[ { "path": ".gitattributes", "content": "*.bat text eol=crlf\n*.cmd text eol=crlf\n*.bin binary\n" }, { "path": ".github/CODEOWNERS", "content": "# CODEOWNERS file for Delta Lake\n# This file defines code owners who must approve changes to specific files/directories.\n# See: https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners\n\n# Build configuration files and directories\n/build/ @tdas\n/build.sbt @tdas\n/project/ @tdas\n/version.sbt @tdas\n\n# Spark V2 and Unified modules\n/spark/v2/ @tdas @huan233usc @TimothyW553 @raveeram-db @murali-db\n/spark-unified/ @tdas @huan233usc @TimothyW553 @raveeram-db @murali-db\n\n# All files in the root directory\n/* @tdas\n" }, { "path": ".github/ISSUE_TEMPLATE/bug-issue.md", "content": "---\nname: Bug Issue\nabout: Use this template for reporting a bug\nlabels: 'bug'\ntitle: '[BUG]'\n\n---\n\n## Bug\n\n#### Which Delta project/connector is this regarding?\n\n\n- [ ] Spark\n- [ ] Standalone\n- [ ] Flink\n- [ ] Kernel\n- [ ] Other (fill in here)\n\n### Describe the problem\n\n#### Steps to reproduce\n\n\n\n#### Observed results\n\n\n\n#### Expected results\n\n\n\n#### Further details\n\n\n\n### Environment information\n\n* Delta Lake version:\n* Spark version:\n* Scala version:\n\n### Willingness to contribute\n\nThe Delta Lake Community encourages bug fix contributions. Would you or another member of your organization be willing to contribute a fix for this bug to the Delta Lake code base?\n\n- [ ] Yes. I can contribute a fix for this bug independently.\n- [ ] Yes. I would be willing to contribute a fix for this bug with guidance from the Delta Lake community.\n- [ ] No. I cannot contribute a bug fix at this time.\n" }, { "path": ".github/ISSUE_TEMPLATE/feature-request.md", "content": "---\nname: Feature Request\nabout: Use this template for raising a feature request\nlabels: 'enhancement'\ntitle: '[Feature Request]'\n\n---\n\n## Feature request\n\n#### Which Delta project/connector is this regarding?\n\n\n- [ ] Spark\n- [ ] Standalone\n- [ ] Flink\n- [ ] Kernel\n- [ ] Other (fill in here)\n\n### Overview\n\n\n\n### Motivation\n\n\n\n### Further details\n\n\n\n### Willingness to contribute\n\nThe Delta Lake Community encourages new feature contributions. Would you or another member of your organization be willing to contribute an implementation of this feature?\n\n- [ ] Yes. I can contribute this feature independently.\n- [ ] Yes. I would be willing to contribute this feature with guidance from the Delta Lake community.\n- [ ] No. I cannot contribute this feature at this time." }, { "path": ".github/ISSUE_TEMPLATE/protocol-rfc.md", "content": "---\nname: Protocol Change Request\nabout: Use this template to propose a new feature that impacts the Delta protocol specification\nlabels: 'protocol'\ntitle: '[PROTOCOL RFC]'\n\n---\n\n## Protocol Change Request\n\n### Description of the protocol change\n\n \n\n\n### Willingness to contribute\n\nThe Delta Lake Community encourages protocol innovations. Would you or another member of your organization be willing to contribute this feature to the Delta Lake code base?\n\n- [ ] Yes. I can contribute.\n- [ ] Yes. I would be willing to contribute with guidance from the Delta Lake community.\n- [ ] No. I cannot contribute at this time.\n\n\n" }, { "path": ".github/PULL_REQUEST_TEMPLATE.md", "content": "\n\n#### Which Delta project/connector is this regarding?\n\n\n- [ ] Spark\n- [ ] Standalone\n- [ ] Flink\n- [ ] Kernel\n- [ ] Other (fill in here)\n\n## Description\n\n\n\n## How was this patch tested?\n\n\n\n## Does this PR introduce _any_ user-facing changes?\n\n\n" }, { "path": ".github/workflows/build.yaml", "content": "name: \"Delta Build\"\non:\n push:\n paths-ignore:\n - '**.md'\n - '**.txt'\n pull_request:\n paths-ignore:\n - '**.md'\n - '**.txt'\njobs:\n test:\n name: \"Build Test\"\n runs-on: ubuntu-24.04\n steps:\n - uses: actions/checkout@v3\n\n - name: Install Java 17\n uses: actions/setup-java@v3\n with:\n distribution: \"zulu\"\n java-version: \"17\"\n\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: '3.9'\n\n - name: Cache Scala, SBT\n uses: actions/cache@v3\n with:\n path: |\n ~/.sbt\n ~/.ivy2\n ~/.cache/coursier\n key: delta-sbt-cache-cross-spark\n\n - name: Run cross-Spark build test\n run: python project/tests/test_cross_spark_publish.py\n" }, { "path": ".github/workflows/flink_test.yaml", "content": "name: \"Delta Flink\"\n\non: [push, pull_request]\n\n# Cancel previous runs when new commits are pushed\nconcurrency:\n group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}\n cancel-in-progress: true\n\nenv:\n # Point SBT to our cache directories for consistency\n SBT_OPTS: \"-Dsbt.coursier.home-dir=/home/runner/.cache/coursier -Dsbt.ivy.home=/home/runner/.ivy2\"\n\njobs:\n test:\n name: \"DF\"\n runs-on: ubuntu-24.04\n steps:\n - name: Show runner specs\n run: |\n echo \"=== GitHub Runner Specs ===\"\n echo \"CPU cores: $(nproc)\"\n echo \"CPU info: $(lscpu | grep 'Model name' | cut -d':' -f2 | xargs)\"\n echo \"Total RAM: $(free -h | grep '^Mem:' | awk '{print $2}')\"\n echo \"Available RAM: $(free -h | grep '^Mem:' | awk '{print $7}')\"\n echo \"Disk space: $(df -h / | tail -1 | awk '{print $2 \" total, \" $4 \" available\"}')\"\n echo \"Runner OS: ${{ runner.os }}\"\n echo \"Runner arch: ${{ runner.arch }}\"\n - name: Checkout code\n uses: actions/checkout@v4\n # Run unit tests with JDK 17. These unit tests depend on Spark, and Spark 4.0+ is JDK 17.\n - name: install java\n uses: actions/setup-java@v4\n with:\n distribution: \"zulu\"\n java-version: \"17\"\n - name: Cache SBT and dependencies\n id: cache-sbt\n uses: actions/cache@v4\n with:\n path: |\n ~/.sbt\n ~/.ivy2/cache\n ~/.coursier/cache\n ~/.cache/coursier\n key: sbt-flink\n - name: Check cache status\n run: |\n if [ \"${{ steps.cache-sbt.outputs.cache-hit }}\" == \"true\" ]; then\n echo \"✅ Cache HIT - using cached dependencies\"\n else\n echo \"❌ Cache MISS - will download dependencies\"\n fi\n - name: Run unit tests\n run: |\n build/sbt flinkGroup/test\n" }, { "path": ".github/workflows/iceberg_test.yaml", "content": "name: \"Delta Iceberg Latest\"\non:\n push:\n paths-ignore:\n - '**.md'\n - '**.txt'\n pull_request:\n paths-ignore:\n - '**.md'\n - '**.txt'\njobs:\n test:\n name: \"DIL: Scala ${{ matrix.scala }}\"\n runs-on: ubuntu-24.04\n strategy:\n matrix:\n # These Scala versions must match those in the build.sbt\n scala: [2.13.16]\n env:\n SCALA_VERSION: ${{ matrix.scala }}\n steps:\n - uses: actions/checkout@v3\n - name: install java\n uses: actions/setup-java@v3\n with:\n distribution: \"zulu\"\n java-version: \"17\"\n - name: Cache Scala, SBT\n uses: actions/cache@v3\n with:\n path: |\n ~/.sbt\n ~/.ivy2\n ~/.cache/coursier\n # Change the key if dependencies are changed. For each key, GitHub Actions will cache the\n # the above directories when we use the key for the first time. After that, each run will\n # just use the cache. The cache is immutable so we need to use a new key when trying to\n # cache new stuff.\n key: delta-sbt-cache-spark4.0-scala${{ matrix.scala }}\n - name: Install Job dependencies\n run: |\n sudo apt-get update\n sudo apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev python3-openssl git\n sudo apt install libedit-dev\n curl -LO https://github.com/bufbuild/buf/releases/download/v1.28.1/buf-Linux-x86_64.tar.gz\n mkdir -p ~/buf\n tar -xvzf buf-Linux-x86_64.tar.gz -C ~/buf --strip-components 1\n rm buf-Linux-x86_64.tar.gz\n sudo apt install python3-pip --fix-missing\n sudo pip3 install pipenv==2024.4.1\n curl https://pyenv.run | bash\n export PATH=\"~/.pyenv/bin:$PATH\"\n eval \"$(pyenv init -)\"\n eval \"$(pyenv virtualenv-init -)\"\n pyenv install 3.8.18\n pyenv global system 3.8.18\n pipenv --python 3.8.18 install\n - name: Run Scala/Java and Python tests\n # when changing TEST_PARALLELISM_COUNT make sure to also change it in spark_master_test.yaml\n run: |\n TEST_PARALLELISM_COUNT=4 pipenv run python run-tests.py --group iceberg --spark-version 4.0\n" }, { "path": ".github/workflows/kernel_docs.yaml", "content": "# Simple workflow for deploying static content to GitHub Pages\nname: Deploy static content to Pages\n\non:\n # Allows you to run this workflow manually from the Actions tab\n workflow_dispatch:\n\n# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages\npermissions:\n contents: read\n pages: write\n id-token: write\n\n# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.\n# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.\nconcurrency:\n group: \"pages\"\n cancel-in-progress: false\n\njobs:\n # Single deploy job since we're just deploying\n deploy_docs:\n environment:\n name: github-pages\n url: ${{ steps.deployment.outputs.page_url }}\n runs-on: ubuntu-latest\n steps:\n - name: Checkout\n uses: actions/checkout@v3\n - name: install java\n uses: actions/setup-java@v3\n with:\n distribution: \"zulu\"\n java-version: \"11\"\n - name: Generate docs\n run: |\n build/sbt kernelGroup/unidoc\n mkdir -p kernel/docs/snapshot/kernel-api/java\n mkdir -p kernel/docs/snapshot/kernel-defaults/java\n cp -r kernel/kernel-api/target/javaunidoc/. kernel/docs/snapshot/kernel-api/java/\n cp -r kernel/kernel-defaults/target/javaunidoc/. kernel/docs/snapshot/kernel-defaults/java/\n - name: Setup Pages\n uses: actions/configure-pages@v3\n - name: Upload artifact\n uses: actions/upload-pages-artifact@v1\n with:\n # Upload kernel docs\n path: kernel/docs\n - name: Deploy to GitHub Pages\n id: deployment\n uses: actions/deploy-pages@v2\n" }, { "path": ".github/workflows/kernel_test.yaml", "content": "name: \"Delta Kernel\"\n\non:\n push:\n paths-ignore:\n - '**.md'\n - '**.txt'\n pull_request:\n paths-ignore:\n - '**.md'\n - '**.txt'\n\n# Cancel previous runs when new commits are pushed\nconcurrency:\n group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}\n cancel-in-progress: true\n\nenv:\n # Point SBT to our cache directories for consistency\n SBT_OPTS: \"-Dsbt.coursier.home-dir=/home/runner/.cache/coursier -Dsbt.ivy.home=/home/runner/.ivy2\"\n\njobs:\n test:\n name: \"DK: Shard ${{ matrix.shard }}\"\n runs-on: ubuntu-24.04\n strategy:\n fail-fast: false # Allow all shards to run even if one fails\n matrix:\n shard: [0, 1, 2, 3]\n env:\n SCALA_VERSION: 2.13.16\n NUM_SHARDS: 4\n DISABLE_UNIDOC: true # Another unidoc workflow will test unidoc.\n TEST_PARALLELISM_COUNT: 4\n steps:\n - name: Show runner specs\n run: |\n echo \"=== GitHub Runner Specs ===\"\n echo \"CPU cores: $(nproc)\"\n echo \"CPU info: $(lscpu | grep 'Model name' | cut -d':' -f2 | xargs)\"\n echo \"Total RAM: $(free -h | grep '^Mem:' | awk '{print $2}')\"\n echo \"Available RAM: $(free -h | grep '^Mem:' | awk '{print $7}')\"\n echo \"Disk space: $(df -h / | tail -1 | awk '{print $2 \" total, \" $4 \" available\"}')\"\n echo \"Runner OS: ${{ runner.os }}\"\n echo \"Runner arch: ${{ runner.arch }}\"\n - name: Checkout code\n uses: actions/checkout@v4\n # Run unit tests with JDK 17. These unit tests depend on Spark, and Spark 4.0+ is JDK 17.\n - name: install java\n uses: actions/setup-java@v4\n with:\n distribution: \"zulu\"\n java-version: \"17\"\n - name: Cache SBT and dependencies\n id: cache-sbt\n uses: actions/cache@v4\n with:\n path: |\n ~/.sbt\n ~/.ivy2/cache\n ~/.coursier/cache\n ~/.cache/coursier\n key: sbt-kernel-${{ runner.os }}-scala${{ env.SCALA_VERSION }}\n - name: Check cache status\n run: |\n if [ \"${{ steps.cache-sbt.outputs.cache-hit }}\" == \"true\" ]; then\n echo \"✅ Cache HIT - using cached dependencies\"\n else\n echo \"❌ Cache MISS - will download dependencies\"\n fi\n - name: Run unit tests\n run: |\n python run-tests.py --group kernel --coverage --shard ${{ matrix.shard }}\n\n integration-test:\n name: \"DK: Integration\"\n runs-on: ubuntu-24.04\n steps:\n - uses: actions/checkout@v3\n # Run integration tests with JDK 11, as they have no Spark dependency\n - name: install java\n uses: actions/setup-java@v3\n with:\n distribution: \"zulu\"\n java-version: \"11\"\n - name: Run integration tests\n run: |\n cd kernel/examples && python run-kernel-examples.py --use-local\n" }, { "path": ".github/workflows/kernel_unitycatalog_test.yaml", "content": "name: \"Kernel Unity Catalog\"\non:\n push:\n paths:\n - 'build.sbt'\n - 'version.sbt'\n - 'kernel/**/*.scala'\n - 'kernel/**/*.java'\n - 'storage/**/*.scala'\n - 'storage/**/*.java'\n - '.github/workflows/kernel_unitycatalog_test.yaml'\n pull_request:\n paths:\n - 'build.sbt'\n - 'version.sbt'\n - 'kernel/**/*.scala'\n - 'kernel/**/*.java'\n - 'storage/**/*.scala'\n - 'storage/**/*.java'\n - '.github/workflows/kernel_unitycatalog_test.yaml'\njobs:\n test:\n name: \"Kernel Unity Catalog Tests\"\n runs-on: ubuntu-24.04\n env:\n SCALA_VERSION: 2.13.16\n steps:\n - uses: actions/checkout@v3\n - name: install java\n uses: actions/setup-java@v3\n with:\n distribution: \"zulu\"\n java-version: \"17\"\n - name: Run Unity tests with coverage\n run: |\n ./build/sbt \"++ ${{ env.SCALA_VERSION }}\" clean coverage kernelUnityCatalog/test coverageAggregate coverageOff -v\n" }, { "path": ".github/workflows/publish_docs.yaml", "content": "name: Publish Docs\n\non:\n push:\n branches:\n - master\n paths:\n - docs/**\n release:\n types:\n - published\n workflow_dispatch:\n\njobs:\n build_api_docs:\n name: Build API docs (${{ matrix.version.name }})\n runs-on: ubuntu-latest\n\n strategy:\n matrix:\n version:\n - name: latest\n ref: v4.0.1\n java: 17\n out_dir: docs/apis/_site/api\n - name: 3.3.2\n ref: v3.3.2\n java: 8\n out_dir: docs/apis/_site/api\n\n steps:\n - uses: actions/checkout@v4\n with:\n repository: delta-io/delta\n ref: ${{ matrix.version.ref }}\n\n - uses: actions/setup-java@v3\n with:\n distribution: \"zulu\"\n java-version: ${{ matrix.version.java }}\n\n - name: Setup python environment\n uses: conda-incubator/setup-miniconda@v3\n with:\n activate-environment: delta_docs\n environment-file: docs/environment.yml\n\n - name: Fix generate_api_docs script\n if: contains(matrix.version.ref, 'v4')\n run: |\n sed -i 's|scala-2\\.12|scala-2.13|g' docs/apis/generate_api_docs.py\n sed -i '/standalone_javadoc_gen_dir,/d' docs/apis/generate_api_docs.py\n sed -i '/flink_javadoc_gen_dir,/d' docs/apis/generate_api_docs.py\n\n - name: Generate API docs\n shell: bash -el {0}\n run: python3 docs/generate_docs.py --api-docs\n env:\n _DELTA_LAKE_RELEASE_VERSION_: ${{ matrix.version.name }}\n\n - name: Move doc contents up one level\n run: |\n find docs/apis/_site/api -type d \\( -name \"unidoc\" -o -name \"javaunidoc\" -o -name \"html\" \\) | while read dir; do\n echo \"Processing $dir\"\n parent=\"$(dirname \"$dir\")\"\n # Move all files (including hidden ones) using find\n find \"$dir\" -maxdepth 1 -type f -exec mv {} \"$parent\"/ \\;\n # Move all subdirectories\n find \"$dir\" -maxdepth 1 -type d ! -path \"$dir\" -exec mv {} \"$parent\"/ \\;\n rmdir \"$dir\"\n done\n\n - name: Upload artifact\n uses: actions/upload-artifact@v4\n with:\n name: ${{ github.run_id }}-apidocs-${{ matrix.version.name }}\n path: ${{ matrix.version.out_dir }}\n\n build_site:\n name: Build site\n needs: build_api_docs\n runs-on: ubuntu-latest\n\n steps:\n - uses: actions/checkout@v4\n\n - name: Install Node.js\n uses: actions/setup-node@v4\n with:\n node-version-file: docs/.nvmrc\n\n - uses: pnpm/action-setup@v4\n name: Install pnpm\n with:\n package_json_file: docs/package.json\n\n - name: Install Node.js dependencies\n run: pnpm install\n working-directory: docs\n\n - name: Download API docs artifacts\n uses: actions/download-artifact@v4\n with:\n pattern: ${{ github.run_id }}-apidocs-*\n path: docs/public/api\n\n - name: Rename API docs artifact folders\n run: |\n for d in docs/public/api/${{ github.run_id }}-apidocs-*; do\n [ -d \"$d\" ] || continue\n new_name=\"$(echo \"$d\" | sed \"s|docs/public/api/${{ github.run_id }}-apidocs-||\")\"\n mv \"$d\" \"docs/public/api/$new_name\"\n done\n\n - name: Generate docs site\n run: python3 docs/generate_docs.py\n\n - name: Install Netlify CLI\n run: pnpm i -g netlify-cli\n\n - name: Publish site to Netlify\n run: netlify deploy --dir=docs/dist --prod\n env:\n NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}\n NETLIFY_SITE_ID: ${{ vars.NETLIFY_SITE_ID }}\n" }, { "path": ".github/workflows/spark_examples_test.yaml", "content": "name: \"Delta Spark Publishing and Examples\"\non:\n push:\n paths-ignore:\n - '**.md'\n - '**.txt'\n pull_request:\n paths-ignore:\n - '**.md'\n - '**.txt'\njobs:\n # Generate Spark versions matrix from CrossSparkVersions.scala\n # This workflow tests against released versions only (no snapshots)\n generate-matrix:\n name: \"Generate Released Spark Versions Matrix\"\n runs-on: ubuntu-24.04\n outputs:\n spark_versions: ${{ steps.generate.outputs.spark_versions }}\n steps:\n - uses: actions/checkout@v3\n - name: install java\n uses: actions/setup-java@v3\n with:\n distribution: \"zulu\"\n java-version: \"17\"\n - name: Generate released Spark versions matrix\n id: generate\n run: |\n # Get only released versions (exclude snapshots)\n SPARK_VERSIONS=$(python3 project/scripts/get_spark_version_info.py --released-spark-versions)\n echo \"spark_versions=$SPARK_VERSIONS\" >> $GITHUB_OUTPUT\n echo \"Generated released Spark versions: $SPARK_VERSIONS\"\n\n test:\n name: \"DSP&E: Spark ${{ matrix.spark_version }}, Scala ${{ matrix.scala }}\"\n runs-on: ubuntu-24.04\n needs: generate-matrix\n strategy:\n matrix:\n # Spark versions are dynamically generated - released versions only\n spark_version: ${{ fromJson(needs.generate-matrix.outputs.spark_versions) }}\n # These Scala versions must match those in the build.sbt\n scala: [2.13.17]\n env:\n SCALA_VERSION: ${{ matrix.scala }}\n steps:\n - uses: actions/checkout@v3\n - name: Get Spark version details\n id: spark-details\n run: |\n # Get JVM version, package suffix, iceberg support, and full version for this Spark version\n JVM_VERSION=$(python3 project/scripts/get_spark_version_info.py --get-field \"${{ matrix.spark_version }}\" targetJvm | jq -r)\n SPARK_PACKAGE_SUFFIX=$(python3 project/scripts/get_spark_version_info.py --get-field \"${{ matrix.spark_version }}\" packageSuffix | jq -r)\n SUPPORT_ICEBERG=$(python3 project/scripts/get_spark_version_info.py --get-field \"${{ matrix.spark_version }}\" supportIceberg | jq -r)\n SPARK_FULL_VERSION=$(python3 project/scripts/get_spark_version_info.py --get-field \"${{ matrix.spark_version }}\" fullVersion | jq -r)\n echo \"jvm_version=$JVM_VERSION\" >> $GITHUB_OUTPUT\n echo \"spark_package_suffix=$SPARK_PACKAGE_SUFFIX\" >> $GITHUB_OUTPUT\n echo \"support_iceberg=$SUPPORT_ICEBERG\" >> $GITHUB_OUTPUT\n echo \"spark_full_version=$SPARK_FULL_VERSION\" >> $GITHUB_OUTPUT\n echo \"Using JVM $JVM_VERSION for Spark $SPARK_FULL_VERSION, package suffix: '$SPARK_PACKAGE_SUFFIX', support iceberg: '$SUPPORT_ICEBERG'\"\n - name: install java\n uses: actions/setup-java@v3\n with:\n distribution: \"zulu\"\n java-version: ${{ steps.spark-details.outputs.jvm_version }}\n - name: Cache Scala, SBT\n uses: actions/cache@v3\n with:\n path: |\n ~/.sbt\n ~/.ivy2\n ~/.cache/coursier\n # Change the key if dependencies are changed. For each key, GitHub Actions will cache the\n # the above directories when we use the key for the first time. After that, each run will\n # just use the cache. The cache is immutable so we need to use a new key when trying to\n # cache new stuff.\n key: delta-sbt-cache-spark${{ matrix.spark_version }}-scala${{ matrix.scala }}\n - name: Install Job dependencies\n run: |\n sudo apt-get update\n sudo apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev python3-openssl git\n sudo apt install libedit-dev\n - name: Run Delta Spark Local Publishing and Examples Compilation\n # examples/scala/build.sbt will compile against the local Delta release version (e.g. 3.2.0-SNAPSHOT).\n # Thus, we need to publishM2 first so those jars are locally accessible.\n # -DsparkVersion is for the Delta project's publishM2 (which Spark version to compile Delta against).\n # SPARK_VERSION/SPARK_PACKAGE_SUFFIX/SUPPORT_ICEBERG are for examples/scala/build.sbt (dependency resolution).\n env:\n SPARK_PACKAGE_SUFFIX: ${{ steps.spark-details.outputs.spark_package_suffix }}\n SUPPORT_ICEBERG: ${{ steps.spark-details.outputs.support_iceberg }}\n SPARK_VERSION: ${{ steps.spark-details.outputs.spark_full_version }}\n run: |\n build/sbt clean\n build/sbt -DsparkVersion=${{ steps.spark-details.outputs.spark_full_version }} publishM2\n cd examples/scala && build/sbt \"++ $SCALA_VERSION compile\"\n - name: Run UC Delta Integration Test\n # Verifies that delta-spark resolved from Maven local includes all kernel module\n # dependencies transitively by running a real UC-backed Delta workload.\n env:\n SPARK_PACKAGE_SUFFIX: ${{ steps.spark-details.outputs.spark_package_suffix }}\n SPARK_VERSION: ${{ steps.spark-details.outputs.spark_full_version }}\n run: |\n cd examples/scala && build/sbt \"++ $SCALA_VERSION runMain example.UnityCatalogQuickstart\"\n" }, { "path": ".github/workflows/spark_python_test.yaml", "content": "name: \"Delta Spark Python\"\non:\n push:\n paths-ignore:\n - '**.md'\n - '**.txt'\n pull_request:\n paths-ignore:\n - '**.md'\n - '**.txt'\njobs:\n # Generate Spark versions matrix from CrossSparkVersions.scala\n # This workflow tests against released versions only (no snapshots)\n generate-matrix:\n name: \"Generate Released Spark Versions Matrix\"\n runs-on: ubuntu-24.04\n outputs:\n spark_versions: ${{ steps.generate.outputs.spark_versions }}\n steps:\n - uses: actions/checkout@v3\n - name: install java\n uses: actions/setup-java@v3\n with:\n distribution: \"zulu\"\n java-version: \"17\"\n - name: Generate released Spark versions matrix\n id: generate\n run: |\n # Get only released versions (exclude snapshots)\n SPARK_VERSIONS=$(python3 project/scripts/get_spark_version_info.py --released-spark-versions)\n echo \"spark_versions=$SPARK_VERSIONS\" >> $GITHUB_OUTPUT\n echo \"Generated released Spark versions: $SPARK_VERSIONS\"\n\n test:\n name: \"DSP (${{ matrix.spark_version }})\"\n runs-on: ubuntu-24.04\n needs: generate-matrix\n strategy:\n matrix:\n # Spark versions are dynamically generated - released versions only\n spark_version: ${{ fromJson(needs.generate-matrix.outputs.spark_versions) }}\n # These Scala versions must match those in the build.sbt\n scala: [2.13.16]\n env:\n SCALA_VERSION: ${{ matrix.scala }}\n SPARK_VERSION: ${{ matrix.spark_version }}\n steps:\n - uses: actions/checkout@v3\n - name: Get Spark version details\n id: spark-details\n run: |\n # Get JVM version and full Spark version for this matrix entry\n JVM_VERSION=$(python3 project/scripts/get_spark_version_info.py --get-field \"${{ matrix.spark_version }}\" targetJvm | jq -r)\n FULL_VERSION=$(python3 project/scripts/get_spark_version_info.py --get-field \"${{ matrix.spark_version }}\" fullVersion | jq -r)\n echo \"jvm_version=$JVM_VERSION\" >> $GITHUB_OUTPUT\n echo \"spark_full_version=$FULL_VERSION\" >> $GITHUB_OUTPUT\n echo \"Using JVM $JVM_VERSION for Spark ${{ matrix.spark_version }} (full: $FULL_VERSION)\"\n - name: install java\n uses: actions/setup-java@v3\n with:\n distribution: \"zulu\"\n java-version: ${{ steps.spark-details.outputs.jvm_version }}\n - name: Cache Scala, SBT\n uses: actions/cache@v3\n with:\n path: |\n ~/.sbt\n ~/.ivy2\n ~/.cache/coursier\n # Change the key if dependencies are changed. For each key, GitHub Actions will cache the\n # the above directories when we use the key for the first time. After that, each run will\n # just use the cache. The cache is immutable so we need to use a new key when trying to\n # cache new stuff.\n key: delta-sbt-cache-spark${{ matrix.spark_version }}-scala${{ matrix.scala }}\n - name: Install Job dependencies\n run: |\n sudo apt-get update\n sudo apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev python3-openssl git\n sudo apt install libedit-dev\n curl -LO https://github.com/bufbuild/buf/releases/download/v1.28.1/buf-Linux-x86_64.tar.gz\n mkdir -p ~/buf\n tar -xvzf buf-Linux-x86_64.tar.gz -C ~/buf --strip-components 1\n rm buf-Linux-x86_64.tar.gz\n sudo apt install python3-pip --fix-missing\n sudo pip3 install pipenv==2024.4.1\n curl https://pyenv.run | bash\n export PATH=\"~/.pyenv/bin:$PATH\"\n eval \"$(pyenv init -)\"\n eval \"$(pyenv virtualenv-init -)\"\n pyenv install 3.10\n pyenv global system 3.10\n pipenv --python 3.10 install\n # Update the pip version to 24.0. By default `pyenv.run` installs the latest pip version\n # available. From version 24.1, `pip` doesn't allow installing python packages\n # with version string containing `-`. In Delta-Spark case, the pypi package generated has\n # `-SNAPSHOT` in version (e.g. `3.3.0-SNAPSHOT`) as the version is picked up from\n # the`version.sbt` file.\n pipenv run pip install pip==24.0 setuptools==69.5.1 wheel==0.43.0\n # Install pyspark matching the full spark version\n pipenv run pip install pyspark==${{ steps.spark-details.outputs.spark_full_version }}\n pipenv run pip install flake8==3.9.0\n pipenv run pip install black==23.12.1\n pipenv run pip install importlib_metadata==3.10.0\n pipenv run pip install mypy==1.8.0\n pipenv run pip install mypy-protobuf==3.3.0\n pipenv run pip install cryptography==37.0.4\n pipenv run pip install twine==4.0.1\n pipenv run pip install wheel==0.33.4\n pipenv run pip install setuptools==41.1.0\n pipenv run pip install pydocstyle==3.0.0\n pipenv run pip install pandas==2.2.0\n pipenv run pip install pyarrow==15.0.0\n pipenv run pip install pypandoc==1.3.3\n pipenv run pip install numpy==1.22.4\n pipenv run pip install googleapis-common-protos-stubs==2.2.0\n pipenv run pip install grpc-stubs==1.24.11\n # Version-specific dependencies for Spark Connect compatibility\n if [[ \"${{ matrix.spark_version }}\" == \"4.0\" ]]; then\n pipenv run pip install grpcio==1.67.0\n pipenv run pip install grpcio-status==1.67.0\n pipenv run pip install googleapis-common-protos==1.65.0\n pipenv run pip install protobuf==5.29.1\n else\n # Spark 4.1+ requirements from https://github.com/apache/spark/blob/branch-4.1/dev/requirements.txt\n pipenv run pip install grpcio==1.76.0\n pipenv run pip install grpcio-status==1.76.0\n pipenv run pip install googleapis-common-protos==1.71.0\n pipenv run pip install protobuf==6.33.0\n pipenv run pip install zstandard==0.25.0\n fi\n - name: Run Python tests\n # when changing TEST_PARALLELISM_COUNT make sure to also change it in spark_test.yaml\n run: |\n TEST_PARALLELISM_COUNT=4 pipenv run python run-tests.py --group spark-python --spark-version ${{ matrix.spark_version }}\n" }, { "path": ".github/workflows/spark_test.yaml", "content": "name: \"Delta Spark\"\non:\n push:\n paths-ignore:\n - '**.md'\n - '**.txt'\n pull_request:\n paths-ignore:\n - '**.md'\n - '**.txt'\njobs:\n # Generate Spark versions matrix from CrossSparkVersions.scala\n # This ensures the workflow always uses the versions defined in the build\n generate-matrix:\n name: \"Generate Spark Versions Matrix\"\n runs-on: ubuntu-24.04\n outputs:\n spark_versions: ${{ steps.generate.outputs.spark_versions }}\n steps:\n - uses: actions/checkout@v3\n - name: install java\n uses: actions/setup-java@v3\n with:\n distribution: \"zulu\"\n java-version: \"17\"\n - name: Generate Spark versions matrix\n id: generate\n run: |\n # The script automatically generates spark-versions.json from CrossSparkVersions.scala\n SPARK_VERSIONS=$(python3 project/scripts/get_spark_version_info.py --all-spark-versions)\n echo \"spark_versions=$SPARK_VERSIONS\" >> $GITHUB_OUTPUT\n echo \"Generated Spark versions: $SPARK_VERSIONS\"\n\n test:\n name: \"DS: Spark ${{ matrix.spark_version }}, Scala ${{ matrix.scala }}, Shard ${{ matrix.shard }}\"\n runs-on: ubuntu-24.04\n needs: generate-matrix\n strategy:\n fail-fast: false\n matrix:\n # Spark versions are dynamically generated from CrossSparkVersions.scala\n # DO NOT hardcode versions here - they are automatically loaded from the build configuration\n spark_version: ${{ fromJson(needs.generate-matrix.outputs.spark_versions) }}\n # These Scala versions must match those in the build.sbt\n scala: [2.13.16]\n # Important: This list of shards must be [0..NUM_SHARDS - 1]\n shard: [0, 1, 2, 3, 4, 5, 6, 7]\n env:\n SCALA_VERSION: ${{ matrix.scala }}\n SPARK_VERSION: ${{ matrix.spark_version }}\n # Important: This must be the same as the length of shards in matrix\n NUM_SHARDS: 8\n steps:\n - uses: actions/checkout@v3\n - name: Get Spark version details\n id: spark-details\n run: |\n # The script automatically generates spark-versions.json if needed\n JVM_VERSION=$(python3 project/scripts/get_spark_version_info.py --get-field \"${{ matrix.spark_version }}\" targetJvm | jq -r)\n echo \"jvm_version=$JVM_VERSION\" >> $GITHUB_OUTPUT\n echo \"Using JVM version: $JVM_VERSION for Spark ${{ matrix.spark_version }}\"\n - name: install java\n uses: actions/setup-java@v3\n with:\n distribution: \"zulu\"\n java-version: ${{ steps.spark-details.outputs.jvm_version }}\n - name: Cache Scala, SBT\n uses: actions/cache@v3\n with:\n path: |\n ~/.sbt\n ~/.ivy2\n ~/.cache/coursier\n # Change the key if dependencies are changed. For each key, GitHub Actions will cache the\n # the above directories when we use the key for the first time. After that, each run will\n # just use the cache. The cache is immutable so we need to use a new key when trying to\n # cache new stuff.\n key: delta-sbt-cache-spark${{ matrix.spark_version }}-scala${{ matrix.scala }}\n - name: Install Job dependencies\n run: |\n sudo apt-get update\n sudo apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev python3-openssl git\n sudo apt install libedit-dev\n curl -LO https://github.com/bufbuild/buf/releases/download/v1.28.1/buf-Linux-x86_64.tar.gz\n mkdir -p ~/buf\n tar -xvzf buf-Linux-x86_64.tar.gz -C ~/buf --strip-components 1\n rm buf-Linux-x86_64.tar.gz\n sudo apt install python3-pip --fix-missing\n sudo pip3 install pipenv==2024.4.1\n curl https://pyenv.run | bash\n export PATH=\"~/.pyenv/bin:$PATH\"\n eval \"$(pyenv init -)\"\n eval \"$(pyenv virtualenv-init -)\"\n pyenv install 3.9\n pyenv global system 3.9\n pipenv --python 3.9 install\n # Update the pip version to 24.0. By default `pyenv.run` installs the latest pip version\n # available. From version 24.1, `pip` doesn't allow installing python packages\n # with version string containing `-`. In Delta-Spark case, the pypi package generated has\n # `-SNAPSHOT` in version (e.g. `3.3.0-SNAPSHOT`) as the version is picked up from\n # the`version.sbt` file.\n pipenv run pip install pip==24.0 setuptools==69.5.1 wheel==0.43.0\n pipenv run pip install flake8==3.9.0\n pipenv run pip install black==23.12.1\n pipenv run pip install importlib_metadata==3.10.0\n pipenv run pip install mypy==1.8.0\n pipenv run pip install mypy-protobuf==3.3.0\n pipenv run pip install cryptography==37.0.4\n pipenv run pip install twine==4.0.1\n pipenv run pip install wheel==0.33.4\n pipenv run pip install setuptools==41.1.0\n pipenv run pip install pydocstyle==3.0.0\n pipenv run pip install pandas==2.2.0\n pipenv run pip install pyarrow==11.0.0\n pipenv run pip install pypandoc==1.3.3\n pipenv run pip install numpy==1.22.4\n pipenv run pip install grpcio==1.67.0\n pipenv run pip install grpcio-status==1.67.0\n pipenv run pip install googleapis-common-protos==1.65.0\n pipenv run pip install protobuf==5.29.1\n pipenv run pip install googleapis-common-protos-stubs==2.2.0\n pipenv run pip install grpc-stubs==1.24.11\n - name: Scala structured logging style check\n run: |\n if [ -f ./dev/spark_structured_logging_style.py ]; then\n python3 ./dev/spark_structured_logging_style.py\n fi\n - name: Run Scala/Java tests\n # when changing TEST_PARALLELISM_COUNT make sure to also change it in spark_python_test.yaml\n run: |\n TEST_PARALLELISM_COUNT=4 pipenv run python run-tests.py --group spark --shard ${{ matrix.shard }} --spark-version ${{ matrix.spark_version }}\n - name: Upload test reports\n if: always()\n uses: actions/upload-artifact@v4\n with:\n name: test-reports-spark${{ matrix.spark_version }}-shard${{ matrix.shard }}\n path: \"**/target/test-reports/*.xml\"\n retention-days: 7\n" }, { "path": ".github/workflows/unidoc.yaml", "content": " name: \"Unidoc\"\n on: [push, pull_request]\n jobs:\n build:\n name: \"U: Scala ${{ matrix.scala }}\"\n runs-on: ubuntu-24.04\n strategy:\n matrix:\n # These Scala versions must match those in the build.sbt\n scala: [2.13.16]\n steps:\n - name: install java\n uses: actions/setup-java@v3\n with:\n distribution: \"zulu\"\n java-version: \"17\"\n - uses: actions/checkout@v3\n - name: generate unidoc\n run: build/sbt \"++ ${{ matrix.scala }}\" unidoc\n" }, { "path": ".gitignore", "content": "*#*#\n*.#*\n*.iml\n*.ipr\n*.iws\n*.pyc\n*.pyo\n*.swp\n*~\n.DS_Store\n.ammonite\n.bloop\n.bsp\n.cache\n.classpath\n.ensime\n.ensime_cache/\n.ensime_lucene\n.generated-mima*\n.idea/\n.idea_modules/\n.metals\n.project\n.pydevproject\n.scala_dependencies\n.settings\n/lib/\nR-unit-tests.log\nR/unit-tests.out\nR/cran-check.out\nR/pkg/vignettes/sparkr-vignettes.html\nR/pkg/tests/fulltests/Rplots.pdf\nbuild/*.jar\nbuild/apache-maven*\nbuild/scala*\nbuild/zinc*\ncache\ncheckpoint\nconf/*.cmd\nconf/*.conf\nconf/*.properties\nconf/*.sh\nconf/*.xml\nconf/java-opts\ndependency-reduced-pom.xml\nderby.log\ndev/create-release/*final\ndev/create-release/*txt\ndev/pr-deps/\ndist/\ndocs/_site\ndocs/api\nsql/docs\nsql/site\nlib_managed/\nlint-r-report.log\nlog/\nlogs/\nmetals.sbt\nout/\nproject/boot/\nproject/build/target/\nproject/plugins/lib_managed/\nproject/plugins/project/build.properties\nproject/plugins/src_managed/\nproject/plugins/target/\npython/lib/pyspark.zip\npython/deps\ndocs/python/_static/\ndocs/python/_templates/\ndocs/python/_build/\npython/test_coverage/coverage_data\npython/test_coverage/htmlcov\npython/pyspark/python\nreports/\nscalastyle-on-compile.generated.xml\nscalastyle-output.xml\nscalastyle.txt\nspark-*-bin-*.tgz\nspark-tests.log\nsrc_managed/\nstreaming-tests.log\ntarget/\nunit-tests.log\nwork/\ndocs/.jekyll-metadata\n\n# For Hive\nTempStatsStore/\nmetastore/\nmetastore_db/\nsql/hive-thriftserver/test_warehouses\nwarehouse/\nspark-warehouse/\n\n# For R session data\n.RData\n.RHistory\n.Rhistory\n*.Rproj\n*.Rproj.*\n\n.Rproj.user\n\n**/src/main/resources/js\n\n# For SBT\n.jvmopts\nsbt-launch-*.jar\n\n# For Python linting\npep8*.py\npycodestyle*.py\n\n# For IDE settings\n.vscode\n\n# For Terraform\n**/.terraform/*\n*.tfstate\n*.tfstate.*\ncrash.log\ncrash.*.log\n*.tfvars\n*.tfvars.json\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n.terraformrc\n.terraform.rc\n\n# Local Netlify folder\n.netlify\n\n# Ignore kernel benchmark report\nkernel/kernel-benchmarks/benchmark_report.json\n\n# Unity Catalog test artifacts\nspark/unitycatalog/etc/\n.scala-build/\n\n" }, { "path": ".sbtopts", "content": "-J-Xmx4G\n" }, { "path": ".scalafmt.conf", "content": "# Copyright (2025) The Delta Lake Project Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nalign = none\nalign.openParenDefnSite = false\nalign.openParenCallSite = false\nalign.tokens = []\nimportSelectors = \"singleLine\"\noptIn.configStyleArguments = false\ncontinuationIndent {\n callSite = 2\n defnSite = 4\n}\ndanglingParentheses {\n defnSite = false\n callSite = false\n}\ndocstrings {\n style = Asterisk\n wrap = no\n}\nliterals.hexDigits = upper\nmaxColumn = 100\nnewlines {\n beforeCurlyLambdaParams = false\n source = keep\n}\nrewrite.rules = [Imports]\nrewrite.imports.sort = scalastyle\nrewrite.imports.groups = [\n [\"java\\\\..*\"],\n [\"scala\\\\..*\"],\n [\"io\\\\.delta\\\\..*\"],\n [\"org\\\\.apache\\\\.spark\\\\.sql\\\\.delta.*\"]\n]\nrunner.dialect = scala212\nversion = 3.8.6\n" }, { "path": "CODE_OF_CONDUCT.md", "content": "# Delta Lake Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as\ncontributors and maintainers pledge to making participation in our project and\nour community a harassment-free experience for everyone, regardless of age, body\nsize, disability, ethnicity, sex characteristics, gender identity and expression,\nlevel of experience, education, socio-economic status, nationality, personal\nappearance, race, religion, or sexual identity and orientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment\ninclude:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\nshipit\n* The use of sexualized language or imagery and unwelcome sexual attention or\n advances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic\n address, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a\n professional setting\nshipit\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable\nbehavior and are expected to take appropriate and fair corrective action in\nresponse to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or\nreject comments, commits, code, wiki edits, issues, and other contributions\nthat are not aligned to this Code of Conduct, or to ban temporarily or\npermanently any contributor for other behaviors that they deem inappropriate,\nthreatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies both within project spaces and in public spaces\nwhen an individual is representing the project or its community. Examples of\nrepresenting a project or community include using an official project e-mail\naddress, posting via an official social media account, or acting as an appointed\nrepresentative at an online or offline event. Representation of a project may be\nfurther defined and clarified by project maintainers.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported by contacting the Technical Steering Committee defined [here](https://github.com/delta-io/delta/blob/master/CONTRIBUTING.md#governance). All\ncomplaints will be reviewed and investigated and will result in a response that\nis deemed necessary and appropriate to the circumstances. The project team is\nobligated to maintain confidentiality with regard to the reporter of an incident.\nFurther details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good\nfaith may face temporary or permanent repercussions as determined by other\nmembers of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,\navailable at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html\n\n[homepage]: https://www.contributor-covenant.org\n\nFor answers to common questions about this code of conduct, see\nhttps://www.contributor-covenant.org/faq\n\n## Linux Foundation Code of Conduct\nYour use is additionally subject to the [Linux Foundation Code of Conduct](https://lfprojects.org/policies/code-of-conduct/)\n" }, { "path": "CONTRIBUTING.md", "content": "We happily welcome contributions to Delta Lake. We use [GitHub Issues](/../../issues/) to track community reported issues and [GitHub Pull Requests ](/../../pulls/) for accepting changes.\n\n# Governance\nDelta Lake is an independent open-source project and not controlled by any single company. To emphasize this we joined the [Delta Lake Project](https://community.linuxfoundation.org/delta-lake/) in 2019, which is a sub-project of the Linux Foundation Projects. Within the project, we make decisions based on [these rules](https://delta.io/pdfs/delta-charter.pdf).\n\nDelta Lake is supported by a wide set of developers from over 50 organizations across multiple repositories. Since 2019, more than 190 developers have contributed to Delta Lake! The Delta Lake community is growing by leaps and bounds with more than 6000 members in the [Delta Users slack](https://go.delta.io/slack)).\n\nFor more information, please refer to the [founding technical charter](https://delta.io/pdfs/delta-charter.pdf).\n\n# Communication\n- Before starting work on a major feature, please reach out to us via [GitHub](https://github.com/delta-io/delta/issues), [Slack](https://go.delta.io/slack), [email](https://groups.google.com/g/delta-users), etc. We will make sure no one else is already working on it and ask you to open a GitHub issue.\n- A \"major feature\" is defined as any change that is > 100 LOC altered (not including tests), or changes any user-facing behavior.\n- We will use the GitHub issue to discuss the feature and come to agreement.\n- This is to prevent your time being wasted, as well as ours.\n- The GitHub review process for major features is also important so that organizations with commit access can come to agreement on design.\n- If it is appropriate to write a design document, the document must be hosted either in the GitHub tracking issue, or linked to from the issue and hosted in a world-readable location. Examples of design documents include [sample 1](https://docs.google.com/document/d/16S7xoAmXpSax7W1OWYYHo5nZ71t5NvrQ-F79pZF6yb8), [sample 2](https://docs.google.com/document/d/1MJhmW_H7doGWY2oty-I78vciziPzBy_nzuuB-Wv5XQ8), and [sample 3](https://docs.google.com/document/d/19CU4eJuBXOwW7FC58uSqyCbcLTsgvQ5P1zoPOPgUSpI).\n- Specifically, if the goal is to add a new extension, please read the extension policy.\n- Small patches and bug fixes don't need prior communication. If you have identified a bug and have ways to solve it, please create an [issue](https://github.com/delta-io/delta/issues) or create a [pull request](https://github.com/delta-io/delta/pulls).\n- If you have an example code that explains a use case or a feature, create a pull request to post under [examples](https://github.com/delta-io/delta/tree/master/examples). \n\n\n# Coding style\nWe generally follow the [Apache Spark Scala Style Guide](https://spark.apache.org/contributing.html).\n\n# Sign your work\nThe sign-off is a simple line at the end of the explanation for the patch. Your signature certifies that you wrote the patch or otherwise have the right to pass it on as an open-source patch. The rules are pretty simple: if you can certify the below (from developercertificate.org):\n\n```\nDeveloper Certificate of Origin\nVersion 1.1\n\nCopyright (C) 2004, 2006 The Linux Foundation and its contributors.\n1 Letterman Drive\nSuite D4700\nSan Francisco, CA, 94129\n\nEveryone is permitted to copy and distribute verbatim copies of this\nlicense document, but changing it is not allowed.\n\n\nDeveloper's Certificate of Origin 1.1\n\nBy making a contribution to this project, I certify that:\n\n(a) The contribution was created in whole or in part by me and I\n have the right to submit it under the open source license\n indicated in the file; or\n\n(b) The contribution is based upon previous work that, to the best\n of my knowledge, is covered under an appropriate open source\n license and I have the right under that license to submit that\n work with modifications, whether created in whole or in part\n by me, under the same open source license (unless I am\n permitted to submit under a different license), as indicated\n in the file; or\n\n(c) The contribution was provided directly to me by some other\n person who certified (a), (b) or (c) and I have not modified\n it.\n\n(d) I understand and agree that this project and the contribution\n are public and that a record of the contribution (including all\n personal information I submit with it, including my sign-off) is\n maintained indefinitely and may be redistributed consistent with\n this project or the open source license(s) involved.\n```\n\nThen you just add a line to every git commit message:\n\n```\nSigned-off-by: Jane Smith \nUse your real name (sorry, no pseudonyms or anonymous contributions.)\n```\n\nIf you set your `user.name` and `user.email` git configs, you can sign your commit automatically with `git commit -s`.\n" }, { "path": "Dockerfile", "content": "#\n# Copyright (2021) The Delta Lake Project Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\nFROM ubuntu:focal-20221019\n\nENV DEBIAN_FRONTEND noninteractive\nENV DEBCONF_NONINTERACTIVE_SEEN true\n\nRUN apt-get update\nRUN apt-get install -y software-properties-common\nRUN apt-get install -y curl\nRUN apt-get install -y wget\nRUN apt-get install -y openjdk-8-jdk\nRUN apt-get install -y python3.8\nRUN apt-get install -y python3-pip\nRUN apt-get install -y git\n\n# Upgrade pip. This is needed to use prebuilt wheels for packages cffi (dep of cryptography) and\n# cryptography. Otherwise, building wheels for these packages fails.\nRUN pip3 install --upgrade pip\n\n# Update the pip version to 24.0. By default `pyenv.run` installs the latest pip version\n# available. From version 24.1, `pip` doesn't allow installing python packages\n# with version string containing `-`. In Delta-Spark case, the pypi package generated has\n# `-SNAPSHOT` in version (e.g. `3.3.0-SNAPSHOT`) as the version is picked up from\n# the`version.sbt` file.\nRUN pip install pip==24.0 setuptools==69.5.1 wheel==0.43.0\n\nRUN pip3 install pyspark==3.5.3\n\nRUN pip3 install mypy==0.982\n\nRUN pip3 install pydocstyle==3.0.0\n\nRUN pip3 install pandas==1.0.5\n\nRUN pip3 install pyarrow==8.0.0\n\nRUN pip3 install numpy==1.20.3\n\nRUN pip3 install importlib_metadata==3.10.0\n\nRUN pip3 install cryptography==37.0.4\n\n# We must install cryptography before twine. Else, twine will pull a newer version of\n# cryptography that requires a newer version of Rust and may break tests.\nRUN pip3 install twine==4.0.1\n\nRUN pip3 install wheel==0.33.4\n\nRUN pip3 install setuptools==41.0.1\n\n# Do not add any non-deterministic changes (e.g., copy from files \n# from repo) in this Dockerfile, so that the docker image \n# generated from this can be reused across builds.\n" }, { "path": "LICENSE.txt", "content": "Copyright (2021) The Delta Lake Project Authors. All rights reserved.\n\n\n Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. Definitions.\n\n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n\n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n\n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n\n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n\n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n\n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n\n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n\n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n\n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. For the purposes of this definition, \"submitted\"\n means any form of electronic, verbal, or written communication sent\n to the Licensor or its representatives, including but not limited to\n communication on electronic mailing lists, source code control systems,\n and issue tracking systems that are managed by, or on behalf of, the\n Licensor for the purpose of discussing and improving the Work, but\n excluding communication that is conspicuously marked or otherwise\n designated in writing by the copyright owner as \"Not a Contribution.\"\n\n \"Contributor\" shall mean Licensor and any individual or Legal Entity\n on behalf of whom a Contribution has been received by Licensor and\n subsequently incorporated within the Work.\n\n 2. Grant of Copyright License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n copyright license to reproduce, prepare Derivative Works of,\n publicly display, publicly perform, sublicense, and distribute the\n Work and such Derivative Works in Source or Object form.\n\n 3. Grant of Patent License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n (except as stated in this section) patent license to make, have made,\n use, offer to sell, sell, import, and otherwise transfer the Work,\n where such license applies only to those patent claims licensable\n by such Contributor that are necessarily infringed by their\n Contribution(s) alone or by combination of their Contribution(s)\n with the Work to which such Contribution(s) was submitted. If You\n institute patent litigation against any entity (including a\n cross-claim or counterclaim in a lawsuit) alleging that the Work\n or a Contribution incorporated within the Work constitutes direct\n or contributory patent infringement, then any patent licenses\n granted to You under this License for that Work shall terminate\n as of the date such litigation is filed.\n\n 4. Redistribution. You may reproduce and distribute copies of the\n Work or Derivative Works thereof in any medium, with or without\n modifications, and in Source or Object form, provided that You\n meet the following conditions:\n\n (a) You must give any other recipients of the Work or\n Derivative Works a copy of this License; and\n\n (b) You must cause any modified files to carry prominent notices\n stating that You changed the files; and\n\n (c) You must retain, in the Source form of any Derivative Works\n that You distribute, all copyright, patent, trademark, and\n attribution notices from the Source form of the Work,\n excluding those notices that do not pertain to any part of\n the Derivative Works; and\n\n (d) If the Work includes a \"NOTICE\" text file as part of its\n distribution, then any Derivative Works that You distribute must\n include a readable copy of the attribution notices contained\n within such NOTICE file, excluding those notices that do not\n pertain to any part of the Derivative Works, in at least one\n of the following places: within a NOTICE text file distributed\n as part of the Derivative Works; within the Source form or\n documentation, if provided along with the Derivative Works; or,\n within a display generated by the Derivative Works, if and\n wherever such third-party notices normally appear. The contents\n of the NOTICE file are for informational purposes only and\n do not modify the License. You may add Your own attribution\n notices within Derivative Works that You distribute, alongside\n or as an addendum to the NOTICE text from the Work, provided\n that such additional attribution notices cannot be construed\n as modifying the License.\n\n You may add Your own copyright statement to Your modifications and\n may provide additional or different license terms and conditions\n for use, reproduction, or distribution of Your modifications, or\n for any such Derivative Works as a whole, provided Your use,\n reproduction, and distribution of the Work otherwise complies with\n the conditions stated in this License.\n\n 5. Submission of Contributions. Unless You explicitly state otherwise,\n any Contribution intentionally submitted for inclusion in the Work\n by You to the Licensor shall be under the terms and conditions of\n this License, without any additional terms or conditions.\n Notwithstanding the above, nothing herein shall supersede or modify\n the terms of any separate license agreement you may have executed\n with Licensor regarding such Contributions.\n\n 6. Trademarks. This License does not grant permission to use the trade\n names, trademarks, service marks, or product names of the Licensor,\n except as required for reasonable and customary use in describing the\n origin of the Work and reproducing the content of the NOTICE file.\n\n 7. Disclaimer of Warranty. Unless required by applicable law or\n agreed to in writing, Licensor provides the Work (and each\n Contributor provides its Contributions) on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n implied, including, without limitation, any warranties or conditions\n of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n PARTICULAR PURPOSE. You are solely responsible for determining the\n appropriateness of using or redistributing the Work and assume any\n risks associated with Your exercise of permissions under this License.\n\n 8. Limitation of Liability. In no event and under no legal theory,\n whether in tort (including negligence), contract, or otherwise,\n unless required by applicable law (such as deliberate and grossly\n negligent acts) or agreed to in writing, shall any Contributor be\n liable to You for damages, including any direct, indirect, special,\n incidental, or consequential damages of any character arising as a\n result of this License or out of the use or inability to use the\n Work (including but not limited to damages for loss of goodwill,\n work stoppage, computer failure or malfunction, or any and all\n other commercial damages or losses), even if such Contributor\n has been advised of the possibility of such damages.\n\n 9. Accepting Warranty or Additional Liability. While redistributing\n the Work or Derivative Works thereof, You may choose to offer,\n and charge a fee for, acceptance of support, warranty, indemnity,\n or other liability obligations and/or rights consistent with this\n License. However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n\n END OF TERMS AND CONDITIONS\n\n\n\n-------------------------------------------------------------------------\nThis project includes code derived from the Apache Spark project.\nThe individual files containing this code carry the original Apache Spark\nlicense, which is reproduced here as well:\n\n Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. Definitions.\n\n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n\n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n\n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n\n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n\n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n\n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n\n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n\n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n\n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. For the purposes of this definition, \"submitted\"\n means any form of electronic, verbal, or written communication sent\n to the Licensor or its representatives, including but not limited to\n communication on electronic mailing lists, source code control systems,\n and issue tracking systems that are managed by, or on behalf of, the\n Licensor for the purpose of discussing and improving the Work, but\n excluding communication that is conspicuously marked or otherwise\n designated in writing by the copyright owner as \"Not a Contribution.\"\n\n \"Contributor\" shall mean Licensor and any individual or Legal Entity\n on behalf of whom a Contribution has been received by Licensor and\n subsequently incorporated within the Work.\n\n 2. Grant of Copyright License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n copyright license to reproduce, prepare Derivative Works of,\n publicly display, publicly perform, sublicense, and distribute the\n Work and such Derivative Works in Source or Object form.\n\n 3. Grant of Patent License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n (except as stated in this section) patent license to make, have made,\n use, offer to sell, sell, import, and otherwise transfer the Work,\n where such license applies only to those patent claims licensable\n by such Contributor that are necessarily infringed by their\n Contribution(s) alone or by combination of their Contribution(s)\n with the Work to which such Contribution(s) was submitted. If You\n institute patent litigation against any entity (including a\n cross-claim or counterclaim in a lawsuit) alleging that the Work\n or a Contribution incorporated within the Work constitutes direct\n or contributory patent infringement, then any patent licenses\n granted to You under this License for that Work shall terminate\n as of the date such litigation is filed.\n\n 4. Redistribution. You may reproduce and distribute copies of the\n Work or Derivative Works thereof in any medium, with or without\n modifications, and in Source or Object form, provided that You\n meet the following conditions:\n\n (a) You must give any other recipients of the Work or\n Derivative Works a copy of this License; and\n\n (b) You must cause any modified files to carry prominent notices\n stating that You changed the files; and\n\n (c) You must retain, in the Source form of any Derivative Works\n that You distribute, all copyright, patent, trademark, and\n attribution notices from the Source form of the Work,\n excluding those notices that do not pertain to any part of\n the Derivative Works; and\n\n (d) If the Work includes a \"NOTICE\" text file as part of its\n distribution, then any Derivative Works that You distribute must\n include a readable copy of the attribution notices contained\n within such NOTICE file, excluding those notices that do not\n pertain to any part of the Derivative Works, in at least one\n of the following places: within a NOTICE text file distributed\n as part of the Derivative Works; within the Source form or\n documentation, if provided along with the Derivative Works; or,\n within a display generated by the Derivative Works, if and\n wherever such third-party notices normally appear. The contents\n of the NOTICE file are for informational purposes only and\n do not modify the License. You may add Your own attribution\n notices within Derivative Works that You distribute, alongside\n or as an addendum to the NOTICE text from the Work, provided\n that such additional attribution notices cannot be construed\n as modifying the License.\n\n You may add Your own copyright statement to Your modifications and\n may provide additional or different license terms and conditions\n for use, reproduction, or distribution of Your modifications, or\n for any such Derivative Works as a whole, provided Your use,\n reproduction, and distribution of the Work otherwise complies with\n the conditions stated in this License.\n\n 5. Submission of Contributions. Unless You explicitly state otherwise,\n any Contribution intentionally submitted for inclusion in the Work\n by You to the Licensor shall be under the terms and conditions of\n this License, without any additional terms or conditions.\n Notwithstanding the above, nothing herein shall supersede or modify\n the terms of any separate license agreement you may have executed\n with Licensor regarding such Contributions.\n\n 6. Trademarks. This License does not grant permission to use the trade\n names, trademarks, service marks, or product names of the Licensor,\n except as required for reasonable and customary use in describing the\n origin of the Work and reproducing the content of the NOTICE file.\n\n 7. Disclaimer of Warranty. Unless required by applicable law or\n agreed to in writing, Licensor provides the Work (and each\n Contributor provides its Contributions) on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n implied, including, without limitation, any warranties or conditions\n of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n PARTICULAR PURPOSE. You are solely responsible for determining the\n appropriateness of using or redistributing the Work and assume any\n risks associated with Your exercise of permissions under this License.\n\n 8. Limitation of Liability. In no event and under no legal theory,\n whether in tort (including negligence), contract, or otherwise,\n unless required by applicable law (such as deliberate and grossly\n negligent acts) or agreed to in writing, shall any Contributor be\n liable to You for damages, including any direct, indirect, special,\n incidental, or consequential damages of any character arising as a\n result of this License or out of the use or inability to use the\n Work (including but not limited to damages for loss of goodwill,\n work stoppage, computer failure or malfunction, or any and all\n other commercial damages or losses), even if such Contributor\n has been advised of the possibility of such damages.\n\n 9. Accepting Warranty or Additional Liability. While redistributing\n the Work or Derivative Works thereof, You may choose to offer,\n and charge a fee for, acceptance of support, warranty, indemnity,\n or other liability obligations and/or rights consistent with this\n License. However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n\n END OF TERMS AND CONDITIONS\n\n APPENDIX: How to apply the Apache License to your work.\n\n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"[]\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright [yyyy] [name of copyright owner]\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n" }, { "path": "NOTICE.txt", "content": "Delta Lake\nCopyright (2021) The Delta Lake Project Authors.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\nhttp://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\nThis project includes software licensed by the Apache Software Foundation (Apache 2.0)\nfrom the Apache Spark project (www.github.com/apache/spark)\n\n----------------------------------------------------------\nApache Spark\nCopyright 2014 and onwards The Apache Software Foundation.\n\nThis product includes software developed at\nThe Apache Software Foundation (http://www.apache.org/).\n" }, { "path": "PROTOCOL.md", "content": "\n\n# Delta Transaction Log Protocol\n\n- [Overview](#overview)\n- [Delta Table Specification](#delta-table-specification)\n - [File Types](#file-types)\n - [Data Files](#data-files)\n - [Deletion Vector Files](#deletion-vector-files)\n - [Change Data Files](#change-data-files)\n - [Delta Log Entries](#delta-log-entries)\n - [Checkpoints](#checkpoints)\n - [Sidecar Files](#sidecar-files)\n - [Log Compaction Files](#log-compaction-files)\n - [Last Checkpoint File](#last-checkpoint-file)\n - [Version Checksum File](#version-checksum-file)\n - [File Size Histogram Schema](#file-size-histogram-schema)\n - [Actions](#actions)\n - [Change Metadata](#change-metadata)\n - [Format Specification](#format-specification)\n - [Add File and Remove File](#add-file-and-remove-file)\n - [Add CDC File](#add-cdc-file)\n - [Writer Requirements for AddCDCFile](#writer-requirements-for-addcdcfile)\n - [Reader Requirements for AddCDCFile](#reader-requirements-for-addcdcfile)\n - [Transaction Identifiers](#transaction-identifiers)\n - [Protocol Evolution](#protocol-evolution)\n - [Commit Provenance Information](#commit-provenance-information)\n - [Domain Metadata](#domain-metadata)\n - [Reader Requirements for Domain Metadata](#reader-requirements-for-domain-metadata)\n - [Writer Requirements for Domain Metadata](#writer-requirements-for-domain-metadata)\n - [Sidecar File Information](#sidecar-file-information)\n - [Checkpoint Metadata](#checkpoint-metadata)\n- [Action Reconciliation](#action-reconciliation)\n- [Table Features](#table-features)\n - [Table Features for New and Existing Tables](#table-features-for-new-and-existing-tables)\n - [Supported Features](#supported-features)\n - [Active Features](#active-features)\n- [Column Mapping](#column-mapping)\n - [Writer Requirements for Column Mapping](#writer-requirements-for-column-mapping)\n - [Reader Requirements for Column Mapping](#reader-requirements-for-column-mapping)\n- [Deletion Vectors](#deletion-vectors)\n - [Deletion Vector Descriptor Schema](#deletion-vector-descriptor-schema)\n - [Derived Fields](#derived-fields)\n - [JSON Example 1 — On Disk with Relative Path (with Random Prefix)](#json-example-1--on-disk-with-relative-path-with-random-prefix)\n - [JSON Example 2 — On Disk with Absolute Path](#json-example-2--on-disk-with-absolute-path)\n - [JSON Example 3 — Inline](#json-example-3--inline)\n - [Reader Requirements for Deletion Vectors](#reader-requirements-for-deletion-vectors)\n - [Writer Requirement for Deletion Vectors](#writer-requirement-for-deletion-vectors)\n- [Iceberg Compatibility V1](#iceberg-compatibility-v1)\n - [Writer Requirements for IcebergCompatV1](#writer-requirements-for-icebergcompatv1)\n- [Iceberg Compatibility V2](#iceberg-compatibility-v2)\n - [Writer Requirement for IcebergCompatV2](#iceberg-compatibility-v2)\n- [Timestamp without timezone (TimestampNtz)](#timestamp-without-timezone-timestampntz)\n- [V2 Checkpoint Table Feature](#v2-checkpoint-table-feature)\n- [Row Tracking](#row-tracking)\n - [Row IDs](#row-ids)\n - [Row Commit Versions](#row-commit-versions)\n - [Reader Requirements for Row Tracking](#reader-requirements-for-row-tracking)\n - [Writer Requirements for Row Tracking](#writer-requirements-for-row-tracking)\n- [VACUUM Protocol Check](#vacuum-protocol-check)\n - [Writer Requirements for Vacuum Protocol Check](#writer-requirements-for-vacuum-protocol-check)\n - [Reader Requirements for Vacuum Protocol Check](#reader-requirements-for-vacuum-protocol-check)\n- [Clustered Table](#clustered-table)\n - [Writer Requirements for Clustered Table](#writer-requirements-for-clustered-table)\n- [Variant Data Type](#variant-data-type)\n - [Variant data in Parquet](#variant-data-in-parquet)\n - [Writer Requirements for Variant Type](#writer-requirements-for-variant-type)\n - [Reader Requirements for Variant Data Type](#reader-requirements-for-variant-data-type)\n - [Compatibility with other Delta Features](#compatibility-with-other-delta-features)\n- [Catalog-managed tables](#catalog-managed-tables)\n - [Terminology: Commits](#terminology-commits)\n - [Terminology: Delta Client](#terminology-delta-client)\n - [Terminology: Catalogs](#terminology-catalogs)\n - [Catalog Responsibilities](#catalog-responsibilities)\n - [Reading Catalog-managed Tables](#reading-catalog-managed-tables)\n - [Commit Protocol](#commit-protocol)\n - [Getting Ratified Commits from the Catalog](#getting-ratified-commits-from-the-catalog)\n - [Publishing Commits](#publishing-commits)\n - [Maintenance Operations on Catalog-managed Tables](#maintenance-operations-on-catalog-managed-tables)\n - [Creating and Dropping Catalog-managed Tables](#creating-and-dropping-catalog-managed-tables)\n - [Catalog-managed Table Enablement](#catalog-managed-table-enablement)\n - [Writer Requirements for Catalog-managed tables](#writer-requirements-for-catalog-managed-tables)\n - [Reader Requirements for Catalog-managed tables](#reader-requirements-for-catalog-managed-tables)\n - [Table Discovery](#table-discovery)\n - [Sample Catalog Client API](#sample-catalog-client-api)\n- [Requirements for Writers](#requirements-for-writers)\n - [Creation of New Log Entries](#creation-of-new-log-entries)\n - [Consistency Between Table Metadata and Data Files](#consistency-between-table-metadata-and-data-files)\n - [Delta Log Entries](#delta-log-entries-1)\n - [Checkpoints](#checkpoints-1)\n - [Checkpoint Specs](#checkpoint-specs)\n - [V2 Spec](#v2-spec)\n - [V1 Spec](#v1-spec)\n - [Checkpoint Naming Scheme](#checkpoint-naming-scheme)\n - [UUID-named checkpoint](#uuid-named-checkpoint)\n - [Classic checkpoint](#classic-checkpoint)\n - [Multi-part checkpoint](#multi-part-checkpoint)\n - [Problems with multi-part checkpoints](#problems-with-multi-part-checkpoints)\n - [Handling Backward compatibility while moving to UUID-named v2 Checkpoints](#handling-backward-compatibility-while-moving-to-uuid-named-v2-checkpoints)\n - [Allowed combinations for `checkpoint spec` <-> `checkpoint file naming`](#allowed-combinations-for-checkpoint-spec---checkpoint-file-naming)\n - [Metadata Cleanup](#metadata-cleanup)\n - [Data Files](#data-files-1)\n - [Append-only Tables](#append-only-tables)\n - [Column Invariants](#column-invariants)\n - [CHECK Constraints](#check-constraints)\n - [Generated Columns](#generated-columns)\n - [Default Columns](#default-columns)\n - [Identity Columns](#identity-columns)\n - [Writer Version Requirements](#writer-version-requirements)\n- [Requirements for Readers](#requirements-for-readers)\n - [Reader Version Requirements](#reader-version-requirements)\n- [Appendix](#appendix)\n - [Valid Feature Names in Table Features](#valid-feature-names-in-table-features)\n - [Deletion Vector Format](#deletion-vector-format)\n - [Deletion Vector File Storage Format](#deletion-vector-file-storage-format)\n - [Per-file Statistics](#per-file-statistics)\n - [Partition Value Serialization](#partition-value-serialization)\n - [Schema Serialization Format](#schema-serialization-format)\n - [Primitive Types](#primitive-types)\n - [Struct Type](#struct-type)\n - [Struct Field](#struct-field)\n - [Array Type](#array-type)\n - [Map Type](#map-type)\n - [Variant Type](#variant-type)\n - [Column Metadata](#column-metadata)\n - [Example](#example)\n - [Checkpoint Schema](#checkpoint-schema)\n - [Last Checkpoint File Schema](#last-checkpoint-file-schema)\n - [JSON checksum](#json-checksum)\n - [How to URL encode keys and string values](#how-to-url-encode-keys-and-string-values)\n - [Delta Data Type to Parquet Type Mappings](#delta-data-type-to-parquet-type-mappings)\n\n\n\n# Overview\nThis document is a specification for the Delta Transaction Protocol, which brings [ACID](https://en.wikipedia.org/wiki/ACID) properties to large collections of data, stored as files, in a distributed file system or object store. The protocol was designed with the following goals in mind:\n\n- **Serializable ACID Writes** - multiple writers can concurrently modify a Delta table while maintaining ACID semantics.\n- **Snapshot Isolation for Reads** - readers can read a consistent snapshot of a Delta table, even in the face of concurrent writes.\n- **Scalability to billions of partitions or files** - queries against a Delta table can be planned on a single machine or in parallel.\n- **Self describing** - all metadata for a Delta table is stored alongside the data. This design eliminates the need to maintain a separate metastore just to read the data and also allows static tables to be copied or moved using standard filesystem tools.\n- **Support for incremental processing** - readers can tail the Delta log to determine what data has been added in a given period of time, allowing for efficient streaming.\n\nDelta's transactions are implemented using multi-version concurrency control (MVCC).\nAs a table changes, Delta's MVCC algorithm keeps multiple copies of the data around rather than immediately replacing files that contain records that are being updated or removed.\n\nReaders of the table ensure that they only see one consistent _snapshot_ of a table at time by using the _transaction log_ to selectively choose which _data files_ to process.\n\nWriters modify the table in two phases:\nFirst, they optimistically write out new data files or updated copies of existing ones.\nThen, they _commit_, creating the latest _atomic version_ of the table by adding a new entry to the log.\nIn this log entry they record which data files to logically add and remove, along with changes to other metadata about the table.\n\nData files that are no longer present in the latest version of the table can be lazily deleted by the vacuum command after a user-specified retention period (default 7 days).\n\n# Delta Table Specification\nA table has a single serial history of atomic versions, which are named using contiguous, monotonically-increasing integers.\nThe state of a table at a given version is called a _snapshot_ and is defined by the following properties:\n - **Delta log protocol** consists of two **protocol versions**, and if applicable, corresponding **table features**, that are required to correctly read or write the table\n - **Reader features** only exists when Reader Version is 3\n - **Writer features** only exists when Writer Version is 7\n - **Metadata** of the table (e.g., the schema, a unique identifier, partition columns, and other configuration properties)\n - **Set of files** present in the table, along with metadata about those files\n - **Set of tombstones** for files that were recently deleted\n - **Set of applications-specific transactions** that have been successfully committed to the table\n\n## File Types\nA Delta table is stored within a directory and is composed of the following different types of files.\n\nHere is an example of a Delta table with four entries in the commit log, stored in the directory `mytable`.\n```\n/mytable/_delta_log/00000000000000000042.json\n/mytable/_delta_log/00000000000000000042.checkpoint.parquet\n/mytable/_delta_log/00000000000000000043.json\n/mytable/_delta_log/00000000000000000044.json\n/mytable/_delta_log/00000000000000000045.json\n/mytable/_delta_log/_last_checkpoint\n/mytable/_change_data/cdc-00000-924d9ac7-21a9-4121-b067-a0a6517aa8ed.c000.snappy.parquet\n/mytable/part-00000-3935a07c-416b-4344-ad97-2a38342ee2fc.c000.snappy.parquet\n/mytable/deletion_vector-0c6cbaaf-5e04-4c9d-8959-1088814f58ef.bin\n```\n\nThis example represents a table after [metadata cleanup](#metadata-cleanup) has removed older log entries. The checkpoint at version 42 contains the complete table state, while versions 43-45 are subsequent commits. Each file type is described in the sections below.\n\n### Data Files\nData files can be stored in the root directory of the table or in any non-hidden subdirectory (i.e., one whose name does not start with an `_`).\nBy default, the reference implementation stores data files in directories that are named based on the partition values for data in that file (i.e. `part1=value1/part2=value2/...`).\nThis directory format is only used to follow existing conventions and is not required by the protocol.\nActual partition values for a file must be read from the transaction log.\n\n### Deletion Vector Files\nDeletion Vector (DV) files are stored in the root directory of the table alongside the data files. A DV file contains one or more serialised DV, each describing the set of *invalidated* (or \"soft deleted\") rows for a particular data file it is associated with.\nFor data with partition values, DV files are *not* kept in the same directory hierarchy as data files, as each one can contain DVs for files from multiple partitions.\nDV files store DVs in a [binary format](#deletion-vector-format).\n\n### Change Data Files\nChange data files are stored in a directory at the root of the table named `_change_data`, and represent the changes for the table version they are in. For data with partition values, it is recommended that the change data files are stored within the `_change_data` directory in their respective partitions (i.e. `_change_data/part1=value1/...`). Writers can _optionally_ produce these change data files as a consequence of operations that change underlying data, like `UPDATE`, `DELETE`, and `MERGE` operations to a Delta Lake table. If an operation only adds new data or removes existing data without updating any existing rows, a writer can write only data files and commit them in `add` or `remove` actions without duplicating the data into change data files. When available, change data readers should use the change data files instead of computing changes from the underlying data files.\n\nIn addition to the data columns, change data files contain additional columns that identify the type of change event:\n\nField Name | Data Type | Description\n-|-|-\n_change_type|`String`| `insert`, `update_preimage` , `update_postimage`, `delete` __(1)__\n\n__(1)__ `preimage` is the value before the update, `postimage` is the value after the update.\n\n### Delta Log Entries\n\nDelta Log Entries, also known as Delta files, are JSON files stored in the `_delta_log`\ndirectory at the root of the table. Together with checkpoints, they make up the log of all changes\nthat have occurred to a table. Delta files are the unit of atomicity for a table, and are named\nusing the next available version number, zero-padded to 20 digits.\n\nFor example:\n\n```\n./_delta_log/00000000000000000000.json\n```\n\nDelta files use newline-delimited JSON format, where every action is stored as a single-line\nJSON document. A Delta file, corresponding to version `v`, contains an atomic set of\n[_actions_](#actions) that should be applied to the previous table state corresponding to version\n`v-1`, in order to construct the `v`th snapshot of the table. An action changes one aspect of the\ntable's state, for example, adding or removing a file.\n\n**Note:** If the [catalogManaged table feature](#catalog-managed-tables) is enabled on the table,\nrecently [ratified commits](#ratified-commit) may not yet be published to the `_delta_log` directory as normal Delta\nfiles - they may be stored directly by the catalog or reside in the `_delta_log/_staged_commits`\ndirectory. Delta clients must contact the table's managing catalog in order to find the information\nabout these [ratified, potentially-unpublished commits](#publishing-commits).\n\nThe `_delta_log/_staged_commits` directory is the staging area for [staged](#staged-commit)\ncommits. Delta files in this directory have a UUID embedded into them and follow the pattern\n`..json`, where the version corresponds to the proposed commit version, zero-padded\nto 20 digits.\n\nFor example:\n\n```\n./_delta_log/_staged_commits/00000000000000000000.3a0d65cd-4056-49b8-937b-95f9e3ee90e5.json\n./_delta_log/_staged_commits/00000000000000000001.7d17ac10-5cc3-401b-bd1a-9c82dd2ea032.json\n./_delta_log/_staged_commits/00000000000000000001.016ae953-37a9-438e-8683-9a9a4a79a395.json\n./_delta_log/_staged_commits/00000000000000000002.3ae45b72-24e1-865a-a211-34987ae02f2a.json\n```\n\nNOTE: The (proposed) version number of a staged commit is authoritative - file\n`00000000000000000100..json` always corresponds to a commit attempt for version 100. Besides\nsimplifying implementations, it also acknowledges the fact that commit files cannot safely be reused\nfor multiple commit attempts. For example, resolving conflicts in a table with [row\ntracking](https://github.com/delta-io/delta/blob/master/PROTOCOL.md#row-tracking) enabled requires\nrewriting all file actions to update their `baseRowId` field.\n\nThe [catalog](#terminology-catalogs) is the source of truth about which staged commit files in\nthe `_delta_log/_staged_commits` directory correspond to ratified versions, and Delta clients should\nnot attempt to directly interpret the contents of that directory. Refer to\n[catalog-managed tables](#catalog-managed-tables) for more details.\n\n### Checkpoints\nCheckpoints are also stored in the `_delta_log` directory, and can be created at any time, for any committed version of the table.\nFor performance reasons, readers should prefer to use the newest complete checkpoint possible.\nFor time travel, the checkpoint used must not be newer than the time travel version.\n\nA checkpoint contains the complete replay of all actions, up to and including the checkpointed table version, with invalid actions removed.\nInvalid actions are those that have been canceled out by subsequent ones (for example removing a file that has been added), using the [rules for reconciliation](#Action-Reconciliation).\nIn addition to above, checkpoint also contains the [_remove tombstones_](#add-file-and-remove-file) until they are expired.\nCheckpoints allow readers to short-cut the cost of reading the log up-to a given point in order to reconstruct a snapshot, and they also allow [Metadata cleanup](#metadata-cleanup) to delete expired JSON Delta log entries.\n\nReaders SHOULD NOT make any assumptions about the existence or frequency of checkpoints, with one exception:\n[Metadata cleanup](#metadata-cleanup) MUST provide a checkpoint for the oldest kept table version, to cover all deleted [Delta log entries](#delta-log-entries).\nThat said, writers are encouraged to checkpoint reasonably frequently, so that readers do not pay excessive log replay costs due to reading large numbers of delta files.\n\nThe checkpoint file name is based on the version of the table that the checkpoint contains.\n\nDelta supports three kinds of checkpoints:\n\n1. UUID-named Checkpoints: These follow [V2 spec](#v2-spec) which uses the following file name: `n.checkpoint.u.{json/parquet}`, where `u` is a UUID and `n` is the\nsnapshot version that this checkpoint represents. Here `n` must be zero padded to have length 20. The UUID-named V2 Checkpoint may be in json or parquet format, and references zero or more checkpoint sidecars\nin the `_delta_log/_sidecars` directory. A checkpoint sidecar is a uniquely-named parquet file: `{unique}.parquet` where `unique` is some unique\nstring such as a UUID.\n\nFor example:\n\n```\n00000000000000000010.checkpoint.80a083e8-7026-4e79-81be-64bd76c43a11.json\n_sidecars/3a0d65cd-4056-49b8-937b-95f9e3ee90e5.parquet\n_sidecars/016ae953-37a9-438e-8683-9a9a4a79a395.parquet\n_sidecars/7d17ac10-5cc3-401b-bd1a-9c82dd2ea032.parquet\n```\n\n2. A [classic checkpoint](#classic-checkpoint) for version `n` of the table consists of a file named `n.checkpoint.parquet`. Here `n` must be zero padded to have length 20.\nThese could follow either [V1 spec](#v1-spec) or [V2 spec](#v2-spec).\nFor example:\n\n```\n00000000000000000010.checkpoint.parquet\n```\n\n\n3. A [multi-part checkpoint](#multi-part-checkpoint) for version `n` consists of `p` \"part\" files (`p > 1`), where\npart `o` of `p` is named `n.checkpoint.o.p.parquet`. Here `n` must be zero padded to have length 20, while `o` and `p` must be zero padded to have length 10. These are always [V1 checkpoints](#v1-spec).\nFor example:\n\n```\n00000000000000000010.checkpoint.0000000001.0000000003.parquet\n00000000000000000010.checkpoint.0000000002.0000000003.parquet\n00000000000000000010.checkpoint.0000000003.0000000003.parquet\n```\n\nA writer can choose to write checkpoints with following constraints:\n- Writers are always allowed create a [classic checkpoint](#classic-checkpoint) following [v1 spec](#v1-spec).\n- Writers are forbidden to create [multi-part checkpoints](#multi-part-checkpoint) if [v2 checkpoints](#v2-checkpoint-table-feature) are enabled.\n- Writers are allowed to create v2 spec checkpoints (either [classic](#classic-checkpoint) or [uuid-named](#uuid-named-checkpoint)) if [v2 checkpoint table feature](#v2-checkpoint-table-feature) is enabled.\n\nMulti-part checkpoints are [deprecated](#problems-with-multi-part-checkpoints), and writers should avoid creating them. Use uuid-named [V2 spec](#v2-spec) checkpoints instead of these.\n\nMultiple checkpoints could exist for the same table version, e.g. if two clients race to create checkpoints at the same time, but with different formats.\nIn such cases, a client can choose which checkpoint to use.\n\nBecause a multi-part checkpoint cannot be created atomically (e.g. vulnerable to slow and/or failed writes), readers must ignore multi-part checkpoints with missing parts.\n\nCheckpoints for a given version must only be created after the associated delta file has been successfully written.\n\n#### Sidecar Files\n\nA sidecar file contains file actions. These files are in parquet format and they must have unique names.\nThese are then [linked](#sidecar-file-information) to checkpoints. Refer to [V2 checkpoint spec](#v2-spec)\nfor more detail. The sidecar files can have only [add file and remove file](#Add-File-and-Remove-File) entries\nas of now. The add and remove file actions are stored as their individual columns in parquet as struct fields.\n\nThese files reside in the `_delta_log/_sidecars` directory.\n\n### Log Compaction Files\n\nLog compaction files reside in the `_delta_log` directory. A log compaction\nfile from a start version `x` to an end version `y` (`y` must be _greater_ than `x`)\nwill have the following name:\n`..compacted.json`. This contains the aggregated\nactions for commit range `[x, y]`. Similar to commits, each row in the log\ncompaction file represents an [action](#actions).\nThe commit files for a given range are created by doing [Action Reconciliation](#action-reconciliation)\nof the corresponding commits.\nInstead of reading the individual commit files in range `[x, y]`, an implementation could choose to read\nthe log compaction file `..compacted.json` to speed up the snapshot construction.\n\nExample:\nSuppose we have `00000000000000000004.json` as:\n```\n{\"commitInfo\":{...}}\n{\"add\":{\"path\":\"f2\",...}}\n{\"remove\":{\"path\":\"f1\",...}}\n```\n`00000000000000000005.json` as:\n```\n{\"commitInfo\":{...}}\n{\"add\":{\"path\":\"f3\",...}}\n{\"add\":{\"path\":\"f4\",...}}\n{\"txn\":{\"appId\":\"3ae45b72-24e1-865a-a211-34987ae02f2a\",\"version\":4389}}\n```\n`00000000000000000006.json` as:\n```\n{\"commitInfo\":{...}}\n{\"remove\":{\"path\":\"f3\",...}}\n{\"txn\":{\"appId\":\"3ae45b72-24e1-865a-a211-34987ae02f2a\",\"version\":4390}}\n```\n\nThen `00000000000000000004.00000000000000000006.compacted.json` will have the following content:\n```\n{\"add\":{\"path\":\"f2\",...}}\n{\"add\":{\"path\":\"f4\",...}}\n{\"remove\":{\"path\":\"f1\",...}}\n{\"remove\":{\"path\":\"f3\",...}}\n{\"txn\":{\"appId\":\"3ae45b72-24e1-865a-a211-34987ae02f2a\",\"version\":4390}}\n```\n\nWriters:\n- Can optionally produce log compactions for any given commit range\n\nReaders:\n- Can optionally consume log compactions, if available\n- The compaction replaces the corresponding commits during action reconciliation\n\n### Last Checkpoint File\nThe Delta transaction log will often contain many (e.g. 10,000+) files.\nListing such a large directory can be prohibitively expensive.\nThe last checkpoint file can help reduce the cost of constructing the latest snapshot of the table by providing a pointer to near the end of the log.\n\nRather than list the entire directory, readers can locate a recent checkpoint by looking at the `_delta_log/_last_checkpoint` file.\nDue to the zero-padded encoding of the files in the log, the version id of this recent checkpoint can be used on storage systems that support lexicographically-sorted, paginated directory listing to enumerate any delta files or newer checkpoints that comprise more recent versions of the table.\n\n### Version Checksum File\n\nThe Delta transaction log must remain an append-only log. To enable the detection of non-compliant modifications to Delta files, writers can optionally emit an auxiliary file with every commit, which contains important information about the state of the table as of that version. This file is referred to as the **Version Checksum** and can be used to validate the integrity of the table.\n\n### Version Checksum File Schema\n\nA Version Checksum file must have the following properties:\n- Be named `{version}.crc` where `version` is zero-padded to 20 digits (e.g., `00000000000000000001.crc`)\n- Be stored directly in the `_delta_log` directory alongside Delta log files\n- Contain exactly one JSON object with the following schema:\n\nField Name | Data Type | Description | optional/required\n-|-|-|-\ntxnId | String | A unique identifier for the transaction that produced this commit. | optional\ntableSizeBytes | Long | Total size of the table in bytes, calculated as the sum of the `size` field of all live `add` actions. | required\nnumFiles | Long | Number of live `add` actions in this table version after Action Reconciliation. | required\nnumMetadata | Long | Number of `metaData` actions. Must be 1. | required\nnumProtocol | Long | Number of `protocol` actions. Must be 1. | required\ninCommitTimestampOpt | Long | The in-commit timestamp of this version. Present if and only if [In-Commit Timestamps](#in-commit-timestamps) are enabled. | optional\nsetTransactions | Array[`txn`] | Live [Transaction Identifier](#transaction-identifiers) actions at this version. | optional\ndomainMetadata | Array[`domainMetadata`] | Live [Domain Metadata](#domain-metadata) actions at this version, excluding tombstones. | optional\nmetadata | Metadata | The table [metadata](#change-metadata) at this version. | required\nprotocol | Protocol | The table [protocol](#protocol-evolution) at this version. | required\nfileSizeHistogram | FileSizeHistogram | Size distribution information of files remaining after [Action Reconciliation](#action-reconciliation). See [FileSizeHistogram](#file-size-histogram-schema) for more details. | optional\nallFiles | Array[`add`] | All live [Add File](#add-file-and-remove-file) actions at this version. | optional\nnumDeletedRecordsOpt | Long | Number of records deleted through Deletion Vectors in this table version. | optional\nnumDeletionVectorsOpt | Long | Number of Deletion Vectors active in this table version. | optional\ndeletedRecordCountsHistogramOpt | DeletedRecordCountsHistogram | Distribution of deleted record counts across files. See [this](#deleted-record-counts-histogram-schema) section for more details. | optional\n\n##### File Size Histogram Schema\n\nThe `FileSizeHistogram` object represents a histogram tracking file counts and total bytes across different size ranges. It has the following schema:\n\nField Name | Data Type | Description | optional/required\n-|-|-|-\nsortedBinBoundaries | Array[Long] | A sorted array of bin boundaries where each element represents the start of a bin (inclusive) and the next element represents the end of the bin (exclusive). The first element must be 0. | required\nfileCounts | Array[Long] | Count of files in each bin. Length must match `sortedBinBoundaries`. | required\ntotalBytes | Array[Long] | Total bytes of files in each bin. Length must match `sortedBinBoundaries`. | required\n\nEach index `i` in these arrays corresponds to a size range from `sortedBinBoundaries[i]` (inclusive) up to but not including `sortedBinBoundaries[i+1]`. The last bin ends at positive infinity. For example, given boundaries `[0, 1024, 4096]`:\n- Bin 0 contains files of size [0, 1024) bytes\n- Bin 1 contains files of size [1024, 4096) bytes\n- Bin 2 contains files of size [4096, ∞) bytes\n\nThe arrays `fileCounts` and `totalBytes` store the number of files and their total size respectively that fall into each bin. This data structure enables efficient analysis of file size distributions in Delta tables.\n\n### Deleted Record Counts Histogram Schema\n\nThe `DeletedRecordCountsHistogram` object represents a histogram tracking the distribution of deleted record counts across files in the table. Each bin in the histogram represents a range of deletion counts and stores the number of files having that many deleted records.\n\nField Name | Data Type | Description | optional/required\n-|-|-|-\ndeletedRecordCounts | Array[Long] | Array of size 10 where each element represents the count of files falling into a specific deletion count range. | required\n\nThe histogram bins correspond to the following ranges:\n- Bin 0: [0, 0] (files with no deletions)\n- Bin 1: [1, 9] (files with 1-9 deleted records)\n- Bin 2: [10, 99] (files with 10-99 deleted records)\n- Bin 3: [100, 999] (files with 100-999 deleted records) \n- Bin 4: [1000, 9999] (files with 1,000-9,999 deleted records)\n- Bin 5: [10000, 99999] (files with 10,000-99,999 deleted records)\n- Bin 6: [100000, 999999] (files with 100,000-999,999 deleted records)\n- Bin 7: [1000000, 9999999] (files with 1,000,000-9,999,999 deleted records)\n- Bin 8: [10000000, 2147483646] (files with 10,000,000 to 2147483646 (i.e. Int.MaxValue-1 in Java) deleted records)\n- Bin 9: [2147483647, ∞) (files with 2147483647 or more deleted records)\n\nThis histogram allows analyzing the distribution of deleted records across files in a Delta table, which can be useful for monitoring and optimizing deletion patterns.\n\n#### State Validation\n\nReaders can validate table state integrity at a particular version by:\n1. Reading the Version Checksum file for that version\n2. Independently computing the same metrics by performing [Action Reconciliation](#action-reconciliation) on the table state\n3. Comparing the computed values against those recorded in the Version Checksum\n\nIf any discrepancy is found between computed and recorded values, the table state at that version should be considered potentially corrupted.\n\n### Writer Requirements\n\n- Writers SHOULD produce a Version Checksum file for each commit\n- Writers MUST ensure all metrics in the Version Checksum accurately reflect table state after Action Reconciliation\n- Writers MUST write the Version Checksum file only after successfully writing the corresponding Delta log entry\n- Writers MUST NOT overwrite existing Version Checksum files\n\n### Reader Requirements\n\n- Readers MAY use Version Checksums to validate table state integrity\n- If performing validation, readers SHOULD verify all required fields match computed values\n- If validation fails, readers SHOULD surface the discrepancy to users via error messaging\n- Readers MUST continue functioning if Version Checksum files are missing\n\n\n## Actions\nActions modify the state of the table and they are stored both in delta files and in checkpoints.\nThis section lists the space of available actions as well as their schema.\n\n### Change Metadata\nThe `metaData` action changes the current metadata of the table.\nThe first version of a table must contain a `metaData` action.\nSubsequent` metaData` actions completely overwrite the current metadata of the table.\n\nThere can be at most one metadata action in a given version of the table.\n\nEvery metadata action **must** include required fields at a minimum.\n\nThe schema of the `metaData` action is as follows:\n\nField Name | Data Type | Description | optional/required\n-|-|-|-\nid|`GUID`|Unique identifier for this table | required\nname|`String`| User-provided identifier for this table | optional\ndescription|`String`| User-provided description for this table | optional\nformat|[Format Struct](#Format-Specification)| Specification of the encoding for the files stored in the table | required\nschemaString|[Schema Struct](#Schema-Serialization-Format)| Schema of the table | required\npartitionColumns|`Array[String]`| An array containing the names of columns by which the data should be partitioned | required\ncreatedTime|`Option[Long]`| The time when this metadata action is created, in milliseconds since the Unix epoch | optional\nconfiguration|`Map[String, String]`| A map containing configuration options for the metadata action | required\n\n#### Format Specification\nField Name | Data Type | Description\n-|-|-\nprovider|`String`|Name of the encoding for files in this table\noptions|`Map[String, String]`|A map containing configuration options for the format\n\nIn the reference implementation, the provider field is used to instantiate a Spark SQL [`FileFormat`](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileFormat.scala). As of Spark 2.4.3 there is built-in `FileFormat` support for `parquet`, `csv`, `orc`, `json`, and `text`.\n\nAs of Delta Lake 0.3.0, user-facing APIs only allow the creation of tables where `format = 'parquet'` and `options = {}`. Support for reading other formats is present both for legacy reasons and to enable possible support for other formats in the future (See [#87](https://github.com/delta-io/delta/issues/87)).\n\nThe following is an example `metaData` action:\n```json\n{\n \"metaData\":{\n \"id\":\"af23c9d7-fff1-4a5a-a2c8-55c59bd782aa\",\n \"format\":{\"provider\":\"parquet\",\"options\":{}},\n \"schemaString\":\"...\",\n \"partitionColumns\":[],\n \"configuration\":{\n \"appendOnly\": \"true\"\n }\n }\n}\n```\n\n\n\n### Add File and Remove File\nThe `add` and `remove` actions are used to modify the data in a table by adding or removing individual _logical files_ respectively.\n\nEvery _logical file_ of the table is represented by a path to a data file, combined with an optional Deletion Vector (DV) that indicates which rows of the data file are no longer in the table. Deletion Vectors are an optional feature, see their [reader requirements](#deletion-vectors) for details.\n\nWhen an `add` action is encountered for a logical file that is already present in the table, statistics and other information from the latest version should replace that from any previous version.\nThe primary key for the entry of a logical file in the set of files is a tuple of the data file's `path` and a unique id describing the DV. If no DV is part of this logical file, then its primary key is `(path, NULL)` instead.\n\nThe `remove` action includes a timestamp that indicates when the removal occurred.\nPhysical deletion of physical files can happen lazily after some user-specified expiration time threshold.\nThis delay allows concurrent readers to continue to execute against a stale snapshot of the data.\nA `remove` action should remain in the state of the table as a _tombstone_ until it has expired.\nA tombstone expires when *current time* (according to the node performing the cleanup) exceeds the expiration threshold added to the `remove` action timestamp.\n\nIn the following statements, `dvId` can refer to either the unique id of a specific Deletion Vector (`deletionVector.uniqueId`) or to `NULL`, indicating that no rows are invalidated. Since actions within a given Delta commit are not guaranteed to be applied in order, a **valid** version is restricted to contain at most one file action *of the same type* (i.e. `add`/`remove`) for any one combination of `path` and `dvId`. Moreover, for simplicity it is required that there is at most one file action of the same type for any `path` (regardless of `dvId`).\nThat means specifically that for any commit…\n\n - it is **legal** for the same `path` to occur in an `add` action and a `remove` action, but with two different `dvId`s.\n - it is **legal** for the same `path` to be added and/or removed and also occur in a `cdc` action.\n - it is **illegal** for the same `path` to occur twice with different `dvId`s within each set of `add` or `remove` actions.\n - it is **illegal** for a `path` to occur in an `add` action that already occurs with a different `dvId` in the list of `add` actions from the snapshot of the version immediately preceeding the commit, unless the commit also contains a remove for the later combination.\n - it is **legal** to commit an existing `path` and `dvId` combination again (this allows metadata updates).\n\nThe `dataChange` flag on either an `add` or a `remove` can be set to `false` to indicate that an action when combined with other actions in the same atomic version only rearranges existing data or adds new statistics.\nFor example, streaming queries that are tailing the transaction log can use this flag to skip actions that would not affect the final results.\n\nThe schema of the `add` action is as follows:\n\nField Name | Data Type | Description | optional/required\n-|-|-|-\npath| String | A relative path to a data file from the root of the table or an absolute path to a file that should be added to the table. The path is a URI as specified by [RFC 2396 URI Generic Syntax](https://www.ietf.org/rfc/rfc2396.txt), which needs to be decoded to get the data file path. | required\npartitionValues| Map[String, String] | A map from partition column to value for this logical file. See also [Partition Value Serialization](#Partition-Value-Serialization) | required\nsize| Long | The size of this data file in bytes | required\nmodificationTime | Long | The time this logical file was created, as milliseconds since the epoch | required\ndataChange | Boolean | When `false` the logical file must already be present in the table or the records in the added file must be contained in one or more `remove` actions in the same version | required\nstats | [Statistics Struct](#Per-file-Statistics) | Contains statistics (e.g., count, min/max values for columns) about the data in this logical file | optional\ntags | Map[String, String] | Map containing metadata about this logical file | optional\ndeletionVector | [DeletionVectorDescriptor Struct](#Deletion-Vectors) | Either null (or absent in JSON) when no DV is associated with this data file, or a struct (described below) that contains necessary information about the DV that is part of this logical file. | optional\nbaseRowId | Long | Default generated Row ID of the first row in the file. The default generated Row IDs of the other rows in the file can be reconstructed by adding the physical index of the row within the file to the base Row ID. See also [Row IDs](#row-ids) | optional\ndefaultRowCommitVersion | Long | First commit version in which an `add` action with the same `path` was committed to the table. | optional\nclusteringProvider | String | The name of the clustering implementation. See also [Clustered Table](#clustered-table)| optional\n\nThe following is an example `add` action for a partitioned table:\n```json\n{\n \"add\": {\n \"path\": \"date=2017-12-10/part-000...c000.gz.parquet\",\n \"partitionValues\": {\"date\": \"2017-12-10\"},\n \"size\": 841454,\n \"modificationTime\": 1512909768000,\n \"dataChange\": true,\n \"baseRowId\": 4071,\n \"defaultRowCommitVersion\": 41,\n \"stats\": \"{\\\"numRecords\\\":1,\\\"minValues\\\":{\\\"val...\"\n }\n}\n```\n\nThe following is an example `add` action for a clustered table:\n```json\n{\n \"add\": {\n \"path\": \"date=2017-12-10/part-000...c000.gz.parquet\",\n \"partitionValues\": {},\n \"size\": 841454,\n \"modificationTime\": 1512909768000,\n \"dataChange\": true,\n \"baseRowId\": 4071,\n \"defaultRowCommitVersion\": 41,\n \"clusteringProvider\": \"liquid\",\n \"stats\": \"{\\\"numRecords\\\":1,\\\"minValues\\\":{\\\"val...\"\n }\n}\n```\n\nThe schema of the `remove` action is as follows:\n\nField Name | Data Type | Description | optional/required\n-|-|-|-\npath| String | A relative path to a file from the root of the table or an absolute path to a file that should be removed from the table. The path is a URI as specified by [RFC 2396 URI Generic Syntax](https://www.ietf.org/rfc/rfc2396.txt), which needs to be decoded to get the data file path. | required\ndeletionTimestamp | Option[Long] | The time the deletion occurred, represented as milliseconds since the epoch | optional\ndataChange | Boolean | When `false` the records in the removed file must be contained in one or more `add` file actions in the same version | required\nextendedFileMetadata | Boolean | When `true` the fields `partitionValues`, `size`, and `tags` are present | optional\npartitionValues| Map[String, String] | A map from partition column to value for this file. See also [Partition Value Serialization](#Partition-Value-Serialization) | optional\nsize| Long | The size of this data file in bytes | optional\nstats | [Statistics Struct](#Per-file-Statistics) | Contains statistics (e.g., count, min/max values for columns) about the data in this logical file | optional\ntags | Map[String, String] | Map containing metadata about this file | optional\ndeletionVector | [DeletionVectorDescriptor Struct](#Deletion-Vectors) | Either null (or absent in JSON) when no DV is associated with this data file, or a struct (described below) that contains necessary information about the DV that is part of this logical file. | optional\nbaseRowId | Long | Default generated Row ID of the first row in the file. The default generated Row IDs of the other rows in the file can be reconstructed by adding the physical index of the row within the file to the base Row ID. See also [Row IDs](#row-ids) | optional\ndefaultRowCommitVersion | Long | First commit version in which an `add` action with the same `path` was committed to the table | optional\n\nThe following is an example `remove` action.\n```json\n{\n \"remove\": {\n \"path\": \"part-00001-9…..snappy.parquet\",\n \"deletionTimestamp\": 1515488792485,\n \"baseRowId\": 4071,\n \"defaultRowCommitVersion\": 41,\n \"dataChange\": true\n }\n}\n```\n\n### Add CDC File\nThe `cdc` action is used to add a [file](#change-data-files) containing only the data that was changed as part of the transaction. The `cdc` action is allowed to add a [Data File](#data-files) that is also added by an `add` action, when it does not contain any copied rows and the `_change_type` column is filled for all rows.\n\nWhen change data readers encounter a `cdc` action in a particular Delta table version, they must read the changes made in that version exclusively using the `cdc` files. If a version has no `cdc` action, then the data in `add` and `remove` actions are read as inserted and deleted rows, respectively.\n\nThe schema of the `cdc` action is as follows:\n\nField Name | Data Type | Description\n-|-|-\npath| String | A relative path to a change data file from the root of the table or an absolute path to a change data file that should be added to the table. The path is a URI as specified by [RFC 2396 URI Generic Syntax](https://www.ietf.org/rfc/rfc2396.txt), which needs to be decoded to get the file path.\npartitionValues| Map[String, String] | A map from partition column to value for this file. See also [Partition Value Serialization](#Partition-Value-Serialization)\nsize| Long | The size of this file in bytes\ndataChange | Boolean | Should always be set to `false` for `cdc` actions because they _do not_ change the underlying data of the table\ntags | Map[String, String] | Map containing metadata about this file\n\nThe following is an example of `cdc` action.\n\n```json\n{\n \"cdc\": {\n \"path\": \"_change_data/cdc-00001-c…..snappy.parquet\",\n \"partitionValues\": {},\n \"size\": 1213,\n \"dataChange\": false\n }\n}\n```\n\n#### Writer Requirements for AddCDCFile\n\nFor [Writer Versions 4 up to 6](#Writer-Version-Requirements), all writers must respect the `delta.enableChangeDataFeed` configuration flag in the metadata of the table. When `delta.enableChangeDataFeed` is `true`, writers must produce the relevant `AddCDCFile`'s for any operation that changes data, as specified in [Change Data Files](#change-data-files).\n\nFor Writer Version 7, all writers must respect the `delta.enableChangeDataFeed` configuration flag in the metadata of the table only if the feature `changeDataFeed` exists in the table `protocol`'s `writerFeatures`.\n\n#### Reader Requirements for AddCDCFile\n\nWhen available, change data readers should use the `cdc` actions in a given table version instead of computing changes from the underlying data files referenced by the `add` and `remove` actions.\nSpecifically, to read the row-level changes made in a version, the following strategy should be used:\n1. If there are `cdc` actions in this version, then read only those to get the row-level changes, and skip the remaining `add` and `remove` actions in this version.\n2. Otherwise, if there are no `cdc` actions in this version, read and treat all the rows in the `add` and `remove` actions as inserted and deleted rows, respectively.\n3. Change data readers should return the following extra columns:\n\n Field Name | Data Type | Description\n -|-|-\n _commit_version|`Long`| The table version containing the change. This can be derived from the name of the Delta log file that contains actions.\n _commit_timestamp|`Timestamp`| The timestamp associated when the commit was created. Depending on whether [In-Commit Timestamps](#in-commit-timestamps) are enabled, this is derived from either the `inCommitTimestamp` field of the `commitInfo` action of the version's Delta log file, or from the Delta log file's modification time.\n\n##### Note for non-change data readers\n\nIn a table with Change Data Feed enabled, the data Parquet files referenced by `add` and `remove` actions are allowed to contain an extra column `_change_type`. This column is not present in the table's schema. When accessing these files, readers should disregard this column and only process columns defined within the table's schema.\n\n### Transaction Identifiers\nIncremental processing systems (e.g., streaming systems) that track progress using their own application-specific versions need to record what progress has been made, in order to avoid duplicating data in the face of failures and retries during a write.\nTransaction identifiers allow this information to be recorded atomically in the transaction log of a delta table along with the other actions that modify the contents of the table.\n\nTransaction identifiers are stored in the form of `appId` `version` pairs, where `appId` is a unique identifier for the process that is modifying the table and `version` is an indication of how much progress has been made by that application.\nThe atomic recording of this information along with modifications to the table enables these external system to make their writes into a Delta table _idempotent_.\n\nFor example, the [Delta Sink for Apache Spark's Structured Streaming](https://github.com/delta-io/delta/blob/master/core/src/main/scala/org/apache/spark/sql/delta/sources/DeltaSink.scala) ensures exactly-once semantics when writing a stream into a table using the following process:\n 1. Record in a write-ahead-log the data that will be written, along with a monotonically increasing identifier for this batch.\n 2. Check the current version of the transaction with `appId = streamId` in the target table. If this value is greater than or equal to the batch being written, then this data has already been added to the table and processing can skip to the next batch.\n 3. Write the data optimistically into the table.\n 4. Attempt to commit the transaction containing both the addition of the data written out and an updated `appId` `version` pair.\n\nThe semantics of the application-specific `version` are left up to the external system.\nDelta only ensures that the latest `version` for a given `appId` is available in the table snapshot.\nThe Delta transaction protocol does not, for example, assume monotonicity of the `version` and it would be valid for the `version` to decrease, possibly representing a \"rollback\" of an earlier transaction.\n\nThe schema of the `txn` action is as follows:\n\nField Name | Data Type | Description | optional/required\n-|-|-|-\nappId | String | A unique identifier for the application performing the transaction | required\nversion | Long | An application-specific numeric identifier for this transaction | required\nlastUpdated | Option[Long] | The time when this transaction action is created, in milliseconds since the Unix epoch | optional\n\nThe following is an example `txn` action:\n```json\n{\n \"txn\": {\n \"appId\":\"3ba13872-2d47-4e17-86a0-21afd2a22395\",\n \"version\":364475\n }\n}\n```\n\n### Protocol Evolution\nThe `protocol` action is used to increase the version of the Delta protocol that is required to read or write a given table.\nProtocol versioning allows a newer client to exclude older readers and/or writers that are missing features required to correctly interpret the transaction log.\nThe _protocol version_ will be increased whenever non-forward-compatible changes are made to this specification.\nIn the case where a client is running an invalid protocol version, an error should be thrown instructing the user to upgrade to a newer protocol version of their Delta client library.\n\nSince breaking changes must be accompanied by an increase in the protocol version recorded in a table or by the addition of a table feature, clients can assume that unrecognized actions, fields, and/or metadata domains are never required in order to correctly interpret the transaction log. Clients must ignore such unrecognized fields, and should not produce an error when reading a table that contains unrecognized fields.\n\nReader Version 3 and Writer Version 7 add two lists of table features to the protocol action. The capability for readers and writers to operate on such a table is not only dependent on their supported protocol versions, but also on whether they support all features listed in `readerFeatures` and `writerFeatures`. See [Table Features](#table-features) section for more information.\n\nThe schema of the `protocol` action is as follows:\n\nField Name | Data Type | Description | optional/required\n-|-|-|-\nminReaderVersion | Int | The minimum version of the Delta read protocol that a client must implement in order to correctly *read* this table | required\nminWriterVersion | Int | The minimum version of the Delta write protocol that a client must implement in order to correctly *write* this table | required\nreaderFeatures | Array[String] | A collection of features that a client must implement in order to correctly read this table (exist only when `minReaderVersion` is set to `3`) | optional\nwriterFeatures | Array[String] | A collection of features that a client must implement in order to correctly write this table (exist only when `minWriterVersion` is set to `7`) | optional\n\nSome example Delta protocols:\n```json\n{\n \"protocol\":{\n \"minReaderVersion\":1,\n \"minWriterVersion\":2\n }\n}\n```\n\nA table that is using table features only for writers:\n```json\n{\n \"protocol\":{\n \"readerVersion\":2,\n \"writerVersion\":7,\n \"writerFeatures\":[\"columnMapping\",\"identityColumns\"]\n }\n}\n```\nReader version 2 in the above example does not support listing reader features but supports Column Mapping. This example is equivalent to the next one, where Column Mapping is represented as a reader table feature.\n\nA table that is using table features for both readers and writers:\n```json\n{\n \"protocol\": {\n \"readerVersion\":3,\n \"writerVersion\":7,\n \"readerFeatures\":[\"columnMapping\"],\n \"writerFeatures\":[\"columnMapping\",\"identityColumns\"]\n }\n}\n```\n\n### Commit Provenance Information\nA delta file can optionally contain additional provenance information about what higher-level operation was being performed as well as who executed it.\n\nWhen the `catalogManaged` table feature is enabled, the `commitInfo` action must have a field\n`txnId` that stores a unique transaction identifier string.\n\nImplementations are free to store any valid JSON-formatted data via the `commitInfo` action.\n\nWhen [In-Commit Timestamps](#in-commit-timestamps) are enabled, writers are required to include a `commitInfo` action with every commit, which must include the `inCommitTimestamp` field. Also, the `commitInfo` action must be first action in the commit.\n\nAn example of storing provenance information related to an `INSERT` operation:\n```json\n{\n \"commitInfo\":{\n \"timestamp\":1515491537026,\n \"userId\":\"100121\",\n \"userName\":\"michael@databricks.com\",\n \"operation\":\"INSERT\",\n \"operationParameters\":{\"mode\":\"Append\",\"partitionBy\":\"[]\"},\n \"notebook\":{\n \"notebookId\":\"4443029\",\n \"notebookPath\":\"Users/michael@databricks.com/actions\"},\n \"clusterId\":\"1027-202406-pooh991\"\n } \n}\n```\n\n### Domain Metadata\nThe domain metadata action contains a configuration (string) for a named metadata domain. Two overlapping transactions conflict if they both contain a domain metadata action for the same metadata domain.\n\nThere are two types of metadata domains:\n1. **User-controlled metadata domains** have names that start with anything other than the `delta.` prefix. Any Delta client implementation or user application can modify these metadata domains, and can allow users to modify them arbitrarily. Delta clients and user applications are encouraged to use a naming convention designed to avoid conflicts with other clients' or users' metadata domains (e.g. `com.databricks.*` or `org.apache.*`).\n2. **System-controlled metadata domains** have names that start with the `delta.` prefix. This prefix is reserved for metadata domains defined by the Delta spec, and Delta client implementations must not allow users to modify the metadata for system-controlled domains. A Delta client implementation should only update metadata for system-controlled domains that it knows about and understands. System-controlled metadata domains are used by various table features and each table feature may impose additional semantics on the metadata domains it uses.\n\nThe schema of the `domainMetadata` action is as follows:\n\nField Name | Data Type | Description\n-|-|-\ndomain | String | Identifier for this domain (system- or user-provided)\nconfiguration | String | String containing configuration for the metadata domain\nremoved | Boolean | When `true`, the action serves as a tombstone to logically delete a metadata domain. Writers should preserve an accurate pre-image of the configuration.\n\nTo support this feature:\n- The table must be on Writer Version 7.\n- A feature name `domainMetadata` must exist in the table's `writerFeatures`.\n\n#### Reader Requirements for Domain Metadata\n- Readers are not required to support domain metadata.\n- Readers who choose not to support domain metadata should ignore metadata domain actions as unrecognized (see [Protocol Evolution](#protocol-evolution)) and snapshots should not include any metadata domains.\n- Readers who choose to support domain metadata must apply [Action Reconciliation](#action-reconciliation) to all metadata domains and snapshots must include them -- even if the reader does not understand them.\n- Any system-controlled domain that imposes any requirements on readers is a [breaking change](#protocol-evolution), and must be part of a reader-writer table feature that specifies the desired behavior.\n\n#### Writer Requirements for Domain Metadata\n- Writers must preserve all domains even if they don't understand them.\n- Writers must not allow users to modify or delete system-controlled domains.\n- Writers must only modify or delete system-controlled domains they understand.\n- Any system-controlled domain that imposes additional requirements on the writer is a [breaking change](#protocol-evolution), and must be part of a writer table feature that specifies the desired behavior.\n\nThe following is an example `domainMetadata` action:\n```json\n{\n \"domainMetadata\": {\n \"domain\": \"delta.deltaTableFeatureX\",\n \"configuration\": \"{\\\"key1\\\":\\\"value1\\\"}\",\n \"removed\": false\n }\n}\n```\n\n### Sidecar File Information\nThe `sidecar` action references a [sidecar file](#sidecar-files) which provides some of the checkpoint's file actions.\nThis action is only allowed in checkpoints following [V2 spec](#v2-spec).\nThe schema of `sidecar` action is as follows:\n\nField Name | Data Type | Description | optional/required\n-|-|-|-\npath | String | URI-encoded path to the sidecar file. Because sidecar files must always reside in the table's own _delta_log/_sidecars directory, implementations are encouraged to store only the file's name (without scheme or parent directories). | required\nsizeInBytes | Long | Size of the sidecar file. | required\nmodificationTime | Long | The time this logical file was created, as milliseconds since the epoch. | required\ntags|`Map[String, String]`|Map containing any additional metadata about the checkpoint sidecar file. | optional\n\nThe following is an example `sidecar` action:\n```json\n{\n \"sidecar\":{\n \"path\": \"016ae953-37a9-438e-8683-9a9a4a79a395.parquet\",\n \"sizeInBytes\": 2304522,\n \"modificationTime\": 1512909768000,\n \"tags\": {}\n }\n}\n```\n\n#### Checkpoint Metadata\nThis action is only allowed in checkpoints following [V2 spec](#v2-spec).\nIt describes the details about the checkpoint. It has the following schema:\n\nField Name | Data Type | Description | optional/required\n-|-|-|-\nversion|`Long`|The checkpoint version.| required\ntags|`Map[String, String]`|Map containing any additional metadata about the v2 spec checkpoint.| optional\n\nE.g.\n```json\n{\n \"checkpointMetadata\":{\n \"version\":1,\n \"tags\":{}\n }\n}\n```\n\n# Action Reconciliation\nA given snapshot of the table can be computed by replaying the events committed to the table in ascending order by commit version. A given snapshot of a Delta table consists of:\n\n - A single `protocol` action\n - A single `metaData` action\n - A collection of `txn` actions with unique `appId`s\n - A collection of `domainMetadata` actions with unique `domain`s.\n - A collection of `add` actions with unique path keys, corresponding to the newest (path, deletionVector.uniqueId) pair encountered for each path.\n - A collection of `remove` actions with unique `(path, deletionVector.uniqueId)` keys. The intersection of the primary keys in the `add` collection and `remove` collection must be empty. That means a logical file cannot exist in both the `remove` and `add` collections at the same time; however, the same *data file* can exist with *different* DVs in the `remove` collection, as logically they represent different content. The `remove` actions act as _tombstones_, and only exist for the benefit of the VACUUM command. Snapshot reads only return `add` actions on the read path.\n \nTo achieve the requirements above, related actions from different delta files need to be reconciled with each other:\n \n - The latest `protocol` action seen wins\n - The latest `metaData` action seen wins\n - For `txn` actions, the latest `version` seen for a given `appId` wins\n - For `domainMetadata`, the latest `domainMetadata` seen for a given `domain` wins. The actions with `removed=true` act as tombstones to suppress earlier versions. Snapshot reads do _not_ return removed `domainMetadata` actions.\n - For `commitInfo` actions, only the `commitInfo` from the commit at the snapshot version is included in the snapshot. [Checkpoints](#checkpoints) and [log compaction files](#log-compaction-files) do not preserve `commitInfo` actions, so this information must be read from the JSON commit file at the snapshot version.\n - Logical files in a table are identified by their `(path, deletionVector.uniqueId)` primary key. File actions (`add` or `remove`) reference logical files, and a log can contain any number of references to a single file.\n - To replay the log, scan all file actions and keep only the newest reference for each logical file.\n - `add` actions in the result identify logical files currently present in the table (for queries). `remove` actions in the result identify tombstones of logical files no longer present in the table (for VACUUM).\n - [v2 checkpoint spec](#v2-spec) actions are not allowed in normal commit files, and do not participate in log replay.\n\n# Table Features\nTable features must only exist on tables that have a supported protocol version. When the table's Reader Version is 3, `readerFeatures` must exist in the `protocol` action, and when the Writer Version is 7, `writerFeatures` must exist in the `protocol` action. `readerFeatures` and `writerFeatures` define the features that readers and writers must implement in order to read and write this table.\n\nReaders and writers must not ignore table features when they are present:\n - to read a table, readers must implement and respect all features listed in `readerFeatures`;\n - to write a table, writers must implement and respect all features listed in `writerFeatures`. Because writers have to read the table (or only the Delta log) before write, they must implement and respect all reader features as well.\n\n## Table Features for New and Existing Tables\nIt is possible to create a new table or upgrade an existing table to the protocol versions that supports the use of table features. A table must support either the use of writer features or both reader and writer features. It is illegal to support reader but not writer features.\n\nFor new tables, when a new table is created with a Reader Version up to 2 and Writer Version 7, its `protocol` action must only contain `writerFeatures`. When a new table is created with Reader Version 3 and Writer Version 7, its `protocol` action must contain both `readerFeatures` and `writerFeatures`. Creating a table with a Reader Version 3 and Writer Version less than 7 is not allowed.\n\nWhen upgrading an existing table to Reader Version 3 and/or Writer Version 7, the client should, on a best effort basis, determine which features supported by the original protocol version are used in any historical version of the table, and add only used features to reader and/or writer feature sets. The client must assume a feature has been used, unless it can prove that the feature is *definitely* not used in any historical version of the table that is reachable by time travel. \n\nFor example, given a table on Reader Version 1 and Writer Version 4, along with four versions:\n 1. Table property change: set `delta.enableChangeDataFeed` to `true`.\n 2. Data change: three rows updated.\n 3. Table property change: unset `delta.enableChangeDataFeed`.\n 4. Table protocol change: upgrade protocol to Reader Version 3 and Writer Version 7.\n\nTo produce Version 4, a writer could look at only Version 3 and discover that Change Data Feed has not been used. But in fact, this feature has been used and the table does contain some Change Data Files for Version 2. This means that, to determine all features that have ever been used by the table, a writer must either scan the whole history (which is very time-consuming) or assume the worst case: all features supported by protocol `(1, 4)` has been used.\n\n## Supported Features\nA feature is supported by a table when its name is in the `protocol` action’s `readerFeatures` and/or `writerFeatures`. Subsequent read and/or write operations on this table must respect the feature. Clients must not remove the feature from the `protocol` action.\n\nWriters are allowed to add support of a feature to the table by adding its name to `readerFeatures` or `writerFeatures`. Reader features should be listed in both `readerFeatures` and `writerFeatures` simultaneously, while writer features should be listed only in `writerFeatures`. It is not allowed to list a feature only in `readerFeatures` but not in `writerFeatures`.\n\nA feature being supported does not imply that it is active. For example, a table may have the [Append-only Tables](#append-only-tables) feature (feature name `appendOnly`) listed in `writerFeatures`, but it does not have a table property `delta.appendOnly` that is set to `true`. In such a case the table is not append-only, and writers are allowed to change, remove, and rearrange data. However, writers must know that the table property `delta.appendOnly` should be checked before writing the table.\n\n## Active Features\nA feature is active on a table when it is supported *and* its metadata requirements are satisfied. Each feature defines its own metadata requirements, as stated in the corresponding sections of this document. For example, the Append-only feature is active when the `appendOnly` feature name is present in a `protocol`'s `writerFeatures` *and* a table property `delta.appendOnly` set to `true`.\n\n# Column Mapping\nDelta can use column mapping to avoid any column naming restrictions, and to support the renaming and dropping of columns without having to rewrite all the data. There are two modes of column mapping, by `name` and by `id`. In both modes, every column - nested or leaf - is assigned a unique _physical_ name, and a unique 32-bit integer as an id. The physical name is stored as part of the column metadata with the key `delta.columnMapping.physicalName`. The column id is stored within the metadata with the key `delta.columnMapping.id`.\n\nThe column mapping is governed by the table property `delta.columnMapping.mode` being one of `none`, `id`, and `name`. The table property should only be honored if the table's protocol has reader and writer versions and/or table features that support the `columnMapping` table feature. For readers this is Reader Version 2, or Reader Version 3 with the `columnMapping` table feature listed as supported. For writers this is Writer Version 5 or 6, or Writer Version 7 with the `columnMapping` table feature supported.\n\nThe following is an example for the column definition of a table that leverages column mapping. See the [appendix](#schema-serialization-format) for a more complete schema definition.\n```json\n{\n \"name\" : \"e\",\n \"type\" : {\n \"type\" : \"array\",\n \"elementType\" : {\n \"type\" : \"struct\",\n \"fields\" : [ {\n \"name\" : \"d\",\n \"type\" : \"integer\",\n \"nullable\" : false,\n \"metadata\" : { \n \"delta.columnMapping.id\": 5,\n \"delta.columnMapping.physicalName\": \"col-a7f4159c-53be-4cb0-b81a-f7e5240cfc49\"\n }\n } ]\n },\n \"containsNull\" : true\n },\n \"nullable\" : true,\n \"metadata\" : { \n \"delta.columnMapping.id\": 4,\n \"delta.columnMapping.physicalName\": \"col-5f422f40-de70-45b2-88ab-1d5c90e94db1\"\n }\n }\n```\n\n## Writer Requirements for Column Mapping\nIn order to support column mapping, writers must:\n - Write `protocol` and `metaData` actions when Column Mapping is turned on for the first time:\n - If the table is on Writer Version 5 or 6: write a `metaData` action to add the `delta.columnMapping.mode` table property;\n - If the table is on Writer Version 7:\n - write a `protocol` action to add the feature `columnMapping` to both `readerFeatures` and `writerFeatures`, and\n - write a `metaData` action to add the `delta.columnMapping.mode` table property.\n - Write data files by using the _physical name_ that is chosen for each column. The physical name of the column is static and can be different than the _display name_ of the column, which is changeable.\n - Write the 32 bit integer column identifier as part of the `field_id` field of the `SchemaElement` struct in the [Parquet Thrift specification](https://github.com/apache/parquet-format/blob/master/src/main/thrift/parquet.thrift).\n - Track partition values, column level statistics, and [clustering column](#clustered-table) names with the physical name of the column in the transaction log.\n - Assign a globally unique identifier as the physical name for each new column that is added to the schema. This is especially important for supporting cheap column deletions in `name` mode. In addition, column identifiers need to be assigned to each column. The maximum id that is assigned to a column is tracked as the table property `delta.columnMapping.maxColumnId`. This is an internal table property that cannot be configured by users. This value must increase monotonically as new columns are introduced and committed to the table alongside the introduction of the new columns to the schema.\n\n## Reader Requirements for Column Mapping\nIf the table is on Reader Version 2, or if the table is on Reader Version 3 and the feature `columnMapping` is present in `readerFeatures`, readers and writers must read the table property `delta.columnMapping.mode` and do one of the following.\n\nIn `none` mode, or if the table property is not present, readers must read the parquet files by using the display names (the `name` field of the column definition) of the columns in the schema.\n\nIn `id ` mode, readers must resolve columns by using the `field_id` in the parquet metadata for each file, as given by the column metadata property `delta.columnMapping.id` in the Delta schema. Partition values and column level statistics must be resolved by their *physical names* for each `add` entry in the transaction log. If a data file does not contain field ids, readers must refuse to read that file or return nulls for each column. For ids that cannot be found in a file, readers must return `null` values for those columns.\n\nIn `name` mode, readers must resolve columns in the data files by their physical names as given by the column metadata property `delta.columnMapping.physicalName` in the Delta schema. Partition values and column level statistics will also be resolved by their physical names. For columns that are not found in the files, `null`s need to be returned. Column ids are not used in this mode for resolution purposes.\n\n# Deletion Vectors\nTo support this feature:\n - To support Deletion Vectors, a table must have Reader Version 3 and Writer Version 7. A feature name `deletionVectors` must exist in the table's `readerFeatures` and `writerFeatures`.\n\nWhen supported:\n - A table may have a metadata property `delta.enableDeletionVectors` in the Delta schema set to `true`. Writers must only write new Deletion Vectors (DVs) when this property is set to `true`.\n - A table's `add` and `remove` actions can optionally include a DV that provides information about logically deleted rows, that are however still physically present in the underlying data file and must thus be skipped during processing. Readers must read the table considering the existence of DVs, even when the `delta.enableDeletionVectors` table property is not set.\n\nDVs can be stored and accessed in different ways, indicated by the `storageType` field. The Delta protocol currently supports inline or on-disk storage, where the latter can be accessed either by a relative path derived from a UUID or an absolute path.\n\n## Deletion Vector Descriptor Schema\n\nThe schema of the `DeletionVectorDescriptor` struct is as follows:\n\nField Name | Data Type | Description\n-|-|-\nstorageType | String | A single character to indicate how to access the DV. Legal options are: `['u', 'i', 'p']`.\npathOrInlineDv | String | Three format options are currently proposed:
  • If `storageType = 'u'` then ``: The deletion vector is stored in a file with a path relative to the data directory of this Delta table, and the file name can be reconstructed from the UUID. See Derived Fields for how to reconstruct the file name. The random prefix is recovered as the extra characters before the (20 characters fixed length) uuid.
  • If `storageType = 'i'` then ``: The deletion vector is stored inline in the log. The format used is the `RoaringBitmapArray` format also used when the DV is stored on disk and described in [Deletion Vector Format](#Deletion-Vector-Format).
  • If `storageType = 'p'` then ``: The DV is stored in a file with an absolute path given by this path, which has the same format as the `path` field in the `add`/`remove` actions.
\noffset | Option[Int] | Start of the data for this DV in number of bytes from the beginning of the file it is stored in. Always `None` (absent in JSON) when `storageType = 'i'`.\nsizeInBytes | Int | Size of the serialized DV in bytes (raw data size, i.e. before base85 encoding, if inline).\ncardinality | Long | Number of rows the given DV logically removes from the file.\n\nThe concrete Base85 variant used is [Z85](https://rfc.zeromq.org/spec/32/), because it is JSON-friendly.\n\n### Derived Fields\n\nSome fields that are necessary to use the DV are not stored explicitly but can be derived in code from the stored fields.\n\nField Name | Data Type | Description | Computed As\n-|-|-|-\nuniqueId | String | Uniquely identifies a DV for a given file. This is used for snapshot reconstruction to differentiate the same file with different DVs in successive versions. | If `offset` is `None` then ``.
Otherwise `@`.\nabsolutePath | String/URI/Path | The absolute path of the DV file. Can be calculated for relative path DVs by providing a parent directory path. | If `storageType='p'`, just use the already absolute path. If `storageType='u'`, the DV is stored at `//deletion_vector_.bin`. This is not a legal field if `storageType='i'`, as an inline DV has no absolute path.\n\n### JSON Example 1 — On Disk with Relative Path (with Random Prefix)\n```json\n{\n \"storageType\" : \"u\",\n \"pathOrInlineDv\" : \"ab^-aqEH.-t@S}K{vb[*k^\",\n \"offset\" : 4,\n \"sizeInBytes\" : 40,\n \"cardinality\" : 6\n}\n```\nAssuming that this DV is stored relative to an `s3://mytable/` directory, the absolute path to be resolved here would be: `s3://mytable/ab/deletion_vector_d2c639aa-8816-431a-aaf6-d3fe2512ff61.bin`.\n\n### JSON Example 2 — On Disk with Absolute Path\n```json\n{\n \"storageType\" : \"p\",\n \"pathOrInlineDv\" : \"s3://mytable/deletion_vector_d2c639aa-8816-431a-aaf6-d3fe2512ff61.bin\",\n \"offset\" : 4,\n \"sizeInBytes\" : 40,\n \"cardinality\" : 6\n}\n```\n\n### JSON Example 3 — Inline\n```json\n{\n \"storageType\" : \"i\",\n \"pathOrInlineDv\" : \"wi5b=000010000siXQKl0rr91000f55c8Xg0@@D72lkbi5=-{L\",\n \"sizeInBytes\" : 40,\n \"cardinality\" : 6\n}\n```\nThe row indexes encoded in this DV are: 3, 4, 7, 11, 18, 29.\n\n## Reader Requirements for Deletion Vectors\nIf a snapshot contains logical files with records that are invalidated by a DV, then these records *must not* be returned in the output.\n\n## Writer Requirement for Deletion Vectors\nWhen adding a logical file with a deletion vector, then that logical file must have correct `numRecords` information for the data file in the `stats` field.\n\n# Catalog-managed tables\n\nWith this feature enabled, the [catalog](#terminology-catalogs) that manages the table becomes the\nsource of truth for whether a given commit attempt succeeded.\n\nThe table feature defines the parts of the [commit protocol](#commit-protocol) that directly impact\nthe Delta table (e.g. atomicity requirements, publishing, etc). The Delta client and catalog\ntogether are responsible for implementing the Delta-specific aspects of commit as defined by this\nspec, but are otherwise free to define their own APIs and protocols for communication with each\nother.\n\n**NOTE**: Filesystem-based access to catalog-managed tables is not supported. Delta clients are\nexpected to discover and access catalog-managed tables through the managing catalog, not by direct\nlisting in the filesystem. This feature is primarily designed to warn filesystem-based readers that\nmight attempt to access a catalog-managed table's storage location without going through the catalog\nfirst, and to block filesystem-based writers who could otherwise corrupt both the table and the\ncatalog by failing to commit through the catalog.\n\nBefore we can go into details of this protocol feature, we must first align our terminology.\n\n## Terminology: Commits\n\nA commit is a set of [actions](#actions) that transform a Delta table from version `v - 1` to `v`.\nIt contains the same kind of content as is stored in a [Delta file](#delta-log-entries).\n\nA commit may be stored in the file system as a Delta file - either _published_ or _staged_ - or\nstored _inline_ in the managing catalog, using whatever format the catalog prefers.\n\nThere are several types of commits:\n\n1. **Proposed commit**: A commit that a Delta client has proposed for the next version of the\n table. It could be _staged_ or _inline_. It will either become _ratified_ or be rejected.\n\n2. **Staged commit**: A commit that is written to disk at\n `_delta_log/_staged_commits/..json`. It has the same content and format as a published\n Delta file.\n - Here, the `uuid` is a random UUID that is generated for each commit and `v` is the version\n which is proposed to be committed, zero-padded to 20 digits.\n - The mere existence of a staged commit does not mean that the file has been ratified or even\n proposed. It might correspond to a failed or in-progress commit attempt.\n - The catalog is the source of truth around which staged commits are ratified.\n - The catalog stores only the location, not the content, of a staged (and ratified) commit.\n\n3. **Inline commit**: A proposed commit that is not written to disk but\n rather has its content sent to the catalog for the catalog to store directly.\n\n4. **Ratified commit**: A proposed commit that a catalog has\n determined has won the commit at the desired version of the table.\n - The catalog must store ratified commits (that is, the staged commit's location or the inline\n commit's content) until they are published to the `_delta_log` directory.\n - A ratified commit may or may not yet be published.\n - A ratified commit may or may not even be stored by the catalog at all - the catalog may\n have just atomically published it to the filesystem directly, relying on PUT-if-absent\n primitives to facilitate the ratification and publication all in one step.\n\n5. **Published commit**: A ratified commit that has been copied into\n the `_delta_log` as a normal Delta file, i.e. `_delta_log/.json`.\n - Here, the `v` is the version which is being committed, zero-padded to 20 digits.\n - The existence of a `.json` file proves that the corresponding version `v` is ratified,\n regardless of whether the table is catalog-managed or filesystem-based. The catalog is allowed\n to return information about published commits, but Delta clients can also use filesystem\n listing operations to directly discover them.\n - Published commits do not need to be stored by the catalog.\n\n## Terminology: Delta Client\n\nThis is the component that implements support for reading and writing Delta tables, and implements\nthe logic required by the `catalogManaged` table feature. Among other things, it\n- triggers the filesystem listing, if needed, to discover published commits\n- generates the commit content (the set of [actions](#actions))\n- works together with the query engine to trigger the commit process and invoke the client-side\n catalog component with the commit content\n\nThe Delta client is also responsible for defining the client-side API that catalogs should target.\nThat is, there must be _some_ API that the [catalog client](#catalog-client) can use to communicate\nto the Delta client the subset of catalog-managed information that the Delta client cares about.\nThis protocol feature is concerned with what information Delta cares about, but leaves to Delta\nclients the design of the API they use to obtain that information from catalog clients.\n\n## Terminology: Catalogs\n\n1. **Catalog**: A catalog is an entity which manages a Delta table, including its creation, writes,\n reads, and eventual deletion.\n - It could be backed by a database, a filesystem, or any other persistence mechanism.\n - Each catalog has its own spec around how catalog clients should interact with them, and how\n they perform a commit.\n\n2. **Catalog Client**: The catalog always has a client-side component\n which the Delta client interacts with directly. This client-side component has two primary\n responsibilities:\n - implement any client-side catalog-specific logic (such as staging or\n [publishing](#publishing-commits) commits)\n - communicate with the Catalog Server, if any\n\n3. **Catalog Server**: The catalog may also involve a server-side component which the client-side\n component would be responsible to communicate with.\n - This server is responsible for coordinating commits and potentially persisting table metadata\n and enforcing authorization policies.\n - Not all catalogs require a server; some may be entirely client-side, e.g. filesystem-backed\n catalogs, or they may make use of a generic database server and implement all of the catalog's\n business logic client-side.\n\n**NOTE**: This specification outlines the responsibilities and actions that catalogs must implement.\nThis spec does its best not to assume any specific catalog _implementation_, though it does call out\nlikely client-side and server-side responsibilities. Nonetheless, what a given catalog does\nclient-side or server-side is up to each catalog implementation to decide for itself.\n\n## Catalog Responsibilities\n\nWhen the `catalogManaged` table feature is enabled, a catalog performs commits to the table on behalf\nof the Delta client.\n\nAs stated above, the Delta spec does not mandate any particular client-server design or API for\ncatalogs that manage Delta tables. However, the catalog does need to provide certain capabilities\nfor reading and writing Delta tables:\n\n- Atomically commit a version `v` with a given set of `actions`. This is explained in detail in the\n [commit protocol](#commit-protocol) section.\n- Retrieve information about recent ratified commits and the latest ratified version on the table.\n This is explained in detail in the [Getting Ratified Commits from the Catalog](#getting-ratified-commits-from-the-catalog) section.\n- Though not required, it is encouraged that catalogs also return the latest table-level metadata,\n such as the latest Protocol and Metadata actions, for the table. This can provide significant\n performance advantages to conforming Delta clients, who may forgo log replay and instead trust\n the information provided by the catalog during query planning.\n\n## Reading Catalog-managed Tables\n\nA catalog-managed table can have a mix of (a) published and (b) ratified but non-published commits.\nThe catalog is the source of truth for ratified commits. Also recall that ratified commits can be\n[staged commits](#staged-commit) that are persisted to the `_delta_log/_staged_commits` directory,\nor [inline commits](#inline-commit) whose content the catalog stores directly.\n\nFor example, suppose the `_delta_log` directory contains the following files:\n\n```\n00000000000000000000.json\n00000000000000000001.json\n00000000000000000002.checkpoint.parquet\n00000000000000000002.json\n00000000000000000003.00000000000000000005.compacted.json\n00000000000000000003.json\n00000000000000000004.json\n00000000000000000005.json\n00000000000000000006.json\n00000000000000000007.json\n_staged_commits/00000000000000000007.016ae953-37a9-438e-8683-9a9a4a79a395.json // ratified and published\n_staged_commits/00000000000000000008.7d17ac10-5cc3-401b-bd1a-9c82dd2ea032.json // ratified\n_staged_commits/00000000000000000008.b91807ba-fe18-488c-a15e-c4807dbd2174.json // rejected\n_staged_commits/00000000000000000010.0f707846-cd18-4e01-b40e-84ee0ae987b0.json // not yet ratified\n_staged_commits/00000000000000000010.7a980438-cb67-4b89-82d2-86f73239b6d6.json // partial file\n```\n\nFurther, suppose the catalog stores the following ratified commits:\n```\n{\n 7 -> \"00000000000000000007.016ae953-37a9-438e-8683-9a9a4a79a395.json\",\n 8 -> \"00000000000000000008.7d17ac10-5cc3-401b-bd1a-9c82dd2ea032.json\",\n 9 -> \n}\n```\n\nSome things to note are:\n- the catalog isn't aware that commit 7 was already published - perhaps the response from the\n filesystem was dropped\n- commit 9 is an inline commit\n- neither of the two staged commits for version 10 have been ratified\n\nTo read such tables, Delta clients must first contact the catalog to get the ratified commits. This\ninforms the Delta client of commits [7, 9] as well as the latest ratified version, 9.\n\nIf this information is insufficient to construct a complete snapshot of the table, Delta clients\nmust LIST the `_delta_log` directory to get information about the published commits. For commits\nthat are both returned by the catalog and already published, Delta clients must treat the catalog's\nversion as authoritative and read the commit returned by the catalog. Additionally, Delta clients\nmust ignore any files with versions greater than the latest ratified commit version returned by the\ncatalog.\n\nCombining these two sets of files and commits enables Delta clients to generate a snapshot at the\nlatest version of the table.\n\n**NOTE**: This spec prescribes the _minimum_ required interactions between Delta clients and\ncatalogs for commits. Catalogs may very well expose APIs and work with Delta clients to be\ninformed of other non-commit [file types](#file-types), such as checkpoint, log\ncompaction, and version checksum files. This would allow catalogs to return additional\ninformation to Delta clients during query and scan planning, potentially allowing Delta\nclients to avoid LISTing the filesystem altogether.\n\n## Commit Protocol\n\nTo start, Delta Clients send the desired actions to be committed to the client-side component of the\ncatalog.\n\nThis component then has several options for proposing, ratifying, and publishing the commit,\ndetailed below.\n\n- Option 1: Write the actions (likely client-side) to a [staged commit file](#staged-commit) in the\n `_delta_log/_staged_commits` directory and then ratify the staged commit (likely server-side) by\n atomically recording (in persistent storage of some kind) that the file corresponds to version `v`.\n- Option 2: Treat this as an [inline commit](#inline-commit) (i.e. likely that the client-side\n component sends the contents to the server-side component) and atomically record (in persistent\n storage of some kind) the content of the commit as version `v` of the table.\n- Option 3: Catalog implementations that use PUT-if-absent (client- or server-side) can ratify and\n publish all-in-one by atomically writing a [published commit file](#published-commit)\n in the `_delta_log` directory. Note that this commit will be considered to have succeeded as soon\n as the file becomes visible in the filesystem, regardless of when or whether the catalog is made\n aware of the successful publish. The catalog does not need to store these files.\n\nA catalog must not ratify version `v` until it has ratified version `v - 1`, and it must ratify\nversion `v` at most once.\n\nThe catalog must store both flavors of ratified commits (staged or inline) and make them available\nto readers until they are [published](#publishing-commits).\n\nFor performance reasons, Delta clients are encouraged to establish an API contract where the catalog\nprovides the latest ratified commit information whenever a commit fails due to version conflict.\n\n## Getting Ratified Commits from the Catalog\n\nEven after a commit is ratified, it is not discoverable through filesystem operations until it is\n[published](#publishing-commits).\n\nThe catalog-client is responsible to implement an API (defined by the Delta client) that Delta clients can\nuse to retrieve the latest ratified commit version (authoritative), as well as the set of ratified\ncommits the catalog is still storing for the table. If some commits needed to complete the snapshot\nare not stored by the catalog, as they are already published, Delta clients can issue a filesystem\nLIST operation to retrieve them.\n\nDelta clients must establish an API contract where the catalog provides ratified commit information\nas part of the standard table resolution process performed at query planning time.\n\n## Publishing Commits\n\nPublishing is the process of copying the ratified commit with version `` to\n`_delta_log/.json`. The ratified commit may be a staged commit located in\n`_delta_log/_staged_commits/..json`, or it may be an inline commit whose content the\ncatalog stores itself. Because the content of a ratified commit is immutable, it does not matter\nwhether the client-side, server-side, or both catalog components initiate publishing.\n\nImplementations are strongly encouraged to publish commits promptly. This reduces the number of\ncommits the catalog needs to store internally (and serve up to readers).\n\nCommits must be published _in order_. That is, version `v - 1` must be published _before_ version\n`v`.\n\n**NOTE**: Because commit publishing can happen at any time after the commit succeeds, the file\nmodification timestamp of the published file will not accurately reflect the original commit time.\nFor this reason, catalog-managed tables must use [in-commit-timestamps](https://github.com/delta-io/delta/blob/master/PROTOCOL.md#in-commit-timestamps)\nto ensure stability of time travel reads. Refer to [Writer Requirements for Catalog-managed Tables](#writer-requirements-for-catalog-managed-tables)\nsection for more details.\n\n## Maintenance Operations on Catalog-managed Tables\n\n[Checkpoints](#checkpoints-1) and [Log Compaction Files](#log-compaction-files) can only be created\nfor versions that are already published in the `_delta_log`. In other words, in order to checkpoint\nversion `v` or produce a log compaction file for commit range `x <= v <= y`, `_delta_log/.json`\nmust exist.\n\nNotably, the [Version Checksum File](#version-checksum-file) for version `v` _can_ be created in the\n`_delta_log` even if the commit for version `v` is not published.\n\nBy default, maintenance operations are prohibited unless the managing catalog explicitly permits\nthe client to run them. The only exceptions are checkpoints, log compaction, and version checksum,\nas they are essential for all basic table operations (e.g. reads and writes) to operate reliably.\nAll other maintenance operations such as the following are not allowed by default.\n- [Log and other metadata files clean up](#metadata-cleanup).\n- Data files cleanup, for example VACUUM.\n- Data layout changes, for example OPTIMIZE and REORG.\n\n## Creating and Dropping Catalog-managed Tables\n\nThe catalog and query engine ultimately dictate how to create and drop catalog-managed tables.\n\nAs one example, table creation often works in three phases:\n\n1. An initial catalog operation to obtain a unique storage location which serves as an unnamed\n \"staging\" table\n2. A table operation that physically initializes a new `catalogManaged`-enabled table at the staging\n location.\n3. A final catalog operation that registers the new table with its intended name.\n\nDelta clients would primarily be involved with the second step, but an implementation could choose\nto combine the second and third steps so that a single catalog call registers the table as part of\nthe table's first commit.\n\nAs another example, dropping a table can be as simple as removing its name from the catalog (a \"soft\ndelete\"), followed at some later point by a \"hard delete\" that physically purges the data. The Delta\nclient would not be involved at all in this process, because no commits are made to the table.\n\n## Catalog-managed Table Enablement\n\nThe `catalogManaged` table feature is supported and active when:\n- The table is on Reader Version 3 and Writer Version 7.\n- The table has a `protocol` action with `readerFeatures` and `writerFeatures` both containing the\n feature `catalogManaged`.\n\n## Writer Requirements for Catalog-managed tables\n\nWhen supported and active:\n\n- Writers must discover and access the table using catalog calls, which happens _before_ the table's\n protocol is known. See [Table Discovery](#table-discovery) for more details.\n- The [in-commit-timestamps](https://github.com/delta-io/delta/blob/master/PROTOCOL.md#in-commit-timestamps)\n table feature must be supported and active.\n- The `commitInfo` action must also contain a field `txnId` that stores a unique transaction\n identifier string\n- Writers must follow the catalog's [commit protocol](#commit-protocol) and must not perform\n ordinary filesystem-based commits against the table.\n- Writers must follow the catalog's [maintenance operation protocol](#maintenance-operations-on-catalog-managed-tables)\n\n## Reader Requirements for Catalog-managed tables\n\nWhen supported and active:\n\n- Readers must discover the table using catalog calls, which happens before the table's protocol\n is known. See [Table Discovery](#table-discovery) for more details.\n- Readers must contact the catalog for information about unpublished ratified commits.\n- Readers must follow the rules described in the [Reading Catalog-managed Tables](#reading-catalog-managed-tables)\n section above. Notably\n - If the catalog said `v` is the latest version, clients must ignore any later versions that may\n have been published\n - When the catalog returns a ratified commit for version `v`, readers must use that\n catalog-supplied commit and ignore any published Delta file for version `v` that might also be\n present.\n\n## Table Discovery\n\nThe requirements above state that readers and writers must discover and access the table using\ncatalog calls, which occurs _before_ the table's protocol is known. This raises an important\nquestion: how can a client discover a `catalogManaged` Delta table without first knowing that it\n_is_, in fact, `catalogManaged` (according to the protocol)?\n\nTo solve this, first note that, in practice, catalog-integrated engines already ask the catalog to\nresolve a table name to its storage location during the name resolution step. This protocol\ntherefore encourages that the same name resolution step also indicate whether the table is\ncatalog-managed. Surfacing this at the very moment the catalog returns the path imposes no extra\nround-trips, yet it lets the client decide — early and unambiguously — whether to follow the\n`catalogManaged` read and write rules.\n\n## Sample Catalog Client API\n\nThe following is an example of a possible API which a Java-based Delta client might require catalog\nimplementations to target:\n\n```scala\n\ninterface CatalogManagedTable {\n /**\n * Commits the given set of `actions` to the given commit `version`.\n *\n * @param version The version we want to commit.\n * @param actions Actions that need to be committed.\n *\n * @return CommitResponse which has details around the new committed delta file.\n */\n def commit(\n version: Long,\n actions: Iterator[String]): CommitResponse\n\n /**\n * Retrieves a (possibly empty) suffix of ratified commits in the range [startVersion,\n * endVersion] for this table.\n * \n * Some of these ratified commits may already have been published. Some of them may be staged,\n * in which case the staged commit file path is returned; others may be inline, in which case\n * the inline commit content is returned.\n * \n * The returned commits are sorted in ascending version number and are contiguous.\n *\n * If neither start nor end version is specified, the catalog will return all available ratified\n * commits (possibly empty, if all commits have been published).\n *\n * In all cases, the response also includes the table's latest ratified commit version.\n *\n * @return GetCommitsResponse which contains an ordered list of ratified commits\n * stored by the catalog, as well as table's latest commit version.\n */\n def getRatifiedCommits(\n startVersion: Option[Long],\n endVersion: Option[Long]): GetCommitsResponse\n}\n```\n\nNote that the above is only one example of a possible Catalog Client API. It is also _NOT_ a catalog\nAPI (no table discovery, ACL, create/drop, etc). The Delta protocol is agnostic to API details, and\nthe API surface Delta clients define should only cover the specific catalog capabilities that Delta\nclient needs to correctly read and write catalog-managed tables.\n\n# Iceberg Compatibility V1\n\nThis table feature (`icebergCompatV1`) ensures that Delta tables can be converted to Apache Iceberg™ format, though this table feature does not implement or specify that conversion.\n\nTo support this feature:\n- Since this table feature depends on Column Mapping, the table must be on Reader Version = 2, or it must be on Reader Version >= 3 and the feature `columnMapping` must exist in the `protocol`'s `readerFeatures`.\n- The table must be on Writer Version 7.\n- The feature `icebergCompatV1` must exist in the table `protocol`'s `writerFeatures`.\n\nThis table feature is enabled when the table property `delta.enableIcebergCompatV1` is set to `true`.\n\n## Writer Requirements for IcebergCompatV1\n\nWhen supported and active, writers must:\n- Require that Column Mapping be enabled and set to either `name` or `id` mode\n- Require that Deletion Vectors are not supported (and, consequently, not active, either). i.e., the `deletionVectors` table feature is not present in the table `protocol`.\n- Require that partition column values are materialized into any Parquet data file that is present in the table, placed *after* the data columns in the parquet schema\n- Require that all `AddFile`s committed to the table have the `numRecords` statistic populated in their `stats` field\n- Block adding `Map`/`Array`/`Void` types to the table schema (and, thus, block writing them, too)\n- Block replacing partitioned tables with a differently-named partition spec\n - e.g. replacing a table partitioned by `part_a INT` with partition spec `part_b INT` must be blocked\n - e.g. replacing a table partitioned by `part_a INT` with partition spec `part_a LONG` is allowed\n- When the [Type Widening](#type-widening) table feature is supported, require that all type changes applied on the table are supported by [Iceberg V2](https://iceberg.apache.org/spec/#schema-evolution), based on the [Type Change Metadata](#type-change-metadata) recorded in the table schema.\n\n# Iceberg Compatibility V2\n\nThis table feature (`icebergCompatV2`) ensures that Delta tables can be converted to Apache Iceberg™ format, though this table feature does not implement or specify that conversion.\n\nTo support this feature:\n- Since this table feature depends on Column Mapping, the table must be on Reader Version = 2, or it must be on Reader Version >= 3 and the feature `columnMapping` must exist in the `protocol`'s `readerFeatures`.\n- The table must be on Writer Version 7.\n- The feature `icebergCompatV2` must exist in the table protocol's `writerFeatures`.\n\nThis table feature is enabled when the table property `delta.enableIcebergCompatV2` is set to `true`. \n\n## Writer Requirements for IcebergCompatV2\n\nWhen this feature is supported and enabled, writers must:\n- Require that Column Mapping be enabled and set to either `name` or `id` mode\n- Require that the nested `element` field of ArrayTypes and the nested `key` and `value` fields of MapTypes be assigned 32 bit integer identifiers. These identifiers must be unique and different from those used in [Column Mapping](#column-mapping), and must be stored in the metadata of their nearest ancestor [StructField](#struct-field) of the Delta table schema. Identifiers belonging to the same `StructField` must be organized as a `Map[String, Long]` and stored in metadata with key `parquet.field.nested.ids`. The keys of the map are \"element\", \"key\", or \"value\", prefixed by the name of the nearest ancestor StructField, separated by dots. The values are the identifiers. The keys for fields in nested arrays or nested maps are prefixed by their parents' key, separated by dots. An [example](#example-of-storing-identifiers-for-nested-fields-in-arraytype-and-maptype) is provided below to demonstrate how the identifiers are stored. These identifiers must be also written to the `field_id` field of the `SchemaElement` struct in the [Parquet Thrift specification](https://github.com/apache/parquet-format/blob/master/src/main/thrift/parquet.thrift) when writing parquet files.\n- Require that IcebergCompatV1 is not active, which means either the `icebergCompatV1` table feature is not present in the table protocol or the table property `delta.enableIcebergCompatV1` is not set to `true`\n- Require that Deletion Vectors are not active, which means either the `deletionVectors` table feature is not present in the table protocol or the table property `delta.enableDeletionVectors` is not set to `true`\n- Require that partition column values be materialized when writing Parquet data files\n- Require that all new `AddFile`s committed to the table have the `numRecords` statistic populated in their `stats` field\n- Require writing timestamp columns as int64\n- Require that the table schema contains only data types in the following allow-list: [`byte`, `short`, `integer`, `long`, `float`, `double`, `decimal`, `string`, `binary`, `boolean`, `timestamp`, `timestampNTZ`, `date`, `array`, `map`, `struct`].\n- Block replacing partitioned tables with a differently-named partition spec\n - e.g. replacing a table partitioned by `part_a INT` with partition spec `part_b INT` must be blocked\n - e.g. replacing a table partitioned by `part_a INT` with partition spec `part_a LONG` is allowed\n- When the [Type Widening](#type-widening) table feature is supported, require that all type changes applied on the table are supported by [Iceberg V2](https://iceberg.apache.org/spec/#schema-evolution), based on the [Type Change Metadata](#type-change-metadata) recorded in the table schema.\n\n### Example of storing identifiers for nested fields in ArrayType and MapType\nThe following is an example of storing the identifiers for nested fields in `ArrayType` and `MapType`, of a table with the following schema,\n```\n|-- col1: array[array[int]] \n|-- col2: map[int, array[int]] \n|-- col3: map[int, struct]\n |-- subcol1: array[int]\n```\nThe identifiers for the nested fields are stored in the metadata as follows: \n```json\n[\n {\n \"name\": \"col1\",\n \"type\": {\n \"type\": \"array\",\n \"elementType\": {\n \"type\": \"array\",\n \"elementType\": \"int\"\n }\n },\n \"metadata\": {\n \"parquet.field.nested.ids\": {\n \"col1.element\": 100,\n \"col1.element.element\": 101\n }\n }\n },\n {\n \"name\": \"col2\",\n \"type\": {\n \"type\": \"map\",\n \"keyType\": \"int\",\n \"valueType\": {\n \"type\": \"array\",\n \"elementType\": \"int\"\n }\n },\n \"metadata\": {\n \"parquet.field.nested.ids\": {\n \"col2.key\": 102,\n \"col2.value\": 103,\n \"col2.value.element\": 104\n }\n }\n },\n {\n \"name\": \"col3\",\n \"type\": {\n \"type\": \"map\",\n \"keyType\": \"int\",\n \"valueType\": {\n \"type\": \"struct\",\n \"fields\": [\n {\n \"name\": \"subcol1\",\n \"type\": {\n \"type\": \"array\",\n \"elementType\": \"int\"\n },\n \"metadata\": {\n \"parquet.field.nested.ids\": {\n \"subcol1.element\": 107\n }\n }\n }\n ]\n }\n },\n \"metadata\": {\n \"parquet.field.nested.ids\": {\n \"col3.key\": 105,\n \"col3.value\": 106\n }\n }\n }\n]\n```\n# Timestamp without timezone (TimestampNtz)\nThis feature introduces a new data type to support timestamps without timezone information. For example: `1970-01-01 00:00:00`, or `1970-01-01 00:00:00.123456`.\nThe serialization method is described in Sections [Partition Value Serialization](#partition-value-serialization) and [Schema Serialization Format](#schema-serialization-format).\n\nTo support this feature:\n- To have a column of TimestampNtz type in a table, the table must have Reader Version 3 and Writer Version 7. A feature name `timestampNtz` must exist in the table's `readerFeatures` and `writerFeatures`.\n\n\n# V2 Checkpoint Table Feature\nTo support this feature:\n- To add [V2 Checkpoints](#v2-spec) support to a table, the table must have Reader Version 3 and Writer Version 7. A feature name `v2Checkpoint` must exist in the table's `readerFeatures` and `writerFeatures`.\n\nWhen supported:\n- A table could use [uuid-named](#uuid-named-checkpoint) [V2 spec Checkpoints](#v2-spec) which must have [checkpoint metadata](#checkpoint-metadata) and may have [sidecar files](#sidecar-files) OR\n- A table could use [classic](#classic-checkpoint) checkpoints which can follow [V1](#v1-spec) or [V2](#v2-spec) spec.\n- A table must not use [multi-part checkpoints](#multi-part-checkpoint)\n\n# Row Tracking\n\nRow Tracking is a feature that allows the tracking of rows across multiple versions of a Delta table.\nIt enables this by exposing two metadata columns: Row IDs, which uniquely identify a row across multiple versions of a table,\nand Row Commit Versions, which make it possible to check whether two rows with the same ID in two different versions of the table represent the same version of the row.\n\nRow Tracking is defined to be **supported** or **enabled** on a table as follows:\n- When the feature `rowTracking` exists in the table `protocol`'s `writerFeatures`, then we say that Row Tracking is **supported**.\n In this situation, writers must assign Row IDs and Commit Versions as long as `delta.rowTrackingSuspended` table property is absent or set to false. However, they cannot yet be relied upon to be present in the table.\n When Row Tracking is supported but not yet enabled writers cannot preserve Row IDs and Commit Versions.\n- When additionally the table property `delta.enableRowTracking` is set to `true`, then we say that Row Tracking is **enabled**.\n In this situation, Row IDs and Row Commit versions can be relied upon to be present in the table for all rows.\n When Row Tracking is enabled writers are expected to preserve Row IDs and Commit Versions.\n- When the table property `delta.rowTrackingSuspended` is set to true, writers should suspend the assignment of Row IDs and Commit Versions.\n Table property `delta.rowTrackingSuspended` should not be enabled together with table property `delta.enableRowTracking`.\n\nEnablement:\n- The table must be on Writer Version 7.\n- The feature `rowTracking` must exist in the table `protocol`'s `writerFeatures`. The feature `domainMetadata` is required in the table `protocol`'s `writerFeatures`.\n- The table property `delta.enableRowTracking` must be set to `true`.\n- The table property `delta.rowTrackingSuspended` should be absent or set to `false`.\n\n## Row IDs\n\nDelta provides Row IDs. Row IDs are integers that are used to uniquely identify rows within a table.\nEvery row has two Row IDs:\n\n- A **fresh** or unstable **Row ID**.\n This ID uniquely identifies the row within one version of the table.\n The fresh ID of a row may change every time the table is updated, even for rows that are not modified. E.g. when a row is copied unchanged during an update operation, it will get a new fresh ID. Fresh IDs can be used to identify rows within one version of the table, e.g. for identifying matching rows in self joins.\n- A **stable Row ID**.\n This ID uniquely identifies the row across versions of the table and across updates.\n When a row is inserted, it is assigned a new stable Row ID that is equal to the fresh Row ID.\n When a row is updated or copied, the stable Row ID for this row is preserved.\n When a row is restored (i.e. the table is restored to an earlier version), its stable Row ID is restored as well.\n\nThe fresh and stable Row IDs are not required to be equal.\n\nRow IDs are stored in two ways:\n\n- **Default generated Row IDs** use the `baseRowId` field stored in `add` and `remove` actions to generate fresh Row IDs.\n The default generated Row IDs for data files are calculated by adding the `baseRowId` of the file in which a row is contained to the (physical) position (index) of the row within the file.\n Default generated Row IDs require little storage overhead but are reassigned every time a row is updated or moved to a different file (for instance when a row is contained in a file that is compacted by OPTIMIZE).\n\n- **Materialized Row IDs** are stored in a column in the data files.\n This column is hidden from readers and writers, i.e. it is not part of the `schemaString` in the table's `metaData`.\n Instead, the name of this column can be found in the value for the `delta.rowTracking.materializedRowIdColumnName` key in the `configuration` of the table's `metaData` action.\n This column may contain `null` values meaning that the corresponding row has no materialized Row ID. This column may be omitted if all its values are `null` in the file.\n Materialized Row IDs provide a mechanism for writers to preserve stable Row IDs for rows that are updated or copied.\n\nThe fresh Row ID of a row is equal to the default generated Row ID. The stable Row ID of a row is equal to the materialized Row ID of the row when that column is present and the value is not NULL, otherwise it is equal to the default generated Row ID.\n\nWhen Row Tracking is enabled:\n- Default generated Row IDs must be assigned to all existing rows.\n This means in particular that all files that are part of the table version that sets the table property `delta.enableRowTracking` to `true` must have `baseRowId` set.\n A backfill operation may be required to commit `add` and `remove` actions with the `baseRowId` field set for all data files before the table property `delta.enableRowTracking` can be set to `true`.\n\n## Row Commit Versions\n\nRow Commit Versions provide versioning of rows.\n\n- **Fresh** or unstable **Row Commit Versions** can be used to identify the first commit version in which the `add` action containing the row was committed.\n The fresh Commit Version of a row may change every time the table is updated, even for rows that are not modified. E.g. when a row is copied unchanged during an update operation, it will get a new fresh Commit Version.\n- **Stable Row Commit Versions** identify the last commit version in which the row (with the same ID) was either inserted or updated.\n When a row is inserted or updated, it is assigned the commit version number of the log entry containing the `add` entry with the new row.\n When a row is copied, the stable Row Commit Version for this row is preserved.\n When a row is restored (i.e. the table is restored to an earlier version), its stable Row Commit Version is restored as well.\n\nThe fresh and stable Row Commit Versions are not required to be equal.\n\nCommit Versions are stored in two ways:\n\n- **Default generated Row Commit Versions** use the `defaultRowCommitVersion` field in `add` and `remove` actions.\n Default generated Row Commit Versions require little storage overhead but are reassigned every time a row is updated or moved to a different file (for instance when a row is contained in a file that is compacted by OPTIMIZE).\n\n- **Materialized Row Commit Versions** are stored in a column in the data files.\n This column is hidden from readers and writers, i.e. it is not part of the `schemaString` in the table's `metaData`.\n Instead, the name of this column can be found in the value for the `delta.rowTracking.materializedRowCommitVersionColumnName` key in the `configuration` of the table's `metaData` action.\n This column may contain `null` values meaning that the corresponding row has no materialized Row Commit Version. This column may be omitted if all its values are `null` in the file.\n Materialized Row Commit Versions provide a mechanism for writers to preserve Row Commit Versions for rows that are copied.\n\nThe fresh Row Commit Version of a row is equal to the default generated Row Commit version.\nThe stable Row Commit Version of a row is equal to the materialized Row Commit Version of the row when that column is present and the value is not NULL, otherwise it is equal to the default generated Commit Version.\n\n## Reader Requirements for Row Tracking\n\nWhen Row Tracking is enabled (when the table property `delta.enableRowTracking` is set to `true`), then:\n- When Row IDs are requested, readers must reconstruct stable Row IDs as follows:\n 1. Readers must use the materialized Row ID if the column determined by `delta.rowTracking.materializedRowIdColumnName` is present in the data file and the column contains a non `null` value for a row.\n 2. Otherwise, readers must use the default generated Row ID of the `add` or `remove` action containing the row in all other cases.\n I.e. readers must add the index of the row in the file to the `baseRowId` of the `add` or `remove` action for the file containing the row.\n- When Row Commit Versions are requested, readers must reconstruct them as follows:\n 1. Readers must use the materialized Row Commit Versions if the column determined by `delta.rowTracking.materializedRowCommitVersionColumnName` is present in the data file and the column contains a non `null` value for a row.\n 2. Otherwise, Readers must use the default generated Row Commit Versions of the `add` or `remove` action containing the row in all other cases.\n I.e. readers must use the `defaultRowCommitVersion` of the `add` or `remove` action for the file containing the row.\n- Readers cannot read Row IDs and Row Commit Versions while reading change data files from `cdc` actions.\n\n## Writer Requirements for Row Tracking\n\nWhen Row Tracking is supported (when the `writerFeatures` field of a table's `protocol` action contains `rowTracking`) and Row Tracking is not suspended (when `delta.rowTrackingSuspended` table property is absent or set to false), then:\n- Writers must assign unique fresh Row IDs to all rows that they commit.\n - Writers must set the `baseRowId` field in all `add` actions that they commit so that all default generated Row IDs are unique in the table version.\n Writers must never commit duplicate Row IDs in the table in any version.\n - Writers must set the `baseRowId` field in recommitted and checkpointed `add` actions and `remove` actions to the `baseRowId` value (if present) of the last committed `add` action with the same `path`.\n - Writers must track the high water mark, i.e. the highest fresh row id assigned.\n - The high water mark must be stored in a `domainMetadata` action with `delta.rowTracking` as the `domain`\n and a `configuration` containing a single key-value pair with `rowIdHighWaterMark` as the key and the highest assigned fresh row id as the value.\n - Writers must include a `domainMetadata` for `delta.rowTracking` whenever they assign new fresh Row IDs that are higher than `rowIdHighWaterMark` value of the current `domainMetadata` for `delta.rowTracking`.\n The `rowIdHighWaterMark` value in the `configuration` of this `domainMetadata` action must always be equal to or greater than the highest fresh Row ID committed so far.\n Writers can either commit this `domainMetadata` in the same commit, or they can reserve the fresh Row IDs in an earlier commit.\n - Writers must set the `baseRowId` field to a value that is higher than the row id high water mark.\n- Writer must assign fresh Row Commit Versions to all rows that they commit.\n - Writers must set the `defaultRowCommitVersion` field in new `add` actions to the version number of the log enty containing the `add` action.\n - Writers must set the `defaultRowCommitVersion` field in recommitted and checkpointed `add` actions and `remove` actions to the `defaultRowCommitVersion` of the last committed `add` action with the same `path`.\n\nOn the other hand, when Row Tracking is supported but suspended (table property `delta.rowTrackingSuspended` is set to `true`), writers should not assign the `baseRowId` or the `defaultRowCommitVersion`.\n\nWriters can enable Row Tracking by setting `delta.enableRowTracking` to `true` in the `configuration` of the table's `metaData`.\nThis is only allowed if the following requirements are satisfied:\n- The feature `rowTracking` has been added to the `writerFeatures` field of a table's `protocol` action either in the same version of the table or in an earlier version of the table.\n- The column name for the materialized Row IDs and Row Commit Versions have been assigned and added to the `configuration` in the table's `metaData` action using the keys `delta.rowTracking.materializedRowIdColumnName` and `delta.rowTracking.materializedRowCommitVersionColumnName` respectively.\n - The assigned column names must be unique. They must not be equal to the name of any other column in the table's schema.\n The assigned column names must remain unique in all future versions of the table.\n If [Column Mapping](#column-mapping) is enabled, then the assigned column name must be distinct from the physical column names of the table.\n- The `baseRowId` and `defaultRowCommitVersion` fields are set for all active `add` actions in the version of the table in which `delta.enableRowTracking` is set to `true`.\n- If the `baseRowId` and `defaultRowCommitVersion` fields are not set in some active `add` action in the table, then writers must first commit new `add` actions that set these fields to replace the `add` actions that do not have these fields set.\n This can be done in the commit that sets `delta.enableRowTracking` to `true` or in an earlier commit.\n The assigned `baseRowId` and `defaultRowCommitVersion` values must satisfy the same requirements as when assigning fresh Row IDs and fresh Row Commit Versions respectively.\nFurthermore, writers should also verify table property `delta.rowTrackingSuspended` is absent or set to false before enabling Row Tracking.\n\nWhen Row Tracking is enabled (when the table property `delta.enableRowTracking` is set to `true`), then:\n- Writers must assign stable Row IDs to all rows.\n - Stable Row IDs must be unique within a version of the table and must not be equal to the fresh Row IDs of other rows in the same version of the table.\n - Writers should preserve the stable Row IDs of rows that are updated or copied using materialized Row IDs.\n - The preserved stable Row ID (i.e. a stable Row ID that is not equal to the fresh Row ID of the same physical row) should be equal to the stable Row ID of the same logical row before it was updated or copied.\n - Materialized Row IDs must be written to the column determined by `delta.rowTracking.materializedRowIdColumnName` in the `configuration` of the table's `metaData` action.\n The value in this column must be set to `NULL` for stable Row IDs that are not preserved.\n- Writers must assign stable Row Commit Versions to all rows.\n - Writers should preserve the stable Row Commit Versions of rows that are copied (but not updated) using materialized Row Commit Versions.\n - The preserved stable Row Commit Version (i.e. a stable Row Commit Version that is not equal to the fresh Row Commit Version of the same physical row) should be equal to the stable Commit Version of the same logical row before it was copied.\n - Materialized Row Commit Versions must be written to the column determined by `delta.rowTracking.materializedRowCommitVersionColumnName` in the `configuration` of the table's `metaData` action.\n The value in this column must be set to `NULL` for stable Row Commit Versions that are not preserved (i.e. that are equal to the fresh Row Commit Version).\n- Writers should set `delta.rowTracking.preserved` in the `tags` of the `commitInfo` action to `true` whenever all the stable Row IDs of rows that are updated or copied and all the stable Row Commit Versions of rows that are copied were preserved.\n In particular, writers should set `delta.rowTracking.preserved` in the `tags` of the `commitInfo` action to `true` if no rows are updated or copied.\n Writers should set that flag to false otherwise.\n\n# VACUUM Protocol Check\n\nThe `vacuumProtocolCheck` ReaderWriter feature ensures consistent application of reader and writer protocol checks during `VACUUM` operations, addressing potential protocol discrepancies and mitigating the risk of data corruption due to skipped writer checks.\n\nEnablement:\n- The table must be on Writer Version 7 and Reader Version 3.\n- The feature `vacuumProtocolCheck` must exist in the table `protocol`'s `writerFeatures` and `readerFeatures`.\n\n## Writer Requirements for Vacuum Protocol Check\n\nThis feature affects only the VACUUM operations; standard commits remain unaffected.\n\nBefore performing a VACUUM operation, writers must ensure that they check the table's write protocol. This is most easily implemented by adding an unconditional write protocol check for all tables, which removes the need to examine individual table properties.\n\nWriters that do not implement VACUUM do not need to change anything and can safely write to tables that enable the feature.\n\n## Reader Requirements for Vacuum Protocol Check\n\nFor tables with Vacuum Protocol Check enabled, readers don’t need to understand or change anything new; they just need to acknowledge the feature exists.\n\nMaking this feature a ReaderWriter feature (rather than solely a Writer feature) ensures that:\n- Older vacuum implementations, which only performed the Reader protocol check and lacked the Writer protocol check, will begin to fail if the table has `vacuumProtocolCheck` enabled. This change allows future writer features to have greater flexibility and safety in managing files within the table directory, eliminating the risk of older Vacuum implementations (that lack the Writer protocol check) accidentally deleting relevant files.\n\n# Clustered Table\n\nThe Clustered Table feature facilitates the physical clustering of rows that share similar values on a predefined set of clustering columns.\nThis enhances query performance when selective filters are applied to these clustering columns through data skipping.\nClustering columns can be specified during the initial creation of a table, or they can be added later, provided that the table doesn't have partition columns.\n\nA table is defined as a clustered table through the following criteria:\n- When the feature `clustering` exists in the table `protocol`'s `writerFeatures`, then we say that the table is a clustered table.\n The feature `domainMetadata` is required in the table `protocol`'s `writerFeatures`.\n\nEnablement:\n- The table must be on Writer Version 7.\n- The feature `clustering` must exist in the table `protocol`'s `writerFeatures`, either during its creation or at a later stage, provided the table does not have partition columns.\n\n## Writer Requirements for Clustered Table\n\nWhen the Clustered Table is supported (when the `writerFeatures` field of a table's `protocol` action contains `clustering`), then:\n- Writers must track clustering column names in a `domainMetadata` action with `delta.clustering` as the `domain` and a `configuration` containing all clustering column names.\n If [Column Mapping](#column-mapping) is enabled, the physical column names should be used.\n- Writers must write out [per-file statistics](#per-file-statistics) and per-column statistics for clustering columns in `add` action. \n If a new column is included in the clustering columns list, it is required for all table files to have statistics for these added columns.\n- When a clustering implementation clusters files, writers must set the name of the clustering implementation in the `clusteringProvider` field when adding `add` actions for clustered files.\n - By default, a clustering implementation must only recluster files that have the field `clusteringProvider` set to the name of the same clustering implementation, or to the names of other clustering implementations that are superseded by the current clustering implementation. In addition, a clustering implementation may cluster any files with an unset `clusteringProvider` field (i.e., unclustered files).\n - Writer is not required to cluster a specific file at any specific moment.\n - A clustering implementation is free to add additional information such as adding a new user-controlled metadata domain to keep track of its metadata.\n- Writers must not define clustered and partitioned table at the same time.\n\nThe following is an example for the `domainMetadata` action definition of a table that leverages column mapping.\n```json\n{\n \"domainMetadata\": {\n \"domain\": \"delta.clustering\",\n \"configuration\": \"{\\\"clusteringColumns\\\":[\\\"col-daadafd7-7c20-4697-98f8-bff70199b1f9\\\", \\\"col-5abe0e80-cf57-47ac-9ffc-a861a3d1077e\\\"]}\",\n \"removed\": false\n }\n}\n```\nThe example above converts `configuration` field into JSON format, including escaping characters. Here's how it looks in plain JSON for better understanding.\n```json\n{\n \"clusteringColumns\": [\n \"col-daadafd7-7c20-4697-98f8-bff70199b1f9\",\n \"col-5abe0e80-cf57-47ac-9ffc-a861a3d1077e\"\n ]\n}\n```\n\n\n# Variant Data Type\n\nThis feature enables support for the `variant` data type, which stores semi-structured data.\nThe schema serialization method is described in [Schema Serialization Format](#schema-serialization-format).\n\nTo support this feature:\n- The table must be on Reader Version 3 and Writer Version 7\n- The feature `variantType` must exist in the table `protocol`'s `readerFeatures` and `writerFeatures`.\n\n## Example JSON-Encoded Delta Table Schema with Variant types\n\n```\n{\n \"type\" : \"struct\",\n \"fields\" : [ {\n \"name\" : \"raw_data\",\n \"type\" : \"variant\",\n \"nullable\" : true,\n \"metadata\" : { }\n }, {\n \"name\" : \"variant_array\",\n \"type\" : {\n \"type\" : \"array\",\n \"elementType\" : {\n \"type\" : \"variant\"\n },\n \"containsNull\" : false\n },\n \"nullable\" : false,\n \"metadata\" : { }\n } ]\n}\n```\n\n## Variant data in Parquet\n\nThe Variant data type is represented as two binary encoded values, according to the [Spark Variant binary encoding specification](https://github.com/apache/spark/blob/master/common/variant/README.md).\nThe two binary values are named `value` and `metadata`.\n\nWhen writing Variant data to parquet files, the Variant data is written as a single Parquet struct, with the following fields:\n\nStruct field name | Parquet primitive type | Description\n-|-|-\nvalue | binary | The binary-encoded Variant value, as described in [Variant binary encoding](https://github.com/apache/spark/blob/master/common/variant/README.md)\nmetadata | binary | The binary-encoded Variant metadata, as described in [Variant binary encoding](https://github.com/apache/spark/blob/master/common/variant/README.md)\n\nThe parquet struct must include the two struct fields `value` and `metadata`.\nSupported writers must write the two binary fields, and supported readers must read the two binary fields.\n\n[Variant shredding](https://github.com/apache/parquet-format/blob/master/VariantShredding.md) will be introduced in a separate `variantShredding` table feature. will be introduced later, as a separate `variantShredding` table feature.\n\n## Writer Requirements for Variant Data Type\n\nWhen Variant type is supported (`writerFeatures` field of a table's `protocol` action contains `variantType`), writers:\n- must write a column of type `variant` to parquet as a struct containing the fields `value` and `metadata` and storing values that conform to the [Variant binary encoding specification](https://github.com/apache/spark/blob/master/common/variant/README.md)\n- must not write a parquet struct field named `typed_value` to avoid confusion with the field required by [Variant shredding](https://github.com/apache/parquet-format/blob/master/VariantShredding.md) with the same name.\n\n## Reader Requirements for Variant Data Type\n\nWhen Variant type is supported (`readerFeatures` field of a table's `protocol` action contains `variantType`), readers:\n- must recognize and tolerate a `variant` data type in a Delta schema\n- must use the correct physical schema (struct-of-binary, with fields `value` and `metadata`) when reading a Variant data type from file\n- must make the column available to the engine:\n - [Recommended] Expose and interpret the struct-of-binary as a single Variant field in accordance with the [Spark Variant binary encoding specification](https://github.com/apache/spark/blob/master/common/variant/README.md).\n - [Alternate] Expose the raw physical struct-of-binary, e.g. if the engine does not support Variant.\n - [Alternate] Convert the struct-of-binary to a string, and expose the string representation, e.g. if the engine does not support Variant.\n\n## Compatibility with other Delta Features\n\nFeature | Support for Variant Data Type\n-|-\nPartition Columns | **Supported:** A Variant column is allowed to be a non-partitioned column of a partitioned table.
**Unsupported:** Variant is not a comparable data type, so it cannot be included in a partition column.\nClustered Tables | **Supported:** A Variant column is allowed to be a non-clustering column of a clustered table.
**Unsupported:** Variant is not a comparable data type, so it cannot be included in a clustering column.\nDelta Column Statistics | **Supported:** A Variant column supports the `nullCount` statistic.
**Unsupported:** Variant is not a comparable data type, so a Variant column does not support the `minValues` and `maxValues` statistics.\nGenerated Columns | **Supported:** A Variant column is allowed to be used as a source in a generated column expression, as long as the Variant type is not the result type of the generated column expression.
**Unsupported:** The Variant data type is not allowed to be the result type of a generated column expression.\nDelta CHECK Constraints | **Supported:** A Variant column is allowed to be used for a CHECK constraint expression.\nDefault Column Values | **Supported:** A Variant column is allowed to have a default column value.\nChange Data Feed | **Supported:** A table using the Variant data type is allowed to enable the Delta Change Data Feed.\n\n# In-Commit Timestamps\n\nThe In-Commit Timestamps writer feature strongly associates a monotonically increasing timestamp with each commit by storing it in the commit's metadata.\n\nEnablement:\n- The table must be on Writer Version 7.\n- The feature `inCommitTimestamp` must exist in the table `protocol`'s `writerFeatures`.\n- The table property `delta.enableInCommitTimestamps` must be set to `true`.\n\n## Writer Requirements for In-Commit Timestamps\n\nWhen In-Commit Timestamps is enabled, then:\n1. Writers must write the `commitInfo` (see [Commit Provenance Information](#commit-provenance-information)) action in the commit.\n2. The `commitInfo` action must be the first action in the commit.\n3. The `commitInfo` action must include a field named `inCommitTimestamp`, of type `long` (see [Primitive Types](#primitive-types)), which represents the time (in milliseconds since the Unix epoch) when the commit is considered to have succeeded. It is the larger of two values:\n - The time, in milliseconds since the Unix epoch, at which the writer attempted the commit\n - One millisecond later than the previous commit's `inCommitTimestamp`\n4. If the table has commits from a period when this feature was not enabled, provenance information around when this feature was enabled must be tracked in table properties:\n - The property `delta.inCommitTimestampEnablementVersion` must be used to track the version of the table when this feature was enabled.\n - The property `delta.inCommitTimestampEnablementTimestamp` must be the same as the `inCommitTimestamp` of the commit when this feature was enabled.\n5. The `inCommitTimestamp` of the commit that enables this feature must be greater than the file modification time of the immediately preceding commit.\n\n## Recommendations for Readers of Tables with In-Commit Timestamps\n\nFor tables with In-Commit timestamps enabled, readers should use the `inCommitTimestamp` as the commit timestamp for operations like time travel and [`DESCRIBE HISTORY`](https://docs.delta.io/latest/delta-utility.html#retrieve-delta-table-history).\nIf a table has commits from a period before In-Commit timestamps were enabled, the table properties `delta.inCommitTimestampEnablementVersion` and `delta.inCommitTimestampEnablementTimestamp` would be set and can be used to identify commits that don't have `inCommitTimestamp`.\nTo correctly determine the commit timestamp for these tables, readers can use the following rules:\n1. For commits with version >= `delta.inCommitTimestampEnablementVersion`, readers should use the `inCommitTimestamp` field of the `commitInfo` action.\n2. For commits with version < `delta.inCommitTimestampEnablementVersion`, readers should use the file modification timestamp.\n\nFurthermore, when attempting timestamp-based time travel where table state must be fetched as of `timestamp X`, readers should use the following rules:\n1. If `timestamp X` >= `delta.inCommitTimestampEnablementTimestamp`, only table versions >= `delta.inCommitTimestampEnablementVersion` should be considered for the query.\n2. Otherwise, only table versions less than `delta.inCommitTimestampEnablementVersion` should be considered for the query.\n\n# Type Widening\n\nThe Type Widening feature enables changing the type of a column or field in an existing Delta table to a wider type.\n\nThe supported type changes are:\n- Integer widening:\n - `Byte` -> `Short` -> `Int` -> `Long`\n- Floating-point widening:\n - `Float` -> `Double`\n - `Byte`, `Short` or `Int` -> `Double`\n- Date widening:\n - `Date` -> `Timestamp without timezone`\n- Decimal widening - `p` and `s` denote the decimal precision and scale respectively.\n - `Decimal(p, s)` -> `Decimal(p + k1, s + k2)` where `k1 >= k2 >= 0`.\n - `Byte`, `Short` or `Int` -> `Decimal(10 + k1, k2)` where `k1 >= k2 >= 0`.\n - `Long` -> `Decimal(20 + k1, k2)` where `k1 >= k2 >= 0`.\n\nTo support this feature:\n- The table must be on Reader version 3 and Writer Version 7.\n- The feature `typeWidening` must exist in the table `protocol`'s `readerFeatures` and `writerFeatures`, either during its creation or at a later stage.\n\nWhen supported:\n - A table may have a metadata property `delta.enableTypeWidening` in the Delta schema set to `true`. Writers must reject widening type changes when this property isn't set to `true`.\n - The `metadata` for a column or field in the table schema may contain the key `delta.typeChanges` storing a history of type changes for that column or field.\n\n### Type Change Metadata\n\nType changes applied to a table are recorded in the table schema and stored in the `metadata` of their nearest ancestor [StructField](#struct-field) using the key `delta.typeChanges`.\nThe value for the key `delta.typeChanges` must be a JSON list of objects, where each object contains the following fields:\nField Name | optional/required | Description\n-|-|-\n`fromType`| required | The type of the column or field before the type change.\n`toType`| required | The type of the column or field after the type change.\n`fieldPath`| optional | When updating the type of a map key/value or array element only: the path from the struct field holding the metadata to the map key/value or array element that was updated.\n\nThe `fieldPath` value is \"key\", \"value\" and \"element\" when updating resp. the type of a map key, map value and array element.\nThe `fieldPath` value for nested maps and nested arrays are prefixed by their parents' path, separated by dots.\n\nThe following is an example for the definition of a column that went through two type changes:\n```json\n{\n \"name\" : \"e\",\n \"type\" : \"long\",\n \"nullable\" : true,\n \"metadata\" : { \n \"delta.typeChanges\": [\n {\n \"fromType\": \"short\",\n \"toType\": \"integer\"\n },\n {\n \"fromType\": \"integer\",\n \"toType\": \"long\"\n }\n ]\n }\n }\n```\n\nThe following is an example for the definition of a column after changing the type of a map key:\n```json\n{\n \"name\" : \"e\",\n \"type\" : {\n \"type\": \"map\",\n \"keyType\": \"double\",\n \"valueType\": \"integer\",\n \"valueContainsNull\": true\n },\n \"nullable\" : true,\n \"metadata\" : { \n \"delta.typeChanges\": [\n {\n \"fromType\": \"float\",\n \"toType\": \"double\",\n \"fieldPath\": \"key\"\n }\n ]\n }\n }\n```\n\nThe following is an example for the definition of a column after changing the type of a map value nested in an array:\n```json\n{\n \"name\" : \"e\",\n \"type\" : {\n \"type\": \"array\",\n \"elementType\": {\n \"type\": \"map\",\n \"keyType\": \"string\",\n \"valueType\": \"decimal(10, 4)\",\n \"valueContainsNull\": true\n },\n \"containsNull\": true\n },\n \"nullable\" : true,\n \"metadata\" : { \n \"delta.typeChanges\": [\n {\n \"fromType\": \"decimal(6, 2)\",\n \"toType\": \"decimal(10, 4)\",\n \"fieldPath\": \"element.value\"\n }\n ]\n }\n }\n```\n\n## Writer Requirements for Type Widening\n\nWhen Type Widening is supported (when the `writerFeatures` field of a table's `protocol` action contains `typeWidening`), then:\n- Writers must reject applying any unsupported type change.\n- Writers must reject applying type changes not supported by [Iceberg V2](https://iceberg.apache.org/spec/#schema-evolution)\n when either the [Iceberg Compatibility V1](#iceberg-compatibility-v1) or [Iceberg Compatibility V2](#iceberg-compatibility-v2) table feature is supported:\n - `Byte`, `Short` or `Int` -> `Double`\n - `Date` -> `Timestamp without timezone`\n - Decimal scale increase\n - `Byte`, `Short`, `Int` or `Long` -> `Decimal`\n- Writers must record type change information in the `metadata` of the nearest ancestor [StructField](#struct-field). See [Type Change Metadata](#type-change-metadata).\n- Writers must preserve the `delta.typeChanges` field in the metadata fields in the schema when the table schema is updated.\n- Writers may remove the `delta.typeChanges` metadata in the table schema if all data files use the same field types as the table schema.\n\nWhen Type Widening is enabled (when the table property `delta.enableTypeWidening` is set to `true`), then:\n- Writers should allow updating the table schema to apply a supported type change to a column, struct field, map key/value or array element.\n\nWhen removing the Type Widening table feature from the table, in the version that removes `typeWidening` from the `writerFeatures` and `readerFeatures` fields of the table's `protocol` action:\n- Writers must ensure no `delta.typeChanges` metadata key is present in the table schema. This may require rewriting existing data files to ensure that all data files use the same field types as the table schema in order to fulfill the requirement to remove type widening metadata.\n- Writers must ensure that the table property `delta.enableTypeWidening` is not set.\n\n## Reader Requirements for Type Widening\nWhen Type Widening is supported (when the `readerFeatures` field of a table's `protocol` action contains `typeWidening`), then:\n- Readers must allow reading data files written before the table underwent any supported type change, and must convert such values to the current, wider type.\n- Readers must validate that they support all type changes in the `delta.typeChanges` field in the table schema for the table version they are reading and fail when finding any unsupported type change.\n\n# Requirements for Writers\nThis section documents additional requirements that writers must follow in order to preserve some of the higher level guarantees that Delta provides.\n\n## Creation of New Log Entries\n - Writers MUST never overwrite an existing log entry. When ever possible they should use atomic primitives of the underlying filesystem to ensure concurrent writers do not overwrite each other's entries.\n\n## Consistency Between Table Metadata and Data Files\n - Any column that exists in a data file present in the table MUST also be present in the metadata of the table.\n - Values for all partition columns present in the schema MUST be present for all files in the table.\n - Columns present in the schema of the table MAY be missing from data files. Readers SHOULD fill these missing columns in with `null`.\n\n## Delta Log Entries\n- A single log entry MUST NOT include more than one action that reconciles with each other.\n - Add / Remove actions with the same `(path, DV)` tuple.\n - More than one Metadata action\n - More than one protocol action\n - More than one SetTransaction with the same `appId`\n\n## Checkpoints\nEach row in the checkpoint corresponds to a single action. The checkpoint **must** contain all information regarding the following actions:\n * The [protocol version](#Protocol-Evolution)\n * The [metadata](#Change-Metadata) of the table\n * Files that have been [added](#Add-File-and-Remove-File) and not yet removed\n * Files that were recently [removed](#Add-File-and-Remove-File) and have not yet expired\n * [Transaction identifiers](#Transaction-Identifiers)\n * [Domain Metadata](#Domain-Metadata)\n * [Checkpoint Metadata](#checkpoint-metadata) - Requires [V2 checkpoints](#v2-spec)\n * [Sidecar File](#sidecar-files) - Requires [V2 checkpoints](#v2-spec)\n\nAll of these actions are stored as their individual columns in parquet as struct fields. Any missing column should be treated as null.\n\nCheckpoints must not preserve [commit provenance information](#commit-provenance-information) nor [change data](#add-cdc-file) actions.\n\nWithin the checkpoint, the `add` struct may or may not contain the following columns based on the configuration of the table:\n - partitionValues_parsed: In this struct, the column names correspond to the partition columns and the values are stored in their corresponding data type. This is a required field when the table is partitioned and the table property `delta.checkpoint.writeStatsAsStruct` is set to `true`. If the table is not partitioned, this column can be omitted. For example, for partition columns `year`, `month` and `event` with data types `int`, `int` and `string` respectively, the schema for this field will look like:\n\n ```\n|-- add: struct\n| |-- partitionValues_parsed: struct\n| | |-- year: int\n| | |-- month: int\n| | |-- event: string\n ```\n\n - stats: Column level statistics can be stored as a JSON string in the checkpoint. This field needs to be written when statistics are available and the table property: `delta.checkpoint.writeStatsAsJson` is set to `true` (which is the default). When this property is set to `false`, this field should be omitted from the checkpoint.\n - stats_parsed: The stats can be stored in their [original format](#Per-file-Statistics). This field needs to be written when statistics are available and the table property: `delta.checkpoint.writeStatsAsStruct` is set to `true`. When this property is set to `false` (which is the default), this field should be omitted from the checkpoint.\n\nWithin the checkpoint, the `remove` struct does not contain the `stats` and `tags` fields because the `remove` actions stored in checkpoints act only as tombstones for VACUUM operations, and VACUUM tombstones do not require `stats` or `tags`. These fields are only stored in Delta JSON commit files.\n\nRefer to the [appendix](#checkpoint-schema) for an example on the schema of the checkpoint.\n\nDelta supports two checkpoint specs and three kind of checkpoint naming schemes.\n\n### Checkpoint Specs\nDelta supports following two checkpoint specs:\n\n#### V2 Spec\nThis checkpoint spec allows putting [add and remove file](#Add-File-and-Remove-File) in the\n[sidecar files](#sidecar-files). This spec can be used only when [v2 checkpoint table feature](#v2-checkpoint-table-feature) is enabled.\nCheckpoints following V2 spec have the following structure:\n- Each v2 spec checkpoint includes exactly one [Checkpoint Metadata](#checkpoint-metadata) action.\n- Remaining rows in the V2 spec checkpoint refer to the other actions mentioned [here](#checkpoints-1)\n- All the non-file actions i.e. all actions except [add and remove file](#Add-File-and-Remove-File)\nmust be part of the v2 spec checkpoint itself.\n- A writer could choose to include the [add and remove file](#Add-File-and-Remove-File) action in the\nV2 spec Checkpoint or they could write the [add and remove file](#Add-File-and-Remove-File) actions in\nseparate [sidecar files](#sidecar-files). These sidecar files will then be referenced in the V2 spec checkpoint.\nAll sidecar files reside in the `_delta_log/_sidecars` directory.\n- A V2 spec Checkpoint could reference zero or more [sidecar file actions](#sidecar-file-information).\n\nNote: A V2 spec Checkpoint can either have all the [add and remove file](#Add-File-and-Remove-File) actions\nembedded inside itself or all of them should be in [sidecar files](#sidecar-files). Having partial\nadd and remove file actions in V2 Checkpoint and partial entries in sidecar files is not allowed.\n\nAfter producing a V2 spec checkpoint, a writer can choose to embed some or all of the V2 spec checkpoint in\nthe `_last_checkpoint` file, so that readers don't have to read the V2 Checkpoint.\n\nE.g. showing the content of V2 spec checkpoint:\n```\n{\"checkpointMetadata\":{\"version\":364475,\"tags\":{}}}\n{\"metaData\":{...}}\n{\"protocol\":{...}}\n{\"txn\":{\"appId\":\"3ba13872-2d47-4e17-86a0-21afd2a22395\",\"version\":364475}}\n{\"txn\":{\"appId\":\"3ae45b72-24e1-865a-a211-34987ae02f2a\",\"version\":4389}}\n{\"sidecar\":{\"path\":\"3a0d65cd-4056-49b8-937b-95f9e3ee90e5.parquet\",\"sizeInBytes\":2341330,\"modificationTime\":1512909768000,\"tags\":{}}\n{\"sidecar\":{\"path\":\"016ae953-37a9-438e-8683-9a9a4a79a395.parquet\",\"sizeInBytes\":8468120,\"modificationTime\":1512909848000,\"tags\":{}}\n```\n\nAnother example of a v2 spec checkpoint without sidecars:\n```\n{\"checkpointMetadata\":{\"version\":364475,\"tags\":{}}}\n{\"metaData\":{...}}\n{\"protocol\":{...}}\n{\"txn\":{\"appId\":\"3ba13872-2d47-4e17-86a0-21afd2a22395\",\"version\":364475}}\n{\"add\":{\"path\":\"date=2017-12-10/part-000...c000.gz.parquet\",...}\n{\"add\":{\"path\":\"date=2017-12-09/part-000...c000.gz.parquet\",...}\n{\"remove\":{\"path\":\"date=2017-12-08/part-000...c000.gz.parquet\",...}\n```\n\n#### V1 Spec\n\nThe V1 Spec does not support [sidecar files](#sidecar-files) and [checkpoint metadata](#checkpoint-metadata).\nThese are flat checkpoints which contains all actions mentioned [here](#checkpoints-1).\n\n### Checkpoint Naming Scheme\nDelta supports three checkpoint naming schemes: UUID-named, classic, and multi-part.\n\n#### UUID-named checkpoint\nThis naming scheme represents a [V2 spec checkpoint](#v2-spec) with following file name: `n.checkpoint.u.{json/parquet}`,\nwhere `u` is a UUID and `n` is the snapshot version that this checkpoint represents.\nThe UUID-named checkpoints may be in JSON or parquet format. Since these are following [V2 spec](#v2-spec), they must\nhave a [checkpoint metadata](#checkpoint-metadata) action and may reference zero or more checkpoint [sidecar files](#sidecar-files).\n\nExample-1: Json UUID-named checkpoint with sidecars\n\n```\n00000000000000000010.checkpoint.80a083e8-7026-4e79-81be-64bd76c43a11.json\n_sidecars/016ae953-37a9-438e-8683-9a9a4a79a395.parquet\n_sidecars/3a0d65cd-4056-49b8-937b-95f9e3ee90e5.parquet\n_sidecars/7d17ac10-5cc3-401b-bd1a-9c82dd2ea032.parquet\n```\n\nExample-2: Parquet UUID-named checkpoint with sidecars\n\n```\n00000000000000000020.checkpoint.80a083e8-7026-4e79-81be-64bd76c43a11.parquet\n_sidecars/016ae953-37a9-438e-8683-9a9a4a79a395.parquet\n_sidecars/3a0d65cd-4056-49b8-937b-95f9e3ee90e5.parquet\n```\n\nExample-3: Json UUID-named checkpoint without sidecars\n\n```\n00000000000000000112.checkpoint.80a083e8-7026-4e79-81be-64bd76c43a11.json\n```\n\n#### Classic checkpoint\n\nA classic checkpoint for version `n` uses the file name `n.checkpoint.parquet`. For example:\n\n```\n00000000000000000010.checkpoint.parquet\n```\n\nIf two checkpoint writers race to create the same classic checkpoint, the latest writer wins.\nHowever, this should not matter because both checkpoints should contain the same information and a\nreader could safely use either one.\n\nA classic checkpoint could:\n1. Either follow [V1 spec](#v1-spec) or\n2. Could follow [V2 spec](#v2-spec). This is possible only when\n[V2 Checkpoint table feature](#v2-checkpoint-table-feature) is enabled. In this case it must include\n[checkpoint metadata](#checkpoint-metadata) and may or may not have [sidecar files](#sidecar-file-information).\n\n#### Multi-part checkpoint\nMulti-part checkpoint uses parquet format.\nThis checkpoint type is [deprecated](#problems-with-multi-part-checkpoints) and writers should avoid using it.\n\nA multi-part checkpoint for version `n` consists of `p` \"part\" files (`p > 1`), where part `o` of `p` is named `n.checkpoint.o.p.parquet`. For example:\n\n```\n00000000000000000010.checkpoint.0000000001.0000000003.parquet\n00000000000000000010.checkpoint.0000000002.0000000003.parquet\n00000000000000000010.checkpoint.0000000003.0000000003.parquet\n```\n\nFor [safety reasons](#problems-with-multi-part-checkpoints), multi-part checkpoints MUST be clustered by\nspark-style hash partitioning. If the table supports [Deletion Vectors](#deletion-vectors), the partitioning\nkey is the logical file identifier `(path, dvId)`; otherwise the key is just `path` (not `(path, NULL)`). This\nensures deterministic content in each part file in case of multiple attempts to write the files -- even when\nolder and newer Delta clients race.\n\n##### Problems with multi-part checkpoints\n\nBecause they cannot be written atomically, multi-part checkpoints have several weaknesses:\n\n1. A writer cannot validate the content of the just-written checkpoint before readers could start using it.\n\n2. Two writers who race to produce the same checkpoint (same version, same number of parts) can overwrite each other, producing an arbitrary mix of checkpoint part files. If an overwrite changes the content of a file in any way, the resulting checkpoint may not produce an accurate snapshot.\n\n3. Not amenable to performance and scalability optimizations. For example, there is no way to store skipping stats for checkpoint parts, nor to reuse checkpoint part files across multiple checkpoints.\n\n4. Multi-part checkpoints also bloat the _delta_log dir and slow down LIST operations.\n\nThe [UUID-named](#uuid-named-checkpoint) checkpoint (which follows [V2 spec](#v2-spec)) solves all\nof these problems and should be preferred over multi-part checkpoints. For this reason, Multi-part\ncheckpoints are forbidden when [V2 Checkpoints table feature](#v2-checkpoint-table-feature) is enabled.\n\n### Handling Backward compatibility while moving to UUID-named v2 Checkpoints\n\nA UUID-named v2 Checkpoint should only be created by clients if the [v2 checkpoint table feature](#v2-checkpoint-table-feature) is enabled.\nWhen UUID-named v2 checkpoints are enabled, Writers should occasionally create a v2 [Classic Checkpoint](#classic-checkpoint)\nto maintain compatibility with older clients which do not support [v2 checkpoint table feature](#v2-checkpoint-table-feature) and\nso do not recognize UUID-named checkpoints. These classic checkpoints have the same content as the UUID-named v2 checkpoint, but older\nclients will recognize the classic file name, allowing them to extract [Protocol](#protocol-evolution) and fail gracefully with an\ninvalid protocol version error on v2-checkpoint-enabled tables. Writers should create classic checkpoints often enough to allow older\nclients to discover them and fail gracefully.\n\n### Allowed combinations for `checkpoint spec` <-> `checkpoint file naming`\n\nCheckpoint Spec | [UUID-named](#uuid-named-checkpoint) | [classic](#classic-checkpoint) | [multi-part](#multi-part-checkpoint)\n-|-|-|-\n[V1](#v1-spec) | Invalid | Valid | Valid\n[V2](#v2-spec) | Valid | Valid | Invalid\n\n### Metadata Cleanup\n\nThe _delta_log directory grows over time as more and more commits and checkpoints are accumulated.\nImplementations are recommended to delete expired commits and checkpoints in order to reduce the directory size.\nThe following steps could be used to do cleanup of the DeltaLog directory:\n1. Identify a threshold (in days) uptil which we want to preserve the deltaLog. Let's refer to\nmidnight UTC of that day as `cutOffTimestamp`. The newest commit not newer than the `cutOffTimestamp` is\nthe `cutoffCommit`, because a commit exactly at midnight is an acceptable cutoff. We want to retain everything including and after the `cutoffCommit`.\n2. Identify the newest checkpoint that is not newer than the `cutOffCommit`. A checkpoint at the `cutOffCommit` is ideal, but an older one will do. Let's call it `cutOffCheckpoint`.\nWe need to preserve the `cutOffCheckpoint` (both the checkpoint file and the JSON commit file at that version) and all published commits after it. The JSON commit file at the `cutOffCheckpoint` version must be preserved because checkpoints do not preserve [commit provenance information](#commit-provenance-information) (e.g., `commitInfo` actions), which may be required by table features such as [In-Commit Timestamps](#in-commit-timestamps). All published commits after `cutOffCheckpoint` must be preserved to enable time travel for commits between `cutOffCheckpoint` and the next available checkpoint.\n - If no `cutOffCheckpoint` can be found, do not proceed with metadata cleanup as there is\n nothing to cleanup.\n3. Delete all [delta log entries](#delta-log-entries), [checkpoint files](#checkpoints), and\n [version checksum files](#version-checksum-file) before the `cutOffCheckpoint` checkpoint. Also delete all the [log compaction files](#log-compaction-files)\n having startVersion <= `cutOffCheckpoint`'s version.\n - Also delete all the [staged commit files](#staged-commit) having version <=\n `cutOffCheckpoint`'s version from the `_delta_log/_staged_commits` directory.\n4. Now read all the available [checkpoints](#checkpoints-1) in the _delta_log directory and identify\nthe corresponding [sidecar files](#sidecar-files). These sidecar files need to be protected.\n5. List all the files in `_delta_log/_sidecars` directory, preserve files that are less than a day\nold (as of midnight UTC), to not break in-progress checkpoints. Also preserve the referenced sidecar files\nidentified in Step-4 above. Delete everything else.\n\n## Data Files\n - Data files MUST be uniquely named and MUST NOT be overwritten. The reference implementation uses a GUID in the name to ensure this property.\n\n## Append-only Tables\nTo support this feature:\n - The table must be on a Writer Version starting from 2 up to 7.\n - If the table is on Writer Version 7, the feature `appendOnly` must exist in the table `protocol`'s `writerFeatures`.\n\nWhen supported, and if the table has a property `delta.appendOnly` set to `true`:\n - New log entries MUST NOT change or remove data from the table.\n - New log entries may rearrange data (i.e. `add` and `remove` actions where `dataChange=false`).\n\nTo remove the append-only restriction, the table property `delta.appendOnly` must be set to `false`, or it must be removed.\n\n## Column Invariants\nTo support this feature\n - If the table is on a Writer Version starting from 2 up to 6, Column Invariants are always enabled.\n - If the table is on Writer Version 7, the feature `invariants` must exist in the table `protocol`'s `writerFeatures`.\n\nWhen supported:\n - The `metadata` for a column in the table schema MAY contain the key `delta.invariants`.\n - The value of `delta.invariants` SHOULD be parsed as a JSON string containing a boolean SQL expression at the key `expression.expression` (that is, `{\"expression\": {\"expression\": \"\"}}`).\n - Writers MUST abort any transaction that adds a row to the table, where an invariant evaluates to `false` or `null`.\n\nFor example, given the schema string (pretty printed for readability. The entire schema string in the log should be a single JSON line):\n\n```json\n{\n \"type\": \"struct\",\n \"fields\": [\n {\n \"name\": \"x\",\n \"type\": \"integer\",\n \"nullable\": true,\n \"metadata\": {\n \"delta.invariants\": \"{\\\"expression\\\": { \\\"expression\\\": \\\"x > 3\\\"} }\"\n }\n }\n ]\n}\n```\n\nWriters should reject any transaction that contains data where the expression `x > 3` returns `false` or `null`.\n\n## CHECK Constraints\n\nTo support this feature:\n- If the table is on a Writer Version starting from 3 up to 6, CHECK Constraints are always supported.\n- If the table is on Writer Version 7, a feature name `checkConstraints` must exist in the table `protocol`'s `writerFeatures`.\n\nCHECK constraints are stored in the map of the `configuration` field in [Metadata](#change-metadata). Each CHECK constraint has a name and is stored as a key value pair. The key format is `delta.constraints.{name}`, and the value is a SQL expression string whose return type must be `Boolean`. Columns referred by the SQL expression must exist in the table schema.\n\nRows in a table must satisfy CHECK constraints. In other words, evaluating the SQL expressions of CHECK constraints must return `true` for each row in a table.\n\nFor example, a key value pair (`delta.constraints.birthDateCheck`, `birthDate > '1900-01-01'`) means there is a CHECK constraint called `birthDateCheck` in the table and the value of the `birthDate` column in each row must be greater than `1900-01-01`.\n\nHence, a writer must follow the rules below:\n- CHECK Constraints may not be added to a table unless the above \"to support this feature\" rules are satisfied. When adding a CHECK Constraint to a table for the first time, writers are allowed to submit a `protocol` change in the same commit to add support of this feature.\n- When adding a CHECK constraint to a table, a writer must validate the existing data in the table and ensure every row satisfies the new CHECK constraint before committing the change. Otherwise, the write operation must fail and the table must stay unchanged.\n- When writing to a table that contains CHECK constraints, every new row being written to the table must satisfy CHECK constraints in the table. Otherwise, the write operation must fail and the table must stay unchanged.\n\n## Generated Columns\n\nTo support this feature:\n - If the table is on a Writer Version starting from 4 up to 6, Generated Columns are always supported.\n - If the table is on Writer Version 7, a feature name `generatedColumns` must exist in the table `protocol`'s `writerFeatures`.\n\nWhen supported:\n - The `metadata` for a column in the table schema MAY contain the key `delta.generationExpression`.\n - The value of `delta.generationExpression` SHOULD be parsed as a SQL expression.\n - Writers MUST enforce that any data writing to the table satisfy the condition `( <=> ) IS TRUE`. `<=>` is the NULL-safe equal operator which performs an equality comparison like the `=` operator but returns `TRUE` rather than NULL if both operands are `NULL`\n\n## Default Columns\n\nDelta supports defining default expressions for columns on Delta tables. Delta will generate default values for columns when users do not explicitly provide values for them when writing to such tables, or when the user explicitly specifies the `DEFAULT` SQL keyword for any such column.\n\nSemantics for write and read operations:\n- Note that this metadata only applies for write operations, not read operations.\n- Table write operations (such as SQL INSERT, UPDATE, and MERGE commands) will use the default values. For example, this SQL command will use default values: `INSERT INTO t VALUES (42, DEFAULT);`\n- Table operations that add new columns (such as SQL ALTER TABLE ... ADD COLUMN commands) MUST not specify a default value for any column in the same command that the column is created. For example, this SQL command is not supported in Delta Lake: `ALTER TABLE t ADD COLUMN c INT DEFAULT 42;`\n- Note that it is acceptable to assign or update default values for columns that were already created in previous commands, however. For example, this SQL command is valid: `ALTER TABLE t ALTER COLUMN c SET DEFAULT 42;`\n\nEnablement:\n- The table must be on Writer Version 7, and a feature name `allowColumnDefaults` must exist in the table `protocol`'s `writerFeatures`.\n\nWhen enabled:\n- The `metadata` for the column in the table schema MAY contain the key `CURRENT_DEFAULT`.\n- The value of `CURRENT_DEFAULT` SHOULD be parsed as a SQL expression.\n- Writers MUST enforce that before writing any rows to the table, for each such requested row that lacks any explicit value (including NULL) for columns with default values, the writing system will assign the result of evaluating the default value expression for each such column as the value for that column in the row. By the same token, if the engine specified the explicit `DEFAULT` SQL keyword for any column, the expression result must be substituted in the same way.\n- All columns of `variant` type must default to null.\n\n## Identity Columns\n\nDelta supports defining Identity columns on Delta tables. Delta will generate unique values for Identity columns when users do not explicitly provide values for them when writing to such tables. To support Identity Columns:\n - The table must be on Writer Version 6, or\n - The table must be on Writer Version 7, and a feature name `identityColumns` must exist in the table `protocol`'s `writerFeatures`.\n\nWhen supported, the `metadata` for a column in the table schema MAY contain the following keys for Identity Column properties:\n- `delta.identity.start`: Starting value for the Identity column. This is a long type value. It should not be changed after table creation.\n- `delta.identity.step`: Increment to the next Identity value. This is a long type value. It cannot be set to 0. It should not be changed after table creation.\n- `delta.identity.highWaterMark`: The highest value generated for the Identity column. This is a long type value. When `delta.identity.step` is positive (negative), this should be the largest (smallest) value in the column.\n- `delta.identity.allowExplicitInsert`: True if this column allows explicitly inserted values. This is a boolean type value. It should not be changed after table creation.\n\nWhen `delta.identity.allowExplicitInsert` is true, writers should meet the following requirements:\n- Users should be allowed to provide their own values for Identity columns.\n\nWhen `delta.identity.allowExplicitInsert` is false, writers should meet the following requirements:\n- Users should not be allowed to provide their own values for Identity columns.\n- Delta should generate values that satisfy the following requirements\n - The new value does not already exist in the column.\n - The new value should satisfy `value = start + k * step` where k is a non-negative integer.\n - The new value should be higher than `delta.identity.highWaterMark`. When `delta.identity.step` is positive (negative), the new value should be the greater (smaller) than `delta.identity.highWaterMark`.\n- Overflow when calculating generated Identity values should be detected and such writes should not be allowed.\n- `delta.identity.highWaterMark` should be updated to the new highest value when the write operation commits.\n\n## Writer Version Requirements\n\nThe requirements of the writers according to the protocol versions are summarized in the table below. Each row inherits the requirements from the preceding row.\n\n
| Requirements\n-|-\nWriter Version 2 | - Respect [Append-only Tables](#append-only-tables)
- Respect [Column Invariants](#column-invariants)\nWriter Version 3 | - Enforce `delta.checkpoint.writeStatsAsJson`
- Enforce `delta.checkpoint.writeStatsAsStruct`
- Respect [`CHECK` constraints](#check-constraints)\nWriter Version 4 | - Respect [Change Data Feed](#add-cdc-file)
- Respect [Generated Columns](#generated-columns)\nWriter Version 5 | Respect [Column Mapping](#column-mapping)\nWriter Version 6 | Respect [Identity Columns](#identity-columns)\nWriter Version 7 | Respect [Table Features](#table-features) for writers\n\n# Requirements for Readers\n\nThis section documents additional requirements that readers must respect in order to produce correct scans of a Delta table.\n\n## Reader Version Requirements\n\nThe requirements of the readers according to the protocol versions are summarized in the table below. Each row inherits the requirements from the preceding row.\n\n
| Requirements\n-|-\nReader Version 2 | Respect [Column Mapping](#column-mapping)\nReader Version 3 | Respect [Table Features](#table-features) for readers
- Writer Version must be 7\n\n# Appendix\n\n## Valid Feature Names in Table Features\n\nFeature | Name | Readers or Writers?\n-|-|-\n[Append-only Tables](#append-only-tables) | `appendOnly` | Writers only\n[Column Invariants](#column-invariants) | `invariants` | Writers only\n[`CHECK` constraints](#check-constraints) | `checkConstraints` | Writers only\n[Generated Columns](#generated-columns) | `generatedColumns` | Writers only\n[Default Columns](#default-columns) | `allowColumnDefaults` | Writers only\n[Change Data Feed](#add-cdc-file) | `changeDataFeed` | Writers only\n[Column Mapping](#column-mapping) | `columnMapping` | Readers and writers\n[Identity Columns](#identity-columns) | `identityColumns` | Writers only\n[Deletion Vectors](#deletion-vectors) | `deletionVectors` | Readers and writers\n[Row Tracking](#row-tracking) | `rowTracking` | Writers only\n[Timestamp without Timezone](#timestamp-without-timezone-timestampNtz) | `timestampNtz` | Readers and writers\n[Domain Metadata](#domain-metadata) | `domainMetadata` | Writers only\n[V2 Checkpoint](#v2-checkpoint-table-feature) | `v2Checkpoint` | Readers and writers\n[Catalog-managed Tables](#catalog-managed-tables) | `catalogManaged` | Readers and writers\n[Iceberg Compatibility V1](#iceberg-compatibility-v1) | `icebergCompatV1` | Writers only\n[Iceberg Compatibility V2](#iceberg-compatibility-v2) | `icebergCompatV2` | Writers only\n[Clustered Table](#clustered-table) | `clustering` | Writers only\n[VACUUM Protocol Check](#vacuum-protocol-check) | `vacuumProtocolCheck` | Readers and Writers\n[In-Commit Timestamps](#in-commit-timestamps) | `inCommitTimestamp` | Writers only\n\n## Deletion Vector Format\n\nDeletion Vectors are basically sets of row indexes, that is 64-bit integers that describe the position (index) of a row in a parquet file starting from zero. We store these sets in a compressed format. The fundamental building block for this is the open source [RoaringBitmap](https://roaringbitmap.org/) library. RoaringBitmap is a flexible format for storing 32-bit integers that automatically switches between three different encodings at the granularity of a 16-bit block (64K values):\n\n- Simple integer array, when the number of values in the block is small.\n- Bitmap-compressed, when the number of values in the block is large and scattered.\n- Run-length encoded, when the number of values in the block is large, but clustered.\n\nThe serialization format is [standardized](https://github.com/RoaringBitmap/RoaringFormatSpec), and both [Java](https://github.com/lemire/RoaringBitmap/) and [C/C++](https://github.com/RoaringBitmap/CRoaring) implementations are available (among others).\n\nThe above description only applies to 32-bit bitmaps, but Deletion Vectors use 64-bit integers. In order to extend coverage from 32 to 64 bits, RoaringBitmaps defines a \"portable\" serialization format in the [RoaringBitmaps Specification](https://github.com/RoaringBitmap/RoaringFormatSpec#extention-for-64-bit-implementations). This format essentially splits the space into an outer part with the most significant 32-bit \"keys\" indexing the least significant 32-bit RoaringBitmaps in ascending sequence. The spec calls these least signficant 32-bit RoaringBitmaps \"buckets\".\n\nBytes | Name | Description\n-|-|-\n0 – 7 | numBuckets | The number of distinct 32-bit buckets in this bitmap.\n`repeat for each bucket b` | | For each bucket in ascending order of keys.\n`` – ` + 3` | key | The most significant 32-bit of all the values in this bucket.\n` + 4` – `` | bucketData | A serialized 32-bit RoaringBitmap with all the least signficant 32-bit entries in this bucket.\n\nThe 32-bit serialization format then consists of a header that describes all the (least signficant) 16-bit containers, their types (s. above), and their key (most significant 16-bits).\nThis is followed by the data for each individual container in a container-specific format.\n\nReference Implementations of the Roaring format:\n\n- [32-bit Java RoaringBitmap](https://github.com/RoaringBitmap/RoaringBitmap/blob/c7993318d7224cd3cc0244dcc99c8bbc5ddb0c87/RoaringBitmap/src/main/java/org/roaringbitmap/RoaringArray.java#L905-L949)\n- [64-bit Java RoaringNavigableBitmap](https://github.com/RoaringBitmap/RoaringBitmap/blob/c7993318d7224cd3cc0244dcc99c8bbc5ddb0c87/RoaringBitmap/src/main/java/org/roaringbitmap/longlong/Roaring64NavigableMap.java#L1253-L1260)\n\nDelta uses the format described above as a black box, but with two additions:\n\n1. We prepend a \"magic number\", which can be used to make sure we are reading the correct format and also retains the ability to evolve the format in the future.\n2. We require that every \"key\" (s. above) in the bitmap has a 0 as its most significant bit. This ensures that in Java, where values are read signed, we never read negative keys. \n\nThe concrete serialization format is as follows (all numerical values are written in little endian byte order):\n\nBytes | Name | Description\n-|-|-\n0 — 3 | magicNumber | 1681511377; Indicates that the following bytes are serialized in this exact format. Future alternative—but related—formats must have a different magic number, for example by incrementing this one.\n4 — end | bitmap | A serialized 64-bit bitmap in the portable standard format as defined in the [RoaringBitmaps Specification](https://github.com/RoaringBitmap/RoaringFormatSpec#extention-for-64-bit-implementations). This can be treated as a black box by any Delta implementation that has a native, standard-compliant RoaringBitmap library available to pass these bytes to.\n\n### Deletion Vector File Storage Format\n\nDeletion Vectors can be stored in files in cloud storage or inline in the Delta log.\nThe format for storing DVs in file storage is one (or more) DV, using the 64-bit RoaringBitmaps described in the previous section, per file, together with a checksum for each DV.\nThe concrete format is as follows, with all numerical values written in big endian byte order:\n\nBytes | Name | Description\n-|-|-\n0 | version | The format version of this file: `1` for the format described here.\n`repeat for each DV i` | | For each DV\n`` — ` + 3` | dataSize | Size of this DV’s data (without the checksum)\n` + 4` — ` + 4 + dataSize - 1` | bitmapData | One 64-bit RoaringBitmap serialised as described above.\n` + 4 + dataSize` — ` + 4 + dataSize + 3` | checksum | CRC-32 checksum of `bitmapData`\n\n## Per-file Statistics\n`add` and `remove` actions can optionally contain statistics about the data in the file being added or removed from the table.\nThese statistics can be used for eliminating files based on query predicates or as inputs to query optimization.\n\nGlobal statistics record information about the entire file.\nThe following global statistic is currently supported:\n\nName | Description\n-|-\nnumRecords | The number of records in this data file.\ntightBounds | Whether per-column statistics are currently **tight** or **wide** (see below).\n\nFor any logical file where `deletionVector` is not `null`, the `numRecords` statistic *must* be present and accurate. That is, it must equal the number of records in the data file, not the valid records in the logical file.\nIn the presence of [Deletion Vectors](#Deletion-Vectors) the statistics may be somewhat outdated, i.e. not reflecting deleted rows yet. The flag `stats.tightBounds` indicates whether we have **tight bounds** (i.e. the min/maxValue exists[^1] in the valid state of the file) or **wide bounds** (i.e. the minValue is <= all valid values in the file, and the maxValue >= all valid values in the file). These upper/lower bounds are sufficient information for data skipping. Note, `stats.tightBounds` should be treated as `true` when it is not explicitly present in the statistics.\n\nPer-column statistics record information for each column in the file and they are encoded, mirroring the schema of the actual data.\nFor example, given the following data schema:\n```\n|-- a: struct\n| |-- b: struct\n| | |-- c: long\n```\n\nStatistics could be stored with the following schema:\n```\n|-- stats: struct\n| |-- numRecords: long\n| |-- tightBounds: boolean\n| |-- minValues: struct\n| | |-- a: struct\n| | | |-- b: struct\n| | | | |-- c: long\n| |-- maxValues: struct\n| | |-- a: struct\n| | | |-- b: struct\n| | | | |-- c: long\n```\n\nThe following per-column statistics are currently supported:\n\nName | Description (`stats.tightBounds=true`) | Description (`stats.tightBounds=false`)\n-|-|-\nnullCount | The number of `null` values for this column |

If the `nullCount` for a column equals the physical number of records (`stats.numRecords`) then **all** valid rows for this column must have `null` values (the reverse is not necessarily true).

If the `nullCount` for a column equals 0 then **all** valid rows are non-`null` in this column (the reverse is not necessarily true).

If the `nullCount` for a column is any value other than these two special cases, the value carries no information and should be treated as if absent.

\nminValues | A value that is equal to the smallest valid value[^1] present in the file for this column. If all valid rows are null, this carries no information. | A value that is less than or equal to all valid values[^1] present in this file for this column. If all valid rows are null, this carries no information.\nmaxValues | A value that is equal to the largest valid value[^1] present in the file for this column. If all valid rows are null, this carries no information. | A value that is greater than or equal to all valid values[^1] present in this file for this column. If all valid rows are null, this carries no information.\n\n[^1]: String columns are cut off at a fixed prefix length. Timestamp columns are truncated down to milliseconds.\n\n## Partition Value Serialization\n\nPartition values are stored as strings, using the following formats. An empty string for any type translates to a `null` partition value.\n\nType | Serialization Format\n-|-\nstring | No translation required\nnumeric types | The string representation of the number\ndate | Encoded as `{year}-{month}-{day}`. For example, `1970-01-01`\ntimestamp | Encoded as `{year}-{month}-{day} {hour}:{minute}:{second}` or `{year}-{month}-{day} {hour}:{minute}:{second}.{microsecond}`. For example: `1970-01-01 00:00:00`, or `1970-01-01 00:00:00.123456`. Timestamps may also be encoded as an ISO8601 formatted timestamp adjusted to UTC timestamp such as `1970-01-01T00:00:00.123456Z`\ntimestamp without timezone | Encoded as `{year}-{month}-{day} {hour}:{minute}:{second}` or `{year}-{month}-{day} {hour}:{minute}:{second}.{microsecond}` For example: `1970-01-01 00:00:00`, or `1970-01-01 00:00:00.123456` To use this type, a table must support a feature `timestampNtz`. See section [Timestamp without timezone (TimestampNtz)](#timestamp-without-timezone-timestampNtz) for more information.\nboolean | Encoded as the string \"true\" or \"false\"\nbinary | Encoded as a string of escaped binary values. For example, `\"\\u0001\\u0002\\u0003\"`\n\nNote: A timestamp value in a partition value may be stored in one of the following ways:\n1. Without a timezone, where the timestamp should be interpreted using the time zone of the system which wrote to the table.\n2. Adjusted to UTC and stored in ISO8601 format.\n\nIt is highly recommended that modern writers adjust the timestamp to UTC and store the timestamp in ISO8601 format as outlined in 2.\n\n## Schema Serialization Format\n\nDelta uses a subset of Spark SQL's JSON Schema representation to record the schema of a table in the transaction log.\nAll column names must be unique regardless of casing.\nA reference implementation can be found in [the catalyst package of the Apache Spark repository](https://github.com/apache/spark/tree/master/sql/catalyst/src/main/scala/org/apache/spark/sql/types).\n\n### Primitive Types\n\nType Name | Description\n-|-\nstring| UTF-8 encoded string of characters\nlong| 8-byte signed integer. Range: -9223372036854775808 to 9223372036854775807\ninteger|4-byte signed integer. Range: -2147483648 to 2147483647\nshort| 2-byte signed integer numbers. Range: -32768 to 32767\nbyte| 1-byte signed integer number. Range: -128 to 127\nfloat| 4-byte single-precision floating-point numbers\ndouble| 8-byte double-precision floating-point numbers\ndecimal| signed decimal number with fixed precision (maximum number of digits) and scale (number of digits on right side of dot). The precision and scale can be up to 38.\nboolean| `true` or `false`\nbinary| A sequence of binary data.\ndate| A calendar date, represented as a year-month-day triple without a timezone.\ntimestamp| Microsecond precision timestamp elapsed since the Unix epoch, 1970-01-01 00:00:00 UTC. When this is stored in a parquet file, its `isAdjustedToUTC` must be set to `true`.\ntimestamp without time zone | Microsecond precision timestamp in a local timezone elapsed since the Unix epoch, 1970-01-01 00:00:00. It doesn't have the timezone information, and a value of this type can map to multiple physical time instants. It should always be displayed in the same way, regardless of the local time zone in effect. When this is stored in a parquet file, its `isAdjustedToUTC` must be set to `false`. To use this type, a table must support a feature `timestampNtz`. See section [Timestamp without timezone (TimestampNtz)](#timestamp-without-timezone-timestampNtz) for more information.\n\nSee Parquet [timestamp type](https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#timestamp) for more details about timestamp and `isAdjustedToUTC`.\n\nNote: Existing tables may have `void` data type columns. Behavior is undefined for `void` data type columns but it is recommended to drop any `void` data type columns on reads (as is implemented by the Spark connector).\n\n### Struct Type\n\nA struct is used to represent both the top-level schema of the table as well as struct columns that contain nested columns. A struct is encoded as a JSON object with the following fields:\n\nField Name | Description\n-|-\ntype | Always the string \"struct\"\nfields | An array of fields\n\n### Struct Field\n\nA struct field represents a top-level or nested column.\n\nField Name | Description\n-|-\nname| Name of this (possibly nested) column\ntype| String containing the name of a primitive type, a struct definition, an array definition or a map definition\nnullable| Boolean denoting whether this field can be null\nmetadata| A JSON map containing information about this column. Keys prefixed with `Delta` are reserved for the implementation. See [Column Metadata](#column-metadata) for more information on column level metadata that clients must handle when writing to a table.\n\n### Array Type\n\nAn array stores a variable length collection of items of some type.\n\nField Name | Description\n-|-\ntype| Always the string \"array\"\nelementType| The type of element stored in this array, represented as a string containing the name of a primitive type, a struct definition, an array definition or a map definition\ncontainsNull| Boolean denoting whether this array can contain one or more null values\n\n### Map Type\n\nA map stores an arbitrary length collection of key-value pairs with a single `keyType` and a single `valueType`.\n\nField Name | Description\n-|-\ntype| Always the string \"map\".\nkeyType| The type of element used for the key of this map, represented as a string containing the name of a primitive type, a struct definition, an array definition or a map definition\nvalueType| The type of element used for the key of this map, represented as a string containing the name of a primitive type, a struct definition, an array definition or a map definition\n\n### Variant Type\n\nVariant data uses the Delta type name `variant` for Delta schema serialization.\n\nField Name | Description\n-|-\ntype | Always the string \"variant\"\n\n### Column Metadata\nA column metadata stores various information about the column.\nFor example, this MAY contain some keys like [`delta.columnMapping`](#column-mapping) or [`delta.generationExpression`](#generated-columns) or [`CURRENT_DEFAULT`](#default-columns). \nField Name | Description\n-|-\ndelta.columnMapping.*| These keys are used to store information about the mapping between the logical column name to the physical name. See [Column Mapping](#column-mapping) for details.\ndelta.identity.*| These keys are for defining identity columns. See [Identity Columns](#identity-columns) for details.\ndelta.invariants| JSON string contains SQL expression information. See [Column Invariants](#column-invariants) for details.\ndelta.generationExpression| SQL expression string. See [Generated Columns](#generated-columns) for details.\ndelta.typeChanges| JSON string containing information about previous type changes applied to this column. See [Type Change Metadata](#type-change-metadata) for details.\n\n### Example\n\nExample Table Schema:\n```\n|-- a: integer (nullable = false)\n|-- b: struct (nullable = true)\n| |-- d: integer (nullable = false)\n|-- c: array (nullable = true)\n| |-- element: integer (containsNull = false)\n|-- e: array (nullable = true)\n| |-- element: struct (containsNull = true)\n| | |-- d: integer (nullable = false)\n|-- f: map (nullable = true)\n| |-- key: string\n| |-- value: string (valueContainsNull = true)\n```\n\nJSON Encoded Table Schema:\n```\n{\n \"type\" : \"struct\",\n \"fields\" : [ {\n \"name\" : \"a\",\n \"type\" : \"integer\",\n \"nullable\" : false,\n \"metadata\" : { }\n }, {\n \"name\" : \"b\",\n \"type\" : {\n \"type\" : \"struct\",\n \"fields\" : [ {\n \"name\" : \"d\",\n \"type\" : \"integer\",\n \"nullable\" : false,\n \"metadata\" : { }\n } ]\n },\n \"nullable\" : true,\n \"metadata\" : { }\n }, {\n \"name\" : \"c\",\n \"type\" : {\n \"type\" : \"array\",\n \"elementType\" : \"integer\",\n \"containsNull\" : false\n },\n \"nullable\" : true,\n \"metadata\" : { }\n }, {\n \"name\" : \"e\",\n \"type\" : {\n \"type\" : \"array\",\n \"elementType\" : {\n \"type\" : \"struct\",\n \"fields\" : [ {\n \"name\" : \"d\",\n \"type\" : \"integer\",\n \"nullable\" : false,\n \"metadata\" : { }\n } ]\n },\n \"containsNull\" : true\n },\n \"nullable\" : true,\n \"metadata\" : { }\n }, {\n \"name\" : \"f\",\n \"type\" : {\n \"type\" : \"map\",\n \"keyType\" : \"string\",\n \"valueType\" : \"string\",\n \"valueContainsNull\" : true\n },\n \"nullable\" : true,\n \"metadata\" : { }\n } ]\n}\n```\n\n## Checkpoint Schema\nThe following examples uses a table with two partition columns: \"date\" and \"region\" of types date and string, respectively, and three data columns: \"asset\", \"quantity\", and \"is_available\" with data types string, double, and boolean. The checkpoint schema will look as follows:\n\n```\n|-- metaData: struct\n| |-- id: string\n| |-- name: string\n| |-- description: string\n| |-- format: struct\n| | |-- provider: string\n| | |-- options: map\n| |-- schemaString: string\n| |-- partitionColumns: array\n| |-- createdTime: long\n| |-- configuration: map\n|-- protocol: struct\n| |-- minReaderVersion: int\n| |-- minWriterVersion: int\n| |-- readerFeatures: array[string]\n| |-- writerFeatures: array[string]\n|-- txn: struct\n| |-- appId: string\n| |-- version: long\n|-- add: struct\n| |-- path: string\n| |-- partitionValues: map\n| |-- size: long\n| |-- modificationTime: long\n| |-- dataChange: boolean\n| |-- stats: string\n| |-- tags: map\n| |-- baseRowId: long\n| |-- defaultRowCommitVersion: long\n| |-- partitionValues_parsed: struct\n| | |-- date: date\n| | |-- region: string\n| |-- stats_parsed: struct\n| | |-- numRecords: long\n| | |-- minValues: struct\n| | | |-- asset: string\n| | | |-- quantity: double\n| | |-- maxValues: struct\n| | | |-- asset: string\n| | | |-- quantity: double\n| | |-- nullCount: struct\n| | | |-- asset: long\n| | | |-- quantity: long\n|-- remove: struct\n| |-- path: string\n| |-- deletionTimestamp: long\n| |-- dataChange: boolean\n|-- checkpointMetadata: struct\n| |-- version: long\n| |-- tags: map\n|-- sidecar: struct\n| |-- path: string\n| |-- sizeInBytes: long\n| |-- modificationTime: long\n| |-- tags: map\n```\n\nObserve that `readerFeatures` and `writerFeatures` fields should comply with:\n- If a table has Reader Version 3, then a writer must write checkpoints with a not-null `readerFeatures` in the schema.\n- If a table has Writer Version 7, then a writer must write checkpoints with a not-null `writerFeatures` in the schema.\n- If a table has neither of the above, then a writer chooses whether to write `readerFeatures` and/or `writerFeatures` into the checkpoint schema. But if it does, their values must be null.\n\nNote that `remove` actions in the checkpoint are tombstones used only by VACUUM, and do not contain the `stats` and `tags` fields.\n\nFor a table that uses column mapping, whether in `id` or `name` mode, the schema of the `add` column will look as follows.\n\nSchema definition:\n```\n{\n \"type\" : \"struct\",\n \"fields\" : [ {\n \"name\" : \"asset\",\n \"type\" : \"string\",\n \"nullable\" : true,\n \"metadata\" : {\n \"delta.columnMapping.id\": 1,\n \"delta.columnMapping.physicalName\": \"col-b96921f0-2329-4cb3-8d79-184b2bdab23b\"\n }\n }, {\n \"name\" : \"quantity\",\n \"type\" : \"double\",\n \"nullable\" : true,\n \"metadata\" : {\n \"delta.columnMapping.id\": 2,\n \"delta.columnMapping.physicalName\": \"col-04ee4877-ee53-4cb9-b1fb-1a4eb74b508c\"\n }\n }, {\n \"name\" : \"date\",\n \"type\" : \"date\",\n \"nullable\" : true,\n \"metadata\" : {\n \"delta.columnMapping.id\": 3,\n \"delta.columnMapping.physicalName\": \"col-798f4abc-c63f-444c-9a04-e2cf1ecba115\"\n }\n }, {\n \"name\" : \"region\",\n \"type\" : \"string\",\n \"nullable\" : true,\n \"metadata\" : {\n \"delta.columnMapping.id\": 4,\n \"delta.columnMapping.physicalName\": \"col-19034dc3-8e3d-4156-82fc-8e05533c088e\"\n }\n } ]\n}\n```\n\nCheckpoint schema (just the `add` column):\n```\n|-- add: struct\n| |-- path: string\n| |-- partitionValues: map\n| |-- size: long\n| |-- modificationTime: long\n| |-- dataChange: boolean\n| |-- stats: string\n| |-- tags: map\n| |-- baseRowId: long\n| |-- defaultRowCommitVersion: long\n| |-- partitionValues_parsed: struct\n| | |-- col-798f4abc-c63f-444c-9a04-e2cf1ecba115: date\n| | |-- col-19034dc3-8e3d-4156-82fc-8e05533c088e: string\n| |-- stats_parsed: struct\n| | |-- numRecords: long\n| | |-- minValues: struct\n| | | |-- col-b96921f0-2329-4cb3-8d79-184b2bdab23b: string\n| | | |-- col-04ee4877-ee53-4cb9-b1fb-1a4eb74b508c: double\n| | |-- maxValues: struct\n| | | |-- col-b96921f0-2329-4cb3-8d79-184b2bdab23b: string\n| | | |-- col-04ee4877-ee53-4cb9-b1fb-1a4eb74b508c: double\n| | |-- nullCount: struct\n| | | |-- col-b96921f0-2329-4cb3-8d79-184b2bdab23b: long\n| | | |-- col-04ee4877-ee53-4cb9-b1fb-1a4eb74b508c: long\n```\n\n## Last Checkpoint File Schema\n\nThis last checkpoint file is encoded as JSON and contains the following information:\n\nField | Description\n-|-\nversion | The version of the table when the last checkpoint was made.\nsize | The number of actions that are stored in the checkpoint.\nparts | The number of fragments if the last checkpoint was written in multiple parts. This field is optional.\nsizeInBytes | The number of bytes of the checkpoint. This field is optional.\nnumOfAddFiles | The number of AddFile actions in the checkpoint. This field is optional.\ncheckpointSchema | The schema of the checkpoint file. This field is optional.\ntags | String-string map containing any additional metadata about the last checkpoint. This field is optional.\nchecksum | The checksum of the last checkpoint JSON. This field is optional.\n\nThe checksum field is an optional field which contains the MD5 checksum for fields of the last checkpoint json file.\nLast checkpoint file readers are encouraged to validate the checksum, if present, and writers are encouraged to write the checksum\nwhile overwriting the file. Refer to [this section](#json-checksum) for rules around calculating the checksum field\nfor the last checkpoint JSON.\n\n### JSON checksum\nTo generate the checksum for the last checkpoint JSON, firstly, the checksum JSON is canonicalized and converted to a string. Then\nthe 32 character MD5 digest is calculated on the resultant string to get the checksum. Rules for [JSON](https://datatracker.ietf.org/doc/html/rfc8259) canonicalization are:\n\n1. Literal values (`true`, `false`, and `null`) are their own canonical form\n2. Numeric values (e.g. `42` or `3.14`) are their own canonical form\n3. String values (e.g. `\"hello world\"`) are canonicalized by preserving the surrounding quotes and [URL-encoding](#how-to-url-encode-keys-and-string-values)\ntheir content, e.g. `\"hello%20world\"`\n4. Object values (e.g. `{\"a\": 10, \"b\": {\"y\": null, \"x\": \"https://delta.io\"} }` are canonicalized by:\n * Canonicalize each scalar (leaf) value following the rule for its type (literal, numeric, string)\n * Canonicalize each (string) name along the path to that value\n * Connect path segments by `+`, e.g. `\"b\"+\"y\"`\n * Connect path and value pairs by `=`, e.g. `\"b\"+\"y\"=null`\n * Sort canonicalized path/value pairs using a byte-order sort on paths. The byte-order sort can be done by converting paths to byte array using UTF-8 charset\\\n and then comparing them, e.g. `\"a\" < \"b\"+\"x\" < \"b\"+\"y\"`\n * Separate ordered pairs by `,`, e.g. `\"a\"=10,\"b\"+\"x\"=\"https%3A%2F%2Fdelta.io\",\"b\"+\"y\"=null`\n\n5. Array values (e.g. `[null, \"hi ho\", 2.71]`) are canonicalized as if they were objects, except the \"name\" has numeric type instead of string type, and gives the (0-based)\nposition of the corresponding array element, e.g. `0=null,1=\"hi%20ho\",2=2.71`\n\n6. Top level `checksum` key is ignored in the canonicalization process. e.g.\n`{\"k1\": \"v1\", \"checksum\": \"\", \"k3\": 23}` is canonicalized to `\"k1\"=\"v1\",\"k3\"=23`\n\n7. Duplicate keys are not allowed in the last checkpoint JSON and such JSON is considered invalid.\n\nGiven the following test sample JSON, a correct implementation of JSON canonicalization should produce the corresponding canonicalized form and checksum value:\ne.g.\nJson: `{\"k0\":\"'v 0'\", \"checksum\": \"adsaskfljadfkjadfkj\", \"k1\":{\"k2\": 2, \"k3\": [\"v3\", [1, 2], {\"k4\": \"v4\", \"k5\": [\"v5\", \"v6\", \"v7\"]}]}}`\\\nCanonicalized form: `\"k0\"=\"%27v%200%27\",\"k1\"+\"k2\"=2,\"k1\"+\"k3\"+0=\"v3\",\"k1\"+\"k3\"+1+0=1,\"k1\"+\"k3\"+1+1=2,\"k1\"+\"k3\"+2+\"k4\"=\"v4\",\"k1\"+\"k3\"+2+\"k5\"+0=\"v5\",\"k1\"+\"k3\"+2+\"k5\"+1=\"v6\",\"k1\"+\"k3\"+2+\"k5\"+2=\"v7\"`\\\nChecksum is `6a92d155a59bf2eecbd4b4ec7fd1f875`\n\n#### How to URL encode keys and string values\nThe [URL Encoding](https://datatracker.ietf.org/doc/html/rfc3986) spec is a bit flexible to give a reliable encoding. e.g. the spec allows both\nuppercase and lowercase as part of percent-encoding. Thus, we require a stricter set of rules for encoding:\n\n1. The string to be encoded must be represented as octets according to the UTF-8 character encoding\n2. All octets except a-z / A-Z / 0-9 / \"-\" / \".\" / \"_\" / \"~\" are reserved\n3. Always [percent-encode](https://datatracker.ietf.org/doc/html/rfc3986#section-2) reserved octets\n4. Never percent-encode non-reserved octets\n5. A percent-encoded octet consists of three characters: `%` followed by its 2-digit hexadecimal value in uppercase letters, e.g. `>` encodes to `%3E`\n\n## Delta Data Type to Parquet Type Mappings\nBelow table captures how each Delta data type is stored physically in Parquet files. Parquet files are used for storing the table data or metadata ([checkpoints](#checkpoints)). Parquet has a limited number of [physical types](https://parquet.apache.org/docs/file-format/types/). Parquet [logical types](https://github.com/apache/parquet-format/blob/master/LogicalTypes.md) are used to extend the types by specifying how the physical types should be interpreted.\n\nFor some of the Delta data types, there are multiple ways store the values physically in Parquet file. For example, `timestamp` can be stored either as `int96` or `int64`. The exact physical type depends on the engine that is writing the Parquet file and/or engine specific configuration options. For a Delta lake table reader, it is recommended that the Parquet file reader support at least the Parquet physical and logical types mentioned in the below table.\n\nDelta Type Name | Parquet Physical Type | Parquet Logical Type\n-|-|-\nboolean| `boolean` |\nbyte| `int32` | `INT(bitwidth = 8, signed = true)`\nshort| `int32` | `INT(bitwidth = 16, signed = true)`\nint| `int32` | `INT(bitwidth = 32, signed = true)`\nlong| `int64` | `INT(bitwidth = 64, signed = true)`\ndate| `int32` | `DATE`\ntimestamp| `int96` or `int64` | `TIMESTAMP(isAdjustedToUTC = true, units = microseconds)`\ntimestamp without time zone| `int96` or `int64` | `TIMESTAMP(isAdjustedToUTC = false, units = microseconds)`\nfloat| `float` |\ndouble| `double` |\ndecimal| `int32`, `int64` or `fixed_length_binary` | `DECIMAL(scale, precision)`\nstring| `binary` | `string (UTF-8)`\nbinary| `binary` |\narray| either as `2-level` or `3-level` representation. Refer to [Parquet documentation](https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#lists) for further details | `LIST`\nmap| either as `2-level` or `3-level` representation. Refer to [Parquet documentation](https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#maps) for further details | `MAP`\nstruct| `group` |\n" }, { "path": "README.md", "content": "\"Delta\n\n[![Test](https://github.com/delta-io/delta/actions/workflows/test.yaml/badge.svg)](https://github.com/delta-io/delta/actions/workflows/test.yaml)\n[![License](https://img.shields.io/badge/license-Apache%202-brightgreen.svg)](https://github.com/delta-io/delta/blob/master/LICENSE.txt)\n[![PyPI](https://img.shields.io/pypi/v/delta-spark.svg)](https://pypi.org/project/delta-spark/)\n[![PyPI - Downloads](https://img.shields.io/pypi/dm/delta-spark)](https://pypistats.org/packages/delta-spark)\n\nDelta Lake is an open-source storage framework that enables building a [Lakehouse architecture](http://cidrdb.org/cidr2021/papers/cidr2021_paper17.pdf) with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs for Scala, Java, Rust, Ruby, and Python. \n* See the [Delta Lake Documentation](https://docs.delta.io) for details.\n* See the [Quick Start Guide](https://docs.delta.io/latest/quick-start.html) to get started with Scala, Java and Python.\n* Note, this repo is one of many Delta Lake repositories in the [delta.io](https://github.com/delta-io) organizations including\n[delta](https://github.com/delta-io/delta), \n[delta-rs](https://github.com/delta-io/delta-rs),\n[delta-sharing](https://github.com/delta-io/delta-sharing),\n[kafka-delta-ingest](https://github.com/delta-io/kafka-delta-ingest), and\n[website](https://github.com/delta-io/website).\n\nThe following are some of the more popular Delta Lake integrations, refer to [delta.io/integrations](https://delta.io/integrations/) for the complete list:\n\n* [Apache Spark™](https://docs.delta.io/): This connector allows Apache Spark™ to read from and write to Delta Lake.\n* [Apache Flink (Preview)](https://github.com/delta-io/delta/tree/master/connectors/flink): This connector allows Apache Flink to write to Delta Lake.\n* [PrestoDB](https://prestodb.io/docs/current/connector/deltalake.html): This connector allows PrestoDB to read from Delta Lake.\n* [Trino](https://trino.io/docs/current/connector/delta-lake.html): This connector allows Trino to read from and write to Delta Lake.\n* [Delta Standalone](https://docs.delta.io/latest/delta-standalone.html): This library allows Scala and Java-based projects (including Apache Flink, Apache Hive, Apache Beam, and PrestoDB) to read from and write to Delta Lake.\n* [Apache Hive](https://docs.delta.io/latest/hive-integration.html): This connector allows Apache Hive to read from Delta Lake.\n* [Delta Rust API](https://docs.rs/deltalake/latest/deltalake/): This library allows Rust (with Python and Ruby bindings) low level access to Delta tables and is intended to be used with data processing frameworks like datafusion, ballista, rust-dataframe, vega, etc.\n\n
\n\n
\nTable of Contents\n\n* [Latest binaries](#latest-binaries)\n* [API Documentation](#api-documentation)\n* [Compatibility](#compatibility)\n * [API Compatibility](#api-compatibility)\n * [Data Storage Compatibility](#data-storage-compatibility)\n* [Roadmap](#roadmap)\n* [Building](#building)\n* [Transaction Protocol](#transaction-protocol)\n* [Requirements for Underlying Storage Systems](#requirements-for-underlying-storage-systems)\n* [Concurrency Control](#concurrency-control)\n* [Reporting issues](#reporting-issues)\n* [Contributing](#contributing)\n* [License](#license)\n* [Community](#community)\n
\n\n\n## Latest Binaries\n\nSee the [online documentation](https://docs.delta.io/latest/) for the latest release.\n\n## API Documentation\n\n* [Scala API docs](https://docs.delta.io/latest/delta-apidoc.html)\n* [Java API docs](https://docs.delta.io/latest/api/java/index.html)\n* [Python API docs](https://docs.delta.io/latest/api/python/index.html)\n\n## Compatibility\n[Delta Standalone](https://docs.delta.io/latest/delta-standalone.html) library is a single-node Java library that can be used to read from and write to Delta tables. Specifically, this library provides APIs to interact with a table’s metadata in the transaction log, implementing the Delta Transaction Log Protocol to achieve the transactional guarantees of the Delta Lake format.\n\n\n### API Compatibility\n\nThere are two types of APIs provided by the Delta Lake project. \n\n- Direct Java/Scala/Python APIs - The classes and methods documented in the [API docs](https://docs.delta.io/latest/delta-apidoc.html) are considered as stable public APIs. All other classes, interfaces, methods that may be directly accessible in code are considered internal, and they are subject to change across releases.\n- Spark-based APIs - You can read Delta tables through the `DataFrameReader`/`Writer` (i.e. `spark.read`, `df.write`, `spark.readStream` and `df.writeStream`). Options to these APIs will remain stable within a major release of Delta Lake (e.g., 1.x.x).\n- See the [online documentation](https://docs.delta.io/latest/releases.html) for the releases and their compatibility with Apache Spark versions.\n\n\n### Data Storage Compatibility\n\nDelta Lake guarantees backward compatibility for all Delta Lake tables (i.e., newer versions of Delta Lake will always be able to read tables written by older versions of Delta Lake). However, we reserve the right to break forward compatibility as new features are introduced to the transaction protocol (i.e., an older version of Delta Lake may not be able to read a table produced by a newer version).\n\nBreaking changes in the protocol are indicated by incrementing the minimum reader/writer version in the `Protocol` [action](https://github.com/delta-io/delta/blob/master/spark/src/test/scala/org/apache/spark/sql/delta/ActionSerializerSuite.scala).\n\n## Roadmap\n\n* For the high-level Delta Lake roadmap, see [Delta Lake 2022H1 roadmap](http://delta.io/roadmap). \n* For the detailed timeline, see the [project roadmap](https://github.com/delta-io/delta/milestones). \n\n## Transaction Protocol\n\n[Delta Transaction Log Protocol](PROTOCOL.md) document provides a specification of the transaction protocol.\n\n## Requirements for Underlying Storage Systems\n\nDelta Lake ACID guarantees are predicated on the atomicity and durability guarantees of the storage system. Specifically, we require the storage system to provide the following.\n\n1. **Atomic visibility**: There must be a way for a file to be visible in its entirety or not visible at all.\n2. **Mutual exclusion**: Only one writer must be able to create (or rename) a file at the final destination.\n3. **Consistent listing**: Once a file has been written in a directory, all future listings for that directory must return that file.\n\nSee the [online documentation on Storage Configuration](https://docs.delta.io/latest/delta-storage.html) for details.\n\n## Concurrency Control\n\nDelta Lake ensures _serializability_ for concurrent reads and writes. Please see [Delta Lake Concurrency Control](https://docs.delta.io/concurrency-control/) for more details.\n\n## Reporting issues\n\nWe use [GitHub Issues](https://github.com/delta-io/delta/issues) to track community reported issues. You can also [contact](#community) the community for getting answers.\n\n## Contributing \n\nWe welcome contributions to Delta Lake. See our [CONTRIBUTING.md](https://github.com/delta-io/delta/blob/master/CONTRIBUTING.md) for more details.\n\nWe also adhere to the [Delta Lake Code of Conduct](https://github.com/delta-io/delta/blob/master/CODE_OF_CONDUCT.md).\n\n## Building\n\nDelta Lake is compiled using [SBT](https://www.scala-sbt.org/1.x/docs/Command-Line-Reference.html).\nEnsure that your Java version is at least 17 (you can verify with `java -version`).\n\nTo compile, run\n\n build/sbt compile\n\nTo generate artifacts, run\n\n build/sbt package\n\nTo execute tests, run\n\n build/sbt test\n\nTo execute a single test suite, run\n\n build/sbt spark/'testOnly org.apache.spark.sql.delta.optimize.OptimizeCompactionSQLSuite'\n\nTo execute a single test within and a single test suite, run\n\n build/sbt spark/'testOnly *.OptimizeCompactionSQLSuite -- -z \"optimize command: on partitioned table - all partitions\"'\n\nRefer to [SBT docs](https://www.scala-sbt.org/1.x/docs/Command-Line-Reference.html) for more commands.\n\n## Running python tests locally\n\n### Setup Environment\n#### Install Conda (Skip if you already installed it)\nFollow [Conda Download](https://www.anaconda.com/download/) to install Anaconda.\n\n#### Create an environment from environment file\nFollow [Create Environment From Environment file](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-from-file) to create a Conda environment from `/python/environment.yml` and activate the newly created `delta_python_tests` environment.\n\n```\n# Note the `--file` argument should be a fully qualified path. Using `~` in file\n# path doesn't work. Example valid path: `/Users/macuser/delta/python/environment.yml`\n\nconda env create --name delta_python_tests --file=/python/environment.yml`\n```\n\n#### JDK Setup\nBuild needs JDK 11. Make sure to setup `JAVA_HOME` that points to JDK 11.\n\n#### Running tests\n```\nconda activate delta_python_tests\npython3 /python/run-tests.py\n```\n\n## IntelliJ Setup\n\nIntelliJ is the recommended IDE to use when developing Delta Lake. To import Delta Lake as a new project:\n1. Clone Delta Lake into, for example, `~/delta`.\n2. In IntelliJ, select `File` > `New Project` > `Project from Existing Sources...` and select `~/delta`.\n3. Under `Import project from external model` select `sbt`. Click `Next`.\n4. Under `Project JDK` specify a valid Java `11` JDK and opt to use SBT shell for `project reload` and `builds`.\n5. Click `Finish`.\n6. In your terminal, run `build/sbt clean package`. Make sure you use Java `11`. The build will generate files \n that are necessary for Intellij to index the repository.\n\n### Setup Verification\n\nAfter waiting for IntelliJ to index, verify your setup by running a test suite in IntelliJ.\n1. Search for and open `DeltaLogSuite`\n2. Next to the class declaration, right click on the two green arrows and select `Run 'DeltaLogSuite'`\n\n### Troubleshooting\n\nIf you see errors of the form\n\n```\nError:(46, 28) object DeltaSqlBaseParser is not a member of package io.delta.sql.parser\nimport io.delta.sql.parser.DeltaSqlBaseParser._\n...\nError:(91, 22) not found: type DeltaSqlBaseParser\n val parser = new DeltaSqlBaseParser(tokenStream)\n```\n\nthen follow these steps:\n1. Ensure you are using Java `11`. You can set this using\n```\nexport JAVA_HOME=`/usr/libexec/java_home -v 11`\n```\n2. Compile using the SBT CLI: `build/sbt clean compile`.\n2. Go to `File` > `Project Structure...` > `Modules` > `delta-spark`.\n3. In the right panel under `Source Folders` remove any `target` folders, e.g. `target/scala-2.12/src_managed/main [generated]`\n4. Click `Apply` and then re-run your test.\n\n## License\nApache License 2.0, see [LICENSE](https://github.com/delta-io/delta/blob/master/LICENSE.txt).\n\n## Community\n\nThere are two mediums of communication within the Delta Lake community.\n\n* Public Slack Channel\n - [Register here](https://go.delta.io/slack)\n - [Login here](https://delta-users.slack.com/)\n* [Linkedin page](https://www.linkedin.com/company/deltalake)\n* [Youtube channel](https://www.youtube.com/c/deltalake)\n* Public [Mailing list](https://groups.google.com/forum/#!forum/delta-users)\n" }, { "path": "benchmarks/README.md", "content": "# Benchmarks \n\n## Overview\nThis is a basic framework for writing benchmarks to measure Delta's performance. It is currently designed to run benchmark on Spark running in an EMR or a Dataproc cluster. However, it can be easily extended for other Spark-based benchmarks. To get started, first download/clone this repository in your local machine. Then you have to set up a cluster and run the benchmark scripts in this directory. See the next section for more details.\n\n## Running TPC-DS benchmark\n\nThis TPC-DS benchmark is constructed such that you have to run the following two steps. \n1. *Load data*: You have to create the TPC-DS database with all the Delta tables. To do that, the raw TPC-DS data has been provided as Apache Parquet files. In this step you will have to use your EMR or a Dataproc cluster to read the parquet files and rewrite them as Delta tables.\n2. *Query data*: Then, using the tables definitions in the Hive Metatore, you can run the 99 benchmark queries. \n\nThe next section will provide the detailed steps of how to setup the necessary Hive Metastore and a cluster, how to test the setup with small-scale data, and then finally run the full scale benchmark.\n\n### Configure cluster with Amazon Web Services\n\n#### Prerequisites\n- An AWS account with necessary permissions to do the following:\n - Manage RDS instances for creating an external Hive Metastore\n - Manage EMR clusters for running the benchmark\n - Read and write to an S3 bucket from the EMR cluster\n- A S3 bucket which will be used to generate the TPC-DS data.\n- A machine which has access to the AWS setup and where this repository has been downloaded or cloned.\n\nThere are two ways to create infrastructure required for benchmarks - using provided [Terraform template](infrastructure/aws/terraform/README.md) or manually (described below).\n\n#### Create external Hive Metastore using Amazon RDS\nCreate an external Hive Metastore in a MySQL database using Amazon RDS with the following specifications:\n- MySQL 8.x on a `db.m5.large`.\n- General purpose SSDs, and no Autoscaling storage.\n- Non-empty password for admin\n- Same region, VPC, subnet as those you will run the EMR cluster. See AWS docs for more guidance.\n - *Note:* Region us-west-2 since that is what this benchmark has been most tested with.\n\nAfter the database is ready, note the JDBC connection details, the username and password. We will need them for the next step. Note that this step needs to be done just once. All EMR clusters can connect and reused this Hive Metastsore. \n \n#### Create EMR cluster\nCreate an EMR cluster that connects to the external Hive Metastore. Here are the specifications of the EMR cluster required for running benchmarks.\n- EMR with Spark and Hive (needed for writing to Hive Metastore). Choose the EMR version based on the Spark version compatible with the format. For example:\n - For Delta 2.0 on Spark 3.2 - EMR 6.6.0\n - For Delta 1.0 on Spark 3.1 - EMR 6.5.0\n- Master - i3.2xlarge\n- Workers - 16 x i3.2xlarge (or just 1 worker if you are just testing by running the 1GB benchmark).\n- Hive-site configuration to connect to the Hive Metastore. See [Using an external MySQL database or Amazon Aurora](https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hive-metastore-external.html) for more details.\n- Same region, VPC, subnet as those of the Hive Metastore.\n - *Note:* Region us-west-2 since that is what this benchmark has been most tested with.\n- No autoscaling, and default EBS storage.\n\nOnce the EMR cluster is ready, note the following: \n- Hostname of the EMR cluster master node.\n- PEM file for SSH into the master node.\nThese will be needed to run the workloads in this framework. \n\n#### Prepare S3 bucket\nCreate a new S3 bucket (or use an existing one) which is in the same region as your EMR cluster.\n\n_________________\n\n### Configure cluster with Google Cloud Platform\n\n#### Prerequisites\n- A GCP account with necessary permissions to do the following:\n - Manage Dataproc clusters for running the benchmark\n - Manage Dataproc Metastore instances\n - Read and write to a GCS bucket from the Dataproc cluster\n- A GCS bucket which will be used to generate the TPC-DS data.\n- A machine which has access to the GCP setup and where this repository has been downloaded or cloned.\n- SSH keys for a user which will be used to access the master node. The user's SSH key can be either [a project-wide key](https://cloud.google.com/compute/docs/connect/add-ssh-keys#add_ssh_keys_to_project_metadata) \n or assigned to the [master node](https://cloud.google.com/compute/docs/connect/add-ssh-keys#after-vm-creation) only.\n- Ideally, all GCP components used in benchmark should be in the same location (Storage bucket, Dataproc Metastore service and Dataproc cluster).\n\nThere are two ways to create infrastructure required for benchmarks - using provided [Terraform template](infrastructure/gcp/terraform/README.md) or manually (described below).\n\n#### Prepare GCS bucket\nCreate a new GCS bucket (or use an existing one) which is in the same region as your Dataproc cluster.\n\n#### Create Dataproc Metastore\nYou can create [Dataproc metastore](https://cloud.google.com/dataproc-metastore/docs/create-service)\neither via Web Console or gcloud command.\n\nSample create command:\n```bash\ngcloud metastore services create dataproc-metastore-for-benchmarks \\\n --location= \\\n --tier=enterprise\n```\n\n#### Create Dataproc cluster\nHere are the specifications of the Dataproc cluster required for running benchmarks.\n- Image version >= 2.0 having Apache Spark 3.1\n- Master - n2-highmem-8 (8 vCPU, 64 GB memory)\n- Workers - 16 x n2-highmem-8 (or just 2 workers if you are just testing by running the 1GB benchmark).\n- The cluster connects to the Dataproc Metastore.\n- Same region and subnet as those of the Dataproc Metastore and GCS bucket.\n- No autoscaling.\n\nSample create command:\n```bash\ngcloud dataproc clusters create delta-performance-benchmarks-cluster \\\n --project \\\n --enable-component-gateway \\\n --region \\\n --zone \\\n --subnet default \\\n --master-machine-type n2-highmem-8 \\\n --master-boot-disk-type pd-ssd \\\n --master-boot-disk-size 100 \\\n --num-master-local-ssds 4 \\\n --master-local-ssd-interface NVME \\\n --num-workers 16 \\\n --worker-machine-type n2-highmem-8 \\\n --worker-boot-disk-type pd-ssd \\\n --worker-boot-disk-size 100 \\\n --num-worker-local-ssds 4 \\\n --worker-local-ssd-interface NVME \\\n --dataproc-metastore projects//locations//services/dataproc-metastore-for-benchmarks \\\n --enable-component-gateway \\\n --image-version 2.0-debian10\n```\n\n#### Input data\nThe benchmark is run using the raw TPC-DS data which has been provided as Apache Parquet files. There are two\npredefined datasets of different size, 1GB and 3TB, located in `s3://devrel-delta-datasets/tpcds-2.13/tpcds_sf1_parquet/`\nand `s3://devrel-delta-datasets/tpcds-2.13/tpcds_sf3000_parquet/`, respectively. Please keep in mind that\n`devrel-delta-datasets` bucket is configured as [Requester Pays](https://docs.aws.amazon.com/AmazonS3/latest/userguide/ObjectsinRequesterPaysBuckets.html) bucket,\nso [access requests have to be configured properly](https://docs.aws.amazon.com/AmazonS3/latest/userguide/ObjectsinRequesterPaysBuckets.html).\n\nUnfortunately, Hadoop in versions available in Dataproc does not support *Requester Pays* feature. It will be available\nas of Hadoop 3.3.4 ([HADOOP-14661](https://issues.apache.org/jira/browse/HADOOP-14661)).\n\nIn consequence, one need to copy the datasets to Google Storage manually before running benchmarks. The simplest\nsolution is to copy the data in two steps: first to a S3 bucket with *Requester Pays* disabled, then copy the data\nusing [Cloud Storage Transfer Service](https://cloud.google.com/storage-transfer/docs/how-to).\n\n_________________\n\n### Test the cluster setup\nNavigate to your local copy of this repository and this benchmark directory. Then run the following steps.\n\n#### Run simple test workload\nVerify that you have the following information\n - : Cluster master node host name\n - : Local path to your PEM file for SSH into the master node.\n - : The username that will be used to SSH into the master node. The username is tied to the SSH key you\n have imported into the cloud. It defaults to `hadoop`.\n - : Path where tables will be created. Make sure your credentials have read/write permission to that path.\n - : Currently either `gcp` or `aws`. For each storage type, different Delta properties might be added.\n \nThen run a simple table write-read test: Run the following in your shell.\n\n```sh\n./run-benchmark.py \\\n --cluster-hostname \\\n -i \\\n --ssh-user \\\n --benchmark-path \\\n --cloud-provider \\\n --benchmark test\n```\n\nIf this works correctly, then you should see an output that look like this.\n \n```text\n>>> Benchmark script generated and uploaded\n\n...\nThere is a screen on:\n12001..ip-172-31-21-247\t(Detached)\n\nFiles for this benchmark:\n20220126-191336-test-benchmarks.jar\n20220126-191336-test-cmd.sh\n20220126-191336-test-out.txt\n>>> Benchmark script started in a screen. Stdout piped into 20220126-191336-test-out.txt.Final report will be generated on completion in 20220126-191336-test-report.json.\n```\n\nThe test workload launched in a `screen` is going to run the following: \n- Spark jobs to run a simple SQL query\n- Create a Delta table in the given location \n- Read it back\n \nTo see whether they worked correctly, SSH into the node and check the output of 20220126-191336-test-out.txt. Once the workload terminates, the last few lines should be something like the following:\n```text\nRESULT:\n{\n \"benchmarkSpecs\" : {\n \"benchmarkPath\" : ...,\n \"benchmarkId\" : \"20220126-191336-test\"\n },\n \"queryResults\" : [ {\n \"name\" : \"sql-test\",\n \"durationMs\" : 11075\n }, {\n \"name\" : \"db-list-test\",\n \"durationMs\" : 208\n }, {\n \"name\" : \"db-create-test\",\n \"durationMs\" : 4070\n }, {\n \"name\" : \"db-use-test\",\n \"durationMs\" : 41\n }, {\n \"name\" : \"table-drop-test\",\n \"durationMs\" : 74\n }, {\n \"name\" : \"table-create-test\",\n \"durationMs\" : 33812\n }, {\n \"name\" : \"table-query-test\",\n \"durationMs\" : 4795\n } ]\n}\nFILE UPLOAD: Uploaded /home/hadoop/20220126-191336-test-report.json to s3:// ...\nSUCCESS\n```\n \nThe above metrics are also written to a json file and uploaded to the given path. Please verify that both the table and report are generated in that path. \n\n#### Run 1GB TPC-DS\nNow that you are familiar with how the framework runs the workload, you can try running the small scale TPC-DS benchmark.\n\n\n1. Load data as Delta tables:\n ```bash\n ./run-benchmark.py \\\n --cluster-hostname \\\n -i \\\n --ssh-user \\\n --benchmark-path \\\n --cloud-provider \\\n --benchmark tpcds-1gb-delta-load\n ```\n If you run the benchmark in GCP you should provide `--source-path ` parameter, where `` is the location of the raw parquet input data files (see *Input data* section).\n ```bash\n ./run-benchmark.py \\\n --cluster-hostname \\\n -i \\\n --ssh-user \\\n --benchmark-path \\\n --source-path \\\n --cloud-provider gcp \\\n --benchmark tpcds-1gb-delta-load\n ```\n\n3. Run queries on Delta tables:\n ```bash\n ./run-benchmark.py \\\n --cluster-hostname \\\n -i \\\n --ssh-user \\\n --benchmark-path \\\n --cloud-provider \\\n --benchmark tpcds-1gb-delta\n ```\n\n### Run 3TB TPC-DS\nFinally, you are all set up to run the full scale benchmark. Similar to the 1GB benchmark, run the following\n\n1. Load data as Delta tables:\n ```bash\n ./run-benchmark.py \\\n --cluster-hostname \\\n -i \\\n --ssh-user \\\n --benchmark-path \\\n --cloud-provider \\\n --benchmark tpcds-3tb-delta-load\n ```\n If you run the benchmark in GCP you should provide `--source-path ` parameter, where `` is the location of the raw parquet input data files (see *Input data* section).\n ```bash\n ./run-benchmark.py \\\n --cluster-hostname \\\n -i \\\n --ssh-user \\\n --benchmark-path \\\n --source-path \\\n --cloud-provider gcp \\\n --benchmark tpcds-3tb-delta-load\n ```\n\n2. Run queries on Delta tables:\n ```bash\n ./run-benchmark.py \\\n --cluster-hostname \\\n -i \\\n --ssh-user \\\n --benchmark-path \\\n --cloud-provider \\\n --benchmark tpcds-3tb-delta\n ```\n\nCompare the results using the generated JSON files.\n\n_________________\n\n## Internals of the framework\n\nStructure of this framework's code\n- `build.sbt`, `project/`, `src/` form the SBT project which contains the Scala code that define the benchmark workload.\n - `Benchmark.scala` is the basic interface, and `TestBenchmark.scala` is a sample implementation.\n- `run-benchmark.py` contains the specification of the benchmarks defined by name (e.g. `tpcds-3tb-delta`). Each benchmark specification is defined by the following: \n - Fully qualified name of the main Scala class to be started.\n - Command line argument for the main function.\n - Additional Maven artifact to load (example `io.delta:delta-core_2.12:1.0.0`).\n - Spark configurations to use.\n- `scripts` has the core python scripts that are called by `run-benchmark.py`\n\nThe script `run-benchmark.py` does the following:\n- Compile the Scala code into a uber jar.\n- Upload it to the given hostname.\n- Using ssh to the hostname, it will launch a screen and start the main class with spark-submit.\n" }, { "path": "benchmarks/build/sbt", "content": "#!/usr/bin/env bash\n\n#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n#\n# This file contains code from the Apache Spark project (original license above).\n# It contains modifications, which are licensed as follows:\n#\n\n#\n# Copyright (2021) The Delta Lake Project Authors.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n\n# When creating new tests for Spark SQL Hive, the HADOOP_CLASSPATH must contain the hive jars so\n# that we can run Hive to generate the golden answer. This is not required for normal development\n# or testing.\nif [ -n \"$HIVE_HOME\" ]; then\n for i in \"$HIVE_HOME\"/lib/*\n do HADOOP_CLASSPATH=\"$HADOOP_CLASSPATH:$i\"\n done\n export HADOOP_CLASSPATH\nfi\n\nrealpath () {\n(\n TARGET_FILE=\"$1\"\n\n cd \"$(dirname \"$TARGET_FILE\")\"\n TARGET_FILE=\"$(basename \"$TARGET_FILE\")\"\n\n COUNT=0\n while [ -L \"$TARGET_FILE\" -a $COUNT -lt 100 ]\n do\n TARGET_FILE=\"$(readlink \"$TARGET_FILE\")\"\n cd $(dirname \"$TARGET_FILE\")\n TARGET_FILE=\"$(basename $TARGET_FILE)\"\n COUNT=$(($COUNT + 1))\n done\n\n echo \"$(pwd -P)/\"$TARGET_FILE\"\"\n)\n}\n\nif [[ \"$JENKINS_URL\" != \"\" ]]; then\n # Make Jenkins use Google Mirror first as Maven Central may ban us\n SBT_REPOSITORIES_CONFIG=\"$(dirname \"$(realpath \"$0\")\")/sbt-config/repositories\"\n export SBT_OPTS=\"-Dsbt.override.build.repos=true -Dsbt.repository.config=$SBT_REPOSITORIES_CONFIG\"\nfi\n\n. \"$(dirname \"$(realpath \"$0\")\")\"/sbt-launch-lib.bash\n\n\ndeclare -r noshare_opts=\"-Dsbt.global.base=project/.sbtboot -Dsbt.boot.directory=project/.boot -Dsbt.ivy.home=project/.ivy\"\ndeclare -r sbt_opts_file=\".sbtopts\"\ndeclare -r etc_sbt_opts_file=\"/etc/sbt/sbtopts\"\n\nusage() {\n cat < path to global settings/plugins directory (default: ~/.sbt)\n -sbt-boot path to shared boot directory (default: ~/.sbt/boot in 0.11 series)\n -ivy path to local Ivy repository (default: ~/.ivy2)\n -mem set memory options (default: $sbt_mem, which is $(get_mem_opts $sbt_mem))\n -no-share use all local caches; no sharing\n -no-global uses global caches, but does not use global ~/.sbt directory.\n -jvm-debug Turn on JVM debugging, open at the given port.\n -batch Disable interactive mode\n\n # sbt version (default: from project/build.properties if present, else latest release)\n -sbt-version use the specified version of sbt\n -sbt-jar use the specified jar as the sbt launcher\n -sbt-rc use an RC version of sbt\n -sbt-snapshot use a snapshot version of sbt\n\n # java version (default: java from PATH, currently $(java -version 2>&1 | grep version))\n -java-home alternate JAVA_HOME\n\n # jvm options and output control\n JAVA_OPTS environment variable, if unset uses \"$java_opts\"\n SBT_OPTS environment variable, if unset uses \"$default_sbt_opts\"\n .sbtopts if this file exists in the current directory, it is\n prepended to the runner args\n /etc/sbt/sbtopts if this file exists, it is prepended to the runner args\n -Dkey=val pass -Dkey=val directly to the java runtime\n -J-X pass option -X directly to the java runtime\n (-J is stripped)\n -S-X add -X to sbt's scalacOptions (-S is stripped)\n -PmavenProfiles Enable a maven profile for the build.\n\nIn the case of duplicated or conflicting options, the order above\nshows precedence: JAVA_OPTS lowest, command line options highest.\nEOM\n}\n\nprocess_my_args () {\n while [[ $# -gt 0 ]]; do\n case \"$1\" in\n -no-colors) addJava \"-Dsbt.log.noformat=true\" && shift ;;\n -no-share) addJava \"$noshare_opts\" && shift ;;\n -no-global) addJava \"-Dsbt.global.base=$(pwd)/project/.sbtboot\" && shift ;;\n -sbt-boot) require_arg path \"$1\" \"$2\" && addJava \"-Dsbt.boot.directory=$2\" && shift 2 ;;\n -sbt-dir) require_arg path \"$1\" \"$2\" && addJava \"-Dsbt.global.base=$2\" && shift 2 ;;\n -debug-inc) addJava \"-Dxsbt.inc.debug=true\" && shift ;;\n -batch) exec /dev/null)\n if [[ ! $? ]]; then\n saved_stty=\"\"\n fi\n}\n\nsaveSttySettings\ntrap onExit INT\n\nrun \"$@\"\n\nexit_status=$?\nonExit\n" }, { "path": "benchmarks/build/sbt-launch-lib.bash", "content": "#!/usr/bin/env bash\n#\n\n# A library to simplify using the SBT launcher from other packages.\n# Note: This should be used by tools like giter8/conscript etc.\n\n# TODO - Should we merge the main SBT script with this library?\n\nif test -z \"$HOME\"; then\n declare -r script_dir=\"$(dirname \"$script_path\")\"\nelse\n declare -r script_dir=\"$HOME/.sbt\"\nfi\n\ndeclare -a residual_args\ndeclare -a java_args\ndeclare -a scalac_args\ndeclare -a sbt_commands\ndeclare -a maven_profiles\n\nif test -x \"$JAVA_HOME/bin/java\"; then\n echo -e \"Using $JAVA_HOME as default JAVA_HOME.\"\n echo \"Note, this will be overridden by -java-home if it is set.\"\n declare java_cmd=\"$JAVA_HOME/bin/java\"\nelse\n declare java_cmd=java\nfi\n\nechoerr () {\n echo 1>&2 \"$@\"\n}\nvlog () {\n [[ $verbose || $debug ]] && echoerr \"$@\"\n}\ndlog () {\n [[ $debug ]] && echoerr \"$@\"\n}\n\nacquire_sbt_jar () {\n SBT_VERSION=`awk -F \"=\" '/sbt\\.version/ {print $2}' ./project/build.properties`\n URL1=${DEFAULT_ARTIFACT_REPOSITORY:-https://repo1.maven.org/maven2/}org/scala-sbt/sbt-launch/${SBT_VERSION}/sbt-launch-${SBT_VERSION}.jar\n JAR=build/sbt-launch-${SBT_VERSION}.jar\n\n sbt_jar=$JAR\n\n if [[ ! -f \"$sbt_jar\" ]]; then\n # Download sbt launch jar if it hasn't been downloaded yet\n if [ ! -f \"${JAR}\" ]; then\n # Download\n printf \"Attempting to fetch sbt\\n\"\n JAR_DL=\"${JAR}.part\"\n if [ $(command -v curl) ]; then\n curl --fail --location --silent ${URL1} > \"${JAR_DL}\" &&\\\n mv \"${JAR_DL}\" \"${JAR}\"\n elif [ $(command -v wget) ]; then\n wget --quiet ${URL1} -O \"${JAR_DL}\" &&\\\n mv \"${JAR_DL}\" \"${JAR}\"\n else\n printf \"You do not have curl or wget installed, please install sbt manually from http://www.scala-sbt.org/\\n\"\n exit -1\n fi\n fi\n if [ ! -f \"${JAR}\" ]; then\n # We failed to download\n printf \"Our attempt to download sbt locally to ${JAR} failed. Please install sbt manually from http://www.scala-sbt.org/\\n\"\n exit -1\n fi\n printf \"Launching sbt from ${JAR}\\n\"\n fi\n}\n\nexecRunner () {\n # print the arguments one to a line, quoting any containing spaces\n [[ $verbose || $debug ]] && echo \"# Executing command line:\" && {\n for arg; do\n if printf \"%s\\n\" \"$arg\" | grep -q ' '; then\n printf \"\\\"%s\\\"\\n\" \"$arg\"\n else\n printf \"%s\\n\" \"$arg\"\n fi\n done\n echo \"\"\n }\n\n \"$@\"\n}\n\naddJava () {\n dlog \"[addJava] arg = '$1'\"\n java_args=( \"${java_args[@]}\" \"$1\" )\n}\n\nenableProfile () {\n dlog \"[enableProfile] arg = '$1'\"\n maven_profiles=( \"${maven_profiles[@]}\" \"$1\" )\n export SBT_MAVEN_PROFILES=\"${maven_profiles[@]}\"\n}\n\naddSbt () {\n dlog \"[addSbt] arg = '$1'\"\n sbt_commands=( \"${sbt_commands[@]}\" \"$1\" )\n}\naddResidual () {\n dlog \"[residual] arg = '$1'\"\n residual_args=( \"${residual_args[@]}\" \"$1\" )\n}\naddDebugger () {\n addJava \"-agentlib:jdwp=transport=dt_socket,server=y,suspend=n,address=$1\"\n}\n\n# a ham-fisted attempt to move some memory settings in concert\n# so they need not be dicked around with individually.\nget_mem_opts () {\n local mem=${1:-1000}\n local perm=$(( $mem / 4 ))\n (( $perm > 256 )) || perm=256\n (( $perm < 4096 )) || perm=4096\n local codecache=$(( $perm / 2 ))\n\n echo \"-Xms${mem}m -Xmx${mem}m -XX:ReservedCodeCacheSize=${codecache}m\"\n}\n\nrequire_arg () {\n local type=\"$1\"\n local opt=\"$2\"\n local arg=\"$3\"\n if [[ -z \"$arg\" ]] || [[ \"${arg:0:1}\" == \"-\" ]]; then\n echo \"$opt requires <$type> argument\" 1>&2\n exit 1\n fi\n}\n\nis_function_defined() {\n declare -f \"$1\" > /dev/null\n}\n\nprocess_args () {\n while [[ $# -gt 0 ]]; do\n case \"$1\" in\n -h|-help) usage; exit 1 ;;\n -v|-verbose) verbose=1 && shift ;;\n -d|-debug) debug=1 && shift ;;\n\n -ivy) require_arg path \"$1\" \"$2\" && addJava \"-Dsbt.ivy.home=$2\" && shift 2 ;;\n -mem) require_arg integer \"$1\" \"$2\" && sbt_mem=\"$2\" && shift 2 ;;\n -jvm-debug) require_arg port \"$1\" \"$2\" && addDebugger $2 && shift 2 ;;\n -batch) exec MergeStrategy.discard\n case x => MergeStrategy.first\n }\n )\n \n" }, { "path": "benchmarks/infrastructure/aws/terraform/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/hashicorp/aws\" {\n version = \"4.15.1\"\n constraints = \"~> 4.15.1\"\n hashes = [\n \"h1:KPu3MdNXCScye05Sp4JlE9WwhS9k3yD9KRoRDHg5sDE=\",\n \"zh:1d944144f8d613b8090c0c8391e4b205ca036086d70aceb4cdf664856fa8410c\",\n \"zh:2a0ca16a6b12c0ac509f64512f80bd2ed6e7ea0ec369212efd4be3fa65e9773d\",\n \"zh:3f9efdce4f1c320ffd061e8715e1d031deac1be0b959eaa60c25a274925653e4\",\n \"zh:4cf82f3267b0c3e08be29b0345f711ab84ea1ea75f0e8ce81f5a2fe635ba67b4\",\n \"zh:58474a0b7da438e1bcd53e87f10e28830836ff9b46cce5f09413c90952ae4f78\",\n \"zh:6eb1be8afb0314b6b8424fe212b13beeb04f3f24692f0f3ee86c5153c7eb2e63\",\n \"zh:8022da7d3b050d452ce6c679844e13729bdb4e1b3e75dcf68931af17a06b9277\",\n \"zh:8e2683d00fff1df43440d6e7c04a2c1eb432c7d5dacff32fe8ce9045bc948fe6\",\n \"zh:9b12af85486a96aedd8d7984b0ff811a4b42e3d88dad1a3fb4c0b580d04fa425\",\n \"zh:b0c22d9a306e8ac2de57b5291a3d0a7a2c1713e33b7d076005662451afdc4d29\",\n \"zh:ba6b7d7d91388b636145b133da6b4e32620cdc8046352e2dc8f3f0f81ff5d2e2\",\n \"zh:d38a816eb60f4419d99303136a3bb61a0d2df3ca8a1dce2ced9b99bf23efa9f7\",\n ]\n}\n" }, { "path": "benchmarks/infrastructure/aws/terraform/README.md", "content": "# Create infrastructure with Terraform\n\n1. Install [Terraform](https://learn.hashicorp.com/tutorials/terraform/install-cli?in=terraform/aws-get-started).\n2. Create an IAM user which will be used to create benchmarks infrastructure. Ensure that your AWS CLI is configured.\n You should either have valid credentials in shared credentials file (e.g. `~/.aws/credentials`)\n ```\n [default]\n aws_access_key_id = anaccesskey\n aws_secret_access_key = asecretkey\n ```\n or export keys as environment variables:\n ```bash\n export AWS_ACCESS_KEY_ID=\"anaccesskey\"\n export AWS_SECRET_ACCESS_KEY=\"asecretkey\"\n ```\n3. Add permissions for the IAM user. You can either assign `AdministratorAccess` AWS managed policy (discouraged)\n or assign AWS managed policies in a more granular way:\n * `IAMFullAccess`\n * `AmazonVPCFullAccess`\n * `AmazonEMRFullAccessPolicy_v2`\n * `AmazonElasticMapReduceFullAccess`\n * `AmazonRDSFullAccess`\n * `AmazonS3FullAccess`\n * a custom policy for EC2 key pairs management\n ```json\n {\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Effect\": \"Allow\",\n \"Action\": [\n \"ec2:ImportKeyPair\",\n \"ec2:CreateKeyPair\",\n \"ec2:DeleteKeyPair\"\n ],\n \"Resource\": \"arn:aws:ec2:*:*:key-pair/benchmarks_key_pair\"\n }\n ]\n }\n ```\n\n4. Create Terraform variable file `benchmarks/infrastructure/aws/terraform/terraform.tfvars` and fill in variable values.\n ```tf\n region = \"\"\n availability_zone1 = \"\"\n availability_zone2 = \"\"\n benchmarks_bucket_name = \"\"\n source_bucket_name = \"\"\n mysql_user = \"\"\n mysql_password = \"\"\n emr_public_key_path = \"\"\n user_ip_address = \"\"\n emr_workers = WORKERS_COUNT\n tags = {\n key1 = \"value1\"\n key2 = \"value2\"\n }\n ```\n Please check `variables.tf` to learn more about each parameter.\n\n5. Run:\n ```bash\n terraform init\n terraform validate\n terraform apply\n ```\n As a result, a new VPC, a S3 bucket, a MySQL instance (metastore) and a EMR cluster will be created.\n The `apply` command returns `master_node_address` that will be used when running benchmarks.\n ```\n Apply complete! Resources: 16 added, 0 changed, 0 destroyed.\n Outputs:\n master_node_address = \"35.165.163.250\"\n ```\n\n6. Once the benchmarks are finished, destroy the resources.\n ```bash\n terraform destroy\n ```\n If the S3 bucket contains any objects, it will not be destroyed automatically.\n One need to do that manually to avoid any accidental data loss.\n ```\n Error: deleting S3 Bucket (my-bucket): BucketNotEmpty: The bucket you tried to delete is not empty \n status code: 409, request id: Q11TYZ5E0B23QGQ2, host id: WdeFY88km5IBhy+bi2hqXzgjBxjrn1+OPtCstsWDjkwGNCyEhXYjq330DZq1jbfNXojBEejH6Wg=\n ```\n" }, { "path": "benchmarks/infrastructure/aws/terraform/main.tf", "content": "module \"networking\" {\n source = \"./modules/networking\"\n\n availability_zone1 = var.availability_zone1\n availability_zone2 = var.availability_zone2\n}\n\nmodule \"storage\" {\n source = \"./modules/storage\"\n\n benchmarks_bucket_name = var.benchmarks_bucket_name\n}\n\nmodule \"processing\" {\n source = \"./modules/processing\"\n\n vpc_id = module.networking.vpc_id\n subnet1_id = module.networking.subnet1_id\n subnet2_id = module.networking.subnet2_id\n\n availability_zone1 = var.availability_zone1\n benchmarks_bucket_name = var.benchmarks_bucket_name\n source_bucket_name = var.source_bucket_name\n mysql_user = var.mysql_user\n mysql_password = var.mysql_password\n emr_public_key_path = var.emr_public_key_path\n emr_workers = var.emr_workers\n user_ip_address = var.user_ip_address\n\n depends_on = [module.networking, module.storage]\n}\n" }, { "path": "benchmarks/infrastructure/aws/terraform/modules/networking/main.tf", "content": "resource \"aws_vpc\" \"this\" {\n cidr_block = \"10.0.0.0/16\"\n}\n\nresource \"aws_subnet\" \"benchmarks_subnet1\" {\n vpc_id = aws_vpc.this.id\n availability_zone = var.availability_zone1\n cidr_block = \"10.0.0.0/17\"\n}\n\n# There are two subnets needed to create an RDS subnet group. In fact this one is unused.\n# If DB subnet group is built using only one AZ, the following error is thrown:\n# The DB subnet group doesn't meet Availability Zone (AZ) coverage requirement.\n# Current AZ coverage: us-west-2a. Add subnets to cover at least 2 AZs.\nresource \"aws_subnet\" \"benchmarks_subnet2\" {\n vpc_id = aws_vpc.this.id\n availability_zone = var.availability_zone2\n cidr_block = \"10.0.128.0/17\"\n}\n\nresource \"aws_internet_gateway\" \"this\" {\n vpc_id = aws_vpc.this.id\n}\n\nresource \"aws_default_route_table\" \"public\" {\n default_route_table_id = aws_vpc.this.default_route_table_id\n\n route {\n cidr_block = \"0.0.0.0/0\"\n gateway_id = aws_internet_gateway.this.id\n }\n}\n" }, { "path": "benchmarks/infrastructure/aws/terraform/modules/networking/outputs.tf", "content": "output \"vpc_id\" {\n value = aws_vpc.this.id\n}\n\noutput \"subnet1_id\" {\n value = aws_subnet.benchmarks_subnet1.id\n}\n\noutput \"subnet2_id\" {\n value = aws_subnet.benchmarks_subnet2.id\n}\n" }, { "path": "benchmarks/infrastructure/aws/terraform/modules/networking/variables.tf", "content": "variable \"availability_zone1\" {\n type = string\n}\n\nvariable \"availability_zone2\" {\n type = string\n}\n" }, { "path": "benchmarks/infrastructure/aws/terraform/modules/processing/main.tf", "content": "resource \"aws_db_instance\" \"metastore_service\" {\n engine = \"mysql\"\n engine_version = \"8.0.28\"\n instance_class = \"db.m5.large\"\n db_name = \"hive\"\n username = var.mysql_user\n password = var.mysql_password\n availability_zone = var.availability_zone1\n skip_final_snapshot = true\n allocated_storage = 50\n db_subnet_group_name = aws_db_subnet_group.metastore_service.name\n vpc_security_group_ids = [aws_security_group.metastore_service.id]\n}\n\nresource \"aws_db_subnet_group\" \"metastore_service\" {\n name = \"benchmarks_subnet_group_for_metastore_service\"\n subnet_ids = [var.subnet1_id, var.subnet2_id]\n}\n\n/* EC2 key used to SSH to EMR cluster nodes. */\nresource \"aws_key_pair\" \"benchmarks\" {\n key_name = \"benchmarks_key_pair\"\n public_key = file(var.emr_public_key_path)\n}\n\nresource \"aws_emr_cluster\" \"benchmarks\" {\n name = \"delta_performance_benchmarks_cluster\"\n release_label = \"emr-6.5.0\"\n applications = [\"Spark\", \"Hive\"]\n termination_protection = false\n keep_job_flow_alive_when_no_steps = true\n ec2_attributes {\n instance_profile = aws_iam_instance_profile.benchmarks_emr_profile.arn\n key_name = aws_key_pair.benchmarks.key_name\n subnet_id = var.subnet1_id\n emr_managed_master_security_group = aws_security_group.emr.id\n emr_managed_slave_security_group = aws_security_group.emr.id\n }\n master_instance_group {\n instance_type = \"i3.2xlarge\"\n }\n core_instance_group {\n instance_type = \"i3.2xlarge\"\n instance_count = var.emr_workers\n }\n\n configurations_json = < 4.15.1\"\n }\n }\n}\n" }, { "path": "benchmarks/infrastructure/gcp/terraform/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/hashicorp/google\" {\n version = \"4.22.0\"\n constraints = \"4.22.0\"\n hashes = [\n \"h1:1HjdT6HX8mPJjdr80lESKArCVkQzTTnvmgDSlZiuRBc=\",\n \"zh:0093d36809054c0ce63fcac0d955511b4afd1acdcbcc7af009cc60650cfc4291\",\n \"zh:343ceca3442a09cca0690f43c5578af39f432691077513cb39eddae270ad3a0d\",\n \"zh:35ca4ba3c048d442bfa72c8df626c4f3eb9102a821b8e0ef4963cbecada78ae6\",\n \"zh:573307f69250fa6f1b20a401c3d2c626553a07843ba36bb0be60bc64221ea765\",\n \"zh:7a5af64663c23848500a4b83c7ac2bfbb460a5efd302e9cd2460a0be4122e2c4\",\n \"zh:989c5a3103d705c1611b7985b8a077518530b707684a47fd9ea9dad390c329c8\",\n \"zh:a583b2f762f28181dde1483fb590a666a69db0b0d82faaf8eceb5f5991fac803\",\n \"zh:c230e2f3fb2aec5878800c946be4b571662d0cb78838e6ad32e99e1e75967639\",\n \"zh:d1d6cf87698de22202ae7d1652c7388ce4142088105cb18b7ed5307da1ff6ef7\",\n \"zh:db99274fa832bcb9d72f7a5e1138c78dd19851a1c3f2574ccdfef6b6f6af8006\",\n \"zh:eb6047a6f7402c8e28db4529fcd43b2a94252bc0fc386eb3eff2a3461c0b2375\",\n \"zh:f569b65999264a9416862bca5cd2a6177d94ccb0424f3a4ef424428912b9cb3c\",\n ]\n}\n\nprovider \"registry.terraform.io/hashicorp/google-beta\" {\n version = \"4.22.0\"\n constraints = \"4.22.0\"\n hashes = [\n \"h1:j0D6Hpugxq6UgFspoSz0yKf9CAwfDVDMtYZ8iRbrWGo=\",\n \"zh:142449e2314d5d6a34d737e749d7d573cb4808f11f8b480c4f3a7a92fd43bdcf\",\n \"zh:181b7f547173e7e3a7fb19005ca86e3b617af79bbfb693afce1f32efc482b2c5\",\n \"zh:2f27fd47e554e1765a1b01dc47a79c8d8bb19ee0dfb5496f71eacbcea407930f\",\n \"zh:51f42ee289b742de0b32048c97bee13189901c0edf2818d7aea7b745366ee54f\",\n \"zh:5e40390e56fad14d6899cfd3bc20308bc2594f6eeb4d508078de49c2e407cb41\",\n \"zh:7b7bfb6631577e06c0e3d8a7019f51b0064822044a4739644ec7ffe7e72b2e3b\",\n \"zh:88dfa69b5cf1c6f75ab0df4d4141373cd1c9ad9b1d854c805515c13da2415c83\",\n \"zh:95ea1b926114c014c7997ad2406a317ad237baed8f0f607e3254f516349fcbc8\",\n \"zh:a8e17c0d134bb39f13fde8c38488bbb004fec4476be93f7b574780ce2c927296\",\n \"zh:b24cec92170f006a795a7a42340846db409effd018e22096a907d07223866a73\",\n \"zh:f569b65999264a9416862bca5cd2a6177d94ccb0424f3a4ef424428912b9cb3c\",\n \"zh:fad38be843066e91456e810d0665fa0e78403d2e22bc8899c08ff076061cecbb\",\n ]\n}\n" }, { "path": "benchmarks/infrastructure/gcp/terraform/README.md", "content": "# Create infrastructure with Terraform\n\n1. Install [Terraform](https://learn.hashicorp.com/tutorials/terraform/install-cli?in=terraform/gcp-get-started).\n\n2. Create and download [service account key](https://cloud.google.com/iam/docs/creating-managing-service-account-keys#iam-service-account-keys-create-console).\n Please note that the key file should be in JSON format.\n The path to the file is required in the next step (`credentials_file`).\n\n3. Make sure the service account has at least the following permissions:\n * `Dataproc Editor`,\n * `Dataproc Metastore Editor`,\n * `Dataproc Service Agent`,\n * `Service Account User`,\n * `Storage Object Admin`.\n\n4. Create Terraform variable file `benchmarks/infrastructure/gcp/terraform/terraform.tfvars` and fill in variable values.\n ```tf\n project = \"\"\n credentials_file = \"\"\n public_key_path = \"\"\n region = \"\"\n zone = \"\"\n bucket_name = \"\"\n dataproc_workers = WORKERS_COUNT\n labels = {\n key1 = \"value1\"\n key2 = \"value2\"\n }\n ```\n Please check `variables.tf` to learn more about each parameter.\n\n5. Run:\n ```bash\n terraform init\n terraform validate\n terraform apply\n ```\n As a result, a new Google Storage bucket, a Dataproc Metastore and a Dataproc cluster will be created.\n The `apply` command returns `master_node_address` that will be used when running benchmarks.\n ```\n Apply complete! Resources: 4 added, 0 changed, 0 destroyed.\n Outputs:\n master_node_address = \"35.165.163.250\"\n ```\n\n6. Once the benchmarks are finished, destroy the resources.\n ```bash\n terraform destroy\n ```\n If the Google Storage bucket contains any objects, it will not be destroyed automatically. One need to do that\n manually to avoid any accidental data loss.\n ```\n Error: Error trying to delete bucket my_bucket containing objects without `force_destroy` set to true\n ```\n" }, { "path": "benchmarks/infrastructure/gcp/terraform/main.tf", "content": "module \"processing\" {\n source = \"./modules/processing\"\n\n project = var.project\n credentials_file = var.credentials_file\n public_key_path = var.public_key_path\n region = var.region\n zone = var.zone\n dataproc_workers = var.dataproc_workers\n labels = var.labels\n}\n\nmodule \"storage\" {\n source = \"./modules/storage\"\n\n bucket_name = var.bucket_name\n region = var.region\n labels = var.labels\n}\n" }, { "path": "benchmarks/infrastructure/gcp/terraform/modules/processing/data.tf", "content": "data \"google_client_openid_userinfo\" \"me\" {\n}\n\ndata \"google_compute_instance\" \"benchmarks_master\" {\n provider = google-beta\n depends_on = [google_dataproc_cluster.benchmarks]\n name = google_dataproc_cluster.benchmarks.cluster_config.0.master_config.0.instance_names.0\n zone = var.zone\n}\n" }, { "path": "benchmarks/infrastructure/gcp/terraform/modules/processing/main.tf", "content": "resource \"google_os_login_ssh_public_key\" \"key_to_login_to_master_node\" {\n user = data.google_client_openid_userinfo.me.email\n key = file(var.public_key_path)\n}\n\nresource \"google_dataproc_metastore_service\" \"this\" {\n provider = google-beta\n service_id = \"dataproc-metastore-for-benchmarks\"\n location = var.region\n tier = \"ENTERPRISE\"\n hive_metastore_config {\n version = \"3.1.2\"\n }\n labels = var.labels\n}\n\nresource \"google_dataproc_cluster\" \"benchmarks\" {\n provider = google-beta\n name = \"delta-performance-benchmarks-cluster\"\n region = var.region\n\n cluster_config {\n master_config {\n num_instances = 1\n machine_type = \"n2-highmem-8\"\n disk_config {\n boot_disk_type = \"pd-ssd\"\n boot_disk_size_gb = 100\n num_local_ssds = 2\n }\n }\n worker_config {\n num_instances = var.dataproc_workers\n machine_type = \"n2-highmem-8\"\n disk_config {\n boot_disk_type = \"pd-ssd\"\n boot_disk_size_gb = 100\n num_local_ssds = 4\n }\n }\n software_config {\n image_version = \"2.0-debian10\"\n }\n metastore_config {\n dataproc_metastore_service = google_dataproc_metastore_service.this.id\n }\n gce_cluster_config {\n zone = var.zone\n }\n endpoint_config {\n enable_http_port_access = \"true\"\n }\n }\n labels = var.labels\n depends_on = [\n google_dataproc_metastore_service.this\n ]\n}\n" }, { "path": "benchmarks/infrastructure/gcp/terraform/modules/processing/outputs.tf", "content": "output \"master_node_address\" {\n value = data.google_compute_instance.benchmarks_master.network_interface.0.access_config.0.nat_ip\n}\n" }, { "path": "benchmarks/infrastructure/gcp/terraform/modules/processing/variables.tf", "content": "variable \"project\" {\n type = string\n}\n\nvariable \"credentials_file\" {\n type = string\n}\n\nvariable \"public_key_path\" {\n type = string\n}\n\nvariable \"region\" {\n type = string\n}\n\nvariable \"zone\" {\n type = string\n}\n\nvariable \"dataproc_workers\" {\n type = number\n}\n\nvariable \"labels\" {\n type = map(string)\n}\n" }, { "path": "benchmarks/infrastructure/gcp/terraform/modules/storage/main.tf", "content": "resource \"google_storage_bucket\" \"benchmarks_data\" {\n provider = google\n name = var.bucket_name\n location = var.region\n storage_class = \"STANDARD\"\n labels = var.labels\n}\n" }, { "path": "benchmarks/infrastructure/gcp/terraform/modules/storage/variables.tf", "content": "variable \"bucket_name\" {\n type = string\n}\n\nvariable \"region\" {\n type = string\n}\n\nvariable \"labels\" {\n type = map(string)\n}\n" }, { "path": "benchmarks/infrastructure/gcp/terraform/outputs.tf", "content": "output \"master_node_address\" {\n value = module.processing.master_node_address\n}\n" }, { "path": "benchmarks/infrastructure/gcp/terraform/providers.tf", "content": "provider \"google\" {\n credentials = file(var.credentials_file)\n project = var.project\n region = var.region\n zone = var.zone\n}\n\nprovider \"google-beta\" {\n credentials = file(var.credentials_file)\n project = var.project\n region = var.region\n zone = var.zone\n}\n" }, { "path": "benchmarks/infrastructure/gcp/terraform/variables.tf", "content": "variable \"project\" {\n description = \"The ID of the GCP project.\"\n type = string\n}\n\nvariable \"credentials_file\" {\n description = \"The path to a service account key file in JSON format.\"\n type = string\n}\n\nvariable \"public_key_path\" {\n description = \"The path to the public key in the typical format, specified in RFC4716. The key is necessary to SSH to Dataproc cluster nodes.\"\n type = string\n default = \"~/.ssh/id_rsa.pub\"\n}\n\nvariable \"region\" {\n description = \"The default region to manage resources in.\"\n type = string\n default = \"us-central1\"\n}\n\nvariable \"zone\" {\n description = \"The default zone to manage resources in.\"\n type = string\n default = \"us-central1-a\"\n}\n\nvariable \"bucket_name\" {\n description = \"The name of the Google Storage bucket that will be used to store benchmark data. Please note that the bucket name has to be globally unique.\"\n type = string\n}\n\nvariable \"dataproc_workers\" {\n description = \"The number of worker nodes in Dataproc cluster.\"\n type = number\n default = 16\n}\n\nvariable \"labels\" {\n description = \"Labels that will be assigned to each resource.\"\n type = map(string)\n default = {}\n}\n" }, { "path": "benchmarks/infrastructure/gcp/terraform/versions.tf", "content": "terraform {\n required_providers {\n google = {\n source = \"hashicorp/google\"\n version = \"4.22.0\"\n }\n google-beta = {\n source = \"hashicorp/google-beta\"\n version = \"4.22.0\"\n }\n }\n}\n" }, { "path": "benchmarks/project/build.properties", "content": "#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n#\n# This file contains code from the Apache Spark project (original license above).\n# It contains modifications, which are licensed as follows:\n#\n\n#\n# Copyright (2021) The Delta Lake Project Authors.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nsbt.version=1.9.9\n" }, { "path": "benchmarks/project/plugins.sbt", "content": "addSbtPlugin(\"com.eed3si9n\" % \"sbt-assembly\" % \"0.15.0\")\n" }, { "path": "benchmarks/run-benchmark.py", "content": "#!/usr/bin/env python3\n\n#\n# Copyright (2021) The Delta Lake Project Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nimport argparse\nfrom scripts.benchmarks import *\n\ndelta_version = \"2.3.0\"\n\n# Benchmark name to their specifications. See the imported benchmarks.py for details of benchmark.\n\nbenchmarks = {\n \"test\":\n DeltaBenchmarkSpec(\n delta_version=delta_version,\n benchmark_main_class=\"benchmark.TestBenchmark\",\n main_class_args=[\"--test-param\", \"value\"],\n ),\n\n # TPC-DS data load\n \"tpcds-1gb-delta-load\": DeltaTPCDSDataLoadSpec(delta_version=delta_version, scale_in_gb=1),\n \"tpcds-3tb-delta-load\": DeltaTPCDSDataLoadSpec(delta_version=delta_version, scale_in_gb=3000),\n \"tpcds-1gb-parquet-load\": ParquetTPCDSDataLoadSpec(scale_in_gb=1),\n \"tpcds-3tb-parquet-load\": ParquetTPCDSDataLoadSpec(scale_in_gb=3000),\n\n # TPC-DS benchmark\n \"tpcds-1gb-delta\": DeltaTPCDSBenchmarkSpec(delta_version=delta_version, scale_in_gb=1),\n \"tpcds-3tb-delta\": DeltaTPCDSBenchmarkSpec(delta_version=delta_version, scale_in_gb=3000),\n \"tpcds-1gb-parquet\": ParquetTPCDSBenchmarkSpec(scale_in_gb=1),\n \"tpcds-3tb-parquet\": ParquetTPCDSBenchmarkSpec(scale_in_gb=3000),\n\n # Merge data load.\n \"merge-1gb-delta-load\": DeltaMergeDataLoadSpec(delta_version=delta_version, scale_in_gb=1),\n \"merge-3tb-delta-load\": DeltaMergeDataLoadSpec(delta_version=delta_version, scale_in_gb=3000),\n\n # Merge benchmark.\n \"merge-1gb-delta\": DeltaMergeBenchmarkSpec(delta_version=delta_version, scale_in_gb=1),\n \"merge-3tb-delta\": DeltaMergeBenchmarkSpec(delta_version=delta_version, scale_in_gb=3000),\n\n}\n\ndelta_log_store_classes = {\n \"aws\": \"spark.delta.logStore.class=org.apache.spark.sql.delta.storage.S3SingleDriverLogStore\",\n \"gcp\": \"spark.delta.logStore.gs.impl=io.delta.storage.GCSLogStore\",\n}\n\nif __name__ == \"__main__\":\n \"\"\"\n Run benchmark on a cluster using ssh.\n\n Example usage:\n\n ./run-benchmark.py --cluster-hostname -i --ssh-user --cloud-provider --benchmark test\n\n \"\"\"\n\n\ndef parse_args():\n # Parse cmd line arguments\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--benchmark\", \"-b\",\n required=True,\n help=\"Run the given benchmark. See this \" +\n \"python file for the list of predefined benchmark names and definitions.\")\n parser.add_argument(\n \"--cluster-hostname\",\n required=True,\n help=\"Hostname or public IP of the cluster driver\")\n parser.add_argument(\n \"--ssh-id-file\", \"-i\",\n required=True,\n help=\"SSH identity file\")\n parser.add_argument(\n \"--spark-conf\",\n action=\"append\",\n help=\"Run benchmark with given spark conf. Use separate --spark-conf for multiple confs.\")\n parser.add_argument(\n \"--resume-benchmark\",\n help=\"Resume waiting for the given running benchmark.\")\n parser.add_argument(\n \"--use-local-delta-dir\",\n help=\"Local path to delta repository which will be used for running the benchmark \" +\n \"instead of the version specified in the specification. Make sure that new delta\" +\n \" version is compatible with version in the spec.\")\n parser.add_argument(\n \"--cloud-provider\",\n choices=delta_log_store_classes.keys(),\n help=\"Cloud where the benchmark will be executed.\")\n parser.add_argument(\n \"--ssh-user\",\n default=\"hadoop\",\n help=\"The user which is used to communicate with the master via SSH.\")\n\n parsed_args, parsed_passthru_args = parser.parse_known_args()\n return parsed_args, parsed_passthru_args\n\n\ndef run_single_benchmark(benchmark_name, benchmark_spec, other_args):\n benchmark_spec.append_spark_confs(other_args.spark_conf)\n benchmark_spec.append_spark_conf(delta_log_store_classes.get(other_args.cloud_provider))\n benchmark_spec.append_main_class_args(passthru_args)\n print(\"------\")\n print(\"Benchmark spec to run:\\n\" + str(vars(benchmark_spec)))\n print(\"------\")\n\n benchmark = Benchmark(benchmark_name, benchmark_spec,\n use_spark_shell=True, local_delta_dir=other_args.use_local_delta_dir)\n benchmark_dir = os.path.dirname(os.path.abspath(__file__))\n with WorkingDirectory(benchmark_dir):\n benchmark.run(other_args.cluster_hostname, other_args.ssh_id_file, other_args.ssh_user)\n\n\nif __name__ == \"__main__\":\n \"\"\"\n Run benchmark on a cluster using ssh.\n\n Example usage:\n\n ./run-benchmark.py --cluster-hostname -i --ssh-user --cloud-provider --benchmark test\n\n \"\"\"\n args, passthru_args = parse_args()\n\n if args.resume_benchmark is not None:\n Benchmark.wait_for_completion(\n args.cluster_hostname, args.ssh_id_file, args.resume_benchmark, args.ssh_user)\n exit(0)\n\n benchmark_names = args.benchmark.split(\",\")\n for benchmark_name in benchmark_names:\n # Create and run the benchmark\n if benchmark_name in benchmarks:\n run_single_benchmark(benchmark_name, benchmarks[benchmark_name], args)\n else:\n raise Exception(\"Could not find benchmark spec for '\" + benchmark_name + \"'.\" +\n \"Must provide one of the predefined benchmark names:\\n\" +\n \"\\n\".join(benchmarks.keys()) +\n \"\\nSee this python file for more details.\")\n" }, { "path": "benchmarks/scripts/benchmarks.py", "content": "#\n# Copyright (2021) The Delta Lake Project Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nfrom scripts.utils import *\nfrom datetime import datetime\nimport time\n\n\nclass BenchmarkSpec:\n \"\"\"\n Specifications of a benchmark.\n\n :param format_name: Spark format name\n :param maven_artifacts: Maven artifact name in x:y:z format\n :param spark_confs: list of spark conf strings in key=value format\n :param benchmark_main_class: Name of main Scala class from the JAR to run\n :param main_class_args: command line args for the main class\n \"\"\"\n def __init__(\n self, format_name, maven_artifacts, spark_confs,\n benchmark_main_class, main_class_args, extra_spark_shell_args=None, **kwargs):\n if main_class_args is None:\n main_class_args = []\n if extra_spark_shell_args is None:\n extra_spark_shell_args = []\n self.format_name = format_name\n self.maven_artifacts = maven_artifacts\n self.spark_confs = spark_confs\n self.benchmark_main_class = benchmark_main_class\n self.benchmark_main_class_args = main_class_args\n self.extra_spark_shell_args = extra_spark_shell_args\n\n def append_spark_conf(self, new_conf):\n if isinstance(new_conf, str):\n self.spark_confs.append(new_conf)\n\n def append_spark_confs(self, new_confs):\n if new_confs is not None and isinstance(new_confs, list):\n self.spark_confs.extend(new_confs)\n\n def append_main_class_args(self, new_args):\n if new_args is not None and isinstance(new_args, list):\n self.benchmark_main_class_args.extend(new_args)\n\n def get_sparksubmit_cmd(self, benchmark_jar_path):\n spark_conf_str = \"\"\n for conf in self.spark_confs:\n print(f\"conf={conf}\")\n spark_conf_str += f\"\"\"--conf \"{conf}\" \"\"\"\n main_class_args = ' '.join(self.benchmark_main_class_args)\n spark_shell_args_str = ' '.join(self.extra_spark_shell_args)\n spark_submit_cmd = (\n f\"spark-submit {spark_shell_args_str} \" +\n (f\"--packages {self.maven_artifacts} \" if self.maven_artifacts else \"\") +\n f\"{spark_conf_str} --class {self.benchmark_main_class} \" +\n f\"{benchmark_jar_path} {main_class_args}\"\n )\n print(spark_submit_cmd)\n return spark_submit_cmd\n\n def get_sparkshell_cmd(self, benchmark_jar_path, benchmark_init_file_path):\n spark_conf_str = \"\"\n for conf in self.spark_confs:\n print(f\"conf={conf}\")\n spark_conf_str += f\"\"\"--conf \"{conf}\" \"\"\"\n spark_shell_args_str = ' '.join(self.extra_spark_shell_args)\n spark_shell_cmd = (\n f\"spark-shell {spark_shell_args_str} \" +\n (f\"--packages {self.maven_artifacts} \" if self.maven_artifacts else \"\") +\n f\"{spark_conf_str} --jars {benchmark_jar_path} -I {benchmark_init_file_path}\"\n )\n print(spark_shell_cmd)\n return spark_shell_cmd\n\n\nclass TPCDSDataLoadSpec(BenchmarkSpec):\n \"\"\"\n Specifications of TPC-DS data load process.\n Always mixin in this first before the base benchmark class.\n \"\"\"\n def __init__(self, scale_in_gb, exclude_nulls=True, **kwargs):\n # forward all keyword args to next constructor\n super().__init__(benchmark_main_class=\"benchmark.TPCDSDataLoad\", **kwargs)\n self.benchmark_main_class_args.extend([\n \"--format\", self.format_name,\n \"--scale-in-gb\", str(scale_in_gb),\n \"--exclude-nulls\", str(exclude_nulls),\n ])\n # To access the public TPCDS parquet files on S3\n self.spark_confs.extend([\"spark.hadoop.fs.s3.useRequesterPaysHeader=true\"])\n\n\nclass TPCDSBenchmarkSpec(BenchmarkSpec):\n \"\"\"\n Specifications of TPC-DS benchmark.\n \"\"\"\n def __init__(self, scale_in_gb, **kwargs):\n # forward all keyword args to next constructor\n super().__init__(benchmark_main_class=\"benchmark.TPCDSBenchmark\", **kwargs)\n # after init of super class, use the format to add main class args\n self.benchmark_main_class_args.extend([\n \"--format\", self.format_name,\n \"--scale-in-gb\", str(scale_in_gb)\n ])\n\n\nclass MergeDataLoadSpec(BenchmarkSpec):\n \"\"\"\n Specifications of Merge data load process.\n Always mixin in this first before the base benchmark class.\n \"\"\"\n def __init__(self, scale_in_gb, exclude_nulls=True, **kwargs):\n # forward all keyword args to next constructor\n super().__init__(benchmark_main_class=\"benchmark.MergeDataLoad\", **kwargs)\n self.benchmark_main_class_args.extend([\n \"--scale-in-gb\", str(scale_in_gb),\n ])\n # To access the public TPCDS parquet files on S3\n self.spark_confs.extend([\"spark.hadoop.fs.s3.useRequesterPaysHeader=true\"])\n\n\nclass MergeBenchmarkSpec(BenchmarkSpec):\n \"\"\"\n Specifications of Merge benchmark.\n \"\"\"\n def __init__(self, scale_in_gb, **kwargs):\n # forward all keyword args to next constructor\n super().__init__(benchmark_main_class=\"benchmark.MergeBenchmark\", **kwargs)\n # after init of super class, use the format to add main class args\n self.benchmark_main_class_args.extend([\n \"--scale-in-gb\", str(scale_in_gb)\n ])\n\n\n\n\n# ============== Delta benchmark specifications ==============\n\n\nclass DeltaBenchmarkSpec(BenchmarkSpec):\n \"\"\"\n Specification of a benchmark using the Delta format.\n \"\"\"\n def __init__(self, delta_version, benchmark_main_class, main_class_args=None, scala_version=\"2.12\", **kwargs):\n delta_spark_confs = [\n \"spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension\",\n \"spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog\"\n ]\n self.scala_version = scala_version\n\n if \"spark_confs\" in kwargs and isinstance(kwargs[\"spark_confs\"], list):\n kwargs[\"spark_confs\"].extend(delta_spark_confs)\n else:\n kwargs[\"spark_confs\"] = delta_spark_confs\n\n super().__init__(\n format_name=\"delta\",\n maven_artifacts=self.delta_maven_artifacts(delta_version, self.scala_version),\n benchmark_main_class=benchmark_main_class,\n main_class_args=main_class_args,\n **kwargs\n )\n\n def update_delta_version(self, new_delta_version):\n self.maven_artifacts = \\\n DeltaBenchmarkSpec.delta_maven_artifacts(new_delta_version, self.scala_version)\n\n @staticmethod\n def delta_maven_artifacts(delta_version, scala_version):\n return f\"io.delta:delta-core_{scala_version}:{delta_version},io.delta:delta-contribs_{scala_version}:{delta_version},io.delta:delta-hive_{scala_version}:0.2.0\"\n\n\nclass DeltaTPCDSDataLoadSpec(TPCDSDataLoadSpec, DeltaBenchmarkSpec):\n def __init__(self, delta_version, scale_in_gb=1):\n super().__init__(delta_version=delta_version, scale_in_gb=scale_in_gb)\n\n\nclass DeltaTPCDSBenchmarkSpec(TPCDSBenchmarkSpec, DeltaBenchmarkSpec):\n def __init__(self, delta_version, scale_in_gb=1):\n super().__init__(delta_version=delta_version, scale_in_gb=scale_in_gb)\n\n\nclass DeltaMergeDataLoadSpec(MergeDataLoadSpec, DeltaBenchmarkSpec):\n def __init__(self, delta_version, scale_in_gb=1):\n super().__init__(delta_version=delta_version, scale_in_gb=scale_in_gb)\n\n\nclass DeltaMergeBenchmarkSpec(MergeBenchmarkSpec, DeltaBenchmarkSpec):\n def __init__(self, delta_version, scale_in_gb=1):\n super().__init__(delta_version=delta_version, scale_in_gb=scale_in_gb)\n\n\n\n# ============== Parquet benchmark specifications ==============\n\n\nclass ParquetBenchmarkSpec(BenchmarkSpec):\n \"\"\"\n Specification of a benchmark using the Parquet format.\n \"\"\"\n def __init__(self, benchmark_main_class, main_class_args=None, **kwargs):\n super().__init__(\n format_name=\"parquet\",\n maven_artifacts=None,\n spark_confs=[],\n benchmark_main_class=benchmark_main_class,\n main_class_args=main_class_args,\n **kwargs\n )\n\nclass ParquetTPCDSDataLoadSpec(TPCDSDataLoadSpec, ParquetBenchmarkSpec):\n def __init__(self, scale_in_gb=1):\n super().__init__(scale_in_gb=scale_in_gb)\n\n\nclass ParquetTPCDSBenchmarkSpec(TPCDSBenchmarkSpec, ParquetBenchmarkSpec):\n def __init__(self, scale_in_gb=1):\n super().__init__(scale_in_gb=scale_in_gb)\n\n\n# ============== General benchmark execution ==============\n\n\nclass Benchmark:\n \"\"\"\n Represents a benchmark that can be run on a remote Spark cluster\n :param benchmark_name: A name to be used for uniquely identifying this benchmark.\n Added to file names generated by this benchmark.\n :param benchmark_spec: Specification of the benchmark. See BenchmarkSpec.\n \"\"\"\n def __init__(self, benchmark_name, benchmark_spec, use_spark_shell, local_delta_dir=None):\n now = datetime.now()\n self.benchmark_id = now.strftime(\"%Y%m%d-%H%M%S\") + \"-\" + benchmark_name\n self.benchmark_spec = benchmark_spec\n\n # Add benchmark id as a spark conf so that it get transferred automatically to scala code\n self.benchmark_spec.append_spark_confs([f\"spark.benchmarkId={self.benchmark_id}\"])\n self.output_file = Benchmark.output_file(self.benchmark_id)\n self.json_report_file = Benchmark.json_report_file(self.benchmark_id)\n self.completed_file = Benchmark.completed_file(self.benchmark_id)\n self.use_spark_shell = use_spark_shell\n self.local_delta_dir = local_delta_dir\n\n def run(self, cluster_hostname, ssh_id_file, ssh_user):\n if self.local_delta_dir and isinstance(self.benchmark_spec, DeltaBenchmarkSpec):\n # Upload new Delta jar to cluster and update spec to use the jar's version\n delta_version_to_use = \\\n self.upload_delta_jars_to_cluster_and_get_version(cluster_hostname, ssh_id_file, ssh_user)\n self.benchmark_spec.update_delta_version(delta_version_to_use)\n\n jar_path_in_cluster = self.upload_jar_to_cluster(cluster_hostname, ssh_id_file, ssh_user)\n self.install_dependencies_via_ssh(cluster_hostname, ssh_id_file, ssh_user)\n self.start_benchmark_via_ssh(cluster_hostname, ssh_id_file, jar_path_in_cluster, ssh_user)\n Benchmark.wait_for_completion(cluster_hostname, ssh_id_file, self.benchmark_id, ssh_user)\n\n def spark_submit_script_content(self, jar_path):\n return f\"\"\"\n#!/bin/bash\njps | grep \"Spark\" | cut -f 1 -d ' ' | xargs kill -9\nset -e\n{self.benchmark_spec.get_sparksubmit_cmd(jar_path)} 2>&1 | tee {self.output_file}\n\"\"\".strip()\n\n def spark_shell_script_content(self, jar_path):\n shell_init_file_name = f\"{self.benchmark_id}_shell_init.scala\"\n benchmark_cmd_line_params_str = \\\n ', '.join(f'\"{w}\"' for w in self.benchmark_spec.benchmark_main_class_args)\n call_main_with_args = \\\n f\"{self.benchmark_spec.benchmark_main_class}.main(Array[String]({benchmark_cmd_line_params_str}))\"\n shell_init_file_content = \\\n \"try { %s } catch { case t => println(t); println(\\\"FAILED\\\"); System.exit(1) } ; System.exit(0)\" % call_main_with_args\n shell_cmd = self.benchmark_spec.get_sparkshell_cmd(jar_path, shell_init_file_name)\n return f\"\"\"\n#!/bin/bash\njps | grep \"Spark\" | cut -f 1 -d ' ' | xargs kill -9\necho '{shell_init_file_content}' > {shell_init_file_name}\n{shell_cmd} 2>&1 | tee {self.output_file}\ntouch {self.completed_file}\n\"\"\".strip()\n\n def upload_jar_to_cluster(self, cluster_hostname, ssh_id_file, ssh_user, delta_version_to_use=None):\n # Compile JAR\n # Note: Deleting existing JARs instead of sbt clean is faster\n if os.path.exists(\"target\"):\n run_cmd(\"\"\"find target -name \"*.jar\" -type f -delete\"\"\", stream_output=True)\n run_cmd(\"build/sbt assembly\", stream_output=True)\n (_, out, _) = run_cmd(\"find target -name *.jar\")\n print(\">>> Benchmark JAR compiled\\n\")\n\n # Upload JAR\n jar_local_path = out.decode(\"utf-8\").strip()\n jar_remote_path = f\"{self.benchmark_id}-benchmarks.jar\"\n scp_cmd = \\\n f\"scp -C -i {ssh_id_file} {jar_local_path} {ssh_user}@{cluster_hostname}:{jar_remote_path}\"\n print(scp_cmd)\n run_cmd(scp_cmd, stream_output=True)\n print(\">>> Benchmark JAR uploaded to cluster\\n\")\n return f\"~/{jar_remote_path}\"\n\n def install_dependencies_via_ssh(self, cluster_hostname, ssh_id_file, ssh_user):\n script_file_name = f\"{self.benchmark_id}-install-deps.sh\"\n script_file_text = \"\"\"\n#!/bin/bash\npackage='screen'\nif [ -x \"$(command -v yum)\" ]; then\n if rpm -q $package; then\n echo \"$package has already been installed\"\n else\n sudo yum -y install $package\n fi\nelif [ -x \"$(command -v apt)\" ]; then\n if dpkg -s $package; then\n echo \"$package has already been installed\"\n else\n sudo apt install $package\n fi\nelse\n echo \"Failed to install packages: Package manager not found. You must manually install: $package\">&2; exit 1;\nfi\n\n\n \"\"\".strip()\n self.copy_script_via_ssh(cluster_hostname, ssh_id_file, ssh_user, script_file_name, script_file_text)\n print(\">>> Install dependencies script generated and uploaded\\n\")\n\n job_cmd = (\n f\"ssh -i {ssh_id_file} {ssh_user}@{cluster_hostname} \" +\n f\"bash {script_file_name}\"\n )\n print(job_cmd)\n run_cmd(job_cmd, stream_output=True)\n print(\">>> Dependencies have been installed\\n\")\n\n def start_benchmark_via_ssh(self, cluster_hostname, ssh_id_file, jar_path, ssh_user):\n # Generate and upload the script to run the benchmark\n script_file_name = f\"{self.benchmark_id}-cmd.sh\"\n if self.use_spark_shell:\n script_file_text = self.spark_shell_script_content(jar_path)\n else:\n script_file_text = self.spark_submit_script_content(jar_path)\n\n self.copy_script_via_ssh(cluster_hostname, ssh_id_file, ssh_user, script_file_name, script_file_text)\n print(\">>> Benchmark script generated and uploaded\\n\")\n\n # Start the script\n job_cmd = (\n f\"ssh -i {ssh_id_file} {ssh_user}@{cluster_hostname} \" +\n f\"screen -d -m bash {script_file_name}\"\n )\n print(job_cmd)\n run_cmd(job_cmd, stream_output=True)\n\n # Print the screen where it is running\n run_cmd(f\"ssh -i {ssh_id_file} {ssh_user}@{cluster_hostname}\" +\n f\"\"\" \"screen -ls ; sleep 2; echo Files for this benchmark: ; ls {self.benchmark_id}*\" \"\"\",\n stream_output=True, throw_on_error=False)\n print(f\">>> Benchmark id {self.benchmark_id} started in a screen. Stdout piped into {self.output_file}. \"\n f\"Final report will be generated on completion in {self.json_report_file}.\\n\")\n\n @staticmethod\n def copy_script_via_ssh(cluster_hostname, ssh_id_file, ssh_user, script_file_name, script_file_text):\n try:\n script_file = open(script_file_name, \"w\")\n script_file.write(script_file_text)\n script_file.close()\n\n scp_cmd = (\n f\"scp -i {ssh_id_file} {script_file_name}\" +\n f\" {ssh_user}@{cluster_hostname}:{script_file_name}\"\n )\n print(scp_cmd)\n run_cmd(scp_cmd, stream_output=True)\n run_cmd_over_ssh(f\"chmod +x {script_file_name}\", cluster_hostname, ssh_id_file, ssh_user,\n throw_on_error=False)\n finally:\n if os.path.exists(script_file_name):\n os.remove(script_file_name)\n\n @staticmethod\n def output_file(benchmark_id):\n return f\"{benchmark_id}-out.txt\"\n\n @staticmethod\n def json_report_file(benchmark_id):\n return f\"{benchmark_id}-report.json\"\n\n @staticmethod\n def csv_report_file(benchmark_id):\n return f\"{benchmark_id}-report.csv\"\n\n @staticmethod\n def completed_file(benchmark_id):\n return f\"{benchmark_id}-completed.txt\"\n\n @staticmethod\n def wait_for_completion(cluster_hostname, ssh_id_file, benchmark_id, ssh_user, copy_report=True):\n completed = False\n succeeded = False\n output_file = Benchmark.output_file(benchmark_id)\n completed_file = Benchmark.completed_file(benchmark_id)\n json_report_file = Benchmark.json_report_file(benchmark_id)\n csv_report_file = Benchmark.csv_report_file(benchmark_id)\n\n print(f\"\\nWaiting for completion of benchmark id {benchmark_id}\")\n while not completed:\n # Print the size of the output file to show progress\n (_, out, _) = run_cmd_over_ssh(f\"stat -c '%n: [%y] [%s bytes]' {output_file}\",\n cluster_hostname, ssh_id_file, ssh_user,\n throw_on_error=False)\n out = out.decode(\"utf-8\").strip()\n print(out)\n if \"No such file\" in out:\n print(\">>> Benchmark failed to start\")\n return\n\n # Check for the existence of the completed file\n (_, out, _) = run_cmd_over_ssh(f\"ls {completed_file}\", cluster_hostname, ssh_id_file, ssh_user,\n throw_on_error=False)\n if completed_file in out.decode(\"utf-8\"):\n completed = True\n else:\n time.sleep(60)\n\n # Check the last few lines of output files to identify success\n (_, out, _) = run_cmd_over_ssh(f\"tail {output_file}\", cluster_hostname, ssh_id_file, ssh_user,\n throw_on_error=False)\n if \"SUCCESS\" in out.decode(\"utf-8\"):\n succeeded = True\n print(\">>> Benchmark completed with success\\n\")\n else:\n print(\">>> Benchmark completed with failure\\n\")\n\n # Download reports\n if copy_report:\n Benchmark.download_file(output_file, cluster_hostname, ssh_id_file, ssh_user)\n if succeeded:\n report_files = [json_report_file, csv_report_file]\n for report_file in report_files:\n Benchmark.download_file(report_file, cluster_hostname, ssh_id_file, ssh_user)\n print(\">>> Downloaded reports to local directory\")\n\n\n @staticmethod\n def download_file(file, cluster_hostname, ssh_id_file, ssh_user):\n run_cmd(f\"scp -C -i {ssh_id_file} \" +\n f\"{ssh_user}@{cluster_hostname}:{file} {file}\",\n stream_output=True)\n\n def upload_delta_jars_to_cluster_and_get_version(self, cluster_hostname, ssh_id_file, ssh_user):\n if not self.local_delta_dir:\n raise Exception(\"Path to delta repo not specified\")\n delta_repo_dir = os.path.abspath(self.local_delta_dir)\n\n with WorkingDirectory(delta_repo_dir):\n # Compile Delta JARs by publishing to local maven cache\n print(f\"Compiling Delta to local dir {delta_repo_dir}\")\n local_maven_delta_dir = os.path.expanduser(\"~/.ivy2/local/io.delta/\")\n if os.path.exists(local_maven_delta_dir):\n run_cmd(f\"rm -rf {local_maven_delta_dir}\", stream_output=True)\n print(f\"Cleared local maven cache at {local_maven_delta_dir}\")\n run_cmd(\"build/sbt publishLocal\", stream_output=False, throw_on_error=True)\n\n # Get the new version\n (_, out, _) = run_cmd(\"\"\"build/sbt \"show version\" \"\"\")\n version = out.decode(\"utf-8\").strip().rsplit(\"\\n\", 1)[-1].rsplit(\" \", 1)[-1].strip()\n if not version:\n raise Exception(f\"Could not find the version from the sbt output:\\n--\\n{out}\\n-\")\n\n # Upload JARs to cluster's local maven cache\n remote_maven_dir = \".ivy2/local/\" # must have \"/\" at the end\n run_cmd_over_ssh(\n f\"rm -rf {remote_maven_dir}/* .ivy2/cache/io.delta .ivy2/jars/io.delta*\",\n cluster_hostname, ssh_id_file, ssh_user, stream_output=True, throw_on_error=False)\n run_cmd_over_ssh(f\"mkdir -p {remote_maven_dir}\", cluster_hostname,\n ssh_id_file, ssh_user, stream_output=True)\n scp_cmd = f\"\"\"scp -r -C -i {ssh_id_file} {local_maven_delta_dir.rstrip(\"/\")} \"\"\" +\\\n f\"{ssh_user}@{cluster_hostname}:{remote_maven_dir}\"\n print(scp_cmd)\n run_cmd(scp_cmd, stream_output=True)\n print(f\">>> Delta {version} JAR uploaded to cluster\\n\")\n return version\n" }, { "path": "benchmarks/scripts/utils.py", "content": "#\n# Copyright (2021) The Delta Lake Project Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nimport os\nimport shlex\nimport subprocess\n\n\ndef run_cmd(cmd, throw_on_error=True, env=None, stream_output=False, **kwargs):\n if isinstance(cmd, str):\n cmd = shlex.split(cmd)\n cmd_env = os.environ.copy()\n if env:\n cmd_env.update(env)\n\n if stream_output:\n child = subprocess.Popen(cmd, env=cmd_env, **kwargs)\n exit_code = child.wait()\n if throw_on_error and exit_code != 0:\n raise Exception(\"Non-zero exitcode: %s\" % (exit_code))\n return exit_code\n else:\n child = subprocess.Popen(\n cmd,\n env=cmd_env,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n **kwargs)\n (stdout, stderr) = child.communicate()\n exit_code = child.wait()\n if throw_on_error and exit_code != 0:\n raise Exception(\n \"Non-zero exitcode: %s\\n\\nSTDOUT:\\n%s\\n\\nSTDERR:%s\" %\n (exit_code, stdout, stderr))\n return exit_code, stdout, stderr\n\n\ndef run_cmd_over_ssh(cmd, host, ssh_id_file, user, **kwargs):\n full_cmd = f\"\"\"ssh -i {ssh_id_file} {user}@{host} \"{cmd}\" \"\"\"\n return run_cmd(full_cmd, **kwargs)\n\n\n# pylint: disable=too-few-public-methods\nclass WorkingDirectory(object):\n def __init__(self, working_directory):\n self.working_directory = working_directory\n self.old_workdir = os.getcwd()\n\n def __enter__(self):\n os.chdir(self.working_directory)\n\n def __exit__(self, tpe, value, traceback):\n os.chdir(self.old_workdir)\n\n" }, { "path": "benchmarks/src/main/scala/benchmark/Benchmark.scala", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage benchmark\n\nimport java.net.URI\nimport java.nio.file.{Files, Paths}\nimport java.nio.charset.StandardCharsets\n\nimport scala.collection.mutable\nimport scala.language.postfixOps\nimport scala.sys.process._\nimport scala.util.control.NonFatal\n\nimport com.fasterxml.jackson.annotation.JsonInclude.Include\nimport com.fasterxml.jackson.annotation.JsonPropertyOrder\nimport com.fasterxml.jackson.databind.{DeserializationFeature, MapperFeature, ObjectMapper}\nimport com.fasterxml.jackson.module.scala.{DefaultScalaModule, ScalaObjectMapper}\n\nimport org.apache.spark.SparkUtils\nimport org.apache.spark.sql.{Row, SparkSession}\nimport org.apache.spark.sql.functions.col\nimport org.apache.spark.sql.internal.SQLConf\n\ntrait BenchmarkConf extends Product {\n /** Cloud path where benchmark data is going to be written. */\n def benchmarkPath: Option[String]\n\n /** Get the database location given the database name and the benchmark path. */\n def dbLocation(dbName: String, suffix: String = \"\"): String = {\n benchmarkPath.map(p => s\"$p/databases/${dbName}_${suffix}\").getOrElse {\n throw new IllegalArgumentException(\"Benchmark path must be specified\")\n }\n }\n\n /** Cloud path where benchmark reports will be uploaded. */\n def reportUploadPath: String = {\n benchmarkPath.map(p => s\"$p/reports/\").getOrElse {\n throw new IllegalArgumentException(\"Benchmark path must be specified\")\n }\n }\n def jsonReportUploadPath: String = s\"$reportUploadPath/json/\"\n def csvReportUploadPath: String = s\"$reportUploadPath/csv/\"\n\n /** Get the benchmark conf details as a map. */\n def asMap: Map[String, String] = SparkUtils.caseClassToMap(this)\n}\n\n@JsonPropertyOrder(alphabetic=true)\ncase class QueryResult(\n name: String,\n iteration: Option[Int],\n durationMs: Option[Long],\n errorMsg: Option[String])\n\n@JsonPropertyOrder(alphabetic=true)\ncase class SparkEnvironmentInfo(\n @JsonPropertyOrder(alphabetic=true)\n sparkBuildInfo: Map[String, String],\n @JsonPropertyOrder(alphabetic=true)\n runtimeInfo: Map[String, String],\n @JsonPropertyOrder(alphabetic=true)\n sparkProps: Map[String, String],\n @JsonPropertyOrder(alphabetic=true)\n hadoopProps: Map[String, String],\n @JsonPropertyOrder(alphabetic=true)\n systemProps: Map[String, String],\n @JsonPropertyOrder(alphabetic=true)\n classpathEntries: Map[String, String])\n\n@JsonPropertyOrder(alphabetic=true)\ncase class BenchmarkReport(\n @JsonPropertyOrder(alphabetic=true)\n benchmarkSpecs: Map[String, String],\n queryResults: Array[QueryResult],\n extraMetrics: Map[String, Double],\n sparkEnvInfo: SparkEnvironmentInfo)\n\n/**\n * Base class for any benchmark with the core functionality of measuring SQL query durations\n * and printing the details as json in a report file.\n */\nabstract class Benchmark(private val conf: BenchmarkConf) {\n\n /* Methods that implementations should override. */\n\n protected def runInternal(): Unit\n\n /* Fields and methods that implementations should not have to override */\n\n final protected lazy val spark = {\n val s = SparkSession.builder()\n .config(\"spark.ui.proxyBase\", \"\")\n .getOrCreate()\n log(\"Spark started with configuration:\\n\" +\n s.conf.getAll.toSeq.sortBy(_._1).map(x => x._1 + \": \" + x._2).mkString(\"\\t\", \"\\n\\t\", \"\\n\"))\n s.sparkContext.setLogLevel(\"WARN\")\n sys.props.update(\"spark.ui.proxyBase\", \"\")\n s\n }\n\n val extraConfs: Map[String, String] = Map(\n SQLConf.BROADCAST_TIMEOUT.key -> \"7200\",\n SQLConf.CROSS_JOINS_ENABLED.key -> \"true\"\n )\n\n private val queryResults = new mutable.ArrayBuffer[QueryResult]\n private val extraMetrics = new mutable.HashMap[String, Double]\n\n protected def run(): Unit = {\n try {\n log(\"=\" * 80)\n log(\"=\" * 80)\n runInternal()\n log(\"=\" * 80)\n } finally {\n generateReport()\n }\n println(s\"SUCCESS\")\n }\n\n protected def runQuery(\n sqlCmd: String,\n queryName: String = \"\",\n iteration: Option[Int] = None,\n printRows: Boolean = false,\n ignoreError: Boolean = true): Seq[Row] = synchronized {\n val iterationStr = iteration.map(i => s\" - iteration $i\").getOrElse(\"\")\n var banner = s\"$queryName$iterationStr\"\n if (banner.trim.isEmpty) {\n banner = sqlCmd.split(\"\\n\")(0).trim + (if (sqlCmd.split(\"\\n\").size > 1) \"...\" else \"\")\n }\n log(\"=\" * 80)\n log(s\"START: $banner\")\n log(\"SQL: \" + sqlCmd.replaceAll(\"\\n\\\\s*\", \" \"))\n spark.sparkContext.setJobGroup(banner, banner, interruptOnCancel = true)\n try {\n val before = System.nanoTime()\n val df = spark.sql(sqlCmd)\n val r = df.collect()\n val after = System.nanoTime()\n if (printRows) df.show(false)\n val durationMs = (after - before) / (1000 * 1000)\n queryResults += QueryResult(queryName, iteration, Some(durationMs), errorMsg = None)\n log(s\"END took $durationMs ms: $banner\")\n log(\"=\" * 80)\n r\n } catch {\n case NonFatal(e) =>\n log(s\"ERROR: $banner\\n${e.getMessage}\")\n queryResults +=\n QueryResult(queryName, iteration, durationMs = None, errorMsg = Some(e.getMessage))\n if (!ignoreError) throw e else Nil\n }\n }\n\n\n protected def runFunc(\n queryName: String = \"\",\n iteration: Option[Int] = None,\n ignoreError: Boolean = true)(f: => Unit): Unit = synchronized {\n val iterationStr = iteration.map(i => s\" - iteration $i\").getOrElse(\"\")\n var banner = s\"$queryName$iterationStr\"\n log(\"=\" * 80)\n log(s\"START: $banner\")\n spark.sparkContext.setJobGroup(banner, banner, interruptOnCancel = true)\n try {\n val before = System.nanoTime()\n f\n val after = System.nanoTime()\n val durationMs = (after - before) / (1000 * 1000)\n queryResults += QueryResult(queryName, iteration, Some(durationMs), errorMsg = None)\n log(s\"END took $durationMs ms: $banner\")\n log(\"=\" * 80)\n } catch {\n case NonFatal(e) =>\n log(s\"ERROR: $banner\\n${e.getMessage}\")\n queryResults +=\n QueryResult(queryName, iteration, durationMs = None, errorMsg = Some(e.getMessage))\n if (!ignoreError) throw e else spark.emptyDataFrame\n }\n }\n\n\n protected def reportExtraMetric(name: String, value: Double): Unit = synchronized {\n extraMetrics += (name -> value)\n }\n\n protected def getQueryResults(): Array[QueryResult] = synchronized { queryResults.toArray }\n\n private def generateJSONReport(report: BenchmarkReport): Unit = synchronized {\n import Benchmark._\n\n val resultJson = toPrettyJson(report)\n val resultFileName =\n if (benchmarkId.trim.isEmpty) \"report.json\" else s\"$benchmarkId-report.json\"\n val reportLocalPath = Paths.get(resultFileName).toAbsolutePath()\n Files.write(reportLocalPath, resultJson.getBytes(StandardCharsets.UTF_8))\n println(s\"RESULT:\\n$resultJson\")\n uploadFile(reportLocalPath.toString, conf.jsonReportUploadPath)\n }\n\n private def generateCSVReport(): Unit = synchronized {\n val csvHeader = \"name,iteration,durationMs\"\n val csvRows = queryResults.map { r =>\n s\"${r.name},${r.iteration.getOrElse(1)},${r.durationMs.getOrElse(-1)}\"\n }\n val csvText = (Seq(csvHeader) ++ csvRows).mkString(\"\\n\")\n val resultFileName =\n if (benchmarkId.trim.isEmpty) \"report.csv\" else s\"$benchmarkId-report.csv\"\n val reportLocalPath = Paths.get(resultFileName).toAbsolutePath()\n Files.write(reportLocalPath, csvText.getBytes(StandardCharsets.UTF_8))\n uploadFile(reportLocalPath.toString, conf.csvReportUploadPath)\n }\n\n private def generateReport(): Unit = synchronized {\n val report = BenchmarkReport(\n benchmarkSpecs = conf.asMap + (\"benchmarkId\" -> benchmarkId),\n queryResults = queryResults.toArray,\n extraMetrics = extraMetrics.toMap,\n sparkEnvInfo = SparkUtils.getEnvironmentInfo(spark.sparkContext)\n )\n generateJSONReport(report)\n generateCSVReport()\n }\n\n private def uploadFile(localPath: String, targetPath: String): Unit = {\n val targetUri = new URI(targetPath)\n val sanitizedTargetPath = targetUri.normalize().toString\n val scheme = new URI(targetPath).getScheme\n try {\n if (scheme.equals(\"s3\")) s\"aws s3 cp $localPath $sanitizedTargetPath/\" !\n else if (scheme.equals(\"gs\")) s\"gsutil cp $localPath $sanitizedTargetPath/\" !\n else throw new IllegalArgumentException(String.format(\"Unsupported scheme %s.\", scheme))\n\n println(s\"FILE UPLOAD: Uploaded $localPath to $sanitizedTargetPath\")\n } catch {\n case NonFatal(e) =>\n log(s\"FILE UPLOAD: Failed to upload $localPath to $sanitizedTargetPath: $e\")\n }\n }\n\n protected def benchmarkId: String =\n sys.env.getOrElse(\"BENCHMARK_ID\", spark.conf.getOption(\"spark.benchmarkId\").getOrElse(\"\"))\n\n protected def log(str: => String): Unit = {\n println(s\"${java.time.LocalDateTime.now} $str\")\n }\n}\n\nobject Benchmark {\n private lazy val mapper = {\n val _mapper = new ObjectMapper with ScalaObjectMapper\n _mapper.setSerializationInclusion(Include.NON_ABSENT)\n _mapper.enable(MapperFeature.SORT_PROPERTIES_ALPHABETICALLY)\n _mapper.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false)\n _mapper.registerModule(DefaultScalaModule)\n _mapper\n }\n\n def toJson[T: Manifest](obj: T): String = {\n mapper.writeValueAsString(obj)\n }\n\n def toPrettyJson[T: Manifest](obj: T): String = {\n mapper.writerWithDefaultPrettyPrinter().writeValueAsString(obj)\n }\n}\n" }, { "path": "benchmarks/src/main/scala/benchmark/MergeBenchmark.scala", "content": "/*\n * Copyright (2023) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage benchmark\n\nimport java.util.UUID\n\nimport org.apache.spark.SparkUtils\nimport org.apache.spark.sql.Row\n\ntrait MergeConf extends BenchmarkConf {\n def scaleInGB: Int\n def tableName: String = \"web_returns\"\n def userDefinedDbName: Option[String]\n def dbName: String = userDefinedDbName.getOrElse(s\"merge_sf${scaleInGB}\")\n def dbLocation: String = dbLocation(dbName)\n}\n\ncase class MergeBenchmarkConf(\n scaleInGB: Int = 0,\n userDefinedDbName: Option[String] = None,\n iterations: Int = 3,\n benchmarkPath: Option[String] = None) extends MergeConf {\n}\n\nobject MergeBenchmarkConf {\n import scopt.OParser\n private val builder = OParser.builder[MergeBenchmarkConf]\n private val argParser = {\n import builder._\n OParser.sequence(\n programName(\"Merge Benchmark\"),\n opt[String](\"scale-in-gb\")\n .required()\n .valueName(\"\")\n .action((x, c) => c.copy(scaleInGB = x.toInt))\n .text(\"Scale factor in GBs of the TPCDS benchmark\"),\n opt[String](\"benchmark-path\")\n .required()\n .valueName(\"\")\n .action((x, c) => c.copy(benchmarkPath = Some(x)))\n .text(\"Cloud path to be used for creating table and generating reports\"),\n opt[String](\"iterations\")\n .optional()\n .valueName(\"\")\n .action((x, c) => c.copy(iterations = x.toInt))\n .text(\"Number of times to run the queries\"))\n }\n\n def parse(args: Array[String]): Option[MergeBenchmarkConf] = {\n OParser.parse(argParser, args, MergeBenchmarkConf())\n }\n}\n\nclass MergeBenchmark(conf: MergeBenchmarkConf) extends Benchmark(conf) {\n /**\n * Runs every merge test case multiple times and records the duration.\n */\n override def runInternal(): Unit = {\n for ((k, v) <- extraConfs) spark.conf.set(k, v)\n spark.sparkContext.setLogLevel(\"WARN\")\n log(\"All configs:\\n\\t\" + spark.conf.getAll.toSeq.sortBy(_._1).mkString(\"\\n\\t\"))\n spark.sql(s\"USE ${conf.dbName}\")\n\n val targetRowCount = spark.read.table(s\"`${conf.dbName}`.`target_${conf.tableName}`\").count\n\n for (iteration <- 1 to conf.iterations) {\n MergeTestCases.testCases.foreach { runMerge(_, targetRowCount, iteration = Some(iteration)) }\n }\n val results = getQueryResults().filter(_.name.startsWith(\"q\"))\n if (results.forall(x => x.errorMsg.isEmpty && x.durationMs.nonEmpty) ) {\n val medianDurationSecPerQuery = results.groupBy(_.name).map { case (q, results) =>\n assert(results.length == conf.iterations)\n val medianMs = SparkUtils.median(results.map(_.durationMs.get), alreadySorted = false)\n (q, medianMs / 1000.0)\n }\n val sumOfMedians = medianDurationSecPerQuery.values.sum\n reportExtraMetric(\"merge-result-seconds\", sumOfMedians)\n }\n }\n\n /**\n * Merge test runner performing the following steps:\n * - Clone a fresh target table.\n * - Run the merge test case.\n * - Check invariants.\n * - Drop the cloned table.\n */\n protected def runMerge(\n testCase: MergeTestCase,\n targetRowCount: Long,\n iteration: Option[Int] = None,\n printRows: Boolean = false,\n ignoreError: Boolean = true): Seq[Row] = synchronized {\n withCloneTargetTable(testCase.name) { targetTable =>\n val result = super.runQuery(\n testCase.sqlCmd(targetTable),\n testCase.name,\n iteration,\n printRows,\n ignoreError)\n testCase.validate(result, targetRowCount)\n result\n }\n }\n\n /**\n * Clones the target table before each test case to use a fresh target table and drops the clone\n * afterwards.\n */\n protected def withCloneTargetTable[T](testCaseName: String)(f: String => T): T = {\n val target = s\"`${conf.dbName}`.`target_${conf.tableName}`\"\n val clonedTableName = s\"`${conf.dbName}`.`${conf.tableName}_${generateShortUUID()}`\"\n runQuery(s\"CREATE TABLE $clonedTableName SHALLOW CLONE $target\", s\"clone-target-$testCaseName\")\n try {\n f(clonedTableName)\n } finally {\n runQuery(s\"DROP TABLE IF EXISTS $clonedTableName\", s\"drop-target-clone-$testCaseName\")\n }\n }\n\n protected def generateShortUUID(): String =\n UUID.randomUUID.toString.replace(\"-\", \"_\").take(8)\n}\n\nobject MergeBenchmark {\n def main(args: Array[String]): Unit = {\n MergeBenchmarkConf.parse(args).foreach { conf =>\n new MergeBenchmark(conf).run()\n }\n }\n}\n" }, { "path": "benchmarks/src/main/scala/benchmark/MergeDataLoad.scala", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage benchmark\n\nimport java.util.Locale\n\nimport org.apache.spark.sql.functions.{col, countDistinct, hash, isnull, max, rand}\n\n\ncase class MergeDataLoadConf(\n scaleInGB: Int = 0,\n userDefinedDbName: Option[String] = None,\n loadFromPath: Option[String] = None,\n benchmarkPath: Option[String] = None,\n excludeNulls: Boolean = true) extends MergeConf {\n}\n\n/**\n * Represents a table configuration used as a source in merge test cases. Each [[MergeTestCase]] has\n * one [[MergeSourceTable]] associated with it, the data loader will collect all source table\n * configurations for all tests and create the required source tables.\n * @param filesMatchedFraction Fraction of files from the base table that will get sampled to\n * create the source table.\n * @param rowsMatchedFraction Fraction of rows from the selected files that will get sampled to form\n * the part of the source table that matches the merge condition.\n * @param rowsNotMatchedFraction Fraction of rows from the selected files that will get sampled to\n * form the part of the source table that doesn't match the merge\n * condition.\n */\ncase class MergeSourceTable(\n filesMatchedFraction: Double,\n rowsMatchedFraction: Double,\n rowsNotMatchedFraction: Double) {\n def name: String = formatTableName(s\"source_\" +\n s\"_filesMatchedFraction_$filesMatchedFraction\" +\n s\"_rowsMatchedFraction_$rowsMatchedFraction\" +\n s\"_rowsNotMatchedFraction_$rowsNotMatchedFraction\")\n\n protected def formatTableName(s: String): String = {\n s.toLowerCase(Locale.ROOT).replaceAll(\"\\\\s+\", \"_\").replaceAll(\"[-,.]\", \"_\")\n }\n}\n\nobject MergeDataLoadConf {\n import scopt.OParser\n private val builder = OParser.builder[MergeDataLoadConf]\n private val argParser = {\n import builder._\n OParser.sequence(\n programName(\"Merge Data Load\"),\n opt[String](\"scale-in-gb\")\n .required()\n .valueName(\"\")\n .action((x, c) => c.copy(scaleInGB = x.toInt))\n .text(\"Scale factor of the Merge benchmark\"),\n opt[String](\"benchmark-path\")\n .required()\n .valueName(\"\")\n .action((x, c) => c.copy(benchmarkPath = Some(x)))\n .text(\"Cloud storage path to be used for creating table and generating reports\"),\n opt[String](\"db-name\")\n .optional()\n .valueName(\"\")\n .action((x, c) => c.copy(userDefinedDbName = Some(x)))\n .text(\"Name of the target database to create with TPC-DS tables in necessary format\"),\n opt[String](\"load-from-path\")\n .optional()\n .valueName(\"\")\n .action((x, c) => c.copy(loadFromPath = Some(x)))\n .text(\"The location of the TPC-DS raw input data\"),\n opt[String](\"exclude-nulls\")\n .optional()\n .valueName(\"true/false\")\n .action((x, c) => c.copy(excludeNulls = x.toBoolean))\n .text(\"Whether to remove null primary keys when loading data, default = false\"))\n }\n\n def parse(args: Array[String]): Option[MergeDataLoadConf] = {\n OParser.parse(argParser, args, MergeDataLoadConf())\n }\n}\n\nclass MergeDataLoad(conf: MergeDataLoadConf) extends Benchmark(conf) {\n\n protected def targetTableFullName = s\"`${conf.dbName}`.`target_${conf.tableName}`\"\n\n protected def dataLoadFromPath: String = conf.loadFromPath.getOrElse {\n s\"s3://devrel-delta-datasets/tpcds-2.13/tpcds_sf${conf.scaleInGB}_parquet/${conf.tableName}/\"\n }\n\n /**\n * Creates the target table and all source table configuration used in merge test cases.\n */\n def runInternal(): Unit = {\n val dbName = conf.dbName\n val dbLocation = conf.dbLocation(dbName, suffix = benchmarkId.replace(\"-\", \"_\"))\n val dbCatalog = \"spark_catalog\"\n\n require(Seq(1, 3000).contains(conf.scaleInGB), \"\")\n\n log(s\"====== Creating database =======\")\n runQuery(s\"DROP DATABASE IF EXISTS ${dbName} CASCADE\", s\"drop-database\")\n runQuery(s\"CREATE DATABASE IF NOT EXISTS ${dbName}\", s\"create-database\")\n\n log(s\"====== Creating merge target table =======\")\n loadMergeTargetTable()\n log(s\"====== Creating merge source tables =======\")\n MergeTestCases.testCases.map(_.sourceTable).distinct.foreach(loadMergeSourceTable)\n log(s\"====== Created all tables in database ${dbName} at '${dbLocation}' =======\")\n\n runQuery(s\"USE $dbCatalog.$dbName;\")\n runQuery(\"SHOW TABLES\", printRows = true)\n }\n\n /**\n * Creates the target Delta table and performs sanity checks. This table will be cloned before\n * each merge test case and the clone serves as a single-use merge target table.\n */\n protected def loadMergeTargetTable(): Unit = {\n val dbLocation = conf.dbLocation(conf.dbName, suffix = benchmarkId.replace(\"-\", \"_\"))\n val location = s\"${dbLocation}/${conf.tableName}/\"\n val format = \"parquet\"\n\n runQuery(s\"DROP TABLE IF EXISTS $targetTableFullName\", s\"drop-table-$targetTableFullName\")\n\n runQuery(\n s\"\"\"CREATE TABLE $targetTableFullName\n USING DELTA\n LOCATION '$location'\n SELECT * FROM `${format}`.`$dataLoadFromPath`\n \"\"\", s\"create-table-$targetTableFullName\", ignoreError = true)\n\n val sourceRowCount =\n spark.sql(s\"SELECT * FROM `${format}`.`$dataLoadFromPath`\").count()\n val targetRowCount = spark.table(targetTableFullName).count()\n val targetFileCount =\n spark.table(targetTableFullName).select(countDistinct(\"_metadata.file_path\"))\n log(s\"Target file count: $targetFileCount\")\n log(s\"Target row count: $targetRowCount\")\n\n assert(targetRowCount == sourceRowCount,\n s\"Row count mismatch: source table = $sourceRowCount, \" +\n s\"target $targetTableFullName = $targetRowCount\")\n }\n\n /**\n * Creates a table that will be used as a merge source table in the merge test cases. The table is\n * created by sampling the merge target table created by [[loadMergeTargetTable]]. The merge test\n * cases don't modify the source table and a single source table is reused across different test\n * cases if the same source table configuration is used.\n */\n protected def loadMergeSourceTable(sourceTableConf: MergeSourceTable): Unit = {\n val fullTableName = s\"`${conf.dbName}`.`${sourceTableConf.name}`\"\n val dbLocation = conf.dbLocation(conf.dbName, suffix = benchmarkId.replace(\"-\", \"_\"))\n\n runQuery(s\"DROP TABLE IF EXISTS $fullTableName\", s\"drop-table-${sourceTableConf.name}\")\n\n val fullTableDF = spark.read.format(\"delta\")\n .load(s\"${dbLocation}/${conf.tableName}/\")\n // Sample files based on their file path.\n val sampledFilesDF = fullTableDF\n .select(\"_metadata.file_path\")\n .distinct\n .sample(sourceTableConf.filesMatchedFraction)\n\n // Read the data from the sampled files and sample two sets of rows for MATCHED clauses and\n // NOT MATCHED clauses respectively.\n val sampledDataDF = fullTableDF\n .withColumn(\"file_path\", col(\"_metadata.file_path\"))\n .join(sampledFilesDF, \"file_path\")\n log(s\"Matching files row count: ${sampledDataDF.count}\")\n\n val numberOfNulls = sampledDataDF.filter(isnull(col(\"wr_order_number\"))).count\n log(s\"wr_order_number contains $numberOfNulls null values\")\n val matchedData = sampledDataDF.sample(sourceTableConf.rowsMatchedFraction)\n val notMatchedData = sampledDataDF.sample(sourceTableConf.rowsNotMatchedFraction)\n .withColumn(\"wr_order_number\", rand())\n .withColumn(\"wr_item_sk\", rand())\n\n val data = matchedData.union(notMatchedData)\n\n val dupes = data.groupBy(\"wr_order_number\", \"wr_item_sk\").count.filter(\"count > 1\")\n log(s\"Duplicates: ${dupes.collect().mkString(\"Array(\", \",\\n\", \")\")}\")\n data.write.format(\"delta\").saveAsTable(fullTableName)\n }\n}\n\nobject MergeDataLoad {\n def main(args: Array[String]): Unit = {\n MergeDataLoadConf.parse(args).foreach { conf =>\n new MergeDataLoad(conf).run()\n }\n }\n}\n" }, { "path": "benchmarks/src/main/scala/benchmark/MergeTestCases.scala", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage benchmark\n\nimport org.apache.spark.sql.Row\n\ntrait MergeTestCase {\n /**\n * Name of the test case used e.p. in the test results.\n */\n def name: String\n\n /**\n * The source table configuration to use for the test case. When a test case is defined,\n * [[MergeDataLoad]] will collect all source table configuration and create the source tables\n * required by all tests.\n */\n def sourceTable: MergeSourceTable\n\n /**\n * The merge command to execute as a SQL string.\n */\n def sqlCmd(targetTable: String): String\n\n /**\n * Each test case can define invariants to check after the merge command runs to ensure that the\n * benchmark results are valid.\n */\n def validate(mergeStats: Seq[Row], targetRowCount: Long): Unit\n}\n\n/**\n * Trait shared by all insert-only merge test cases.\n */\ntrait InsertOnlyTestCase extends MergeTestCase {\n val filesMatchedFraction: Double\n val rowsNotMatchedFraction: Double\n\n override def sourceTable: MergeSourceTable = MergeSourceTable(\n filesMatchedFraction,\n rowsMatchedFraction = 0,\n rowsNotMatchedFraction)\n\n override def validate(mergeStats: Seq[Row], targetRowCount: Long): Unit = {\n assert(mergeStats.length == 1)\n assert(mergeStats.head.getAs[Long](\"num_updated_rows\") == 0)\n assert(mergeStats.head.getAs[Long](\"num_deleted_rows\") == 0)\n }\n}\n\n/**\n * A merge test case with a single WHEN NOT MATCHED THEN INSERT * clause.\n */\ncase class SingleInsertOnlyTestCase(\n filesMatchedFraction: Double,\n rowsNotMatchedFraction: Double) extends InsertOnlyTestCase {\n\n override val name: String = \"single_insert_only\" +\n s\"_filesMatchedFraction_$filesMatchedFraction\" +\n s\"_rowsNotMatchedFraction_$rowsNotMatchedFraction\"\n\n\n override def sqlCmd(targetTable: String): String = {\n s\"\"\"MERGE INTO $targetTable t\n |USING ${sourceTable.name} s\n |ON t.wr_order_number = s.wr_order_number AND t.wr_item_sk = s.wr_item_sk\n |WHEN NOT MATCHED THEN INSERT *\"\"\".stripMargin\n }\n}\n\n/**\n * A merge test case with two WHEN NOT MATCHED (AND condition) THEN INSERT * clauses.\n */\ncase class MultipleInsertOnlyTestCase(\n filesMatchedFraction: Double,\n rowsNotMatchedFraction: Double) extends InsertOnlyTestCase {\n\n override val name: String = \"multiple_insert_only\" +\n s\"_filesMatchedFraction_$filesMatchedFraction\" +\n s\"_rowsNotMatchedFraction_$rowsNotMatchedFraction\"\n\n override def sqlCmd(targetTable: String): String = {\n s\"\"\"MERGE INTO $targetTable t\n |USING ${sourceTable.name} s\n |ON t.wr_order_number = s.wr_order_number AND t.wr_item_sk = s.wr_item_sk\n |WHEN NOT MATCHED AND s.wr_item_sk % 2 = 0 THEN INSERT *\n |WHEN NOT MATCHED THEN INSERT *\"\"\".stripMargin\n }\n}\n\n/**\n * A merge test case with a single WHEN MATCHED THEN DELETED clause.\n */\ncase class DeleteOnlyTestCase(\n filesMatchedFraction: Double,\n rowsMatchedFraction: Double) extends MergeTestCase {\n\n override val name: String = \"delete_only\" +\n s\"_filesMatchedFraction_$filesMatchedFraction\" +\n s\"_rowsMatchedFraction_$rowsMatchedFraction\"\n\n override def sourceTable: MergeSourceTable = MergeSourceTable(\n filesMatchedFraction,\n rowsMatchedFraction,\n rowsNotMatchedFraction = 0)\n\n override def sqlCmd(targetTable: String): String = {\n s\"\"\"MERGE INTO $targetTable t\n |USING ${sourceTable.name} s\n |ON t.wr_order_number = s.wr_order_number AND t.wr_item_sk = s.wr_item_sk\n |WHEN MATCHED THEN DELETE\"\"\".stripMargin\n }\n\n override def validate(mergeStats: Seq[Row], targetRowCount: Long): Unit = {\n assert(mergeStats.length == 1)\n assert(mergeStats.head.getAs[Long](\"num_updated_rows\") == 0)\n assert(mergeStats.head.getAs[Long](\"num_inserted_rows\") == 0)\n }\n}\n\n/**\n * A merge test case with a MATCHED UPDATE and a NOT MATCHED INSERT clause.\n */\ncase class UpsertTestCase(\n filesMatchedFraction: Double,\n rowsMatchedFraction: Double,\n rowsNotMatchedFraction: Double) extends MergeTestCase {\n\n override val name: String = \"upsert\" +\n s\"_filesMatchedFraction_$filesMatchedFraction\" +\n s\"_rowsMatchedFraction_$rowsMatchedFraction\" +\n s\"_rowsNotMatchedFraction_$rowsNotMatchedFraction\"\n\n override def sourceTable: MergeSourceTable = MergeSourceTable(\n filesMatchedFraction,\n rowsMatchedFraction,\n rowsNotMatchedFraction)\n\n override def sqlCmd(targetTable: String): String = {\n s\"\"\"MERGE INTO $targetTable t\n |USING ${sourceTable.name} s\n |ON t.wr_order_number = s.wr_order_number AND t.wr_item_sk = s.wr_item_sk\n |WHEN MATCHED THEN UPDATE SET *\n |WHEN NOT MATCHED THEN INSERT *\"\"\".stripMargin\n }\n\n override def validate(mergeStats: Seq[Row], targetRowCount: Long): Unit = {\n assert(mergeStats.length == 1)\n assert(mergeStats.head.getAs[Long](\"num_deleted_rows\") == 0)\n }\n}\n\nobject MergeTestCases {\n def testCases: Seq[MergeTestCase] =\n insertOnlyTestCases ++\n deleteOnlyTestCases ++\n upsertTestCases\n\n def insertOnlyTestCases: Seq[MergeTestCase] =\n Seq(0.05, 0.5, 1.0).flatMap { rowsNotMatchedFraction =>\n Seq(\n SingleInsertOnlyTestCase(\n filesMatchedFraction = 0.05,\n rowsNotMatchedFraction),\n\n MultipleInsertOnlyTestCase(\n filesMatchedFraction = 0.05,\n rowsNotMatchedFraction)\n )\n }\n\n def deleteOnlyTestCases: Seq[MergeTestCase] = Seq(\n DeleteOnlyTestCase(\n filesMatchedFraction = 0.05,\n rowsMatchedFraction = 0.05))\n\n def upsertTestCases: Seq[MergeTestCase] = Seq(\n Seq(0.0, 0.01, 0.1).map { rowsMatchedFraction =>\n UpsertTestCase(\n filesMatchedFraction = 0.05,\n rowsMatchedFraction,\n rowsNotMatchedFraction = 0.1)\n },\n\n Seq(0.5, 0.99, 1.0).map { rowsMatchedFraction =>\n UpsertTestCase(\n filesMatchedFraction = 0.05,\n rowsMatchedFraction,\n rowsNotMatchedFraction = 0.001)\n },\n\n Seq(\n UpsertTestCase(\n filesMatchedFraction = 0.05,\n rowsMatchedFraction = 0.1,\n rowsNotMatchedFraction = 0.0),\n\n UpsertTestCase(\n filesMatchedFraction = 0.5,\n rowsMatchedFraction = 0.01,\n rowsNotMatchedFraction = 0.001),\n\n UpsertTestCase(\n filesMatchedFraction = 1.0,\n rowsMatchedFraction = 0.01,\n rowsNotMatchedFraction = 0.001)\n )\n ).flatten\n}\n" }, { "path": "benchmarks/src/main/scala/benchmark/TPCDSBenchmark.scala", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage benchmark\n\nimport benchmark.TPCDSBenchmarkQueries._\n\ntrait TPCDSConf extends BenchmarkConf {\n protected def format: Option[String]\n def scaleInGB: Int\n def userDefinedDbName: Option[String]\n\n def formatName: String = format.getOrElse {\n throw new IllegalArgumentException(\"format must be specified\")\n }\n def dbName: String = userDefinedDbName.getOrElse(s\"tpcds_sf${scaleInGB}_${formatName}\")\n def dbLocation: String = dbLocation(dbName)\n}\n\ncase class TPCDSBenchmarkConf(\n protected val format: Option[String] = None,\n scaleInGB: Int = 0,\n userDefinedDbName: Option[String] = None,\n iterations: Int = 3,\n benchmarkPath: Option[String] = None) extends TPCDSConf\n\nobject TPCDSBenchmarkConf {\n import scopt.OParser\n private val builder = OParser.builder[TPCDSBenchmarkConf]\n private val argParser = {\n import builder._\n OParser.sequence(\n programName(\"TPC-DS Benchmark\"),\n opt[String](\"format\")\n .required()\n .action((x, c) => c.copy(format = Some(x)))\n .text(\"Spark's short name for the file format to use\"),\n opt[String](\"scale-in-gb\")\n .required()\n .valueName(\"\")\n .action((x, c) => c.copy(scaleInGB = x.toInt))\n .text(\"Scale factor of the TPCDS benchmark\"),\n opt[String](\"benchmark-path\")\n .required()\n .valueName(\"\")\n .action((x, c) => c.copy(benchmarkPath = Some(x)))\n .text(\"Cloud path to be used for creating table and generating reports\"),\n opt[String](\"iterations\")\n .optional()\n .valueName(\"\")\n .action((x, c) => c.copy(iterations = x.toInt))\n .text(\"Number of times to run the queries\"),\n )\n }\n\n def parse(args: Array[String]): Option[TPCDSBenchmarkConf] = {\n OParser.parse(argParser, args, TPCDSBenchmarkConf())\n }\n}\n\nclass TPCDSBenchmark(conf: TPCDSBenchmarkConf) extends Benchmark(conf) {\n val queries: Map[String, String] = {\n if (conf.scaleInGB <= 3000) TPCDSQueries3TB\n else if (conf.scaleInGB == 10) TPCDSQueries10TB\n else throw new IllegalArgumentException(\n s\"Unsupported scale factor of ${conf.scaleInGB} GB\")\n }\n\n val dbName = conf.dbName\n\n def runInternal(): Unit = {\n for ((k, v) <- extraConfs) spark.conf.set(k, v)\n spark.sparkContext.setLogLevel(\"WARN\")\n log(\"All configs:\\n\\t\" + spark.conf.getAll.toSeq.sortBy(_._1).mkString(\"\\n\\t\"))\n spark.sql(s\"USE $dbName\")\n for (iteration <- 1 to conf.iterations) {\n queries.toSeq.sortBy(_._1).foreach { case (name, sql) =>\n runQuery(sql, iteration = Some(iteration), queryName = name)\n }\n }\n val results = getQueryResults().filter(_.name.startsWith(\"q\"))\n if (results.forall(x => x.errorMsg.isEmpty && x.durationMs.nonEmpty) ) {\n val medianDurationSecPerQuery = results.groupBy(_.name).map { case (q, results) =>\n assert(results.size == conf.iterations)\n val medianMs = results.map(_.durationMs.get).sorted\n .drop(math.floor(conf.iterations / 2.0).toInt).head\n (q, medianMs / 1000.0)\n }\n val sumOfMedians = medianDurationSecPerQuery.map(_._2).sum\n reportExtraMetric(\"tpcds-result-seconds\", sumOfMedians)\n }\n }\n}\n\nobject TPCDSBenchmark {\n def main(args: Array[String]): Unit = {\n TPCDSBenchmarkConf.parse(args).foreach { conf =>\n new TPCDSBenchmark(conf).run()\n }\n }\n}\n\n" }, { "path": "benchmarks/src/main/scala/benchmark/TPCDSBenchmarkQueries.scala", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage benchmark\n\nobject TPCDSBenchmarkQueries {\n val TPCDSQueries3TB = Map(\n \"q1\" ->\n \"\"\"\nwith customer_total_return as\n(select sr_customer_sk as ctr_customer_sk\n,sr_store_sk as ctr_store_sk\n,sum(SR_FEE) as ctr_total_return\nfrom store_returns\n,date_dim\nwhere sr_returned_date_sk = d_date_sk\nand d_year =2000\ngroup by sr_customer_sk\n,sr_store_sk)\n select c_customer_id\nfrom customer_total_return ctr1\n,store\n,customer\nwhere ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\nfrom customer_total_return ctr2\nwhere ctr1.ctr_store_sk = ctr2.ctr_store_sk)\nand s_store_sk = ctr1.ctr_store_sk\nand s_state = 'TN'\nand ctr1.ctr_customer_sk = c_customer_sk\norder by c_customer_id\nlimit 100\"\"\",\n \"q2\" ->\n \"\"\"\nwith wscs as\n (select sold_date_sk\n ,sales_price\n from (select ws_sold_date_sk sold_date_sk\n ,ws_ext_sales_price sales_price\n from web_sales\n union all\n select cs_sold_date_sk sold_date_sk\n ,cs_ext_sales_price sales_price\n from catalog_sales)),\n wswscs as\n (select d_week_seq,\n sum(case when (d_day_name='Sunday') then sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then sales_price else null end) sat_sales\n from wscs\n ,date_dim\n where d_date_sk = sold_date_sk\n group by d_week_seq)\n select d_week_seq1\n ,round(sun_sales1/sun_sales2,2)\n ,round(mon_sales1/mon_sales2,2)\n ,round(tue_sales1/tue_sales2,2)\n ,round(wed_sales1/wed_sales2,2)\n ,round(thu_sales1/thu_sales2,2)\n ,round(fri_sales1/fri_sales2,2)\n ,round(sat_sales1/sat_sales2,2)\n from\n (select wswscs.d_week_seq d_week_seq1\n ,sun_sales sun_sales1\n ,mon_sales mon_sales1\n ,tue_sales tue_sales1\n ,wed_sales wed_sales1\n ,thu_sales thu_sales1\n ,fri_sales fri_sales1\n ,sat_sales sat_sales1\n from wswscs,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998) y,\n (select wswscs.d_week_seq d_week_seq2\n ,sun_sales sun_sales2\n ,mon_sales mon_sales2\n ,tue_sales tue_sales2\n ,wed_sales wed_sales2\n ,thu_sales thu_sales2\n ,fri_sales fri_sales2\n ,sat_sales sat_sales2\n from wswscs\n ,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998+1) z\n where d_week_seq1=d_week_seq2-53\n order by d_week_seq1\"\"\",\n \"q3\" ->\n \"\"\"\nselect dt.d_year\n ,item.i_brand_id brand_id\n ,item.i_brand brand\n ,sum(ss_sales_price) sum_agg\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manufact_id = 816\n and dt.d_moy=11\n group by dt.d_year\n ,item.i_brand\n ,item.i_brand_id\n order by dt.d_year\n ,sum_agg desc\n ,brand_id\n limit 100\"\"\",\n \"q4\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(((ss_ext_list_price-ss_ext_wholesale_cost-ss_ext_discount_amt)+ss_ext_sales_price)/2) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((cs_ext_list_price-cs_ext_wholesale_cost-cs_ext_discount_amt)+cs_ext_sales_price)/2) ) year_total\n ,'c' sale_type\n from customer\n ,catalog_sales\n ,date_dim\n where c_customer_sk = cs_bill_customer_sk\n and cs_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\nunion all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((ws_ext_list_price-ws_ext_wholesale_cost-ws_ext_discount_amt)+ws_ext_sales_price)/2) ) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_c_firstyear\n ,year_total t_c_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_c_secyear.customer_id\n and t_s_firstyear.customer_id = t_c_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_c_firstyear.sale_type = 'c'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_c_secyear.sale_type = 'c'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 1999\n and t_s_secyear.dyear = 1999+1\n and t_c_firstyear.dyear = 1999\n and t_c_secyear.dyear = 1999+1\n and t_w_firstyear.dyear = 1999\n and t_w_secyear.dyear = 1999+1\n and t_s_firstyear.year_total > 0\n and t_c_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\nlimit 100\"\"\",\n \"q5\" ->\n \"\"\"\nwith ssr as\n (select s_store_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ss_store_sk as store_sk,\n ss_sold_date_sk as date_sk,\n ss_ext_sales_price as sales_price,\n ss_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from store_sales\n union all\n select sr_store_sk as store_sk,\n sr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n sr_return_amt as return_amt,\n sr_net_loss as net_loss\n from store_returns\n ) salesreturns,\n date_dim,\n store\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and store_sk = s_store_sk\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select cs_catalog_page_sk as page_sk,\n cs_sold_date_sk as date_sk,\n cs_ext_sales_price as sales_price,\n cs_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from catalog_sales\n union all\n select cr_catalog_page_sk as page_sk,\n cr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n cr_return_amount as return_amt,\n cr_net_loss as net_loss\n from catalog_returns\n ) salesreturns,\n date_dim,\n catalog_page\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and page_sk = cp_catalog_page_sk\n group by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ws_web_site_sk as wsr_web_site_sk,\n ws_sold_date_sk as date_sk,\n ws_ext_sales_price as sales_price,\n ws_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from web_sales\n union all\n select ws_web_site_sk as wsr_web_site_sk,\n wr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n wr_return_amt as return_amt,\n wr_net_loss as net_loss\n from web_returns left outer join web_sales on\n ( wr_item_sk = ws_item_sk\n and wr_order_number = ws_order_number)\n ) salesreturns,\n date_dim,\n web_site\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and wsr_web_site_sk = web_site_sk\n group by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || s_store_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || cp_catalog_page_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q6\" ->\n \"\"\"\nselect a.ca_state state, count(*) cnt\n from customer_address a\n ,customer c\n ,store_sales s\n ,date_dim d\n ,item i\n where a.ca_address_sk = c.c_current_addr_sk\n \tand c.c_customer_sk = s.ss_customer_sk\n \tand s.ss_sold_date_sk = d.d_date_sk\n \tand s.ss_item_sk = i.i_item_sk\n \tand d.d_month_seq =\n \t (select distinct (d_month_seq)\n \t from date_dim\n where d_year = 2002\n \t and d_moy = 3 )\n \tand i.i_current_price > 1.2 *\n (select avg(j.i_current_price)\n \t from item j\n \t where j.i_category = i.i_category)\n group by a.ca_state\n having count(*) >= 10\n order by cnt, a.ca_state\n limit 100\"\"\",\n \"q7\" ->\n \"\"\"\nselect i_item_id,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, item, promotion\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_cdemo_sk = cd_demo_sk and\n ss_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'W' and\n cd_education_status = 'College' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 2001\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q8\" ->\n \"\"\"\nselect s_store_name\n ,sum(ss_net_profit)\n from store_sales\n ,date_dim\n ,store,\n (select ca_zip\n from (\n SELECT substr(ca_zip,1,5) ca_zip\n FROM customer_address\n WHERE substr(ca_zip,1,5) IN (\n '47602','16704','35863','28577','83910','36201',\n '58412','48162','28055','41419','80332',\n '38607','77817','24891','16226','18410',\n '21231','59345','13918','51089','20317',\n '17167','54585','67881','78366','47770',\n '18360','51717','73108','14440','21800',\n '89338','45859','65501','34948','25973',\n '73219','25333','17291','10374','18829',\n '60736','82620','41351','52094','19326',\n '25214','54207','40936','21814','79077',\n '25178','75742','77454','30621','89193',\n '27369','41232','48567','83041','71948',\n '37119','68341','14073','16891','62878',\n '49130','19833','24286','27700','40979',\n '50412','81504','94835','84844','71954',\n '39503','57649','18434','24987','12350',\n '86379','27413','44529','98569','16515',\n '27287','24255','21094','16005','56436',\n '91110','68293','56455','54558','10298',\n '83647','32754','27052','51766','19444',\n '13869','45645','94791','57631','20712',\n '37788','41807','46507','21727','71836',\n '81070','50632','88086','63991','20244',\n '31655','51782','29818','63792','68605',\n '94898','36430','57025','20601','82080',\n '33869','22728','35834','29086','92645',\n '98584','98072','11652','78093','57553',\n '43830','71144','53565','18700','90209',\n '71256','38353','54364','28571','96560',\n '57839','56355','50679','45266','84680',\n '34306','34972','48530','30106','15371',\n '92380','84247','92292','68852','13338',\n '34594','82602','70073','98069','85066',\n '47289','11686','98862','26217','47529',\n '63294','51793','35926','24227','14196',\n '24594','32489','99060','49472','43432',\n '49211','14312','88137','47369','56877',\n '20534','81755','15794','12318','21060',\n '73134','41255','63073','81003','73873',\n '66057','51184','51195','45676','92696',\n '70450','90669','98338','25264','38919',\n '59226','58581','60298','17895','19489',\n '52301','80846','95464','68770','51634',\n '19988','18367','18421','11618','67975',\n '25494','41352','95430','15734','62585',\n '97173','33773','10425','75675','53535',\n '17879','41967','12197','67998','79658',\n '59130','72592','14851','43933','68101',\n '50636','25717','71286','24660','58058',\n '72991','95042','15543','33122','69280',\n '11912','59386','27642','65177','17672',\n '33467','64592','36335','54010','18767',\n '63193','42361','49254','33113','33159',\n '36479','59080','11855','81963','31016',\n '49140','29392','41836','32958','53163',\n '13844','73146','23952','65148','93498',\n '14530','46131','58454','13376','13378',\n '83986','12320','17193','59852','46081',\n '98533','52389','13086','68843','31013',\n '13261','60560','13443','45533','83583',\n '11489','58218','19753','22911','25115',\n '86709','27156','32669','13123','51933',\n '39214','41331','66943','14155','69998',\n '49101','70070','35076','14242','73021',\n '59494','15782','29752','37914','74686',\n '83086','34473','15751','81084','49230',\n '91894','60624','17819','28810','63180',\n '56224','39459','55233','75752','43639',\n '55349','86057','62361','50788','31830',\n '58062','18218','85761','60083','45484',\n '21204','90229','70041','41162','35390',\n '16364','39500','68908','26689','52868',\n '81335','40146','11340','61527','61794',\n '71997','30415','59004','29450','58117',\n '69952','33562','83833','27385','61860',\n '96435','48333','23065','32961','84919',\n '61997','99132','22815','56600','68730',\n '48017','95694','32919','88217','27116',\n '28239','58032','18884','16791','21343',\n '97462','18569','75660','15475')\n intersect\n select ca_zip\n from (SELECT substr(ca_zip,1,5) ca_zip,count(*) cnt\n FROM customer_address, customer\n WHERE ca_address_sk = c_current_addr_sk and\n c_preferred_cust_flag='Y'\n group by ca_zip\n having count(*) > 10)A1)A2) V1\n where ss_store_sk = s_store_sk\n and ss_sold_date_sk = d_date_sk\n and d_qoy = 2 and d_year = 1998\n and (substr(s_zip,1,2) = substr(V1.ca_zip,1,2))\n group by s_store_name\n order by s_store_name\n limit 100\"\"\",\n \"q9\" ->\n \"\"\"\nselect case when (select count(*)\n from store_sales\n where ss_quantity between 1 and 20) > 2972190\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 1 and 20)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 1 and 20) end bucket1 ,\n case when (select count(*)\n from store_sales\n where ss_quantity between 21 and 40) > 111711138\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 21 and 40)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 21 and 40) end bucket2,\n case when (select count(*)\n from store_sales\n where ss_quantity between 41 and 60) > 127958920\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 41 and 60)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 41 and 60) end bucket3,\n case when (select count(*)\n from store_sales\n where ss_quantity between 61 and 80) > 41162107\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 61 and 80)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 61 and 80) end bucket4,\n case when (select count(*)\n from store_sales\n where ss_quantity between 81 and 100) > 25211875\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 81 and 100)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 81 and 100) end bucket5\nfrom reason\nwhere r_reason_sk = 1\"\"\",\n \"q10\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3,\n cd_dep_count,\n count(*) cnt4,\n cd_dep_employed_count,\n count(*) cnt5,\n cd_dep_college_count,\n count(*) cnt6\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_county in ('Allen County','Jefferson County','Lamar County','Dakota County','Park County') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_moy between 4 and 4+3) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_moy between 4 ANd 4+3) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_moy between 4 and 4+3))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\nlimit 100\"\"\",\n \"q11\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ss_ext_list_price-ss_ext_discount_amt) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ws_ext_list_price-ws_ext_discount_amt) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_login\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 1998\n and t_s_secyear.dyear = 1998+1\n and t_w_firstyear.dyear = 1998\n and t_w_secyear.dyear = 1998+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else 0.0 end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else 0.0 end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_login\nlimit 100\"\"\",\n \"q12\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ws_ext_sales_price) as itemrevenue\n ,sum(ws_ext_sales_price)*100/sum(sum(ws_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tweb_sales\n \t,item\n \t,date_dim\nwhere\n\tws_item_sk = i_item_sk\n \tand i_category in ('Men', 'Books', 'Children')\n \tand ws_sold_date_sk = d_date_sk\n\tand d_date between cast('1998-03-28' as date)\n\t\t\t\tand (cast('1998-03-28' as date) + INTERVAL 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q13\" ->\n \"\"\"\nselect avg(ss_quantity)\n ,avg(ss_ext_sales_price)\n ,avg(ss_ext_wholesale_cost)\n ,sum(ss_ext_wholesale_cost)\n from store_sales\n ,store\n ,customer_demographics\n ,household_demographics\n ,customer_address\n ,date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 2001\n and((ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'U'\n and cd_education_status = 'Unknown'\n and ss_sales_price between 100.00 and 150.00\n and hd_dep_count = 3\n )or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'W'\n and cd_education_status = '2 yr Degree'\n and ss_sales_price between 50.00 and 100.00\n and hd_dep_count = 1\n ) or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'S'\n and cd_education_status = 'College'\n and ss_sales_price between 150.00 and 200.00\n and hd_dep_count = 1\n ))\n and((ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('WV', 'GA', 'TX')\n and ss_net_profit between 100 and 200\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('TN', 'KY', 'SC')\n and ss_net_profit between 150 and 300\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('OK', 'NE', 'CA')\n and ss_net_profit between 50 and 250\n ))\"\"\",\n \"q14a\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 1998 AND 1998 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 1998 AND 1998 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 1998 AND 1998 + 2)\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n (select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2) x)\n select channel, i_brand_id,i_class_id,i_category_id,sum(sales), sum(number_sales)\n from(\n select 'store' channel, i_brand_id,i_class_id\n ,i_category_id,sum(ss_quantity*ss_list_price) sales\n , count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1998+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)\n union all\n select 'catalog' channel, i_brand_id,i_class_id,i_category_id, sum(cs_quantity*cs_list_price) sales, count(*) number_sales\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk in (select ss_item_sk from cross_items)\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1998+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(cs_quantity*cs_list_price) > (select average_sales from avg_sales)\n union all\n select 'web' channel, i_brand_id,i_class_id,i_category_id, sum(ws_quantity*ws_list_price) sales , count(*) number_sales\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk in (select ss_item_sk from cross_items)\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1998+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ws_quantity*ws_list_price) > (select average_sales from avg_sales)\n ) y\n group by rollup (channel, i_brand_id,i_class_id,i_category_id)\n order by channel,i_brand_id,i_class_id,i_category_id\n limit 100\"\"\",\n \"q14b\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 1998 AND 1998 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 1998 AND 1998 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 1998 AND 1998 + 2) x\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n(select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2) x)\n select this_year.channel ty_channel\n ,this_year.i_brand_id ty_brand\n ,this_year.i_class_id ty_class\n ,this_year.i_category_id ty_category\n ,this_year.sales ty_sales\n ,this_year.number_sales ty_number_sales\n ,last_year.channel ly_channel\n ,last_year.i_brand_id ly_brand\n ,last_year.i_class_id ly_class\n ,last_year.i_category_id ly_category\n ,last_year.sales ly_sales\n ,last_year.number_sales ly_number_sales\n from\n (select 'store' channel, i_brand_id,i_class_id,i_category_id\n ,sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 1998 + 1\n and d_moy = 12\n and d_dom = 20)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) this_year,\n (select 'store' channel, i_brand_id,i_class_id\n ,i_category_id, sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 1998\n and d_moy = 12\n and d_dom = 20)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) last_year\n where this_year.i_brand_id= last_year.i_brand_id\n and this_year.i_class_id = last_year.i_class_id\n and this_year.i_category_id = last_year.i_category_id\n order by this_year.channel, this_year.i_brand_id, this_year.i_class_id, this_year.i_category_id\n limit 100\"\"\",\n \"q15\" ->\n \"\"\"\nselect ca_zip\n ,sum(cs_sales_price)\n from catalog_sales\n ,customer\n ,customer_address\n ,date_dim\n where cs_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475',\n '85392', '85460', '80348', '81792')\n \t or ca_state in ('CA','WA','GA')\n \t or cs_sales_price > 500)\n \tand cs_sold_date_sk = d_date_sk\n \tand d_qoy = 1 and d_year = 2000\n group by ca_zip\n order by ca_zip\n limit 100\"\"\",\n \"q16\" ->\n \"\"\"\nselect\n count(distinct cs_order_number) as `order count`\n ,sum(cs_ext_ship_cost) as `total shipping cost`\n ,sum(cs_net_profit) as `total net profit`\nfrom\n catalog_sales cs1\n ,date_dim\n ,customer_address\n ,call_center\nwhere\n d_date between '2001-2-01' and\n (cast('2001-2-01' as date) + INTERVAL 60 days)\nand cs1.cs_ship_date_sk = d_date_skq\nand cs1.cs_ship_addr_sk = ca_address_sk\nand ca_state = 'MS'\nand cs1.cs_call_center_sk = cc_call_center_sk\nand cc_county in ('Jackson County','Daviess County','Walker County','Dauphin County',\n 'Mobile County'\n)\nand exists (select *\n from catalog_sales cs2\n where cs1.cs_order_number = cs2.cs_order_number\n and cs1.cs_warehouse_sk <> cs2.cs_warehouse_sk)\nand not exists(select *\n from catalog_returns cr1\n where cs1.cs_order_number = cr1.cr_order_number)\norder by count(distinct cs_order_number)\nlimit 100\"\"\",\n \"q17\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,s_state\n ,count(ss_quantity) as store_sales_quantitycount\n ,avg(ss_quantity) as store_sales_quantityave\n ,stddev_samp(ss_quantity) as store_sales_quantitystdev\n ,stddev_samp(ss_quantity)/avg(ss_quantity) as store_sales_quantitycov\n ,count(sr_return_quantity) as store_returns_quantitycount\n ,avg(sr_return_quantity) as store_returns_quantityave\n ,stddev_samp(sr_return_quantity) as store_returns_quantitystdev\n ,stddev_samp(sr_return_quantity)/avg(sr_return_quantity) as store_returns_quantitycov\n ,count(cs_quantity) as catalog_sales_quantitycount ,avg(cs_quantity) as catalog_sales_quantityave\n ,stddev_samp(cs_quantity) as catalog_sales_quantitystdev\n ,stddev_samp(cs_quantity)/avg(cs_quantity) as catalog_sales_quantitycov\n from store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where d1.d_quarter_name = '1999Q1'\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_quarter_name in ('1999Q1','1999Q2','1999Q3')\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_quarter_name in ('1999Q1','1999Q2','1999Q3')\n group by i_item_id\n ,i_item_desc\n ,s_state\n order by i_item_id\n ,i_item_desc\n ,s_state\nlimit 100\"\"\",\n \"q18\" ->\n \"\"\"\nselect i_item_id,\n ca_country,\n ca_state,\n ca_county,\n avg( cast(cs_quantity as decimal(12,2))) agg1,\n avg( cast(cs_list_price as decimal(12,2))) agg2,\n avg( cast(cs_coupon_amt as decimal(12,2))) agg3,\n avg( cast(cs_sales_price as decimal(12,2))) agg4,\n avg( cast(cs_net_profit as decimal(12,2))) agg5,\n avg( cast(c_birth_year as decimal(12,2))) agg6,\n avg( cast(cd1.cd_dep_count as decimal(12,2))) agg7\n from catalog_sales, customer_demographics cd1,\n customer_demographics cd2, customer, customer_address, date_dim, item\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd1.cd_demo_sk and\n cs_bill_customer_sk = c_customer_sk and\n cd1.cd_gender = 'F' and\n cd1.cd_education_status = 'Primary' and\n c_current_cdemo_sk = cd2.cd_demo_sk and\n c_current_addr_sk = ca_address_sk and\n c_birth_month in (6,7,3,11,12,8) and\n d_year = 1999 and\n ca_state in ('IL','WV','KS'\n ,'GA','LA','PA','TX')\n group by rollup (i_item_id, ca_country, ca_state, ca_county)\n order by ca_country,\n ca_state,\n ca_county,\n\ti_item_id\n limit 100\"\"\",\n \"q19\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand, i_manufact_id, i_manufact,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item,customer,customer_address,store\n where d_date_sk = ss_sold_date_sk\n and ss_item_sk = i_item_sk\n and i_manager_id=26\n and d_moy=12\n and d_year=2000\n and ss_customer_sk = c_customer_sk\n and c_current_addr_sk = ca_address_sk\n and substr(ca_zip,1,5) <> substr(s_zip,1,5)\n and ss_store_sk = s_store_sk\n group by i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\n order by ext_price desc\n ,i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\nlimit 100 \"\"\",\n \"q20\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(cs_ext_sales_price) as itemrevenue\n ,sum(cs_ext_sales_price)*100/sum(sum(cs_ext_sales_price)) over\n (partition by i_class) as revenueratio\n from\tcatalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and i_category in ('Books', 'Home', 'Jewelry')\n and cs_sold_date_sk = d_date_sk\n and d_date between cast('1998-05-08' as date)\n \t\t\t\tand (cast('1998-05-08' as date) + INTERVAL 30 days)\n group by i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n order by i_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q21\" ->\n \"\"\"\nselect *\n from(select w_warehouse_name\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('2000-05-22' as date))\n\t then inv_quantity_on_hand\n else 0 end) as inv_before\n ,sum(case when (cast(d_date as date) >= cast ('2000-05-22' as date))\n then inv_quantity_on_hand\n else 0 end) as inv_after\n from inventory\n ,warehouse\n ,item\n ,date_dim\n where i_current_price between 0.99 and 1.49\n and i_item_sk = inv_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_date between (cast ('2000-05-22' as date) - INTERVAL 30 days)\n and (cast ('2000-05-22' as date) + INTERVAL 30 days)\n group by w_warehouse_name, i_item_id) x\n where (case when inv_before > 0\n then inv_after / inv_before\n else null\n end) between 2.0/3.0 and 3.0/2.0\n order by w_warehouse_name\n ,i_item_id\n limit 100\"\"\",\n \"q22\" ->\n \"\"\"\nselect i_product_name\n ,i_brand\n ,i_class\n ,i_category\n ,avg(inv_quantity_on_hand) qoh\n from inventory\n ,date_dim\n ,item\n where inv_date_sk=d_date_sk\n and inv_item_sk=i_item_sk\n and d_month_seq between 1199 and 1199 + 11\n group by rollup(i_product_name\n ,i_brand\n ,i_class\n ,i_category)\norder by qoh, i_product_name, i_brand, i_class, i_category\nlimit 100\"\"\",\n \"q23a\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\nfrom\n max_store_sales))\n select sum(sales)\n from (select cs_quantity*cs_list_price sales\n from catalog_sales\n ,date_dim\n where d_year = 2000\n and d_moy = 5\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n union all\n select ws_quantity*ws_list_price sales\n from web_sales\n ,date_dim\n where d_year = 2000\n and d_moy = 5\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer))\n limit 100\"\"\",\n \"q23b\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (2000,2000 + 1,2000 + 2,2000 + 3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\n from max_store_sales))\n select c_last_name,c_first_name,sales\n from (select c_last_name,c_first_name,sum(cs_quantity*cs_list_price) sales\n from catalog_sales\n ,customer\n ,date_dim\n where d_year = 2000\n and d_moy = 5\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and cs_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name\n union all\n select c_last_name,c_first_name,sum(ws_quantity*ws_list_price) sales\n from web_sales\n ,customer\n ,date_dim\n where d_year = 2000\n and d_moy = 5\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and ws_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name)\n order by c_last_name,c_first_name,sales\n limit 100\"\"\",\n \"q24a\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_net_paid_inc_tax) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\nand s_market_id=10\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'navy'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q24b\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_net_paid_inc_tax) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\n and s_market_id = 10\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'beige'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q25\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,sum(ss_net_profit) as store_sales_profit\n ,sum(sr_net_loss) as store_returns_loss\n ,sum(cs_net_profit) as catalog_sales_profit\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 2002\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 10\n and d2.d_year = 2002\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_moy between 4 and 10\n and d3.d_year = 2002\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q26\" ->\n \"\"\"\nselect i_item_id,\n avg(cs_quantity) agg1,\n avg(cs_list_price) agg2,\n avg(cs_coupon_amt) agg3,\n avg(cs_sales_price) agg4\n from catalog_sales, customer_demographics, date_dim, item, promotion\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd_demo_sk and\n cs_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'M' and\n cd_education_status = '2 yr Degree' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 2002\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q27\" ->\n \"\"\"\nselect i_item_id,\n s_state, grouping(s_state) g_state,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, store, item\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_store_sk = s_store_sk and\n ss_cdemo_sk = cd_demo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'S' and\n cd_education_status = 'Advanced Degree' and\n d_year = 2000 and\n s_state in ('WA','LA', 'LA', 'TX', 'AL', 'PA')\n group by rollup (i_item_id, s_state)\n order by i_item_id\n ,s_state\n limit 100\"\"\",\n \"q28\" ->\n \"\"\"\nselect *\nfrom (select avg(ss_list_price) B1_LP\n ,count(ss_list_price) B1_CNT\n ,count(distinct ss_list_price) B1_CNTD\n from store_sales\n where ss_quantity between 0 and 5\n and (ss_list_price between 189 and 189+10\n or ss_coupon_amt between 4483 and 4483+1000\n or ss_wholesale_cost between 24 and 24+20)) B1,\n (select avg(ss_list_price) B2_LP\n ,count(ss_list_price) B2_CNT\n ,count(distinct ss_list_price) B2_CNTD\n from store_sales\n where ss_quantity between 6 and 10\n and (ss_list_price between 71 and 71+10\n or ss_coupon_amt between 14775 and 14775+1000\n or ss_wholesale_cost between 38 and 38+20)) B2,\n (select avg(ss_list_price) B3_LP\n ,count(ss_list_price) B3_CNT\n ,count(distinct ss_list_price) B3_CNTD\n from store_sales\n where ss_quantity between 11 and 15\n and (ss_list_price between 183 and 183+10\n or ss_coupon_amt between 13456 and 13456+1000\n or ss_wholesale_cost between 31 and 31+20)) B3,\n (select avg(ss_list_price) B4_LP\n ,count(ss_list_price) B4_CNT\n ,count(distinct ss_list_price) B4_CNTD\n from store_sales\n where ss_quantity between 16 and 20\n and (ss_list_price between 135 and 135+10\n or ss_coupon_amt between 4905 and 4905+1000\n or ss_wholesale_cost between 27 and 27+20)) B4,\n (select avg(ss_list_price) B5_LP\n ,count(ss_list_price) B5_CNT\n ,count(distinct ss_list_price) B5_CNTD\n from store_sales\n where ss_quantity between 21 and 25\n and (ss_list_price between 180 and 180+10\n or ss_coupon_amt between 17430 and 17430+1000\n or ss_wholesale_cost between 57 and 57+20)) B5,\n (select avg(ss_list_price) B6_LP\n ,count(ss_list_price) B6_CNT\n ,count(distinct ss_list_price) B6_CNTD\n from store_sales\n where ss_quantity between 26 and 30\n and (ss_list_price between 49 and 49+10\n or ss_coupon_amt between 2950 and 2950+1000\n or ss_wholesale_cost between 52 and 52+20)) B6\nlimit 100\"\"\",\n \"q29\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,stddev_samp(ss_quantity) as store_sales_quantity\n ,stddev_samp(sr_return_quantity) as store_returns_quantity\n ,stddev_samp(cs_quantity) as catalog_sales_quantity\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 1998\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 4 + 3\n and d2.d_year = 1998\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_year in (1998,1998+1,1998+2)\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q30\" ->\n \"\"\"\nwith customer_total_return as\n (select wr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(wr_return_amt) as ctr_total_return\n from web_returns\n ,date_dim\n ,customer_address\n where wr_returned_date_sk = d_date_sk\n and d_year =2000\n and wr_returning_addr_sk = ca_address_sk\n group by wr_returning_customer_sk\n ,ca_state)\n select c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'GA'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\nlimit 100\"\"\",\n \"q31\" ->\n \"\"\"\nwith ss as\n (select ca_county,d_qoy, d_year,sum(ss_ext_sales_price) as store_sales\n from store_sales,date_dim,customer_address\n where ss_sold_date_sk = d_date_sk\n and ss_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year),\n ws as\n (select ca_county,d_qoy, d_year,sum(ws_ext_sales_price) as web_sales\n from web_sales,date_dim,customer_address\n where ws_sold_date_sk = d_date_sk\n and ws_bill_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year)\n select\n ss1.ca_county\n ,ss1.d_year\n ,ws2.web_sales/ws1.web_sales web_q1_q2_increase\n ,ss2.store_sales/ss1.store_sales store_q1_q2_increase\n ,ws3.web_sales/ws2.web_sales web_q2_q3_increase\n ,ss3.store_sales/ss2.store_sales store_q2_q3_increase\n from\n ss ss1\n ,ss ss2\n ,ss ss3\n ,ws ws1\n ,ws ws2\n ,ws ws3\n where\n ss1.d_qoy = 1\n and ss1.d_year = 1998\n and ss1.ca_county = ss2.ca_county\n and ss2.d_qoy = 2\n and ss2.d_year = 1998\n and ss2.ca_county = ss3.ca_county\n and ss3.d_qoy = 3\n and ss3.d_year = 1998\n and ss1.ca_county = ws1.ca_county\n and ws1.d_qoy = 1\n and ws1.d_year = 1998\n and ws1.ca_county = ws2.ca_county\n and ws2.d_qoy = 2\n and ws2.d_year = 1998\n and ws1.ca_county = ws3.ca_county\n and ws3.d_qoy = 3\n and ws3.d_year =1998\n and case when ws1.web_sales > 0 then ws2.web_sales/ws1.web_sales else null end\n > case when ss1.store_sales > 0 then ss2.store_sales/ss1.store_sales else null end\n and case when ws2.web_sales > 0 then ws3.web_sales/ws2.web_sales else null end\n > case when ss2.store_sales > 0 then ss3.store_sales/ss2.store_sales else null end\n order by ss1.ca_county\"\"\",\n \"q32\" ->\n \"\"\"\nselect sum(cs_ext_discount_amt) as `excess discount amount`\nfrom\n catalog_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 948\nand i_item_sk = cs_item_sk\nand d_date between '1998-02-03' and\n (cast('1998-02-03' as date) + INTERVAL 90 days)\nand d_date_sk = cs_sold_date_sk\nand cs_ext_discount_amt\n > (\n select\n 1.3 * avg(cs_ext_discount_amt)\n from\n catalog_sales\n ,date_dim\n where\n cs_item_sk = i_item_sk\n and d_date between '1998-02-03' and\n (cast('1998-02-03' as date) + INTERVAL 90 days)\n and d_date_sk = cs_sold_date_sk\n )\nlimit 100\"\"\",\n \"q33\" ->\n \"\"\"\nwith ss as (\n select\n i_manufact_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Electronics'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 2\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id),\n cs as (\n select\n i_manufact_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Electronics'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 2\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id),\n ws as (\n select\n i_manufact_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Electronics'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 2\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id)\n select i_manufact_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_manufact_id\n order by total_sales\nlimit 100\"\"\",\n \"q34\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (date_dim.d_dom between 1 and 3 or date_dim.d_dom between 25 and 28)\n and (household_demographics.hd_buy_potential = '>10000' or\n household_demographics.hd_buy_potential = '5001-10000')\n and household_demographics.hd_vehicle_count > 0\n and (case when household_demographics.hd_vehicle_count > 0\n\tthen household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count\n\telse null\n\tend) > 1.2\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_county in ('Jefferson Davis Parish','Levy County','Coal County','Oglethorpe County',\n 'Mobile County','Gage County','Richland County','Gogebic County')\n group by ss_ticket_number,ss_customer_sk) dn,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 15 and 20\n order by c_last_name,c_first_name,c_salutation,c_preferred_cust_flag desc, ss_ticket_number\"\"\",\n \"q35\" ->\n \"\"\"\nselect\n ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n count(*) cnt1,\n stddev_samp(cd_dep_count),\n stddev_samp(cd_dep_count),\n min(cd_dep_count),\n cd_dep_employed_count,\n count(*) cnt2,\n stddev_samp(cd_dep_employed_count),\n stddev_samp(cd_dep_employed_count),\n min(cd_dep_employed_count),\n cd_dep_college_count,\n count(*) cnt3,\n stddev_samp(cd_dep_college_count),\n stddev_samp(cd_dep_college_count),\n min(cd_dep_college_count)\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2002 and\n d_qoy < 4) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2002 and\n d_qoy < 4) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2002 and\n d_qoy < 4))\n group by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n limit 100\"\"\",\n \"q36\" ->\n \"\"\"\nselect\n sum(ss_net_profit)/sum(ss_ext_sales_price) as gross_margin\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ss_net_profit)/sum(ss_ext_sales_price) asc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,item\n ,store\n where\n d1.d_year = 1998\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and s_state in ('OH','WV','PA','TN',\n 'MN','MO','NM','MI')\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then i_category end\n ,rank_within_parent\n limit 100\"\"\",\n \"q37\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, catalog_sales\n where i_current_price between 35 and 35 + 30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2001-01-20' as date) and (cast('2001-01-20' as date) + interval 60 days)\n and i_manufact_id in (928,715,942,861)\n and inv_quantity_on_hand between 100 and 500\n and cs_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q38\" ->\n \"\"\"\nselect count(*) from (\n select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1222 and 1222 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1222 and 1222 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1222 and 1222 + 11\n) hot_cust\nlimit 100\"\"\",\n \"q39a\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =1998\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=4\n and inv2.d_moy=4+1\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q39b\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =1998\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=4\n and inv2.d_moy=4+1\n and inv1.cov > 1.5\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q40\" ->\n \"\"\"\nselect\n w_state\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('1999-02-02' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_before\n ,sum(case when (cast(d_date as date) >= cast ('1999-02-02' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_after\n from\n catalog_sales left outer join catalog_returns on\n (cs_order_number = cr_order_number\n and cs_item_sk = cr_item_sk)\n ,warehouse\n ,item\n ,date_dim\n where\n i_current_price between 0.99 and 1.49\n and i_item_sk = cs_item_sk\n and cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and d_date between (cast ('1999-02-02' as date) - INTERVAL 30 days)\n and (cast ('1999-02-02' as date) + INTERVAL 30 days)\n group by\n w_state,i_item_id\n order by w_state,i_item_id\nlimit 100\"\"\",\n \"q41\" ->\n \"\"\"\nselect distinct(i_product_name)\n from item i1\n where i_manufact_id between 732 and 732+40\n and (select count(*) as item_cnt\n from item\n where (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'beige' or i_color = 'spring') and\n (i_units = 'Tsp' or i_units = 'Ton') and\n (i_size = 'petite' or i_size = 'extra large')\n ) or\n (i_category = 'Women' and\n (i_color = 'white' or i_color = 'pale') and\n (i_units = 'Box' or i_units = 'Dram') and\n (i_size = 'large' or i_size = 'economy')\n ) or\n (i_category = 'Men' and\n (i_color = 'midnight' or i_color = 'frosted') and\n (i_units = 'Bunch' or i_units = 'Carton') and\n (i_size = 'small' or i_size = 'N/A')\n ) or\n (i_category = 'Men' and\n (i_color = 'azure' or i_color = 'goldenrod') and\n (i_units = 'Pallet' or i_units = 'Gross') and\n (i_size = 'petite' or i_size = 'extra large')\n ))) or\n (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'brown' or i_color = 'hot') and\n (i_units = 'Tbl' or i_units = 'Cup') and\n (i_size = 'petite' or i_size = 'extra large')\n ) or\n (i_category = 'Women' and\n (i_color = 'powder' or i_color = 'honeydew') and\n (i_units = 'Bundle' or i_units = 'Unknown') and\n (i_size = 'large' or i_size = 'economy')\n ) or\n (i_category = 'Men' and\n (i_color = 'antique' or i_color = 'purple') and\n (i_units = 'N/A' or i_units = 'Dozen') and\n (i_size = 'small' or i_size = 'N/A')\n ) or\n (i_category = 'Men' and\n (i_color = 'lavender' or i_color = 'tomato') and\n (i_units = 'Lb' or i_units = 'Oz') and\n (i_size = 'petite' or i_size = 'extra large')\n )))) > 0\n order by i_product_name\n limit 100\"\"\",\n \"q42\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_category_id\n \t,item.i_category\n \t,sum(ss_ext_sales_price)\n from \tdate_dim dt\n \t,store_sales\n \t,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n \tand store_sales.ss_item_sk = item.i_item_sk\n \tand item.i_manager_id = 1\n \tand dt.d_moy=11\n \tand dt.d_year=2002\n group by \tdt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\n order by sum(ss_ext_sales_price) desc,dt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\nlimit 100 \"\"\",\n \"q43\" ->\n \"\"\"\nselect s_store_name, s_store_id,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from date_dim, store_sales, store\n where d_date_sk = ss_sold_date_sk and\n s_store_sk = ss_store_sk and\n s_gmt_offset = -6 and\n d_year = 1999\n group by s_store_name, s_store_id\n order by s_store_name, s_store_id,sun_sales,mon_sales,tue_sales,wed_sales,thu_sales,fri_sales,sat_sales\n limit 100\"\"\",\n \"q44\" ->\n \"\"\"\nselect asceding.rnk, i1.i_product_name best_performing, i2.i_product_name worst_performing\nfrom(select *\n from (select item_sk,rank() over (order by rank_col asc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 321\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 321\n and ss_addr_sk is null\n group by ss_store_sk))V1)V11\n where rnk < 11) asceding,\n (select *\n from (select item_sk,rank() over (order by rank_col desc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 321\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 321\n and ss_addr_sk is null\n group by ss_store_sk))V2)V21\n where rnk < 11) descending,\nitem i1,\nitem i2\nwhere asceding.rnk = descending.rnk\n and i1.i_item_sk=asceding.item_sk\n and i2.i_item_sk=descending.item_sk\norder by asceding.rnk\nlimit 100\"\"\",\n \"q45\" ->\n \"\"\"\nselect ca_zip, ca_county, sum(ws_sales_price)\n from web_sales, customer, customer_address, date_dim, item\n where ws_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ws_item_sk = i_item_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475', '85392', '85460', '80348', '81792')\n \t or\n \t i_item_id in (select i_item_id\n from item\n where i_item_sk in (2, 3, 5, 7, 11, 13, 17, 19, 23, 29)\n )\n \t )\n \tand ws_sold_date_sk = d_date_sk\n \tand d_qoy = 2 and d_year = 1999\n group by ca_zip, ca_county\n order by ca_zip, ca_county\n limit 100\"\"\",\n \"q46\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and (household_demographics.hd_dep_count = 2 or\n household_demographics.hd_vehicle_count= 2)\n and date_dim.d_dow in (6,0)\n and date_dim.d_year in (1998,1998+1,1998+2)\n and store.s_city in ('Antioch','Mount Vernon','Jamestown','Wilson','Farmington')\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,ca_city) dn,customer,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n limit 100\"\"\",\n \"q47\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand,\n s_store_name, s_company_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n s_store_name, s_company_name\n order by d_year, d_moy) rn\n from item, store_sales, date_dim, store\n where ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n (\n d_year = 2001 or\n ( d_year = 2001-1 and d_moy =12) or\n ( d_year = 2001+1 and d_moy =1)\n )\n group by i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy),\n v2 as(\n select v1.s_company_name\n ,v1.d_year, v1.d_moy\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1.s_store_name = v1_lag.s_store_name and\n v1.s_store_name = v1_lead.s_store_name and\n v1.s_company_name = v1_lag.s_company_name and\n v1.s_company_name = v1_lead.s_company_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 2001 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, avg_monthly_sales\n limit 100\"\"\",\n \"q48\" ->\n \"\"\"\nselect sum (ss_quantity)\n from store_sales, store, customer_demographics, customer_address, date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 1999\n and\n (\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'D'\n and\n cd_education_status = 'College'\n and\n ss_sales_price between 100.00 and 150.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'W'\n and\n cd_education_status = 'Secondary'\n and\n ss_sales_price between 50.00 and 100.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'M'\n and\n cd_education_status = '2 yr Degree'\n and\n ss_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('NE', 'IA', 'NY')\n and ss_net_profit between 0 and 2000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('IN', 'TN', 'OH')\n and ss_net_profit between 150 and 3000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('KS', 'CA', 'CO')\n and ss_net_profit between 50 and 25000\n )\n )\"\"\",\n \"q49\" ->\n \"\"\"\nselect channel, item, return_ratio, return_rank, currency_rank from\n (select\n 'web' as channel\n ,web.item\n ,web.return_ratio\n ,web.return_rank\n ,web.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect ws.ws_item_sk as item\n \t\t,(cast(sum(coalesce(wr.wr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(wr.wr_return_amt,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\t web_sales ws left outer join web_returns wr\n \t\t\ton (ws.ws_order_number = wr.wr_order_number and\n \t\t\tws.ws_item_sk = wr.wr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\twr.wr_return_amt > 10000\n \t\t\tand ws.ws_net_profit > 1\n and ws.ws_net_paid > 0\n and ws.ws_quantity > 0\n and ws_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 11\n \t\tgroup by ws.ws_item_sk\n \t) in_web\n ) web\n where\n (\n web.return_rank <= 10\n or\n web.currency_rank <= 10\n )\n union\n select\n 'catalog' as channel\n ,catalog.item\n ,catalog.return_ratio\n ,catalog.return_rank\n ,catalog.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect\n \t\tcs.cs_item_sk as item\n \t\t,(cast(sum(coalesce(cr.cr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(cr.cr_return_amount,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tcatalog_sales cs left outer join catalog_returns cr\n \t\t\ton (cs.cs_order_number = cr.cr_order_number and\n \t\t\tcs.cs_item_sk = cr.cr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tcr.cr_return_amount > 10000\n \t\t\tand cs.cs_net_profit > 1\n and cs.cs_net_paid > 0\n and cs.cs_quantity > 0\n and cs_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 11\n group by cs.cs_item_sk\n \t) in_cat\n ) catalog\n where\n (\n catalog.return_rank <= 10\n or\n catalog.currency_rank <=10\n )\n union\n select\n 'store' as channel\n ,store.item\n ,store.return_ratio\n ,store.return_rank\n ,store.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect sts.ss_item_sk as item\n \t\t,(cast(sum(coalesce(sr.sr_return_quantity,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(sr.sr_return_amt,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tstore_sales sts left outer join store_returns sr\n \t\t\ton (sts.ss_ticket_number = sr.sr_ticket_number and sts.ss_item_sk = sr.sr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tsr.sr_return_amt > 10000\n \t\t\tand sts.ss_net_profit > 1\n and sts.ss_net_paid > 0\n and sts.ss_quantity > 0\n and ss_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 11\n \t\tgroup by sts.ss_item_sk\n \t) in_store\n ) store\n where (\n store.return_rank <= 10\n or\n store.currency_rank <= 10\n )\n )\n order by 1,4,5,2\n limit 100\"\"\",\n \"q50\" ->\n \"\"\"\nselect\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 30) and\n (sr_returned_date_sk - ss_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 60) and\n (sr_returned_date_sk - ss_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 90) and\n (sr_returned_date_sk - ss_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n store_sales\n ,store_returns\n ,store\n ,date_dim d1\n ,date_dim d2\nwhere\n d2.d_year = 1999\nand d2.d_moy = 9\nand ss_ticket_number = sr_ticket_number\nand ss_item_sk = sr_item_sk\nand ss_sold_date_sk = d1.d_date_sk\nand sr_returned_date_sk = d2.d_date_sk\nand ss_customer_sk = sr_customer_sk\nand ss_store_sk = s_store_sk\ngroup by\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\norder by s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\nlimit 100\"\"\",\n \"q51\" ->\n \"\"\"\nWITH web_v1 as (\nselect\n ws_item_sk item_sk, d_date,\n sum(sum(ws_sales_price))\n over (partition by ws_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom web_sales\n ,date_dim\nwhere ws_sold_date_sk=d_date_sk\n and d_month_seq between 1176 and 1176+11\n and ws_item_sk is not NULL\ngroup by ws_item_sk, d_date),\nstore_v1 as (\nselect\n ss_item_sk item_sk, d_date,\n sum(sum(ss_sales_price))\n over (partition by ss_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom store_sales\n ,date_dim\nwhere ss_sold_date_sk=d_date_sk\n and d_month_seq between 1176 and 1176+11\n and ss_item_sk is not NULL\ngroup by ss_item_sk, d_date)\n select *\nfrom (select item_sk\n ,d_date\n ,web_sales\n ,store_sales\n ,max(web_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) web_cumulative\n ,max(store_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) store_cumulative\n from (select case when web.item_sk is not null then web.item_sk else store.item_sk end item_sk\n ,case when web.d_date is not null then web.d_date else store.d_date end d_date\n ,web.cume_sales web_sales\n ,store.cume_sales store_sales\n from web_v1 web full outer join store_v1 store on (web.item_sk = store.item_sk\n and web.d_date = store.d_date)\n )x )y\nwhere web_cumulative > store_cumulative\norder by item_sk\n ,d_date\nlimit 100\"\"\",\n \"q52\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_brand_id brand_id\n \t,item.i_brand brand\n \t,sum(ss_ext_sales_price) ext_price\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manager_id = 1\n and dt.d_moy=11\n and dt.d_year=2001\n group by dt.d_year\n \t,item.i_brand\n \t,item.i_brand_id\n order by dt.d_year\n \t,ext_price desc\n \t,brand_id\nlimit 100 \"\"\",\n \"q53\" ->\n \"\"\"\nselect * from\n(select i_manufact_id,\nsum(ss_sales_price) sum_sales,\navg(sum(ss_sales_price)) over (partition by i_manufact_id) avg_quarterly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\nss_sold_date_sk = d_date_sk and\nss_store_sk = s_store_sk and\nd_month_seq in (1218,1218+1,1218+2,1218+3,1218+4,1218+5,1218+6,1218+7,1218+8,1218+9,1218+10,1218+11) and\n((i_category in ('Books','Children','Electronics') and\ni_class in ('personal','portable','reference','self-help') and\ni_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t'exportiunivamalg #9','scholaramalgamalg #9'))\nor(i_category in ('Women','Music','Men') and\ni_class in ('accessories','classical','fragrances','pants') and\ni_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t'importoamalg #1')))\ngroup by i_manufact_id, d_qoy ) tmp1\nwhere case when avg_quarterly_sales > 0\n\tthen abs (sum_sales - avg_quarterly_sales)/ avg_quarterly_sales\n\telse null end > 0.1\norder by avg_quarterly_sales,\n\t sum_sales,\n\t i_manufact_id\nlimit 100\"\"\",\n \"q54\" ->\n \"\"\"\nwith my_customers as (\n select distinct c_customer_sk\n , c_current_addr_sk\n from\n ( select cs_sold_date_sk sold_date_sk,\n cs_bill_customer_sk customer_sk,\n cs_item_sk item_sk\n from catalog_sales\n union all\n select ws_sold_date_sk sold_date_sk,\n ws_bill_customer_sk customer_sk,\n ws_item_sk item_sk\n from web_sales\n ) cs_or_ws_sales,\n item,\n date_dim,\n customer\n where sold_date_sk = d_date_sk\n and item_sk = i_item_sk\n and i_category = 'Music'\n and i_class = 'country'\n and c_customer_sk = cs_or_ws_sales.customer_sk\n and d_moy = 7\n and d_year = 2001\n )\n , my_revenue as (\n select c_customer_sk,\n sum(ss_ext_sales_price) as revenue\n from my_customers,\n store_sales,\n customer_address,\n store,\n date_dim\n where c_current_addr_sk = ca_address_sk\n and ca_county = s_county\n and ca_state = s_state\n and ss_sold_date_sk = d_date_sk\n and c_customer_sk = ss_customer_sk\n and d_month_seq between (select distinct d_month_seq+1\n from date_dim where d_year = 2001 and d_moy = 7)\n and (select distinct d_month_seq+3\n from date_dim where d_year = 2001 and d_moy = 7)\n group by c_customer_sk\n )\n , segments as\n (select cast((revenue/50) as int) as segment\n from my_revenue\n )\n select segment, count(*) as num_customers, segment*50 as segment_base\n from segments\n group by segment\n order by segment, num_customers\n limit 100\"\"\",\n \"q55\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item\n where d_date_sk = ss_sold_date_sk\n \tand ss_item_sk = i_item_sk\n \tand i_manager_id=87\n \tand d_moy=11\n \tand d_year=2001\n group by i_brand, i_brand_id\n order by ext_price desc, i_brand_id\nlimit 100 \"\"\",\n \"q56\" ->\n \"\"\"\nwith ss as (\n select i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('tan','lace','gainsboro'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 3\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n cs as (\n select i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('tan','lace','gainsboro'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 3\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n ws as (\n select i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('tan','lace','gainsboro'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 3\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id)\n select i_item_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by total_sales,\n i_item_id\n limit 100\"\"\",\n \"q57\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n cc_name,\n d_year, d_moy,\n sum(cs_sales_price) sum_sales,\n avg(sum(cs_sales_price)) over\n (partition by i_category, i_brand,\n cc_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n cc_name\n order by d_year, d_moy) rn\n from item, catalog_sales, date_dim, call_center\n where cs_item_sk = i_item_sk and\n cs_sold_date_sk = d_date_sk and\n cc_call_center_sk= cs_call_center_sk and\n (\n d_year = 2001 or\n ( d_year = 2001-1 and d_moy =12) or\n ( d_year = 2001+1 and d_moy =1)\n )\n group by i_category, i_brand,\n cc_name , d_year, d_moy),\n v2 as(\n select v1.i_category, v1.i_brand, v1.cc_name\n ,v1.d_year, v1.d_moy\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1. cc_name = v1_lag. cc_name and\n v1. cc_name = v1_lead. cc_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 2001 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, avg_monthly_sales\n limit 100\"\"\",\n \"q58\" ->\n \"\"\"\nwith ss_items as\n (select i_item_id item_id\n ,sum(ss_ext_sales_price) ss_item_rev\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '2000-03-26'))\n and ss_sold_date_sk = d_date_sk\n group by i_item_id),\n cs_items as\n (select i_item_id item_id\n ,sum(cs_ext_sales_price) cs_item_rev\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '2000-03-26'))\n and cs_sold_date_sk = d_date_sk\n group by i_item_id),\n ws_items as\n (select i_item_id item_id\n ,sum(ws_ext_sales_price) ws_item_rev\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq =(select d_week_seq\n from date_dim\n where d_date = '2000-03-26'))\n and ws_sold_date_sk = d_date_sk\n group by i_item_id)\n select ss_items.item_id\n ,ss_item_rev\n ,ss_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ss_dev\n ,cs_item_rev\n ,cs_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 cs_dev\n ,ws_item_rev\n ,ws_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ws_dev\n ,(ss_item_rev+cs_item_rev+ws_item_rev)/3 average\n from ss_items,cs_items,ws_items\n where ss_items.item_id=cs_items.item_id\n and ss_items.item_id=ws_items.item_id\n and ss_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n and ss_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and cs_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and cs_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and ws_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and ws_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n order by item_id\n ,ss_item_rev\n limit 100\"\"\",\n \"q59\" ->\n \"\"\"\nwith wss as\n (select d_week_seq,\n ss_store_sk,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n group by d_week_seq,ss_store_sk\n )\n select s_store_name1,s_store_id1,d_week_seq1\n ,sun_sales1/sun_sales2,mon_sales1/mon_sales2\n ,tue_sales1/tue_sales2,wed_sales1/wed_sales2,thu_sales1/thu_sales2\n ,fri_sales1/fri_sales2,sat_sales1/sat_sales2\n from\n (select s_store_name s_store_name1,wss.d_week_seq d_week_seq1\n ,s_store_id s_store_id1,sun_sales sun_sales1\n ,mon_sales mon_sales1,tue_sales tue_sales1\n ,wed_sales wed_sales1,thu_sales thu_sales1\n ,fri_sales fri_sales1,sat_sales sat_sales1\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1199 and 1199 + 11) y,\n (select s_store_name s_store_name2,wss.d_week_seq d_week_seq2\n ,s_store_id s_store_id2,sun_sales sun_sales2\n ,mon_sales mon_sales2,tue_sales tue_sales2\n ,wed_sales wed_sales2,thu_sales thu_sales2\n ,fri_sales fri_sales2,sat_sales sat_sales2\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1199+ 12 and 1199 + 23) x\n where s_store_id1=s_store_id2\n and d_week_seq1=d_week_seq2-52\n order by s_store_name1,s_store_id1,d_week_seq1\nlimit 100\"\"\",\n \"q60\" ->\n \"\"\"\nwith ss as (\n select\n i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Men'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 9\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n cs as (\n select\n i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Men'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 9\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n ws as (\n select\n i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Men'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 9\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id)\n select\n i_item_id\n,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by i_item_id\n ,total_sales\n limit 100\"\"\",\n \"q61\" ->\n \"\"\"\nselect promotions,total,cast(promotions as decimal(15,4))/cast(total as decimal(15,4))*100\nfrom\n (select sum(ss_ext_sales_price) promotions\n from store_sales\n ,store\n ,promotion\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_promo_sk = p_promo_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -7\n and i_category = 'Electronics'\n and (p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y')\n and s_gmt_offset = -7\n and d_year = 2001\n and d_moy = 11) promotional_sales,\n (select sum(ss_ext_sales_price) total\n from store_sales\n ,store\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -7\n and i_category = 'Electronics'\n and s_gmt_offset = -7\n and d_year = 2001\n and d_moy = 11) all_sales\norder by promotions, total\nlimit 100\"\"\",\n \"q62\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 30) and\n (ws_ship_date_sk - ws_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 60) and\n (ws_ship_date_sk - ws_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 90) and\n (ws_ship_date_sk - ws_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n web_sales\n ,warehouse\n ,ship_mode\n ,web_site\n ,date_dim\nwhere\n d_month_seq between 1194 and 1194 + 11\nand ws_ship_date_sk = d_date_sk\nand ws_warehouse_sk = w_warehouse_sk\nand ws_ship_mode_sk = sm_ship_mode_sk\nand ws_web_site_sk = web_site_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\nlimit 100\"\"\",\n \"q63\" ->\n \"\"\"\nselect *\nfrom (select i_manager_id\n ,sum(ss_sales_price) sum_sales\n ,avg(sum(ss_sales_price)) over (partition by i_manager_id) avg_monthly_sales\n from item\n ,store_sales\n ,date_dim\n ,store\n where ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and d_month_seq in (1205,1205+1,1205+2,1205+3,1205+4,1205+5,1205+6,1205+7,1205+8,1205+9,1205+10,1205+11)\n and (( i_category in ('Books','Children','Electronics')\n and i_class in ('personal','portable','reference','self-help')\n and i_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t 'exportiunivamalg #9','scholaramalgamalg #9'))\n or( i_category in ('Women','Music','Men')\n and i_class in ('accessories','classical','fragrances','pants')\n and i_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t 'importoamalg #1')))\ngroup by i_manager_id, d_moy) tmp1\nwhere case when avg_monthly_sales > 0 then abs (sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\norder by i_manager_id\n ,avg_monthly_sales\n ,sum_sales\nlimit 100\"\"\",\n \"q64\" ->\n \"\"\"\nwith cs_ui as\n (select cs_item_sk\n ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund\n from catalog_sales\n ,catalog_returns\n where cs_item_sk = cr_item_sk\n and cs_order_number = cr_order_number\n group by cs_item_sk\n having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit)),\ncross_sales as\n (select i_product_name product_name\n ,i_item_sk item_sk\n ,s_store_name store_name\n ,s_zip store_zip\n ,ad1.ca_street_number b_street_number\n ,ad1.ca_street_name b_street_name\n ,ad1.ca_city b_city\n ,ad1.ca_zip b_zip\n ,ad2.ca_street_number c_street_number\n ,ad2.ca_street_name c_street_name\n ,ad2.ca_city c_city\n ,ad2.ca_zip c_zip\n ,d1.d_year as syear\n ,d2.d_year as fsyear\n ,d3.d_year s2year\n ,count(*) cnt\n ,sum(ss_wholesale_cost) s1\n ,sum(ss_list_price) s2\n ,sum(ss_coupon_amt) s3\n FROM store_sales\n ,store_returns\n ,cs_ui\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,customer\n ,customer_demographics cd1\n ,customer_demographics cd2\n ,promotion\n ,household_demographics hd1\n ,household_demographics hd2\n ,customer_address ad1\n ,customer_address ad2\n ,income_band ib1\n ,income_band ib2\n ,item\n WHERE ss_store_sk = s_store_sk AND\n ss_sold_date_sk = d1.d_date_sk AND\n ss_customer_sk = c_customer_sk AND\n ss_cdemo_sk= cd1.cd_demo_sk AND\n ss_hdemo_sk = hd1.hd_demo_sk AND\n ss_addr_sk = ad1.ca_address_sk and\n ss_item_sk = i_item_sk and\n ss_item_sk = sr_item_sk and\n ss_ticket_number = sr_ticket_number and\n ss_item_sk = cs_ui.cs_item_sk and\n c_current_cdemo_sk = cd2.cd_demo_sk AND\n c_current_hdemo_sk = hd2.hd_demo_sk AND\n c_current_addr_sk = ad2.ca_address_sk and\n c_first_sales_date_sk = d2.d_date_sk and\n c_first_shipto_date_sk = d3.d_date_sk and\n ss_promo_sk = p_promo_sk and\n hd1.hd_income_band_sk = ib1.ib_income_band_sk and\n hd2.hd_income_band_sk = ib2.ib_income_band_sk and\n cd1.cd_marital_status <> cd2.cd_marital_status and\n i_color in ('peach','misty','drab','chocolate','almond','saddle') and\n i_current_price between 75 and 75 + 10 and\n i_current_price between 75 + 1 and 75 + 15\ngroup by i_product_name\n ,i_item_sk\n ,s_store_name\n ,s_zip\n ,ad1.ca_street_number\n ,ad1.ca_street_name\n ,ad1.ca_city\n ,ad1.ca_zip\n ,ad2.ca_street_number\n ,ad2.ca_street_name\n ,ad2.ca_city\n ,ad2.ca_zip\n ,d1.d_year\n ,d2.d_year\n ,d3.d_year\n)\nselect cs1.product_name\n ,cs1.store_name\n ,cs1.store_zip\n ,cs1.b_street_number\n ,cs1.b_street_name\n ,cs1.b_city\n ,cs1.b_zip\n ,cs1.c_street_number\n ,cs1.c_street_name\n ,cs1.c_city\n ,cs1.c_zip\n ,cs1.syear\n ,cs1.cnt\n ,cs1.s1 as s11\n ,cs1.s2 as s21\n ,cs1.s3 as s31\n ,cs2.s1 as s12\n ,cs2.s2 as s22\n ,cs2.s3 as s32\n ,cs2.syear\n ,cs2.cnt\nfrom cross_sales cs1,cross_sales cs2\nwhere cs1.item_sk=cs2.item_sk and\n cs1.syear = 2000 and\n cs2.syear = 2000 + 1 and\n cs2.cnt <= cs1.cnt and\n cs1.store_name = cs2.store_name and\n cs1.store_zip = cs2.store_zip\norder by cs1.product_name\n ,cs1.store_name\n ,cs2.cnt\n ,cs1.s1\n ,cs2.s1\"\"\",\n \"q65\" ->\n \"\"\"\nselect\n\ts_store_name,\n\ti_item_desc,\n\tsc.revenue,\n\ti_current_price,\n\ti_wholesale_cost,\n\ti_brand\n from store, item,\n (select ss_store_sk, avg(revenue) as ave\n \tfrom\n \t (select ss_store_sk, ss_item_sk,\n \t\t sum(ss_sales_price) as revenue\n \t\tfrom store_sales, date_dim\n \t\twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1208 and 1208+11\n \t\tgroup by ss_store_sk, ss_item_sk) sa\n \tgroup by ss_store_sk) sb,\n (select ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue\n \tfrom store_sales, date_dim\n \twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1208 and 1208+11\n \tgroup by ss_store_sk, ss_item_sk) sc\n where sb.ss_store_sk = sc.ss_store_sk and\n sc.revenue <= 0.1 * sb.ave and\n s_store_sk = sc.ss_store_sk and\n i_item_sk = sc.ss_item_sk\n order by s_store_name, i_item_desc\nlimit 100\"\"\",\n \"q66\" ->\n \"\"\"\nselect\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,ship_carriers\n ,year\n \t,sum(jan_sales) as jan_sales\n \t,sum(feb_sales) as feb_sales\n \t,sum(mar_sales) as mar_sales\n \t,sum(apr_sales) as apr_sales\n \t,sum(may_sales) as may_sales\n \t,sum(jun_sales) as jun_sales\n \t,sum(jul_sales) as jul_sales\n \t,sum(aug_sales) as aug_sales\n \t,sum(sep_sales) as sep_sales\n \t,sum(oct_sales) as oct_sales\n \t,sum(nov_sales) as nov_sales\n \t,sum(dec_sales) as dec_sales\n \t,sum(jan_sales/w_warehouse_sq_ft) as jan_sales_per_sq_foot\n \t,sum(feb_sales/w_warehouse_sq_ft) as feb_sales_per_sq_foot\n \t,sum(mar_sales/w_warehouse_sq_ft) as mar_sales_per_sq_foot\n \t,sum(apr_sales/w_warehouse_sq_ft) as apr_sales_per_sq_foot\n \t,sum(may_sales/w_warehouse_sq_ft) as may_sales_per_sq_foot\n \t,sum(jun_sales/w_warehouse_sq_ft) as jun_sales_per_sq_foot\n \t,sum(jul_sales/w_warehouse_sq_ft) as jul_sales_per_sq_foot\n \t,sum(aug_sales/w_warehouse_sq_ft) as aug_sales_per_sq_foot\n \t,sum(sep_sales/w_warehouse_sq_ft) as sep_sales_per_sq_foot\n \t,sum(oct_sales/w_warehouse_sq_ft) as oct_sales_per_sq_foot\n \t,sum(nov_sales/w_warehouse_sq_ft) as nov_sales_per_sq_foot\n \t,sum(dec_sales/w_warehouse_sq_ft) as dec_sales_per_sq_foot\n \t,sum(jan_net) as jan_net\n \t,sum(feb_net) as feb_net\n \t,sum(mar_net) as mar_net\n \t,sum(apr_net) as apr_net\n \t,sum(may_net) as may_net\n \t,sum(jun_net) as jun_net\n \t,sum(jul_net) as jul_net\n \t,sum(aug_net) as aug_net\n \t,sum(sep_net) as sep_net\n \t,sum(oct_net) as oct_net\n \t,sum(nov_net) as nov_net\n \t,sum(dec_net) as dec_net\n from (\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'HARMSTORF' || ',' || 'USPS' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen ws_sales_price* ws_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen ws_sales_price* ws_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen ws_sales_price* ws_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen ws_sales_price* ws_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen ws_sales_price* ws_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen ws_sales_price* ws_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen ws_sales_price* ws_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen ws_sales_price* ws_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen ws_sales_price* ws_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen ws_sales_price* ws_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen ws_sales_price* ws_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen ws_sales_price* ws_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as dec_net\n from\n web_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n ws_warehouse_sk = w_warehouse_sk\n and ws_sold_date_sk = d_date_sk\n and ws_sold_time_sk = t_time_sk\n \tand ws_ship_mode_sk = sm_ship_mode_sk\n and d_year = 2002\n \tand t_time between 24285 and 24285+28800\n \tand sm_carrier in ('HARMSTORF','USPS')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n union all\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'HARMSTORF' || ',' || 'USPS' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen cs_net_paid * cs_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen cs_net_paid * cs_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen cs_net_paid * cs_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen cs_net_paid * cs_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen cs_net_paid * cs_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen cs_net_paid * cs_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen cs_net_paid * cs_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen cs_net_paid * cs_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen cs_net_paid * cs_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen cs_net_paid * cs_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen cs_net_paid * cs_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen cs_net_paid * cs_quantity else 0 end) as dec_net\n from\n catalog_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and cs_sold_time_sk = t_time_sk\n \tand cs_ship_mode_sk = sm_ship_mode_sk\n and d_year = 2002\n \tand t_time between 24285 AND 24285+28800\n \tand sm_carrier in ('HARMSTORF','USPS')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n ) x\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,ship_carriers\n ,year\n order by w_warehouse_name\n limit 100\"\"\",\n \"q67\" ->\n \"\"\"\nselect *\nfrom (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rank() over (partition by i_category order by sumsales desc) rk\n from (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sum(coalesce(ss_sales_price*ss_quantity,0)) sumsales\n from store_sales\n ,date_dim\n ,store\n ,item\n where ss_sold_date_sk=d_date_sk\n and ss_item_sk=i_item_sk\n and ss_store_sk = s_store_sk\n and d_month_seq between 1196 and 1196+11\n group by rollup(i_category, i_class, i_brand, i_product_name, d_year, d_qoy, d_moy,s_store_id))dw1) dw2\nwhere rk <= 100\norder by i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rk\nlimit 100\"\"\",\n \"q68\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,extended_price\n ,extended_tax\n ,list_price\n from (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_ext_sales_price) extended_price\n ,sum(ss_ext_list_price) list_price\n ,sum(ss_ext_tax) extended_tax\n from store_sales\n ,date_dim\n ,store\n ,household_demographics\n ,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_dep_count = 1 or\n household_demographics.hd_vehicle_count= -1)\n and date_dim.d_year in (1998,1998+1,1998+2)\n and store.s_city in ('Bethel','Summit')\n group by ss_ticket_number\n ,ss_customer_sk\n ,ss_addr_sk,ca_city) dn\n ,customer\n ,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,ss_ticket_number\n limit 100\"\"\",\n \"q69\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_state in ('OK','GA','VA') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2004 and\n d_moy between 4 and 4+2) and\n (not exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2004 and\n d_moy between 4 and 4+2) and\n not exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2004 and\n d_moy between 4 and 4+2))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n limit 100\"\"\",\n \"q70\" ->\n \"\"\"\nselect\n sum(ss_net_profit) as total_sum\n ,s_state\n ,s_county\n ,grouping(s_state)+grouping(s_county) as lochierarchy\n ,rank() over (\n \tpartition by grouping(s_state)+grouping(s_county),\n \tcase when grouping(s_county) = 0 then s_state end\n \torder by sum(ss_net_profit) desc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,store\n where\n d1.d_month_seq between 1197 and 1197+11\n and d1.d_date_sk = ss_sold_date_sk\n and s_store_sk = ss_store_sk\n and s_state in\n ( select s_state\n from (select s_state as s_state,\n \t\t\t rank() over ( partition by s_state order by sum(ss_net_profit) desc) as ranking\n from store_sales, store, date_dim\n where d_month_seq between 1197 and 1197+11\n \t\t\t and d_date_sk = ss_sold_date_sk\n \t\t\t and s_store_sk = ss_store_sk\n group by s_state\n ) tmp1\n where ranking <= 5\n )\n group by rollup(s_state,s_county)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then s_state end\n ,rank_within_parent\n limit 100\"\"\",\n \"q71\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,t_hour,t_minute,\n \tsum(ext_price) ext_price\n from item, (select ws_ext_sales_price as ext_price,\n ws_sold_date_sk as sold_date_sk,\n ws_item_sk as sold_item_sk,\n ws_sold_time_sk as time_sk\n from web_sales,date_dim\n where d_date_sk = ws_sold_date_sk\n and d_moy=12\n and d_year=1999\n union all\n select cs_ext_sales_price as ext_price,\n cs_sold_date_sk as sold_date_sk,\n cs_item_sk as sold_item_sk,\n cs_sold_time_sk as time_sk\n from catalog_sales,date_dim\n where d_date_sk = cs_sold_date_sk\n and d_moy=12\n and d_year=1999\n union all\n select ss_ext_sales_price as ext_price,\n ss_sold_date_sk as sold_date_sk,\n ss_item_sk as sold_item_sk,\n ss_sold_time_sk as time_sk\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n and d_moy=12\n and d_year=1999\n ) tmp,time_dim\n where\n sold_item_sk = i_item_sk\n and i_manager_id=1\n and time_sk = t_time_sk\n and (t_meal_time = 'breakfast' or t_meal_time = 'dinner')\n group by i_brand, i_brand_id,t_hour,t_minute\n order by ext_price desc, i_brand_id\n \"\"\",\n \"q72\" ->\n \"\"\"\nselect i_item_desc\n ,w_warehouse_name\n ,d1.d_week_seq\n ,sum(case when p_promo_sk is null then 1 else 0 end) no_promo\n ,sum(case when p_promo_sk is not null then 1 else 0 end) promo\n ,count(*) total_cnt\nfrom catalog_sales\njoin inventory on (cs_item_sk = inv_item_sk)\njoin warehouse on (w_warehouse_sk=inv_warehouse_sk)\njoin item on (i_item_sk = cs_item_sk)\njoin customer_demographics on (cs_bill_cdemo_sk = cd_demo_sk)\njoin household_demographics on (cs_bill_hdemo_sk = hd_demo_sk)\njoin date_dim d1 on (cs_sold_date_sk = d1.d_date_sk)\njoin date_dim d2 on (inv_date_sk = d2.d_date_sk)\njoin date_dim d3 on (cs_ship_date_sk = d3.d_date_sk)\nleft outer join promotion on (cs_promo_sk=p_promo_sk)\nleft outer join catalog_returns on (cr_item_sk = cs_item_sk and cr_order_number = cs_order_number)\nwhere d1.d_week_seq = d2.d_week_seq\n and inv_quantity_on_hand < cs_quantity\n and d3.d_date > d1.d_date + interval 5 days\n and hd_buy_potential = '>10000'\n and d1.d_year = 2002\n and cd_marital_status = 'D'\ngroup by i_item_desc,w_warehouse_name,d1.d_week_seq\norder by total_cnt desc, i_item_desc, w_warehouse_name, d_week_seq\nlimit 100\"\"\",\n \"q73\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_buy_potential = '501-1000' or\n household_demographics.hd_buy_potential = 'Unknown')\n and household_demographics.hd_vehicle_count > 0\n and case when household_demographics.hd_vehicle_count > 0 then\n household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count else null end > 1\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_county in ('Franklin Parish','Ziebach County','Luce County','Williamson County')\n group by ss_ticket_number,ss_customer_sk) dj,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 1 and 5\n order by cnt desc, c_last_name asc\"\"\",\n \"q74\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,max(ss_net_paid) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2001,2001+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,max(ws_net_paid) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n and d_year in (2001,2001+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n )\n select\n t_s_secyear.customer_id, t_s_secyear.customer_first_name, t_s_secyear.customer_last_name\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.year = 2001\n and t_s_secyear.year = 2001+1\n and t_w_firstyear.year = 2001\n and t_w_secyear.year = 2001+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n order by 3,1,2\nlimit 100\"\"\",\n \"q75\" ->\n \"\"\"\nWITH all_sales AS (\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,SUM(sales_cnt) AS sales_cnt\n ,SUM(sales_amt) AS sales_amt\n FROM (SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,cs_quantity - COALESCE(cr_return_quantity,0) AS sales_cnt\n ,cs_ext_sales_price - COALESCE(cr_return_amount,0.0) AS sales_amt\n FROM catalog_sales JOIN item ON i_item_sk=cs_item_sk\n JOIN date_dim ON d_date_sk=cs_sold_date_sk\n LEFT JOIN catalog_returns ON (cs_order_number=cr_order_number\n AND cs_item_sk=cr_item_sk)\n WHERE i_category='Sports'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ss_quantity - COALESCE(sr_return_quantity,0) AS sales_cnt\n ,ss_ext_sales_price - COALESCE(sr_return_amt,0.0) AS sales_amt\n FROM store_sales JOIN item ON i_item_sk=ss_item_sk\n JOIN date_dim ON d_date_sk=ss_sold_date_sk\n LEFT JOIN store_returns ON (ss_ticket_number=sr_ticket_number\n AND ss_item_sk=sr_item_sk)\n WHERE i_category='Sports'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ws_quantity - COALESCE(wr_return_quantity,0) AS sales_cnt\n ,ws_ext_sales_price - COALESCE(wr_return_amt,0.0) AS sales_amt\n FROM web_sales JOIN item ON i_item_sk=ws_item_sk\n JOIN date_dim ON d_date_sk=ws_sold_date_sk\n LEFT JOIN web_returns ON (ws_order_number=wr_order_number\n AND ws_item_sk=wr_item_sk)\n WHERE i_category='Sports') sales_detail\n GROUP BY d_year, i_brand_id, i_class_id, i_category_id, i_manufact_id)\n SELECT prev_yr.d_year AS prev_year\n ,curr_yr.d_year AS year\n ,curr_yr.i_brand_id\n ,curr_yr.i_class_id\n ,curr_yr.i_category_id\n ,curr_yr.i_manufact_id\n ,prev_yr.sales_cnt AS prev_yr_cnt\n ,curr_yr.sales_cnt AS curr_yr_cnt\n ,curr_yr.sales_cnt-prev_yr.sales_cnt AS sales_cnt_diff\n ,curr_yr.sales_amt-prev_yr.sales_amt AS sales_amt_diff\n FROM all_sales curr_yr, all_sales prev_yr\n WHERE curr_yr.i_brand_id=prev_yr.i_brand_id\n AND curr_yr.i_class_id=prev_yr.i_class_id\n AND curr_yr.i_category_id=prev_yr.i_category_id\n AND curr_yr.i_manufact_id=prev_yr.i_manufact_id\n AND curr_yr.d_year=2001\n AND prev_yr.d_year=2001-1\n AND CAST(curr_yr.sales_cnt AS DECIMAL(17,2))/CAST(prev_yr.sales_cnt AS DECIMAL(17,2))<0.9\n ORDER BY sales_cnt_diff,sales_amt_diff\n limit 100\"\"\",\n \"q76\" ->\n \"\"\"\nselect channel, col_name, d_year, d_qoy, i_category, COUNT(*) sales_cnt, SUM(ext_sales_price) sales_amt FROM (\n SELECT 'store' as channel, 'ss_cdemo_sk' col_name, d_year, d_qoy, i_category, ss_ext_sales_price ext_sales_price\n FROM store_sales, item, date_dim\n WHERE ss_cdemo_sk IS NULL\n AND ss_sold_date_sk=d_date_sk\n AND ss_item_sk=i_item_sk\n UNION ALL\n SELECT 'web' as channel, 'ws_ship_hdemo_sk' col_name, d_year, d_qoy, i_category, ws_ext_sales_price ext_sales_price\n FROM web_sales, item, date_dim\n WHERE ws_ship_hdemo_sk IS NULL\n AND ws_sold_date_sk=d_date_sk\n AND ws_item_sk=i_item_sk\n UNION ALL\n SELECT 'catalog' as channel, 'cs_ship_customer_sk' col_name, d_year, d_qoy, i_category, cs_ext_sales_price ext_sales_price\n FROM catalog_sales, item, date_dim\n WHERE cs_ship_customer_sk IS NULL\n AND cs_sold_date_sk=d_date_sk\n AND cs_item_sk=i_item_sk) foo\nGROUP BY channel, col_name, d_year, d_qoy, i_category\nORDER BY channel, col_name, d_year, d_qoy, i_category\nlimit 100\"\"\",\n \"q77\" ->\n \"\"\"\nwith ss as\n (select s_store_sk,\n sum(ss_ext_sales_price) as sales,\n sum(ss_net_profit) as profit\n from store_sales,\n date_dim,\n store\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-27' as date)\n and (cast('2001-08-27' as date) + INTERVAL 30 days)\n and ss_store_sk = s_store_sk\n group by s_store_sk)\n ,\n sr as\n (select s_store_sk,\n sum(sr_return_amt) as returns,\n sum(sr_net_loss) as profit_loss\n from store_returns,\n date_dim,\n store\n where sr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-27' as date)\n and (cast('2001-08-27' as date) + INTERVAL 30 days)\n and sr_store_sk = s_store_sk\n group by s_store_sk),\n cs as\n (select cs_call_center_sk,\n sum(cs_ext_sales_price) as sales,\n sum(cs_net_profit) as profit\n from catalog_sales,\n date_dim\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-27' as date)\n and (cast('2001-08-27' as date) + INTERVAL 30 days)\n group by cs_call_center_sk\n ),\n cr as\n (select cr_call_center_sk,\n sum(cr_return_amount) as returns,\n sum(cr_net_loss) as profit_loss\n from catalog_returns,\n date_dim\n where cr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-27' as date)\n and (cast('2001-08-27' as date) + INTERVAL 30 days)\n group by cr_call_center_sk\n ),\n ws as\n ( select wp_web_page_sk,\n sum(ws_ext_sales_price) as sales,\n sum(ws_net_profit) as profit\n from web_sales,\n date_dim,\n web_page\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-27' as date)\n and (cast('2001-08-27' as date) + INTERVAL 30 days)\n and ws_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk),\n wr as\n (select wp_web_page_sk,\n sum(wr_return_amt) as returns,\n sum(wr_net_loss) as profit_loss\n from web_returns,\n date_dim,\n web_page\n where wr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-27' as date)\n and (cast('2001-08-27' as date) + INTERVAL 30 days)\n and wr_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , ss.s_store_sk as id\n , sales\n , coalesce(returns, 0) as returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ss left join sr\n on ss.s_store_sk = sr.s_store_sk\n union all\n select 'catalog channel' as channel\n , cs_call_center_sk as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from cs\n , cr\n union all\n select 'web channel' as channel\n , ws.wp_web_page_sk as id\n , sales\n , coalesce(returns, 0) returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ws left join wr\n on ws.wp_web_page_sk = wr.wp_web_page_sk\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q78\" ->\n \"\"\"\nwith ws as\n (select d_year AS ws_sold_year, ws_item_sk,\n ws_bill_customer_sk ws_customer_sk,\n sum(ws_quantity) ws_qty,\n sum(ws_wholesale_cost) ws_wc,\n sum(ws_sales_price) ws_sp\n from web_sales\n left join web_returns on wr_order_number=ws_order_number and ws_item_sk=wr_item_sk\n join date_dim on ws_sold_date_sk = d_date_sk\n where wr_order_number is null\n group by d_year, ws_item_sk, ws_bill_customer_sk\n ),\ncs as\n (select d_year AS cs_sold_year, cs_item_sk,\n cs_bill_customer_sk cs_customer_sk,\n sum(cs_quantity) cs_qty,\n sum(cs_wholesale_cost) cs_wc,\n sum(cs_sales_price) cs_sp\n from catalog_sales\n left join catalog_returns on cr_order_number=cs_order_number and cs_item_sk=cr_item_sk\n join date_dim on cs_sold_date_sk = d_date_sk\n where cr_order_number is null\n group by d_year, cs_item_sk, cs_bill_customer_sk\n ),\nss as\n (select d_year AS ss_sold_year, ss_item_sk,\n ss_customer_sk,\n sum(ss_quantity) ss_qty,\n sum(ss_wholesale_cost) ss_wc,\n sum(ss_sales_price) ss_sp\n from store_sales\n left join store_returns on sr_ticket_number=ss_ticket_number and ss_item_sk=sr_item_sk\n join date_dim on ss_sold_date_sk = d_date_sk\n where sr_ticket_number is null\n group by d_year, ss_item_sk, ss_customer_sk\n )\n select\nss_sold_year, ss_item_sk, ss_customer_sk,\nround(ss_qty/(coalesce(ws_qty,0)+coalesce(cs_qty,0)),2) ratio,\nss_qty store_qty, ss_wc store_wholesale_cost, ss_sp store_sales_price,\ncoalesce(ws_qty,0)+coalesce(cs_qty,0) other_chan_qty,\ncoalesce(ws_wc,0)+coalesce(cs_wc,0) other_chan_wholesale_cost,\ncoalesce(ws_sp,0)+coalesce(cs_sp,0) other_chan_sales_price\nfrom ss\nleft join ws on (ws_sold_year=ss_sold_year and ws_item_sk=ss_item_sk and ws_customer_sk=ss_customer_sk)\nleft join cs on (cs_sold_year=ss_sold_year and cs_item_sk=ss_item_sk and cs_customer_sk=ss_customer_sk)\nwhere (coalesce(ws_qty,0)>0 or coalesce(cs_qty, 0)>0) and ss_sold_year=2002\norder by\n ss_sold_year, ss_item_sk, ss_customer_sk,\n ss_qty desc, ss_wc desc, ss_sp desc,\n other_chan_qty,\n other_chan_wholesale_cost,\n other_chan_sales_price,\n ratio\nlimit 100\"\"\",\n \"q79\" ->\n \"\"\"\nselect\n c_last_name,c_first_name,substr(s_city,1,30),ss_ticket_number,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,store.s_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (household_demographics.hd_dep_count = 0 or household_demographics.hd_vehicle_count > 0)\n and date_dim.d_dow = 1\n and date_dim.d_year in (2000,2000+1,2000+2)\n and store.s_number_employees between 200 and 295\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,store.s_city) ms,customer\n where ss_customer_sk = c_customer_sk\n order by c_last_name,c_first_name,substr(s_city,1,30), profit\nlimit 100\"\"\",\n \"q80\" ->\n \"\"\"\nwith ssr as\n (select s_store_id as store_id,\n sum(ss_ext_sales_price) as sales,\n sum(coalesce(sr_return_amt, 0)) as returns,\n sum(ss_net_profit - coalesce(sr_net_loss, 0)) as profit\n from store_sales left outer join store_returns on\n (ss_item_sk = sr_item_sk and ss_ticket_number = sr_ticket_number),\n date_dim,\n store,\n item,\n promotion\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('1999-08-12' as date)\n and (cast('1999-08-12' as date) + INTERVAL 60 days)\n and ss_store_sk = s_store_sk\n and ss_item_sk = i_item_sk\n and i_current_price > 50\n and ss_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id as catalog_page_id,\n sum(cs_ext_sales_price) as sales,\n sum(coalesce(cr_return_amount, 0)) as returns,\n sum(cs_net_profit - coalesce(cr_net_loss, 0)) as profit\n from catalog_sales left outer join catalog_returns on\n (cs_item_sk = cr_item_sk and cs_order_number = cr_order_number),\n date_dim,\n catalog_page,\n item,\n promotion\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('1999-08-12' as date)\n and (cast('1999-08-12' as date) + INTERVAL 60 days)\n and cs_catalog_page_sk = cp_catalog_page_sk\n and cs_item_sk = i_item_sk\n and i_current_price > 50\n and cs_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(ws_ext_sales_price) as sales,\n sum(coalesce(wr_return_amt, 0)) as returns,\n sum(ws_net_profit - coalesce(wr_net_loss, 0)) as profit\n from web_sales left outer join web_returns on\n (ws_item_sk = wr_item_sk and ws_order_number = wr_order_number),\n date_dim,\n web_site,\n item,\n promotion\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('1999-08-12' as date)\n and (cast('1999-08-12' as date) + INTERVAL 60 days)\n and ws_web_site_sk = web_site_sk\n and ws_item_sk = i_item_sk\n and i_current_price > 50\n and ws_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || store_id as id\n , sales\n , returns\n , profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || catalog_page_id as id\n , sales\n , returns\n , profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q81\" ->\n \"\"\"\nwith customer_total_return as\n (select cr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(cr_return_amt_inc_tax) as ctr_total_return\n from catalog_returns\n ,date_dim\n ,customer_address\n where cr_returned_date_sk = d_date_sk\n and d_year =2001\n and cr_returning_addr_sk = ca_address_sk\n group by cr_returning_customer_sk\n ,ca_state )\n select c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'NC'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n limit 100\"\"\",\n \"q82\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, store_sales\n where i_current_price between 82 and 82+30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2002-03-10' as date) and (cast('2002-03-10' as date) + INTERVAL 60 days)\n and i_manufact_id in (941,920,105,693)\n and inv_quantity_on_hand between 100 and 500\n and ss_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q83\" ->\n \"\"\"\nwith sr_items as\n (select i_item_id item_id,\n sum(sr_return_quantity) sr_item_qty\n from store_returns,\n item,\n date_dim\n where sr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('1999-04-14','1999-09-28','1999-11-12')))\n and sr_returned_date_sk = d_date_sk\n group by i_item_id),\n cr_items as\n (select i_item_id item_id,\n sum(cr_return_quantity) cr_item_qty\n from catalog_returns,\n item,\n date_dim\n where cr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('1999-04-14','1999-09-28','1999-11-12')))\n and cr_returned_date_sk = d_date_sk\n group by i_item_id),\n wr_items as\n (select i_item_id item_id,\n sum(wr_return_quantity) wr_item_qty\n from web_returns,\n item,\n date_dim\n where wr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t\twhere d_date in ('1999-04-14','1999-09-28','1999-11-12')))\n and wr_returned_date_sk = d_date_sk\n group by i_item_id)\n select sr_items.item_id\n ,sr_item_qty\n ,sr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 sr_dev\n ,cr_item_qty\n ,cr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 cr_dev\n ,wr_item_qty\n ,wr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 wr_dev\n ,(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 average\n from sr_items\n ,cr_items\n ,wr_items\n where sr_items.item_id=cr_items.item_id\n and sr_items.item_id=wr_items.item_id\n order by sr_items.item_id\n ,sr_item_qty\n limit 100\"\"\",\n \"q84\" ->\n \"\"\"\nselect c_customer_id as customer_id\n , coalesce(c_last_name,'') || ', ' || coalesce(c_first_name,'') as customername\n from customer\n ,customer_address\n ,customer_demographics\n ,household_demographics\n ,income_band\n ,store_returns\n where ca_city\t = 'Antioch'\n and c_current_addr_sk = ca_address_sk\n and ib_lower_bound >= 55019\n and ib_upper_bound <= 55019 + 50000\n and ib_income_band_sk = hd_income_band_sk\n and cd_demo_sk = c_current_cdemo_sk\n and hd_demo_sk = c_current_hdemo_sk\n and sr_cdemo_sk = cd_demo_sk\n order by c_customer_id\n limit 100\"\"\",\n \"q85\" ->\n \"\"\"\nselect substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\n from web_sales, web_returns, web_page, customer_demographics cd1,\n customer_demographics cd2, customer_address, date_dim, reason\n where ws_web_page_sk = wp_web_page_sk\n and ws_item_sk = wr_item_sk\n and ws_order_number = wr_order_number\n and ws_sold_date_sk = d_date_sk and d_year = 2001\n and cd1.cd_demo_sk = wr_refunded_cdemo_sk\n and cd2.cd_demo_sk = wr_returning_cdemo_sk\n and ca_address_sk = wr_refunded_addr_sk\n and r_reason_sk = wr_reason_sk\n and\n (\n (\n cd1.cd_marital_status = 'S'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = '2 yr Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 100.00 and 150.00\n )\n or\n (\n cd1.cd_marital_status = 'D'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'Advanced Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 50.00 and 100.00\n )\n or\n (\n cd1.cd_marital_status = 'W'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = '4 yr Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ca_country = 'United States'\n and\n ca_state in ('OK', 'TX', 'MO')\n and ws_net_profit between 100 and 200\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('GA', 'KS', 'NC')\n and ws_net_profit between 150 and 300\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('VA', 'WI', 'WV')\n and ws_net_profit between 50 and 250\n )\n )\ngroup by r_reason_desc\norder by substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\nlimit 100\"\"\",\n \"q86\" ->\n \"\"\"\nselect\n sum(ws_net_paid) as total_sum\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ws_net_paid) desc) as rank_within_parent\n from\n web_sales\n ,date_dim d1\n ,item\n where\n d1.d_month_seq between 1180 and 1180+11\n and d1.d_date_sk = ws_sold_date_sk\n and i_item_sk = ws_item_sk\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc,\n case when lochierarchy = 0 then i_category end,\n rank_within_parent\n limit 100\"\"\",\n \"q87\" ->\n \"\"\"\nselect count(*)\nfrom ((select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1204 and 1204+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1204 and 1204+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1204 and 1204+11)\n) cool_cust\"\"\",\n \"q88\" ->\n \"\"\"\nselect *\nfrom\n (select count(*) h8_30_to_9\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 8\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s1,\n (select count(*) h9_to_9_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s2,\n (select count(*) h9_30_to_10\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s3,\n (select count(*) h10_to_10_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s4,\n (select count(*) h10_30_to_11\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s5,\n (select count(*) h11_to_11_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s6,\n (select count(*) h11_30_to_12\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s7,\n (select count(*) h12_to_12_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 12\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s8\"\"\",\n \"q89\" ->\n \"\"\"\nselect *\nfrom(\nselect i_category, i_class, i_brand,\n s_store_name, s_company_name,\n d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand, s_store_name, s_company_name)\n avg_monthly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n d_year in (2001) and\n ((i_category in ('Women','Music','Home') and\n i_class in ('fragrances','pop','bedding')\n )\n or (i_category in ('Books','Men','Children') and\n i_class in ('home repair','sports-apparel','infants')\n ))\ngroup by i_category, i_class, i_brand,\n s_store_name, s_company_name, d_moy) tmp1\nwhere case when (avg_monthly_sales <> 0) then (abs(sum_sales - avg_monthly_sales) / avg_monthly_sales) else null end > 0.1\norder by sum_sales - avg_monthly_sales, s_store_name\nlimit 100\"\"\",\n \"q90\" ->\n \"\"\"\nselect cast(amc as decimal(15,4))/cast(pmc as decimal(15,4)) am_pm_ratio\n from ( select count(*) amc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 8 and 8+1\n and household_demographics.hd_dep_count = 4\n and web_page.wp_char_count between 5000 and 5200) at,\n ( select count(*) pmc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 19 and 19+1\n and household_demographics.hd_dep_count = 4\n and web_page.wp_char_count between 5000 and 5200) pt\n order by am_pm_ratio\n limit 100\"\"\",\n \"q91\" ->\n \"\"\"\nselect\n cc_call_center_id Call_Center,\n cc_name Call_Center_Name,\n cc_manager Manager,\n sum(cr_net_loss) Returns_Loss\nfrom\n call_center,\n catalog_returns,\n date_dim,\n customer,\n customer_address,\n customer_demographics,\n household_demographics\nwhere\n cr_call_center_sk = cc_call_center_sk\nand cr_returned_date_sk = d_date_sk\nand cr_returning_customer_sk= c_customer_sk\nand cd_demo_sk = c_current_cdemo_sk\nand hd_demo_sk = c_current_hdemo_sk\nand ca_address_sk = c_current_addr_sk\nand d_year = 2002\nand d_moy = 11\nand ( (cd_marital_status = 'M' and cd_education_status = 'Unknown')\n or(cd_marital_status = 'W' and cd_education_status = 'Advanced Degree'))\nand hd_buy_potential like '5001-10000%'\nand ca_gmt_offset = -6\ngroup by cc_call_center_id,cc_name,cc_manager,cd_marital_status,cd_education_status\norder by sum(cr_net_loss) desc\"\"\",\n \"q92\" ->\n \"\"\"\nselect\n sum(ws_ext_discount_amt) as `Excess Discount Amount`\nfrom\n web_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 561\nand i_item_sk = ws_item_sk\nand d_date between '2001-03-13' and\n (cast('2001-03-13' as date) + INTERVAL 90 days)\nand d_date_sk = ws_sold_date_sk\nand ws_ext_discount_amt\n > (\n SELECT\n 1.3 * avg(ws_ext_discount_amt)\n FROM\n web_sales\n ,date_dim\n WHERE\n ws_item_sk = i_item_sk\n and d_date between '2001-03-13' and\n (cast('2001-03-13' as date) + INTERVAL 90 days)\n and d_date_sk = ws_sold_date_sk\n )\norder by sum(ws_ext_discount_amt)\nlimit 100\"\"\",\n \"q93\" ->\n \"\"\"\nselect ss_customer_sk\n ,sum(act_sales) sumsales\n from (select ss_item_sk\n ,ss_ticket_number\n ,ss_customer_sk\n ,case when sr_return_quantity is not null then (ss_quantity-sr_return_quantity)*ss_sales_price\n else (ss_quantity*ss_sales_price) end act_sales\n from store_sales left outer join store_returns on (sr_item_sk = ss_item_sk\n and sr_ticket_number = ss_ticket_number)\n ,reason\n where sr_reason_sk = r_reason_sk\n and r_reason_desc = 'reason 64') t\n group by ss_customer_sk\n order by sumsales, ss_customer_sk\nlimit 100\"\"\",\n \"q94\" ->\n \"\"\"\nselect\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2001-5-01' and\n (cast('2001-5-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'TX'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand exists (select *\n from web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\nand not exists(select *\n from web_returns wr1\n where ws1.ws_order_number = wr1.wr_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q95\" ->\n \"\"\"\nwith ws_wh as\n(select ws1.ws_order_number,ws1.ws_warehouse_sk wh1,ws2.ws_warehouse_sk wh2\n from web_sales ws1,web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\n select\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2000-3-01' and\n (cast('2000-3-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'TN'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand ws1.ws_order_number in (select ws_order_number\n from ws_wh)\nand ws1.ws_order_number in (select wr_order_number\n from web_returns,ws_wh\n where wr_order_number = ws_wh.ws_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q96\" ->\n \"\"\"\nselect count(*)\nfrom store_sales\n ,household_demographics\n ,time_dim, store\nwhere ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 16\n and time_dim.t_minute >= 30\n and household_demographics.hd_dep_count = 4\n and store.s_store_name = 'ese'\norder by count(*)\nlimit 100\"\"\",\n \"q97\" ->\n \"\"\"\nwith ssci as (\nselect ss_customer_sk customer_sk\n ,ss_item_sk item_sk\nfrom store_sales,date_dim\nwhere ss_sold_date_sk = d_date_sk\n and d_month_seq between 1209 and 1209 + 11\ngroup by ss_customer_sk\n ,ss_item_sk),\ncsci as(\n select cs_bill_customer_sk customer_sk\n ,cs_item_sk item_sk\nfrom catalog_sales,date_dim\nwhere cs_sold_date_sk = d_date_sk\n and d_month_seq between 1209 and 1209 + 11\ngroup by cs_bill_customer_sk\n ,cs_item_sk)\n select sum(case when ssci.customer_sk is not null and csci.customer_sk is null then 1 else 0 end) store_only\n ,sum(case when ssci.customer_sk is null and csci.customer_sk is not null then 1 else 0 end) catalog_only\n ,sum(case when ssci.customer_sk is not null and csci.customer_sk is not null then 1 else 0 end) store_and_catalog\nfrom ssci full outer join csci on (ssci.customer_sk=csci.customer_sk\n and ssci.item_sk = csci.item_sk)\nlimit 100\"\"\",\n \"q98\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ss_ext_sales_price) as itemrevenue\n ,sum(ss_ext_sales_price)*100/sum(sum(ss_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tstore_sales\n \t,item\n \t,date_dim\nwhere\n\tss_item_sk = i_item_sk\n \tand i_category in ('Jewelry', 'Home', 'Shoes')\n \tand ss_sold_date_sk = d_date_sk\n\tand d_date between cast('2001-04-12' as date)\n\t\t\t\tand (cast('2001-04-12' as date) + interval 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\"\"\",\n \"q99\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 30) and\n (cs_ship_date_sk - cs_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 60) and\n (cs_ship_date_sk - cs_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 90) and\n (cs_ship_date_sk - cs_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n catalog_sales\n ,warehouse\n ,ship_mode\n ,call_center\n ,date_dim\nwhere\n d_month_seq between 1203 and 1203 + 11\nand cs_ship_date_sk = d_date_sk\nand cs_warehouse_sk = w_warehouse_sk\nand cs_ship_mode_sk = sm_ship_mode_sk\nand cs_call_center_sk = cc_call_center_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\nlimit 100\"\"\",\n \"q1\" ->\n \"\"\"\nwith customer_total_return as\n(select sr_customer_sk as ctr_customer_sk\n,sr_store_sk as ctr_store_sk\n,sum(SR_FEE) as ctr_total_return\nfrom store_returns\n,date_dim\nwhere sr_returned_date_sk = d_date_sk\nand d_year =2000\ngroup by sr_customer_sk\n,sr_store_sk)\n select c_customer_id\nfrom customer_total_return ctr1\n,store\n,customer\nwhere ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\nfrom customer_total_return ctr2\nwhere ctr1.ctr_store_sk = ctr2.ctr_store_sk)\nand s_store_sk = ctr1.ctr_store_sk\nand s_state = 'NM'\nand ctr1.ctr_customer_sk = c_customer_sk\norder by c_customer_id\nlimit 100\"\"\",\n \"q2\" ->\n \"\"\"\nwith wscs as\n (select sold_date_sk\n ,sales_price\n from (select ws_sold_date_sk sold_date_sk\n ,ws_ext_sales_price sales_price\n from web_sales\n union all\n select cs_sold_date_sk sold_date_sk\n ,cs_ext_sales_price sales_price\n from catalog_sales)),\n wswscs as\n (select d_week_seq,\n sum(case when (d_day_name='Sunday') then sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then sales_price else null end) sat_sales\n from wscs\n ,date_dim\n where d_date_sk = sold_date_sk\n group by d_week_seq)\n select d_week_seq1\n ,round(sun_sales1/sun_sales2,2)\n ,round(mon_sales1/mon_sales2,2)\n ,round(tue_sales1/tue_sales2,2)\n ,round(wed_sales1/wed_sales2,2)\n ,round(thu_sales1/thu_sales2,2)\n ,round(fri_sales1/fri_sales2,2)\n ,round(sat_sales1/sat_sales2,2)\n from\n (select wswscs.d_week_seq d_week_seq1\n ,sun_sales sun_sales1\n ,mon_sales mon_sales1\n ,tue_sales tue_sales1\n ,wed_sales wed_sales1\n ,thu_sales thu_sales1\n ,fri_sales fri_sales1\n ,sat_sales sat_sales1\n from wswscs,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998) y,\n (select wswscs.d_week_seq d_week_seq2\n ,sun_sales sun_sales2\n ,mon_sales mon_sales2\n ,tue_sales tue_sales2\n ,wed_sales wed_sales2\n ,thu_sales thu_sales2\n ,fri_sales fri_sales2\n ,sat_sales sat_sales2\n from wswscs\n ,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998+1) z\n where d_week_seq1=d_week_seq2-53\n order by d_week_seq1\"\"\",\n \"q3\" ->\n \"\"\"\nselect dt.d_year\n ,item.i_brand_id brand_id\n ,item.i_brand brand\n ,sum(ss_sales_price) sum_agg\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manufact_id = 816\n and dt.d_moy=11\n group by dt.d_year\n ,item.i_brand\n ,item.i_brand_id\n order by dt.d_year\n ,sum_agg desc\n ,brand_id\n limit 100\"\"\",\n \"q4\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(((ss_ext_list_price-ss_ext_wholesale_cost-ss_ext_discount_amt)+ss_ext_sales_price)/2) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((cs_ext_list_price-cs_ext_wholesale_cost-cs_ext_discount_amt)+cs_ext_sales_price)/2) ) year_total\n ,'c' sale_type\n from customer\n ,catalog_sales\n ,date_dim\n where c_customer_sk = cs_bill_customer_sk\n and cs_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\nunion all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((ws_ext_list_price-ws_ext_wholesale_cost-ws_ext_discount_amt)+ws_ext_sales_price)/2) ) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_c_firstyear\n ,year_total t_c_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_c_secyear.customer_id\n and t_s_firstyear.customer_id = t_c_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_c_firstyear.sale_type = 'c'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_c_secyear.sale_type = 'c'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 1999\n and t_s_secyear.dyear = 1999+1\n and t_c_firstyear.dyear = 1999\n and t_c_secyear.dyear = 1999+1\n and t_w_firstyear.dyear = 1999\n and t_w_secyear.dyear = 1999+1\n and t_s_firstyear.year_total > 0\n and t_c_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\nlimit 100\"\"\",\n \"q5\" ->\n \"\"\"\nwith ssr as\n (select s_store_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ss_store_sk as store_sk,\n ss_sold_date_sk as date_sk,\n ss_ext_sales_price as sales_price,\n ss_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from store_sales\n union all\n select sr_store_sk as store_sk,\n sr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n sr_return_amt as return_amt,\n sr_net_loss as net_loss\n from store_returns\n ) salesreturns,\n date_dim,\n store\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and store_sk = s_store_sk\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select cs_catalog_page_sk as page_sk,\n cs_sold_date_sk as date_sk,\n cs_ext_sales_price as sales_price,\n cs_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from catalog_sales\n union all\n select cr_catalog_page_sk as page_sk,\n cr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n cr_return_amount as return_amt,\n cr_net_loss as net_loss\n from catalog_returns\n ) salesreturns,\n date_dim,\n catalog_page\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and page_sk = cp_catalog_page_sk\n group by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ws_web_site_sk as wsr_web_site_sk,\n ws_sold_date_sk as date_sk,\n ws_ext_sales_price as sales_price,\n ws_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from web_sales\n union all\n select ws_web_site_sk as wsr_web_site_sk,\n wr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n wr_return_amt as return_amt,\n wr_net_loss as net_loss\n from web_returns left outer join web_sales on\n ( wr_item_sk = ws_item_sk\n and wr_order_number = ws_order_number)\n ) salesreturns,\n date_dim,\n web_site\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and wsr_web_site_sk = web_site_sk\n group by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || s_store_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || cp_catalog_page_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q6\" ->\n \"\"\"\nselect a.ca_state state, count(*) cnt\n from customer_address a\n ,customer c\n ,store_sales s\n ,date_dim d\n ,item i\n where a.ca_address_sk = c.c_current_addr_sk\n \tand c.c_customer_sk = s.ss_customer_sk\n \tand s.ss_sold_date_sk = d.d_date_sk\n \tand s.ss_item_sk = i.i_item_sk\n \tand d.d_month_seq =\n \t (select distinct (d_month_seq)\n \t from date_dim\n where d_year = 2002\n \t and d_moy = 3 )\n \tand i.i_current_price > 1.2 *\n (select avg(j.i_current_price)\n \t from item j\n \t where j.i_category = i.i_category)\n group by a.ca_state\n having count(*) >= 10\n order by cnt, a.ca_state\n limit 100\"\"\",\n \"q7\" ->\n \"\"\"\nselect i_item_id,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, item, promotion\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_cdemo_sk = cd_demo_sk and\n ss_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'W' and\n cd_education_status = 'College' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 2001\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q8\" ->\n \"\"\"\nselect s_store_name\n ,sum(ss_net_profit)\n from store_sales\n ,date_dim\n ,store,\n (select ca_zip\n from (\n SELECT substr(ca_zip,1,5) ca_zip\n FROM customer_address\n WHERE substr(ca_zip,1,5) IN (\n '47602','16704','35863','28577','83910','36201',\n '58412','48162','28055','41419','80332',\n '38607','77817','24891','16226','18410',\n '21231','59345','13918','51089','20317',\n '17167','54585','67881','78366','47770',\n '18360','51717','73108','14440','21800',\n '89338','45859','65501','34948','25973',\n '73219','25333','17291','10374','18829',\n '60736','82620','41351','52094','19326',\n '25214','54207','40936','21814','79077',\n '25178','75742','77454','30621','89193',\n '27369','41232','48567','83041','71948',\n '37119','68341','14073','16891','62878',\n '49130','19833','24286','27700','40979',\n '50412','81504','94835','84844','71954',\n '39503','57649','18434','24987','12350',\n '86379','27413','44529','98569','16515',\n '27287','24255','21094','16005','56436',\n '91110','68293','56455','54558','10298',\n '83647','32754','27052','51766','19444',\n '13869','45645','94791','57631','20712',\n '37788','41807','46507','21727','71836',\n '81070','50632','88086','63991','20244',\n '31655','51782','29818','63792','68605',\n '94898','36430','57025','20601','82080',\n '33869','22728','35834','29086','92645',\n '98584','98072','11652','78093','57553',\n '43830','71144','53565','18700','90209',\n '71256','38353','54364','28571','96560',\n '57839','56355','50679','45266','84680',\n '34306','34972','48530','30106','15371',\n '92380','84247','92292','68852','13338',\n '34594','82602','70073','98069','85066',\n '47289','11686','98862','26217','47529',\n '63294','51793','35926','24227','14196',\n '24594','32489','99060','49472','43432',\n '49211','14312','88137','47369','56877',\n '20534','81755','15794','12318','21060',\n '73134','41255','63073','81003','73873',\n '66057','51184','51195','45676','92696',\n '70450','90669','98338','25264','38919',\n '59226','58581','60298','17895','19489',\n '52301','80846','95464','68770','51634',\n '19988','18367','18421','11618','67975',\n '25494','41352','95430','15734','62585',\n '97173','33773','10425','75675','53535',\n '17879','41967','12197','67998','79658',\n '59130','72592','14851','43933','68101',\n '50636','25717','71286','24660','58058',\n '72991','95042','15543','33122','69280',\n '11912','59386','27642','65177','17672',\n '33467','64592','36335','54010','18767',\n '63193','42361','49254','33113','33159',\n '36479','59080','11855','81963','31016',\n '49140','29392','41836','32958','53163',\n '13844','73146','23952','65148','93498',\n '14530','46131','58454','13376','13378',\n '83986','12320','17193','59852','46081',\n '98533','52389','13086','68843','31013',\n '13261','60560','13443','45533','83583',\n '11489','58218','19753','22911','25115',\n '86709','27156','32669','13123','51933',\n '39214','41331','66943','14155','69998',\n '49101','70070','35076','14242','73021',\n '59494','15782','29752','37914','74686',\n '83086','34473','15751','81084','49230',\n '91894','60624','17819','28810','63180',\n '56224','39459','55233','75752','43639',\n '55349','86057','62361','50788','31830',\n '58062','18218','85761','60083','45484',\n '21204','90229','70041','41162','35390',\n '16364','39500','68908','26689','52868',\n '81335','40146','11340','61527','61794',\n '71997','30415','59004','29450','58117',\n '69952','33562','83833','27385','61860',\n '96435','48333','23065','32961','84919',\n '61997','99132','22815','56600','68730',\n '48017','95694','32919','88217','27116',\n '28239','58032','18884','16791','21343',\n '97462','18569','75660','15475')\n intersect\n select ca_zip\n from (SELECT substr(ca_zip,1,5) ca_zip,count(*) cnt\n FROM customer_address, customer\n WHERE ca_address_sk = c_current_addr_sk and\n c_preferred_cust_flag='Y'\n group by ca_zip\n having count(*) > 10)A1)A2) V1\n where ss_store_sk = s_store_sk\n and ss_sold_date_sk = d_date_sk\n and d_qoy = 2 and d_year = 1998\n and (substr(s_zip,1,2) = substr(V1.ca_zip,1,2))\n group by s_store_name\n order by s_store_name\n limit 100\"\"\",\n \"q9\" ->\n \"\"\"\nselect case when (select count(*)\n from store_sales\n where ss_quantity between 1 and 20) > 98972190\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 1 and 20)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 1 and 20) end bucket1 ,\n case when (select count(*)\n from store_sales\n where ss_quantity between 21 and 40) > 160856845\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 21 and 40)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 21 and 40) end bucket2,\n case when (select count(*)\n from store_sales\n where ss_quantity between 41 and 60) > 12733327\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 41 and 60)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 41 and 60) end bucket3,\n case when (select count(*)\n from store_sales\n where ss_quantity between 61 and 80) > 96251173\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 61 and 80)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 61 and 80) end bucket4,\n case when (select count(*)\n from store_sales\n where ss_quantity between 81 and 100) > 80049606\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 81 and 100)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 81 and 100) end bucket5\nfrom reason\nwhere r_reason_sk = 1\"\"\",\n \"q10\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3,\n cd_dep_count,\n count(*) cnt4,\n cd_dep_employed_count,\n count(*) cnt5,\n cd_dep_college_count,\n count(*) cnt6\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_county in ('Fillmore County','McPherson County','Bonneville County','Boone County','Brown County') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2000 and\n d_moy between 3 and 3+3) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2000 and\n d_moy between 3 ANd 3+3) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2000 and\n d_moy between 3 and 3+3))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\nlimit 100\"\"\",\n \"q11\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ss_ext_list_price-ss_ext_discount_amt) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ws_ext_list_price-ws_ext_discount_amt) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 1999\n and t_s_secyear.dyear = 1999+1\n and t_w_firstyear.dyear = 1999\n and t_w_secyear.dyear = 1999+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else 0.0 end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else 0.0 end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\nlimit 100\"\"\",\n \"q12\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ws_ext_sales_price) as itemrevenue\n ,sum(ws_ext_sales_price)*100/sum(sum(ws_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tweb_sales\n \t,item\n \t,date_dim\nwhere\n\tws_item_sk = i_item_sk\n \tand i_category in ('Electronics', 'Books', 'Women')\n \tand ws_sold_date_sk = d_date_sk\n\tand d_date between cast('1998-01-06' as date)\n\t\t\t\tand (cast('1998-01-06' as date) + INTERVAL 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q13\" ->\n \"\"\"\nselect avg(ss_quantity)\n ,avg(ss_ext_sales_price)\n ,avg(ss_ext_wholesale_cost)\n ,sum(ss_ext_wholesale_cost)\n from store_sales\n ,store\n ,customer_demographics\n ,household_demographics\n ,customer_address\n ,date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 2001\n and((ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'U'\n and cd_education_status = 'Secondary'\n and ss_sales_price between 100.00 and 150.00\n and hd_dep_count = 3\n )or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'W'\n and cd_education_status = 'College'\n and ss_sales_price between 50.00 and 100.00\n and hd_dep_count = 1\n ) or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'D'\n and cd_education_status = 'Primary'\n and ss_sales_price between 150.00 and 200.00\n and hd_dep_count = 1\n ))\n and((ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('TX', 'OK', 'MI')\n and ss_net_profit between 100 and 200\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('WA', 'NC', 'OH')\n and ss_net_profit between 150 and 300\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('MT', 'FL', 'GA')\n and ss_net_profit between 50 and 250\n ))\"\"\",\n \"q14a\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 2000 AND 2000 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 2000 AND 2000 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 2000 AND 2000 + 2)\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n (select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 2000 and 2000 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 2000 and 2000 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 2000 and 2000 + 2) x)\n select channel, i_brand_id,i_class_id,i_category_id,sum(sales), sum(number_sales)\n from(\n select 'store' channel, i_brand_id,i_class_id\n ,i_category_id,sum(ss_quantity*ss_list_price) sales\n , count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 2000+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)\n union all\n select 'catalog' channel, i_brand_id,i_class_id,i_category_id, sum(cs_quantity*cs_list_price) sales, count(*) number_sales\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk in (select ss_item_sk from cross_items)\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 2000+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(cs_quantity*cs_list_price) > (select average_sales from avg_sales)\n union all\n select 'web' channel, i_brand_id,i_class_id,i_category_id, sum(ws_quantity*ws_list_price) sales , count(*) number_sales\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk in (select ss_item_sk from cross_items)\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 2000+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ws_quantity*ws_list_price) > (select average_sales from avg_sales)\n ) y\n group by rollup (channel, i_brand_id,i_class_id,i_category_id)\n order by channel,i_brand_id,i_class_id,i_category_id\n limit 100\"\"\",\n \"q14b\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 2000 AND 2000 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 2000 AND 2000 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 2000 AND 2000 + 2) x\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n(select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 2000 and 2000 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 2000 and 2000 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 2000 and 2000 + 2) x)\n select this_year.channel ty_channel\n ,this_year.i_brand_id ty_brand\n ,this_year.i_class_id ty_class\n ,this_year.i_category_id ty_category\n ,this_year.sales ty_sales\n ,this_year.number_sales ty_number_sales\n ,last_year.channel ly_channel\n ,last_year.i_brand_id ly_brand\n ,last_year.i_class_id ly_class\n ,last_year.i_category_id ly_category\n ,last_year.sales ly_sales\n ,last_year.number_sales ly_number_sales\n from\n (select 'store' channel, i_brand_id,i_class_id,i_category_id\n ,sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 2000 + 1\n and d_moy = 12\n and d_dom = 15)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) this_year,\n (select 'store' channel, i_brand_id,i_class_id\n ,i_category_id, sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 2000\n and d_moy = 12\n and d_dom = 15)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) last_year\n where this_year.i_brand_id= last_year.i_brand_id\n and this_year.i_class_id = last_year.i_class_id\n and this_year.i_category_id = last_year.i_category_id\n order by this_year.channel, this_year.i_brand_id, this_year.i_class_id, this_year.i_category_id\n limit 100\"\"\",\n \"q15\" ->\n \"\"\"\nselect ca_zip\n ,sum(cs_sales_price)\n from catalog_sales\n ,customer\n ,customer_address\n ,date_dim\n where cs_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475',\n '85392', '85460', '80348', '81792')\n \t or ca_state in ('CA','WA','GA')\n \t or cs_sales_price > 500)\n \tand cs_sold_date_sk = d_date_sk\n \tand d_qoy = 2 and d_year = 1998\n group by ca_zip\n order by ca_zip\n limit 100\"\"\",\n \"q16\" ->\n \"\"\"\nselect\n count(distinct cs_order_number) as `order count`\n ,sum(cs_ext_ship_cost) as `total shipping cost`\n ,sum(cs_net_profit) as `total net profit`\nfrom\n catalog_sales cs1\n ,date_dim\n ,customer_address\n ,call_center\nwhere\n d_date between '1999-4-01' and\n (cast('1999-4-01' as date) + INTERVAL 60 days)\nand cs1.cs_ship_date_sk = d_date_sk\nand cs1.cs_ship_addr_sk = ca_address_sk\nand ca_state = 'IL'\nand cs1.cs_call_center_sk = cc_call_center_sk\nand cc_county in ('Richland County','Bronx County','Maverick County','Mesa County',\n 'Raleigh County'\n)\nand exists (select *\n from catalog_sales cs2\n where cs1.cs_order_number = cs2.cs_order_number\n and cs1.cs_warehouse_sk <> cs2.cs_warehouse_sk)\nand not exists(select *\n from catalog_returns cr1\n where cs1.cs_order_number = cr1.cr_order_number)\norder by count(distinct cs_order_number)\nlimit 100\"\"\",\n \"q17\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,s_state\n ,count(ss_quantity) as store_sales_quantitycount\n ,avg(ss_quantity) as store_sales_quantityave\n ,stddev_samp(ss_quantity) as store_sales_quantitystdev\n ,stddev_samp(ss_quantity)/avg(ss_quantity) as store_sales_quantitycov\n ,count(sr_return_quantity) as store_returns_quantitycount\n ,avg(sr_return_quantity) as store_returns_quantityave\n ,stddev_samp(sr_return_quantity) as store_returns_quantitystdev\n ,stddev_samp(sr_return_quantity)/avg(sr_return_quantity) as store_returns_quantitycov\n ,count(cs_quantity) as catalog_sales_quantitycount ,avg(cs_quantity) as catalog_sales_quantityave\n ,stddev_samp(cs_quantity) as catalog_sales_quantitystdev\n ,stddev_samp(cs_quantity)/avg(cs_quantity) as catalog_sales_quantitycov\n from store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where d1.d_quarter_name = '2000Q1'\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_quarter_name in ('2000Q1','2000Q2','2000Q3')\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_quarter_name in ('2000Q1','2000Q2','2000Q3')\n group by i_item_id\n ,i_item_desc\n ,s_state\n order by i_item_id\n ,i_item_desc\n ,s_state\nlimit 100\"\"\",\n \"q18\" ->\n \"\"\"\nselect i_item_id,\n ca_country,\n ca_state,\n ca_county,\n avg( cast(cs_quantity as decimal(12,2))) agg1,\n avg( cast(cs_list_price as decimal(12,2))) agg2,\n avg( cast(cs_coupon_amt as decimal(12,2))) agg3,\n avg( cast(cs_sales_price as decimal(12,2))) agg4,\n avg( cast(cs_net_profit as decimal(12,2))) agg5,\n avg( cast(c_birth_year as decimal(12,2))) agg6,\n avg( cast(cd1.cd_dep_count as decimal(12,2))) agg7\n from catalog_sales, customer_demographics cd1,\n customer_demographics cd2, customer, customer_address, date_dim, item\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd1.cd_demo_sk and\n cs_bill_customer_sk = c_customer_sk and\n cd1.cd_gender = 'M' and\n cd1.cd_education_status = 'Unknown' and\n c_current_cdemo_sk = cd2.cd_demo_sk and\n c_current_addr_sk = ca_address_sk and\n c_birth_month in (5,1,4,7,8,9) and\n d_year = 2002 and\n ca_state in ('AR','TX','NC'\n ,'GA','MS','WV','AL')\n group by rollup (i_item_id, ca_country, ca_state, ca_county)\n order by ca_country,\n ca_state,\n ca_county,\n\ti_item_id\n limit 100\"\"\",\n \"q19\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand, i_manufact_id, i_manufact,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item,customer,customer_address,store\n where d_date_sk = ss_sold_date_sk\n and ss_item_sk = i_item_sk\n and i_manager_id=16\n and d_moy=12\n and d_year=1998\n and ss_customer_sk = c_customer_sk\n and c_current_addr_sk = ca_address_sk\n and substr(ca_zip,1,5) <> substr(s_zip,1,5)\n and ss_store_sk = s_store_sk\n group by i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\n order by ext_price desc\n ,i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\nlimit 100 \"\"\",\n \"q20\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(cs_ext_sales_price) as itemrevenue\n ,sum(cs_ext_sales_price)*100/sum(sum(cs_ext_sales_price)) over\n (partition by i_class) as revenueratio\n from\tcatalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and i_category in ('Shoes', 'Electronics', 'Children')\n and cs_sold_date_sk = d_date_sk\n and d_date between cast('2001-03-14' as date)\n \t\t\t\tand (cast('2001-03-14' as date) + INTERVAL 30 days)\n group by i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n order by i_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q21\" ->\n \"\"\"\nselect *\n from(select w_warehouse_name\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('1999-03-20' as date))\n\t then inv_quantity_on_hand\n else 0 end) as inv_before\n ,sum(case when (cast(d_date as date) >= cast ('1999-03-20' as date))\n then inv_quantity_on_hand\n else 0 end) as inv_after\n from inventory\n ,warehouse\n ,item\n ,date_dim\n where i_current_price between 0.99 and 1.49\n and i_item_sk = inv_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_date between (cast ('1999-03-20' as date) - INTERVAL 30 days)\n and (cast ('1999-03-20' as date) + INTERVAL 30 days)\n group by w_warehouse_name, i_item_id) x\n where (case when inv_before > 0\n then inv_after / inv_before\n else null\n end) between 2.0/3.0 and 3.0/2.0\n order by w_warehouse_name\n ,i_item_id\n limit 100\"\"\",\n \"q22\" ->\n \"\"\"\nselect i_product_name\n ,i_brand\n ,i_class\n ,i_category\n ,avg(inv_quantity_on_hand) qoh\n from inventory\n ,date_dim\n ,item\n where inv_date_sk=d_date_sk\n and inv_item_sk=i_item_sk\n and d_month_seq between 1186 and 1186 + 11\n group by rollup(i_product_name\n ,i_brand\n ,i_class\n ,i_category)\norder by qoh, i_product_name, i_brand, i_class, i_category\nlimit 100\"\"\",\n \"q23a\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\nfrom\n max_store_sales))\n select sum(sales)\n from (select cs_quantity*cs_list_price sales\n from catalog_sales\n ,date_dim\n where d_year = 2000\n and d_moy = 3\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n union all\n select ws_quantity*ws_list_price sales\n from web_sales\n ,date_dim\n where d_year = 2000\n and d_moy = 3\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer))\n limit 100\"\"\",\n \"q23b\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (2000,2000 + 1,2000 + 2,2000 + 3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\n from max_store_sales))\n select c_last_name,c_first_name,sales\n from (select c_last_name,c_first_name,sum(cs_quantity*cs_list_price) sales\n from catalog_sales\n ,customer\n ,date_dim\n where d_year = 2000\n and d_moy = 3\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and cs_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name\n union all\n select c_last_name,c_first_name,sum(ws_quantity*ws_list_price) sales\n from web_sales\n ,customer\n ,date_dim\n where d_year = 2000\n and d_moy = 3\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and ws_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name)\n order by c_last_name,c_first_name,sales\n limit 100\"\"\",\n \"q24a\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_sales_price) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\nand s_market_id=10\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'snow'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q24b\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_sales_price) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\n and s_market_id = 10\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'chiffon'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q25\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,sum(ss_net_profit) as store_sales_profit\n ,sum(sr_net_loss) as store_returns_loss\n ,sum(cs_net_profit) as catalog_sales_profit\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 2000\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 10\n and d2.d_year = 2000\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_moy between 4 and 10\n and d3.d_year = 2000\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q26\" ->\n \"\"\"\nselect i_item_id,\n avg(cs_quantity) agg1,\n avg(cs_list_price) agg2,\n avg(cs_coupon_amt) agg3,\n avg(cs_sales_price) agg4\n from catalog_sales, customer_demographics, date_dim, item, promotion\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd_demo_sk and\n cs_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'S' and\n cd_education_status = 'College' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 1998\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q27\" ->\n \"\"\"\nselect i_item_id,\n s_state, grouping(s_state) g_state,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, store, item\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_store_sk = s_store_sk and\n ss_cdemo_sk = cd_demo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'U' and\n cd_education_status = '2 yr Degree' and\n d_year = 2000 and\n s_state in ('AL','IN', 'SC', 'NY', 'OH', 'FL')\n group by rollup (i_item_id, s_state)\n order by i_item_id\n ,s_state\n limit 100\"\"\",\n \"q28\" ->\n \"\"\"\nselect *\nfrom (select avg(ss_list_price) B1_LP\n ,count(ss_list_price) B1_CNT\n ,count(distinct ss_list_price) B1_CNTD\n from store_sales\n where ss_quantity between 0 and 5\n and (ss_list_price between 73 and 73+10\n or ss_coupon_amt between 7826 and 7826+1000\n or ss_wholesale_cost between 70 and 70+20)) B1,\n (select avg(ss_list_price) B2_LP\n ,count(ss_list_price) B2_CNT\n ,count(distinct ss_list_price) B2_CNTD\n from store_sales\n where ss_quantity between 6 and 10\n and (ss_list_price between 152 and 152+10\n or ss_coupon_amt between 2196 and 2196+1000\n or ss_wholesale_cost between 56 and 56+20)) B2,\n (select avg(ss_list_price) B3_LP\n ,count(ss_list_price) B3_CNT\n ,count(distinct ss_list_price) B3_CNTD\n from store_sales\n where ss_quantity between 11 and 15\n and (ss_list_price between 53 and 53+10\n or ss_coupon_amt between 3430 and 3430+1000\n or ss_wholesale_cost between 13 and 13+20)) B3,\n (select avg(ss_list_price) B4_LP\n ,count(ss_list_price) B4_CNT\n ,count(distinct ss_list_price) B4_CNTD\n from store_sales\n where ss_quantity between 16 and 20\n and (ss_list_price between 182 and 182+10\n or ss_coupon_amt between 3262 and 3262+1000\n or ss_wholesale_cost between 20 and 20+20)) B4,\n (select avg(ss_list_price) B5_LP\n ,count(ss_list_price) B5_CNT\n ,count(distinct ss_list_price) B5_CNTD\n from store_sales\n where ss_quantity between 21 and 25\n and (ss_list_price between 85 and 85+10\n or ss_coupon_amt between 3310 and 3310+1000\n or ss_wholesale_cost between 37 and 37+20)) B5,\n (select avg(ss_list_price) B6_LP\n ,count(ss_list_price) B6_CNT\n ,count(distinct ss_list_price) B6_CNTD\n from store_sales\n where ss_quantity between 26 and 30\n and (ss_list_price between 180 and 180+10\n or ss_coupon_amt between 12592 and 12592+1000\n or ss_wholesale_cost between 22 and 22+20)) B6\nlimit 100\"\"\",\n \"q29\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,stddev_samp(ss_quantity) as store_sales_quantity\n ,stddev_samp(sr_return_quantity) as store_returns_quantity\n ,stddev_samp(cs_quantity) as catalog_sales_quantity\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 1998\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 4 + 3\n and d2.d_year = 1998\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_year in (1998,1998+1,1998+2)\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q30\" ->\n \"\"\"\nwith customer_total_return as\n (select wr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(wr_return_amt) as ctr_total_return\n from web_returns\n ,date_dim\n ,customer_address\n where wr_returned_date_sk = d_date_sk\n and d_year =2000\n and wr_returning_addr_sk = ca_address_sk\n group by wr_returning_customer_sk\n ,ca_state)\n select c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'GA'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\nlimit 100\"\"\",\n \"q31\" ->\n \"\"\"\nwith ss as\n (select ca_county,d_qoy, d_year,sum(ss_ext_sales_price) as store_sales\n from store_sales,date_dim,customer_address\n where ss_sold_date_sk = d_date_sk\n and ss_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year),\n ws as\n (select ca_county,d_qoy, d_year,sum(ws_ext_sales_price) as web_sales\n from web_sales,date_dim,customer_address\n where ws_sold_date_sk = d_date_sk\n and ws_bill_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year)\n select\n ss1.ca_county\n ,ss1.d_year\n ,ws2.web_sales/ws1.web_sales web_q1_q2_increase\n ,ss2.store_sales/ss1.store_sales store_q1_q2_increase\n ,ws3.web_sales/ws2.web_sales web_q2_q3_increase\n ,ss3.store_sales/ss2.store_sales store_q2_q3_increase\n from\n ss ss1\n ,ss ss2\n ,ss ss3\n ,ws ws1\n ,ws ws2\n ,ws ws3\n where\n ss1.d_qoy = 1\n and ss1.d_year = 1999\n and ss1.ca_county = ss2.ca_county\n and ss2.d_qoy = 2\n and ss2.d_year = 1999\n and ss2.ca_county = ss3.ca_county\n and ss3.d_qoy = 3\n and ss3.d_year = 1999\n and ss1.ca_county = ws1.ca_county\n and ws1.d_qoy = 1\n and ws1.d_year = 1999\n and ws1.ca_county = ws2.ca_county\n and ws2.d_qoy = 2\n and ws2.d_year = 1999\n and ws1.ca_county = ws3.ca_county\n and ws3.d_qoy = 3\n and ws3.d_year =1999\n and case when ws1.web_sales > 0 then ws2.web_sales/ws1.web_sales else null end\n > case when ss1.store_sales > 0 then ss2.store_sales/ss1.store_sales else null end\n and case when ws2.web_sales > 0 then ws3.web_sales/ws2.web_sales else null end\n > case when ss2.store_sales > 0 then ss3.store_sales/ss2.store_sales else null end\n order by ss1.d_year\"\"\",\n \"q32\" ->\n \"\"\"\nselect sum(cs_ext_discount_amt) as `excess discount amount`\nfrom\n catalog_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 66\nand i_item_sk = cs_item_sk\nand d_date between '2002-03-29' and\n (cast('2002-03-29' as date) + INTERVAL 90 days)\nand d_date_sk = cs_sold_date_sk\nand cs_ext_discount_amt\n > (\n select\n 1.3 * avg(cs_ext_discount_amt)\n from\n catalog_sales\n ,date_dim\n where\n cs_item_sk = i_item_sk\n and d_date between '2002-03-29' and\n (cast('2002-03-29' as date) + INTERVAL 90 days)\n and d_date_sk = cs_sold_date_sk\n )\nlimit 100\"\"\",\n \"q33\" ->\n \"\"\"\nwith ss as (\n select\n i_manufact_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Home'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 5\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id),\n cs as (\n select\n i_manufact_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Home'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 5\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id),\n ws as (\n select\n i_manufact_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Home'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 5\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id)\n select i_manufact_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_manufact_id\n order by total_sales\nlimit 100\"\"\",\n \"q34\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (date_dim.d_dom between 1 and 3 or date_dim.d_dom between 25 and 28)\n and (household_demographics.hd_buy_potential = '>10000' or\n household_demographics.hd_buy_potential = 'Unknown')\n and household_demographics.hd_vehicle_count > 0\n and (case when household_demographics.hd_vehicle_count > 0\n\tthen household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count\n\telse null\n\tend) > 1.2\n and date_dim.d_year in (2000,2000+1,2000+2)\n and store.s_county in ('Salem County','Terrell County','Arthur County','Oglethorpe County',\n 'Lunenburg County','Perry County','Halifax County','Sumner County')\n group by ss_ticket_number,ss_customer_sk) dn,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 15 and 20\n order by c_last_name,c_first_name,c_salutation,c_preferred_cust_flag desc, ss_ticket_number\"\"\",\n \"q35\" ->\n \"\"\"\nselect\n ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n count(*) cnt1,\n avg(cd_dep_count),\n min(cd_dep_count),\n stddev_samp(cd_dep_count),\n cd_dep_employed_count,\n count(*) cnt2,\n avg(cd_dep_employed_count),\n min(cd_dep_employed_count),\n stddev_samp(cd_dep_employed_count),\n cd_dep_college_count,\n count(*) cnt3,\n avg(cd_dep_college_count),\n min(cd_dep_college_count),\n stddev_samp(cd_dep_college_count)\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_qoy < 4) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_qoy < 4) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_qoy < 4))\n group by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n limit 100\"\"\",\n \"q36\" ->\n \"\"\"\nselect\n sum(ss_net_profit)/sum(ss_ext_sales_price) as gross_margin\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ss_net_profit)/sum(ss_ext_sales_price) asc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,item\n ,store\n where\n d1.d_year = 1999\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and s_state in ('IN','AL','MI','MN',\n 'TN','LA','FL','NM')\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then i_category end\n ,rank_within_parent\n limit 100\"\"\",\n \"q37\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, catalog_sales\n where i_current_price between 39 and 39 + 30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2001-01-16' as date) and (cast('2001-01-16' as date) + interval 60 days)\n and i_manufact_id in (765,886,889,728)\n and inv_quantity_on_hand between 100 and 500\n and cs_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q38\" ->\n \"\"\"\nselect count(*) from (\n select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1186 and 1186 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1186 and 1186 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1186 and 1186 + 11\n) hot_cust\nlimit 100\"\"\",\n \"q39a\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =2000\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=2\n and inv2.d_moy=2+1\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q39b\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =2000\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=2\n and inv2.d_moy=2+1\n and inv1.cov > 1.5\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q40\" ->\n \"\"\"\nselect\n w_state\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('2000-03-18' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_before\n ,sum(case when (cast(d_date as date) >= cast ('2000-03-18' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_after\n from\n catalog_sales left outer join catalog_returns on\n (cs_order_number = cr_order_number\n and cs_item_sk = cr_item_sk)\n ,warehouse\n ,item\n ,date_dim\n where\n i_current_price between 0.99 and 1.49\n and i_item_sk = cs_item_sk\n and cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and d_date between (cast ('2000-03-18' as date) - INTERVAL 30 days)\n and (cast ('2000-03-18' as date) + INTERVAL 30 days)\n group by\n w_state,i_item_id\n order by w_state,i_item_id\nlimit 100\"\"\",\n \"q41\" ->\n \"\"\"\nselect distinct(i_product_name)\n from item i1\n where i_manufact_id between 970 and 970+40\n and (select count(*) as item_cnt\n from item\n where (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'frosted' or i_color = 'rose') and\n (i_units = 'Lb' or i_units = 'Gross') and\n (i_size = 'medium' or i_size = 'large')\n ) or\n (i_category = 'Women' and\n (i_color = 'chocolate' or i_color = 'black') and\n (i_units = 'Box' or i_units = 'Dram') and\n (i_size = 'economy' or i_size = 'petite')\n ) or\n (i_category = 'Men' and\n (i_color = 'slate' or i_color = 'magenta') and\n (i_units = 'Carton' or i_units = 'Bundle') and\n (i_size = 'N/A' or i_size = 'small')\n ) or\n (i_category = 'Men' and\n (i_color = 'cornflower' or i_color = 'firebrick') and\n (i_units = 'Pound' or i_units = 'Oz') and\n (i_size = 'medium' or i_size = 'large')\n ))) or\n (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'almond' or i_color = 'steel') and\n (i_units = 'Tsp' or i_units = 'Case') and\n (i_size = 'medium' or i_size = 'large')\n ) or\n (i_category = 'Women' and\n (i_color = 'purple' or i_color = 'aquamarine') and\n (i_units = 'Bunch' or i_units = 'Gram') and\n (i_size = 'economy' or i_size = 'petite')\n ) or\n (i_category = 'Men' and\n (i_color = 'lavender' or i_color = 'papaya') and\n (i_units = 'Pallet' or i_units = 'Cup') and\n (i_size = 'N/A' or i_size = 'small')\n ) or\n (i_category = 'Men' and\n (i_color = 'maroon' or i_color = 'cyan') and\n (i_units = 'Each' or i_units = 'N/A') and\n (i_size = 'medium' or i_size = 'large')\n )))) > 0\n order by i_product_name\n limit 100\"\"\",\n \"q42\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_category_id\n \t,item.i_category\n \t,sum(ss_ext_sales_price)\n from \tdate_dim dt\n \t,store_sales\n \t,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n \tand store_sales.ss_item_sk = item.i_item_sk\n \tand item.i_manager_id = 1\n \tand dt.d_moy=12\n \tand dt.d_year=1998\n group by \tdt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\n order by sum(ss_ext_sales_price) desc,dt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\nlimit 100 \"\"\",\n \"q43\" ->\n \"\"\"\nselect s_store_name, s_store_id,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from date_dim, store_sales, store\n where d_date_sk = ss_sold_date_sk and\n s_store_sk = ss_store_sk and\n s_gmt_offset = -6 and\n d_year = 2001\n group by s_store_name, s_store_id\n order by s_store_name, s_store_id,sun_sales,mon_sales,tue_sales,wed_sales,thu_sales,fri_sales,sat_sales\n limit 100\"\"\",\n \"q44\" ->\n \"\"\"\nselect asceding.rnk, i1.i_product_name best_performing, i2.i_product_name worst_performing\nfrom(select *\n from (select item_sk,rank() over (order by rank_col asc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 366\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 366\n and ss_cdemo_sk is null\n group by ss_store_sk))V1)V11\n where rnk < 11) asceding,\n (select *\n from (select item_sk,rank() over (order by rank_col desc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 366\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 366\n and ss_cdemo_sk is null\n group by ss_store_sk))V2)V21\n where rnk < 11) descending,\nitem i1,\nitem i2\nwhere asceding.rnk = descending.rnk\n and i1.i_item_sk=asceding.item_sk\n and i2.i_item_sk=descending.item_sk\norder by asceding.rnk\nlimit 100\"\"\",\n \"q45\" ->\n \"\"\"\nselect ca_zip, ca_county, sum(ws_sales_price)\n from web_sales, customer, customer_address, date_dim, item\n where ws_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ws_item_sk = i_item_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475', '85392', '85460', '80348', '81792')\n \t or\n \t i_item_id in (select i_item_id\n from item\n where i_item_sk in (2, 3, 5, 7, 11, 13, 17, 19, 23, 29)\n )\n \t )\n \tand ws_sold_date_sk = d_date_sk\n \tand d_qoy = 1 and d_year = 1998\n group by ca_zip, ca_county\n order by ca_zip, ca_county\n limit 100\"\"\",\n \"q46\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and (household_demographics.hd_dep_count = 0 or\n household_demographics.hd_vehicle_count= 1)\n and date_dim.d_dow in (6,0)\n and date_dim.d_year in (2000,2000+1,2000+2)\n and store.s_city in ('Five Forks','Oakland','Fairview','Winchester','Farmington')\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,ca_city) dn,customer,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n limit 100\"\"\",\n \"q47\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand,\n s_store_name, s_company_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n s_store_name, s_company_name\n order by d_year, d_moy) rn\n from item, store_sales, date_dim, store\n where ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n (\n d_year = 1999 or\n ( d_year = 1999-1 and d_moy =12) or\n ( d_year = 1999+1 and d_moy =1)\n )\n group by i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy),\n v2 as(\n select v1.s_store_name\n ,v1.d_year, v1.d_moy\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1.s_store_name = v1_lag.s_store_name and\n v1.s_store_name = v1_lead.s_store_name and\n v1.s_company_name = v1_lag.s_company_name and\n v1.s_company_name = v1_lead.s_company_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 1999 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, sum_sales\n limit 100\"\"\",\n \"q48\" ->\n \"\"\"\nselect sum (ss_quantity)\n from store_sales, store, customer_demographics, customer_address, date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 1998\n and\n (\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'M'\n and\n cd_education_status = 'Unknown'\n and\n ss_sales_price between 100.00 and 150.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'W'\n and\n cd_education_status = 'College'\n and\n ss_sales_price between 50.00 and 100.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'D'\n and\n cd_education_status = 'Primary'\n and\n ss_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('MI', 'GA', 'NH')\n and ss_net_profit between 0 and 2000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('TX', 'KY', 'SD')\n and ss_net_profit between 150 and 3000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('NY', 'OH', 'FL')\n and ss_net_profit between 50 and 25000\n )\n )\"\"\",\n \"q49\" ->\n \"\"\"\nselect channel, item, return_ratio, return_rank, currency_rank from\n (select\n 'web' as channel\n ,web.item\n ,web.return_ratio\n ,web.return_rank\n ,web.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect ws.ws_item_sk as item\n \t\t,(cast(sum(coalesce(wr.wr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(wr.wr_return_amt,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\t web_sales ws left outer join web_returns wr\n \t\t\ton (ws.ws_order_number = wr.wr_order_number and\n \t\t\tws.ws_item_sk = wr.wr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\twr.wr_return_amt > 10000\n \t\t\tand ws.ws_net_profit > 1\n and ws.ws_net_paid > 0\n and ws.ws_quantity > 0\n and ws_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 12\n \t\tgroup by ws.ws_item_sk\n \t) in_web\n ) web\n where\n (\n web.return_rank <= 10\n or\n web.currency_rank <= 10\n )\n union\n select\n 'catalog' as channel\n ,catalog.item\n ,catalog.return_ratio\n ,catalog.return_rank\n ,catalog.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect\n \t\tcs.cs_item_sk as item\n \t\t,(cast(sum(coalesce(cr.cr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(cr.cr_return_amount,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tcatalog_sales cs left outer join catalog_returns cr\n \t\t\ton (cs.cs_order_number = cr.cr_order_number and\n \t\t\tcs.cs_item_sk = cr.cr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tcr.cr_return_amount > 10000\n \t\t\tand cs.cs_net_profit > 1\n and cs.cs_net_paid > 0\n and cs.cs_quantity > 0\n and cs_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 12\n group by cs.cs_item_sk\n \t) in_cat\n ) catalog\n where\n (\n catalog.return_rank <= 10\n or\n catalog.currency_rank <=10\n )\n union\n select\n 'store' as channel\n ,store.item\n ,store.return_ratio\n ,store.return_rank\n ,store.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect sts.ss_item_sk as item\n \t\t,(cast(sum(coalesce(sr.sr_return_quantity,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(sr.sr_return_amt,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tstore_sales sts left outer join store_returns sr\n \t\t\ton (sts.ss_ticket_number = sr.sr_ticket_number and sts.ss_item_sk = sr.sr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tsr.sr_return_amt > 10000\n \t\t\tand sts.ss_net_profit > 1\n and sts.ss_net_paid > 0\n and sts.ss_quantity > 0\n and ss_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 12\n \t\tgroup by sts.ss_item_sk\n \t) in_store\n ) store\n where (\n store.return_rank <= 10\n or\n store.currency_rank <= 10\n )\n )\n order by 1,4,5,2\n limit 100\"\"\",\n \"q50\" ->\n \"\"\"\nselect\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 30) and\n (sr_returned_date_sk - ss_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 60) and\n (sr_returned_date_sk - ss_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 90) and\n (sr_returned_date_sk - ss_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n store_sales\n ,store_returns\n ,store\n ,date_dim d1\n ,date_dim d2\nwhere\n d2.d_year = 1998\nand d2.d_moy = 9\nand ss_ticket_number = sr_ticket_number\nand ss_item_sk = sr_item_sk\nand ss_sold_date_sk = d1.d_date_sk\nand sr_returned_date_sk = d2.d_date_sk\nand ss_customer_sk = sr_customer_sk\nand ss_store_sk = s_store_sk\ngroup by\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\norder by s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\nlimit 100\"\"\",\n \"q51\" ->\n \"\"\"\nWITH web_v1 as (\nselect\n ws_item_sk item_sk, d_date,\n sum(sum(ws_sales_price))\n over (partition by ws_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom web_sales\n ,date_dim\nwhere ws_sold_date_sk=d_date_sk\n and d_month_seq between 1214 and 1214+11\n and ws_item_sk is not NULL\ngroup by ws_item_sk, d_date),\nstore_v1 as (\nselect\n ss_item_sk item_sk, d_date,\n sum(sum(ss_sales_price))\n over (partition by ss_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom store_sales\n ,date_dim\nwhere ss_sold_date_sk=d_date_sk\n and d_month_seq between 1214 and 1214+11\n and ss_item_sk is not NULL\ngroup by ss_item_sk, d_date)\n select *\nfrom (select item_sk\n ,d_date\n ,web_sales\n ,store_sales\n ,max(web_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) web_cumulative\n ,max(store_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) store_cumulative\n from (select case when web.item_sk is not null then web.item_sk else store.item_sk end item_sk\n ,case when web.d_date is not null then web.d_date else store.d_date end d_date\n ,web.cume_sales web_sales\n ,store.cume_sales store_sales\n from web_v1 web full outer join store_v1 store on (web.item_sk = store.item_sk\n and web.d_date = store.d_date)\n )x )y\nwhere web_cumulative > store_cumulative\norder by item_sk\n ,d_date\nlimit 100\"\"\",\n \"q52\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_brand_id brand_id\n \t,item.i_brand brand\n \t,sum(ss_ext_sales_price) ext_price\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manager_id = 1\n and dt.d_moy=12\n and dt.d_year=2000\n group by dt.d_year\n \t,item.i_brand\n \t,item.i_brand_id\n order by dt.d_year\n \t,ext_price desc\n \t,brand_id\nlimit 100 \"\"\",\n \"q53\" ->\n \"\"\"\nselect * from\n(select i_manufact_id,\nsum(ss_sales_price) sum_sales,\navg(sum(ss_sales_price)) over (partition by i_manufact_id) avg_quarterly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\nss_sold_date_sk = d_date_sk and\nss_store_sk = s_store_sk and\nd_month_seq in (1212,1212+1,1212+2,1212+3,1212+4,1212+5,1212+6,1212+7,1212+8,1212+9,1212+10,1212+11) and\n((i_category in ('Books','Children','Electronics') and\ni_class in ('personal','portable','reference','self-help') and\ni_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t'exportiunivamalg #9','scholaramalgamalg #9'))\nor(i_category in ('Women','Music','Men') and\ni_class in ('accessories','classical','fragrances','pants') and\ni_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t'importoamalg #1')))\ngroup by i_manufact_id, d_qoy ) tmp1\nwhere case when avg_quarterly_sales > 0\n\tthen abs (sum_sales - avg_quarterly_sales)/ avg_quarterly_sales\n\telse null end > 0.1\norder by avg_quarterly_sales,\n\t sum_sales,\n\t i_manufact_id\nlimit 100\"\"\",\n \"q54\" ->\n \"\"\"\nwith my_customers as (\n select distinct c_customer_sk\n , c_current_addr_sk\n from\n ( select cs_sold_date_sk sold_date_sk,\n cs_bill_customer_sk customer_sk,\n cs_item_sk item_sk\n from catalog_sales\n union all\n select ws_sold_date_sk sold_date_sk,\n ws_bill_customer_sk customer_sk,\n ws_item_sk item_sk\n from web_sales\n ) cs_or_ws_sales,\n item,\n date_dim,\n customer\n where sold_date_sk = d_date_sk\n and item_sk = i_item_sk\n and i_category = 'Books'\n and i_class = 'business'\n and c_customer_sk = cs_or_ws_sales.customer_sk\n and d_moy = 2\n and d_year = 2000\n )\n , my_revenue as (\n select c_customer_sk,\n sum(ss_ext_sales_price) as revenue\n from my_customers,\n store_sales,\n customer_address,\n store,\n date_dim\n where c_current_addr_sk = ca_address_sk\n and ca_county = s_county\n and ca_state = s_state\n and ss_sold_date_sk = d_date_sk\n and c_customer_sk = ss_customer_sk\n and d_month_seq between (select distinct d_month_seq+1\n from date_dim where d_year = 2000 and d_moy = 2)\n and (select distinct d_month_seq+3\n from date_dim where d_year = 2000 and d_moy = 2)\n group by c_customer_sk\n )\n , segments as\n (select cast((revenue/50) as int) as segment\n from my_revenue\n )\n select segment, count(*) as num_customers, segment*50 as segment_base\n from segments\n group by segment\n order by segment, num_customers\n limit 100\"\"\",\n \"q55\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item\n where d_date_sk = ss_sold_date_sk\n \tand ss_item_sk = i_item_sk\n \tand i_manager_id=13\n \tand d_moy=11\n \tand d_year=1999\n group by i_brand, i_brand_id\n order by ext_price desc, i_brand_id\nlimit 100 \"\"\",\n \"q56\" ->\n \"\"\"\nwith ss as (\n select i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('chiffon','smoke','lace'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 2001\n and d_moy = 5\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_item_id),\n cs as (\n select i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('chiffon','smoke','lace'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 2001\n and d_moy = 5\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_item_id),\n ws as (\n select i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('chiffon','smoke','lace'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 2001\n and d_moy = 5\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_item_id)\n select i_item_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by total_sales,\n i_item_id\n limit 100\"\"\",\n \"q57\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n cc_name,\n d_year, d_moy,\n sum(cs_sales_price) sum_sales,\n avg(sum(cs_sales_price)) over\n (partition by i_category, i_brand,\n cc_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n cc_name\n order by d_year, d_moy) rn\n from item, catalog_sales, date_dim, call_center\n where cs_item_sk = i_item_sk and\n cs_sold_date_sk = d_date_sk and\n cc_call_center_sk= cs_call_center_sk and\n (\n d_year = 1999 or\n ( d_year = 1999-1 and d_moy =12) or\n ( d_year = 1999+1 and d_moy =1)\n )\n group by i_category, i_brand,\n cc_name , d_year, d_moy),\n v2 as(\n select v1.i_category, v1.i_brand\n ,v1.d_year, v1.d_moy\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1. cc_name = v1_lag. cc_name and\n v1. cc_name = v1_lead. cc_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 1999 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, avg_monthly_sales\n limit 100\"\"\",\n \"q58\" ->\n \"\"\"\nwith ss_items as\n (select i_item_id item_id\n ,sum(ss_ext_sales_price) ss_item_rev\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '1998-02-21'))\n and ss_sold_date_sk = d_date_sk\n group by i_item_id),\n cs_items as\n (select i_item_id item_id\n ,sum(cs_ext_sales_price) cs_item_rev\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '1998-02-21'))\n and cs_sold_date_sk = d_date_sk\n group by i_item_id),\n ws_items as\n (select i_item_id item_id\n ,sum(ws_ext_sales_price) ws_item_rev\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq =(select d_week_seq\n from date_dim\n where d_date = '1998-02-21'))\n and ws_sold_date_sk = d_date_sk\n group by i_item_id)\n select ss_items.item_id\n ,ss_item_rev\n ,ss_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ss_dev\n ,cs_item_rev\n ,cs_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 cs_dev\n ,ws_item_rev\n ,ws_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ws_dev\n ,(ss_item_rev+cs_item_rev+ws_item_rev)/3 average\n from ss_items,cs_items,ws_items\n where ss_items.item_id=cs_items.item_id\n and ss_items.item_id=ws_items.item_id\n and ss_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n and ss_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and cs_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and cs_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and ws_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and ws_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n order by item_id\n ,ss_item_rev\n limit 100\"\"\",\n \"q59\" ->\n \"\"\"\nwith wss as\n (select d_week_seq,\n ss_store_sk,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n group by d_week_seq,ss_store_sk\n )\n select s_store_name1,s_store_id1,d_week_seq1\n ,sun_sales1/sun_sales2,mon_sales1/mon_sales2\n ,tue_sales1/tue_sales2,wed_sales1/wed_sales2,thu_sales1/thu_sales2\n ,fri_sales1/fri_sales2,sat_sales1/sat_sales2\n from\n (select s_store_name s_store_name1,wss.d_week_seq d_week_seq1\n ,s_store_id s_store_id1,sun_sales sun_sales1\n ,mon_sales mon_sales1,tue_sales tue_sales1\n ,wed_sales wed_sales1,thu_sales thu_sales1\n ,fri_sales fri_sales1,sat_sales sat_sales1\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1205 and 1205 + 11) y,\n (select s_store_name s_store_name2,wss.d_week_seq d_week_seq2\n ,s_store_id s_store_id2,sun_sales sun_sales2\n ,mon_sales mon_sales2,tue_sales tue_sales2\n ,wed_sales wed_sales2,thu_sales thu_sales2\n ,fri_sales fri_sales2,sat_sales sat_sales2\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1205+ 12 and 1205 + 23) x\n where s_store_id1=s_store_id2\n and d_week_seq1=d_week_seq2-52\n order by s_store_name1,s_store_id1,d_week_seq1\nlimit 100\"\"\",\n \"q60\" ->\n \"\"\"\nwith ss as (\n select\n i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Children'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 10\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n cs as (\n select\n i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Children'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 10\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n ws as (\n select\n i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Children'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 10\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id)\n select\n i_item_id\n,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by i_item_id\n ,total_sales\n limit 100\"\"\",\n \"q61\" ->\n \"\"\"\nselect promotions,total,cast(promotions as decimal(15,4))/cast(total as decimal(15,4))*100\nfrom\n (select sum(ss_ext_sales_price) promotions\n from store_sales\n ,store\n ,promotion\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_promo_sk = p_promo_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -6\n and i_category = 'Sports'\n and (p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y')\n and s_gmt_offset = -6\n and d_year = 2001\n and d_moy = 12) promotional_sales,\n (select sum(ss_ext_sales_price) total\n from store_sales\n ,store\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -6\n and i_category = 'Sports'\n and s_gmt_offset = -6\n and d_year = 2001\n and d_moy = 12) all_sales\norder by promotions, total\nlimit 100\"\"\",\n \"q62\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 30) and\n (ws_ship_date_sk - ws_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 60) and\n (ws_ship_date_sk - ws_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 90) and\n (ws_ship_date_sk - ws_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n web_sales\n ,warehouse\n ,ship_mode\n ,web_site\n ,date_dim\nwhere\n d_month_seq between 1215 and 1215 + 11\nand ws_ship_date_sk = d_date_sk\nand ws_warehouse_sk = w_warehouse_sk\nand ws_ship_mode_sk = sm_ship_mode_sk\nand ws_web_site_sk = web_site_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\nlimit 100\"\"\",\n \"q63\" ->\n \"\"\"\nselect *\nfrom (select i_manager_id\n ,sum(ss_sales_price) sum_sales\n ,avg(sum(ss_sales_price)) over (partition by i_manager_id) avg_monthly_sales\n from item\n ,store_sales\n ,date_dim\n ,store\n where ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and d_month_seq in (1211,1211+1,1211+2,1211+3,1211+4,1211+5,1211+6,1211+7,1211+8,1211+9,1211+10,1211+11)\n and (( i_category in ('Books','Children','Electronics')\n and i_class in ('personal','portable','reference','self-help')\n and i_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t 'exportiunivamalg #9','scholaramalgamalg #9'))\n or( i_category in ('Women','Music','Men')\n and i_class in ('accessories','classical','fragrances','pants')\n and i_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t 'importoamalg #1')))\ngroup by i_manager_id, d_moy) tmp1\nwhere case when avg_monthly_sales > 0 then abs (sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\norder by i_manager_id\n ,avg_monthly_sales\n ,sum_sales\nlimit 100\"\"\",\n \"q64\" ->\n \"\"\"\nwith cs_ui as\n (select cs_item_sk\n ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund\n from catalog_sales\n ,catalog_returns\n where cs_item_sk = cr_item_sk\n and cs_order_number = cr_order_number\n group by cs_item_sk\n having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit)),\ncross_sales as\n (select i_product_name product_name\n ,i_item_sk item_sk\n ,s_store_name store_name\n ,s_zip store_zip\n ,ad1.ca_street_number b_street_number\n ,ad1.ca_street_name b_street_name\n ,ad1.ca_city b_city\n ,ad1.ca_zip b_zip\n ,ad2.ca_street_number c_street_number\n ,ad2.ca_street_name c_street_name\n ,ad2.ca_city c_city\n ,ad2.ca_zip c_zip\n ,d1.d_year as syear\n ,d2.d_year as fsyear\n ,d3.d_year s2year\n ,count(*) cnt\n ,sum(ss_wholesale_cost) s1\n ,sum(ss_list_price) s2\n ,sum(ss_coupon_amt) s3\n FROM store_sales\n ,store_returns\n ,cs_ui\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,customer\n ,customer_demographics cd1\n ,customer_demographics cd2\n ,promotion\n ,household_demographics hd1\n ,household_demographics hd2\n ,customer_address ad1\n ,customer_address ad2\n ,income_band ib1\n ,income_band ib2\n ,item\n WHERE ss_store_sk = s_store_sk AND\n ss_sold_date_sk = d1.d_date_sk AND\n ss_customer_sk = c_customer_sk AND\n ss_cdemo_sk= cd1.cd_demo_sk AND\n ss_hdemo_sk = hd1.hd_demo_sk AND\n ss_addr_sk = ad1.ca_address_sk and\n ss_item_sk = i_item_sk and\n ss_item_sk = sr_item_sk and\n ss_ticket_number = sr_ticket_number and\n ss_item_sk = cs_ui.cs_item_sk and\n c_current_cdemo_sk = cd2.cd_demo_sk AND\n c_current_hdemo_sk = hd2.hd_demo_sk AND\n c_current_addr_sk = ad2.ca_address_sk and\n c_first_sales_date_sk = d2.d_date_sk and\n c_first_shipto_date_sk = d3.d_date_sk and\n ss_promo_sk = p_promo_sk and\n hd1.hd_income_band_sk = ib1.ib_income_band_sk and\n hd2.hd_income_band_sk = ib2.ib_income_band_sk and\n cd1.cd_marital_status <> cd2.cd_marital_status and\n i_color in ('azure','gainsboro','misty','blush','hot','lemon') and\n i_current_price between 80 and 80 + 10 and\n i_current_price between 80 + 1 and 80 + 15\ngroup by i_product_name\n ,i_item_sk\n ,s_store_name\n ,s_zip\n ,ad1.ca_street_number\n ,ad1.ca_street_name\n ,ad1.ca_city\n ,ad1.ca_zip\n ,ad2.ca_street_number\n ,ad2.ca_street_name\n ,ad2.ca_city\n ,ad2.ca_zip\n ,d1.d_year\n ,d2.d_year\n ,d3.d_year\n)\nselect cs1.product_name\n ,cs1.store_name\n ,cs1.store_zip\n ,cs1.b_street_number\n ,cs1.b_street_name\n ,cs1.b_city\n ,cs1.b_zip\n ,cs1.c_street_number\n ,cs1.c_street_name\n ,cs1.c_city\n ,cs1.c_zip\n ,cs1.syear\n ,cs1.cnt\n ,cs1.s1 as s11\n ,cs1.s2 as s21\n ,cs1.s3 as s31\n ,cs2.s1 as s12\n ,cs2.s2 as s22\n ,cs2.s3 as s32\n ,cs2.syear\n ,cs2.cnt\nfrom cross_sales cs1,cross_sales cs2\nwhere cs1.item_sk=cs2.item_sk and\n cs1.syear = 1999 and\n cs2.syear = 1999 + 1 and\n cs2.cnt <= cs1.cnt and\n cs1.store_name = cs2.store_name and\n cs1.store_zip = cs2.store_zip\norder by cs1.product_name\n ,cs1.store_name\n ,cs2.cnt\n ,cs1.s1\n ,cs2.s1\"\"\",\n \"q65\" ->\n \"\"\"\nselect\n\ts_store_name,\n\ti_item_desc,\n\tsc.revenue,\n\ti_current_price,\n\ti_wholesale_cost,\n\ti_brand\n from store, item,\n (select ss_store_sk, avg(revenue) as ave\n \tfrom\n \t (select ss_store_sk, ss_item_sk,\n \t\t sum(ss_sales_price) as revenue\n \t\tfrom store_sales, date_dim\n \t\twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1186 and 1186+11\n \t\tgroup by ss_store_sk, ss_item_sk) sa\n \tgroup by ss_store_sk) sb,\n (select ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue\n \tfrom store_sales, date_dim\n \twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1186 and 1186+11\n \tgroup by ss_store_sk, ss_item_sk) sc\n where sb.ss_store_sk = sc.ss_store_sk and\n sc.revenue <= 0.1 * sb.ave and\n s_store_sk = sc.ss_store_sk and\n i_item_sk = sc.ss_item_sk\n order by s_store_name, i_item_desc\nlimit 100\"\"\",\n \"q66\" ->\n \"\"\"\nselect\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,ship_carriers\n ,year\n \t,sum(jan_sales) as jan_sales\n \t,sum(feb_sales) as feb_sales\n \t,sum(mar_sales) as mar_sales\n \t,sum(apr_sales) as apr_sales\n \t,sum(may_sales) as may_sales\n \t,sum(jun_sales) as jun_sales\n \t,sum(jul_sales) as jul_sales\n \t,sum(aug_sales) as aug_sales\n \t,sum(sep_sales) as sep_sales\n \t,sum(oct_sales) as oct_sales\n \t,sum(nov_sales) as nov_sales\n \t,sum(dec_sales) as dec_sales\n \t,sum(jan_sales/w_warehouse_sq_ft) as jan_sales_per_sq_foot\n \t,sum(feb_sales/w_warehouse_sq_ft) as feb_sales_per_sq_foot\n \t,sum(mar_sales/w_warehouse_sq_ft) as mar_sales_per_sq_foot\n \t,sum(apr_sales/w_warehouse_sq_ft) as apr_sales_per_sq_foot\n \t,sum(may_sales/w_warehouse_sq_ft) as may_sales_per_sq_foot\n \t,sum(jun_sales/w_warehouse_sq_ft) as jun_sales_per_sq_foot\n \t,sum(jul_sales/w_warehouse_sq_ft) as jul_sales_per_sq_foot\n \t,sum(aug_sales/w_warehouse_sq_ft) as aug_sales_per_sq_foot\n \t,sum(sep_sales/w_warehouse_sq_ft) as sep_sales_per_sq_foot\n \t,sum(oct_sales/w_warehouse_sq_ft) as oct_sales_per_sq_foot\n \t,sum(nov_sales/w_warehouse_sq_ft) as nov_sales_per_sq_foot\n \t,sum(dec_sales/w_warehouse_sq_ft) as dec_sales_per_sq_foot\n \t,sum(jan_net) as jan_net\n \t,sum(feb_net) as feb_net\n \t,sum(mar_net) as mar_net\n \t,sum(apr_net) as apr_net\n \t,sum(may_net) as may_net\n \t,sum(jun_net) as jun_net\n \t,sum(jul_net) as jul_net\n \t,sum(aug_net) as aug_net\n \t,sum(sep_net) as sep_net\n \t,sum(oct_net) as oct_net\n \t,sum(nov_net) as nov_net\n \t,sum(dec_net) as dec_net\n from (\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'MSC' || ',' || 'GERMA' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen ws_sales_price* ws_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen ws_sales_price* ws_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen ws_sales_price* ws_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen ws_sales_price* ws_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen ws_sales_price* ws_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen ws_sales_price* ws_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen ws_sales_price* ws_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen ws_sales_price* ws_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen ws_sales_price* ws_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen ws_sales_price* ws_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen ws_sales_price* ws_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen ws_sales_price* ws_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as dec_net\n from\n web_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n ws_warehouse_sk = w_warehouse_sk\n and ws_sold_date_sk = d_date_sk\n and ws_sold_time_sk = t_time_sk\n \tand ws_ship_mode_sk = sm_ship_mode_sk\n and d_year = 2001\n \tand t_time between 9453 and 9453+28800\n \tand sm_carrier in ('MSC','GERMA')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n union all\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'MSC' || ',' || 'GERMA' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as dec_net\n from\n catalog_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and cs_sold_time_sk = t_time_sk\n \tand cs_ship_mode_sk = sm_ship_mode_sk\n and d_year = 2001\n \tand t_time between 9453 AND 9453+28800\n \tand sm_carrier in ('MSC','GERMA')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n ) x\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,ship_carriers\n ,year\n order by w_warehouse_name\n limit 100\"\"\",\n \"q67\" ->\n \"\"\"\nselect *\nfrom (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rank() over (partition by i_category order by sumsales desc) rk\n from (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sum(coalesce(ss_sales_price*ss_quantity,0)) sumsales\n from store_sales\n ,date_dim\n ,store\n ,item\n where ss_sold_date_sk=d_date_sk\n and ss_item_sk=i_item_sk\n and ss_store_sk = s_store_sk\n and d_month_seq between 1185 and 1185+11\n group by rollup(i_category, i_class, i_brand, i_product_name, d_year, d_qoy, d_moy,s_store_id))dw1) dw2\nwhere rk <= 100\norder by i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rk\nlimit 100\"\"\",\n \"q68\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,extended_price\n ,extended_tax\n ,list_price\n from (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_ext_sales_price) extended_price\n ,sum(ss_ext_list_price) list_price\n ,sum(ss_ext_tax) extended_tax\n from store_sales\n ,date_dim\n ,store\n ,household_demographics\n ,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_dep_count = 4 or\n household_demographics.hd_vehicle_count= 0)\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_city in ('Pleasant Hill','Bethel')\n group by ss_ticket_number\n ,ss_customer_sk\n ,ss_addr_sk,ca_city) dn\n ,customer\n ,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,ss_ticket_number\n limit 100\"\"\",\n \"q69\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_state in ('MO','MN','AZ') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2003 and\n d_moy between 2 and 2+2) and\n (not exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2003 and\n d_moy between 2 and 2+2) and\n not exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2003 and\n d_moy between 2 and 2+2))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n limit 100\"\"\",\n \"q70\" ->\n \"\"\"\nselect\n sum(ss_net_profit) as total_sum\n ,s_state\n ,s_county\n ,grouping(s_state)+grouping(s_county) as lochierarchy\n ,rank() over (\n \tpartition by grouping(s_state)+grouping(s_county),\n \tcase when grouping(s_county) = 0 then s_state end\n \torder by sum(ss_net_profit) desc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,store\n where\n d1.d_month_seq between 1218 and 1218+11\n and d1.d_date_sk = ss_sold_date_sk\n and s_store_sk = ss_store_sk\n and s_state in\n ( select s_state\n from (select s_state as s_state,\n \t\t\t rank() over ( partition by s_state order by sum(ss_net_profit) desc) as ranking\n from store_sales, store, date_dim\n where d_month_seq between 1218 and 1218+11\n \t\t\t and d_date_sk = ss_sold_date_sk\n \t\t\t and s_store_sk = ss_store_sk\n group by s_state\n ) tmp1\n where ranking <= 5\n )\n group by rollup(s_state,s_county)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then s_state end\n ,rank_within_parent\n limit 100\"\"\",\n \"q71\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,t_hour,t_minute,\n \tsum(ext_price) ext_price\n from item, (select ws_ext_sales_price as ext_price,\n ws_sold_date_sk as sold_date_sk,\n ws_item_sk as sold_item_sk,\n ws_sold_time_sk as time_sk\n from web_sales,date_dim\n where d_date_sk = ws_sold_date_sk\n and d_moy=12\n and d_year=2000\n union all\n select cs_ext_sales_price as ext_price,\n cs_sold_date_sk as sold_date_sk,\n cs_item_sk as sold_item_sk,\n cs_sold_time_sk as time_sk\n from catalog_sales,date_dim\n where d_date_sk = cs_sold_date_sk\n and d_moy=12\n and d_year=2000\n union all\n select ss_ext_sales_price as ext_price,\n ss_sold_date_sk as sold_date_sk,\n ss_item_sk as sold_item_sk,\n ss_sold_time_sk as time_sk\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n and d_moy=12\n and d_year=2000\n ) tmp,time_dim\n where\n sold_item_sk = i_item_sk\n and i_manager_id=1\n and time_sk = t_time_sk\n and (t_meal_time = 'breakfast' or t_meal_time = 'dinner')\n group by i_brand, i_brand_id,t_hour,t_minute\n order by ext_price desc, i_brand_id\n \"\"\",\n \"q72\" ->\n \"\"\"\nselect i_item_desc\n ,w_warehouse_name\n ,d1.d_week_seq\n ,sum(case when p_promo_sk is null then 1 else 0 end) no_promo\n ,sum(case when p_promo_sk is not null then 1 else 0 end) promo\n ,count(*) total_cnt\nfrom catalog_sales\njoin inventory on (cs_item_sk = inv_item_sk)\njoin warehouse on (w_warehouse_sk=inv_warehouse_sk)\njoin item on (i_item_sk = cs_item_sk)\njoin customer_demographics on (cs_bill_cdemo_sk = cd_demo_sk)\njoin household_demographics on (cs_bill_hdemo_sk = hd_demo_sk)\njoin date_dim d1 on (cs_sold_date_sk = d1.d_date_sk)\njoin date_dim d2 on (inv_date_sk = d2.d_date_sk)\njoin date_dim d3 on (cs_ship_date_sk = d3.d_date_sk)\nleft outer join promotion on (cs_promo_sk=p_promo_sk)\nleft outer join catalog_returns on (cr_item_sk = cs_item_sk and cr_order_number = cs_order_number)\nwhere d1.d_week_seq = d2.d_week_seq\n and inv_quantity_on_hand < cs_quantity\n and d3.d_date > d1.d_date + interval 5 days\n and hd_buy_potential = '1001-5000'\n and d1.d_year = 2000\n and cd_marital_status = 'D'\ngroup by i_item_desc,w_warehouse_name,d1.d_week_seq\norder by total_cnt desc, i_item_desc, w_warehouse_name, d_week_seq\nlimit 100\"\"\",\n \"q73\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_buy_potential = '>10000' or\n household_demographics.hd_buy_potential = '5001-10000')\n and household_demographics.hd_vehicle_count > 0\n and case when household_demographics.hd_vehicle_count > 0 then\n household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count else null end > 1\n and date_dim.d_year in (2000,2000+1,2000+2)\n and store.s_county in ('Lea County','Furnas County','Pennington County','Bronx County')\n group by ss_ticket_number,ss_customer_sk) dj,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 1 and 5\n order by cnt desc, c_last_name asc\"\"\",\n \"q74\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,sum(ss_net_paid) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (1998,1998+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,sum(ws_net_paid) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n and d_year in (1998,1998+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n )\n select\n t_s_secyear.customer_id, t_s_secyear.customer_first_name, t_s_secyear.customer_last_name\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.year = 1998\n and t_s_secyear.year = 1998+1\n and t_w_firstyear.year = 1998\n and t_w_secyear.year = 1998+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n order by 3,1,2\nlimit 100\"\"\",\n \"q75\" ->\n \"\"\"\nWITH all_sales AS (\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,SUM(sales_cnt) AS sales_cnt\n ,SUM(sales_amt) AS sales_amt\n FROM (SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,cs_quantity - COALESCE(cr_return_quantity,0) AS sales_cnt\n ,cs_ext_sales_price - COALESCE(cr_return_amount,0.0) AS sales_amt\n FROM catalog_sales JOIN item ON i_item_sk=cs_item_sk\n JOIN date_dim ON d_date_sk=cs_sold_date_sk\n LEFT JOIN catalog_returns ON (cs_order_number=cr_order_number\n AND cs_item_sk=cr_item_sk)\n WHERE i_category='Sports'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ss_quantity - COALESCE(sr_return_quantity,0) AS sales_cnt\n ,ss_ext_sales_price - COALESCE(sr_return_amt,0.0) AS sales_amt\n FROM store_sales JOIN item ON i_item_sk=ss_item_sk\n JOIN date_dim ON d_date_sk=ss_sold_date_sk\n LEFT JOIN store_returns ON (ss_ticket_number=sr_ticket_number\n AND ss_item_sk=sr_item_sk)\n WHERE i_category='Sports'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ws_quantity - COALESCE(wr_return_quantity,0) AS sales_cnt\n ,ws_ext_sales_price - COALESCE(wr_return_amt,0.0) AS sales_amt\n FROM web_sales JOIN item ON i_item_sk=ws_item_sk\n JOIN date_dim ON d_date_sk=ws_sold_date_sk\n LEFT JOIN web_returns ON (ws_order_number=wr_order_number\n AND ws_item_sk=wr_item_sk)\n WHERE i_category='Sports') sales_detail\n GROUP BY d_year, i_brand_id, i_class_id, i_category_id, i_manufact_id)\n SELECT prev_yr.d_year AS prev_year\n ,curr_yr.d_year AS year\n ,curr_yr.i_brand_id\n ,curr_yr.i_class_id\n ,curr_yr.i_category_id\n ,curr_yr.i_manufact_id\n ,prev_yr.sales_cnt AS prev_yr_cnt\n ,curr_yr.sales_cnt AS curr_yr_cnt\n ,curr_yr.sales_cnt-prev_yr.sales_cnt AS sales_cnt_diff\n ,curr_yr.sales_amt-prev_yr.sales_amt AS sales_amt_diff\n FROM all_sales curr_yr, all_sales prev_yr\n WHERE curr_yr.i_brand_id=prev_yr.i_brand_id\n AND curr_yr.i_class_id=prev_yr.i_class_id\n AND curr_yr.i_category_id=prev_yr.i_category_id\n AND curr_yr.i_manufact_id=prev_yr.i_manufact_id\n AND curr_yr.d_year=2001\n AND prev_yr.d_year=2001-1\n AND CAST(curr_yr.sales_cnt AS DECIMAL(17,2))/CAST(prev_yr.sales_cnt AS DECIMAL(17,2))<0.9\n ORDER BY sales_cnt_diff,sales_amt_diff\n limit 100\"\"\",\n \"q76\" ->\n \"\"\"\nselect channel, col_name, d_year, d_qoy, i_category, COUNT(*) sales_cnt, SUM(ext_sales_price) sales_amt FROM (\n SELECT 'store' as channel, 'ss_customer_sk' col_name, d_year, d_qoy, i_category, ss_ext_sales_price ext_sales_price\n FROM store_sales, item, date_dim\n WHERE ss_customer_sk IS NULL\n AND ss_sold_date_sk=d_date_sk\n AND ss_item_sk=i_item_sk\n UNION ALL\n SELECT 'web' as channel, 'ws_ship_addr_sk' col_name, d_year, d_qoy, i_category, ws_ext_sales_price ext_sales_price\n FROM web_sales, item, date_dim\n WHERE ws_ship_addr_sk IS NULL\n AND ws_sold_date_sk=d_date_sk\n AND ws_item_sk=i_item_sk\n UNION ALL\n SELECT 'catalog' as channel, 'cs_ship_mode_sk' col_name, d_year, d_qoy, i_category, cs_ext_sales_price ext_sales_price\n FROM catalog_sales, item, date_dim\n WHERE cs_ship_mode_sk IS NULL\n AND cs_sold_date_sk=d_date_sk\n AND cs_item_sk=i_item_sk) foo\nGROUP BY channel, col_name, d_year, d_qoy, i_category\nORDER BY channel, col_name, d_year, d_qoy, i_category\nlimit 100\"\"\",\n \"q77\" ->\n \"\"\"\nwith ss as\n (select s_store_sk,\n sum(ss_ext_sales_price) as sales,\n sum(ss_net_profit) as profit\n from store_sales,\n date_dim,\n store\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2000-08-16' as date)\n and (cast('2000-08-16' as date) + INTERVAL 30 days)\n and ss_store_sk = s_store_sk\n group by s_store_sk)\n ,\n sr as\n (select s_store_sk,\n sum(sr_return_amt) as returns,\n sum(sr_net_loss) as profit_loss\n from store_returns,\n date_dim,\n store\n where sr_returned_date_sk = d_date_sk\n and d_date between cast('2000-08-16' as date)\n and (cast('2000-08-16' as date) + INTERVAL 30 days)\n and sr_store_sk = s_store_sk\n group by s_store_sk),\n cs as\n (select cs_call_center_sk,\n sum(cs_ext_sales_price) as sales,\n sum(cs_net_profit) as profit\n from catalog_sales,\n date_dim\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2000-08-16' as date)\n and (cast('2000-08-16' as date) + INTERVAL 30 days)\n group by cs_call_center_sk\n ),\n cr as\n (select cr_call_center_sk,\n sum(cr_return_amount) as returns,\n sum(cr_net_loss) as profit_loss\n from catalog_returns,\n date_dim\n where cr_returned_date_sk = d_date_sk\n and d_date between cast('2000-08-16' as date)\n and (cast('2000-08-16' as date) + INTERVAL 30 days)\n group by cr_call_center_sk\n ),\n ws as\n ( select wp_web_page_sk,\n sum(ws_ext_sales_price) as sales,\n sum(ws_net_profit) as profit\n from web_sales,\n date_dim,\n web_page\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2000-08-16' as date)\n and (cast('2000-08-16' as date) + INTERVAL 30 days)\n and ws_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk),\n wr as\n (select wp_web_page_sk,\n sum(wr_return_amt) as returns,\n sum(wr_net_loss) as profit_loss\n from web_returns,\n date_dim,\n web_page\n where wr_returned_date_sk = d_date_sk\n and d_date between cast('2000-08-16' as date)\n and (cast('2000-08-16' as date) + INTERVAL 30 days)\n and wr_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , ss.s_store_sk as id\n , sales\n , coalesce(returns, 0) as returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ss left join sr\n on ss.s_store_sk = sr.s_store_sk\n union all\n select 'catalog channel' as channel\n , cs_call_center_sk as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from cs\n , cr\n union all\n select 'web channel' as channel\n , ws.wp_web_page_sk as id\n , sales\n , coalesce(returns, 0) returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ws left join wr\n on ws.wp_web_page_sk = wr.wp_web_page_sk\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q78\" ->\n \"\"\"\nwith ws as\n (select d_year AS ws_sold_year, ws_item_sk,\n ws_bill_customer_sk ws_customer_sk,\n sum(ws_quantity) ws_qty,\n sum(ws_wholesale_cost) ws_wc,\n sum(ws_sales_price) ws_sp\n from web_sales\n left join web_returns on wr_order_number=ws_order_number and ws_item_sk=wr_item_sk\n join date_dim on ws_sold_date_sk = d_date_sk\n where wr_order_number is null\n group by d_year, ws_item_sk, ws_bill_customer_sk\n ),\ncs as\n (select d_year AS cs_sold_year, cs_item_sk,\n cs_bill_customer_sk cs_customer_sk,\n sum(cs_quantity) cs_qty,\n sum(cs_wholesale_cost) cs_wc,\n sum(cs_sales_price) cs_sp\n from catalog_sales\n left join catalog_returns on cr_order_number=cs_order_number and cs_item_sk=cr_item_sk\n join date_dim on cs_sold_date_sk = d_date_sk\n where cr_order_number is null\n group by d_year, cs_item_sk, cs_bill_customer_sk\n ),\nss as\n (select d_year AS ss_sold_year, ss_item_sk,\n ss_customer_sk,\n sum(ss_quantity) ss_qty,\n sum(ss_wholesale_cost) ss_wc,\n sum(ss_sales_price) ss_sp\n from store_sales\n left join store_returns on sr_ticket_number=ss_ticket_number and ss_item_sk=sr_item_sk\n join date_dim on ss_sold_date_sk = d_date_sk\n where sr_ticket_number is null\n group by d_year, ss_item_sk, ss_customer_sk\n )\n select\nss_customer_sk,\nround(ss_qty/(coalesce(ws_qty,0)+coalesce(cs_qty,0)),2) ratio,\nss_qty store_qty, ss_wc store_wholesale_cost, ss_sp store_sales_price,\ncoalesce(ws_qty,0)+coalesce(cs_qty,0) other_chan_qty,\ncoalesce(ws_wc,0)+coalesce(cs_wc,0) other_chan_wholesale_cost,\ncoalesce(ws_sp,0)+coalesce(cs_sp,0) other_chan_sales_price\nfrom ss\nleft join ws on (ws_sold_year=ss_sold_year and ws_item_sk=ss_item_sk and ws_customer_sk=ss_customer_sk)\nleft join cs on (cs_sold_year=ss_sold_year and cs_item_sk=ss_item_sk and cs_customer_sk=ss_customer_sk)\nwhere (coalesce(ws_qty,0)>0 or coalesce(cs_qty, 0)>0) and ss_sold_year=2001\norder by\n ss_customer_sk,\n ss_qty desc, ss_wc desc, ss_sp desc,\n other_chan_qty,\n other_chan_wholesale_cost,\n other_chan_sales_price,\n ratio\nlimit 100\"\"\",\n \"q79\" ->\n \"\"\"\nselect\n c_last_name,c_first_name,substr(s_city,1,30),ss_ticket_number,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,store.s_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (household_demographics.hd_dep_count = 0 or household_demographics.hd_vehicle_count > 3)\n and date_dim.d_dow = 1\n and date_dim.d_year in (1998,1998+1,1998+2)\n and store.s_number_employees between 200 and 295\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,store.s_city) ms,customer\n where ss_customer_sk = c_customer_sk\n order by c_last_name,c_first_name,substr(s_city,1,30), profit\nlimit 100\"\"\",\n \"q80\" ->\n \"\"\"\nwith ssr as\n (select s_store_id as store_id,\n sum(ss_ext_sales_price) as sales,\n sum(coalesce(sr_return_amt, 0)) as returns,\n sum(ss_net_profit - coalesce(sr_net_loss, 0)) as profit\n from store_sales left outer join store_returns on\n (ss_item_sk = sr_item_sk and ss_ticket_number = sr_ticket_number),\n date_dim,\n store,\n item,\n promotion\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2002-08-06' as date)\n and (cast('2002-08-06' as date) + INTERVAL 60 days)\n and ss_store_sk = s_store_sk\n and ss_item_sk = i_item_sk\n and i_current_price > 50\n and ss_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id as catalog_page_id,\n sum(cs_ext_sales_price) as sales,\n sum(coalesce(cr_return_amount, 0)) as returns,\n sum(cs_net_profit - coalesce(cr_net_loss, 0)) as profit\n from catalog_sales left outer join catalog_returns on\n (cs_item_sk = cr_item_sk and cs_order_number = cr_order_number),\n date_dim,\n catalog_page,\n item,\n promotion\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2002-08-06' as date)\n and (cast('2002-08-06' as date) + INTERVAL 60 days)\n and cs_catalog_page_sk = cp_catalog_page_sk\n and cs_item_sk = i_item_sk\n and i_current_price > 50\n and cs_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(ws_ext_sales_price) as sales,\n sum(coalesce(wr_return_amt, 0)) as returns,\n sum(ws_net_profit - coalesce(wr_net_loss, 0)) as profit\n from web_sales left outer join web_returns on\n (ws_item_sk = wr_item_sk and ws_order_number = wr_order_number),\n date_dim,\n web_site,\n item,\n promotion\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2002-08-06' as date)\n and (cast('2002-08-06' as date) + INTERVAL 60 days)\n and ws_web_site_sk = web_site_sk\n and ws_item_sk = i_item_sk\n and i_current_price > 50\n and ws_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || store_id as id\n , sales\n , returns\n , profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || catalog_page_id as id\n , sales\n , returns\n , profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q81\" ->\n \"\"\"\nwith customer_total_return as\n (select cr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(cr_return_amt_inc_tax) as ctr_total_return\n from catalog_returns\n ,date_dim\n ,customer_address\n where cr_returned_date_sk = d_date_sk\n and d_year =1998\n and cr_returning_addr_sk = ca_address_sk\n group by cr_returning_customer_sk\n ,ca_state )\n select c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'TX'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n limit 100\"\"\",\n \"q82\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, store_sales\n where i_current_price between 49 and 49+30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2001-01-28' as date) and (cast('2001-01-28' as date) + INTERVAL 60 days)\n and i_manufact_id in (80,675,292,17)\n and inv_quantity_on_hand between 100 and 500\n and ss_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q83\" ->\n \"\"\"\nwith sr_items as\n (select i_item_id item_id,\n sum(sr_return_quantity) sr_item_qty\n from store_returns,\n item,\n date_dim\n where sr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('2000-06-17','2000-08-22','2000-11-17')))\n and sr_returned_date_sk = d_date_sk\n group by i_item_id),\n cr_items as\n (select i_item_id item_id,\n sum(cr_return_quantity) cr_item_qty\n from catalog_returns,\n item,\n date_dim\n where cr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('2000-06-17','2000-08-22','2000-11-17')))\n and cr_returned_date_sk = d_date_sk\n group by i_item_id),\n wr_items as\n (select i_item_id item_id,\n sum(wr_return_quantity) wr_item_qty\n from web_returns,\n item,\n date_dim\n where wr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t\twhere d_date in ('2000-06-17','2000-08-22','2000-11-17')))\n and wr_returned_date_sk = d_date_sk\n group by i_item_id)\n select sr_items.item_id\n ,sr_item_qty\n ,sr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 sr_dev\n ,cr_item_qty\n ,cr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 cr_dev\n ,wr_item_qty\n ,wr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 wr_dev\n ,(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 average\n from sr_items\n ,cr_items\n ,wr_items\n where sr_items.item_id=cr_items.item_id\n and sr_items.item_id=wr_items.item_id\n order by sr_items.item_id\n ,sr_item_qty\n limit 100\"\"\",\n \"q84\" ->\n \"\"\"\nselect c_customer_id as customer_id\n , coalesce(c_last_name,'') || ', ' || coalesce(c_first_name,'') as customername\n from customer\n ,customer_address\n ,customer_demographics\n ,household_demographics\n ,income_band\n ,store_returns\n where ca_city\t = 'Hopewell'\n and c_current_addr_sk = ca_address_sk\n and ib_lower_bound >= 37855\n and ib_upper_bound <= 37855 + 50000\n and ib_income_band_sk = hd_income_band_sk\n and cd_demo_sk = c_current_cdemo_sk\n and hd_demo_sk = c_current_hdemo_sk\n and sr_cdemo_sk = cd_demo_sk\n order by c_customer_id\n limit 100\"\"\",\n \"q85\" ->\n \"\"\"\nselect substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\n from web_sales, web_returns, web_page, customer_demographics cd1,\n customer_demographics cd2, customer_address, date_dim, reason\n where ws_web_page_sk = wp_web_page_sk\n and ws_item_sk = wr_item_sk\n and ws_order_number = wr_order_number\n and ws_sold_date_sk = d_date_sk and d_year = 2001\n and cd1.cd_demo_sk = wr_refunded_cdemo_sk\n and cd2.cd_demo_sk = wr_returning_cdemo_sk\n and ca_address_sk = wr_refunded_addr_sk\n and r_reason_sk = wr_reason_sk\n and\n (\n (\n cd1.cd_marital_status = 'M'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = '4 yr Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 100.00 and 150.00\n )\n or\n (\n cd1.cd_marital_status = 'S'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'College'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 50.00 and 100.00\n )\n or\n (\n cd1.cd_marital_status = 'D'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'Secondary'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ca_country = 'United States'\n and\n ca_state in ('TX', 'VA', 'CA')\n and ws_net_profit between 100 and 200\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('AR', 'NE', 'MO')\n and ws_net_profit between 150 and 300\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('IA', 'MS', 'WA')\n and ws_net_profit between 50 and 250\n )\n )\ngroup by r_reason_desc\norder by substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\nlimit 100\"\"\",\n \"q86\" ->\n \"\"\"\nselect\n sum(ws_net_paid) as total_sum\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ws_net_paid) desc) as rank_within_parent\n from\n web_sales\n ,date_dim d1\n ,item\n where\n d1.d_month_seq between 1215 and 1215+11\n and d1.d_date_sk = ws_sold_date_sk\n and i_item_sk = ws_item_sk\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc,\n case when lochierarchy = 0 then i_category end,\n rank_within_parent\n limit 100\"\"\",\n \"q87\" ->\n \"\"\"\nselect count(*)\nfrom ((select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1221 and 1221+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1221 and 1221+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1221 and 1221+11)\n) cool_cust\"\"\",\n \"q88\" ->\n \"\"\"\nselect *\nfrom\n (select count(*) h8_30_to_9\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 8\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s1,\n (select count(*) h9_to_9_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s2,\n (select count(*) h9_30_to_10\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s3,\n (select count(*) h10_to_10_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s4,\n (select count(*) h10_30_to_11\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s5,\n (select count(*) h11_to_11_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s6,\n (select count(*) h11_30_to_12\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s7,\n (select count(*) h12_to_12_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 12\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s8\"\"\",\n \"q89\" ->\n \"\"\"\nselect *\nfrom(\nselect i_category, i_class, i_brand,\n s_store_name, s_company_name,\n d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand, s_store_name, s_company_name)\n avg_monthly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n d_year in (2000) and\n ((i_category in ('Home','Music','Books') and\n i_class in ('glassware','classical','fiction')\n )\n or (i_category in ('Jewelry','Sports','Women') and\n i_class in ('semi-precious','baseball','dresses')\n ))\ngroup by i_category, i_class, i_brand,\n s_store_name, s_company_name, d_moy) tmp1\nwhere case when (avg_monthly_sales <> 0) then (abs(sum_sales - avg_monthly_sales) / avg_monthly_sales) else null end > 0.1\norder by sum_sales - avg_monthly_sales, s_store_name\nlimit 100\"\"\",\n \"q90\" ->\n \"\"\"\nselect cast(amc as decimal(15,4))/cast(pmc as decimal(15,4)) am_pm_ratio\n from ( select count(*) amc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 9 and 9+1\n and household_demographics.hd_dep_count = 3\n and web_page.wp_char_count between 5000 and 5200) at,\n ( select count(*) pmc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 16 and 16+1\n and household_demographics.hd_dep_count = 3\n and web_page.wp_char_count between 5000 and 5200) pt\n order by am_pm_ratio\n limit 100\"\"\",\n \"q91\" ->\n \"\"\"\nselect\n cc_call_center_id Call_Center,\n cc_name Call_Center_Name,\n cc_manager Manager,\n sum(cr_net_loss) Returns_Loss\nfrom\n call_center,\n catalog_returns,\n date_dim,\n customer,\n customer_address,\n customer_demographics,\n household_demographics\nwhere\n cr_call_center_sk = cc_call_center_sk\nand cr_returned_date_sk = d_date_sk\nand cr_returning_customer_sk= c_customer_sk\nand cd_demo_sk = c_current_cdemo_sk\nand hd_demo_sk = c_current_hdemo_sk\nand ca_address_sk = c_current_addr_sk\nand d_year = 2000\nand d_moy = 12\nand ( (cd_marital_status = 'M' and cd_education_status = 'Unknown')\n or(cd_marital_status = 'W' and cd_education_status = 'Advanced Degree'))\nand hd_buy_potential like 'Unknown%'\nand ca_gmt_offset = -7\ngroup by cc_call_center_id,cc_name,cc_manager,cd_marital_status,cd_education_status\norder by sum(cr_net_loss) desc\"\"\",\n \"q92\" ->\n \"\"\"\nselect\n sum(ws_ext_discount_amt) as `Excess Discount Amount`\nfrom\n web_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 356\nand i_item_sk = ws_item_sk\nand d_date between '2001-03-12' and\n (cast('2001-03-12' as date) + INTERVAL 90 days)\nand d_date_sk = ws_sold_date_sk\nand ws_ext_discount_amt\n > (\n SELECT\n 1.3 * avg(ws_ext_discount_amt)\n FROM\n web_sales\n ,date_dim\n WHERE\n ws_item_sk = i_item_sk\n and d_date between '2001-03-12' and\n (cast('2001-03-12' as date) + INTERVAL 90 days)\n and d_date_sk = ws_sold_date_sk\n )\norder by sum(ws_ext_discount_amt)\nlimit 100\"\"\",\n \"q93\" ->\n \"\"\"\nselect ss_customer_sk\n ,sum(act_sales) sumsales\n from (select ss_item_sk\n ,ss_ticket_number\n ,ss_customer_sk\n ,case when sr_return_quantity is not null then (ss_quantity-sr_return_quantity)*ss_sales_price\n else (ss_quantity*ss_sales_price) end act_sales\n from store_sales left outer join store_returns on (sr_item_sk = ss_item_sk\n and sr_ticket_number = ss_ticket_number)\n ,reason\n where sr_reason_sk = r_reason_sk\n and r_reason_desc = 'reason 66') t\n group by ss_customer_sk\n order by sumsales, ss_customer_sk\nlimit 100\"\"\",\n \"q94\" ->\n \"\"\"\nselect\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '1999-4-01' and\n (cast('1999-4-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'NE'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand exists (select *\n from web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\nand not exists(select *\n from web_returns wr1\n where ws1.ws_order_number = wr1.wr_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q95\" ->\n \"\"\"\nwith ws_wh as\n(select ws1.ws_order_number,ws1.ws_warehouse_sk wh1,ws2.ws_warehouse_sk wh2\n from web_sales ws1,web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\n select\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2002-4-01' and\n (cast('2002-4-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'AL'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand ws1.ws_order_number in (select ws_order_number\n from ws_wh)\nand ws1.ws_order_number in (select wr_order_number\n from web_returns,ws_wh\n where wr_order_number = ws_wh.ws_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q96\" ->\n \"\"\"\nselect count(*)\nfrom store_sales\n ,household_demographics\n ,time_dim, store\nwhere ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 16\n and time_dim.t_minute >= 30\n and household_demographics.hd_dep_count = 6\n and store.s_store_name = 'ese'\norder by count(*)\nlimit 100\"\"\",\n \"q97\" ->\n \"\"\"\nwith ssci as (\nselect ss_customer_sk customer_sk\n ,ss_item_sk item_sk\nfrom store_sales,date_dim\nwhere ss_sold_date_sk = d_date_sk\n and d_month_seq between 1190 and 1190 + 11\ngroup by ss_customer_sk\n ,ss_item_sk),\ncsci as(\n select cs_bill_customer_sk customer_sk\n ,cs_item_sk item_sk\nfrom catalog_sales,date_dim\nwhere cs_sold_date_sk = d_date_sk\n and d_month_seq between 1190 and 1190 + 11\ngroup by cs_bill_customer_sk\n ,cs_item_sk)\n select sum(case when ssci.customer_sk is not null and csci.customer_sk is null then 1 else 0 end) store_only\n ,sum(case when ssci.customer_sk is null and csci.customer_sk is not null then 1 else 0 end) catalog_only\n ,sum(case when ssci.customer_sk is not null and csci.customer_sk is not null then 1 else 0 end) store_and_catalog\nfrom ssci full outer join csci on (ssci.customer_sk=csci.customer_sk\n and ssci.item_sk = csci.item_sk)\nlimit 100\"\"\",\n \"q98\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ss_ext_sales_price) as itemrevenue\n ,sum(ss_ext_sales_price)*100/sum(sum(ss_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tstore_sales\n \t,item\n \t,date_dim\nwhere\n\tss_item_sk = i_item_sk\n \tand i_category in ('Home', 'Sports', 'Men')\n \tand ss_sold_date_sk = d_date_sk\n\tand d_date between cast('2002-01-05' as date)\n\t\t\t\tand (cast('2002-01-05' as date) + interval 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\"\"\",\n \"q99\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 30) and\n (cs_ship_date_sk - cs_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 60) and\n (cs_ship_date_sk - cs_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 90) and\n (cs_ship_date_sk - cs_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n catalog_sales\n ,warehouse\n ,ship_mode\n ,call_center\n ,date_dim\nwhere\n d_month_seq between 1178 and 1178 + 11\nand cs_ship_date_sk = d_date_sk\nand cs_warehouse_sk = w_warehouse_sk\nand cs_ship_mode_sk = sm_ship_mode_sk\nand cs_call_center_sk = cc_call_center_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\nlimit 100\"\"\",\n \"q1\" ->\n \"\"\"\nwith customer_total_return as\n(select sr_customer_sk as ctr_customer_sk\n,sr_store_sk as ctr_store_sk\n,sum(SR_FEE) as ctr_total_return\nfrom store_returns\n,date_dim\nwhere sr_returned_date_sk = d_date_sk\nand d_year =2000\ngroup by sr_customer_sk\n,sr_store_sk)\n select c_customer_id\nfrom customer_total_return ctr1\n,store\n,customer\nwhere ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\nfrom customer_total_return ctr2\nwhere ctr1.ctr_store_sk = ctr2.ctr_store_sk)\nand s_store_sk = ctr1.ctr_store_sk\nand s_state = 'NY'\nand ctr1.ctr_customer_sk = c_customer_sk\norder by c_customer_id\nlimit 100\"\"\",\n \"q2\" ->\n \"\"\"\nwith wscs as\n (select sold_date_sk\n ,sales_price\n from (select ws_sold_date_sk sold_date_sk\n ,ws_ext_sales_price sales_price\n from web_sales\n union all\n select cs_sold_date_sk sold_date_sk\n ,cs_ext_sales_price sales_price\n from catalog_sales)),\n wswscs as\n (select d_week_seq,\n sum(case when (d_day_name='Sunday') then sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then sales_price else null end) sat_sales\n from wscs\n ,date_dim\n where d_date_sk = sold_date_sk\n group by d_week_seq)\n select d_week_seq1\n ,round(sun_sales1/sun_sales2,2)\n ,round(mon_sales1/mon_sales2,2)\n ,round(tue_sales1/tue_sales2,2)\n ,round(wed_sales1/wed_sales2,2)\n ,round(thu_sales1/thu_sales2,2)\n ,round(fri_sales1/fri_sales2,2)\n ,round(sat_sales1/sat_sales2,2)\n from\n (select wswscs.d_week_seq d_week_seq1\n ,sun_sales sun_sales1\n ,mon_sales mon_sales1\n ,tue_sales tue_sales1\n ,wed_sales wed_sales1\n ,thu_sales thu_sales1\n ,fri_sales fri_sales1\n ,sat_sales sat_sales1\n from wswscs,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998) y,\n (select wswscs.d_week_seq d_week_seq2\n ,sun_sales sun_sales2\n ,mon_sales mon_sales2\n ,tue_sales tue_sales2\n ,wed_sales wed_sales2\n ,thu_sales thu_sales2\n ,fri_sales fri_sales2\n ,sat_sales sat_sales2\n from wswscs\n ,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998+1) z\n where d_week_seq1=d_week_seq2-53\n order by d_week_seq1\"\"\",\n \"q3\" ->\n \"\"\"\nselect dt.d_year\n ,item.i_brand_id brand_id\n ,item.i_brand brand\n ,sum(ss_sales_price) sum_agg\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manufact_id = 816\n and dt.d_moy=11\n group by dt.d_year\n ,item.i_brand\n ,item.i_brand_id\n order by dt.d_year\n ,sum_agg desc\n ,brand_id\n limit 100\"\"\",\n \"q4\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(((ss_ext_list_price-ss_ext_wholesale_cost-ss_ext_discount_amt)+ss_ext_sales_price)/2) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((cs_ext_list_price-cs_ext_wholesale_cost-cs_ext_discount_amt)+cs_ext_sales_price)/2) ) year_total\n ,'c' sale_type\n from customer\n ,catalog_sales\n ,date_dim\n where c_customer_sk = cs_bill_customer_sk\n and cs_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\nunion all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((ws_ext_list_price-ws_ext_wholesale_cost-ws_ext_discount_amt)+ws_ext_sales_price)/2) ) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_c_firstyear\n ,year_total t_c_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_c_secyear.customer_id\n and t_s_firstyear.customer_id = t_c_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_c_firstyear.sale_type = 'c'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_c_secyear.sale_type = 'c'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 1999\n and t_s_secyear.dyear = 1999+1\n and t_c_firstyear.dyear = 1999\n and t_c_secyear.dyear = 1999+1\n and t_w_firstyear.dyear = 1999\n and t_w_secyear.dyear = 1999+1\n and t_s_firstyear.year_total > 0\n and t_c_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\nlimit 100\"\"\",\n \"q5\" ->\n \"\"\"\nwith ssr as\n (select s_store_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ss_store_sk as store_sk,\n ss_sold_date_sk as date_sk,\n ss_ext_sales_price as sales_price,\n ss_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from store_sales\n union all\n select sr_store_sk as store_sk,\n sr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n sr_return_amt as return_amt,\n sr_net_loss as net_loss\n from store_returns\n ) salesreturns,\n date_dim,\n store\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and store_sk = s_store_sk\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select cs_catalog_page_sk as page_sk,\n cs_sold_date_sk as date_sk,\n cs_ext_sales_price as sales_price,\n cs_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from catalog_sales\n union all\n select cr_catalog_page_sk as page_sk,\n cr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n cr_return_amount as return_amt,\n cr_net_loss as net_loss\n from catalog_returns\n ) salesreturns,\n date_dim,\n catalog_page\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and page_sk = cp_catalog_page_sk\n group by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ws_web_site_sk as wsr_web_site_sk,\n ws_sold_date_sk as date_sk,\n ws_ext_sales_price as sales_price,\n ws_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from web_sales\n union all\n select ws_web_site_sk as wsr_web_site_sk,\n wr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n wr_return_amt as return_amt,\n wr_net_loss as net_loss\n from web_returns left outer join web_sales on\n ( wr_item_sk = ws_item_sk\n and wr_order_number = ws_order_number)\n ) salesreturns,\n date_dim,\n web_site\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and wsr_web_site_sk = web_site_sk\n group by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || s_store_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || cp_catalog_page_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q6\" ->\n \"\"\"\nselect a.ca_state state, count(*) cnt\n from customer_address a\n ,customer c\n ,store_sales s\n ,date_dim d\n ,item i\n where a.ca_address_sk = c.c_current_addr_sk\n \tand c.c_customer_sk = s.ss_customer_sk\n \tand s.ss_sold_date_sk = d.d_date_sk\n \tand s.ss_item_sk = i.i_item_sk\n \tand d.d_month_seq =\n \t (select distinct (d_month_seq)\n \t from date_dim\n where d_year = 2002\n \t and d_moy = 3 )\n \tand i.i_current_price > 1.2 *\n (select avg(j.i_current_price)\n \t from item j\n \t where j.i_category = i.i_category)\n group by a.ca_state\n having count(*) >= 10\n order by cnt, a.ca_state\n limit 100\"\"\",\n \"q7\" ->\n \"\"\"\nselect i_item_id,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, item, promotion\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_cdemo_sk = cd_demo_sk and\n ss_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'W' and\n cd_education_status = 'College' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 2001\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q8\" ->\n \"\"\"\nselect s_store_name\n ,sum(ss_net_profit)\n from store_sales\n ,date_dim\n ,store,\n (select ca_zip\n from (\n SELECT substr(ca_zip,1,5) ca_zip\n FROM customer_address\n WHERE substr(ca_zip,1,5) IN (\n '47602','16704','35863','28577','83910','36201',\n '58412','48162','28055','41419','80332',\n '38607','77817','24891','16226','18410',\n '21231','59345','13918','51089','20317',\n '17167','54585','67881','78366','47770',\n '18360','51717','73108','14440','21800',\n '89338','45859','65501','34948','25973',\n '73219','25333','17291','10374','18829',\n '60736','82620','41351','52094','19326',\n '25214','54207','40936','21814','79077',\n '25178','75742','77454','30621','89193',\n '27369','41232','48567','83041','71948',\n '37119','68341','14073','16891','62878',\n '49130','19833','24286','27700','40979',\n '50412','81504','94835','84844','71954',\n '39503','57649','18434','24987','12350',\n '86379','27413','44529','98569','16515',\n '27287','24255','21094','16005','56436',\n '91110','68293','56455','54558','10298',\n '83647','32754','27052','51766','19444',\n '13869','45645','94791','57631','20712',\n '37788','41807','46507','21727','71836',\n '81070','50632','88086','63991','20244',\n '31655','51782','29818','63792','68605',\n '94898','36430','57025','20601','82080',\n '33869','22728','35834','29086','92645',\n '98584','98072','11652','78093','57553',\n '43830','71144','53565','18700','90209',\n '71256','38353','54364','28571','96560',\n '57839','56355','50679','45266','84680',\n '34306','34972','48530','30106','15371',\n '92380','84247','92292','68852','13338',\n '34594','82602','70073','98069','85066',\n '47289','11686','98862','26217','47529',\n '63294','51793','35926','24227','14196',\n '24594','32489','99060','49472','43432',\n '49211','14312','88137','47369','56877',\n '20534','81755','15794','12318','21060',\n '73134','41255','63073','81003','73873',\n '66057','51184','51195','45676','92696',\n '70450','90669','98338','25264','38919',\n '59226','58581','60298','17895','19489',\n '52301','80846','95464','68770','51634',\n '19988','18367','18421','11618','67975',\n '25494','41352','95430','15734','62585',\n '97173','33773','10425','75675','53535',\n '17879','41967','12197','67998','79658',\n '59130','72592','14851','43933','68101',\n '50636','25717','71286','24660','58058',\n '72991','95042','15543','33122','69280',\n '11912','59386','27642','65177','17672',\n '33467','64592','36335','54010','18767',\n '63193','42361','49254','33113','33159',\n '36479','59080','11855','81963','31016',\n '49140','29392','41836','32958','53163',\n '13844','73146','23952','65148','93498',\n '14530','46131','58454','13376','13378',\n '83986','12320','17193','59852','46081',\n '98533','52389','13086','68843','31013',\n '13261','60560','13443','45533','83583',\n '11489','58218','19753','22911','25115',\n '86709','27156','32669','13123','51933',\n '39214','41331','66943','14155','69998',\n '49101','70070','35076','14242','73021',\n '59494','15782','29752','37914','74686',\n '83086','34473','15751','81084','49230',\n '91894','60624','17819','28810','63180',\n '56224','39459','55233','75752','43639',\n '55349','86057','62361','50788','31830',\n '58062','18218','85761','60083','45484',\n '21204','90229','70041','41162','35390',\n '16364','39500','68908','26689','52868',\n '81335','40146','11340','61527','61794',\n '71997','30415','59004','29450','58117',\n '69952','33562','83833','27385','61860',\n '96435','48333','23065','32961','84919',\n '61997','99132','22815','56600','68730',\n '48017','95694','32919','88217','27116',\n '28239','58032','18884','16791','21343',\n '97462','18569','75660','15475')\n intersect\n select ca_zip\n from (SELECT substr(ca_zip,1,5) ca_zip,count(*) cnt\n FROM customer_address, customer\n WHERE ca_address_sk = c_current_addr_sk and\n c_preferred_cust_flag='Y'\n group by ca_zip\n having count(*) > 10)A1)A2) V1\n where ss_store_sk = s_store_sk\n and ss_sold_date_sk = d_date_sk\n and d_qoy = 2 and d_year = 1998\n and (substr(s_zip,1,2) = substr(V1.ca_zip,1,2))\n group by s_store_name\n order by s_store_name\n limit 100\"\"\",\n \"q9\" ->\n \"\"\"\nselect case when (select count(*)\n from store_sales\n where ss_quantity between 1 and 20) > 578972190\n then (select avg(ss_ext_list_price)\n from store_sales\n where ss_quantity between 1 and 20)\n else (select avg(ss_net_paid_inc_tax)\n from store_sales\n where ss_quantity between 1 and 20) end bucket1 ,\n case when (select count(*)\n from store_sales\n where ss_quantity between 21 and 40) > 536856786\n then (select avg(ss_ext_list_price)\n from store_sales\n where ss_quantity between 21 and 40)\n else (select avg(ss_net_paid_inc_tax)\n from store_sales\n where ss_quantity between 21 and 40) end bucket2,\n case when (select count(*)\n from store_sales\n where ss_quantity between 41 and 60) > 12733327\n then (select avg(ss_ext_list_price)\n from store_sales\n where ss_quantity between 41 and 60)\n else (select avg(ss_net_paid_inc_tax)\n from store_sales\n where ss_quantity between 41 and 60) end bucket3,\n case when (select count(*)\n from store_sales\n where ss_quantity between 61 and 80) > 205136171\n then (select avg(ss_ext_list_price)\n from store_sales\n where ss_quantity between 61 and 80)\n else (select avg(ss_net_paid_inc_tax)\n from store_sales\n where ss_quantity between 61 and 80) end bucket4,\n case when (select count(*)\n from store_sales\n where ss_quantity between 81 and 100) > 1192341092\n then (select avg(ss_ext_list_price)\n from store_sales\n where ss_quantity between 81 and 100)\n else (select avg(ss_net_paid_inc_tax)\n from store_sales\n where ss_quantity between 81 and 100) end bucket5\nfrom reason\nwhere r_reason_sk = 1\"\"\",\n \"q10\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3,\n cd_dep_count,\n count(*) cnt4,\n cd_dep_employed_count,\n count(*) cnt5,\n cd_dep_college_count,\n count(*) cnt6\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_county in ('Baltimore city','Stafford County','Greene County','Ballard County','Franklin County') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2000 and\n d_moy between 1 and 1+3) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2000 and\n d_moy between 1 ANd 1+3) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2000 and\n d_moy between 1 and 1+3))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\nlimit 100\"\"\",\n \"q11\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ss_ext_list_price-ss_ext_discount_amt) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ws_ext_list_price-ws_ext_discount_amt) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_preferred_cust_flag\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 2001\n and t_s_secyear.dyear = 2001+1\n and t_w_firstyear.dyear = 2001\n and t_w_secyear.dyear = 2001+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else 0.0 end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else 0.0 end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_preferred_cust_flag\nlimit 100\"\"\",\n \"q12\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ws_ext_sales_price) as itemrevenue\n ,sum(ws_ext_sales_price)*100/sum(sum(ws_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tweb_sales\n \t,item\n \t,date_dim\nwhere\n\tws_item_sk = i_item_sk\n \tand i_category in ('Children', 'Shoes', 'Women')\n \tand ws_sold_date_sk = d_date_sk\n\tand d_date between cast('1998-06-19' as date)\n\t\t\t\tand (cast('1998-06-19' as date) + INTERVAL 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q13\" ->\n \"\"\"\nselect avg(ss_quantity)\n ,avg(ss_ext_sales_price)\n ,avg(ss_ext_wholesale_cost)\n ,sum(ss_ext_wholesale_cost)\n from store_sales\n ,store\n ,customer_demographics\n ,household_demographics\n ,customer_address\n ,date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 2001\n and((ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'D'\n and cd_education_status = '2 yr Degree'\n and ss_sales_price between 100.00 and 150.00\n and hd_dep_count = 3\n )or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'U'\n and cd_education_status = 'College'\n and ss_sales_price between 50.00 and 100.00\n and hd_dep_count = 1\n ) or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'S'\n and cd_education_status = 'Primary'\n and ss_sales_price between 150.00 and 200.00\n and hd_dep_count = 1\n ))\n and((ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('GA', 'IN', 'NY')\n and ss_net_profit between 100 and 200\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('ND', 'WV', 'TX')\n and ss_net_profit between 150 and 300\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('KS', 'NC', 'NM')\n and ss_net_profit between 50 and 250\n ))\"\"\",\n \"q14a\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 1998 AND 1998 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 1998 AND 1998 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 1998 AND 1998 + 2)\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n (select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2) x)\n select channel, i_brand_id,i_class_id,i_category_id,sum(sales), sum(number_sales)\n from(\n select 'store' channel, i_brand_id,i_class_id\n ,i_category_id,sum(ss_quantity*ss_list_price) sales\n , count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1998+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)\n union all\n select 'catalog' channel, i_brand_id,i_class_id,i_category_id, sum(cs_quantity*cs_list_price) sales, count(*) number_sales\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk in (select ss_item_sk from cross_items)\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1998+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(cs_quantity*cs_list_price) > (select average_sales from avg_sales)\n union all\n select 'web' channel, i_brand_id,i_class_id,i_category_id, sum(ws_quantity*ws_list_price) sales , count(*) number_sales\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk in (select ss_item_sk from cross_items)\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1998+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ws_quantity*ws_list_price) > (select average_sales from avg_sales)\n ) y\n group by rollup (channel, i_brand_id,i_class_id,i_category_id)\n order by channel,i_brand_id,i_class_id,i_category_id\n limit 100\"\"\",\n \"q14b\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 1998 AND 1998 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 1998 AND 1998 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 1998 AND 1998 + 2) x\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n(select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2) x)\n select this_year.channel ty_channel\n ,this_year.i_brand_id ty_brand\n ,this_year.i_class_id ty_class\n ,this_year.i_category_id ty_category\n ,this_year.sales ty_sales\n ,this_year.number_sales ty_number_sales\n ,last_year.channel ly_channel\n ,last_year.i_brand_id ly_brand\n ,last_year.i_class_id ly_class\n ,last_year.i_category_id ly_category\n ,last_year.sales ly_sales\n ,last_year.number_sales ly_number_sales\n from\n (select 'store' channel, i_brand_id,i_class_id,i_category_id\n ,sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 1998 + 1\n and d_moy = 12\n and d_dom = 17)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) this_year,\n (select 'store' channel, i_brand_id,i_class_id\n ,i_category_id, sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 1998\n and d_moy = 12\n and d_dom = 17)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) last_year\n where this_year.i_brand_id= last_year.i_brand_id\n and this_year.i_class_id = last_year.i_class_id\n and this_year.i_category_id = last_year.i_category_id\n order by this_year.channel, this_year.i_brand_id, this_year.i_class_id, this_year.i_category_id\n limit 100\"\"\",\n \"q15\" ->\n \"\"\"\nselect ca_zip\n ,sum(cs_sales_price)\n from catalog_sales\n ,customer\n ,customer_address\n ,date_dim\n where cs_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475',\n '85392', '85460', '80348', '81792')\n \t or ca_state in ('CA','WA','GA')\n \t or cs_sales_price > 500)\n \tand cs_sold_date_sk = d_date_sk\n \tand d_qoy = 1 and d_year = 2002\n group by ca_zip\n order by ca_zip\n limit 100\"\"\",\n \"q16\" ->\n \"\"\"\nselect\n count(distinct cs_order_number) as `order count`\n ,sum(cs_ext_ship_cost) as `total shipping cost`\n ,sum(cs_net_profit) as `total net profit`\nfrom\n catalog_sales cs1\n ,date_dim\n ,customer_address\n ,call_center\nwhere\n d_date between '2001-3-01' and\n (cast('2001-3-01' as date) + INTERVAL 60 days)\nand cs1.cs_ship_date_sk = d_date_sk\nand cs1.cs_ship_addr_sk = ca_address_sk\nand ca_state = 'PA'\nand cs1.cs_call_center_sk = cc_call_center_sk\nand cc_county in ('Luce County','Franklin Parish','Sierra County','Williamson County',\n 'Kittitas County'\n)\nand exists (select *\n from catalog_sales cs2\n where cs1.cs_order_number = cs2.cs_order_number\n and cs1.cs_warehouse_sk <> cs2.cs_warehouse_sk)\nand not exists(select *\n from catalog_returns cr1\n where cs1.cs_order_number = cr1.cr_order_number)\norder by count(distinct cs_order_number)\nlimit 100\"\"\",\n \"q17\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,s_state\n ,count(ss_quantity) as store_sales_quantitycount\n ,avg(ss_quantity) as store_sales_quantityave\n ,stddev_samp(ss_quantity) as store_sales_quantitystdev\n ,stddev_samp(ss_quantity)/avg(ss_quantity) as store_sales_quantitycov\n ,count(sr_return_quantity) as store_returns_quantitycount\n ,avg(sr_return_quantity) as store_returns_quantityave\n ,stddev_samp(sr_return_quantity) as store_returns_quantitystdev\n ,stddev_samp(sr_return_quantity)/avg(sr_return_quantity) as store_returns_quantitycov\n ,count(cs_quantity) as catalog_sales_quantitycount ,avg(cs_quantity) as catalog_sales_quantityave\n ,stddev_samp(cs_quantity) as catalog_sales_quantitystdev\n ,stddev_samp(cs_quantity)/avg(cs_quantity) as catalog_sales_quantitycov\n from store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where d1.d_quarter_name = '2001Q1'\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_quarter_name in ('2001Q1','2001Q2','2001Q3')\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_quarter_name in ('2001Q1','2001Q2','2001Q3')\n group by i_item_id\n ,i_item_desc\n ,s_state\n order by i_item_id\n ,i_item_desc\n ,s_state\nlimit 100\"\"\",\n \"q18\" ->\n \"\"\"\nselect i_item_id,\n ca_country,\n ca_state,\n ca_county,\n avg( cast(cs_quantity as decimal(12,2))) agg1,\n avg( cast(cs_list_price as decimal(12,2))) agg2,\n avg( cast(cs_coupon_amt as decimal(12,2))) agg3,\n avg( cast(cs_sales_price as decimal(12,2))) agg4,\n avg( cast(cs_net_profit as decimal(12,2))) agg5,\n avg( cast(c_birth_year as decimal(12,2))) agg6,\n avg( cast(cd1.cd_dep_count as decimal(12,2))) agg7\n from catalog_sales, customer_demographics cd1,\n customer_demographics cd2, customer, customer_address, date_dim, item\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd1.cd_demo_sk and\n cs_bill_customer_sk = c_customer_sk and\n cd1.cd_gender = 'M' and\n cd1.cd_education_status = 'Unknown' and\n c_current_cdemo_sk = cd2.cd_demo_sk and\n c_current_addr_sk = ca_address_sk and\n c_birth_month in (5,7,8,6,12,4) and\n d_year = 2000 and\n ca_state in ('MO','NY','ME'\n ,'MI','IA','OH','MS')\n group by rollup (i_item_id, ca_country, ca_state, ca_county)\n order by ca_country,\n ca_state,\n ca_county,\n\ti_item_id\n limit 100\"\"\",\n \"q19\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand, i_manufact_id, i_manufact,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item,customer,customer_address,store\n where d_date_sk = ss_sold_date_sk\n and ss_item_sk = i_item_sk\n and i_manager_id=55\n and d_moy=11\n and d_year=1998\n and ss_customer_sk = c_customer_sk\n and c_current_addr_sk = ca_address_sk\n and substr(ca_zip,1,5) <> substr(s_zip,1,5)\n and ss_store_sk = s_store_sk\n group by i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\n order by ext_price desc\n ,i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\nlimit 100 \"\"\",\n \"q20\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(cs_ext_sales_price) as itemrevenue\n ,sum(cs_ext_sales_price)*100/sum(sum(cs_ext_sales_price)) over\n (partition by i_class) as revenueratio\n from\tcatalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and i_category in ('Shoes', 'Electronics', 'Home')\n and cs_sold_date_sk = d_date_sk\n and d_date between cast('2000-05-15' as date)\n \t\t\t\tand (cast('2000-05-15' as date) + INTERVAL 30 days)\n group by i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n order by i_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q21\" ->\n \"\"\"\nselect *\n from(select w_warehouse_name\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('2002-02-15' as date))\n\t then inv_quantity_on_hand\n else 0 end) as inv_before\n ,sum(case when (cast(d_date as date) >= cast ('2002-02-15' as date))\n then inv_quantity_on_hand\n else 0 end) as inv_after\n from inventory\n ,warehouse\n ,item\n ,date_dim\n where i_current_price between 0.99 and 1.49\n and i_item_sk = inv_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_date between (cast ('2002-02-15' as date) - INTERVAL 30 days)\n and (cast ('2002-02-15' as date) + INTERVAL 30 days)\n group by w_warehouse_name, i_item_id) x\n where (case when inv_before > 0\n then inv_after / inv_before\n else null\n end) between 2.0/3.0 and 3.0/2.0\n order by w_warehouse_name\n ,i_item_id\n limit 100\"\"\",\n \"q22\" ->\n \"\"\"\nselect i_product_name\n ,i_brand\n ,i_class\n ,i_category\n ,avg(inv_quantity_on_hand) qoh\n from inventory\n ,date_dim\n ,item\n where inv_date_sk=d_date_sk\n and inv_item_sk=i_item_sk\n and d_month_seq between 1202 and 1202 + 11\n group by rollup(i_product_name\n ,i_brand\n ,i_class\n ,i_category)\norder by qoh, i_product_name, i_brand, i_class, i_category\nlimit 100\"\"\",\n \"q23a\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\nfrom\n max_store_sales))\n select sum(sales)\n from (select cs_quantity*cs_list_price sales\n from catalog_sales\n ,date_dim\n where d_year = 2000\n and d_moy = 4\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n union all\n select ws_quantity*ws_list_price sales\n from web_sales\n ,date_dim\n where d_year = 2000\n and d_moy = 4\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer))\n limit 100\"\"\",\n \"q23b\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (2000,2000 + 1,2000 + 2,2000 + 3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\n from max_store_sales))\n select c_last_name,c_first_name,sales\n from (select c_last_name,c_first_name,sum(cs_quantity*cs_list_price) sales\n from catalog_sales\n ,customer\n ,date_dim\n where d_year = 2000\n and d_moy = 4\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and cs_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name\n union all\n select c_last_name,c_first_name,sum(ws_quantity*ws_list_price) sales\n from web_sales\n ,customer\n ,date_dim\n where d_year = 2000\n and d_moy = 4\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and ws_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name)\n order by c_last_name,c_first_name,sales\n limit 100\"\"\",\n \"q24a\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_net_paid_inc_tax) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\nand s_market_id=5\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'cyan'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q24b\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_net_paid_inc_tax) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\n and s_market_id = 5\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'ivory'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q25\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,stddev_samp(ss_net_profit) as store_sales_profit\n ,stddev_samp(sr_net_loss) as store_returns_loss\n ,stddev_samp(cs_net_profit) as catalog_sales_profit\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 2000\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 10\n and d2.d_year = 2000\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_moy between 4 and 10\n and d3.d_year = 2000\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q26\" ->\n \"\"\"\nselect i_item_id,\n avg(cs_quantity) agg1,\n avg(cs_list_price) agg2,\n avg(cs_coupon_amt) agg3,\n avg(cs_sales_price) agg4\n from catalog_sales, customer_demographics, date_dim, item, promotion\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd_demo_sk and\n cs_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'M' and\n cd_education_status = 'Unknown' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 2001\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q27\" ->\n \"\"\"\nselect i_item_id,\n s_state, grouping(s_state) g_state,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, store, item\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_store_sk = s_store_sk and\n ss_cdemo_sk = cd_demo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'D' and\n cd_education_status = '2 yr Degree' and\n d_year = 1999 and\n s_state in ('MI','WV', 'MI', 'NY', 'TN', 'MI')\n group by rollup (i_item_id, s_state)\n order by i_item_id\n ,s_state\n limit 100\"\"\",\n \"q28\" ->\n \"\"\"\nselect *\nfrom (select avg(ss_list_price) B1_LP\n ,count(ss_list_price) B1_CNT\n ,count(distinct ss_list_price) B1_CNTD\n from store_sales\n where ss_quantity between 0 and 5\n and (ss_list_price between 151 and 151+10\n or ss_coupon_amt between 4349 and 4349+1000\n or ss_wholesale_cost between 75 and 75+20)) B1,\n (select avg(ss_list_price) B2_LP\n ,count(ss_list_price) B2_CNT\n ,count(distinct ss_list_price) B2_CNTD\n from store_sales\n where ss_quantity between 6 and 10\n and (ss_list_price between 45 and 45+10\n or ss_coupon_amt between 12490 and 12490+1000\n or ss_wholesale_cost between 37 and 37+20)) B2,\n (select avg(ss_list_price) B3_LP\n ,count(ss_list_price) B3_CNT\n ,count(distinct ss_list_price) B3_CNTD\n from store_sales\n where ss_quantity between 11 and 15\n and (ss_list_price between 54 and 54+10\n or ss_coupon_amt between 13038 and 13038+1000\n or ss_wholesale_cost between 17 and 17+20)) B3,\n (select avg(ss_list_price) B4_LP\n ,count(ss_list_price) B4_CNT\n ,count(distinct ss_list_price) B4_CNTD\n from store_sales\n where ss_quantity between 16 and 20\n and (ss_list_price between 178 and 178+10\n or ss_coupon_amt between 10744 and 10744+1000\n or ss_wholesale_cost between 51 and 51+20)) B4,\n (select avg(ss_list_price) B5_LP\n ,count(ss_list_price) B5_CNT\n ,count(distinct ss_list_price) B5_CNTD\n from store_sales\n where ss_quantity between 21 and 25\n and (ss_list_price between 49 and 49+10\n or ss_coupon_amt between 8494 and 8494+1000\n or ss_wholesale_cost between 56 and 56+20)) B5,\n (select avg(ss_list_price) B6_LP\n ,count(ss_list_price) B6_CNT\n ,count(distinct ss_list_price) B6_CNTD\n from store_sales\n where ss_quantity between 26 and 30\n and (ss_list_price between 0 and 0+10\n or ss_coupon_amt between 17854 and 17854+1000\n or ss_wholesale_cost between 31 and 31+20)) B6\nlimit 100\"\"\",\n \"q29\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,max(ss_quantity) as store_sales_quantity\n ,max(sr_return_quantity) as store_returns_quantity\n ,max(cs_quantity) as catalog_sales_quantity\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 1999\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 4 + 3\n and d2.d_year = 1999\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_year in (1999,1999+1,1999+2)\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q30\" ->\n \"\"\"\nwith customer_total_return as\n (select wr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(wr_return_amt) as ctr_total_return\n from web_returns\n ,date_dim\n ,customer_address\n where wr_returned_date_sk = d_date_sk\n and d_year =2000\n and wr_returning_addr_sk = ca_address_sk\n group by wr_returning_customer_sk\n ,ca_state)\n select c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'MD'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\nlimit 100\"\"\",\n \"q31\" ->\n \"\"\"\nwith ss as\n (select ca_county,d_qoy, d_year,sum(ss_ext_sales_price) as store_sales\n from store_sales,date_dim,customer_address\n where ss_sold_date_sk = d_date_sk\n and ss_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year),\n ws as\n (select ca_county,d_qoy, d_year,sum(ws_ext_sales_price) as web_sales\n from web_sales,date_dim,customer_address\n where ws_sold_date_sk = d_date_sk\n and ws_bill_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year)\n select\n ss1.ca_county\n ,ss1.d_year\n ,ws2.web_sales/ws1.web_sales web_q1_q2_increase\n ,ss2.store_sales/ss1.store_sales store_q1_q2_increase\n ,ws3.web_sales/ws2.web_sales web_q2_q3_increase\n ,ss3.store_sales/ss2.store_sales store_q2_q3_increase\n from\n ss ss1\n ,ss ss2\n ,ss ss3\n ,ws ws1\n ,ws ws2\n ,ws ws3\n where\n ss1.d_qoy = 1\n and ss1.d_year = 1999\n and ss1.ca_county = ss2.ca_county\n and ss2.d_qoy = 2\n and ss2.d_year = 1999\n and ss2.ca_county = ss3.ca_county\n and ss3.d_qoy = 3\n and ss3.d_year = 1999\n and ss1.ca_county = ws1.ca_county\n and ws1.d_qoy = 1\n and ws1.d_year = 1999\n and ws1.ca_county = ws2.ca_county\n and ws2.d_qoy = 2\n and ws2.d_year = 1999\n and ws1.ca_county = ws3.ca_county\n and ws3.d_qoy = 3\n and ws3.d_year =1999\n and case when ws1.web_sales > 0 then ws2.web_sales/ws1.web_sales else null end\n > case when ss1.store_sales > 0 then ss2.store_sales/ss1.store_sales else null end\n and case when ws2.web_sales > 0 then ws3.web_sales/ws2.web_sales else null end\n > case when ss2.store_sales > 0 then ss3.store_sales/ss2.store_sales else null end\n order by store_q2_q3_increase\"\"\",\n \"q32\" ->\n \"\"\"\nselect sum(cs_ext_discount_amt) as `excess discount amount`\nfrom\n catalog_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 7\nand i_item_sk = cs_item_sk\nand d_date between '2000-01-21' and\n (cast('2000-01-21' as date) + INTERVAL 90 days)\nand d_date_sk = cs_sold_date_sk\nand cs_ext_discount_amt\n > (\n select\n 1.3 * avg(cs_ext_discount_amt)\n from\n catalog_sales\n ,date_dim\n where\n cs_item_sk = i_item_sk\n and d_date between '2000-01-21' and\n (cast('2000-01-21' as date) + INTERVAL 90 days)\n and d_date_sk = cs_sold_date_sk\n )\nlimit 100\"\"\",\n \"q33\" ->\n \"\"\"\nwith ss as (\n select\n i_manufact_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Books'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 6\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_manufact_id),\n cs as (\n select\n i_manufact_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Books'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 6\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_manufact_id),\n ws as (\n select\n i_manufact_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Books'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 6\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_manufact_id)\n select i_manufact_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_manufact_id\n order by total_sales\nlimit 100\"\"\",\n \"q34\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (date_dim.d_dom between 1 and 3 or date_dim.d_dom between 25 and 28)\n and (household_demographics.hd_buy_potential = '501-1000' or\n household_demographics.hd_buy_potential = '5001-10000')\n and household_demographics.hd_vehicle_count > 0\n and (case when household_demographics.hd_vehicle_count > 0\n\tthen household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count\n\telse null\n\tend) > 1.2\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_county in ('Levy County','Val Verde County','Porter County','Nowata County',\n 'Lincoln County','Brazos County','Franklin Parish','Pipestone County')\n group by ss_ticket_number,ss_customer_sk) dn,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 15 and 20\n order by c_last_name,c_first_name,c_salutation,c_preferred_cust_flag desc, ss_ticket_number\"\"\",\n \"q35\" ->\n \"\"\"\nselect\n ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n count(*) cnt1,\n sum(cd_dep_count),\n sum(cd_dep_count),\n sum(cd_dep_count),\n cd_dep_employed_count,\n count(*) cnt2,\n sum(cd_dep_employed_count),\n sum(cd_dep_employed_count),\n sum(cd_dep_employed_count),\n cd_dep_college_count,\n count(*) cnt3,\n sum(cd_dep_college_count),\n sum(cd_dep_college_count),\n sum(cd_dep_college_count)\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_qoy < 4) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_qoy < 4) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_qoy < 4))\n group by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n limit 100\"\"\",\n \"q36\" ->\n \"\"\"\nselect\n sum(ss_net_profit)/sum(ss_ext_sales_price) as gross_margin\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ss_net_profit)/sum(ss_ext_sales_price) asc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,item\n ,store\n where\n d1.d_year = 1999\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and s_state in ('MO','AL','OH','WV',\n 'AL','MN','TN','WA')\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then i_category end\n ,rank_within_parent\n limit 100\"\"\",\n \"q37\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, catalog_sales\n where i_current_price between 57 and 57 + 30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2001-04-19' as date) and (cast('2001-04-19' as date) + interval 60 days)\n and i_manufact_id in (804,916,707,680)\n and inv_quantity_on_hand between 100 and 500\n and cs_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q38\" ->\n \"\"\"\nselect count(*) from (\n select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1189 and 1189 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1189 and 1189 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1189 and 1189 + 11\n) hot_cust\nlimit 100\"\"\",\n \"q39a\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =2000\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=3\n and inv2.d_moy=3+1\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q39b\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =2000\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=3\n and inv2.d_moy=3+1\n and inv1.cov > 1.5\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q40\" ->\n \"\"\"\nselect\n w_state\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('2000-04-09' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_before\n ,sum(case when (cast(d_date as date) >= cast ('2000-04-09' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_after\n from\n catalog_sales left outer join catalog_returns on\n (cs_order_number = cr_order_number\n and cs_item_sk = cr_item_sk)\n ,warehouse\n ,item\n ,date_dim\n where\n i_current_price between 0.99 and 1.49\n and i_item_sk = cs_item_sk\n and cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and d_date between (cast ('2000-04-09' as date) - INTERVAL 30 days)\n and (cast ('2000-04-09' as date) + INTERVAL 30 days)\n group by\n w_state,i_item_id\n order by w_state,i_item_id\nlimit 100\"\"\",\n \"q41\" ->\n \"\"\"\nselect distinct(i_product_name)\n from item i1\n where i_manufact_id between 917 and 917+40\n and (select count(*) as item_cnt\n from item\n where (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'antique' or i_color = 'pale') and\n (i_units = 'Tbl' or i_units = 'Case') and\n (i_size = 'small' or i_size = 'extra large')\n ) or\n (i_category = 'Women' and\n (i_color = 'snow' or i_color = 'lemon') and\n (i_units = 'Box' or i_units = 'Ounce') and\n (i_size = 'economy' or i_size = 'N/A')\n ) or\n (i_category = 'Men' and\n (i_color = 'green' or i_color = 'blue') and\n (i_units = 'Gross' or i_units = 'Ton') and\n (i_size = 'large' or i_size = 'petite')\n ) or\n (i_category = 'Men' and\n (i_color = 'cream' or i_color = 'frosted') and\n (i_units = 'Bundle' or i_units = 'Gram') and\n (i_size = 'small' or i_size = 'extra large')\n ))) or\n (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'orange' or i_color = 'spring') and\n (i_units = 'Lb' or i_units = 'Carton') and\n (i_size = 'small' or i_size = 'extra large')\n ) or\n (i_category = 'Women' and\n (i_color = 'lawn' or i_color = 'violet') and\n (i_units = 'Oz' or i_units = 'Cup') and\n (i_size = 'economy' or i_size = 'N/A')\n ) or\n (i_category = 'Men' and\n (i_color = 'navy' or i_color = 'linen') and\n (i_units = 'Pound' or i_units = 'Unknown') and\n (i_size = 'large' or i_size = 'petite')\n ) or\n (i_category = 'Men' and\n (i_color = 'almond' or i_color = 'olive') and\n (i_units = 'Pallet' or i_units = 'Bunch') and\n (i_size = 'small' or i_size = 'extra large')\n )))) > 0\n order by i_product_name\n limit 100\"\"\",\n \"q42\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_category_id\n \t,item.i_category\n \t,sum(ss_ext_sales_price)\n from \tdate_dim dt\n \t,store_sales\n \t,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n \tand store_sales.ss_item_sk = item.i_item_sk\n \tand item.i_manager_id = 1\n \tand dt.d_moy=11\n \tand dt.d_year=1998\n group by \tdt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\n order by sum(ss_ext_sales_price) desc,dt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\nlimit 100 \"\"\",\n \"q43\" ->\n \"\"\"\nselect s_store_name, s_store_id,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from date_dim, store_sales, store\n where d_date_sk = ss_sold_date_sk and\n s_store_sk = ss_store_sk and\n s_gmt_offset = -6 and\n d_year = 2000\n group by s_store_name, s_store_id\n order by s_store_name, s_store_id,sun_sales,mon_sales,tue_sales,wed_sales,thu_sales,fri_sales,sat_sales\n limit 100\"\"\",\n \"q44\" ->\n \"\"\"\nselect asceding.rnk, i1.i_product_name best_performing, i2.i_product_name worst_performing\nfrom(select *\n from (select item_sk,rank() over (order by rank_col asc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 731\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 731\n and ss_promo_sk is null\n group by ss_store_sk))V1)V11\n where rnk < 11) asceding,\n (select *\n from (select item_sk,rank() over (order by rank_col desc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 731\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 731\n and ss_promo_sk is null\n group by ss_store_sk))V2)V21\n where rnk < 11) descending,\nitem i1,\nitem i2\nwhere asceding.rnk = descending.rnk\n and i1.i_item_sk=asceding.item_sk\n and i2.i_item_sk=descending.item_sk\norder by asceding.rnk\nlimit 100\"\"\",\n \"q45\" ->\n \"\"\"\nselect ca_zip, ca_city, sum(ws_sales_price)\n from web_sales, customer, customer_address, date_dim, item\n where ws_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ws_item_sk = i_item_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475', '85392', '85460', '80348', '81792')\n \t or\n \t i_item_id in (select i_item_id\n from item\n where i_item_sk in (2, 3, 5, 7, 11, 13, 17, 19, 23, 29)\n )\n \t )\n \tand ws_sold_date_sk = d_date_sk\n \tand d_qoy = 1 and d_year = 2000\n group by ca_zip, ca_city\n order by ca_zip, ca_city\n limit 100\"\"\",\n \"q46\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and (household_demographics.hd_dep_count = 1 or\n household_demographics.hd_vehicle_count= 2)\n and date_dim.d_dow in (6,0)\n and date_dim.d_year in (2000,2000+1,2000+2)\n and store.s_city in ('Buena Vista','Friendship','Monroe','Oak Hill','Randolph')\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,ca_city) dn,customer,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n limit 100\"\"\",\n \"q47\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand,\n s_store_name, s_company_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n s_store_name, s_company_name\n order by d_year, d_moy) rn\n from item, store_sales, date_dim, store\n where ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n (\n d_year = 1999 or\n ( d_year = 1999-1 and d_moy =12) or\n ( d_year = 1999+1 and d_moy =1)\n )\n group by i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy),\n v2 as(\n select v1.i_category\n ,v1.d_year, v1.d_moy\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1.s_store_name = v1_lag.s_store_name and\n v1.s_store_name = v1_lead.s_store_name and\n v1.s_company_name = v1_lag.s_company_name and\n v1.s_company_name = v1_lead.s_company_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 1999 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, sum_sales\n limit 100\"\"\",\n \"q48\" ->\n \"\"\"\nselect sum (ss_quantity)\n from store_sales, store, customer_demographics, customer_address, date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 1999\n and\n (\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'S'\n and\n cd_education_status = 'Primary'\n and\n ss_sales_price between 100.00 and 150.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'D'\n and\n cd_education_status = 'College'\n and\n ss_sales_price between 50.00 and 100.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'U'\n and\n cd_education_status = '2 yr Degree'\n and\n ss_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('ND', 'NC', 'TX')\n and ss_net_profit between 0 and 2000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('VA', 'IA', 'AR')\n and ss_net_profit between 150 and 3000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('MA', 'FL', 'TN')\n and ss_net_profit between 50 and 25000\n )\n )\"\"\",\n \"q49\" ->\n \"\"\"\nselect channel, item, return_ratio, return_rank, currency_rank from\n (select\n 'web' as channel\n ,web.item\n ,web.return_ratio\n ,web.return_rank\n ,web.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect ws.ws_item_sk as item\n \t\t,(cast(sum(coalesce(wr.wr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(wr.wr_return_amt,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\t web_sales ws left outer join web_returns wr\n \t\t\ton (ws.ws_order_number = wr.wr_order_number and\n \t\t\tws.ws_item_sk = wr.wr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\twr.wr_return_amt > 10000\n \t\t\tand ws.ws_net_profit > 1\n and ws.ws_net_paid > 0\n and ws.ws_quantity > 0\n and ws_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 11\n \t\tgroup by ws.ws_item_sk\n \t) in_web\n ) web\n where\n (\n web.return_rank <= 10\n or\n web.currency_rank <= 10\n )\n union\n select\n 'catalog' as channel\n ,catalog.item\n ,catalog.return_ratio\n ,catalog.return_rank\n ,catalog.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect\n \t\tcs.cs_item_sk as item\n \t\t,(cast(sum(coalesce(cr.cr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(cr.cr_return_amount,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tcatalog_sales cs left outer join catalog_returns cr\n \t\t\ton (cs.cs_order_number = cr.cr_order_number and\n \t\t\tcs.cs_item_sk = cr.cr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tcr.cr_return_amount > 10000\n \t\t\tand cs.cs_net_profit > 1\n and cs.cs_net_paid > 0\n and cs.cs_quantity > 0\n and cs_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 11\n group by cs.cs_item_sk\n \t) in_cat\n ) catalog\n where\n (\n catalog.return_rank <= 10\n or\n catalog.currency_rank <=10\n )\n union\n select\n 'store' as channel\n ,store.item\n ,store.return_ratio\n ,store.return_rank\n ,store.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect sts.ss_item_sk as item\n \t\t,(cast(sum(coalesce(sr.sr_return_quantity,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(sr.sr_return_amt,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tstore_sales sts left outer join store_returns sr\n \t\t\ton (sts.ss_ticket_number = sr.sr_ticket_number and sts.ss_item_sk = sr.sr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tsr.sr_return_amt > 10000\n \t\t\tand sts.ss_net_profit > 1\n and sts.ss_net_paid > 0\n and sts.ss_quantity > 0\n and ss_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 11\n \t\tgroup by sts.ss_item_sk\n \t) in_store\n ) store\n where (\n store.return_rank <= 10\n or\n store.currency_rank <= 10\n )\n )\n order by 1,4,5,2\n limit 100\"\"\",\n \"q50\" ->\n \"\"\"\nselect\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 30) and\n (sr_returned_date_sk - ss_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 60) and\n (sr_returned_date_sk - ss_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 90) and\n (sr_returned_date_sk - ss_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n store_sales\n ,store_returns\n ,store\n ,date_dim d1\n ,date_dim d2\nwhere\n d2.d_year = 2002\nand d2.d_moy = 10\nand ss_ticket_number = sr_ticket_number\nand ss_item_sk = sr_item_sk\nand ss_sold_date_sk = d1.d_date_sk\nand sr_returned_date_sk = d2.d_date_sk\nand ss_customer_sk = sr_customer_sk\nand ss_store_sk = s_store_sk\ngroup by\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\norder by s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\nlimit 100\"\"\",\n \"q51\" ->\n \"\"\"\nWITH web_v1 as (\nselect\n ws_item_sk item_sk, d_date,\n sum(sum(ws_sales_price))\n over (partition by ws_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom web_sales\n ,date_dim\nwhere ws_sold_date_sk=d_date_sk\n and d_month_seq between 1213 and 1213+11\n and ws_item_sk is not NULL\ngroup by ws_item_sk, d_date),\nstore_v1 as (\nselect\n ss_item_sk item_sk, d_date,\n sum(sum(ss_sales_price))\n over (partition by ss_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom store_sales\n ,date_dim\nwhere ss_sold_date_sk=d_date_sk\n and d_month_seq between 1213 and 1213+11\n and ss_item_sk is not NULL\ngroup by ss_item_sk, d_date)\n select *\nfrom (select item_sk\n ,d_date\n ,web_sales\n ,store_sales\n ,max(web_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) web_cumulative\n ,max(store_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) store_cumulative\n from (select case when web.item_sk is not null then web.item_sk else store.item_sk end item_sk\n ,case when web.d_date is not null then web.d_date else store.d_date end d_date\n ,web.cume_sales web_sales\n ,store.cume_sales store_sales\n from web_v1 web full outer join store_v1 store on (web.item_sk = store.item_sk\n and web.d_date = store.d_date)\n )x )y\nwhere web_cumulative > store_cumulative\norder by item_sk\n ,d_date\nlimit 100\"\"\",\n \"q52\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_brand_id brand_id\n \t,item.i_brand brand\n \t,sum(ss_ext_sales_price) ext_price\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manager_id = 1\n and dt.d_moy=11\n and dt.d_year=1998\n group by dt.d_year\n \t,item.i_brand\n \t,item.i_brand_id\n order by dt.d_year\n \t,ext_price desc\n \t,brand_id\nlimit 100 \"\"\",\n \"q53\" ->\n \"\"\"\nselect * from\n(select i_manufact_id,\nsum(ss_sales_price) sum_sales,\navg(sum(ss_sales_price)) over (partition by i_manufact_id) avg_quarterly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\nss_sold_date_sk = d_date_sk and\nss_store_sk = s_store_sk and\nd_month_seq in (1219,1219+1,1219+2,1219+3,1219+4,1219+5,1219+6,1219+7,1219+8,1219+9,1219+10,1219+11) and\n((i_category in ('Books','Children','Electronics') and\ni_class in ('personal','portable','reference','self-help') and\ni_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t'exportiunivamalg #9','scholaramalgamalg #9'))\nor(i_category in ('Women','Music','Men') and\ni_class in ('accessories','classical','fragrances','pants') and\ni_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t'importoamalg #1')))\ngroup by i_manufact_id, d_qoy ) tmp1\nwhere case when avg_quarterly_sales > 0\n\tthen abs (sum_sales - avg_quarterly_sales)/ avg_quarterly_sales\n\telse null end > 0.1\norder by avg_quarterly_sales,\n\t sum_sales,\n\t i_manufact_id\nlimit 100\"\"\",\n \"q54\" ->\n \"\"\"\nwith my_customers as (\n select distinct c_customer_sk\n , c_current_addr_sk\n from\n ( select cs_sold_date_sk sold_date_sk,\n cs_bill_customer_sk customer_sk,\n cs_item_sk item_sk\n from catalog_sales\n union all\n select ws_sold_date_sk sold_date_sk,\n ws_bill_customer_sk customer_sk,\n ws_item_sk item_sk\n from web_sales\n ) cs_or_ws_sales,\n item,\n date_dim,\n customer\n where sold_date_sk = d_date_sk\n and item_sk = i_item_sk\n and i_category = 'Men'\n and i_class = 'shirts'\n and c_customer_sk = cs_or_ws_sales.customer_sk\n and d_moy = 2\n and d_year = 1999\n )\n , my_revenue as (\n select c_customer_sk,\n sum(ss_ext_sales_price) as revenue\n from my_customers,\n store_sales,\n customer_address,\n store,\n date_dim\n where c_current_addr_sk = ca_address_sk\n and ca_county = s_county\n and ca_state = s_state\n and ss_sold_date_sk = d_date_sk\n and c_customer_sk = ss_customer_sk\n and d_month_seq between (select distinct d_month_seq+1\n from date_dim where d_year = 1999 and d_moy = 2)\n and (select distinct d_month_seq+3\n from date_dim where d_year = 1999 and d_moy = 2)\n group by c_customer_sk\n )\n , segments as\n (select cast((revenue/50) as int) as segment\n from my_revenue\n )\n select segment, count(*) as num_customers, segment*50 as segment_base\n from segments\n group by segment\n order by segment, num_customers\n limit 100\"\"\",\n \"q55\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item\n where d_date_sk = ss_sold_date_sk\n \tand ss_item_sk = i_item_sk\n \tand i_manager_id=96\n \tand d_moy=11\n \tand d_year=2000\n group by i_brand, i_brand_id\n order by ext_price desc, i_brand_id\nlimit 100 \"\"\",\n \"q56\" ->\n \"\"\"\nwith ss as (\n select i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('antique','white','smoke'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 6\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_item_id),\n cs as (\n select i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('antique','white','smoke'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 6\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_item_id),\n ws as (\n select i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('antique','white','smoke'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 6\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_item_id)\n select i_item_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by total_sales,\n i_item_id\n limit 100\"\"\",\n \"q57\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n cc_name,\n d_year, d_moy,\n sum(cs_sales_price) sum_sales,\n avg(sum(cs_sales_price)) over\n (partition by i_category, i_brand,\n cc_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n cc_name\n order by d_year, d_moy) rn\n from item, catalog_sales, date_dim, call_center\n where cs_item_sk = i_item_sk and\n cs_sold_date_sk = d_date_sk and\n cc_call_center_sk= cs_call_center_sk and\n (\n d_year = 2000 or\n ( d_year = 2000-1 and d_moy =12) or\n ( d_year = 2000+1 and d_moy =1)\n )\n group by i_category, i_brand,\n cc_name , d_year, d_moy),\n v2 as(\n select v1.i_category, v1.i_brand, v1.cc_name\n ,v1.d_year\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1. cc_name = v1_lag. cc_name and\n v1. cc_name = v1_lead. cc_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 2000 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, sum_sales\n limit 100\"\"\",\n \"q58\" ->\n \"\"\"\nwith ss_items as\n (select i_item_id item_id\n ,sum(ss_ext_sales_price) ss_item_rev\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '2001-01-27'))\n and ss_sold_date_sk = d_date_sk\n group by i_item_id),\n cs_items as\n (select i_item_id item_id\n ,sum(cs_ext_sales_price) cs_item_rev\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '2001-01-27'))\n and cs_sold_date_sk = d_date_sk\n group by i_item_id),\n ws_items as\n (select i_item_id item_id\n ,sum(ws_ext_sales_price) ws_item_rev\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq =(select d_week_seq\n from date_dim\n where d_date = '2001-01-27'))\n and ws_sold_date_sk = d_date_sk\n group by i_item_id)\n select ss_items.item_id\n ,ss_item_rev\n ,ss_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ss_dev\n ,cs_item_rev\n ,cs_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 cs_dev\n ,ws_item_rev\n ,ws_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ws_dev\n ,(ss_item_rev+cs_item_rev+ws_item_rev)/3 average\n from ss_items,cs_items,ws_items\n where ss_items.item_id=cs_items.item_id\n and ss_items.item_id=ws_items.item_id\n and ss_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n and ss_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and cs_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and cs_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and ws_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and ws_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n order by item_id\n ,ss_item_rev\n limit 100\"\"\",\n \"q59\" ->\n \"\"\"\nwith wss as\n (select d_week_seq,\n ss_store_sk,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n group by d_week_seq,ss_store_sk\n )\n select s_store_name1,s_store_id1,d_week_seq1\n ,sun_sales1/sun_sales2,mon_sales1/mon_sales2\n ,tue_sales1/tue_sales2,wed_sales1/wed_sales2,thu_sales1/thu_sales2\n ,fri_sales1/fri_sales2,sat_sales1/sat_sales2\n from\n (select s_store_name s_store_name1,wss.d_week_seq d_week_seq1\n ,s_store_id s_store_id1,sun_sales sun_sales1\n ,mon_sales mon_sales1,tue_sales tue_sales1\n ,wed_sales wed_sales1,thu_sales thu_sales1\n ,fri_sales fri_sales1,sat_sales sat_sales1\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1177 and 1177 + 11) y,\n (select s_store_name s_store_name2,wss.d_week_seq d_week_seq2\n ,s_store_id s_store_id2,sun_sales sun_sales2\n ,mon_sales mon_sales2,tue_sales tue_sales2\n ,wed_sales wed_sales2,thu_sales thu_sales2\n ,fri_sales fri_sales2,sat_sales sat_sales2\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1177+ 12 and 1177 + 23) x\n where s_store_id1=s_store_id2\n and d_week_seq1=d_week_seq2-52\n order by s_store_name1,s_store_id1,d_week_seq1\nlimit 100\"\"\",\n \"q60\" ->\n \"\"\"\nwith ss as (\n select\n i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Shoes'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 2002\n and d_moy = 8\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_item_id),\n cs as (\n select\n i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Shoes'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 2002\n and d_moy = 8\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_item_id),\n ws as (\n select\n i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Shoes'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 2002\n and d_moy = 8\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_item_id)\n select\n i_item_id\n,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by i_item_id\n ,total_sales\n limit 100\"\"\",\n \"q61\" ->\n \"\"\"\nselect promotions,total,cast(promotions as decimal(15,4))/cast(total as decimal(15,4))*100\nfrom\n (select sum(ss_ext_sales_price) promotions\n from store_sales\n ,store\n ,promotion\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_promo_sk = p_promo_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -6\n and i_category = 'Home'\n and (p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y')\n and s_gmt_offset = -6\n and d_year = 1999\n and d_moy = 12) promotional_sales,\n (select sum(ss_ext_sales_price) total\n from store_sales\n ,store\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -6\n and i_category = 'Home'\n and s_gmt_offset = -6\n and d_year = 1999\n and d_moy = 12) all_sales\norder by promotions, total\nlimit 100\"\"\",\n \"q62\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 30) and\n (ws_ship_date_sk - ws_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 60) and\n (ws_ship_date_sk - ws_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 90) and\n (ws_ship_date_sk - ws_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n web_sales\n ,warehouse\n ,ship_mode\n ,web_site\n ,date_dim\nwhere\n d_month_seq between 1191 and 1191 + 11\nand ws_ship_date_sk = d_date_sk\nand ws_warehouse_sk = w_warehouse_sk\nand ws_ship_mode_sk = sm_ship_mode_sk\nand ws_web_site_sk = web_site_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\nlimit 100\"\"\",\n \"q63\" ->\n \"\"\"\nselect *\nfrom (select i_manager_id\n ,sum(ss_sales_price) sum_sales\n ,avg(sum(ss_sales_price)) over (partition by i_manager_id) avg_monthly_sales\n from item\n ,store_sales\n ,date_dim\n ,store\n where ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and d_month_seq in (1193,1193+1,1193+2,1193+3,1193+4,1193+5,1193+6,1193+7,1193+8,1193+9,1193+10,1193+11)\n and (( i_category in ('Books','Children','Electronics')\n and i_class in ('personal','portable','reference','self-help')\n and i_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t 'exportiunivamalg #9','scholaramalgamalg #9'))\n or( i_category in ('Women','Music','Men')\n and i_class in ('accessories','classical','fragrances','pants')\n and i_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t 'importoamalg #1')))\ngroup by i_manager_id, d_moy) tmp1\nwhere case when avg_monthly_sales > 0 then abs (sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\norder by i_manager_id\n ,avg_monthly_sales\n ,sum_sales\nlimit 100\"\"\",\n \"q64\" ->\n \"\"\"\nwith cs_ui as\n (select cs_item_sk\n ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund\n from catalog_sales\n ,catalog_returns\n where cs_item_sk = cr_item_sk\n and cs_order_number = cr_order_number\n group by cs_item_sk\n having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit)),\ncross_sales as\n (select i_product_name product_name\n ,i_item_sk item_sk\n ,s_store_name store_name\n ,s_zip store_zip\n ,ad1.ca_street_number b_street_number\n ,ad1.ca_street_name b_street_name\n ,ad1.ca_city b_city\n ,ad1.ca_zip b_zip\n ,ad2.ca_street_number c_street_number\n ,ad2.ca_street_name c_street_name\n ,ad2.ca_city c_city\n ,ad2.ca_zip c_zip\n ,d1.d_year as syear\n ,d2.d_year as fsyear\n ,d3.d_year s2year\n ,count(*) cnt\n ,sum(ss_wholesale_cost) s1\n ,sum(ss_list_price) s2\n ,sum(ss_coupon_amt) s3\n FROM store_sales\n ,store_returns\n ,cs_ui\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,customer\n ,customer_demographics cd1\n ,customer_demographics cd2\n ,promotion\n ,household_demographics hd1\n ,household_demographics hd2\n ,customer_address ad1\n ,customer_address ad2\n ,income_band ib1\n ,income_band ib2\n ,item\n WHERE ss_store_sk = s_store_sk AND\n ss_sold_date_sk = d1.d_date_sk AND\n ss_customer_sk = c_customer_sk AND\n ss_cdemo_sk= cd1.cd_demo_sk AND\n ss_hdemo_sk = hd1.hd_demo_sk AND\n ss_addr_sk = ad1.ca_address_sk and\n ss_item_sk = i_item_sk and\n ss_item_sk = sr_item_sk and\n ss_ticket_number = sr_ticket_number and\n ss_item_sk = cs_ui.cs_item_sk and\n c_current_cdemo_sk = cd2.cd_demo_sk AND\n c_current_hdemo_sk = hd2.hd_demo_sk AND\n c_current_addr_sk = ad2.ca_address_sk and\n c_first_sales_date_sk = d2.d_date_sk and\n c_first_shipto_date_sk = d3.d_date_sk and\n ss_promo_sk = p_promo_sk and\n hd1.hd_income_band_sk = ib1.ib_income_band_sk and\n hd2.hd_income_band_sk = ib2.ib_income_band_sk and\n cd1.cd_marital_status <> cd2.cd_marital_status and\n i_color in ('orange','aquamarine','olive','linen','smoke','coral') and\n i_current_price between 74 and 74 + 10 and\n i_current_price between 74 + 1 and 74 + 15\ngroup by i_product_name\n ,i_item_sk\n ,s_store_name\n ,s_zip\n ,ad1.ca_street_number\n ,ad1.ca_street_name\n ,ad1.ca_city\n ,ad1.ca_zip\n ,ad2.ca_street_number\n ,ad2.ca_street_name\n ,ad2.ca_city\n ,ad2.ca_zip\n ,d1.d_year\n ,d2.d_year\n ,d3.d_year\n)\nselect cs1.product_name\n ,cs1.store_name\n ,cs1.store_zip\n ,cs1.b_street_number\n ,cs1.b_street_name\n ,cs1.b_city\n ,cs1.b_zip\n ,cs1.c_street_number\n ,cs1.c_street_name\n ,cs1.c_city\n ,cs1.c_zip\n ,cs1.syear\n ,cs1.cnt\n ,cs1.s1 as s11\n ,cs1.s2 as s21\n ,cs1.s3 as s31\n ,cs2.s1 as s12\n ,cs2.s2 as s22\n ,cs2.s3 as s32\n ,cs2.syear\n ,cs2.cnt\nfrom cross_sales cs1,cross_sales cs2\nwhere cs1.item_sk=cs2.item_sk and\n cs1.syear = 2001 and\n cs2.syear = 2001 + 1 and\n cs2.cnt <= cs1.cnt and\n cs1.store_name = cs2.store_name and\n cs1.store_zip = cs2.store_zip\norder by cs1.product_name\n ,cs1.store_name\n ,cs2.cnt\n ,cs1.s1\n ,cs2.s1\"\"\",\n \"q65\" ->\n \"\"\"\nselect\n\ts_store_name,\n\ti_item_desc,\n\tsc.revenue,\n\ti_current_price,\n\ti_wholesale_cost,\n\ti_brand\n from store, item,\n (select ss_store_sk, avg(revenue) as ave\n \tfrom\n \t (select ss_store_sk, ss_item_sk,\n \t\t sum(ss_sales_price) as revenue\n \t\tfrom store_sales, date_dim\n \t\twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1195 and 1195+11\n \t\tgroup by ss_store_sk, ss_item_sk) sa\n \tgroup by ss_store_sk) sb,\n (select ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue\n \tfrom store_sales, date_dim\n \twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1195 and 1195+11\n \tgroup by ss_store_sk, ss_item_sk) sc\n where sb.ss_store_sk = sc.ss_store_sk and\n sc.revenue <= 0.1 * sb.ave and\n s_store_sk = sc.ss_store_sk and\n i_item_sk = sc.ss_item_sk\n order by s_store_name, i_item_desc\nlimit 100\"\"\",\n \"q66\" ->\n \"\"\"\nselect\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,ship_carriers\n ,year\n \t,sum(jan_sales) as jan_sales\n \t,sum(feb_sales) as feb_sales\n \t,sum(mar_sales) as mar_sales\n \t,sum(apr_sales) as apr_sales\n \t,sum(may_sales) as may_sales\n \t,sum(jun_sales) as jun_sales\n \t,sum(jul_sales) as jul_sales\n \t,sum(aug_sales) as aug_sales\n \t,sum(sep_sales) as sep_sales\n \t,sum(oct_sales) as oct_sales\n \t,sum(nov_sales) as nov_sales\n \t,sum(dec_sales) as dec_sales\n \t,sum(jan_sales/w_warehouse_sq_ft) as jan_sales_per_sq_foot\n \t,sum(feb_sales/w_warehouse_sq_ft) as feb_sales_per_sq_foot\n \t,sum(mar_sales/w_warehouse_sq_ft) as mar_sales_per_sq_foot\n \t,sum(apr_sales/w_warehouse_sq_ft) as apr_sales_per_sq_foot\n \t,sum(may_sales/w_warehouse_sq_ft) as may_sales_per_sq_foot\n \t,sum(jun_sales/w_warehouse_sq_ft) as jun_sales_per_sq_foot\n \t,sum(jul_sales/w_warehouse_sq_ft) as jul_sales_per_sq_foot\n \t,sum(aug_sales/w_warehouse_sq_ft) as aug_sales_per_sq_foot\n \t,sum(sep_sales/w_warehouse_sq_ft) as sep_sales_per_sq_foot\n \t,sum(oct_sales/w_warehouse_sq_ft) as oct_sales_per_sq_foot\n \t,sum(nov_sales/w_warehouse_sq_ft) as nov_sales_per_sq_foot\n \t,sum(dec_sales/w_warehouse_sq_ft) as dec_sales_per_sq_foot\n \t,sum(jan_net) as jan_net\n \t,sum(feb_net) as feb_net\n \t,sum(mar_net) as mar_net\n \t,sum(apr_net) as apr_net\n \t,sum(may_net) as may_net\n \t,sum(jun_net) as jun_net\n \t,sum(jul_net) as jul_net\n \t,sum(aug_net) as aug_net\n \t,sum(sep_net) as sep_net\n \t,sum(oct_net) as oct_net\n \t,sum(nov_net) as nov_net\n \t,sum(dec_net) as dec_net\n from (\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'LATVIAN' || ',' || 'ALLIANCE' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as dec_net\n from\n web_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n ws_warehouse_sk = w_warehouse_sk\n and ws_sold_date_sk = d_date_sk\n and ws_sold_time_sk = t_time_sk\n \tand ws_ship_mode_sk = sm_ship_mode_sk\n and d_year = 1998\n \tand t_time between 16224 and 16224+28800\n \tand sm_carrier in ('LATVIAN','ALLIANCE')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n union all\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'LATVIAN' || ',' || 'ALLIANCE' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as dec_net\n from\n catalog_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and cs_sold_time_sk = t_time_sk\n \tand cs_ship_mode_sk = sm_ship_mode_sk\n and d_year = 1998\n \tand t_time between 16224 AND 16224+28800\n \tand sm_carrier in ('LATVIAN','ALLIANCE')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n ) x\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,ship_carriers\n ,year\n order by w_warehouse_name\n limit 100\"\"\",\n \"q67\" ->\n \"\"\"\nselect *\nfrom (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rank() over (partition by i_category order by sumsales desc) rk\n from (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sum(coalesce(ss_sales_price*ss_quantity,0)) sumsales\n from store_sales\n ,date_dim\n ,store\n ,item\n where ss_sold_date_sk=d_date_sk\n and ss_item_sk=i_item_sk\n and ss_store_sk = s_store_sk\n and d_month_seq between 1203 and 1203+11\n group by rollup(i_category, i_class, i_brand, i_product_name, d_year, d_qoy, d_moy,s_store_id))dw1) dw2\nwhere rk <= 100\norder by i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rk\nlimit 100\"\"\",\n \"q68\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,extended_price\n ,extended_tax\n ,list_price\n from (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_ext_sales_price) extended_price\n ,sum(ss_ext_list_price) list_price\n ,sum(ss_ext_tax) extended_tax\n from store_sales\n ,date_dim\n ,store\n ,household_demographics\n ,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_dep_count = 3 or\n household_demographics.hd_vehicle_count= -1)\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_city in ('Jamestown','Pine Hill')\n group by ss_ticket_number\n ,ss_customer_sk\n ,ss_addr_sk,ca_city) dn\n ,customer\n ,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,ss_ticket_number\n limit 100\"\"\",\n \"q69\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_state in ('CA','MT','SD') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2003 and\n d_moy between 2 and 2+2) and\n (not exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2003 and\n d_moy between 2 and 2+2) and\n not exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2003 and\n d_moy between 2 and 2+2))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n limit 100\"\"\",\n \"q70\" ->\n \"\"\"\nselect\n sum(ss_net_profit) as total_sum\n ,s_state\n ,s_county\n ,grouping(s_state)+grouping(s_county) as lochierarchy\n ,rank() over (\n \tpartition by grouping(s_state)+grouping(s_county),\n \tcase when grouping(s_county) = 0 then s_state end\n \torder by sum(ss_net_profit) desc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,store\n where\n d1.d_month_seq between 1215 and 1215+11\n and d1.d_date_sk = ss_sold_date_sk\n and s_store_sk = ss_store_sk\n and s_state in\n ( select s_state\n from (select s_state as s_state,\n \t\t\t rank() over ( partition by s_state order by sum(ss_net_profit) desc) as ranking\n from store_sales, store, date_dim\n where d_month_seq between 1215 and 1215+11\n \t\t\t and d_date_sk = ss_sold_date_sk\n \t\t\t and s_store_sk = ss_store_sk\n group by s_state\n ) tmp1\n where ranking <= 5\n )\n group by rollup(s_state,s_county)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then s_state end\n ,rank_within_parent\n limit 100\"\"\",\n \"q71\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,t_hour,t_minute,\n \tsum(ext_price) ext_price\n from item, (select ws_ext_sales_price as ext_price,\n ws_sold_date_sk as sold_date_sk,\n ws_item_sk as sold_item_sk,\n ws_sold_time_sk as time_sk\n from web_sales,date_dim\n where d_date_sk = ws_sold_date_sk\n and d_moy=11\n and d_year=1998\n union all\n select cs_ext_sales_price as ext_price,\n cs_sold_date_sk as sold_date_sk,\n cs_item_sk as sold_item_sk,\n cs_sold_time_sk as time_sk\n from catalog_sales,date_dim\n where d_date_sk = cs_sold_date_sk\n and d_moy=11\n and d_year=1998\n union all\n select ss_ext_sales_price as ext_price,\n ss_sold_date_sk as sold_date_sk,\n ss_item_sk as sold_item_sk,\n ss_sold_time_sk as time_sk\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n and d_moy=11\n and d_year=1998\n ) tmp,time_dim\n where\n sold_item_sk = i_item_sk\n and i_manager_id=1\n and time_sk = t_time_sk\n and (t_meal_time = 'breakfast' or t_meal_time = 'dinner')\n group by i_brand, i_brand_id,t_hour,t_minute\n order by ext_price desc, i_brand_id\n \"\"\",\n \"q72\" ->\n \"\"\"\nselect i_item_desc\n ,w_warehouse_name\n ,d1.d_week_seq\n ,sum(case when p_promo_sk is null then 1 else 0 end) no_promo\n ,sum(case when p_promo_sk is not null then 1 else 0 end) promo\n ,count(*) total_cnt\nfrom catalog_sales\njoin inventory on (cs_item_sk = inv_item_sk)\njoin warehouse on (w_warehouse_sk=inv_warehouse_sk)\njoin item on (i_item_sk = cs_item_sk)\njoin customer_demographics on (cs_bill_cdemo_sk = cd_demo_sk)\njoin household_demographics on (cs_bill_hdemo_sk = hd_demo_sk)\njoin date_dim d1 on (cs_sold_date_sk = d1.d_date_sk)\njoin date_dim d2 on (inv_date_sk = d2.d_date_sk)\njoin date_dim d3 on (cs_ship_date_sk = d3.d_date_sk)\nleft outer join promotion on (cs_promo_sk=p_promo_sk)\nleft outer join catalog_returns on (cr_item_sk = cs_item_sk and cr_order_number = cs_order_number)\nwhere d1.d_week_seq = d2.d_week_seq\n and inv_quantity_on_hand < cs_quantity\n and d3.d_date > d1.d_date + interval 5 days\n and hd_buy_potential = '1001-5000'\n and d1.d_year = 1998\n and cd_marital_status = 'S'\ngroup by i_item_desc,w_warehouse_name,d1.d_week_seq\norder by total_cnt desc, i_item_desc, w_warehouse_name, d_week_seq\nlimit 100\"\"\",\n \"q73\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_buy_potential = '>10000' or\n household_demographics.hd_buy_potential = 'Unknown')\n and household_demographics.hd_vehicle_count > 0\n and case when household_demographics.hd_vehicle_count > 0 then\n household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count else null end > 1\n and date_dim.d_year in (1998,1998+1,1998+2)\n and store.s_county in ('Van Buren County','Terrell County','Belknap County','Kootenai County')\n group by ss_ticket_number,ss_customer_sk) dj,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 1 and 5\n order by cnt desc, c_last_name asc\"\"\",\n \"q74\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,max(ss_net_paid) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2001,2001+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,max(ws_net_paid) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n and d_year in (2001,2001+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n )\n select\n t_s_secyear.customer_id, t_s_secyear.customer_first_name, t_s_secyear.customer_last_name\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.year = 2001\n and t_s_secyear.year = 2001+1\n and t_w_firstyear.year = 2001\n and t_w_secyear.year = 2001+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n order by 2,3,1\nlimit 100\"\"\",\n \"q75\" ->\n \"\"\"\nWITH all_sales AS (\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,SUM(sales_cnt) AS sales_cnt\n ,SUM(sales_amt) AS sales_amt\n FROM (SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,cs_quantity - COALESCE(cr_return_quantity,0) AS sales_cnt\n ,cs_ext_sales_price - COALESCE(cr_return_amount,0.0) AS sales_amt\n FROM catalog_sales JOIN item ON i_item_sk=cs_item_sk\n JOIN date_dim ON d_date_sk=cs_sold_date_sk\n LEFT JOIN catalog_returns ON (cs_order_number=cr_order_number\n AND cs_item_sk=cr_item_sk)\n WHERE i_category='Music'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ss_quantity - COALESCE(sr_return_quantity,0) AS sales_cnt\n ,ss_ext_sales_price - COALESCE(sr_return_amt,0.0) AS sales_amt\n FROM store_sales JOIN item ON i_item_sk=ss_item_sk\n JOIN date_dim ON d_date_sk=ss_sold_date_sk\n LEFT JOIN store_returns ON (ss_ticket_number=sr_ticket_number\n AND ss_item_sk=sr_item_sk)\n WHERE i_category='Music'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ws_quantity - COALESCE(wr_return_quantity,0) AS sales_cnt\n ,ws_ext_sales_price - COALESCE(wr_return_amt,0.0) AS sales_amt\n FROM web_sales JOIN item ON i_item_sk=ws_item_sk\n JOIN date_dim ON d_date_sk=ws_sold_date_sk\n LEFT JOIN web_returns ON (ws_order_number=wr_order_number\n AND ws_item_sk=wr_item_sk)\n WHERE i_category='Music') sales_detail\n GROUP BY d_year, i_brand_id, i_class_id, i_category_id, i_manufact_id)\n SELECT prev_yr.d_year AS prev_year\n ,curr_yr.d_year AS year\n ,curr_yr.i_brand_id\n ,curr_yr.i_class_id\n ,curr_yr.i_category_id\n ,curr_yr.i_manufact_id\n ,prev_yr.sales_cnt AS prev_yr_cnt\n ,curr_yr.sales_cnt AS curr_yr_cnt\n ,curr_yr.sales_cnt-prev_yr.sales_cnt AS sales_cnt_diff\n ,curr_yr.sales_amt-prev_yr.sales_amt AS sales_amt_diff\n FROM all_sales curr_yr, all_sales prev_yr\n WHERE curr_yr.i_brand_id=prev_yr.i_brand_id\n AND curr_yr.i_class_id=prev_yr.i_class_id\n AND curr_yr.i_category_id=prev_yr.i_category_id\n AND curr_yr.i_manufact_id=prev_yr.i_manufact_id\n AND curr_yr.d_year=2001\n AND prev_yr.d_year=2001-1\n AND CAST(curr_yr.sales_cnt AS DECIMAL(17,2))/CAST(prev_yr.sales_cnt AS DECIMAL(17,2))<0.9\n ORDER BY sales_cnt_diff,sales_amt_diff\n limit 100\"\"\",\n \"q76\" ->\n \"\"\"\nselect channel, col_name, d_year, d_qoy, i_category, COUNT(*) sales_cnt, SUM(ext_sales_price) sales_amt FROM (\n SELECT 'store' as channel, 'ss_promo_sk' col_name, d_year, d_qoy, i_category, ss_ext_sales_price ext_sales_price\n FROM store_sales, item, date_dim\n WHERE ss_promo_sk IS NULL\n AND ss_sold_date_sk=d_date_sk\n AND ss_item_sk=i_item_sk\n UNION ALL\n SELECT 'web' as channel, 'ws_web_site_sk' col_name, d_year, d_qoy, i_category, ws_ext_sales_price ext_sales_price\n FROM web_sales, item, date_dim\n WHERE ws_web_site_sk IS NULL\n AND ws_sold_date_sk=d_date_sk\n AND ws_item_sk=i_item_sk\n UNION ALL\n SELECT 'catalog' as channel, 'cs_bill_addr_sk' col_name, d_year, d_qoy, i_category, cs_ext_sales_price ext_sales_price\n FROM catalog_sales, item, date_dim\n WHERE cs_bill_addr_sk IS NULL\n AND cs_sold_date_sk=d_date_sk\n AND cs_item_sk=i_item_sk) foo\nGROUP BY channel, col_name, d_year, d_qoy, i_category\nORDER BY channel, col_name, d_year, d_qoy, i_category\nlimit 100\"\"\",\n \"q77\" ->\n \"\"\"\nwith ss as\n (select s_store_sk,\n sum(ss_ext_sales_price) as sales,\n sum(ss_net_profit) as profit\n from store_sales,\n date_dim,\n store\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-22' as date)\n and (cast('2001-08-22' as date) + INTERVAL 30 days)\n and ss_store_sk = s_store_sk\n group by s_store_sk)\n ,\n sr as\n (select s_store_sk,\n sum(sr_return_amt) as returns,\n sum(sr_net_loss) as profit_loss\n from store_returns,\n date_dim,\n store\n where sr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-22' as date)\n and (cast('2001-08-22' as date) + INTERVAL 30 days)\n and sr_store_sk = s_store_sk\n group by s_store_sk),\n cs as\n (select cs_call_center_sk,\n sum(cs_ext_sales_price) as sales,\n sum(cs_net_profit) as profit\n from catalog_sales,\n date_dim\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-22' as date)\n and (cast('2001-08-22' as date) + INTERVAL 30 days)\n group by cs_call_center_sk\n ),\n cr as\n (select cr_call_center_sk,\n sum(cr_return_amount) as returns,\n sum(cr_net_loss) as profit_loss\n from catalog_returns,\n date_dim\n where cr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-22' as date)\n and (cast('2001-08-22' as date) + INTERVAL 30 days)\n group by cr_call_center_sk\n ),\n ws as\n ( select wp_web_page_sk,\n sum(ws_ext_sales_price) as sales,\n sum(ws_net_profit) as profit\n from web_sales,\n date_dim,\n web_page\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-22' as date)\n and (cast('2001-08-22' as date) + INTERVAL 30 days)\n and ws_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk),\n wr as\n (select wp_web_page_sk,\n sum(wr_return_amt) as returns,\n sum(wr_net_loss) as profit_loss\n from web_returns,\n date_dim,\n web_page\n where wr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-22' as date)\n and (cast('2001-08-22' as date) + INTERVAL 30 days)\n and wr_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , ss.s_store_sk as id\n , sales\n , coalesce(returns, 0) as returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ss left join sr\n on ss.s_store_sk = sr.s_store_sk\n union all\n select 'catalog channel' as channel\n , cs_call_center_sk as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from cs\n , cr\n union all\n select 'web channel' as channel\n , ws.wp_web_page_sk as id\n , sales\n , coalesce(returns, 0) returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ws left join wr\n on ws.wp_web_page_sk = wr.wp_web_page_sk\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q78\" ->\n \"\"\"\nwith ws as\n (select d_year AS ws_sold_year, ws_item_sk,\n ws_bill_customer_sk ws_customer_sk,\n sum(ws_quantity) ws_qty,\n sum(ws_wholesale_cost) ws_wc,\n sum(ws_sales_price) ws_sp\n from web_sales\n left join web_returns on wr_order_number=ws_order_number and ws_item_sk=wr_item_sk\n join date_dim on ws_sold_date_sk = d_date_sk\n where wr_order_number is null\n group by d_year, ws_item_sk, ws_bill_customer_sk\n ),\ncs as\n (select d_year AS cs_sold_year, cs_item_sk,\n cs_bill_customer_sk cs_customer_sk,\n sum(cs_quantity) cs_qty,\n sum(cs_wholesale_cost) cs_wc,\n sum(cs_sales_price) cs_sp\n from catalog_sales\n left join catalog_returns on cr_order_number=cs_order_number and cs_item_sk=cr_item_sk\n join date_dim on cs_sold_date_sk = d_date_sk\n where cr_order_number is null\n group by d_year, cs_item_sk, cs_bill_customer_sk\n ),\nss as\n (select d_year AS ss_sold_year, ss_item_sk,\n ss_customer_sk,\n sum(ss_quantity) ss_qty,\n sum(ss_wholesale_cost) ss_wc,\n sum(ss_sales_price) ss_sp\n from store_sales\n left join store_returns on sr_ticket_number=ss_ticket_number and ss_item_sk=sr_item_sk\n join date_dim on ss_sold_date_sk = d_date_sk\n where sr_ticket_number is null\n group by d_year, ss_item_sk, ss_customer_sk\n )\n select\nss_sold_year, ss_item_sk, ss_customer_sk,\nround(ss_qty/(coalesce(ws_qty,0)+coalesce(cs_qty,0)),2) ratio,\nss_qty store_qty, ss_wc store_wholesale_cost, ss_sp store_sales_price,\ncoalesce(ws_qty,0)+coalesce(cs_qty,0) other_chan_qty,\ncoalesce(ws_wc,0)+coalesce(cs_wc,0) other_chan_wholesale_cost,\ncoalesce(ws_sp,0)+coalesce(cs_sp,0) other_chan_sales_price\nfrom ss\nleft join ws on (ws_sold_year=ss_sold_year and ws_item_sk=ss_item_sk and ws_customer_sk=ss_customer_sk)\nleft join cs on (cs_sold_year=ss_sold_year and cs_item_sk=ss_item_sk and cs_customer_sk=ss_customer_sk)\nwhere (coalesce(ws_qty,0)>0 or coalesce(cs_qty, 0)>0) and ss_sold_year=2001\norder by\n ss_sold_year, ss_item_sk, ss_customer_sk,\n ss_qty desc, ss_wc desc, ss_sp desc,\n other_chan_qty,\n other_chan_wholesale_cost,\n other_chan_sales_price,\n ratio\nlimit 100\"\"\",\n \"q79\" ->\n \"\"\"\nselect\n c_last_name,c_first_name,substr(s_city,1,30),ss_ticket_number,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,store.s_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (household_demographics.hd_dep_count = 6 or household_demographics.hd_vehicle_count > -1)\n and date_dim.d_dow = 1\n and date_dim.d_year in (1998,1998+1,1998+2)\n and store.s_number_employees between 200 and 295\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,store.s_city) ms,customer\n where ss_customer_sk = c_customer_sk\n order by c_last_name,c_first_name,substr(s_city,1,30), profit\nlimit 100\"\"\",\n \"q80\" ->\n \"\"\"\nwith ssr as\n (select s_store_id as store_id,\n sum(ss_ext_sales_price) as sales,\n sum(coalesce(sr_return_amt, 0)) as returns,\n sum(ss_net_profit - coalesce(sr_net_loss, 0)) as profit\n from store_sales left outer join store_returns on\n (ss_item_sk = sr_item_sk and ss_ticket_number = sr_ticket_number),\n date_dim,\n store,\n item,\n promotion\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2000-08-25' as date)\n and (cast('2000-08-25' as date) + INTERVAL 60 days)\n and ss_store_sk = s_store_sk\n and ss_item_sk = i_item_sk\n and i_current_price > 50\n and ss_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id as catalog_page_id,\n sum(cs_ext_sales_price) as sales,\n sum(coalesce(cr_return_amount, 0)) as returns,\n sum(cs_net_profit - coalesce(cr_net_loss, 0)) as profit\n from catalog_sales left outer join catalog_returns on\n (cs_item_sk = cr_item_sk and cs_order_number = cr_order_number),\n date_dim,\n catalog_page,\n item,\n promotion\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2000-08-25' as date)\n and (cast('2000-08-25' as date) + INTERVAL 60 days)\n and cs_catalog_page_sk = cp_catalog_page_sk\n and cs_item_sk = i_item_sk\n and i_current_price > 50\n and cs_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(ws_ext_sales_price) as sales,\n sum(coalesce(wr_return_amt, 0)) as returns,\n sum(ws_net_profit - coalesce(wr_net_loss, 0)) as profit\n from web_sales left outer join web_returns on\n (ws_item_sk = wr_item_sk and ws_order_number = wr_order_number),\n date_dim,\n web_site,\n item,\n promotion\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2000-08-25' as date)\n and (cast('2000-08-25' as date) + INTERVAL 60 days)\n and ws_web_site_sk = web_site_sk\n and ws_item_sk = i_item_sk\n and i_current_price > 50\n and ws_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || store_id as id\n , sales\n , returns\n , profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || catalog_page_id as id\n , sales\n , returns\n , profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q81\" ->\n \"\"\"\nwith customer_total_return as\n (select cr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(cr_return_amt_inc_tax) as ctr_total_return\n from catalog_returns\n ,date_dim\n ,customer_address\n where cr_returned_date_sk = d_date_sk\n and d_year =2000\n and cr_returning_addr_sk = ca_address_sk\n group by cr_returning_customer_sk\n ,ca_state )\n select c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'SC'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n limit 100\"\"\",\n \"q82\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, store_sales\n where i_current_price between 6 and 6+30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2001-02-23' as date) and (cast('2001-02-23' as date) + INTERVAL 60 days)\n and i_manufact_id in (669,623,578,379)\n and inv_quantity_on_hand between 100 and 500\n and ss_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q83\" ->\n \"\"\"\nwith sr_items as\n (select i_item_id item_id,\n sum(sr_return_quantity) sr_item_qty\n from store_returns,\n item,\n date_dim\n where sr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('2001-01-15','2001-09-03','2001-11-17')))\n and sr_returned_date_sk = d_date_sk\n group by i_item_id),\n cr_items as\n (select i_item_id item_id,\n sum(cr_return_quantity) cr_item_qty\n from catalog_returns,\n item,\n date_dim\n where cr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('2001-01-15','2001-09-03','2001-11-17')))\n and cr_returned_date_sk = d_date_sk\n group by i_item_id),\n wr_items as\n (select i_item_id item_id,\n sum(wr_return_quantity) wr_item_qty\n from web_returns,\n item,\n date_dim\n where wr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t\twhere d_date in ('2001-01-15','2001-09-03','2001-11-17')))\n and wr_returned_date_sk = d_date_sk\n group by i_item_id)\n select sr_items.item_id\n ,sr_item_qty\n ,sr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 sr_dev\n ,cr_item_qty\n ,cr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 cr_dev\n ,wr_item_qty\n ,wr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 wr_dev\n ,(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 average\n from sr_items\n ,cr_items\n ,wr_items\n where sr_items.item_id=cr_items.item_id\n and sr_items.item_id=wr_items.item_id\n order by sr_items.item_id\n ,sr_item_qty\n limit 100\"\"\",\n \"q84\" ->\n \"\"\"\nselect c_customer_id as customer_id\n , coalesce(c_last_name,'') || ', ' || coalesce(c_first_name,'') as customername\n from customer\n ,customer_address\n ,customer_demographics\n ,household_demographics\n ,income_band\n ,store_returns\n where ca_city\t = 'Walnut Grove'\n and c_current_addr_sk = ca_address_sk\n and ib_lower_bound >= 53669\n and ib_upper_bound <= 53669 + 50000\n and ib_income_band_sk = hd_income_band_sk\n and cd_demo_sk = c_current_cdemo_sk\n and hd_demo_sk = c_current_hdemo_sk\n and sr_cdemo_sk = cd_demo_sk\n order by c_customer_id\n limit 100\"\"\",\n \"q85\" ->\n \"\"\"\nselect substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\n from web_sales, web_returns, web_page, customer_demographics cd1,\n customer_demographics cd2, customer_address, date_dim, reason\n where ws_web_page_sk = wp_web_page_sk\n and ws_item_sk = wr_item_sk\n and ws_order_number = wr_order_number\n and ws_sold_date_sk = d_date_sk and d_year = 2001\n and cd1.cd_demo_sk = wr_refunded_cdemo_sk\n and cd2.cd_demo_sk = wr_returning_cdemo_sk\n and ca_address_sk = wr_refunded_addr_sk\n and r_reason_sk = wr_reason_sk\n and\n (\n (\n cd1.cd_marital_status = 'S'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'Secondary'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 100.00 and 150.00\n )\n or\n (\n cd1.cd_marital_status = 'D'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'Advanced Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 50.00 and 100.00\n )\n or\n (\n cd1.cd_marital_status = 'W'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'Primary'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ca_country = 'United States'\n and\n ca_state in ('AZ', 'SD', 'TN')\n and ws_net_profit between 100 and 200\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('TX', 'GA', 'IA')\n and ws_net_profit between 150 and 300\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('WI', 'VT', 'AL')\n and ws_net_profit between 50 and 250\n )\n )\ngroup by r_reason_desc\norder by substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\nlimit 100\"\"\",\n \"q86\" ->\n \"\"\"\nselect\n sum(ws_net_paid) as total_sum\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ws_net_paid) desc) as rank_within_parent\n from\n web_sales\n ,date_dim d1\n ,item\n where\n d1.d_month_seq between 1195 and 1195+11\n and d1.d_date_sk = ws_sold_date_sk\n and i_item_sk = ws_item_sk\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc,\n case when lochierarchy = 0 then i_category end,\n rank_within_parent\n limit 100\"\"\",\n \"q87\" ->\n \"\"\"\nselect count(*)\nfrom ((select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1194 and 1194+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1194 and 1194+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1194 and 1194+11)\n) cool_cust\"\"\",\n \"q88\" ->\n \"\"\"\nselect *\nfrom\n (select count(*) h8_30_to_9\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 8\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s1,\n (select count(*) h9_to_9_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s2,\n (select count(*) h9_30_to_10\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s3,\n (select count(*) h10_to_10_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s4,\n (select count(*) h10_30_to_11\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s5,\n (select count(*) h11_to_11_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s6,\n (select count(*) h11_30_to_12\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s7,\n (select count(*) h12_to_12_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 12\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s8\"\"\",\n \"q89\" ->\n \"\"\"\nselect *\nfrom(\nselect i_category, i_class, i_brand,\n s_store_name, s_company_name,\n d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand, s_store_name, s_company_name)\n avg_monthly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n d_year in (2000) and\n ((i_category in ('Home','Shoes','Electronics') and\n i_class in ('flatware','mens','televisions')\n )\n or (i_category in ('Women','Sports','Music') and\n i_class in ('maternity','camping','rock')\n ))\ngroup by i_category, i_class, i_brand,\n s_store_name, s_company_name, d_moy) tmp1\nwhere case when (avg_monthly_sales <> 0) then (abs(sum_sales - avg_monthly_sales) / avg_monthly_sales) else null end > 0.1\norder by sum_sales - avg_monthly_sales, s_store_name\nlimit 100\"\"\",\n \"q90\" ->\n \"\"\"\nselect cast(amc as decimal(15,4))/cast(pmc as decimal(15,4)) am_pm_ratio\n from ( select count(*) amc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 8 and 8+1\n and household_demographics.hd_dep_count = 4\n and web_page.wp_char_count between 5000 and 5200) at,\n ( select count(*) pmc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 20 and 20+1\n and household_demographics.hd_dep_count = 4\n and web_page.wp_char_count between 5000 and 5200) pt\n order by am_pm_ratio\n limit 100\"\"\",\n \"q91\" ->\n \"\"\"\nselect\n cc_call_center_id Call_Center,\n cc_name Call_Center_Name,\n cc_manager Manager,\n sum(cr_net_loss) Returns_Loss\nfrom\n call_center,\n catalog_returns,\n date_dim,\n customer,\n customer_address,\n customer_demographics,\n household_demographics\nwhere\n cr_call_center_sk = cc_call_center_sk\nand cr_returned_date_sk = d_date_sk\nand cr_returning_customer_sk= c_customer_sk\nand cd_demo_sk = c_current_cdemo_sk\nand hd_demo_sk = c_current_hdemo_sk\nand ca_address_sk = c_current_addr_sk\nand d_year = 2001\nand d_moy = 12\nand ( (cd_marital_status = 'M' and cd_education_status = 'Unknown')\n or(cd_marital_status = 'W' and cd_education_status = 'Advanced Degree'))\nand hd_buy_potential like 'Unknown%'\nand ca_gmt_offset = -6\ngroup by cc_call_center_id,cc_name,cc_manager,cd_marital_status,cd_education_status\norder by sum(cr_net_loss) desc\"\"\",\n \"q92\" ->\n \"\"\"\nselect\n sum(ws_ext_discount_amt) as `Excess Discount Amount`\nfrom\n web_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 7\nand i_item_sk = ws_item_sk\nand d_date between '2000-01-16' and\n (cast('2000-01-16' as date) + INTERVAL 90 days)\nand d_date_sk = ws_sold_date_sk\nand ws_ext_discount_amt\n > (\n SELECT\n 1.3 * avg(ws_ext_discount_amt)\n FROM\n web_sales\n ,date_dim\n WHERE\n ws_item_sk = i_item_sk\n and d_date between '2000-01-16' and\n (cast('2000-01-16' as date) + INTERVAL 90 days)\n and d_date_sk = ws_sold_date_sk\n )\norder by sum(ws_ext_discount_amt)\nlimit 100\"\"\",\n \"q93\" ->\n \"\"\"\nselect ss_customer_sk\n ,sum(act_sales) sumsales\n from (select ss_item_sk\n ,ss_ticket_number\n ,ss_customer_sk\n ,case when sr_return_quantity is not null then (ss_quantity-sr_return_quantity)*ss_sales_price\n else (ss_quantity*ss_sales_price) end act_sales\n from store_sales left outer join store_returns on (sr_item_sk = ss_item_sk\n and sr_ticket_number = ss_ticket_number)\n ,reason\n where sr_reason_sk = r_reason_sk\n and r_reason_desc = 'reason 24') t\n group by ss_customer_sk\n order by sumsales, ss_customer_sk\nlimit 100\"\"\",\n \"q94\" ->\n \"\"\"\nselect\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2001-2-01' and\n (cast('2001-2-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'VT'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand exists (select *\n from web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\nand not exists(select *\n from web_returns wr1\n where ws1.ws_order_number = wr1.wr_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q95\" ->\n \"\"\"\nwith ws_wh as\n(select ws1.ws_order_number,ws1.ws_warehouse_sk wh1,ws2.ws_warehouse_sk wh2\n from web_sales ws1,web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\n select\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2001-3-01' and\n (cast('2001-3-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'TN'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand ws1.ws_order_number in (select ws_order_number\n from ws_wh)\nand ws1.ws_order_number in (select wr_order_number\n from web_returns,ws_wh\n where wr_order_number = ws_wh.ws_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q96\" ->\n \"\"\"\nselect count(*)\nfrom store_sales\n ,household_demographics\n ,time_dim, store\nwhere ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 20\n and time_dim.t_minute >= 30\n and household_demographics.hd_dep_count = 6\n and store.s_store_name = 'ese'\norder by count(*)\nlimit 100\"\"\",\n \"q97\" ->\n \"\"\"\nwith ssci as (\nselect ss_customer_sk customer_sk\n ,ss_item_sk item_sk\nfrom store_sales,date_dim\nwhere ss_sold_date_sk = d_date_sk\n and d_month_seq between 1206 and 1206 + 11\ngroup by ss_customer_sk\n ,ss_item_sk),\ncsci as(\n select cs_bill_customer_sk customer_sk\n ,cs_item_sk item_sk\nfrom catalog_sales,date_dim\nwhere cs_sold_date_sk = d_date_sk\n and d_month_seq between 1206 and 1206 + 11\ngroup by cs_bill_customer_sk\n ,cs_item_sk)\n select sum(case when ssci.customer_sk is not null and csci.customer_sk is null then 1 else 0 end) store_only\n ,sum(case when ssci.customer_sk is null and csci.customer_sk is not null then 1 else 0 end) catalog_only\n ,sum(case when ssci.customer_sk is not null and csci.customer_sk is not null then 1 else 0 end) store_and_catalog\nfrom ssci full outer join csci on (ssci.customer_sk=csci.customer_sk\n and ssci.item_sk = csci.item_sk)\nlimit 100\"\"\",\n \"q98\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ss_ext_sales_price) as itemrevenue\n ,sum(ss_ext_sales_price)*100/sum(sum(ss_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tstore_sales\n \t,item\n \t,date_dim\nwhere\n\tss_item_sk = i_item_sk\n \tand i_category in ('Sports', 'Books', 'Electronics')\n \tand ss_sold_date_sk = d_date_sk\n\tand d_date between cast('2002-06-29' as date)\n\t\t\t\tand (cast('2002-06-29' as date) + interval 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\"\"\",\n \"q99\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 30) and\n (cs_ship_date_sk - cs_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 60) and\n (cs_ship_date_sk - cs_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 90) and\n (cs_ship_date_sk - cs_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n catalog_sales\n ,warehouse\n ,ship_mode\n ,call_center\n ,date_dim\nwhere\n d_month_seq between 1199 and 1199 + 11\nand cs_ship_date_sk = d_date_sk\nand cs_warehouse_sk = w_warehouse_sk\nand cs_ship_mode_sk = sm_ship_mode_sk\nand cs_call_center_sk = cc_call_center_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\nlimit 100\"\"\",\n \"q1\" ->\n \"\"\"\nwith customer_total_return as\n(select sr_customer_sk as ctr_customer_sk\n,sr_store_sk as ctr_store_sk\n,sum(SR_FEE) as ctr_total_return\nfrom store_returns\n,date_dim\nwhere sr_returned_date_sk = d_date_sk\nand d_year =2000\ngroup by sr_customer_sk\n,sr_store_sk)\n select c_customer_id\nfrom customer_total_return ctr1\n,store\n,customer\nwhere ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\nfrom customer_total_return ctr2\nwhere ctr1.ctr_store_sk = ctr2.ctr_store_sk)\nand s_store_sk = ctr1.ctr_store_sk\nand s_state = 'MO'\nand ctr1.ctr_customer_sk = c_customer_sk\norder by c_customer_id\nlimit 100\"\"\",\n \"q2\" ->\n \"\"\"\nwith wscs as\n (select sold_date_sk\n ,sales_price\n from (select ws_sold_date_sk sold_date_sk\n ,ws_ext_sales_price sales_price\n from web_sales\n union all\n select cs_sold_date_sk sold_date_sk\n ,cs_ext_sales_price sales_price\n from catalog_sales)),\n wswscs as\n (select d_week_seq,\n sum(case when (d_day_name='Sunday') then sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then sales_price else null end) sat_sales\n from wscs\n ,date_dim\n where d_date_sk = sold_date_sk\n group by d_week_seq)\n select d_week_seq1\n ,round(sun_sales1/sun_sales2,2)\n ,round(mon_sales1/mon_sales2,2)\n ,round(tue_sales1/tue_sales2,2)\n ,round(wed_sales1/wed_sales2,2)\n ,round(thu_sales1/thu_sales2,2)\n ,round(fri_sales1/fri_sales2,2)\n ,round(sat_sales1/sat_sales2,2)\n from\n (select wswscs.d_week_seq d_week_seq1\n ,sun_sales sun_sales1\n ,mon_sales mon_sales1\n ,tue_sales tue_sales1\n ,wed_sales wed_sales1\n ,thu_sales thu_sales1\n ,fri_sales fri_sales1\n ,sat_sales sat_sales1\n from wswscs,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998) y,\n (select wswscs.d_week_seq d_week_seq2\n ,sun_sales sun_sales2\n ,mon_sales mon_sales2\n ,tue_sales tue_sales2\n ,wed_sales wed_sales2\n ,thu_sales thu_sales2\n ,fri_sales fri_sales2\n ,sat_sales sat_sales2\n from wswscs\n ,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998+1) z\n where d_week_seq1=d_week_seq2-53\n order by d_week_seq1\"\"\",\n \"q3\" ->\n \"\"\"\nselect dt.d_year\n ,item.i_brand_id brand_id\n ,item.i_brand brand\n ,sum(ss_sales_price) sum_agg\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manufact_id = 816\n and dt.d_moy=11\n group by dt.d_year\n ,item.i_brand\n ,item.i_brand_id\n order by dt.d_year\n ,sum_agg desc\n ,brand_id\n limit 100\"\"\",\n \"q4\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(((ss_ext_list_price-ss_ext_wholesale_cost-ss_ext_discount_amt)+ss_ext_sales_price)/2) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((cs_ext_list_price-cs_ext_wholesale_cost-cs_ext_discount_amt)+cs_ext_sales_price)/2) ) year_total\n ,'c' sale_type\n from customer\n ,catalog_sales\n ,date_dim\n where c_customer_sk = cs_bill_customer_sk\n and cs_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\nunion all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((ws_ext_list_price-ws_ext_wholesale_cost-ws_ext_discount_amt)+ws_ext_sales_price)/2) ) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_c_firstyear\n ,year_total t_c_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_c_secyear.customer_id\n and t_s_firstyear.customer_id = t_c_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_c_firstyear.sale_type = 'c'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_c_secyear.sale_type = 'c'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 1999\n and t_s_secyear.dyear = 1999+1\n and t_c_firstyear.dyear = 1999\n and t_c_secyear.dyear = 1999+1\n and t_w_firstyear.dyear = 1999\n and t_w_secyear.dyear = 1999+1\n and t_s_firstyear.year_total > 0\n and t_c_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\nlimit 100\"\"\",\n \"q5\" ->\n \"\"\"\nwith ssr as\n (select s_store_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ss_store_sk as store_sk,\n ss_sold_date_sk as date_sk,\n ss_ext_sales_price as sales_price,\n ss_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from store_sales\n union all\n select sr_store_sk as store_sk,\n sr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n sr_return_amt as return_amt,\n sr_net_loss as net_loss\n from store_returns\n ) salesreturns,\n date_dim,\n store\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and store_sk = s_store_sk\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select cs_catalog_page_sk as page_sk,\n cs_sold_date_sk as date_sk,\n cs_ext_sales_price as sales_price,\n cs_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from catalog_sales\n union all\n select cr_catalog_page_sk as page_sk,\n cr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n cr_return_amount as return_amt,\n cr_net_loss as net_loss\n from catalog_returns\n ) salesreturns,\n date_dim,\n catalog_page\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and page_sk = cp_catalog_page_sk\n group by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ws_web_site_sk as wsr_web_site_sk,\n ws_sold_date_sk as date_sk,\n ws_ext_sales_price as sales_price,\n ws_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from web_sales\n union all\n select ws_web_site_sk as wsr_web_site_sk,\n wr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n wr_return_amt as return_amt,\n wr_net_loss as net_loss\n from web_returns left outer join web_sales on\n ( wr_item_sk = ws_item_sk\n and wr_order_number = ws_order_number)\n ) salesreturns,\n date_dim,\n web_site\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and wsr_web_site_sk = web_site_sk\n group by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || s_store_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || cp_catalog_page_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q6\" ->\n \"\"\"\nselect a.ca_state state, count(*) cnt\n from customer_address a\n ,customer c\n ,store_sales s\n ,date_dim d\n ,item i\n where a.ca_address_sk = c.c_current_addr_sk\n \tand c.c_customer_sk = s.ss_customer_sk\n \tand s.ss_sold_date_sk = d.d_date_sk\n \tand s.ss_item_sk = i.i_item_sk\n \tand d.d_month_seq =\n \t (select distinct (d_month_seq)\n \t from date_dim\n where d_year = 2002\n \t and d_moy = 3 )\n \tand i.i_current_price > 1.2 *\n (select avg(j.i_current_price)\n \t from item j\n \t where j.i_category = i.i_category)\n group by a.ca_state\n having count(*) >= 10\n order by cnt, a.ca_state\n limit 100\"\"\",\n \"q7\" ->\n \"\"\"\nselect i_item_id,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, item, promotion\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_cdemo_sk = cd_demo_sk and\n ss_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'W' and\n cd_education_status = 'College' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 2001\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q8\" ->\n \"\"\"\nselect s_store_name\n ,sum(ss_net_profit)\n from store_sales\n ,date_dim\n ,store,\n (select ca_zip\n from (\n SELECT substr(ca_zip,1,5) ca_zip\n FROM customer_address\n WHERE substr(ca_zip,1,5) IN (\n '47602','16704','35863','28577','83910','36201',\n '58412','48162','28055','41419','80332',\n '38607','77817','24891','16226','18410',\n '21231','59345','13918','51089','20317',\n '17167','54585','67881','78366','47770',\n '18360','51717','73108','14440','21800',\n '89338','45859','65501','34948','25973',\n '73219','25333','17291','10374','18829',\n '60736','82620','41351','52094','19326',\n '25214','54207','40936','21814','79077',\n '25178','75742','77454','30621','89193',\n '27369','41232','48567','83041','71948',\n '37119','68341','14073','16891','62878',\n '49130','19833','24286','27700','40979',\n '50412','81504','94835','84844','71954',\n '39503','57649','18434','24987','12350',\n '86379','27413','44529','98569','16515',\n '27287','24255','21094','16005','56436',\n '91110','68293','56455','54558','10298',\n '83647','32754','27052','51766','19444',\n '13869','45645','94791','57631','20712',\n '37788','41807','46507','21727','71836',\n '81070','50632','88086','63991','20244',\n '31655','51782','29818','63792','68605',\n '94898','36430','57025','20601','82080',\n '33869','22728','35834','29086','92645',\n '98584','98072','11652','78093','57553',\n '43830','71144','53565','18700','90209',\n '71256','38353','54364','28571','96560',\n '57839','56355','50679','45266','84680',\n '34306','34972','48530','30106','15371',\n '92380','84247','92292','68852','13338',\n '34594','82602','70073','98069','85066',\n '47289','11686','98862','26217','47529',\n '63294','51793','35926','24227','14196',\n '24594','32489','99060','49472','43432',\n '49211','14312','88137','47369','56877',\n '20534','81755','15794','12318','21060',\n '73134','41255','63073','81003','73873',\n '66057','51184','51195','45676','92696',\n '70450','90669','98338','25264','38919',\n '59226','58581','60298','17895','19489',\n '52301','80846','95464','68770','51634',\n '19988','18367','18421','11618','67975',\n '25494','41352','95430','15734','62585',\n '97173','33773','10425','75675','53535',\n '17879','41967','12197','67998','79658',\n '59130','72592','14851','43933','68101',\n '50636','25717','71286','24660','58058',\n '72991','95042','15543','33122','69280',\n '11912','59386','27642','65177','17672',\n '33467','64592','36335','54010','18767',\n '63193','42361','49254','33113','33159',\n '36479','59080','11855','81963','31016',\n '49140','29392','41836','32958','53163',\n '13844','73146','23952','65148','93498',\n '14530','46131','58454','13376','13378',\n '83986','12320','17193','59852','46081',\n '98533','52389','13086','68843','31013',\n '13261','60560','13443','45533','83583',\n '11489','58218','19753','22911','25115',\n '86709','27156','32669','13123','51933',\n '39214','41331','66943','14155','69998',\n '49101','70070','35076','14242','73021',\n '59494','15782','29752','37914','74686',\n '83086','34473','15751','81084','49230',\n '91894','60624','17819','28810','63180',\n '56224','39459','55233','75752','43639',\n '55349','86057','62361','50788','31830',\n '58062','18218','85761','60083','45484',\n '21204','90229','70041','41162','35390',\n '16364','39500','68908','26689','52868',\n '81335','40146','11340','61527','61794',\n '71997','30415','59004','29450','58117',\n '69952','33562','83833','27385','61860',\n '96435','48333','23065','32961','84919',\n '61997','99132','22815','56600','68730',\n '48017','95694','32919','88217','27116',\n '28239','58032','18884','16791','21343',\n '97462','18569','75660','15475')\n intersect\n select ca_zip\n from (SELECT substr(ca_zip,1,5) ca_zip,count(*) cnt\n FROM customer_address, customer\n WHERE ca_address_sk = c_current_addr_sk and\n c_preferred_cust_flag='Y'\n group by ca_zip\n having count(*) > 10)A1)A2) V1\n where ss_store_sk = s_store_sk\n and ss_sold_date_sk = d_date_sk\n and d_qoy = 2 and d_year = 1998\n and (substr(s_zip,1,2) = substr(V1.ca_zip,1,2))\n group by s_store_name\n order by s_store_name\n limit 100\"\"\",\n \"q9\" ->\n \"\"\"\nselect case when (select count(*)\n from store_sales\n where ss_quantity between 1 and 20) > 4502397049\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 1 and 20)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 1 and 20) end bucket1 ,\n case when (select count(*)\n from store_sales\n where ss_quantity between 21 and 40) > 4756228269\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 21 and 40)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 21 and 40) end bucket2,\n case when (select count(*)\n from store_sales\n where ss_quantity between 41 and 60) > 4101835064\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 41 and 60)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 41 and 60) end bucket3,\n case when (select count(*)\n from store_sales\n where ss_quantity between 61 and 80) > 4583261513\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 61 and 80)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 61 and 80) end bucket4,\n case when (select count(*)\n from store_sales\n where ss_quantity between 81 and 100) > 4208819283\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 81 and 100)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 81 and 100) end bucket5\nfrom reason\nwhere r_reason_sk = 1\"\"\",\n \"q10\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3,\n cd_dep_count,\n count(*) cnt4,\n cd_dep_employed_count,\n count(*) cnt5,\n cd_dep_college_count,\n count(*) cnt6\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_county in ('Grady County','Marion County','Decatur County','Lyman County','Beaver County') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 1999 and\n d_moy between 2 and 2+3) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 1999 and\n d_moy between 2 ANd 2+3) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 1999 and\n d_moy between 2 and 2+3))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\nlimit 100\"\"\",\n \"q11\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ss_ext_list_price-ss_ext_discount_amt) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ws_ext_list_price-ws_ext_discount_amt) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_preferred_cust_flag\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 2001\n and t_s_secyear.dyear = 2001+1\n and t_w_firstyear.dyear = 2001\n and t_w_secyear.dyear = 2001+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else 0.0 end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else 0.0 end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_preferred_cust_flag\nlimit 100\"\"\",\n \"q12\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ws_ext_sales_price) as itemrevenue\n ,sum(ws_ext_sales_price)*100/sum(sum(ws_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tweb_sales\n \t,item\n \t,date_dim\nwhere\n\tws_item_sk = i_item_sk\n \tand i_category in ('Children', 'Jewelry', 'Music')\n \tand ws_sold_date_sk = d_date_sk\n\tand d_date between cast('2001-05-11' as date)\n\t\t\t\tand (cast('2001-05-11' as date) + INTERVAL 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q13\" ->\n \"\"\"\nselect avg(ss_quantity)\n ,avg(ss_ext_sales_price)\n ,avg(ss_ext_wholesale_cost)\n ,sum(ss_ext_wholesale_cost)\n from store_sales\n ,store\n ,customer_demographics\n ,household_demographics\n ,customer_address\n ,date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 2001\n and((ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'M'\n and cd_education_status = 'Primary'\n and ss_sales_price between 100.00 and 150.00\n and hd_dep_count = 3\n )or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'S'\n and cd_education_status = '4 yr Degree'\n and ss_sales_price between 50.00 and 100.00\n and hd_dep_count = 1\n ) or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'W'\n and cd_education_status = '2 yr Degree'\n and ss_sales_price between 150.00 and 200.00\n and hd_dep_count = 1\n ))\n and((ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('SC', 'WY', 'TX')\n and ss_net_profit between 100 and 200\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('NY', 'NE', 'GA')\n and ss_net_profit between 150 and 300\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('AL', 'AR', 'MI')\n and ss_net_profit between 50 and 250\n ))\"\"\",\n \"q14a\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 1999 AND 1999 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 1999 AND 1999 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 1999 AND 1999 + 2)\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n (select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 1999 and 1999 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 1999 and 1999 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 1999 and 1999 + 2) x)\n select channel, i_brand_id,i_class_id,i_category_id,sum(sales), sum(number_sales)\n from(\n select 'store' channel, i_brand_id,i_class_id\n ,i_category_id,sum(ss_quantity*ss_list_price) sales\n , count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1999+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)\n union all\n select 'catalog' channel, i_brand_id,i_class_id,i_category_id, sum(cs_quantity*cs_list_price) sales, count(*) number_sales\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk in (select ss_item_sk from cross_items)\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1999+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(cs_quantity*cs_list_price) > (select average_sales from avg_sales)\n union all\n select 'web' channel, i_brand_id,i_class_id,i_category_id, sum(ws_quantity*ws_list_price) sales , count(*) number_sales\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk in (select ss_item_sk from cross_items)\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1999+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ws_quantity*ws_list_price) > (select average_sales from avg_sales)\n ) y\n group by rollup (channel, i_brand_id,i_class_id,i_category_id)\n order by channel,i_brand_id,i_class_id,i_category_id\n limit 100\"\"\",\n \"q14b\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 1999 AND 1999 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 1999 AND 1999 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 1999 AND 1999 + 2) x\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n(select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 1999 and 1999 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 1999 and 1999 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 1999 and 1999 + 2) x)\n select this_year.channel ty_channel\n ,this_year.i_brand_id ty_brand\n ,this_year.i_class_id ty_class\n ,this_year.i_category_id ty_category\n ,this_year.sales ty_sales\n ,this_year.number_sales ty_number_sales\n ,last_year.channel ly_channel\n ,last_year.i_brand_id ly_brand\n ,last_year.i_class_id ly_class\n ,last_year.i_category_id ly_category\n ,last_year.sales ly_sales\n ,last_year.number_sales ly_number_sales\n from\n (select 'store' channel, i_brand_id,i_class_id,i_category_id\n ,sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 1999 + 1\n and d_moy = 12\n and d_dom = 5)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) this_year,\n (select 'store' channel, i_brand_id,i_class_id\n ,i_category_id, sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 1999\n and d_moy = 12\n and d_dom = 5)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) last_year\n where this_year.i_brand_id= last_year.i_brand_id\n and this_year.i_class_id = last_year.i_class_id\n and this_year.i_category_id = last_year.i_category_id\n order by this_year.channel, this_year.i_brand_id, this_year.i_class_id, this_year.i_category_id\n limit 100\"\"\",\n \"q15\" ->\n \"\"\"\nselect ca_zip\n ,sum(cs_sales_price)\n from catalog_sales\n ,customer\n ,customer_address\n ,date_dim\n where cs_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475',\n '85392', '85460', '80348', '81792')\n \t or ca_state in ('CA','WA','GA')\n \t or cs_sales_price > 500)\n \tand cs_sold_date_sk = d_date_sk\n \tand d_qoy = 1 and d_year = 1998\n group by ca_zip\n order by ca_zip\n limit 100\"\"\",\n \"q16\" ->\n \"\"\"\nselect\n count(distinct cs_order_number) as `order count`\n ,sum(cs_ext_ship_cost) as `total shipping cost`\n ,sum(cs_net_profit) as `total net profit`\nfrom\n catalog_sales cs1\n ,date_dim\n ,customer_address\n ,call_center\nwhere\n d_date between '2000-3-01' and\n (cast('2000-3-01' as date) + INTERVAL 60 days)\nand cs1.cs_ship_date_sk = d_date_sk\nand cs1.cs_ship_addr_sk = ca_address_sk\nand ca_state = 'IA'\nand cs1.cs_call_center_sk = cc_call_center_sk\nand cc_county in ('Luce County','Wadena County','Jefferson Davis Parish','Daviess County',\n 'Williamson County'\n)\nand exists (select *\n from catalog_sales cs2\n where cs1.cs_order_number = cs2.cs_order_number\n and cs1.cs_warehouse_sk <> cs2.cs_warehouse_sk)\nand not exists(select *\n from catalog_returns cr1\n where cs1.cs_order_number = cr1.cr_order_number)\norder by count(distinct cs_order_number)\nlimit 100\"\"\",\n \"q17\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,s_state\n ,count(ss_quantity) as store_sales_quantitycount\n ,avg(ss_quantity) as store_sales_quantityave\n ,stddev_samp(ss_quantity) as store_sales_quantitystdev\n ,stddev_samp(ss_quantity)/avg(ss_quantity) as store_sales_quantitycov\n ,count(sr_return_quantity) as store_returns_quantitycount\n ,avg(sr_return_quantity) as store_returns_quantityave\n ,stddev_samp(sr_return_quantity) as store_returns_quantitystdev\n ,stddev_samp(sr_return_quantity)/avg(sr_return_quantity) as store_returns_quantitycov\n ,count(cs_quantity) as catalog_sales_quantitycount ,avg(cs_quantity) as catalog_sales_quantityave\n ,stddev_samp(cs_quantity) as catalog_sales_quantitystdev\n ,stddev_samp(cs_quantity)/avg(cs_quantity) as catalog_sales_quantitycov\n from store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where d1.d_quarter_name = '1999Q1'\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_quarter_name in ('1999Q1','1999Q2','1999Q3')\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_quarter_name in ('1999Q1','1999Q2','1999Q3')\n group by i_item_id\n ,i_item_desc\n ,s_state\n order by i_item_id\n ,i_item_desc\n ,s_state\nlimit 100\"\"\",\n \"q18\" ->\n \"\"\"\nselect i_item_id,\n ca_country,\n ca_state,\n ca_county,\n avg( cast(cs_quantity as decimal(12,2))) agg1,\n avg( cast(cs_list_price as decimal(12,2))) agg2,\n avg( cast(cs_coupon_amt as decimal(12,2))) agg3,\n avg( cast(cs_sales_price as decimal(12,2))) agg4,\n avg( cast(cs_net_profit as decimal(12,2))) agg5,\n avg( cast(c_birth_year as decimal(12,2))) agg6,\n avg( cast(cd1.cd_dep_count as decimal(12,2))) agg7\n from catalog_sales, customer_demographics cd1,\n customer_demographics cd2, customer, customer_address, date_dim, item\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd1.cd_demo_sk and\n cs_bill_customer_sk = c_customer_sk and\n cd1.cd_gender = 'F' and\n cd1.cd_education_status = 'Unknown' and\n c_current_cdemo_sk = cd2.cd_demo_sk and\n c_current_addr_sk = ca_address_sk and\n c_birth_month in (4,8,12,10,11,9) and\n d_year = 2001 and\n ca_state in ('AR','IA','TX'\n ,'KS','LA','NC','SD')\n group by rollup (i_item_id, ca_country, ca_state, ca_county)\n order by ca_country,\n ca_state,\n ca_county,\n\ti_item_id\n limit 100\"\"\",\n \"q19\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand, i_manufact_id, i_manufact,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item,customer,customer_address,store\n where d_date_sk = ss_sold_date_sk\n and ss_item_sk = i_item_sk\n and i_manager_id=63\n and d_moy=11\n and d_year=2002\n and ss_customer_sk = c_customer_sk\n and c_current_addr_sk = ca_address_sk\n and substr(ca_zip,1,5) <> substr(s_zip,1,5)\n and ss_store_sk = s_store_sk\n group by i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\n order by ext_price desc\n ,i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\nlimit 100 \"\"\",\n \"q20\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(cs_ext_sales_price) as itemrevenue\n ,sum(cs_ext_sales_price)*100/sum(sum(cs_ext_sales_price)) over\n (partition by i_class) as revenueratio\n from\tcatalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and i_category in ('Electronics', 'Children', 'Home')\n and cs_sold_date_sk = d_date_sk\n and d_date between cast('2002-03-19' as date)\n \t\t\t\tand (cast('2002-03-19' as date) + INTERVAL 30 days)\n group by i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n order by i_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q21\" ->\n \"\"\"\nselect *\n from(select w_warehouse_name\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('1999-04-12' as date))\n\t then inv_quantity_on_hand\n else 0 end) as inv_before\n ,sum(case when (cast(d_date as date) >= cast ('1999-04-12' as date))\n then inv_quantity_on_hand\n else 0 end) as inv_after\n from inventory\n ,warehouse\n ,item\n ,date_dim\n where i_current_price between 0.99 and 1.49\n and i_item_sk = inv_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_date between (cast ('1999-04-12' as date) - INTERVAL 30 days)\n and (cast ('1999-04-12' as date) + INTERVAL 30 days)\n group by w_warehouse_name, i_item_id) x\n where (case when inv_before > 0\n then inv_after / inv_before\n else null\n end) between 2.0/3.0 and 3.0/2.0\n order by w_warehouse_name\n ,i_item_id\n limit 100\"\"\",\n \"q22\" ->\n \"\"\"\nselect i_product_name\n ,i_brand\n ,i_class\n ,i_category\n ,avg(inv_quantity_on_hand) qoh\n from inventory\n ,date_dim\n ,item\n where inv_date_sk=d_date_sk\n and inv_item_sk=i_item_sk\n and d_month_seq between 1188 and 1188 + 11\n group by rollup(i_product_name\n ,i_brand\n ,i_class\n ,i_category)\norder by qoh, i_product_name, i_brand, i_class, i_category\nlimit 100\"\"\",\n \"q23a\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (1998,1998+1,1998+2,1998+3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (1998,1998+1,1998+2,1998+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\nfrom\n max_store_sales))\n select sum(sales)\n from (select cs_quantity*cs_list_price sales\n from catalog_sales\n ,date_dim\n where d_year = 1998\n and d_moy = 7\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n union all\n select ws_quantity*ws_list_price sales\n from web_sales\n ,date_dim\n where d_year = 1998\n and d_moy = 7\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer))\n limit 100\"\"\",\n \"q23b\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (1998,1998 + 1,1998 + 2,1998 + 3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (1998,1998+1,1998+2,1998+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\n from max_store_sales))\n select c_last_name,c_first_name,sales\n from (select c_last_name,c_first_name,sum(cs_quantity*cs_list_price) sales\n from catalog_sales\n ,customer\n ,date_dim\n where d_year = 1998\n and d_moy = 7\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and cs_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name\n union all\n select c_last_name,c_first_name,sum(ws_quantity*ws_list_price) sales\n from web_sales\n ,customer\n ,date_dim\n where d_year = 1998\n and d_moy = 7\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and ws_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name)\n order by c_last_name,c_first_name,sales\n limit 100\"\"\",\n \"q24a\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_sales_price) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\nand s_market_id=7\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'goldenrod'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q24b\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_sales_price) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\n and s_market_id = 7\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'magenta'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q25\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,min(ss_net_profit) as store_sales_profit\n ,min(sr_net_loss) as store_returns_loss\n ,min(cs_net_profit) as catalog_sales_profit\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 2002\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 10\n and d2.d_year = 2002\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_moy between 4 and 10\n and d3.d_year = 2002\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q26\" ->\n \"\"\"\nselect i_item_id,\n avg(cs_quantity) agg1,\n avg(cs_list_price) agg2,\n avg(cs_coupon_amt) agg3,\n avg(cs_sales_price) agg4\n from catalog_sales, customer_demographics, date_dim, item, promotion\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd_demo_sk and\n cs_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'M' and\n cd_education_status = '4 yr Degree' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 1998\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q27\" ->\n \"\"\"\nselect i_item_id,\n s_state, grouping(s_state) g_state,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, store, item\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_store_sk = s_store_sk and\n ss_cdemo_sk = cd_demo_sk and\n cd_gender = 'M' and\n cd_marital_status = 'M' and\n cd_education_status = 'Secondary' and\n d_year = 1999 and\n s_state in ('AL','FL', 'TX', 'NM', 'MI', 'GA')\n group by rollup (i_item_id, s_state)\n order by i_item_id\n ,s_state\n limit 100\"\"\",\n \"q28\" ->\n \"\"\"\nselect *\nfrom (select avg(ss_list_price) B1_LP\n ,count(ss_list_price) B1_CNT\n ,count(distinct ss_list_price) B1_CNTD\n from store_sales\n where ss_quantity between 0 and 5\n and (ss_list_price between 74 and 74+10\n or ss_coupon_amt between 2949 and 2949+1000\n or ss_wholesale_cost between 49 and 49+20)) B1,\n (select avg(ss_list_price) B2_LP\n ,count(ss_list_price) B2_CNT\n ,count(distinct ss_list_price) B2_CNTD\n from store_sales\n where ss_quantity between 6 and 10\n and (ss_list_price between 136 and 136+10\n or ss_coupon_amt between 10027 and 10027+1000\n or ss_wholesale_cost between 53 and 53+20)) B2,\n (select avg(ss_list_price) B3_LP\n ,count(ss_list_price) B3_CNT\n ,count(distinct ss_list_price) B3_CNTD\n from store_sales\n where ss_quantity between 11 and 15\n and (ss_list_price between 73 and 73+10\n or ss_coupon_amt between 1451 and 1451+1000\n or ss_wholesale_cost between 78 and 78+20)) B3,\n (select avg(ss_list_price) B4_LP\n ,count(ss_list_price) B4_CNT\n ,count(distinct ss_list_price) B4_CNTD\n from store_sales\n where ss_quantity between 16 and 20\n and (ss_list_price between 87 and 87+10\n or ss_coupon_amt between 17007 and 17007+1000\n or ss_wholesale_cost between 55 and 55+20)) B4,\n (select avg(ss_list_price) B5_LP\n ,count(ss_list_price) B5_CNT\n ,count(distinct ss_list_price) B5_CNTD\n from store_sales\n where ss_quantity between 21 and 25\n and (ss_list_price between 112 and 112+10\n or ss_coupon_amt between 17243 and 17243+1000\n or ss_wholesale_cost between 2 and 2+20)) B5,\n (select avg(ss_list_price) B6_LP\n ,count(ss_list_price) B6_CNT\n ,count(distinct ss_list_price) B6_CNTD\n from store_sales\n where ss_quantity between 26 and 30\n and (ss_list_price between 119 and 119+10\n or ss_coupon_amt between 4954 and 4954+1000\n or ss_wholesale_cost between 22 and 22+20)) B6\nlimit 100\"\"\",\n \"q29\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,stddev_samp(ss_quantity) as store_sales_quantity\n ,stddev_samp(sr_return_quantity) as store_returns_quantity\n ,stddev_samp(cs_quantity) as catalog_sales_quantity\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 2000\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 4 + 3\n and d2.d_year = 2000\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_year in (2000,2000+1,2000+2)\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q30\" ->\n \"\"\"\nwith customer_total_return as\n (select wr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(wr_return_amt) as ctr_total_return\n from web_returns\n ,date_dim\n ,customer_address\n where wr_returned_date_sk = d_date_sk\n and d_year =2001\n and wr_returning_addr_sk = ca_address_sk\n group by wr_returning_customer_sk\n ,ca_state)\n select c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'MI'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\nlimit 100\"\"\",\n \"q31\" ->\n \"\"\"\nwith ss as\n (select ca_county,d_qoy, d_year,sum(ss_ext_sales_price) as store_sales\n from store_sales,date_dim,customer_address\n where ss_sold_date_sk = d_date_sk\n and ss_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year),\n ws as\n (select ca_county,d_qoy, d_year,sum(ws_ext_sales_price) as web_sales\n from web_sales,date_dim,customer_address\n where ws_sold_date_sk = d_date_sk\n and ws_bill_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year)\n select\n ss1.ca_county\n ,ss1.d_year\n ,ws2.web_sales/ws1.web_sales web_q1_q2_increase\n ,ss2.store_sales/ss1.store_sales store_q1_q2_increase\n ,ws3.web_sales/ws2.web_sales web_q2_q3_increase\n ,ss3.store_sales/ss2.store_sales store_q2_q3_increase\n from\n ss ss1\n ,ss ss2\n ,ss ss3\n ,ws ws1\n ,ws ws2\n ,ws ws3\n where\n ss1.d_qoy = 1\n and ss1.d_year = 2000\n and ss1.ca_county = ss2.ca_county\n and ss2.d_qoy = 2\n and ss2.d_year = 2000\n and ss2.ca_county = ss3.ca_county\n and ss3.d_qoy = 3\n and ss3.d_year = 2000\n and ss1.ca_county = ws1.ca_county\n and ws1.d_qoy = 1\n and ws1.d_year = 2000\n and ws1.ca_county = ws2.ca_county\n and ws2.d_qoy = 2\n and ws2.d_year = 2000\n and ws1.ca_county = ws3.ca_county\n and ws3.d_qoy = 3\n and ws3.d_year =2000\n and case when ws1.web_sales > 0 then ws2.web_sales/ws1.web_sales else null end\n > case when ss1.store_sales > 0 then ss2.store_sales/ss1.store_sales else null end\n and case when ws2.web_sales > 0 then ws3.web_sales/ws2.web_sales else null end\n > case when ss2.store_sales > 0 then ss3.store_sales/ss2.store_sales else null end\n order by store_q1_q2_increase\"\"\",\n \"q32\" ->\n \"\"\"\nselect sum(cs_ext_discount_amt) as `excess discount amount`\nfrom\n catalog_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 490\nand i_item_sk = cs_item_sk\nand d_date between '1999-01-27' and\n (cast('1999-01-27' as date) + INTERVAL 90 days)\nand d_date_sk = cs_sold_date_sk\nand cs_ext_discount_amt\n > (\n select\n 1.3 * avg(cs_ext_discount_amt)\n from\n catalog_sales\n ,date_dim\n where\n cs_item_sk = i_item_sk\n and d_date between '1999-01-27' and\n (cast('1999-01-27' as date) + INTERVAL 90 days)\n and d_date_sk = cs_sold_date_sk\n )\nlimit 100\"\"\",\n \"q33\" ->\n \"\"\"\nwith ss as (\n select\n i_manufact_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Electronics'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 2001\n and d_moy = 1\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id),\n cs as (\n select\n i_manufact_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Electronics'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 2001\n and d_moy = 1\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id),\n ws as (\n select\n i_manufact_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Electronics'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 2001\n and d_moy = 1\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id)\n select i_manufact_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_manufact_id\n order by total_sales\nlimit 100\"\"\",\n \"q34\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (date_dim.d_dom between 1 and 3 or date_dim.d_dom between 25 and 28)\n and (household_demographics.hd_buy_potential = '1001-5000' or\n household_demographics.hd_buy_potential = 'Unknown')\n and household_demographics.hd_vehicle_count > 0\n and (case when household_demographics.hd_vehicle_count > 0\n\tthen household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count\n\telse null\n\tend) > 1.2\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_county in ('Nez Perce County','Murray County','Surry County','Calhoun County',\n 'Wilkinson County','Brown County','Wallace County','Carter County')\n group by ss_ticket_number,ss_customer_sk) dn,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 15 and 20\n order by c_last_name,c_first_name,c_salutation,c_preferred_cust_flag desc, ss_ticket_number\"\"\",\n \"q35\" ->\n \"\"\"\nselect\n ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n count(*) cnt1,\n stddev_samp(cd_dep_count),\n sum(cd_dep_count),\n min(cd_dep_count),\n cd_dep_employed_count,\n count(*) cnt2,\n stddev_samp(cd_dep_employed_count),\n sum(cd_dep_employed_count),\n min(cd_dep_employed_count),\n cd_dep_college_count,\n count(*) cnt3,\n stddev_samp(cd_dep_college_count),\n sum(cd_dep_college_count),\n min(cd_dep_college_count)\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2002 and\n d_qoy < 4) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2002 and\n d_qoy < 4) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2002 and\n d_qoy < 4))\n group by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n limit 100\"\"\",\n \"q36\" ->\n \"\"\"\nselect\n sum(ss_net_profit)/sum(ss_ext_sales_price) as gross_margin\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ss_net_profit)/sum(ss_ext_sales_price) asc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,item\n ,store\n where\n d1.d_year = 2000\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and s_state in ('MN','TX','TX','IN',\n 'CA','LA','NM','TX')\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then i_category end\n ,rank_within_parent\n limit 100\"\"\",\n \"q37\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, catalog_sales\n where i_current_price between 16 and 16 + 30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2002-06-05' as date) and (cast('2002-06-05' as date) + interval 60 days)\n and i_manufact_id in (841,790,796,739)\n and inv_quantity_on_hand between 100 and 500\n and cs_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q38\" ->\n \"\"\"\nselect count(*) from (\n select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1203 and 1203 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1203 and 1203 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1203 and 1203 + 11\n) hot_cust\nlimit 100\"\"\",\n \"q39a\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =1999\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=3\n and inv2.d_moy=3+1\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q39b\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =1999\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=3\n and inv2.d_moy=3+1\n and inv1.cov > 1.5\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q40\" ->\n \"\"\"\nselect\n w_state\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('1999-04-27' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_before\n ,sum(case when (cast(d_date as date) >= cast ('1999-04-27' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_after\n from\n catalog_sales left outer join catalog_returns on\n (cs_order_number = cr_order_number\n and cs_item_sk = cr_item_sk)\n ,warehouse\n ,item\n ,date_dim\n where\n i_current_price between 0.99 and 1.49\n and i_item_sk = cs_item_sk\n and cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and d_date between (cast ('1999-04-27' as date) - INTERVAL 30 days)\n and (cast ('1999-04-27' as date) + INTERVAL 30 days)\n group by\n w_state,i_item_id\n order by w_state,i_item_id\nlimit 100\"\"\",\n \"q41\" ->\n \"\"\"\nselect distinct(i_product_name)\n from item i1\n where i_manufact_id between 841 and 841+40\n and (select count(*) as item_cnt\n from item\n where (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'bisque' or i_color = 'khaki') and\n (i_units = 'Carton' or i_units = 'Box') and\n (i_size = 'large' or i_size = 'extra large')\n ) or\n (i_category = 'Women' and\n (i_color = 'antique' or i_color = 'sandy') and\n (i_units = 'Pallet' or i_units = 'Cup') and\n (i_size = 'petite' or i_size = 'small')\n ) or\n (i_category = 'Men' and\n (i_color = 'forest' or i_color = 'brown') and\n (i_units = 'Dram' or i_units = 'Ton') and\n (i_size = 'economy' or i_size = 'medium')\n ) or\n (i_category = 'Men' and\n (i_color = 'chartreuse' or i_color = 'light') and\n (i_units = 'Pound' or i_units = 'Dozen') and\n (i_size = 'large' or i_size = 'extra large')\n ))) or\n (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'turquoise' or i_color = 'chocolate') and\n (i_units = 'Bundle' or i_units = 'Unknown') and\n (i_size = 'large' or i_size = 'extra large')\n ) or\n (i_category = 'Women' and\n (i_color = 'maroon' or i_color = 'pale') and\n (i_units = 'Each' or i_units = 'Tbl') and\n (i_size = 'petite' or i_size = 'small')\n ) or\n (i_category = 'Men' and\n (i_color = 'almond' or i_color = 'floral') and\n (i_units = 'Gross' or i_units = 'N/A') and\n (i_size = 'economy' or i_size = 'medium')\n ) or\n (i_category = 'Men' and\n (i_color = 'drab' or i_color = 'plum') and\n (i_units = 'Bunch' or i_units = 'Case') and\n (i_size = 'large' or i_size = 'extra large')\n )))) > 0\n order by i_product_name\n limit 100\"\"\",\n \"q42\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_category_id\n \t,item.i_category\n \t,sum(ss_ext_sales_price)\n from \tdate_dim dt\n \t,store_sales\n \t,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n \tand store_sales.ss_item_sk = item.i_item_sk\n \tand item.i_manager_id = 1\n \tand dt.d_moy=11\n \tand dt.d_year=2002\n group by \tdt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\n order by sum(ss_ext_sales_price) desc,dt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\nlimit 100 \"\"\",\n \"q43\" ->\n \"\"\"\nselect s_store_name, s_store_id,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from date_dim, store_sales, store\n where d_date_sk = ss_sold_date_sk and\n s_store_sk = ss_store_sk and\n s_gmt_offset = -5 and\n d_year = 2002\n group by s_store_name, s_store_id\n order by s_store_name, s_store_id,sun_sales,mon_sales,tue_sales,wed_sales,thu_sales,fri_sales,sat_sales\n limit 100\"\"\",\n \"q44\" ->\n \"\"\"\nselect asceding.rnk, i1.i_product_name best_performing, i2.i_product_name worst_performing\nfrom(select *\n from (select item_sk,rank() over (order by rank_col asc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 709\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 709\n and ss_addr_sk is null\n group by ss_store_sk))V1)V11\n where rnk < 11) asceding,\n (select *\n from (select item_sk,rank() over (order by rank_col desc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 709\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 709\n and ss_addr_sk is null\n group by ss_store_sk))V2)V21\n where rnk < 11) descending,\nitem i1,\nitem i2\nwhere asceding.rnk = descending.rnk\n and i1.i_item_sk=asceding.item_sk\n and i2.i_item_sk=descending.item_sk\norder by asceding.rnk\nlimit 100\"\"\",\n \"q45\" ->\n \"\"\"\nselect ca_zip, ca_state, sum(ws_sales_price)\n from web_sales, customer, customer_address, date_dim, item\n where ws_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ws_item_sk = i_item_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475', '85392', '85460', '80348', '81792')\n \t or\n \t i_item_id in (select i_item_id\n from item\n where i_item_sk in (2, 3, 5, 7, 11, 13, 17, 19, 23, 29)\n )\n \t )\n \tand ws_sold_date_sk = d_date_sk\n \tand d_qoy = 2 and d_year = 2002\n group by ca_zip, ca_state\n order by ca_zip, ca_state\n limit 100\"\"\",\n \"q46\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and (household_demographics.hd_dep_count = 0 or\n household_demographics.hd_vehicle_count= 1)\n and date_dim.d_dow in (6,0)\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_city in ('Johnson','Norwood','Cambridge','Klondike','Rock Hill')\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,ca_city) dn,customer,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n limit 100\"\"\",\n \"q47\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand,\n s_store_name, s_company_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n s_store_name, s_company_name\n order by d_year, d_moy) rn\n from item, store_sales, date_dim, store\n where ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n (\n d_year = 2001 or\n ( d_year = 2001-1 and d_moy =12) or\n ( d_year = 2001+1 and d_moy =1)\n )\n group by i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy),\n v2 as(\n select v1.i_category, v1.i_brand, v1.s_store_name, v1.s_company_name\n ,v1.d_year, v1.d_moy\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1.s_store_name = v1_lag.s_store_name and\n v1.s_store_name = v1_lead.s_store_name and\n v1.s_company_name = v1_lag.s_company_name and\n v1.s_company_name = v1_lead.s_company_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 2001 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, psum\n limit 100\"\"\",\n \"q48\" ->\n \"\"\"\nselect sum (ss_quantity)\n from store_sales, store, customer_demographics, customer_address, date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 2000\n and\n (\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'U'\n and\n cd_education_status = '2 yr Degree'\n and\n ss_sales_price between 100.00 and 150.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'S'\n and\n cd_education_status = 'Primary'\n and\n ss_sales_price between 50.00 and 100.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'W'\n and\n cd_education_status = '4 yr Degree'\n and\n ss_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('MT', 'OH', 'GA')\n and ss_net_profit between 0 and 2000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('WV', 'AZ', 'NM')\n and ss_net_profit between 150 and 3000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('NY', 'PA', 'KY')\n and ss_net_profit between 50 and 25000\n )\n )\"\"\",\n \"q49\" ->\n \"\"\"\nselect channel, item, return_ratio, return_rank, currency_rank from\n (select\n 'web' as channel\n ,web.item\n ,web.return_ratio\n ,web.return_rank\n ,web.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect ws.ws_item_sk as item\n \t\t,(cast(sum(coalesce(wr.wr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(wr.wr_return_amt,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\t web_sales ws left outer join web_returns wr\n \t\t\ton (ws.ws_order_number = wr.wr_order_number and\n \t\t\tws.ws_item_sk = wr.wr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\twr.wr_return_amt > 10000\n \t\t\tand ws.ws_net_profit > 1\n and ws.ws_net_paid > 0\n and ws.ws_quantity > 0\n and ws_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 11\n \t\tgroup by ws.ws_item_sk\n \t) in_web\n ) web\n where\n (\n web.return_rank <= 10\n or\n web.currency_rank <= 10\n )\n union\n select\n 'catalog' as channel\n ,catalog.item\n ,catalog.return_ratio\n ,catalog.return_rank\n ,catalog.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect\n \t\tcs.cs_item_sk as item\n \t\t,(cast(sum(coalesce(cr.cr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(cr.cr_return_amount,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tcatalog_sales cs left outer join catalog_returns cr\n \t\t\ton (cs.cs_order_number = cr.cr_order_number and\n \t\t\tcs.cs_item_sk = cr.cr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tcr.cr_return_amount > 10000\n \t\t\tand cs.cs_net_profit > 1\n and cs.cs_net_paid > 0\n and cs.cs_quantity > 0\n and cs_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 11\n group by cs.cs_item_sk\n \t) in_cat\n ) catalog\n where\n (\n catalog.return_rank <= 10\n or\n catalog.currency_rank <=10\n )\n union\n select\n 'store' as channel\n ,store.item\n ,store.return_ratio\n ,store.return_rank\n ,store.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect sts.ss_item_sk as item\n \t\t,(cast(sum(coalesce(sr.sr_return_quantity,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(sr.sr_return_amt,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tstore_sales sts left outer join store_returns sr\n \t\t\ton (sts.ss_ticket_number = sr.sr_ticket_number and sts.ss_item_sk = sr.sr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tsr.sr_return_amt > 10000\n \t\t\tand sts.ss_net_profit > 1\n and sts.ss_net_paid > 0\n and sts.ss_quantity > 0\n and ss_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 11\n \t\tgroup by sts.ss_item_sk\n \t) in_store\n ) store\n where (\n store.return_rank <= 10\n or\n store.currency_rank <= 10\n )\n )\n order by 1,4,5,2\n limit 100\"\"\",\n \"q50\" ->\n \"\"\"\nselect\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 30) and\n (sr_returned_date_sk - ss_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 60) and\n (sr_returned_date_sk - ss_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 90) and\n (sr_returned_date_sk - ss_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n store_sales\n ,store_returns\n ,store\n ,date_dim d1\n ,date_dim d2\nwhere\n d2.d_year = 2000\nand d2.d_moy = 9\nand ss_ticket_number = sr_ticket_number\nand ss_item_sk = sr_item_sk\nand ss_sold_date_sk = d1.d_date_sk\nand sr_returned_date_sk = d2.d_date_sk\nand ss_customer_sk = sr_customer_sk\nand ss_store_sk = s_store_sk\ngroup by\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\norder by s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\nlimit 100\"\"\",\n \"q51\" ->\n \"\"\"\nWITH web_v1 as (\nselect\n ws_item_sk item_sk, d_date,\n sum(sum(ws_sales_price))\n over (partition by ws_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom web_sales\n ,date_dim\nwhere ws_sold_date_sk=d_date_sk\n and d_month_seq between 1177 and 1177+11\n and ws_item_sk is not NULL\ngroup by ws_item_sk, d_date),\nstore_v1 as (\nselect\n ss_item_sk item_sk, d_date,\n sum(sum(ss_sales_price))\n over (partition by ss_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom store_sales\n ,date_dim\nwhere ss_sold_date_sk=d_date_sk\n and d_month_seq between 1177 and 1177+11\n and ss_item_sk is not NULL\ngroup by ss_item_sk, d_date)\n select *\nfrom (select item_sk\n ,d_date\n ,web_sales\n ,store_sales\n ,max(web_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) web_cumulative\n ,max(store_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) store_cumulative\n from (select case when web.item_sk is not null then web.item_sk else store.item_sk end item_sk\n ,case when web.d_date is not null then web.d_date else store.d_date end d_date\n ,web.cume_sales web_sales\n ,store.cume_sales store_sales\n from web_v1 web full outer join store_v1 store on (web.item_sk = store.item_sk\n and web.d_date = store.d_date)\n )x )y\nwhere web_cumulative > store_cumulative\norder by item_sk\n ,d_date\nlimit 100\"\"\",\n \"q52\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_brand_id brand_id\n \t,item.i_brand brand\n \t,sum(ss_ext_sales_price) ext_price\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manager_id = 1\n and dt.d_moy=12\n and dt.d_year=2001\n group by dt.d_year\n \t,item.i_brand\n \t,item.i_brand_id\n order by dt.d_year\n \t,ext_price desc\n \t,brand_id\nlimit 100 \"\"\",\n \"q53\" ->\n \"\"\"\nselect * from\n(select i_manufact_id,\nsum(ss_sales_price) sum_sales,\navg(sum(ss_sales_price)) over (partition by i_manufact_id) avg_quarterly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\nss_sold_date_sk = d_date_sk and\nss_store_sk = s_store_sk and\nd_month_seq in (1188,1188+1,1188+2,1188+3,1188+4,1188+5,1188+6,1188+7,1188+8,1188+9,1188+10,1188+11) and\n((i_category in ('Books','Children','Electronics') and\ni_class in ('personal','portable','reference','self-help') and\ni_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t'exportiunivamalg #9','scholaramalgamalg #9'))\nor(i_category in ('Women','Music','Men') and\ni_class in ('accessories','classical','fragrances','pants') and\ni_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t'importoamalg #1')))\ngroup by i_manufact_id, d_qoy ) tmp1\nwhere case when avg_quarterly_sales > 0\n\tthen abs (sum_sales - avg_quarterly_sales)/ avg_quarterly_sales\n\telse null end > 0.1\norder by avg_quarterly_sales,\n\t sum_sales,\n\t i_manufact_id\nlimit 100\"\"\",\n \"q54\" ->\n \"\"\"\nwith my_customers as (\n select distinct c_customer_sk\n , c_current_addr_sk\n from\n ( select cs_sold_date_sk sold_date_sk,\n cs_bill_customer_sk customer_sk,\n cs_item_sk item_sk\n from catalog_sales\n union all\n select ws_sold_date_sk sold_date_sk,\n ws_bill_customer_sk customer_sk,\n ws_item_sk item_sk\n from web_sales\n ) cs_or_ws_sales,\n item,\n date_dim,\n customer\n where sold_date_sk = d_date_sk\n and item_sk = i_item_sk\n and i_category = 'Men'\n and i_class = 'pants'\n and c_customer_sk = cs_or_ws_sales.customer_sk\n and d_moy = 5\n and d_year = 2002\n )\n , my_revenue as (\n select c_customer_sk,\n sum(ss_ext_sales_price) as revenue\n from my_customers,\n store_sales,\n customer_address,\n store,\n date_dim\n where c_current_addr_sk = ca_address_sk\n and ca_county = s_county\n and ca_state = s_state\n and ss_sold_date_sk = d_date_sk\n and c_customer_sk = ss_customer_sk\n and d_month_seq between (select distinct d_month_seq+1\n from date_dim where d_year = 2002 and d_moy = 5)\n and (select distinct d_month_seq+3\n from date_dim where d_year = 2002 and d_moy = 5)\n group by c_customer_sk\n )\n , segments as\n (select cast((revenue/50) as int) as segment\n from my_revenue\n )\n select segment, count(*) as num_customers, segment*50 as segment_base\n from segments\n group by segment\n order by segment, num_customers\n limit 100\"\"\",\n \"q55\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item\n where d_date_sk = ss_sold_date_sk\n \tand ss_item_sk = i_item_sk\n \tand i_manager_id=67\n \tand d_moy=11\n \tand d_year=2001\n group by i_brand, i_brand_id\n order by ext_price desc, i_brand_id\nlimit 100 \"\"\",\n \"q56\" ->\n \"\"\"\nwith ss as (\n select i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('blanched','spring','seashell'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 6\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_item_id),\n cs as (\n select i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('blanched','spring','seashell'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 6\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_item_id),\n ws as (\n select i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('blanched','spring','seashell'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 6\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_item_id)\n select i_item_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by total_sales,\n i_item_id\n limit 100\"\"\",\n \"q57\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n cc_name,\n d_year, d_moy,\n sum(cs_sales_price) sum_sales,\n avg(sum(cs_sales_price)) over\n (partition by i_category, i_brand,\n cc_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n cc_name\n order by d_year, d_moy) rn\n from item, catalog_sales, date_dim, call_center\n where cs_item_sk = i_item_sk and\n cs_sold_date_sk = d_date_sk and\n cc_call_center_sk= cs_call_center_sk and\n (\n d_year = 2000 or\n ( d_year = 2000-1 and d_moy =12) or\n ( d_year = 2000+1 and d_moy =1)\n )\n group by i_category, i_brand,\n cc_name , d_year, d_moy),\n v2 as(\n select v1.i_category, v1.i_brand\n ,v1.d_year\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1. cc_name = v1_lag. cc_name and\n v1. cc_name = v1_lead. cc_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 2000 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, sum_sales\n limit 100\"\"\",\n \"q58\" ->\n \"\"\"\nwith ss_items as\n (select i_item_id item_id\n ,sum(ss_ext_sales_price) ss_item_rev\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '2000-05-24'))\n and ss_sold_date_sk = d_date_sk\n group by i_item_id),\n cs_items as\n (select i_item_id item_id\n ,sum(cs_ext_sales_price) cs_item_rev\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '2000-05-24'))\n and cs_sold_date_sk = d_date_sk\n group by i_item_id),\n ws_items as\n (select i_item_id item_id\n ,sum(ws_ext_sales_price) ws_item_rev\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq =(select d_week_seq\n from date_dim\n where d_date = '2000-05-24'))\n and ws_sold_date_sk = d_date_sk\n group by i_item_id)\n select ss_items.item_id\n ,ss_item_rev\n ,ss_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ss_dev\n ,cs_item_rev\n ,cs_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 cs_dev\n ,ws_item_rev\n ,ws_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ws_dev\n ,(ss_item_rev+cs_item_rev+ws_item_rev)/3 average\n from ss_items,cs_items,ws_items\n where ss_items.item_id=cs_items.item_id\n and ss_items.item_id=ws_items.item_id\n and ss_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n and ss_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and cs_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and cs_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and ws_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and ws_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n order by item_id\n ,ss_item_rev\n limit 100\"\"\",\n \"q59\" ->\n \"\"\"\nwith wss as\n (select d_week_seq,\n ss_store_sk,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n group by d_week_seq,ss_store_sk\n )\n select s_store_name1,s_store_id1,d_week_seq1\n ,sun_sales1/sun_sales2,mon_sales1/mon_sales2\n ,tue_sales1/tue_sales2,wed_sales1/wed_sales2,thu_sales1/thu_sales2\n ,fri_sales1/fri_sales2,sat_sales1/sat_sales2\n from\n (select s_store_name s_store_name1,wss.d_week_seq d_week_seq1\n ,s_store_id s_store_id1,sun_sales sun_sales1\n ,mon_sales mon_sales1,tue_sales tue_sales1\n ,wed_sales wed_sales1,thu_sales thu_sales1\n ,fri_sales fri_sales1,sat_sales sat_sales1\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1197 and 1197 + 11) y,\n (select s_store_name s_store_name2,wss.d_week_seq d_week_seq2\n ,s_store_id s_store_id2,sun_sales sun_sales2\n ,mon_sales mon_sales2,tue_sales tue_sales2\n ,wed_sales wed_sales2,thu_sales thu_sales2\n ,fri_sales fri_sales2,sat_sales sat_sales2\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1197+ 12 and 1197 + 23) x\n where s_store_id1=s_store_id2\n and d_week_seq1=d_week_seq2-52\n order by s_store_name1,s_store_id1,d_week_seq1\nlimit 100\"\"\",\n \"q60\" ->\n \"\"\"\nwith ss as (\n select\n i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Shoes'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 10\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n cs as (\n select\n i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Shoes'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 10\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n ws as (\n select\n i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Shoes'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 10\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id)\n select\n i_item_id\n,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by i_item_id\n ,total_sales\n limit 100\"\"\",\n \"q61\" ->\n \"\"\"\nselect promotions,total,cast(promotions as decimal(15,4))/cast(total as decimal(15,4))*100\nfrom\n (select sum(ss_ext_sales_price) promotions\n from store_sales\n ,store\n ,promotion\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_promo_sk = p_promo_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -7\n and i_category = 'Jewelry'\n and (p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y')\n and s_gmt_offset = -7\n and d_year = 2002\n and d_moy = 11) promotional_sales,\n (select sum(ss_ext_sales_price) total\n from store_sales\n ,store\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -7\n and i_category = 'Jewelry'\n and s_gmt_offset = -7\n and d_year = 2002\n and d_moy = 11) all_sales\norder by promotions, total\nlimit 100\"\"\",\n \"q62\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 30) and\n (ws_ship_date_sk - ws_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 60) and\n (ws_ship_date_sk - ws_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 90) and\n (ws_ship_date_sk - ws_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n web_sales\n ,warehouse\n ,ship_mode\n ,web_site\n ,date_dim\nwhere\n d_month_seq between 1194 and 1194 + 11\nand ws_ship_date_sk = d_date_sk\nand ws_warehouse_sk = w_warehouse_sk\nand ws_ship_mode_sk = sm_ship_mode_sk\nand ws_web_site_sk = web_site_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\nlimit 100\"\"\",\n \"q63\" ->\n \"\"\"\nselect *\nfrom (select i_manager_id\n ,sum(ss_sales_price) sum_sales\n ,avg(sum(ss_sales_price)) over (partition by i_manager_id) avg_monthly_sales\n from item\n ,store_sales\n ,date_dim\n ,store\n where ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and d_month_seq in (1222,1222+1,1222+2,1222+3,1222+4,1222+5,1222+6,1222+7,1222+8,1222+9,1222+10,1222+11)\n and (( i_category in ('Books','Children','Electronics')\n and i_class in ('personal','portable','reference','self-help')\n and i_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t 'exportiunivamalg #9','scholaramalgamalg #9'))\n or( i_category in ('Women','Music','Men')\n and i_class in ('accessories','classical','fragrances','pants')\n and i_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t 'importoamalg #1')))\ngroup by i_manager_id, d_moy) tmp1\nwhere case when avg_monthly_sales > 0 then abs (sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\norder by i_manager_id\n ,avg_monthly_sales\n ,sum_sales\nlimit 100\"\"\",\n \"q64\" ->\n \"\"\"\nwith cs_ui as\n (select cs_item_sk\n ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund\n from catalog_sales\n ,catalog_returns\n where cs_item_sk = cr_item_sk\n and cs_order_number = cr_order_number\n group by cs_item_sk\n having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit)),\ncross_sales as\n (select i_product_name product_name\n ,i_item_sk item_sk\n ,s_store_name store_name\n ,s_zip store_zip\n ,ad1.ca_street_number b_street_number\n ,ad1.ca_street_name b_street_name\n ,ad1.ca_city b_city\n ,ad1.ca_zip b_zip\n ,ad2.ca_street_number c_street_number\n ,ad2.ca_street_name c_street_name\n ,ad2.ca_city c_city\n ,ad2.ca_zip c_zip\n ,d1.d_year as syear\n ,d2.d_year as fsyear\n ,d3.d_year s2year\n ,count(*) cnt\n ,sum(ss_wholesale_cost) s1\n ,sum(ss_list_price) s2\n ,sum(ss_coupon_amt) s3\n FROM store_sales\n ,store_returns\n ,cs_ui\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,customer\n ,customer_demographics cd1\n ,customer_demographics cd2\n ,promotion\n ,household_demographics hd1\n ,household_demographics hd2\n ,customer_address ad1\n ,customer_address ad2\n ,income_band ib1\n ,income_band ib2\n ,item\n WHERE ss_store_sk = s_store_sk AND\n ss_sold_date_sk = d1.d_date_sk AND\n ss_customer_sk = c_customer_sk AND\n ss_cdemo_sk= cd1.cd_demo_sk AND\n ss_hdemo_sk = hd1.hd_demo_sk AND\n ss_addr_sk = ad1.ca_address_sk and\n ss_item_sk = i_item_sk and\n ss_item_sk = sr_item_sk and\n ss_ticket_number = sr_ticket_number and\n ss_item_sk = cs_ui.cs_item_sk and\n c_current_cdemo_sk = cd2.cd_demo_sk AND\n c_current_hdemo_sk = hd2.hd_demo_sk AND\n c_current_addr_sk = ad2.ca_address_sk and\n c_first_sales_date_sk = d2.d_date_sk and\n c_first_shipto_date_sk = d3.d_date_sk and\n ss_promo_sk = p_promo_sk and\n hd1.hd_income_band_sk = ib1.ib_income_band_sk and\n hd2.hd_income_band_sk = ib2.ib_income_band_sk and\n cd1.cd_marital_status <> cd2.cd_marital_status and\n i_color in ('ivory','purple','almond','bisque','lawn','azure') and\n i_current_price between 60 and 60 + 10 and\n i_current_price between 60 + 1 and 60 + 15\ngroup by i_product_name\n ,i_item_sk\n ,s_store_name\n ,s_zip\n ,ad1.ca_street_number\n ,ad1.ca_street_name\n ,ad1.ca_city\n ,ad1.ca_zip\n ,ad2.ca_street_number\n ,ad2.ca_street_name\n ,ad2.ca_city\n ,ad2.ca_zip\n ,d1.d_year\n ,d2.d_year\n ,d3.d_year\n)\nselect cs1.product_name\n ,cs1.store_name\n ,cs1.store_zip\n ,cs1.b_street_number\n ,cs1.b_street_name\n ,cs1.b_city\n ,cs1.b_zip\n ,cs1.c_street_number\n ,cs1.c_street_name\n ,cs1.c_city\n ,cs1.c_zip\n ,cs1.syear\n ,cs1.cnt\n ,cs1.s1 as s11\n ,cs1.s2 as s21\n ,cs1.s3 as s31\n ,cs2.s1 as s12\n ,cs2.s2 as s22\n ,cs2.s3 as s32\n ,cs2.syear\n ,cs2.cnt\nfrom cross_sales cs1,cross_sales cs2\nwhere cs1.item_sk=cs2.item_sk and\n cs1.syear = 2001 and\n cs2.syear = 2001 + 1 and\n cs2.cnt <= cs1.cnt and\n cs1.store_name = cs2.store_name and\n cs1.store_zip = cs2.store_zip\norder by cs1.product_name\n ,cs1.store_name\n ,cs2.cnt\n ,cs1.s1\n ,cs2.s1\"\"\",\n \"q65\" ->\n \"\"\"\nselect\n\ts_store_name,\n\ti_item_desc,\n\tsc.revenue,\n\ti_current_price,\n\ti_wholesale_cost,\n\ti_brand\n from store, item,\n (select ss_store_sk, avg(revenue) as ave\n \tfrom\n \t (select ss_store_sk, ss_item_sk,\n \t\t sum(ss_sales_price) as revenue\n \t\tfrom store_sales, date_dim\n \t\twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1185 and 1185+11\n \t\tgroup by ss_store_sk, ss_item_sk) sa\n \tgroup by ss_store_sk) sb,\n (select ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue\n \tfrom store_sales, date_dim\n \twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1185 and 1185+11\n \tgroup by ss_store_sk, ss_item_sk) sc\n where sb.ss_store_sk = sc.ss_store_sk and\n sc.revenue <= 0.1 * sb.ave and\n s_store_sk = sc.ss_store_sk and\n i_item_sk = sc.ss_item_sk\n order by s_store_name, i_item_desc\nlimit 100\"\"\",\n \"q66\" ->\n \"\"\"\nselect\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,ship_carriers\n ,year\n \t,sum(jan_sales) as jan_sales\n \t,sum(feb_sales) as feb_sales\n \t,sum(mar_sales) as mar_sales\n \t,sum(apr_sales) as apr_sales\n \t,sum(may_sales) as may_sales\n \t,sum(jun_sales) as jun_sales\n \t,sum(jul_sales) as jul_sales\n \t,sum(aug_sales) as aug_sales\n \t,sum(sep_sales) as sep_sales\n \t,sum(oct_sales) as oct_sales\n \t,sum(nov_sales) as nov_sales\n \t,sum(dec_sales) as dec_sales\n \t,sum(jan_sales/w_warehouse_sq_ft) as jan_sales_per_sq_foot\n \t,sum(feb_sales/w_warehouse_sq_ft) as feb_sales_per_sq_foot\n \t,sum(mar_sales/w_warehouse_sq_ft) as mar_sales_per_sq_foot\n \t,sum(apr_sales/w_warehouse_sq_ft) as apr_sales_per_sq_foot\n \t,sum(may_sales/w_warehouse_sq_ft) as may_sales_per_sq_foot\n \t,sum(jun_sales/w_warehouse_sq_ft) as jun_sales_per_sq_foot\n \t,sum(jul_sales/w_warehouse_sq_ft) as jul_sales_per_sq_foot\n \t,sum(aug_sales/w_warehouse_sq_ft) as aug_sales_per_sq_foot\n \t,sum(sep_sales/w_warehouse_sq_ft) as sep_sales_per_sq_foot\n \t,sum(oct_sales/w_warehouse_sq_ft) as oct_sales_per_sq_foot\n \t,sum(nov_sales/w_warehouse_sq_ft) as nov_sales_per_sq_foot\n \t,sum(dec_sales/w_warehouse_sq_ft) as dec_sales_per_sq_foot\n \t,sum(jan_net) as jan_net\n \t,sum(feb_net) as feb_net\n \t,sum(mar_net) as mar_net\n \t,sum(apr_net) as apr_net\n \t,sum(may_net) as may_net\n \t,sum(jun_net) as jun_net\n \t,sum(jul_net) as jul_net\n \t,sum(aug_net) as aug_net\n \t,sum(sep_net) as sep_net\n \t,sum(oct_net) as oct_net\n \t,sum(nov_net) as nov_net\n \t,sum(dec_net) as dec_net\n from (\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'FEDEX' || ',' || 'MSC' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen ws_net_profit * ws_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen ws_net_profit * ws_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen ws_net_profit * ws_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen ws_net_profit * ws_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen ws_net_profit * ws_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen ws_net_profit * ws_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen ws_net_profit * ws_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen ws_net_profit * ws_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen ws_net_profit * ws_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen ws_net_profit * ws_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen ws_net_profit * ws_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen ws_net_profit * ws_quantity else 0 end) as dec_net\n from\n web_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n ws_warehouse_sk = w_warehouse_sk\n and ws_sold_date_sk = d_date_sk\n and ws_sold_time_sk = t_time_sk\n \tand ws_ship_mode_sk = sm_ship_mode_sk\n and d_year = 2002\n \tand t_time between 2662 and 2662+28800\n \tand sm_carrier in ('FEDEX','MSC')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n union all\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'FEDEX' || ',' || 'MSC' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen cs_net_profit * cs_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen cs_net_profit * cs_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen cs_net_profit * cs_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen cs_net_profit * cs_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen cs_net_profit * cs_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen cs_net_profit * cs_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen cs_net_profit * cs_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen cs_net_profit * cs_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen cs_net_profit * cs_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen cs_net_profit * cs_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen cs_net_profit * cs_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen cs_net_profit * cs_quantity else 0 end) as dec_net\n from\n catalog_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and cs_sold_time_sk = t_time_sk\n \tand cs_ship_mode_sk = sm_ship_mode_sk\n and d_year = 2002\n \tand t_time between 2662 AND 2662+28800\n \tand sm_carrier in ('FEDEX','MSC')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n ) x\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,ship_carriers\n ,year\n order by w_warehouse_name\n limit 100\"\"\",\n \"q67\" ->\n \"\"\"\nselect *\nfrom (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rank() over (partition by i_category order by sumsales desc) rk\n from (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sum(coalesce(ss_sales_price*ss_quantity,0)) sumsales\n from store_sales\n ,date_dim\n ,store\n ,item\n where ss_sold_date_sk=d_date_sk\n and ss_item_sk=i_item_sk\n and ss_store_sk = s_store_sk\n and d_month_seq between 1177 and 1177+11\n group by rollup(i_category, i_class, i_brand, i_product_name, d_year, d_qoy, d_moy,s_store_id))dw1) dw2\nwhere rk <= 100\norder by i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rk\nlimit 100\"\"\",\n \"q68\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,extended_price\n ,extended_tax\n ,list_price\n from (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_ext_sales_price) extended_price\n ,sum(ss_ext_list_price) list_price\n ,sum(ss_ext_tax) extended_tax\n from store_sales\n ,date_dim\n ,store\n ,household_demographics\n ,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_dep_count = 5 or\n household_demographics.hd_vehicle_count= 4)\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_city in ('Lodi','Richmond')\n group by ss_ticket_number\n ,ss_customer_sk\n ,ss_addr_sk,ca_city) dn\n ,customer\n ,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,ss_ticket_number\n limit 100\"\"\",\n \"q69\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_state in ('IL','FL','SD') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 1999 and\n d_moy between 1 and 1+2) and\n (not exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 1999 and\n d_moy between 1 and 1+2) and\n not exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 1999 and\n d_moy between 1 and 1+2))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n limit 100\"\"\",\n \"q70\" ->\n \"\"\"\nselect\n sum(ss_net_profit) as total_sum\n ,s_state\n ,s_county\n ,grouping(s_state)+grouping(s_county) as lochierarchy\n ,rank() over (\n \tpartition by grouping(s_state)+grouping(s_county),\n \tcase when grouping(s_county) = 0 then s_state end\n \torder by sum(ss_net_profit) desc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,store\n where\n d1.d_month_seq between 1206 and 1206+11\n and d1.d_date_sk = ss_sold_date_sk\n and s_store_sk = ss_store_sk\n and s_state in\n ( select s_state\n from (select s_state as s_state,\n \t\t\t rank() over ( partition by s_state order by sum(ss_net_profit) desc) as ranking\n from store_sales, store, date_dim\n where d_month_seq between 1206 and 1206+11\n \t\t\t and d_date_sk = ss_sold_date_sk\n \t\t\t and s_store_sk = ss_store_sk\n group by s_state\n ) tmp1\n where ranking <= 5\n )\n group by rollup(s_state,s_county)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then s_state end\n ,rank_within_parent\n limit 100\"\"\",\n \"q71\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,t_hour,t_minute,\n \tsum(ext_price) ext_price\n from item, (select ws_ext_sales_price as ext_price,\n ws_sold_date_sk as sold_date_sk,\n ws_item_sk as sold_item_sk,\n ws_sold_time_sk as time_sk\n from web_sales,date_dim\n where d_date_sk = ws_sold_date_sk\n and d_moy=11\n and d_year=1999\n union all\n select cs_ext_sales_price as ext_price,\n cs_sold_date_sk as sold_date_sk,\n cs_item_sk as sold_item_sk,\n cs_sold_time_sk as time_sk\n from catalog_sales,date_dim\n where d_date_sk = cs_sold_date_sk\n and d_moy=11\n and d_year=1999\n union all\n select ss_ext_sales_price as ext_price,\n ss_sold_date_sk as sold_date_sk,\n ss_item_sk as sold_item_sk,\n ss_sold_time_sk as time_sk\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n and d_moy=11\n and d_year=1999\n ) tmp,time_dim\n where\n sold_item_sk = i_item_sk\n and i_manager_id=1\n and time_sk = t_time_sk\n and (t_meal_time = 'breakfast' or t_meal_time = 'dinner')\n group by i_brand, i_brand_id,t_hour,t_minute\n order by ext_price desc, i_brand_id\n \"\"\",\n \"q72\" ->\n \"\"\"\nselect i_item_desc\n ,w_warehouse_name\n ,d1.d_week_seq\n ,sum(case when p_promo_sk is null then 1 else 0 end) no_promo\n ,sum(case when p_promo_sk is not null then 1 else 0 end) promo\n ,count(*) total_cnt\nfrom catalog_sales\njoin inventory on (cs_item_sk = inv_item_sk)\njoin warehouse on (w_warehouse_sk=inv_warehouse_sk)\njoin item on (i_item_sk = cs_item_sk)\njoin customer_demographics on (cs_bill_cdemo_sk = cd_demo_sk)\njoin household_demographics on (cs_bill_hdemo_sk = hd_demo_sk)\njoin date_dim d1 on (cs_sold_date_sk = d1.d_date_sk)\njoin date_dim d2 on (inv_date_sk = d2.d_date_sk)\njoin date_dim d3 on (cs_ship_date_sk = d3.d_date_sk)\nleft outer join promotion on (cs_promo_sk=p_promo_sk)\nleft outer join catalog_returns on (cr_item_sk = cs_item_sk and cr_order_number = cs_order_number)\nwhere d1.d_week_seq = d2.d_week_seq\n and inv_quantity_on_hand < cs_quantity\n and d3.d_date > d1.d_date + interval 5 days\n and hd_buy_potential = '1001-5000'\n and d1.d_year = 2000\n and cd_marital_status = 'S'\ngroup by i_item_desc,w_warehouse_name,d1.d_week_seq\norder by total_cnt desc, i_item_desc, w_warehouse_name, d_week_seq\nlimit 100\"\"\",\n \"q73\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_buy_potential = '1001-5000' or\n household_demographics.hd_buy_potential = 'Unknown')\n and household_demographics.hd_vehicle_count > 0\n and case when household_demographics.hd_vehicle_count > 0 then\n household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count else null end > 1\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_county in ('Humboldt County','Hickman County','Galax city','Abbeville County')\n group by ss_ticket_number,ss_customer_sk) dj,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 1 and 5\n order by cnt desc, c_last_name asc\"\"\",\n \"q74\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,max(ss_net_paid) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2001,2001+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,max(ws_net_paid) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n and d_year in (2001,2001+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n )\n select\n t_s_secyear.customer_id, t_s_secyear.customer_first_name, t_s_secyear.customer_last_name\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.year = 2001\n and t_s_secyear.year = 2001+1\n and t_w_firstyear.year = 2001\n and t_w_secyear.year = 2001+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n order by 3,1,2\nlimit 100\"\"\",\n \"q75\" ->\n \"\"\"\nWITH all_sales AS (\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,SUM(sales_cnt) AS sales_cnt\n ,SUM(sales_amt) AS sales_amt\n FROM (SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,cs_quantity - COALESCE(cr_return_quantity,0) AS sales_cnt\n ,cs_ext_sales_price - COALESCE(cr_return_amount,0.0) AS sales_amt\n FROM catalog_sales JOIN item ON i_item_sk=cs_item_sk\n JOIN date_dim ON d_date_sk=cs_sold_date_sk\n LEFT JOIN catalog_returns ON (cs_order_number=cr_order_number\n AND cs_item_sk=cr_item_sk)\n WHERE i_category='Books'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ss_quantity - COALESCE(sr_return_quantity,0) AS sales_cnt\n ,ss_ext_sales_price - COALESCE(sr_return_amt,0.0) AS sales_amt\n FROM store_sales JOIN item ON i_item_sk=ss_item_sk\n JOIN date_dim ON d_date_sk=ss_sold_date_sk\n LEFT JOIN store_returns ON (ss_ticket_number=sr_ticket_number\n AND ss_item_sk=sr_item_sk)\n WHERE i_category='Books'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ws_quantity - COALESCE(wr_return_quantity,0) AS sales_cnt\n ,ws_ext_sales_price - COALESCE(wr_return_amt,0.0) AS sales_amt\n FROM web_sales JOIN item ON i_item_sk=ws_item_sk\n JOIN date_dim ON d_date_sk=ws_sold_date_sk\n LEFT JOIN web_returns ON (ws_order_number=wr_order_number\n AND ws_item_sk=wr_item_sk)\n WHERE i_category='Books') sales_detail\n GROUP BY d_year, i_brand_id, i_class_id, i_category_id, i_manufact_id)\n SELECT prev_yr.d_year AS prev_year\n ,curr_yr.d_year AS year\n ,curr_yr.i_brand_id\n ,curr_yr.i_class_id\n ,curr_yr.i_category_id\n ,curr_yr.i_manufact_id\n ,prev_yr.sales_cnt AS prev_yr_cnt\n ,curr_yr.sales_cnt AS curr_yr_cnt\n ,curr_yr.sales_cnt-prev_yr.sales_cnt AS sales_cnt_diff\n ,curr_yr.sales_amt-prev_yr.sales_amt AS sales_amt_diff\n FROM all_sales curr_yr, all_sales prev_yr\n WHERE curr_yr.i_brand_id=prev_yr.i_brand_id\n AND curr_yr.i_class_id=prev_yr.i_class_id\n AND curr_yr.i_category_id=prev_yr.i_category_id\n AND curr_yr.i_manufact_id=prev_yr.i_manufact_id\n AND curr_yr.d_year=2001\n AND prev_yr.d_year=2001-1\n AND CAST(curr_yr.sales_cnt AS DECIMAL(17,2))/CAST(prev_yr.sales_cnt AS DECIMAL(17,2))<0.9\n ORDER BY sales_cnt_diff,sales_amt_diff\n limit 100\"\"\",\n \"q76\" ->\n \"\"\"\nselect channel, col_name, d_year, d_qoy, i_category, COUNT(*) sales_cnt, SUM(ext_sales_price) sales_amt FROM (\n SELECT 'store' as channel, 'ss_promo_sk' col_name, d_year, d_qoy, i_category, ss_ext_sales_price ext_sales_price\n FROM store_sales, item, date_dim\n WHERE ss_promo_sk IS NULL\n AND ss_sold_date_sk=d_date_sk\n AND ss_item_sk=i_item_sk\n UNION ALL\n SELECT 'web' as channel, 'ws_ship_addr_sk' col_name, d_year, d_qoy, i_category, ws_ext_sales_price ext_sales_price\n FROM web_sales, item, date_dim\n WHERE ws_ship_addr_sk IS NULL\n AND ws_sold_date_sk=d_date_sk\n AND ws_item_sk=i_item_sk\n UNION ALL\n SELECT 'catalog' as channel, 'cs_ship_customer_sk' col_name, d_year, d_qoy, i_category, cs_ext_sales_price ext_sales_price\n FROM catalog_sales, item, date_dim\n WHERE cs_ship_customer_sk IS NULL\n AND cs_sold_date_sk=d_date_sk\n AND cs_item_sk=i_item_sk) foo\nGROUP BY channel, col_name, d_year, d_qoy, i_category\nORDER BY channel, col_name, d_year, d_qoy, i_category\nlimit 100\"\"\",\n \"q77\" ->\n \"\"\"\nwith ss as\n (select s_store_sk,\n sum(ss_ext_sales_price) as sales,\n sum(ss_net_profit) as profit\n from store_sales,\n date_dim,\n store\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-16' as date)\n and (cast('2001-08-16' as date) + INTERVAL 30 days)\n and ss_store_sk = s_store_sk\n group by s_store_sk)\n ,\n sr as\n (select s_store_sk,\n sum(sr_return_amt) as returns,\n sum(sr_net_loss) as profit_loss\n from store_returns,\n date_dim,\n store\n where sr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-16' as date)\n and (cast('2001-08-16' as date) + INTERVAL 30 days)\n and sr_store_sk = s_store_sk\n group by s_store_sk),\n cs as\n (select cs_call_center_sk,\n sum(cs_ext_sales_price) as sales,\n sum(cs_net_profit) as profit\n from catalog_sales,\n date_dim\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-16' as date)\n and (cast('2001-08-16' as date) + INTERVAL 30 days)\n group by cs_call_center_sk\n ),\n cr as\n (select cr_call_center_sk,\n sum(cr_return_amount) as returns,\n sum(cr_net_loss) as profit_loss\n from catalog_returns,\n date_dim\n where cr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-16' as date)\n and (cast('2001-08-16' as date) + INTERVAL 30 days)\n group by cr_call_center_sk\n ),\n ws as\n ( select wp_web_page_sk,\n sum(ws_ext_sales_price) as sales,\n sum(ws_net_profit) as profit\n from web_sales,\n date_dim,\n web_page\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-16' as date)\n and (cast('2001-08-16' as date) + INTERVAL 30 days)\n and ws_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk),\n wr as\n (select wp_web_page_sk,\n sum(wr_return_amt) as returns,\n sum(wr_net_loss) as profit_loss\n from web_returns,\n date_dim,\n web_page\n where wr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-16' as date)\n and (cast('2001-08-16' as date) + INTERVAL 30 days)\n and wr_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , ss.s_store_sk as id\n , sales\n , coalesce(returns, 0) as returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ss left join sr\n on ss.s_store_sk = sr.s_store_sk\n union all\n select 'catalog channel' as channel\n , cs_call_center_sk as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from cs\n , cr\n union all\n select 'web channel' as channel\n , ws.wp_web_page_sk as id\n , sales\n , coalesce(returns, 0) returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ws left join wr\n on ws.wp_web_page_sk = wr.wp_web_page_sk\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q78\" ->\n \"\"\"\nwith ws as\n (select d_year AS ws_sold_year, ws_item_sk,\n ws_bill_customer_sk ws_customer_sk,\n sum(ws_quantity) ws_qty,\n sum(ws_wholesale_cost) ws_wc,\n sum(ws_sales_price) ws_sp\n from web_sales\n left join web_returns on wr_order_number=ws_order_number and ws_item_sk=wr_item_sk\n join date_dim on ws_sold_date_sk = d_date_sk\n where wr_order_number is null\n group by d_year, ws_item_sk, ws_bill_customer_sk\n ),\ncs as\n (select d_year AS cs_sold_year, cs_item_sk,\n cs_bill_customer_sk cs_customer_sk,\n sum(cs_quantity) cs_qty,\n sum(cs_wholesale_cost) cs_wc,\n sum(cs_sales_price) cs_sp\n from catalog_sales\n left join catalog_returns on cr_order_number=cs_order_number and cs_item_sk=cr_item_sk\n join date_dim on cs_sold_date_sk = d_date_sk\n where cr_order_number is null\n group by d_year, cs_item_sk, cs_bill_customer_sk\n ),\nss as\n (select d_year AS ss_sold_year, ss_item_sk,\n ss_customer_sk,\n sum(ss_quantity) ss_qty,\n sum(ss_wholesale_cost) ss_wc,\n sum(ss_sales_price) ss_sp\n from store_sales\n left join store_returns on sr_ticket_number=ss_ticket_number and ss_item_sk=sr_item_sk\n join date_dim on ss_sold_date_sk = d_date_sk\n where sr_ticket_number is null\n group by d_year, ss_item_sk, ss_customer_sk\n )\n select\nss_item_sk,\nround(ss_qty/(coalesce(ws_qty,0)+coalesce(cs_qty,0)),2) ratio,\nss_qty store_qty, ss_wc store_wholesale_cost, ss_sp store_sales_price,\ncoalesce(ws_qty,0)+coalesce(cs_qty,0) other_chan_qty,\ncoalesce(ws_wc,0)+coalesce(cs_wc,0) other_chan_wholesale_cost,\ncoalesce(ws_sp,0)+coalesce(cs_sp,0) other_chan_sales_price\nfrom ss\nleft join ws on (ws_sold_year=ss_sold_year and ws_item_sk=ss_item_sk and ws_customer_sk=ss_customer_sk)\nleft join cs on (cs_sold_year=ss_sold_year and cs_item_sk=ss_item_sk and cs_customer_sk=ss_customer_sk)\nwhere (coalesce(ws_qty,0)>0 or coalesce(cs_qty, 0)>0) and ss_sold_year=2000\norder by\n ss_item_sk,\n ss_qty desc, ss_wc desc, ss_sp desc,\n other_chan_qty,\n other_chan_wholesale_cost,\n other_chan_sales_price,\n ratio\nlimit 100\"\"\",\n \"q79\" ->\n \"\"\"\nselect\n c_last_name,c_first_name,substr(s_city,1,30),ss_ticket_number,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,store.s_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (household_demographics.hd_dep_count = 5 or household_demographics.hd_vehicle_count > -1)\n and date_dim.d_dow = 1\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_number_employees between 200 and 295\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,store.s_city) ms,customer\n where ss_customer_sk = c_customer_sk\n order by c_last_name,c_first_name,substr(s_city,1,30), profit\nlimit 100\"\"\",\n \"q80\" ->\n \"\"\"\nwith ssr as\n (select s_store_id as store_id,\n sum(ss_ext_sales_price) as sales,\n sum(coalesce(sr_return_amt, 0)) as returns,\n sum(ss_net_profit - coalesce(sr_net_loss, 0)) as profit\n from store_sales left outer join store_returns on\n (ss_item_sk = sr_item_sk and ss_ticket_number = sr_ticket_number),\n date_dim,\n store,\n item,\n promotion\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-19' as date)\n and (cast('2001-08-19' as date) + INTERVAL 60 days)\n and ss_store_sk = s_store_sk\n and ss_item_sk = i_item_sk\n and i_current_price > 50\n and ss_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id as catalog_page_id,\n sum(cs_ext_sales_price) as sales,\n sum(coalesce(cr_return_amount, 0)) as returns,\n sum(cs_net_profit - coalesce(cr_net_loss, 0)) as profit\n from catalog_sales left outer join catalog_returns on\n (cs_item_sk = cr_item_sk and cs_order_number = cr_order_number),\n date_dim,\n catalog_page,\n item,\n promotion\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-19' as date)\n and (cast('2001-08-19' as date) + INTERVAL 60 days)\n and cs_catalog_page_sk = cp_catalog_page_sk\n and cs_item_sk = i_item_sk\n and i_current_price > 50\n and cs_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(ws_ext_sales_price) as sales,\n sum(coalesce(wr_return_amt, 0)) as returns,\n sum(ws_net_profit - coalesce(wr_net_loss, 0)) as profit\n from web_sales left outer join web_returns on\n (ws_item_sk = wr_item_sk and ws_order_number = wr_order_number),\n date_dim,\n web_site,\n item,\n promotion\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-19' as date)\n and (cast('2001-08-19' as date) + INTERVAL 60 days)\n and ws_web_site_sk = web_site_sk\n and ws_item_sk = i_item_sk\n and i_current_price > 50\n and ws_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || store_id as id\n , sales\n , returns\n , profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || catalog_page_id as id\n , sales\n , returns\n , profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q81\" ->\n \"\"\"\nwith customer_total_return as\n (select cr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(cr_return_amt_inc_tax) as ctr_total_return\n from catalog_returns\n ,date_dim\n ,customer_address\n where cr_returned_date_sk = d_date_sk\n and d_year =1999\n and cr_returning_addr_sk = ca_address_sk\n group by cr_returning_customer_sk\n ,ca_state )\n select c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'MO'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n limit 100\"\"\",\n \"q82\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, store_sales\n where i_current_price between 68 and 68+30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2002-05-08' as date) and (cast('2002-05-08' as date) + INTERVAL 60 days)\n and i_manufact_id in (562,370,230,182)\n and inv_quantity_on_hand between 100 and 500\n and ss_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q83\" ->\n \"\"\"\nwith sr_items as\n (select i_item_id item_id,\n sum(sr_return_quantity) sr_item_qty\n from store_returns,\n item,\n date_dim\n where sr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('2000-02-20','2000-10-08','2000-11-04')))\n and sr_returned_date_sk = d_date_sk\n group by i_item_id),\n cr_items as\n (select i_item_id item_id,\n sum(cr_return_quantity) cr_item_qty\n from catalog_returns,\n item,\n date_dim\n where cr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('2000-02-20','2000-10-08','2000-11-04')))\n and cr_returned_date_sk = d_date_sk\n group by i_item_id),\n wr_items as\n (select i_item_id item_id,\n sum(wr_return_quantity) wr_item_qty\n from web_returns,\n item,\n date_dim\n where wr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t\twhere d_date in ('2000-02-20','2000-10-08','2000-11-04')))\n and wr_returned_date_sk = d_date_sk\n group by i_item_id)\n select sr_items.item_id\n ,sr_item_qty\n ,sr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 sr_dev\n ,cr_item_qty\n ,cr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 cr_dev\n ,wr_item_qty\n ,wr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 wr_dev\n ,(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 average\n from sr_items\n ,cr_items\n ,wr_items\n where sr_items.item_id=cr_items.item_id\n and sr_items.item_id=wr_items.item_id\n order by sr_items.item_id\n ,sr_item_qty\n limit 100\"\"\",\n \"q84\" ->\n \"\"\"\nselect c_customer_id as customer_id\n , coalesce(c_last_name,'') || ', ' || coalesce(c_first_name,'') as customername\n from customer\n ,customer_address\n ,customer_demographics\n ,household_demographics\n ,income_band\n ,store_returns\n where ca_city\t = 'Buena Vista'\n and c_current_addr_sk = ca_address_sk\n and ib_lower_bound >= 49786\n and ib_upper_bound <= 49786 + 50000\n and ib_income_band_sk = hd_income_band_sk\n and cd_demo_sk = c_current_cdemo_sk\n and hd_demo_sk = c_current_hdemo_sk\n and sr_cdemo_sk = cd_demo_sk\n order by c_customer_id\n limit 100\"\"\",\n \"q85\" ->\n \"\"\"\nselect substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\n from web_sales, web_returns, web_page, customer_demographics cd1,\n customer_demographics cd2, customer_address, date_dim, reason\n where ws_web_page_sk = wp_web_page_sk\n and ws_item_sk = wr_item_sk\n and ws_order_number = wr_order_number\n and ws_sold_date_sk = d_date_sk and d_year = 2001\n and cd1.cd_demo_sk = wr_refunded_cdemo_sk\n and cd2.cd_demo_sk = wr_returning_cdemo_sk\n and ca_address_sk = wr_refunded_addr_sk\n and r_reason_sk = wr_reason_sk\n and\n (\n (\n cd1.cd_marital_status = 'D'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = '4 yr Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 100.00 and 150.00\n )\n or\n (\n cd1.cd_marital_status = 'M'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'Primary'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 50.00 and 100.00\n )\n or\n (\n cd1.cd_marital_status = 'U'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = '2 yr Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ca_country = 'United States'\n and\n ca_state in ('IA', 'ND', 'FL')\n and ws_net_profit between 100 and 200\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('OH', 'MS', 'VA')\n and ws_net_profit between 150 and 300\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('MN', 'LA', 'TX')\n and ws_net_profit between 50 and 250\n )\n )\ngroup by r_reason_desc\norder by substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\nlimit 100\"\"\",\n \"q86\" ->\n \"\"\"\nselect\n sum(ws_net_paid) as total_sum\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ws_net_paid) desc) as rank_within_parent\n from\n web_sales\n ,date_dim d1\n ,item\n where\n d1.d_month_seq between 1217 and 1217+11\n and d1.d_date_sk = ws_sold_date_sk\n and i_item_sk = ws_item_sk\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc,\n case when lochierarchy = 0 then i_category end,\n rank_within_parent\n limit 100\"\"\",\n \"q87\" ->\n \"\"\"\nselect count(*)\nfrom ((select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1224 and 1224+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1224 and 1224+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1224 and 1224+11)\n) cool_cust\"\"\",\n \"q88\" ->\n \"\"\"\nselect *\nfrom\n (select count(*) h8_30_to_9\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 8\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s1,\n (select count(*) h9_to_9_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s2,\n (select count(*) h9_30_to_10\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s3,\n (select count(*) h10_to_10_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s4,\n (select count(*) h10_30_to_11\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s5,\n (select count(*) h11_to_11_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s6,\n (select count(*) h11_30_to_12\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s7,\n (select count(*) h12_to_12_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 12\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s8\"\"\",\n \"q89\" ->\n \"\"\"\nselect *\nfrom(\nselect i_category, i_class, i_brand,\n s_store_name, s_company_name,\n d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand, s_store_name, s_company_name)\n avg_monthly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n d_year in (2001) and\n ((i_category in ('Children','Home','Women') and\n i_class in ('toddlers','flatware','fragrances')\n )\n or (i_category in ('Music','Electronics','Shoes') and\n i_class in ('country','dvd/vcr players','mens')\n ))\ngroup by i_category, i_class, i_brand,\n s_store_name, s_company_name, d_moy) tmp1\nwhere case when (avg_monthly_sales <> 0) then (abs(sum_sales - avg_monthly_sales) / avg_monthly_sales) else null end > 0.1\norder by sum_sales - avg_monthly_sales, s_store_name\nlimit 100\"\"\",\n \"q90\" ->\n \"\"\"\nselect cast(amc as decimal(15,4))/cast(pmc as decimal(15,4)) am_pm_ratio\n from ( select count(*) amc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 7 and 7+1\n and household_demographics.hd_dep_count = 1\n and web_page.wp_char_count between 5000 and 5200) at,\n ( select count(*) pmc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 20 and 20+1\n and household_demographics.hd_dep_count = 1\n and web_page.wp_char_count between 5000 and 5200) pt\n order by am_pm_ratio\n limit 100\"\"\",\n \"q91\" ->\n \"\"\"\nselect\n cc_call_center_id Call_Center,\n cc_name Call_Center_Name,\n cc_manager Manager,\n sum(cr_net_loss) Returns_Loss\nfrom\n call_center,\n catalog_returns,\n date_dim,\n customer,\n customer_address,\n customer_demographics,\n household_demographics\nwhere\n cr_call_center_sk = cc_call_center_sk\nand cr_returned_date_sk = d_date_sk\nand cr_returning_customer_sk= c_customer_sk\nand cd_demo_sk = c_current_cdemo_sk\nand hd_demo_sk = c_current_hdemo_sk\nand ca_address_sk = c_current_addr_sk\nand d_year = 1998\nand d_moy = 12\nand ( (cd_marital_status = 'M' and cd_education_status = 'Unknown')\n or(cd_marital_status = 'W' and cd_education_status = 'Advanced Degree'))\nand hd_buy_potential like 'Unknown%'\nand ca_gmt_offset = -6\ngroup by cc_call_center_id,cc_name,cc_manager,cd_marital_status,cd_education_status\norder by sum(cr_net_loss) desc\"\"\",\n \"q92\" ->\n \"\"\"\nselect\n sum(ws_ext_discount_amt) as `Excess Discount Amount`\nfrom\n web_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 172\nand i_item_sk = ws_item_sk\nand d_date between '1999-01-12' and\n (cast('1999-01-12' as date) + INTERVAL 90 days)\nand d_date_sk = ws_sold_date_sk\nand ws_ext_discount_amt\n > (\n SELECT\n 1.3 * avg(ws_ext_discount_amt)\n FROM\n web_sales\n ,date_dim\n WHERE\n ws_item_sk = i_item_sk\n and d_date between '1999-01-12' and\n (cast('1999-01-12' as date) + INTERVAL 90 days)\n and d_date_sk = ws_sold_date_sk\n )\norder by sum(ws_ext_discount_amt)\nlimit 100\"\"\",\n \"q93\" ->\n \"\"\"\nselect ss_customer_sk\n ,sum(act_sales) sumsales\n from (select ss_item_sk\n ,ss_ticket_number\n ,ss_customer_sk\n ,case when sr_return_quantity is not null then (ss_quantity-sr_return_quantity)*ss_sales_price\n else (ss_quantity*ss_sales_price) end act_sales\n from store_sales left outer join store_returns on (sr_item_sk = ss_item_sk\n and sr_ticket_number = ss_ticket_number)\n ,reason\n where sr_reason_sk = r_reason_sk\n and r_reason_desc = 'reason 58') t\n group by ss_customer_sk\n order by sumsales, ss_customer_sk\nlimit 100\"\"\",\n \"q94\" ->\n \"\"\"\nselect\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2002-3-01' and\n (cast('2002-3-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'GA'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand exists (select *\n from web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\nand not exists(select *\n from web_returns wr1\n where ws1.ws_order_number = wr1.wr_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q95\" ->\n \"\"\"\nwith ws_wh as\n(select ws1.ws_order_number,ws1.ws_warehouse_sk wh1,ws2.ws_warehouse_sk wh2\n from web_sales ws1,web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\n select\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2001-3-01' and\n (cast('2001-3-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'NE'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand ws1.ws_order_number in (select ws_order_number\n from ws_wh)\nand ws1.ws_order_number in (select wr_order_number\n from web_returns,ws_wh\n where wr_order_number = ws_wh.ws_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q96\" ->\n \"\"\"\nselect count(*)\nfrom store_sales\n ,household_demographics\n ,time_dim, store\nwhere ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 16\n and time_dim.t_minute >= 30\n and household_demographics.hd_dep_count = 0\n and store.s_store_name = 'ese'\norder by count(*)\nlimit 100\"\"\",\n \"q97\" ->\n \"\"\"\nwith ssci as (\nselect ss_customer_sk customer_sk\n ,ss_item_sk item_sk\nfrom store_sales,date_dim\nwhere ss_sold_date_sk = d_date_sk\n and d_month_seq between 1219 and 1219 + 11\ngroup by ss_customer_sk\n ,ss_item_sk),\ncsci as(\n select cs_bill_customer_sk customer_sk\n ,cs_item_sk item_sk\nfrom catalog_sales,date_dim\nwhere cs_sold_date_sk = d_date_sk\n and d_month_seq between 1219 and 1219 + 11\ngroup by cs_bill_customer_sk\n ,cs_item_sk)\n select sum(case when ssci.customer_sk is not null and csci.customer_sk is null then 1 else 0 end) store_only\n ,sum(case when ssci.customer_sk is null and csci.customer_sk is not null then 1 else 0 end) catalog_only\n ,sum(case when ssci.customer_sk is not null and csci.customer_sk is not null then 1 else 0 end) store_and_catalog\nfrom ssci full outer join csci on (ssci.customer_sk=csci.customer_sk\n and ssci.item_sk = csci.item_sk)\nlimit 100\"\"\",\n \"q98\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ss_ext_sales_price) as itemrevenue\n ,sum(ss_ext_sales_price)*100/sum(sum(ss_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tstore_sales\n \t,item\n \t,date_dim\nwhere\n\tss_item_sk = i_item_sk\n \tand i_category in ('Books', 'Children', 'Sports')\n \tand ss_sold_date_sk = d_date_sk\n\tand d_date between cast('2001-03-10' as date)\n\t\t\t\tand (cast('2001-03-10' as date) + interval 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\"\"\",\n \"q99\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 30) and\n (cs_ship_date_sk - cs_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 60) and\n (cs_ship_date_sk - cs_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 90) and\n (cs_ship_date_sk - cs_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n catalog_sales\n ,warehouse\n ,ship_mode\n ,call_center\n ,date_dim\nwhere\n d_month_seq between 1205 and 1205 + 11\nand cs_ship_date_sk = d_date_sk\nand cs_warehouse_sk = w_warehouse_sk\nand cs_ship_mode_sk = sm_ship_mode_sk\nand cs_call_center_sk = cc_call_center_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\nlimit 100\"\"\"\n )\n\n val TPCDSQueries10TB = Map(\n \"q1\" ->\n \"\"\"\nwith customer_total_return as\n(select sr_customer_sk as ctr_customer_sk\n,sr_store_sk as ctr_store_sk\n,sum(SR_FEE) as ctr_total_return\nfrom store_returns\n,date_dim\nwhere sr_returned_date_sk = d_date_sk\nand d_year =2000\ngroup by sr_customer_sk\n,sr_store_sk)\n select c_customer_id\nfrom customer_total_return ctr1\n,store\n,customer\nwhere ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\nfrom customer_total_return ctr2\nwhere ctr1.ctr_store_sk = ctr2.ctr_store_sk)\nand s_store_sk = ctr1.ctr_store_sk\nand s_state = 'TN'\nand ctr1.ctr_customer_sk = c_customer_sk\norder by c_customer_id\nlimit 100\"\"\",\n \"q2\" ->\n \"\"\"\nwith wscs as\n (select sold_date_sk\n ,sales_price\n from (select ws_sold_date_sk sold_date_sk\n ,ws_ext_sales_price sales_price\n from web_sales\n union all\n select cs_sold_date_sk sold_date_sk\n ,cs_ext_sales_price sales_price\n from catalog_sales)),\n wswscs as\n (select d_week_seq,\n sum(case when (d_day_name='Sunday') then sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then sales_price else null end) sat_sales\n from wscs\n ,date_dim\n where d_date_sk = sold_date_sk\n group by d_week_seq)\n select d_week_seq1\n ,round(sun_sales1/sun_sales2,2)\n ,round(mon_sales1/mon_sales2,2)\n ,round(tue_sales1/tue_sales2,2)\n ,round(wed_sales1/wed_sales2,2)\n ,round(thu_sales1/thu_sales2,2)\n ,round(fri_sales1/fri_sales2,2)\n ,round(sat_sales1/sat_sales2,2)\n from\n (select wswscs.d_week_seq d_week_seq1\n ,sun_sales sun_sales1\n ,mon_sales mon_sales1\n ,tue_sales tue_sales1\n ,wed_sales wed_sales1\n ,thu_sales thu_sales1\n ,fri_sales fri_sales1\n ,sat_sales sat_sales1\n from wswscs,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998) y,\n (select wswscs.d_week_seq d_week_seq2\n ,sun_sales sun_sales2\n ,mon_sales mon_sales2\n ,tue_sales tue_sales2\n ,wed_sales wed_sales2\n ,thu_sales thu_sales2\n ,fri_sales fri_sales2\n ,sat_sales sat_sales2\n from wswscs\n ,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998+1) z\n where d_week_seq1=d_week_seq2-53\n order by d_week_seq1\"\"\",\n \"q3\" ->\n \"\"\"\nselect dt.d_year\n ,item.i_brand_id brand_id\n ,item.i_brand brand\n ,sum(ss_sales_price) sum_agg\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manufact_id = 816\n and dt.d_moy=11\n group by dt.d_year\n ,item.i_brand\n ,item.i_brand_id\n order by dt.d_year\n ,sum_agg desc\n ,brand_id\n limit 100\"\"\",\n \"q4\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(((ss_ext_list_price-ss_ext_wholesale_cost-ss_ext_discount_amt)+ss_ext_sales_price)/2) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((cs_ext_list_price-cs_ext_wholesale_cost-cs_ext_discount_amt)+cs_ext_sales_price)/2) ) year_total\n ,'c' sale_type\n from customer\n ,catalog_sales\n ,date_dim\n where c_customer_sk = cs_bill_customer_sk\n and cs_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\nunion all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((ws_ext_list_price-ws_ext_wholesale_cost-ws_ext_discount_amt)+ws_ext_sales_price)/2) ) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_c_firstyear\n ,year_total t_c_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_c_secyear.customer_id\n and t_s_firstyear.customer_id = t_c_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_c_firstyear.sale_type = 'c'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_c_secyear.sale_type = 'c'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 1999\n and t_s_secyear.dyear = 1999+1\n and t_c_firstyear.dyear = 1999\n and t_c_secyear.dyear = 1999+1\n and t_w_firstyear.dyear = 1999\n and t_w_secyear.dyear = 1999+1\n and t_s_firstyear.year_total > 0\n and t_c_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\nlimit 100\"\"\",\n \"q5\" ->\n \"\"\"\nwith ssr as\n (select s_store_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ss_store_sk as store_sk,\n ss_sold_date_sk as date_sk,\n ss_ext_sales_price as sales_price,\n ss_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from store_sales\n union all\n select sr_store_sk as store_sk,\n sr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n sr_return_amt as return_amt,\n sr_net_loss as net_loss\n from store_returns\n ) salesreturns,\n date_dim,\n store\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and store_sk = s_store_sk\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select cs_catalog_page_sk as page_sk,\n cs_sold_date_sk as date_sk,\n cs_ext_sales_price as sales_price,\n cs_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from catalog_sales\n union all\n select cr_catalog_page_sk as page_sk,\n cr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n cr_return_amount as return_amt,\n cr_net_loss as net_loss\n from catalog_returns\n ) salesreturns,\n date_dim,\n catalog_page\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and page_sk = cp_catalog_page_sk\n group by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ws_web_site_sk as wsr_web_site_sk,\n ws_sold_date_sk as date_sk,\n ws_ext_sales_price as sales_price,\n ws_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from web_sales\n union all\n select ws_web_site_sk as wsr_web_site_sk,\n wr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n wr_return_amt as return_amt,\n wr_net_loss as net_loss\n from web_returns left outer join web_sales on\n ( wr_item_sk = ws_item_sk\n and wr_order_number = ws_order_number)\n ) salesreturns,\n date_dim,\n web_site\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and wsr_web_site_sk = web_site_sk\n group by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || s_store_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || cp_catalog_page_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q6\" ->\n \"\"\"\nselect a.ca_state state, count(*) cnt\n from customer_address a\n ,customer c\n ,store_sales s\n ,date_dim d\n ,item i\n where a.ca_address_sk = c.c_current_addr_sk\n \tand c.c_customer_sk = s.ss_customer_sk\n \tand s.ss_sold_date_sk = d.d_date_sk\n \tand s.ss_item_sk = i.i_item_sk\n \tand d.d_month_seq =\n \t (select distinct (d_month_seq)\n \t from date_dim\n where d_year = 2002\n \t and d_moy = 3 )\n \tand i.i_current_price > 1.2 *\n (select avg(j.i_current_price)\n \t from item j\n \t where j.i_category = i.i_category)\n group by a.ca_state\n having count(*) >= 10\n order by cnt, a.ca_state\n limit 100\"\"\",\n \"q7\" ->\n \"\"\"\nselect i_item_id,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, item, promotion\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_cdemo_sk = cd_demo_sk and\n ss_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'W' and\n cd_education_status = 'College' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 2001\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q8\" ->\n \"\"\"\nselect s_store_name\n ,sum(ss_net_profit)\n from store_sales\n ,date_dim\n ,store,\n (select ca_zip\n from (\n SELECT substr(ca_zip,1,5) ca_zip\n FROM customer_address\n WHERE substr(ca_zip,1,5) IN (\n '47602','16704','35863','28577','83910','36201',\n '58412','48162','28055','41419','80332',\n '38607','77817','24891','16226','18410',\n '21231','59345','13918','51089','20317',\n '17167','54585','67881','78366','47770',\n '18360','51717','73108','14440','21800',\n '89338','45859','65501','34948','25973',\n '73219','25333','17291','10374','18829',\n '60736','82620','41351','52094','19326',\n '25214','54207','40936','21814','79077',\n '25178','75742','77454','30621','89193',\n '27369','41232','48567','83041','71948',\n '37119','68341','14073','16891','62878',\n '49130','19833','24286','27700','40979',\n '50412','81504','94835','84844','71954',\n '39503','57649','18434','24987','12350',\n '86379','27413','44529','98569','16515',\n '27287','24255','21094','16005','56436',\n '91110','68293','56455','54558','10298',\n '83647','32754','27052','51766','19444',\n '13869','45645','94791','57631','20712',\n '37788','41807','46507','21727','71836',\n '81070','50632','88086','63991','20244',\n '31655','51782','29818','63792','68605',\n '94898','36430','57025','20601','82080',\n '33869','22728','35834','29086','92645',\n '98584','98072','11652','78093','57553',\n '43830','71144','53565','18700','90209',\n '71256','38353','54364','28571','96560',\n '57839','56355','50679','45266','84680',\n '34306','34972','48530','30106','15371',\n '92380','84247','92292','68852','13338',\n '34594','82602','70073','98069','85066',\n '47289','11686','98862','26217','47529',\n '63294','51793','35926','24227','14196',\n '24594','32489','99060','49472','43432',\n '49211','14312','88137','47369','56877',\n '20534','81755','15794','12318','21060',\n '73134','41255','63073','81003','73873',\n '66057','51184','51195','45676','92696',\n '70450','90669','98338','25264','38919',\n '59226','58581','60298','17895','19489',\n '52301','80846','95464','68770','51634',\n '19988','18367','18421','11618','67975',\n '25494','41352','95430','15734','62585',\n '97173','33773','10425','75675','53535',\n '17879','41967','12197','67998','79658',\n '59130','72592','14851','43933','68101',\n '50636','25717','71286','24660','58058',\n '72991','95042','15543','33122','69280',\n '11912','59386','27642','65177','17672',\n '33467','64592','36335','54010','18767',\n '63193','42361','49254','33113','33159',\n '36479','59080','11855','81963','31016',\n '49140','29392','41836','32958','53163',\n '13844','73146','23952','65148','93498',\n '14530','46131','58454','13376','13378',\n '83986','12320','17193','59852','46081',\n '98533','52389','13086','68843','31013',\n '13261','60560','13443','45533','83583',\n '11489','58218','19753','22911','25115',\n '86709','27156','32669','13123','51933',\n '39214','41331','66943','14155','69998',\n '49101','70070','35076','14242','73021',\n '59494','15782','29752','37914','74686',\n '83086','34473','15751','81084','49230',\n '91894','60624','17819','28810','63180',\n '56224','39459','55233','75752','43639',\n '55349','86057','62361','50788','31830',\n '58062','18218','85761','60083','45484',\n '21204','90229','70041','41162','35390',\n '16364','39500','68908','26689','52868',\n '81335','40146','11340','61527','61794',\n '71997','30415','59004','29450','58117',\n '69952','33562','83833','27385','61860',\n '96435','48333','23065','32961','84919',\n '61997','99132','22815','56600','68730',\n '48017','95694','32919','88217','27116',\n '28239','58032','18884','16791','21343',\n '97462','18569','75660','15475')\n intersect\n select ca_zip\n from (SELECT substr(ca_zip,1,5) ca_zip,count(*) cnt\n FROM customer_address, customer\n WHERE ca_address_sk = c_current_addr_sk and\n c_preferred_cust_flag='Y'\n group by ca_zip\n having count(*) > 10)A1)A2) V1\n where ss_store_sk = s_store_sk\n and ss_sold_date_sk = d_date_sk\n and d_qoy = 2 and d_year = 1998\n and (substr(s_zip,1,2) = substr(V1.ca_zip,1,2))\n group by s_store_name\n order by s_store_name\n limit 100\"\"\",\n \"q9\" ->\n \"\"\"\nselect case when (select count(*)\n from store_sales\n where ss_quantity between 1 and 20) > 2972190\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 1 and 20)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 1 and 20) end bucket1 ,\n case when (select count(*)\n from store_sales\n where ss_quantity between 21 and 40) > 111711138\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 21 and 40)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 21 and 40) end bucket2,\n case when (select count(*)\n from store_sales\n where ss_quantity between 41 and 60) > 127958920\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 41 and 60)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 41 and 60) end bucket3,\n case when (select count(*)\n from store_sales\n where ss_quantity between 61 and 80) > 41162107\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 61 and 80)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 61 and 80) end bucket4,\n case when (select count(*)\n from store_sales\n where ss_quantity between 81 and 100) > 25211875\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 81 and 100)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 81 and 100) end bucket5\nfrom reason\nwhere r_reason_sk = 1\"\"\",\n \"q10\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3,\n cd_dep_count,\n count(*) cnt4,\n cd_dep_employed_count,\n count(*) cnt5,\n cd_dep_college_count,\n count(*) cnt6\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_county in ('Allen County','Jefferson County','Lamar County','Dakota County','Park County') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_moy between 4 and 4+3) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_moy between 4 ANd 4+3) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_moy between 4 and 4+3))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\nlimit 100\"\"\",\n \"q11\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ss_ext_list_price-ss_ext_discount_amt) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ws_ext_list_price-ws_ext_discount_amt) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_login\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 1998\n and t_s_secyear.dyear = 1998+1\n and t_w_firstyear.dyear = 1998\n and t_w_secyear.dyear = 1998+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else 0.0 end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else 0.0 end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_login\nlimit 100\"\"\",\n \"q12\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ws_ext_sales_price) as itemrevenue\n ,sum(ws_ext_sales_price)*100/sum(sum(ws_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tweb_sales\n \t,item\n \t,date_dim\nwhere\n\tws_item_sk = i_item_sk\n \tand i_category in ('Men', 'Books', 'Children')\n \tand ws_sold_date_sk = d_date_sk\n\tand d_date between cast('1998-03-28' as date)\n\t\t\t\tand (cast('1998-03-28' as date) + INTERVAL 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q13\" ->\n \"\"\"\nselect avg(ss_quantity)\n ,avg(ss_ext_sales_price)\n ,avg(ss_ext_wholesale_cost)\n ,sum(ss_ext_wholesale_cost)\n from store_sales\n ,store\n ,customer_demographics\n ,household_demographics\n ,customer_address\n ,date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 2001\n and((ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'U'\n and cd_education_status = 'Unknown'\n and ss_sales_price between 100.00 and 150.00\n and hd_dep_count = 3\n )or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'W'\n and cd_education_status = '2 yr Degree'\n and ss_sales_price between 50.00 and 100.00\n and hd_dep_count = 1\n ) or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'S'\n and cd_education_status = 'College'\n and ss_sales_price between 150.00 and 200.00\n and hd_dep_count = 1\n ))\n and((ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('WV', 'GA', 'TX')\n and ss_net_profit between 100 and 200\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('TN', 'KY', 'SC')\n and ss_net_profit between 150 and 300\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('OK', 'NE', 'CA')\n and ss_net_profit between 50 and 250\n ))\"\"\",\n \"q14a\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 1998 AND 1998 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 1998 AND 1998 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 1998 AND 1998 + 2)\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n (select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2) x)\n select channel, i_brand_id,i_class_id,i_category_id,sum(sales), sum(number_sales)\n from(\n select 'store' channel, i_brand_id,i_class_id\n ,i_category_id,sum(ss_quantity*ss_list_price) sales\n , count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1998+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)\n union all\n select 'catalog' channel, i_brand_id,i_class_id,i_category_id, sum(cs_quantity*cs_list_price) sales, count(*) number_sales\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk in (select ss_item_sk from cross_items)\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1998+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(cs_quantity*cs_list_price) > (select average_sales from avg_sales)\n union all\n select 'web' channel, i_brand_id,i_class_id,i_category_id, sum(ws_quantity*ws_list_price) sales , count(*) number_sales\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk in (select ss_item_sk from cross_items)\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1998+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ws_quantity*ws_list_price) > (select average_sales from avg_sales)\n ) y\n group by rollup (channel, i_brand_id,i_class_id,i_category_id)\n order by channel,i_brand_id,i_class_id,i_category_id\n limit 100\"\"\",\n \"q14b\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 1998 AND 1998 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 1998 AND 1998 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 1998 AND 1998 + 2) x\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n(select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2) x)\n select this_year.channel ty_channel\n ,this_year.i_brand_id ty_brand\n ,this_year.i_class_id ty_class\n ,this_year.i_category_id ty_category\n ,this_year.sales ty_sales\n ,this_year.number_sales ty_number_sales\n ,last_year.channel ly_channel\n ,last_year.i_brand_id ly_brand\n ,last_year.i_class_id ly_class\n ,last_year.i_category_id ly_category\n ,last_year.sales ly_sales\n ,last_year.number_sales ly_number_sales\n from\n (select 'store' channel, i_brand_id,i_class_id,i_category_id\n ,sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 1998 + 1\n and d_moy = 12\n and d_dom = 20)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) this_year,\n (select 'store' channel, i_brand_id,i_class_id\n ,i_category_id, sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 1998\n and d_moy = 12\n and d_dom = 20)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) last_year\n where this_year.i_brand_id= last_year.i_brand_id\n and this_year.i_class_id = last_year.i_class_id\n and this_year.i_category_id = last_year.i_category_id\n order by this_year.channel, this_year.i_brand_id, this_year.i_class_id, this_year.i_category_id\n limit 100\"\"\",\n \"q15\" ->\n \"\"\"\nselect ca_zip\n ,sum(cs_sales_price)\n from catalog_sales\n ,customer\n ,customer_address\n ,date_dim\n where cs_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475',\n '85392', '85460', '80348', '81792')\n \t or ca_state in ('CA','WA','GA')\n \t or cs_sales_price > 500)\n \tand cs_sold_date_sk = d_date_sk\n \tand d_qoy = 1 and d_year = 2000\n group by ca_zip\n order by ca_zip\n limit 100\"\"\",\n \"q16\" ->\n \"\"\"\nselect\n count(distinct cs_order_number) as `order count`\n ,sum(cs_ext_ship_cost) as `total shipping cost`\n ,sum(cs_net_profit) as `total net profit`\nfrom\n catalog_sales cs1\n ,date_dim\n ,customer_address\n ,call_center\nwhere\n d_date between '2001-2-01' and\n (cast('2001-2-01' as date) + INTERVAL 60 days)\nand cs1.cs_ship_date_sk = d_date_sk\nand cs1.cs_ship_addr_sk = ca_address_sk\nand ca_state = 'MS'\nand cs1.cs_call_center_sk = cc_call_center_sk\nand cc_county in ('Jackson County','Daviess County','Walker County','Dauphin County',\n 'Mobile County'\n)\nand exists (select *\n from catalog_sales cs2\n where cs1.cs_order_number = cs2.cs_order_number\n and cs1.cs_warehouse_sk <> cs2.cs_warehouse_sk)\nand not exists(select *\n from catalog_returns cr1\n where cs1.cs_order_number = cr1.cr_order_number)\norder by count(distinct cs_order_number)\nlimit 100\"\"\",\n \"q17\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,s_state\n ,count(ss_quantity) as store_sales_quantitycount\n ,avg(ss_quantity) as store_sales_quantityave\n ,stddev_samp(ss_quantity) as store_sales_quantitystdev\n ,stddev_samp(ss_quantity)/avg(ss_quantity) as store_sales_quantitycov\n ,count(sr_return_quantity) as store_returns_quantitycount\n ,avg(sr_return_quantity) as store_returns_quantityave\n ,stddev_samp(sr_return_quantity) as store_returns_quantitystdev\n ,stddev_samp(sr_return_quantity)/avg(sr_return_quantity) as store_returns_quantitycov\n ,count(cs_quantity) as catalog_sales_quantitycount ,avg(cs_quantity) as catalog_sales_quantityave\n ,stddev_samp(cs_quantity) as catalog_sales_quantitystdev\n ,stddev_samp(cs_quantity)/avg(cs_quantity) as catalog_sales_quantitycov\n from store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where d1.d_quarter_name = '1999Q1'\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_quarter_name in ('1999Q1','1999Q2','1999Q3')\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_quarter_name in ('1999Q1','1999Q2','1999Q3')\n group by i_item_id\n ,i_item_desc\n ,s_state\n order by i_item_id\n ,i_item_desc\n ,s_state\nlimit 100\"\"\",\n \"q18\" ->\n \"\"\"\nselect i_item_id,\n ca_country,\n ca_state,\n ca_county,\n avg( cast(cs_quantity as decimal(12,2))) agg1,\n avg( cast(cs_list_price as decimal(12,2))) agg2,\n avg( cast(cs_coupon_amt as decimal(12,2))) agg3,\n avg( cast(cs_sales_price as decimal(12,2))) agg4,\n avg( cast(cs_net_profit as decimal(12,2))) agg5,\n avg( cast(c_birth_year as decimal(12,2))) agg6,\n avg( cast(cd1.cd_dep_count as decimal(12,2))) agg7\n from catalog_sales, customer_demographics cd1,\n customer_demographics cd2, customer, customer_address, date_dim, item\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd1.cd_demo_sk and\n cs_bill_customer_sk = c_customer_sk and\n cd1.cd_gender = 'F' and\n cd1.cd_education_status = 'Primary' and\n c_current_cdemo_sk = cd2.cd_demo_sk and\n c_current_addr_sk = ca_address_sk and\n c_birth_month in (6,7,3,11,12,8) and\n d_year = 1999 and\n ca_state in ('IL','WV','KS'\n ,'GA','LA','PA','TX')\n group by rollup (i_item_id, ca_country, ca_state, ca_county)\n order by ca_country,\n ca_state,\n ca_county,\n\ti_item_id\n limit 100\"\"\",\n \"q19\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand, i_manufact_id, i_manufact,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item,customer,customer_address,store\n where d_date_sk = ss_sold_date_sk\n and ss_item_sk = i_item_sk\n and i_manager_id=26\n and d_moy=12\n and d_year=2000\n and ss_customer_sk = c_customer_sk\n and c_current_addr_sk = ca_address_sk\n and substr(ca_zip,1,5) <> substr(s_zip,1,5)\n and ss_store_sk = s_store_sk\n group by i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\n order by ext_price desc\n ,i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\nlimit 100 \"\"\",\n \"q20\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(cs_ext_sales_price) as itemrevenue\n ,sum(cs_ext_sales_price)*100/sum(sum(cs_ext_sales_price)) over\n (partition by i_class) as revenueratio\n from\tcatalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and i_category in ('Books', 'Home', 'Jewelry')\n and cs_sold_date_sk = d_date_sk\n and d_date between cast('1998-05-08' as date)\n \t\t\t\tand (cast('1998-05-08' as date) + INTERVAL 30 days)\n group by i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n order by i_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q21\" ->\n \"\"\"\nselect *\n from(select w_warehouse_name\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('2000-05-22' as date))\n\t then inv_quantity_on_hand\n else 0 end) as inv_before\n ,sum(case when (cast(d_date as date) >= cast ('2000-05-22' as date))\n then inv_quantity_on_hand\n else 0 end) as inv_after\n from inventory\n ,warehouse\n ,item\n ,date_dim\n where i_current_price between 0.99 and 1.49\n and i_item_sk = inv_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_date between (cast ('2000-05-22' as date) - INTERVAL 30 days)\n and (cast ('2000-05-22' as date) + INTERVAL 30 days)\n group by w_warehouse_name, i_item_id) x\n where (case when inv_before > 0\n then inv_after / inv_before\n else null\n end) between 2.0/3.0 and 3.0/2.0\n order by w_warehouse_name\n ,i_item_id\n limit 100\"\"\",\n \"q22\" ->\n \"\"\"\nselect i_product_name\n ,i_brand\n ,i_class\n ,i_category\n ,avg(inv_quantity_on_hand) qoh\n from inventory\n ,date_dim\n ,item\n where inv_date_sk=d_date_sk\n and inv_item_sk=i_item_sk\n and d_month_seq between 1199 and 1199 + 11\n group by rollup(i_product_name\n ,i_brand\n ,i_class\n ,i_category)\norder by qoh, i_product_name, i_brand, i_class, i_category\nlimit 100\"\"\",\n \"q23a\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\nfrom\n max_store_sales))\n select sum(sales)\n from (select cs_quantity*cs_list_price sales\n from catalog_sales\n ,date_dim\n where d_year = 2000\n and d_moy = 5\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n union all\n select ws_quantity*ws_list_price sales\n from web_sales\n ,date_dim\n where d_year = 2000\n and d_moy = 5\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer))\n limit 100\"\"\",\n \"q23b\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (2000,2000 + 1,2000 + 2,2000 + 3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\n from max_store_sales))\n select c_last_name,c_first_name,sales\n from (select c_last_name,c_first_name,sum(cs_quantity*cs_list_price) sales\n from catalog_sales\n ,customer\n ,date_dim\n where d_year = 2000\n and d_moy = 5\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and cs_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name\n union all\n select c_last_name,c_first_name,sum(ws_quantity*ws_list_price) sales\n from web_sales\n ,customer\n ,date_dim\n where d_year = 2000\n and d_moy = 5\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and ws_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name)\n order by c_last_name,c_first_name,sales\n limit 100\"\"\",\n \"q24a\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_net_paid_inc_tax) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\nand s_market_id=10\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'navy'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q24b\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_net_paid_inc_tax) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\n and s_market_id = 10\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'beige'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q25\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,sum(ss_net_profit) as store_sales_profit\n ,sum(sr_net_loss) as store_returns_loss\n ,sum(cs_net_profit) as catalog_sales_profit\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 2002\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 10\n and d2.d_year = 2002\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_moy between 4 and 10\n and d3.d_year = 2002\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q26\" ->\n \"\"\"\nselect i_item_id,\n avg(cs_quantity) agg1,\n avg(cs_list_price) agg2,\n avg(cs_coupon_amt) agg3,\n avg(cs_sales_price) agg4\n from catalog_sales, customer_demographics, date_dim, item, promotion\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd_demo_sk and\n cs_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'M' and\n cd_education_status = '2 yr Degree' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 2002\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q27\" ->\n \"\"\"\nselect i_item_id,\n s_state, grouping(s_state) g_state,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, store, item\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_store_sk = s_store_sk and\n ss_cdemo_sk = cd_demo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'S' and\n cd_education_status = 'Advanced Degree' and\n d_year = 2000 and\n s_state in ('WA','LA', 'LA', 'TX', 'AL', 'PA')\n group by rollup (i_item_id, s_state)\n order by i_item_id\n ,s_state\n limit 100\"\"\",\n \"q28\" ->\n \"\"\"\nselect *\nfrom (select avg(ss_list_price) B1_LP\n ,count(ss_list_price) B1_CNT\n ,count(distinct ss_list_price) B1_CNTD\n from store_sales\n where ss_quantity between 0 and 5\n and (ss_list_price between 189 and 189+10\n or ss_coupon_amt between 4483 and 4483+1000\n or ss_wholesale_cost between 24 and 24+20)) B1,\n (select avg(ss_list_price) B2_LP\n ,count(ss_list_price) B2_CNT\n ,count(distinct ss_list_price) B2_CNTD\n from store_sales\n where ss_quantity between 6 and 10\n and (ss_list_price between 71 and 71+10\n or ss_coupon_amt between 14775 and 14775+1000\n or ss_wholesale_cost between 38 and 38+20)) B2,\n (select avg(ss_list_price) B3_LP\n ,count(ss_list_price) B3_CNT\n ,count(distinct ss_list_price) B3_CNTD\n from store_sales\n where ss_quantity between 11 and 15\n and (ss_list_price between 183 and 183+10\n or ss_coupon_amt between 13456 and 13456+1000\n or ss_wholesale_cost between 31 and 31+20)) B3,\n (select avg(ss_list_price) B4_LP\n ,count(ss_list_price) B4_CNT\n ,count(distinct ss_list_price) B4_CNTD\n from store_sales\n where ss_quantity between 16 and 20\n and (ss_list_price between 135 and 135+10\n or ss_coupon_amt between 4905 and 4905+1000\n or ss_wholesale_cost between 27 and 27+20)) B4,\n (select avg(ss_list_price) B5_LP\n ,count(ss_list_price) B5_CNT\n ,count(distinct ss_list_price) B5_CNTD\n from store_sales\n where ss_quantity between 21 and 25\n and (ss_list_price between 180 and 180+10\n or ss_coupon_amt between 17430 and 17430+1000\n or ss_wholesale_cost between 57 and 57+20)) B5,\n (select avg(ss_list_price) B6_LP\n ,count(ss_list_price) B6_CNT\n ,count(distinct ss_list_price) B6_CNTD\n from store_sales\n where ss_quantity between 26 and 30\n and (ss_list_price between 49 and 49+10\n or ss_coupon_amt between 2950 and 2950+1000\n or ss_wholesale_cost between 52 and 52+20)) B6\nlimit 100\"\"\",\n \"q29\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,stddev_samp(ss_quantity) as store_sales_quantity\n ,stddev_samp(sr_return_quantity) as store_returns_quantity\n ,stddev_samp(cs_quantity) as catalog_sales_quantity\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 1998\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 4 + 3\n and d2.d_year = 1998\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_year in (1998,1998+1,1998+2)\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q30\" ->\n \"\"\"\nwith customer_total_return as\n (select wr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(wr_return_amt) as ctr_total_return\n from web_returns\n ,date_dim\n ,customer_address\n where wr_returned_date_sk = d_date_sk\n and d_year =2000\n and wr_returning_addr_sk = ca_address_sk\n group by wr_returning_customer_sk\n ,ca_state)\n select c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'GA'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\nlimit 100\"\"\",\n \"q31\" ->\n \"\"\"\nwith ss as\n (select ca_county,d_qoy, d_year,sum(ss_ext_sales_price) as store_sales\n from store_sales,date_dim,customer_address\n where ss_sold_date_sk = d_date_sk\n and ss_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year),\n ws as\n (select ca_county,d_qoy, d_year,sum(ws_ext_sales_price) as web_sales\n from web_sales,date_dim,customer_address\n where ws_sold_date_sk = d_date_sk\n and ws_bill_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year)\n select\n ss1.ca_county\n ,ss1.d_year\n ,ws2.web_sales/ws1.web_sales web_q1_q2_increase\n ,ss2.store_sales/ss1.store_sales store_q1_q2_increase\n ,ws3.web_sales/ws2.web_sales web_q2_q3_increase\n ,ss3.store_sales/ss2.store_sales store_q2_q3_increase\n from\n ss ss1\n ,ss ss2\n ,ss ss3\n ,ws ws1\n ,ws ws2\n ,ws ws3\n where\n ss1.d_qoy = 1\n and ss1.d_year = 1998\n and ss1.ca_county = ss2.ca_county\n and ss2.d_qoy = 2\n and ss2.d_year = 1998\n and ss2.ca_county = ss3.ca_county\n and ss3.d_qoy = 3\n and ss3.d_year = 1998\n and ss1.ca_county = ws1.ca_county\n and ws1.d_qoy = 1\n and ws1.d_year = 1998\n and ws1.ca_county = ws2.ca_county\n and ws2.d_qoy = 2\n and ws2.d_year = 1998\n and ws1.ca_county = ws3.ca_county\n and ws3.d_qoy = 3\n and ws3.d_year =1998\n and case when ws1.web_sales > 0 then ws2.web_sales/ws1.web_sales else null end\n > case when ss1.store_sales > 0 then ss2.store_sales/ss1.store_sales else null end\n and case when ws2.web_sales > 0 then ws3.web_sales/ws2.web_sales else null end\n > case when ss2.store_sales > 0 then ss3.store_sales/ss2.store_sales else null end\n order by ss1.ca_county\"\"\",\n \"q32\" ->\n \"\"\"\nselect sum(cs_ext_discount_amt) as `excess discount amount`\nfrom\n catalog_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 948\nand i_item_sk = cs_item_sk\nand d_date between '1998-02-03' and\n (cast('1998-02-03' as date) + INTERVAL 90 days)\nand d_date_sk = cs_sold_date_sk\nand cs_ext_discount_amt\n > (\n select\n 1.3 * avg(cs_ext_discount_amt)\n from\n catalog_sales\n ,date_dim\n where\n cs_item_sk = i_item_sk\n and d_date between '1998-02-03' and\n (cast('1998-02-03' as date) + INTERVAL 90 days)\n and d_date_sk = cs_sold_date_sk\n )\nlimit 100\"\"\",\n \"q33\" ->\n \"\"\"\nwith ss as (\n select\n i_manufact_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Electronics'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 2\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id),\n cs as (\n select\n i_manufact_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Electronics'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 2\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id),\n ws as (\n select\n i_manufact_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Electronics'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 2\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id)\n select i_manufact_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_manufact_id\n order by total_sales\nlimit 100\"\"\",\n \"q34\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (date_dim.d_dom between 1 and 3 or date_dim.d_dom between 25 and 28)\n and (household_demographics.hd_buy_potential = '>10000' or\n household_demographics.hd_buy_potential = '5001-10000')\n and household_demographics.hd_vehicle_count > 0\n and (case when household_demographics.hd_vehicle_count > 0\n\tthen household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count\n\telse null\n\tend) > 1.2\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_county in ('Jefferson Davis Parish','Levy County','Coal County','Oglethorpe County',\n 'Mobile County','Gage County','Richland County','Gogebic County')\n group by ss_ticket_number,ss_customer_sk) dn,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 15 and 20\n order by c_last_name,c_first_name,c_salutation,c_preferred_cust_flag desc, ss_ticket_number\"\"\",\n \"q35\" ->\n \"\"\"\nselect\n ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n count(*) cnt1,\n stddev_samp(cd_dep_count),\n stddev_samp(cd_dep_count),\n min(cd_dep_count),\n cd_dep_employed_count,\n count(*) cnt2,\n stddev_samp(cd_dep_employed_count),\n stddev_samp(cd_dep_employed_count),\n min(cd_dep_employed_count),\n cd_dep_college_count,\n count(*) cnt3,\n stddev_samp(cd_dep_college_count),\n stddev_samp(cd_dep_college_count),\n min(cd_dep_college_count)\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2002 and\n d_qoy < 4) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2002 and\n d_qoy < 4) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2002 and\n d_qoy < 4))\n group by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n limit 100\"\"\",\n \"q36\" ->\n \"\"\"\nselect\n sum(ss_net_profit)/sum(ss_ext_sales_price) as gross_margin\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ss_net_profit)/sum(ss_ext_sales_price) asc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,item\n ,store\n where\n d1.d_year = 1998\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and s_state in ('OH','WV','PA','TN',\n 'MN','MO','NM','MI')\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then i_category end\n ,rank_within_parent\n limit 100\"\"\",\n \"q37\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, catalog_sales\n where i_current_price between 35 and 35 + 30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2001-01-20' as date) and (cast('2001-01-20' as date) + interval 60 days)\n and i_manufact_id in (928,715,942,861)\n and inv_quantity_on_hand between 100 and 500\n and cs_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q38\" ->\n \"\"\"\nselect count(*) from (\n select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1222 and 1222 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1222 and 1222 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1222 and 1222 + 11\n) hot_cust\nlimit 100\"\"\",\n \"q39a\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =1998\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=4\n and inv2.d_moy=4+1\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q39b\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =1998\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=4\n and inv2.d_moy=4+1\n and inv1.cov > 1.5\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q40\" ->\n \"\"\"\nselect\n w_state\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('1999-02-02' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_before\n ,sum(case when (cast(d_date as date) >= cast ('1999-02-02' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_after\n from\n catalog_sales left outer join catalog_returns on\n (cs_order_number = cr_order_number\n and cs_item_sk = cr_item_sk)\n ,warehouse\n ,item\n ,date_dim\n where\n i_current_price between 0.99 and 1.49\n and i_item_sk = cs_item_sk\n and cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and d_date between (cast ('1999-02-02' as date) - INTERVAL 30 days)\n and (cast ('1999-02-02' as date) + INTERVAL 30 days)\n group by\n w_state,i_item_id\n order by w_state,i_item_id\nlimit 100\"\"\",\n \"q41\" ->\n \"\"\"\nselect distinct(i_product_name)\n from item i1\n where i_manufact_id between 732 and 732+40\n and (select count(*) as item_cnt\n from item\n where (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'beige' or i_color = 'spring') and\n (i_units = 'Tsp' or i_units = 'Ton') and\n (i_size = 'petite' or i_size = 'extra large')\n ) or\n (i_category = 'Women' and\n (i_color = 'white' or i_color = 'pale') and\n (i_units = 'Box' or i_units = 'Dram') and\n (i_size = 'large' or i_size = 'economy')\n ) or\n (i_category = 'Men' and\n (i_color = 'midnight' or i_color = 'frosted') and\n (i_units = 'Bunch' or i_units = 'Carton') and\n (i_size = 'small' or i_size = 'N/A')\n ) or\n (i_category = 'Men' and\n (i_color = 'azure' or i_color = 'goldenrod') and\n (i_units = 'Pallet' or i_units = 'Gross') and\n (i_size = 'petite' or i_size = 'extra large')\n ))) or\n (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'brown' or i_color = 'hot') and\n (i_units = 'Tbl' or i_units = 'Cup') and\n (i_size = 'petite' or i_size = 'extra large')\n ) or\n (i_category = 'Women' and\n (i_color = 'powder' or i_color = 'honeydew') and\n (i_units = 'Bundle' or i_units = 'Unknown') and\n (i_size = 'large' or i_size = 'economy')\n ) or\n (i_category = 'Men' and\n (i_color = 'antique' or i_color = 'purple') and\n (i_units = 'N/A' or i_units = 'Dozen') and\n (i_size = 'small' or i_size = 'N/A')\n ) or\n (i_category = 'Men' and\n (i_color = 'lavender' or i_color = 'tomato') and\n (i_units = 'Lb' or i_units = 'Oz') and\n (i_size = 'petite' or i_size = 'extra large')\n )))) > 0\n order by i_product_name\n limit 100\"\"\",\n \"q42\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_category_id\n \t,item.i_category\n \t,sum(ss_ext_sales_price)\n from \tdate_dim dt\n \t,store_sales\n \t,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n \tand store_sales.ss_item_sk = item.i_item_sk\n \tand item.i_manager_id = 1\n \tand dt.d_moy=11\n \tand dt.d_year=2002\n group by \tdt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\n order by sum(ss_ext_sales_price) desc,dt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\nlimit 100 \"\"\",\n \"q43\" ->\n \"\"\"\nselect s_store_name, s_store_id,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from date_dim, store_sales, store\n where d_date_sk = ss_sold_date_sk and\n s_store_sk = ss_store_sk and\n s_gmt_offset = -6 and\n d_year = 1999\n group by s_store_name, s_store_id\n order by s_store_name, s_store_id,sun_sales,mon_sales,tue_sales,wed_sales,thu_sales,fri_sales,sat_sales\n limit 100\"\"\",\n \"q44\" ->\n \"\"\"\nselect asceding.rnk, i1.i_product_name best_performing, i2.i_product_name worst_performing\nfrom(select *\n from (select item_sk,rank() over (order by rank_col asc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 321\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 321\n and ss_addr_sk is null\n group by ss_store_sk))V1)V11\n where rnk < 11) asceding,\n (select *\n from (select item_sk,rank() over (order by rank_col desc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 321\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 321\n and ss_addr_sk is null\n group by ss_store_sk))V2)V21\n where rnk < 11) descending,\nitem i1,\nitem i2\nwhere asceding.rnk = descending.rnk\n and i1.i_item_sk=asceding.item_sk\n and i2.i_item_sk=descending.item_sk\norder by asceding.rnk\nlimit 100\"\"\",\n \"q45\" ->\n \"\"\"\nselect ca_zip, ca_county, sum(ws_sales_price)\n from web_sales, customer, customer_address, date_dim, item\n where ws_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ws_item_sk = i_item_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475', '85392', '85460', '80348', '81792')\n \t or\n \t i_item_id in (select i_item_id\n from item\n where i_item_sk in (2, 3, 5, 7, 11, 13, 17, 19, 23, 29)\n )\n \t )\n \tand ws_sold_date_sk = d_date_sk\n \tand d_qoy = 2 and d_year = 1999\n group by ca_zip, ca_county\n order by ca_zip, ca_county\n limit 100\"\"\",\n \"q46\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and (household_demographics.hd_dep_count = 2 or\n household_demographics.hd_vehicle_count= 2)\n and date_dim.d_dow in (6,0)\n and date_dim.d_year in (1998,1998+1,1998+2)\n and store.s_city in ('Antioch','Mount Vernon','Jamestown','Wilson','Farmington')\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,ca_city) dn,customer,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n limit 100\"\"\",\n \"q47\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand,\n s_store_name, s_company_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n s_store_name, s_company_name\n order by d_year, d_moy) rn\n from item, store_sales, date_dim, store\n where ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n (\n d_year = 2001 or\n ( d_year = 2001-1 and d_moy =12) or\n ( d_year = 2001+1 and d_moy =1)\n )\n group by i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy),\n v2 as(\n select v1.s_company_name\n ,v1.d_year, v1.d_moy\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1.s_store_name = v1_lag.s_store_name and\n v1.s_store_name = v1_lead.s_store_name and\n v1.s_company_name = v1_lag.s_company_name and\n v1.s_company_name = v1_lead.s_company_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 2001 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, avg_monthly_sales\n limit 100\"\"\",\n \"q48\" ->\n \"\"\"\nselect sum (ss_quantity)\n from store_sales, store, customer_demographics, customer_address, date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 1999\n and\n (\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'D'\n and\n cd_education_status = 'College'\n and\n ss_sales_price between 100.00 and 150.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'W'\n and\n cd_education_status = 'Secondary'\n and\n ss_sales_price between 50.00 and 100.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'M'\n and\n cd_education_status = '2 yr Degree'\n and\n ss_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('NE', 'IA', 'NY')\n and ss_net_profit between 0 and 2000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('IN', 'TN', 'OH')\n and ss_net_profit between 150 and 3000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('KS', 'CA', 'CO')\n and ss_net_profit between 50 and 25000\n )\n )\"\"\",\n \"q49\" ->\n \"\"\"\nselect channel, item, return_ratio, return_rank, currency_rank from\n (select\n 'web' as channel\n ,web.item\n ,web.return_ratio\n ,web.return_rank\n ,web.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect ws.ws_item_sk as item\n \t\t,(cast(sum(coalesce(wr.wr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(wr.wr_return_amt,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\t web_sales ws left outer join web_returns wr\n \t\t\ton (ws.ws_order_number = wr.wr_order_number and\n \t\t\tws.ws_item_sk = wr.wr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\twr.wr_return_amt > 10000\n \t\t\tand ws.ws_net_profit > 1\n and ws.ws_net_paid > 0\n and ws.ws_quantity > 0\n and ws_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 11\n \t\tgroup by ws.ws_item_sk\n \t) in_web\n ) web\n where\n (\n web.return_rank <= 10\n or\n web.currency_rank <= 10\n )\n union\n select\n 'catalog' as channel\n ,catalog.item\n ,catalog.return_ratio\n ,catalog.return_rank\n ,catalog.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect\n \t\tcs.cs_item_sk as item\n \t\t,(cast(sum(coalesce(cr.cr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(cr.cr_return_amount,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tcatalog_sales cs left outer join catalog_returns cr\n \t\t\ton (cs.cs_order_number = cr.cr_order_number and\n \t\t\tcs.cs_item_sk = cr.cr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tcr.cr_return_amount > 10000\n \t\t\tand cs.cs_net_profit > 1\n and cs.cs_net_paid > 0\n and cs.cs_quantity > 0\n and cs_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 11\n group by cs.cs_item_sk\n \t) in_cat\n ) catalog\n where\n (\n catalog.return_rank <= 10\n or\n catalog.currency_rank <=10\n )\n union\n select\n 'store' as channel\n ,store.item\n ,store.return_ratio\n ,store.return_rank\n ,store.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect sts.ss_item_sk as item\n \t\t,(cast(sum(coalesce(sr.sr_return_quantity,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(sr.sr_return_amt,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tstore_sales sts left outer join store_returns sr\n \t\t\ton (sts.ss_ticket_number = sr.sr_ticket_number and sts.ss_item_sk = sr.sr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tsr.sr_return_amt > 10000\n \t\t\tand sts.ss_net_profit > 1\n and sts.ss_net_paid > 0\n and sts.ss_quantity > 0\n and ss_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 11\n \t\tgroup by sts.ss_item_sk\n \t) in_store\n ) store\n where (\n store.return_rank <= 10\n or\n store.currency_rank <= 10\n )\n )\n order by 1,4,5,2\n limit 100\"\"\",\n \"q50\" ->\n \"\"\"\nselect\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 30) and\n (sr_returned_date_sk - ss_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 60) and\n (sr_returned_date_sk - ss_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 90) and\n (sr_returned_date_sk - ss_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n store_sales\n ,store_returns\n ,store\n ,date_dim d1\n ,date_dim d2\nwhere\n d2.d_year = 1999\nand d2.d_moy = 9\nand ss_ticket_number = sr_ticket_number\nand ss_item_sk = sr_item_sk\nand ss_sold_date_sk = d1.d_date_sk\nand sr_returned_date_sk = d2.d_date_sk\nand ss_customer_sk = sr_customer_sk\nand ss_store_sk = s_store_sk\ngroup by\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\norder by s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\nlimit 100\"\"\",\n \"q51\" ->\n \"\"\"\nWITH web_v1 as (\nselect\n ws_item_sk item_sk, d_date,\n sum(sum(ws_sales_price))\n over (partition by ws_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom web_sales\n ,date_dim\nwhere ws_sold_date_sk=d_date_sk\n and d_month_seq between 1176 and 1176+11\n and ws_item_sk is not NULL\ngroup by ws_item_sk, d_date),\nstore_v1 as (\nselect\n ss_item_sk item_sk, d_date,\n sum(sum(ss_sales_price))\n over (partition by ss_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom store_sales\n ,date_dim\nwhere ss_sold_date_sk=d_date_sk\n and d_month_seq between 1176 and 1176+11\n and ss_item_sk is not NULL\ngroup by ss_item_sk, d_date)\n select *\nfrom (select item_sk\n ,d_date\n ,web_sales\n ,store_sales\n ,max(web_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) web_cumulative\n ,max(store_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) store_cumulative\n from (select case when web.item_sk is not null then web.item_sk else store.item_sk end item_sk\n ,case when web.d_date is not null then web.d_date else store.d_date end d_date\n ,web.cume_sales web_sales\n ,store.cume_sales store_sales\n from web_v1 web full outer join store_v1 store on (web.item_sk = store.item_sk\n and web.d_date = store.d_date)\n )x )y\nwhere web_cumulative > store_cumulative\norder by item_sk\n ,d_date\nlimit 100\"\"\",\n \"q52\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_brand_id brand_id\n \t,item.i_brand brand\n \t,sum(ss_ext_sales_price) ext_price\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manager_id = 1\n and dt.d_moy=11\n and dt.d_year=2001\n group by dt.d_year\n \t,item.i_brand\n \t,item.i_brand_id\n order by dt.d_year\n \t,ext_price desc\n \t,brand_id\nlimit 100 \"\"\",\n \"q53\" ->\n \"\"\"\nselect * from\n(select i_manufact_id,\nsum(ss_sales_price) sum_sales,\navg(sum(ss_sales_price)) over (partition by i_manufact_id) avg_quarterly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\nss_sold_date_sk = d_date_sk and\nss_store_sk = s_store_sk and\nd_month_seq in (1218,1218+1,1218+2,1218+3,1218+4,1218+5,1218+6,1218+7,1218+8,1218+9,1218+10,1218+11) and\n((i_category in ('Books','Children','Electronics') and\ni_class in ('personal','portable','reference','self-help') and\ni_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t'exportiunivamalg #9','scholaramalgamalg #9'))\nor(i_category in ('Women','Music','Men') and\ni_class in ('accessories','classical','fragrances','pants') and\ni_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t'importoamalg #1')))\ngroup by i_manufact_id, d_qoy ) tmp1\nwhere case when avg_quarterly_sales > 0\n\tthen abs (sum_sales - avg_quarterly_sales)/ avg_quarterly_sales\n\telse null end > 0.1\norder by avg_quarterly_sales,\n\t sum_sales,\n\t i_manufact_id\nlimit 100\"\"\",\n \"q54\" ->\n \"\"\"\nwith my_customers as (\n select distinct c_customer_sk\n , c_current_addr_sk\n from\n ( select cs_sold_date_sk sold_date_sk,\n cs_bill_customer_sk customer_sk,\n cs_item_sk item_sk\n from catalog_sales\n union all\n select ws_sold_date_sk sold_date_sk,\n ws_bill_customer_sk customer_sk,\n ws_item_sk item_sk\n from web_sales\n ) cs_or_ws_sales,\n item,\n date_dim,\n customer\n where sold_date_sk = d_date_sk\n and item_sk = i_item_sk\n and i_category = 'Music'\n and i_class = 'country'\n and c_customer_sk = cs_or_ws_sales.customer_sk\n and d_moy = 7\n and d_year = 2001\n )\n , my_revenue as (\n select c_customer_sk,\n sum(ss_ext_sales_price) as revenue\n from my_customers,\n store_sales,\n customer_address,\n store,\n date_dim\n where c_current_addr_sk = ca_address_sk\n and ca_county = s_county\n and ca_state = s_state\n and ss_sold_date_sk = d_date_sk\n and c_customer_sk = ss_customer_sk\n and d_month_seq between (select distinct d_month_seq+1\n from date_dim where d_year = 2001 and d_moy = 7)\n and (select distinct d_month_seq+3\n from date_dim where d_year = 2001 and d_moy = 7)\n group by c_customer_sk\n )\n , segments as\n (select cast((revenue/50) as int) as segment\n from my_revenue\n )\n select segment, count(*) as num_customers, segment*50 as segment_base\n from segments\n group by segment\n order by segment, num_customers\n limit 100\"\"\",\n \"q55\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item\n where d_date_sk = ss_sold_date_sk\n \tand ss_item_sk = i_item_sk\n \tand i_manager_id=87\n \tand d_moy=11\n \tand d_year=2001\n group by i_brand, i_brand_id\n order by ext_price desc, i_brand_id\nlimit 100 \"\"\",\n \"q56\" ->\n \"\"\"\nwith ss as (\n select i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('tan','lace','gainsboro'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 3\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n cs as (\n select i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('tan','lace','gainsboro'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 3\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n ws as (\n select i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('tan','lace','gainsboro'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 3\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id)\n select i_item_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by total_sales,\n i_item_id\n limit 100\"\"\",\n \"q57\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n cc_name,\n d_year, d_moy,\n sum(cs_sales_price) sum_sales,\n avg(sum(cs_sales_price)) over\n (partition by i_category, i_brand,\n cc_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n cc_name\n order by d_year, d_moy) rn\n from item, catalog_sales, date_dim, call_center\n where cs_item_sk = i_item_sk and\n cs_sold_date_sk = d_date_sk and\n cc_call_center_sk= cs_call_center_sk and\n (\n d_year = 2001 or\n ( d_year = 2001-1 and d_moy =12) or\n ( d_year = 2001+1 and d_moy =1)\n )\n group by i_category, i_brand,\n cc_name , d_year, d_moy),\n v2 as(\n select v1.i_category, v1.i_brand, v1.cc_name\n ,v1.d_year, v1.d_moy\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1. cc_name = v1_lag. cc_name and\n v1. cc_name = v1_lead. cc_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 2001 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, avg_monthly_sales\n limit 100\"\"\",\n \"q58\" ->\n \"\"\"\nwith ss_items as\n (select i_item_id item_id\n ,sum(ss_ext_sales_price) ss_item_rev\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '2000-03-26'))\n and ss_sold_date_sk = d_date_sk\n group by i_item_id),\n cs_items as\n (select i_item_id item_id\n ,sum(cs_ext_sales_price) cs_item_rev\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '2000-03-26'))\n and cs_sold_date_sk = d_date_sk\n group by i_item_id),\n ws_items as\n (select i_item_id item_id\n ,sum(ws_ext_sales_price) ws_item_rev\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq =(select d_week_seq\n from date_dim\n where d_date = '2000-03-26'))\n and ws_sold_date_sk = d_date_sk\n group by i_item_id)\n select ss_items.item_id\n ,ss_item_rev\n ,ss_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ss_dev\n ,cs_item_rev\n ,cs_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 cs_dev\n ,ws_item_rev\n ,ws_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ws_dev\n ,(ss_item_rev+cs_item_rev+ws_item_rev)/3 average\n from ss_items,cs_items,ws_items\n where ss_items.item_id=cs_items.item_id\n and ss_items.item_id=ws_items.item_id\n and ss_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n and ss_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and cs_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and cs_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and ws_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and ws_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n order by item_id\n ,ss_item_rev\n limit 100\"\"\",\n \"q59\" ->\n \"\"\"\nwith wss as\n (select d_week_seq,\n ss_store_sk,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n group by d_week_seq,ss_store_sk\n )\n select s_store_name1,s_store_id1,d_week_seq1\n ,sun_sales1/sun_sales2,mon_sales1/mon_sales2\n ,tue_sales1/tue_sales2,wed_sales1/wed_sales2,thu_sales1/thu_sales2\n ,fri_sales1/fri_sales2,sat_sales1/sat_sales2\n from\n (select s_store_name s_store_name1,wss.d_week_seq d_week_seq1\n ,s_store_id s_store_id1,sun_sales sun_sales1\n ,mon_sales mon_sales1,tue_sales tue_sales1\n ,wed_sales wed_sales1,thu_sales thu_sales1\n ,fri_sales fri_sales1,sat_sales sat_sales1\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1199 and 1199 + 11) y,\n (select s_store_name s_store_name2,wss.d_week_seq d_week_seq2\n ,s_store_id s_store_id2,sun_sales sun_sales2\n ,mon_sales mon_sales2,tue_sales tue_sales2\n ,wed_sales wed_sales2,thu_sales thu_sales2\n ,fri_sales fri_sales2,sat_sales sat_sales2\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1199+ 12 and 1199 + 23) x\n where s_store_id1=s_store_id2\n and d_week_seq1=d_week_seq2-52\n order by s_store_name1,s_store_id1,d_week_seq1\nlimit 100\"\"\",\n \"q60\" ->\n \"\"\"\nwith ss as (\n select\n i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Men'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 9\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n cs as (\n select\n i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Men'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 9\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n ws as (\n select\n i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Men'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 9\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id)\n select\n i_item_id\n,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by i_item_id\n ,total_sales\n limit 100\"\"\",\n \"q61\" ->\n \"\"\"\nselect promotions,total,cast(promotions as decimal(15,4))/cast(total as decimal(15,4))*100\nfrom\n (select sum(ss_ext_sales_price) promotions\n from store_sales\n ,store\n ,promotion\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_promo_sk = p_promo_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -7\n and i_category = 'Electronics'\n and (p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y')\n and s_gmt_offset = -7\n and d_year = 2001\n and d_moy = 11) promotional_sales,\n (select sum(ss_ext_sales_price) total\n from store_sales\n ,store\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -7\n and i_category = 'Electronics'\n and s_gmt_offset = -7\n and d_year = 2001\n and d_moy = 11) all_sales\norder by promotions, total\nlimit 100\"\"\",\n \"q62\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 30) and\n (ws_ship_date_sk - ws_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 60) and\n (ws_ship_date_sk - ws_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 90) and\n (ws_ship_date_sk - ws_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n web_sales\n ,warehouse\n ,ship_mode\n ,web_site\n ,date_dim\nwhere\n d_month_seq between 1194 and 1194 + 11\nand ws_ship_date_sk = d_date_sk\nand ws_warehouse_sk = w_warehouse_sk\nand ws_ship_mode_sk = sm_ship_mode_sk\nand ws_web_site_sk = web_site_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\nlimit 100\"\"\",\n \"q63\" ->\n \"\"\"\nselect *\nfrom (select i_manager_id\n ,sum(ss_sales_price) sum_sales\n ,avg(sum(ss_sales_price)) over (partition by i_manager_id) avg_monthly_sales\n from item\n ,store_sales\n ,date_dim\n ,store\n where ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and d_month_seq in (1205,1205+1,1205+2,1205+3,1205+4,1205+5,1205+6,1205+7,1205+8,1205+9,1205+10,1205+11)\n and (( i_category in ('Books','Children','Electronics')\n and i_class in ('personal','portable','reference','self-help')\n and i_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t 'exportiunivamalg #9','scholaramalgamalg #9'))\n or( i_category in ('Women','Music','Men')\n and i_class in ('accessories','classical','fragrances','pants')\n and i_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t 'importoamalg #1')))\ngroup by i_manager_id, d_moy) tmp1\nwhere case when avg_monthly_sales > 0 then abs (sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\norder by i_manager_id\n ,avg_monthly_sales\n ,sum_sales\nlimit 100\"\"\",\n \"q64\" ->\n \"\"\"\nwith cs_ui as\n (select cs_item_sk\n ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund\n from catalog_sales\n ,catalog_returns\n where cs_item_sk = cr_item_sk\n and cs_order_number = cr_order_number\n group by cs_item_sk\n having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit)),\ncross_sales as\n (select i_product_name product_name\n ,i_item_sk item_sk\n ,s_store_name store_name\n ,s_zip store_zip\n ,ad1.ca_street_number b_street_number\n ,ad1.ca_street_name b_street_name\n ,ad1.ca_city b_city\n ,ad1.ca_zip b_zip\n ,ad2.ca_street_number c_street_number\n ,ad2.ca_street_name c_street_name\n ,ad2.ca_city c_city\n ,ad2.ca_zip c_zip\n ,d1.d_year as syear\n ,d2.d_year as fsyear\n ,d3.d_year s2year\n ,count(*) cnt\n ,sum(ss_wholesale_cost) s1\n ,sum(ss_list_price) s2\n ,sum(ss_coupon_amt) s3\n FROM store_sales\n ,store_returns\n ,cs_ui\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,customer\n ,customer_demographics cd1\n ,customer_demographics cd2\n ,promotion\n ,household_demographics hd1\n ,household_demographics hd2\n ,customer_address ad1\n ,customer_address ad2\n ,income_band ib1\n ,income_band ib2\n ,item\n WHERE ss_store_sk = s_store_sk AND\n ss_sold_date_sk = d1.d_date_sk AND\n ss_customer_sk = c_customer_sk AND\n ss_cdemo_sk= cd1.cd_demo_sk AND\n ss_hdemo_sk = hd1.hd_demo_sk AND\n ss_addr_sk = ad1.ca_address_sk and\n ss_item_sk = i_item_sk and\n ss_item_sk = sr_item_sk and\n ss_ticket_number = sr_ticket_number and\n ss_item_sk = cs_ui.cs_item_sk and\n c_current_cdemo_sk = cd2.cd_demo_sk AND\n c_current_hdemo_sk = hd2.hd_demo_sk AND\n c_current_addr_sk = ad2.ca_address_sk and\n c_first_sales_date_sk = d2.d_date_sk and\n c_first_shipto_date_sk = d3.d_date_sk and\n ss_promo_sk = p_promo_sk and\n hd1.hd_income_band_sk = ib1.ib_income_band_sk and\n hd2.hd_income_band_sk = ib2.ib_income_band_sk and\n cd1.cd_marital_status <> cd2.cd_marital_status and\n i_color in ('peach','misty','drab','chocolate','almond','saddle') and\n i_current_price between 75 and 75 + 10 and\n i_current_price between 75 + 1 and 75 + 15\ngroup by i_product_name\n ,i_item_sk\n ,s_store_name\n ,s_zip\n ,ad1.ca_street_number\n ,ad1.ca_street_name\n ,ad1.ca_city\n ,ad1.ca_zip\n ,ad2.ca_street_number\n ,ad2.ca_street_name\n ,ad2.ca_city\n ,ad2.ca_zip\n ,d1.d_year\n ,d2.d_year\n ,d3.d_year\n)\nselect cs1.product_name\n ,cs1.store_name\n ,cs1.store_zip\n ,cs1.b_street_number\n ,cs1.b_street_name\n ,cs1.b_city\n ,cs1.b_zip\n ,cs1.c_street_number\n ,cs1.c_street_name\n ,cs1.c_city\n ,cs1.c_zip\n ,cs1.syear\n ,cs1.cnt\n ,cs1.s1 as s11\n ,cs1.s2 as s21\n ,cs1.s3 as s31\n ,cs2.s1 as s12\n ,cs2.s2 as s22\n ,cs2.s3 as s32\n ,cs2.syear\n ,cs2.cnt\nfrom cross_sales cs1,cross_sales cs2\nwhere cs1.item_sk=cs2.item_sk and\n cs1.syear = 2000 and\n cs2.syear = 2000 + 1 and\n cs2.cnt <= cs1.cnt and\n cs1.store_name = cs2.store_name and\n cs1.store_zip = cs2.store_zip\norder by cs1.product_name\n ,cs1.store_name\n ,cs2.cnt\n ,cs1.s1\n ,cs2.s1\"\"\",\n \"q65\" ->\n \"\"\"\nselect\n\ts_store_name,\n\ti_item_desc,\n\tsc.revenue,\n\ti_current_price,\n\ti_wholesale_cost,\n\ti_brand\n from store, item,\n (select ss_store_sk, avg(revenue) as ave\n \tfrom\n \t (select ss_store_sk, ss_item_sk,\n \t\t sum(ss_sales_price) as revenue\n \t\tfrom store_sales, date_dim\n \t\twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1208 and 1208+11\n \t\tgroup by ss_store_sk, ss_item_sk) sa\n \tgroup by ss_store_sk) sb,\n (select ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue\n \tfrom store_sales, date_dim\n \twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1208 and 1208+11\n \tgroup by ss_store_sk, ss_item_sk) sc\n where sb.ss_store_sk = sc.ss_store_sk and\n sc.revenue <= 0.1 * sb.ave and\n s_store_sk = sc.ss_store_sk and\n i_item_sk = sc.ss_item_sk\n order by s_store_name, i_item_desc\nlimit 100\"\"\",\n \"q66\" ->\n \"\"\"\nselect\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,ship_carriers\n ,year\n \t,sum(jan_sales) as jan_sales\n \t,sum(feb_sales) as feb_sales\n \t,sum(mar_sales) as mar_sales\n \t,sum(apr_sales) as apr_sales\n \t,sum(may_sales) as may_sales\n \t,sum(jun_sales) as jun_sales\n \t,sum(jul_sales) as jul_sales\n \t,sum(aug_sales) as aug_sales\n \t,sum(sep_sales) as sep_sales\n \t,sum(oct_sales) as oct_sales\n \t,sum(nov_sales) as nov_sales\n \t,sum(dec_sales) as dec_sales\n \t,sum(jan_sales/w_warehouse_sq_ft) as jan_sales_per_sq_foot\n \t,sum(feb_sales/w_warehouse_sq_ft) as feb_sales_per_sq_foot\n \t,sum(mar_sales/w_warehouse_sq_ft) as mar_sales_per_sq_foot\n \t,sum(apr_sales/w_warehouse_sq_ft) as apr_sales_per_sq_foot\n \t,sum(may_sales/w_warehouse_sq_ft) as may_sales_per_sq_foot\n \t,sum(jun_sales/w_warehouse_sq_ft) as jun_sales_per_sq_foot\n \t,sum(jul_sales/w_warehouse_sq_ft) as jul_sales_per_sq_foot\n \t,sum(aug_sales/w_warehouse_sq_ft) as aug_sales_per_sq_foot\n \t,sum(sep_sales/w_warehouse_sq_ft) as sep_sales_per_sq_foot\n \t,sum(oct_sales/w_warehouse_sq_ft) as oct_sales_per_sq_foot\n \t,sum(nov_sales/w_warehouse_sq_ft) as nov_sales_per_sq_foot\n \t,sum(dec_sales/w_warehouse_sq_ft) as dec_sales_per_sq_foot\n \t,sum(jan_net) as jan_net\n \t,sum(feb_net) as feb_net\n \t,sum(mar_net) as mar_net\n \t,sum(apr_net) as apr_net\n \t,sum(may_net) as may_net\n \t,sum(jun_net) as jun_net\n \t,sum(jul_net) as jul_net\n \t,sum(aug_net) as aug_net\n \t,sum(sep_net) as sep_net\n \t,sum(oct_net) as oct_net\n \t,sum(nov_net) as nov_net\n \t,sum(dec_net) as dec_net\n from (\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'HARMSTORF' || ',' || 'USPS' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen ws_sales_price* ws_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen ws_sales_price* ws_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen ws_sales_price* ws_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen ws_sales_price* ws_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen ws_sales_price* ws_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen ws_sales_price* ws_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen ws_sales_price* ws_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen ws_sales_price* ws_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen ws_sales_price* ws_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen ws_sales_price* ws_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen ws_sales_price* ws_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen ws_sales_price* ws_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen ws_net_paid_inc_tax * ws_quantity else 0 end) as dec_net\n from\n web_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n ws_warehouse_sk = w_warehouse_sk\n and ws_sold_date_sk = d_date_sk\n and ws_sold_time_sk = t_time_sk\n \tand ws_ship_mode_sk = sm_ship_mode_sk\n and d_year = 2002\n \tand t_time between 24285 and 24285+28800\n \tand sm_carrier in ('HARMSTORF','USPS')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n union all\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'HARMSTORF' || ',' || 'USPS' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen cs_net_paid * cs_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen cs_net_paid * cs_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen cs_net_paid * cs_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen cs_net_paid * cs_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen cs_net_paid * cs_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen cs_net_paid * cs_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen cs_net_paid * cs_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen cs_net_paid * cs_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen cs_net_paid * cs_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen cs_net_paid * cs_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen cs_net_paid * cs_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen cs_net_paid * cs_quantity else 0 end) as dec_net\n from\n catalog_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and cs_sold_time_sk = t_time_sk\n \tand cs_ship_mode_sk = sm_ship_mode_sk\n and d_year = 2002\n \tand t_time between 24285 AND 24285+28800\n \tand sm_carrier in ('HARMSTORF','USPS')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n ) x\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,ship_carriers\n ,year\n order by w_warehouse_name\n limit 100\"\"\",\n \"q67\" ->\n \"\"\"\nselect *\nfrom (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rank() over (partition by i_category order by sumsales desc) rk\n from (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sum(coalesce(ss_sales_price*ss_quantity,0)) sumsales\n from store_sales\n ,date_dim\n ,store\n ,item\n where ss_sold_date_sk=d_date_sk\n and ss_item_sk=i_item_sk\n and ss_store_sk = s_store_sk\n and d_month_seq between 1196 and 1196+11\n group by rollup(i_category, i_class, i_brand, i_product_name, d_year, d_qoy, d_moy,s_store_id))dw1) dw2\nwhere rk <= 100\norder by i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rk\nlimit 100\"\"\",\n \"q68\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,extended_price\n ,extended_tax\n ,list_price\n from (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_ext_sales_price) extended_price\n ,sum(ss_ext_list_price) list_price\n ,sum(ss_ext_tax) extended_tax\n from store_sales\n ,date_dim\n ,store\n ,household_demographics\n ,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_dep_count = 1 or\n household_demographics.hd_vehicle_count= -1)\n and date_dim.d_year in (1998,1998+1,1998+2)\n and store.s_city in ('Bethel','Summit')\n group by ss_ticket_number\n ,ss_customer_sk\n ,ss_addr_sk,ca_city) dn\n ,customer\n ,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,ss_ticket_number\n limit 100\"\"\",\n \"q69\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_state in ('OK','GA','VA') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2004 and\n d_moy between 4 and 4+2) and\n (not exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2004 and\n d_moy between 4 and 4+2) and\n not exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2004 and\n d_moy between 4 and 4+2))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n limit 100\"\"\",\n \"q70\" ->\n \"\"\"\nselect\n sum(ss_net_profit) as total_sum\n ,s_state\n ,s_county\n ,grouping(s_state)+grouping(s_county) as lochierarchy\n ,rank() over (\n \tpartition by grouping(s_state)+grouping(s_county),\n \tcase when grouping(s_county) = 0 then s_state end\n \torder by sum(ss_net_profit) desc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,store\n where\n d1.d_month_seq between 1197 and 1197+11\n and d1.d_date_sk = ss_sold_date_sk\n and s_store_sk = ss_store_sk\n and s_state in\n ( select s_state\n from (select s_state as s_state,\n \t\t\t rank() over ( partition by s_state order by sum(ss_net_profit) desc) as ranking\n from store_sales, store, date_dim\n where d_month_seq between 1197 and 1197+11\n \t\t\t and d_date_sk = ss_sold_date_sk\n \t\t\t and s_store_sk = ss_store_sk\n group by s_state\n ) tmp1\n where ranking <= 5\n )\n group by rollup(s_state,s_county)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then s_state end\n ,rank_within_parent\n limit 100\"\"\",\n \"q71\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,t_hour,t_minute,\n \tsum(ext_price) ext_price\n from item, (select ws_ext_sales_price as ext_price,\n ws_sold_date_sk as sold_date_sk,\n ws_item_sk as sold_item_sk,\n ws_sold_time_sk as time_sk\n from web_sales,date_dim\n where d_date_sk = ws_sold_date_sk\n and d_moy=12\n and d_year=1999\n union all\n select cs_ext_sales_price as ext_price,\n cs_sold_date_sk as sold_date_sk,\n cs_item_sk as sold_item_sk,\n cs_sold_time_sk as time_sk\n from catalog_sales,date_dim\n where d_date_sk = cs_sold_date_sk\n and d_moy=12\n and d_year=1999\n union all\n select ss_ext_sales_price as ext_price,\n ss_sold_date_sk as sold_date_sk,\n ss_item_sk as sold_item_sk,\n ss_sold_time_sk as time_sk\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n and d_moy=12\n and d_year=1999\n ) tmp,time_dim\n where\n sold_item_sk = i_item_sk\n and i_manager_id=1\n and time_sk = t_time_sk\n and (t_meal_time = 'breakfast' or t_meal_time = 'dinner')\n group by i_brand, i_brand_id,t_hour,t_minute\n order by ext_price desc, i_brand_id\n \"\"\",\n \"q72\" ->\n \"\"\"\nselect i_item_desc\n ,w_warehouse_name\n ,d1.d_week_seq\n ,sum(case when p_promo_sk is null then 1 else 0 end) no_promo\n ,sum(case when p_promo_sk is not null then 1 else 0 end) promo\n ,count(*) total_cnt\nfrom catalog_sales\njoin inventory on (cs_item_sk = inv_item_sk)\njoin warehouse on (w_warehouse_sk=inv_warehouse_sk)\njoin item on (i_item_sk = cs_item_sk)\njoin customer_demographics on (cs_bill_cdemo_sk = cd_demo_sk)\njoin household_demographics on (cs_bill_hdemo_sk = hd_demo_sk)\njoin date_dim d1 on (cs_sold_date_sk = d1.d_date_sk)\njoin date_dim d2 on (inv_date_sk = d2.d_date_sk)\njoin date_dim d3 on (cs_ship_date_sk = d3.d_date_sk)\nleft outer join promotion on (cs_promo_sk=p_promo_sk)\nleft outer join catalog_returns on (cr_item_sk = cs_item_sk and cr_order_number = cs_order_number)\nwhere d1.d_week_seq = d2.d_week_seq\n and inv_quantity_on_hand < cs_quantity\n and d3.d_date > d1.d_date + interval 5 days\n and hd_buy_potential = '>10000'\n and d1.d_year = 2002\n and cd_marital_status = 'D'\ngroup by i_item_desc,w_warehouse_name,d1.d_week_seq\norder by total_cnt desc, i_item_desc, w_warehouse_name, d_week_seq\nlimit 100\"\"\",\n \"q73\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_buy_potential = '501-1000' or\n household_demographics.hd_buy_potential = 'Unknown')\n and household_demographics.hd_vehicle_count > 0\n and case when household_demographics.hd_vehicle_count > 0 then\n household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count else null end > 1\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_county in ('Franklin Parish','Ziebach County','Luce County','Williamson County')\n group by ss_ticket_number,ss_customer_sk) dj,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 1 and 5\n order by cnt desc, c_last_name asc\"\"\",\n \"q74\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,max(ss_net_paid) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2001,2001+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,max(ws_net_paid) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n and d_year in (2001,2001+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n )\n select\n t_s_secyear.customer_id, t_s_secyear.customer_first_name, t_s_secyear.customer_last_name\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.year = 2001\n and t_s_secyear.year = 2001+1\n and t_w_firstyear.year = 2001\n and t_w_secyear.year = 2001+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n order by 3,1,2\nlimit 100\"\"\",\n \"q75\" ->\n \"\"\"\nWITH all_sales AS (\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,SUM(sales_cnt) AS sales_cnt\n ,SUM(sales_amt) AS sales_amt\n FROM (SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,cs_quantity - COALESCE(cr_return_quantity,0) AS sales_cnt\n ,cs_ext_sales_price - COALESCE(cr_return_amount,0.0) AS sales_amt\n FROM catalog_sales JOIN item ON i_item_sk=cs_item_sk\n JOIN date_dim ON d_date_sk=cs_sold_date_sk\n LEFT JOIN catalog_returns ON (cs_order_number=cr_order_number\n AND cs_item_sk=cr_item_sk)\n WHERE i_category='Sports'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ss_quantity - COALESCE(sr_return_quantity,0) AS sales_cnt\n ,ss_ext_sales_price - COALESCE(sr_return_amt,0.0) AS sales_amt\n FROM store_sales JOIN item ON i_item_sk=ss_item_sk\n JOIN date_dim ON d_date_sk=ss_sold_date_sk\n LEFT JOIN store_returns ON (ss_ticket_number=sr_ticket_number\n AND ss_item_sk=sr_item_sk)\n WHERE i_category='Sports'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ws_quantity - COALESCE(wr_return_quantity,0) AS sales_cnt\n ,ws_ext_sales_price - COALESCE(wr_return_amt,0.0) AS sales_amt\n FROM web_sales JOIN item ON i_item_sk=ws_item_sk\n JOIN date_dim ON d_date_sk=ws_sold_date_sk\n LEFT JOIN web_returns ON (ws_order_number=wr_order_number\n AND ws_item_sk=wr_item_sk)\n WHERE i_category='Sports') sales_detail\n GROUP BY d_year, i_brand_id, i_class_id, i_category_id, i_manufact_id)\n SELECT prev_yr.d_year AS prev_year\n ,curr_yr.d_year AS year\n ,curr_yr.i_brand_id\n ,curr_yr.i_class_id\n ,curr_yr.i_category_id\n ,curr_yr.i_manufact_id\n ,prev_yr.sales_cnt AS prev_yr_cnt\n ,curr_yr.sales_cnt AS curr_yr_cnt\n ,curr_yr.sales_cnt-prev_yr.sales_cnt AS sales_cnt_diff\n ,curr_yr.sales_amt-prev_yr.sales_amt AS sales_amt_diff\n FROM all_sales curr_yr, all_sales prev_yr\n WHERE curr_yr.i_brand_id=prev_yr.i_brand_id\n AND curr_yr.i_class_id=prev_yr.i_class_id\n AND curr_yr.i_category_id=prev_yr.i_category_id\n AND curr_yr.i_manufact_id=prev_yr.i_manufact_id\n AND curr_yr.d_year=2001\n AND prev_yr.d_year=2001-1\n AND CAST(curr_yr.sales_cnt AS DECIMAL(17,2))/CAST(prev_yr.sales_cnt AS DECIMAL(17,2))<0.9\n ORDER BY sales_cnt_diff,sales_amt_diff\n limit 100\"\"\",\n \"q76\" ->\n \"\"\"\nselect channel, col_name, d_year, d_qoy, i_category, COUNT(*) sales_cnt, SUM(ext_sales_price) sales_amt FROM (\n SELECT 'store' as channel, 'ss_cdemo_sk' col_name, d_year, d_qoy, i_category, ss_ext_sales_price ext_sales_price\n FROM store_sales, item, date_dim\n WHERE ss_cdemo_sk IS NULL\n AND ss_sold_date_sk=d_date_sk\n AND ss_item_sk=i_item_sk\n UNION ALL\n SELECT 'web' as channel, 'ws_ship_hdemo_sk' col_name, d_year, d_qoy, i_category, ws_ext_sales_price ext_sales_price\n FROM web_sales, item, date_dim\n WHERE ws_ship_hdemo_sk IS NULL\n AND ws_sold_date_sk=d_date_sk\n AND ws_item_sk=i_item_sk\n UNION ALL\n SELECT 'catalog' as channel, 'cs_ship_customer_sk' col_name, d_year, d_qoy, i_category, cs_ext_sales_price ext_sales_price\n FROM catalog_sales, item, date_dim\n WHERE cs_ship_customer_sk IS NULL\n AND cs_sold_date_sk=d_date_sk\n AND cs_item_sk=i_item_sk) foo\nGROUP BY channel, col_name, d_year, d_qoy, i_category\nORDER BY channel, col_name, d_year, d_qoy, i_category\nlimit 100\"\"\",\n \"q77\" ->\n \"\"\"\nwith ss as\n (select s_store_sk,\n sum(ss_ext_sales_price) as sales,\n sum(ss_net_profit) as profit\n from store_sales,\n date_dim,\n store\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-27' as date)\n and (cast('2001-08-27' as date) + INTERVAL 30 days)\n and ss_store_sk = s_store_sk\n group by s_store_sk)\n ,\n sr as\n (select s_store_sk,\n sum(sr_return_amt) as returns,\n sum(sr_net_loss) as profit_loss\n from store_returns,\n date_dim,\n store\n where sr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-27' as date)\n and (cast('2001-08-27' as date) + INTERVAL 30 days)\n and sr_store_sk = s_store_sk\n group by s_store_sk),\n cs as\n (select cs_call_center_sk,\n sum(cs_ext_sales_price) as sales,\n sum(cs_net_profit) as profit\n from catalog_sales,\n date_dim\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-27' as date)\n and (cast('2001-08-27' as date) + INTERVAL 30 days)\n group by cs_call_center_sk\n ),\n cr as\n (select cr_call_center_sk,\n sum(cr_return_amount) as returns,\n sum(cr_net_loss) as profit_loss\n from catalog_returns,\n date_dim\n where cr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-27' as date)\n and (cast('2001-08-27' as date) + INTERVAL 30 days)\n group by cr_call_center_sk\n ),\n ws as\n ( select wp_web_page_sk,\n sum(ws_ext_sales_price) as sales,\n sum(ws_net_profit) as profit\n from web_sales,\n date_dim,\n web_page\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-27' as date)\n and (cast('2001-08-27' as date) + INTERVAL 30 days)\n and ws_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk),\n wr as\n (select wp_web_page_sk,\n sum(wr_return_amt) as returns,\n sum(wr_net_loss) as profit_loss\n from web_returns,\n date_dim,\n web_page\n where wr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-27' as date)\n and (cast('2001-08-27' as date) + INTERVAL 30 days)\n and wr_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , ss.s_store_sk as id\n , sales\n , coalesce(returns, 0) as returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ss left join sr\n on ss.s_store_sk = sr.s_store_sk\n union all\n select 'catalog channel' as channel\n , cs_call_center_sk as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from cs\n , cr\n union all\n select 'web channel' as channel\n , ws.wp_web_page_sk as id\n , sales\n , coalesce(returns, 0) returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ws left join wr\n on ws.wp_web_page_sk = wr.wp_web_page_sk\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q78\" ->\n \"\"\"\nwith ws as\n (select d_year AS ws_sold_year, ws_item_sk,\n ws_bill_customer_sk ws_customer_sk,\n sum(ws_quantity) ws_qty,\n sum(ws_wholesale_cost) ws_wc,\n sum(ws_sales_price) ws_sp\n from web_sales\n left join web_returns on wr_order_number=ws_order_number and ws_item_sk=wr_item_sk\n join date_dim on ws_sold_date_sk = d_date_sk\n where wr_order_number is null\n group by d_year, ws_item_sk, ws_bill_customer_sk\n ),\ncs as\n (select d_year AS cs_sold_year, cs_item_sk,\n cs_bill_customer_sk cs_customer_sk,\n sum(cs_quantity) cs_qty,\n sum(cs_wholesale_cost) cs_wc,\n sum(cs_sales_price) cs_sp\n from catalog_sales\n left join catalog_returns on cr_order_number=cs_order_number and cs_item_sk=cr_item_sk\n join date_dim on cs_sold_date_sk = d_date_sk\n where cr_order_number is null\n group by d_year, cs_item_sk, cs_bill_customer_sk\n ),\nss as\n (select d_year AS ss_sold_year, ss_item_sk,\n ss_customer_sk,\n sum(ss_quantity) ss_qty,\n sum(ss_wholesale_cost) ss_wc,\n sum(ss_sales_price) ss_sp\n from store_sales\n left join store_returns on sr_ticket_number=ss_ticket_number and ss_item_sk=sr_item_sk\n join date_dim on ss_sold_date_sk = d_date_sk\n where sr_ticket_number is null\n group by d_year, ss_item_sk, ss_customer_sk\n )\n select\nss_sold_year, ss_item_sk, ss_customer_sk,\nround(ss_qty/(coalesce(ws_qty,0)+coalesce(cs_qty,0)),2) ratio,\nss_qty store_qty, ss_wc store_wholesale_cost, ss_sp store_sales_price,\ncoalesce(ws_qty,0)+coalesce(cs_qty,0) other_chan_qty,\ncoalesce(ws_wc,0)+coalesce(cs_wc,0) other_chan_wholesale_cost,\ncoalesce(ws_sp,0)+coalesce(cs_sp,0) other_chan_sales_price\nfrom ss\nleft join ws on (ws_sold_year=ss_sold_year and ws_item_sk=ss_item_sk and ws_customer_sk=ss_customer_sk)\nleft join cs on (cs_sold_year=ss_sold_year and cs_item_sk=ss_item_sk and cs_customer_sk=ss_customer_sk)\nwhere (coalesce(ws_qty,0)>0 or coalesce(cs_qty, 0)>0) and ss_sold_year=2002\norder by\n ss_sold_year, ss_item_sk, ss_customer_sk,\n ss_qty desc, ss_wc desc, ss_sp desc,\n other_chan_qty,\n other_chan_wholesale_cost,\n other_chan_sales_price,\n ratio\nlimit 100\"\"\",\n \"q79\" ->\n \"\"\"\nselect\n c_last_name,c_first_name,substr(s_city,1,30),ss_ticket_number,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,store.s_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (household_demographics.hd_dep_count = 0 or household_demographics.hd_vehicle_count > 0)\n and date_dim.d_dow = 1\n and date_dim.d_year in (2000,2000+1,2000+2)\n and store.s_number_employees between 200 and 295\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,store.s_city) ms,customer\n where ss_customer_sk = c_customer_sk\n order by c_last_name,c_first_name,substr(s_city,1,30), profit\nlimit 100\"\"\",\n \"q80\" ->\n \"\"\"\nwith ssr as\n (select s_store_id as store_id,\n sum(ss_ext_sales_price) as sales,\n sum(coalesce(sr_return_amt, 0)) as returns,\n sum(ss_net_profit - coalesce(sr_net_loss, 0)) as profit\n from store_sales left outer join store_returns on\n (ss_item_sk = sr_item_sk and ss_ticket_number = sr_ticket_number),\n date_dim,\n store,\n item,\n promotion\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('1999-08-12' as date)\n and (cast('1999-08-12' as date) + INTERVAL 60 days)\n and ss_store_sk = s_store_sk\n and ss_item_sk = i_item_sk\n and i_current_price > 50\n and ss_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id as catalog_page_id,\n sum(cs_ext_sales_price) as sales,\n sum(coalesce(cr_return_amount, 0)) as returns,\n sum(cs_net_profit - coalesce(cr_net_loss, 0)) as profit\n from catalog_sales left outer join catalog_returns on\n (cs_item_sk = cr_item_sk and cs_order_number = cr_order_number),\n date_dim,\n catalog_page,\n item,\n promotion\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('1999-08-12' as date)\n and (cast('1999-08-12' as date) + INTERVAL 60 days)\n and cs_catalog_page_sk = cp_catalog_page_sk\n and cs_item_sk = i_item_sk\n and i_current_price > 50\n and cs_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(ws_ext_sales_price) as sales,\n sum(coalesce(wr_return_amt, 0)) as returns,\n sum(ws_net_profit - coalesce(wr_net_loss, 0)) as profit\n from web_sales left outer join web_returns on\n (ws_item_sk = wr_item_sk and ws_order_number = wr_order_number),\n date_dim,\n web_site,\n item,\n promotion\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('1999-08-12' as date)\n and (cast('1999-08-12' as date) + INTERVAL 60 days)\n and ws_web_site_sk = web_site_sk\n and ws_item_sk = i_item_sk\n and i_current_price > 50\n and ws_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || store_id as id\n , sales\n , returns\n , profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || catalog_page_id as id\n , sales\n , returns\n , profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q81\" ->\n \"\"\"\nwith customer_total_return as\n (select cr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(cr_return_amt_inc_tax) as ctr_total_return\n from catalog_returns\n ,date_dim\n ,customer_address\n where cr_returned_date_sk = d_date_sk\n and d_year =2001\n and cr_returning_addr_sk = ca_address_sk\n group by cr_returning_customer_sk\n ,ca_state )\n select c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'NC'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n limit 100\"\"\",\n \"q82\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, store_sales\n where i_current_price between 82 and 82+30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2002-03-10' as date) and (cast('2002-03-10' as date) + INTERVAL 60 days)\n and i_manufact_id in (941,920,105,693)\n and inv_quantity_on_hand between 100 and 500\n and ss_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q83\" ->\n \"\"\"\nwith sr_items as\n (select i_item_id item_id,\n sum(sr_return_quantity) sr_item_qty\n from store_returns,\n item,\n date_dim\n where sr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('1999-04-14','1999-09-28','1999-11-12')))\n and sr_returned_date_sk = d_date_sk\n group by i_item_id),\n cr_items as\n (select i_item_id item_id,\n sum(cr_return_quantity) cr_item_qty\n from catalog_returns,\n item,\n date_dim\n where cr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('1999-04-14','1999-09-28','1999-11-12')))\n and cr_returned_date_sk = d_date_sk\n group by i_item_id),\n wr_items as\n (select i_item_id item_id,\n sum(wr_return_quantity) wr_item_qty\n from web_returns,\n item,\n date_dim\n where wr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t\twhere d_date in ('1999-04-14','1999-09-28','1999-11-12')))\n and wr_returned_date_sk = d_date_sk\n group by i_item_id)\n select sr_items.item_id\n ,sr_item_qty\n ,sr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 sr_dev\n ,cr_item_qty\n ,cr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 cr_dev\n ,wr_item_qty\n ,wr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 wr_dev\n ,(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 average\n from sr_items\n ,cr_items\n ,wr_items\n where sr_items.item_id=cr_items.item_id\n and sr_items.item_id=wr_items.item_id\n order by sr_items.item_id\n ,sr_item_qty\n limit 100\"\"\",\n \"q84\" ->\n \"\"\"\nselect c_customer_id as customer_id\n , coalesce(c_last_name,'') || ', ' || coalesce(c_first_name,'') as customername\n from customer\n ,customer_address\n ,customer_demographics\n ,household_demographics\n ,income_band\n ,store_returns\n where ca_city\t = 'Antioch'\n and c_current_addr_sk = ca_address_sk\n and ib_lower_bound >= 55019\n and ib_upper_bound <= 55019 + 50000\n and ib_income_band_sk = hd_income_band_sk\n and cd_demo_sk = c_current_cdemo_sk\n and hd_demo_sk = c_current_hdemo_sk\n and sr_cdemo_sk = cd_demo_sk\n order by c_customer_id\n limit 100\"\"\",\n \"q85\" ->\n \"\"\"\nselect substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\n from web_sales, web_returns, web_page, customer_demographics cd1,\n customer_demographics cd2, customer_address, date_dim, reason\n where ws_web_page_sk = wp_web_page_sk\n and ws_item_sk = wr_item_sk\n and ws_order_number = wr_order_number\n and ws_sold_date_sk = d_date_sk and d_year = 2001\n and cd1.cd_demo_sk = wr_refunded_cdemo_sk\n and cd2.cd_demo_sk = wr_returning_cdemo_sk\n and ca_address_sk = wr_refunded_addr_sk\n and r_reason_sk = wr_reason_sk\n and\n (\n (\n cd1.cd_marital_status = 'S'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = '2 yr Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 100.00 and 150.00\n )\n or\n (\n cd1.cd_marital_status = 'D'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'Advanced Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 50.00 and 100.00\n )\n or\n (\n cd1.cd_marital_status = 'W'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = '4 yr Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ca_country = 'United States'\n and\n ca_state in ('OK', 'TX', 'MO')\n and ws_net_profit between 100 and 200\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('GA', 'KS', 'NC')\n and ws_net_profit between 150 and 300\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('VA', 'WI', 'WV')\n and ws_net_profit between 50 and 250\n )\n )\ngroup by r_reason_desc\norder by substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\nlimit 100\"\"\",\n \"q86\" ->\n \"\"\"\nselect\n sum(ws_net_paid) as total_sum\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ws_net_paid) desc) as rank_within_parent\n from\n web_sales\n ,date_dim d1\n ,item\n where\n d1.d_month_seq between 1180 and 1180+11\n and d1.d_date_sk = ws_sold_date_sk\n and i_item_sk = ws_item_sk\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc,\n case when lochierarchy = 0 then i_category end,\n rank_within_parent\n limit 100\"\"\",\n \"q87\" ->\n \"\"\"\nselect count(*)\nfrom ((select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1204 and 1204+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1204 and 1204+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1204 and 1204+11)\n) cool_cust\"\"\",\n \"q88\" ->\n \"\"\"\nselect *\nfrom\n (select count(*) h8_30_to_9\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 8\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s1,\n (select count(*) h9_to_9_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s2,\n (select count(*) h9_30_to_10\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s3,\n (select count(*) h10_to_10_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s4,\n (select count(*) h10_30_to_11\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s5,\n (select count(*) h11_to_11_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s6,\n (select count(*) h11_30_to_12\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s7,\n (select count(*) h12_to_12_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 12\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2))\n and store.s_store_name = 'ese') s8\"\"\",\n \"q89\" ->\n \"\"\"\nselect *\nfrom(\nselect i_category, i_class, i_brand,\n s_store_name, s_company_name,\n d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand, s_store_name, s_company_name)\n avg_monthly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n d_year in (2001) and\n ((i_category in ('Women','Music','Home') and\n i_class in ('fragrances','pop','bedding')\n )\n or (i_category in ('Books','Men','Children') and\n i_class in ('home repair','sports-apparel','infants')\n ))\ngroup by i_category, i_class, i_brand,\n s_store_name, s_company_name, d_moy) tmp1\nwhere case when (avg_monthly_sales <> 0) then (abs(sum_sales - avg_monthly_sales) / avg_monthly_sales) else null end > 0.1\norder by sum_sales - avg_monthly_sales, s_store_name\nlimit 100\"\"\",\n \"q90\" ->\n \"\"\"\nselect cast(amc as decimal(15,4))/cast(pmc as decimal(15,4)) am_pm_ratio\n from ( select count(*) amc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 8 and 8+1\n and household_demographics.hd_dep_count = 4\n and web_page.wp_char_count between 5000 and 5200) at,\n ( select count(*) pmc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 19 and 19+1\n and household_demographics.hd_dep_count = 4\n and web_page.wp_char_count between 5000 and 5200) pt\n order by am_pm_ratio\n limit 100\"\"\",\n \"q91\" ->\n \"\"\"\nselect\n cc_call_center_id Call_Center,\n cc_name Call_Center_Name,\n cc_manager Manager,\n sum(cr_net_loss) Returns_Loss\nfrom\n call_center,\n catalog_returns,\n date_dim,\n customer,\n customer_address,\n customer_demographics,\n household_demographics\nwhere\n cr_call_center_sk = cc_call_center_sk\nand cr_returned_date_sk = d_date_sk\nand cr_returning_customer_sk= c_customer_sk\nand cd_demo_sk = c_current_cdemo_sk\nand hd_demo_sk = c_current_hdemo_sk\nand ca_address_sk = c_current_addr_sk\nand d_year = 2002\nand d_moy = 11\nand ( (cd_marital_status = 'M' and cd_education_status = 'Unknown')\n or(cd_marital_status = 'W' and cd_education_status = 'Advanced Degree'))\nand hd_buy_potential like '5001-10000%'\nand ca_gmt_offset = -6\ngroup by cc_call_center_id,cc_name,cc_manager,cd_marital_status,cd_education_status\norder by sum(cr_net_loss) desc\"\"\",\n \"q92\" ->\n \"\"\"\nselect\n sum(ws_ext_discount_amt) as `Excess Discount Amount`\nfrom\n web_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 561\nand i_item_sk = ws_item_sk\nand d_date between '2001-03-13' and\n (cast('2001-03-13' as date) + INTERVAL 90 days)\nand d_date_sk = ws_sold_date_sk\nand ws_ext_discount_amt\n > (\n SELECT\n 1.3 * avg(ws_ext_discount_amt)\n FROM\n web_sales\n ,date_dim\n WHERE\n ws_item_sk = i_item_sk\n and d_date between '2001-03-13' and\n (cast('2001-03-13' as date) + INTERVAL 90 days)\n and d_date_sk = ws_sold_date_sk\n )\norder by sum(ws_ext_discount_amt)\nlimit 100\"\"\",\n \"q93\" ->\n \"\"\"\nselect ss_customer_sk\n ,sum(act_sales) sumsales\n from (select ss_item_sk\n ,ss_ticket_number\n ,ss_customer_sk\n ,case when sr_return_quantity is not null then (ss_quantity-sr_return_quantity)*ss_sales_price\n else (ss_quantity*ss_sales_price) end act_sales\n from store_sales left outer join store_returns on (sr_item_sk = ss_item_sk\n and sr_ticket_number = ss_ticket_number)\n ,reason\n where sr_reason_sk = r_reason_sk\n and r_reason_desc = 'reason 64') t\n group by ss_customer_sk\n order by sumsales, ss_customer_sk\nlimit 100\"\"\",\n \"q94\" ->\n \"\"\"\nselect\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2001-5-01' and\n (cast('2001-5-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'TX'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand exists (select *\n from web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\nand not exists(select *\n from web_returns wr1\n where ws1.ws_order_number = wr1.wr_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q95\" ->\n \"\"\"\nwith ws_wh as\n(select ws1.ws_order_number,ws1.ws_warehouse_sk wh1,ws2.ws_warehouse_sk wh2\n from web_sales ws1,web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\n select\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2000-3-01' and\n (cast('2000-3-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'TN'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand ws1.ws_order_number in (select ws_order_number\n from ws_wh)\nand ws1.ws_order_number in (select wr_order_number\n from web_returns,ws_wh\n where wr_order_number = ws_wh.ws_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q96\" ->\n \"\"\"\nselect count(*)\nfrom store_sales\n ,household_demographics\n ,time_dim, store\nwhere ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 16\n and time_dim.t_minute >= 30\n and household_demographics.hd_dep_count = 4\n and store.s_store_name = 'ese'\norder by count(*)\nlimit 100\"\"\",\n \"q97\" ->\n \"\"\"\nwith ssci as (\nselect ss_customer_sk customer_sk\n ,ss_item_sk item_sk\nfrom store_sales,date_dim\nwhere ss_sold_date_sk = d_date_sk\n and d_month_seq between 1209 and 1209 + 11\ngroup by ss_customer_sk\n ,ss_item_sk),\ncsci as(\n select cs_bill_customer_sk customer_sk\n ,cs_item_sk item_sk\nfrom catalog_sales,date_dim\nwhere cs_sold_date_sk = d_date_sk\n and d_month_seq between 1209 and 1209 + 11\ngroup by cs_bill_customer_sk\n ,cs_item_sk)\n select sum(case when ssci.customer_sk is not null and csci.customer_sk is null then 1 else 0 end) store_only\n ,sum(case when ssci.customer_sk is null and csci.customer_sk is not null then 1 else 0 end) catalog_only\n ,sum(case when ssci.customer_sk is not null and csci.customer_sk is not null then 1 else 0 end) store_and_catalog\nfrom ssci full outer join csci on (ssci.customer_sk=csci.customer_sk\n and ssci.item_sk = csci.item_sk)\nlimit 100\"\"\",\n \"q98\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ss_ext_sales_price) as itemrevenue\n ,sum(ss_ext_sales_price)*100/sum(sum(ss_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tstore_sales\n \t,item\n \t,date_dim\nwhere\n\tss_item_sk = i_item_sk\n \tand i_category in ('Jewelry', 'Home', 'Shoes')\n \tand ss_sold_date_sk = d_date_sk\n\tand d_date between cast('2001-04-12' as date)\n\t\t\t\tand (cast('2001-04-12' as date) + interval 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\"\"\",\n \"q99\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 30) and\n (cs_ship_date_sk - cs_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 60) and\n (cs_ship_date_sk - cs_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 90) and\n (cs_ship_date_sk - cs_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n catalog_sales\n ,warehouse\n ,ship_mode\n ,call_center\n ,date_dim\nwhere\n d_month_seq between 1203 and 1203 + 11\nand cs_ship_date_sk = d_date_sk\nand cs_warehouse_sk = w_warehouse_sk\nand cs_ship_mode_sk = sm_ship_mode_sk\nand cs_call_center_sk = cc_call_center_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\nlimit 100\"\"\",\n \"q1\" ->\n \"\"\"\nwith customer_total_return as\n(select sr_customer_sk as ctr_customer_sk\n,sr_store_sk as ctr_store_sk\n,sum(SR_FEE) as ctr_total_return\nfrom store_returns\n,date_dim\nwhere sr_returned_date_sk = d_date_sk\nand d_year =2000\ngroup by sr_customer_sk\n,sr_store_sk)\n select c_customer_id\nfrom customer_total_return ctr1\n,store\n,customer\nwhere ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\nfrom customer_total_return ctr2\nwhere ctr1.ctr_store_sk = ctr2.ctr_store_sk)\nand s_store_sk = ctr1.ctr_store_sk\nand s_state = 'NM'\nand ctr1.ctr_customer_sk = c_customer_sk\norder by c_customer_id\nlimit 100\"\"\",\n \"q2\" ->\n \"\"\"\nwith wscs as\n (select sold_date_sk\n ,sales_price\n from (select ws_sold_date_sk sold_date_sk\n ,ws_ext_sales_price sales_price\n from web_sales\n union all\n select cs_sold_date_sk sold_date_sk\n ,cs_ext_sales_price sales_price\n from catalog_sales)),\n wswscs as\n (select d_week_seq,\n sum(case when (d_day_name='Sunday') then sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then sales_price else null end) sat_sales\n from wscs\n ,date_dim\n where d_date_sk = sold_date_sk\n group by d_week_seq)\n select d_week_seq1\n ,round(sun_sales1/sun_sales2,2)\n ,round(mon_sales1/mon_sales2,2)\n ,round(tue_sales1/tue_sales2,2)\n ,round(wed_sales1/wed_sales2,2)\n ,round(thu_sales1/thu_sales2,2)\n ,round(fri_sales1/fri_sales2,2)\n ,round(sat_sales1/sat_sales2,2)\n from\n (select wswscs.d_week_seq d_week_seq1\n ,sun_sales sun_sales1\n ,mon_sales mon_sales1\n ,tue_sales tue_sales1\n ,wed_sales wed_sales1\n ,thu_sales thu_sales1\n ,fri_sales fri_sales1\n ,sat_sales sat_sales1\n from wswscs,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998) y,\n (select wswscs.d_week_seq d_week_seq2\n ,sun_sales sun_sales2\n ,mon_sales mon_sales2\n ,tue_sales tue_sales2\n ,wed_sales wed_sales2\n ,thu_sales thu_sales2\n ,fri_sales fri_sales2\n ,sat_sales sat_sales2\n from wswscs\n ,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998+1) z\n where d_week_seq1=d_week_seq2-53\n order by d_week_seq1\"\"\",\n \"q3\" ->\n \"\"\"\nselect dt.d_year\n ,item.i_brand_id brand_id\n ,item.i_brand brand\n ,sum(ss_sales_price) sum_agg\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manufact_id = 816\n and dt.d_moy=11\n group by dt.d_year\n ,item.i_brand\n ,item.i_brand_id\n order by dt.d_year\n ,sum_agg desc\n ,brand_id\n limit 100\"\"\",\n \"q4\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(((ss_ext_list_price-ss_ext_wholesale_cost-ss_ext_discount_amt)+ss_ext_sales_price)/2) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((cs_ext_list_price-cs_ext_wholesale_cost-cs_ext_discount_amt)+cs_ext_sales_price)/2) ) year_total\n ,'c' sale_type\n from customer\n ,catalog_sales\n ,date_dim\n where c_customer_sk = cs_bill_customer_sk\n and cs_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\nunion all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((ws_ext_list_price-ws_ext_wholesale_cost-ws_ext_discount_amt)+ws_ext_sales_price)/2) ) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_c_firstyear\n ,year_total t_c_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_c_secyear.customer_id\n and t_s_firstyear.customer_id = t_c_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_c_firstyear.sale_type = 'c'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_c_secyear.sale_type = 'c'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 1999\n and t_s_secyear.dyear = 1999+1\n and t_c_firstyear.dyear = 1999\n and t_c_secyear.dyear = 1999+1\n and t_w_firstyear.dyear = 1999\n and t_w_secyear.dyear = 1999+1\n and t_s_firstyear.year_total > 0\n and t_c_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\nlimit 100\"\"\",\n \"q5\" ->\n \"\"\"\nwith ssr as\n (select s_store_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ss_store_sk as store_sk,\n ss_sold_date_sk as date_sk,\n ss_ext_sales_price as sales_price,\n ss_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from store_sales\n union all\n select sr_store_sk as store_sk,\n sr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n sr_return_amt as return_amt,\n sr_net_loss as net_loss\n from store_returns\n ) salesreturns,\n date_dim,\n store\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and store_sk = s_store_sk\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select cs_catalog_page_sk as page_sk,\n cs_sold_date_sk as date_sk,\n cs_ext_sales_price as sales_price,\n cs_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from catalog_sales\n union all\n select cr_catalog_page_sk as page_sk,\n cr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n cr_return_amount as return_amt,\n cr_net_loss as net_loss\n from catalog_returns\n ) salesreturns,\n date_dim,\n catalog_page\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and page_sk = cp_catalog_page_sk\n group by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ws_web_site_sk as wsr_web_site_sk,\n ws_sold_date_sk as date_sk,\n ws_ext_sales_price as sales_price,\n ws_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from web_sales\n union all\n select ws_web_site_sk as wsr_web_site_sk,\n wr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n wr_return_amt as return_amt,\n wr_net_loss as net_loss\n from web_returns left outer join web_sales on\n ( wr_item_sk = ws_item_sk\n and wr_order_number = ws_order_number)\n ) salesreturns,\n date_dim,\n web_site\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and wsr_web_site_sk = web_site_sk\n group by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || s_store_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || cp_catalog_page_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q6\" ->\n \"\"\"\nselect a.ca_state state, count(*) cnt\n from customer_address a\n ,customer c\n ,store_sales s\n ,date_dim d\n ,item i\n where a.ca_address_sk = c.c_current_addr_sk\n \tand c.c_customer_sk = s.ss_customer_sk\n \tand s.ss_sold_date_sk = d.d_date_sk\n \tand s.ss_item_sk = i.i_item_sk\n \tand d.d_month_seq =\n \t (select distinct (d_month_seq)\n \t from date_dim\n where d_year = 2002\n \t and d_moy = 3 )\n \tand i.i_current_price > 1.2 *\n (select avg(j.i_current_price)\n \t from item j\n \t where j.i_category = i.i_category)\n group by a.ca_state\n having count(*) >= 10\n order by cnt, a.ca_state\n limit 100\"\"\",\n \"q7\" ->\n \"\"\"\nselect i_item_id,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, item, promotion\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_cdemo_sk = cd_demo_sk and\n ss_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'W' and\n cd_education_status = 'College' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 2001\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q8\" ->\n \"\"\"\nselect s_store_name\n ,sum(ss_net_profit)\n from store_sales\n ,date_dim\n ,store,\n (select ca_zip\n from (\n SELECT substr(ca_zip,1,5) ca_zip\n FROM customer_address\n WHERE substr(ca_zip,1,5) IN (\n '47602','16704','35863','28577','83910','36201',\n '58412','48162','28055','41419','80332',\n '38607','77817','24891','16226','18410',\n '21231','59345','13918','51089','20317',\n '17167','54585','67881','78366','47770',\n '18360','51717','73108','14440','21800',\n '89338','45859','65501','34948','25973',\n '73219','25333','17291','10374','18829',\n '60736','82620','41351','52094','19326',\n '25214','54207','40936','21814','79077',\n '25178','75742','77454','30621','89193',\n '27369','41232','48567','83041','71948',\n '37119','68341','14073','16891','62878',\n '49130','19833','24286','27700','40979',\n '50412','81504','94835','84844','71954',\n '39503','57649','18434','24987','12350',\n '86379','27413','44529','98569','16515',\n '27287','24255','21094','16005','56436',\n '91110','68293','56455','54558','10298',\n '83647','32754','27052','51766','19444',\n '13869','45645','94791','57631','20712',\n '37788','41807','46507','21727','71836',\n '81070','50632','88086','63991','20244',\n '31655','51782','29818','63792','68605',\n '94898','36430','57025','20601','82080',\n '33869','22728','35834','29086','92645',\n '98584','98072','11652','78093','57553',\n '43830','71144','53565','18700','90209',\n '71256','38353','54364','28571','96560',\n '57839','56355','50679','45266','84680',\n '34306','34972','48530','30106','15371',\n '92380','84247','92292','68852','13338',\n '34594','82602','70073','98069','85066',\n '47289','11686','98862','26217','47529',\n '63294','51793','35926','24227','14196',\n '24594','32489','99060','49472','43432',\n '49211','14312','88137','47369','56877',\n '20534','81755','15794','12318','21060',\n '73134','41255','63073','81003','73873',\n '66057','51184','51195','45676','92696',\n '70450','90669','98338','25264','38919',\n '59226','58581','60298','17895','19489',\n '52301','80846','95464','68770','51634',\n '19988','18367','18421','11618','67975',\n '25494','41352','95430','15734','62585',\n '97173','33773','10425','75675','53535',\n '17879','41967','12197','67998','79658',\n '59130','72592','14851','43933','68101',\n '50636','25717','71286','24660','58058',\n '72991','95042','15543','33122','69280',\n '11912','59386','27642','65177','17672',\n '33467','64592','36335','54010','18767',\n '63193','42361','49254','33113','33159',\n '36479','59080','11855','81963','31016',\n '49140','29392','41836','32958','53163',\n '13844','73146','23952','65148','93498',\n '14530','46131','58454','13376','13378',\n '83986','12320','17193','59852','46081',\n '98533','52389','13086','68843','31013',\n '13261','60560','13443','45533','83583',\n '11489','58218','19753','22911','25115',\n '86709','27156','32669','13123','51933',\n '39214','41331','66943','14155','69998',\n '49101','70070','35076','14242','73021',\n '59494','15782','29752','37914','74686',\n '83086','34473','15751','81084','49230',\n '91894','60624','17819','28810','63180',\n '56224','39459','55233','75752','43639',\n '55349','86057','62361','50788','31830',\n '58062','18218','85761','60083','45484',\n '21204','90229','70041','41162','35390',\n '16364','39500','68908','26689','52868',\n '81335','40146','11340','61527','61794',\n '71997','30415','59004','29450','58117',\n '69952','33562','83833','27385','61860',\n '96435','48333','23065','32961','84919',\n '61997','99132','22815','56600','68730',\n '48017','95694','32919','88217','27116',\n '28239','58032','18884','16791','21343',\n '97462','18569','75660','15475')\n intersect\n select ca_zip\n from (SELECT substr(ca_zip,1,5) ca_zip,count(*) cnt\n FROM customer_address, customer\n WHERE ca_address_sk = c_current_addr_sk and\n c_preferred_cust_flag='Y'\n group by ca_zip\n having count(*) > 10)A1)A2) V1\n where ss_store_sk = s_store_sk\n and ss_sold_date_sk = d_date_sk\n and d_qoy = 2 and d_year = 1998\n and (substr(s_zip,1,2) = substr(V1.ca_zip,1,2))\n group by s_store_name\n order by s_store_name\n limit 100\"\"\",\n \"q9\" ->\n \"\"\"\nselect case when (select count(*)\n from store_sales\n where ss_quantity between 1 and 20) > 98972190\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 1 and 20)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 1 and 20) end bucket1 ,\n case when (select count(*)\n from store_sales\n where ss_quantity between 21 and 40) > 160856845\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 21 and 40)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 21 and 40) end bucket2,\n case when (select count(*)\n from store_sales\n where ss_quantity between 41 and 60) > 12733327\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 41 and 60)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 41 and 60) end bucket3,\n case when (select count(*)\n from store_sales\n where ss_quantity between 61 and 80) > 96251173\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 61 and 80)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 61 and 80) end bucket4,\n case when (select count(*)\n from store_sales\n where ss_quantity between 81 and 100) > 80049606\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 81 and 100)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 81 and 100) end bucket5\nfrom reason\nwhere r_reason_sk = 1\"\"\",\n \"q10\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3,\n cd_dep_count,\n count(*) cnt4,\n cd_dep_employed_count,\n count(*) cnt5,\n cd_dep_college_count,\n count(*) cnt6\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_county in ('Fillmore County','McPherson County','Bonneville County','Boone County','Brown County') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2000 and\n d_moy between 3 and 3+3) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2000 and\n d_moy between 3 ANd 3+3) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2000 and\n d_moy between 3 and 3+3))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\nlimit 100\"\"\",\n \"q11\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ss_ext_list_price-ss_ext_discount_amt) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ws_ext_list_price-ws_ext_discount_amt) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 1999\n and t_s_secyear.dyear = 1999+1\n and t_w_firstyear.dyear = 1999\n and t_w_secyear.dyear = 1999+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else 0.0 end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else 0.0 end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\nlimit 100\"\"\",\n \"q12\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ws_ext_sales_price) as itemrevenue\n ,sum(ws_ext_sales_price)*100/sum(sum(ws_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tweb_sales\n \t,item\n \t,date_dim\nwhere\n\tws_item_sk = i_item_sk\n \tand i_category in ('Electronics', 'Books', 'Women')\n \tand ws_sold_date_sk = d_date_sk\n\tand d_date between cast('1998-01-06' as date)\n\t\t\t\tand (cast('1998-01-06' as date) + INTERVAL 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q13\" ->\n \"\"\"\nselect avg(ss_quantity)\n ,avg(ss_ext_sales_price)\n ,avg(ss_ext_wholesale_cost)\n ,sum(ss_ext_wholesale_cost)\n from store_sales\n ,store\n ,customer_demographics\n ,household_demographics\n ,customer_address\n ,date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 2001\n and((ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'U'\n and cd_education_status = 'Secondary'\n and ss_sales_price between 100.00 and 150.00\n and hd_dep_count = 3\n )or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'W'\n and cd_education_status = 'College'\n and ss_sales_price between 50.00 and 100.00\n and hd_dep_count = 1\n ) or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'D'\n and cd_education_status = 'Primary'\n and ss_sales_price between 150.00 and 200.00\n and hd_dep_count = 1\n ))\n and((ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('TX', 'OK', 'MI')\n and ss_net_profit between 100 and 200\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('WA', 'NC', 'OH')\n and ss_net_profit between 150 and 300\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('MT', 'FL', 'GA')\n and ss_net_profit between 50 and 250\n ))\"\"\",\n \"q14a\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 2000 AND 2000 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 2000 AND 2000 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 2000 AND 2000 + 2)\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n (select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 2000 and 2000 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 2000 and 2000 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 2000 and 2000 + 2) x)\n select channel, i_brand_id,i_class_id,i_category_id,sum(sales), sum(number_sales)\n from(\n select 'store' channel, i_brand_id,i_class_id\n ,i_category_id,sum(ss_quantity*ss_list_price) sales\n , count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 2000+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)\n union all\n select 'catalog' channel, i_brand_id,i_class_id,i_category_id, sum(cs_quantity*cs_list_price) sales, count(*) number_sales\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk in (select ss_item_sk from cross_items)\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 2000+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(cs_quantity*cs_list_price) > (select average_sales from avg_sales)\n union all\n select 'web' channel, i_brand_id,i_class_id,i_category_id, sum(ws_quantity*ws_list_price) sales , count(*) number_sales\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk in (select ss_item_sk from cross_items)\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 2000+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ws_quantity*ws_list_price) > (select average_sales from avg_sales)\n ) y\n group by rollup (channel, i_brand_id,i_class_id,i_category_id)\n order by channel,i_brand_id,i_class_id,i_category_id\n limit 100\"\"\",\n \"q14b\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 2000 AND 2000 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 2000 AND 2000 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 2000 AND 2000 + 2) x\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n(select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 2000 and 2000 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 2000 and 2000 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 2000 and 2000 + 2) x)\n select this_year.channel ty_channel\n ,this_year.i_brand_id ty_brand\n ,this_year.i_class_id ty_class\n ,this_year.i_category_id ty_category\n ,this_year.sales ty_sales\n ,this_year.number_sales ty_number_sales\n ,last_year.channel ly_channel\n ,last_year.i_brand_id ly_brand\n ,last_year.i_class_id ly_class\n ,last_year.i_category_id ly_category\n ,last_year.sales ly_sales\n ,last_year.number_sales ly_number_sales\n from\n (select 'store' channel, i_brand_id,i_class_id,i_category_id\n ,sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 2000 + 1\n and d_moy = 12\n and d_dom = 15)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) this_year,\n (select 'store' channel, i_brand_id,i_class_id\n ,i_category_id, sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 2000\n and d_moy = 12\n and d_dom = 15)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) last_year\n where this_year.i_brand_id= last_year.i_brand_id\n and this_year.i_class_id = last_year.i_class_id\n and this_year.i_category_id = last_year.i_category_id\n order by this_year.channel, this_year.i_brand_id, this_year.i_class_id, this_year.i_category_id\n limit 100\"\"\",\n \"q15\" ->\n \"\"\"\nselect ca_zip\n ,sum(cs_sales_price)\n from catalog_sales\n ,customer\n ,customer_address\n ,date_dim\n where cs_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475',\n '85392', '85460', '80348', '81792')\n \t or ca_state in ('CA','WA','GA')\n \t or cs_sales_price > 500)\n \tand cs_sold_date_sk = d_date_sk\n \tand d_qoy = 2 and d_year = 1998\n group by ca_zip\n order by ca_zip\n limit 100\"\"\",\n \"q16\" ->\n \"\"\"\nselect\n count(distinct cs_order_number) as `order count`\n ,sum(cs_ext_ship_cost) as `total shipping cost`\n ,sum(cs_net_profit) as `total net profit`\nfrom\n catalog_sales cs1\n ,date_dim\n ,customer_address\n ,call_center\nwhere\n d_date between '1999-4-01' and\n (cast('1999-4-01' as date) + INTERVAL 60 days)\nand cs1.cs_ship_date_sk = d_date_sk\nand cs1.cs_ship_addr_sk = ca_address_sk\nand ca_state = 'IL'\nand cs1.cs_call_center_sk = cc_call_center_sk\nand cc_county in ('Richland County','Bronx County','Maverick County','Mesa County',\n 'Raleigh County'\n)\nand exists (select *\n from catalog_sales cs2\n where cs1.cs_order_number = cs2.cs_order_number\n and cs1.cs_warehouse_sk <> cs2.cs_warehouse_sk)\nand not exists(select *\n from catalog_returns cr1\n where cs1.cs_order_number = cr1.cr_order_number)\norder by count(distinct cs_order_number)\nlimit 100\"\"\",\n \"q17\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,s_state\n ,count(ss_quantity) as store_sales_quantitycount\n ,avg(ss_quantity) as store_sales_quantityave\n ,stddev_samp(ss_quantity) as store_sales_quantitystdev\n ,stddev_samp(ss_quantity)/avg(ss_quantity) as store_sales_quantitycov\n ,count(sr_return_quantity) as store_returns_quantitycount\n ,avg(sr_return_quantity) as store_returns_quantityave\n ,stddev_samp(sr_return_quantity) as store_returns_quantitystdev\n ,stddev_samp(sr_return_quantity)/avg(sr_return_quantity) as store_returns_quantitycov\n ,count(cs_quantity) as catalog_sales_quantitycount ,avg(cs_quantity) as catalog_sales_quantityave\n ,stddev_samp(cs_quantity) as catalog_sales_quantitystdev\n ,stddev_samp(cs_quantity)/avg(cs_quantity) as catalog_sales_quantitycov\n from store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where d1.d_quarter_name = '2000Q1'\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_quarter_name in ('2000Q1','2000Q2','2000Q3')\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_quarter_name in ('2000Q1','2000Q2','2000Q3')\n group by i_item_id\n ,i_item_desc\n ,s_state\n order by i_item_id\n ,i_item_desc\n ,s_state\nlimit 100\"\"\",\n \"q18\" ->\n \"\"\"\nselect i_item_id,\n ca_country,\n ca_state,\n ca_county,\n avg( cast(cs_quantity as decimal(12,2))) agg1,\n avg( cast(cs_list_price as decimal(12,2))) agg2,\n avg( cast(cs_coupon_amt as decimal(12,2))) agg3,\n avg( cast(cs_sales_price as decimal(12,2))) agg4,\n avg( cast(cs_net_profit as decimal(12,2))) agg5,\n avg( cast(c_birth_year as decimal(12,2))) agg6,\n avg( cast(cd1.cd_dep_count as decimal(12,2))) agg7\n from catalog_sales, customer_demographics cd1,\n customer_demographics cd2, customer, customer_address, date_dim, item\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd1.cd_demo_sk and\n cs_bill_customer_sk = c_customer_sk and\n cd1.cd_gender = 'M' and\n cd1.cd_education_status = 'Unknown' and\n c_current_cdemo_sk = cd2.cd_demo_sk and\n c_current_addr_sk = ca_address_sk and\n c_birth_month in (5,1,4,7,8,9) and\n d_year = 2002 and\n ca_state in ('AR','TX','NC'\n ,'GA','MS','WV','AL')\n group by rollup (i_item_id, ca_country, ca_state, ca_county)\n order by ca_country,\n ca_state,\n ca_county,\n\ti_item_id\n limit 100\"\"\",\n \"q19\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand, i_manufact_id, i_manufact,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item,customer,customer_address,store\n where d_date_sk = ss_sold_date_sk\n and ss_item_sk = i_item_sk\n and i_manager_id=16\n and d_moy=12\n and d_year=1998\n and ss_customer_sk = c_customer_sk\n and c_current_addr_sk = ca_address_sk\n and substr(ca_zip,1,5) <> substr(s_zip,1,5)\n and ss_store_sk = s_store_sk\n group by i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\n order by ext_price desc\n ,i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\nlimit 100 \"\"\",\n \"q20\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(cs_ext_sales_price) as itemrevenue\n ,sum(cs_ext_sales_price)*100/sum(sum(cs_ext_sales_price)) over\n (partition by i_class) as revenueratio\n from\tcatalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and i_category in ('Shoes', 'Electronics', 'Children')\n and cs_sold_date_sk = d_date_sk\n and d_date between cast('2001-03-14' as date)\n \t\t\t\tand (cast('2001-03-14' as date) + INTERVAL 30 days)\n group by i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n order by i_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q21\" ->\n \"\"\"\nselect *\n from(select w_warehouse_name\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('1999-03-20' as date))\n\t then inv_quantity_on_hand\n else 0 end) as inv_before\n ,sum(case when (cast(d_date as date) >= cast ('1999-03-20' as date))\n then inv_quantity_on_hand\n else 0 end) as inv_after\n from inventory\n ,warehouse\n ,item\n ,date_dim\n where i_current_price between 0.99 and 1.49\n and i_item_sk = inv_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_date between (cast ('1999-03-20' as date) - INTERVAL 30 days)\n and (cast ('1999-03-20' as date) + INTERVAL 30 days)\n group by w_warehouse_name, i_item_id) x\n where (case when inv_before > 0\n then inv_after / inv_before\n else null\n end) between 2.0/3.0 and 3.0/2.0\n order by w_warehouse_name\n ,i_item_id\n limit 100\"\"\",\n \"q22\" ->\n \"\"\"\nselect i_product_name\n ,i_brand\n ,i_class\n ,i_category\n ,avg(inv_quantity_on_hand) qoh\n from inventory\n ,date_dim\n ,item\n where inv_date_sk=d_date_sk\n and inv_item_sk=i_item_sk\n and d_month_seq between 1186 and 1186 + 11\n group by rollup(i_product_name\n ,i_brand\n ,i_class\n ,i_category)\norder by qoh, i_product_name, i_brand, i_class, i_category\nlimit 100\"\"\",\n \"q23a\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\nfrom\n max_store_sales))\n select sum(sales)\n from (select cs_quantity*cs_list_price sales\n from catalog_sales\n ,date_dim\n where d_year = 2000\n and d_moy = 3\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n union all\n select ws_quantity*ws_list_price sales\n from web_sales\n ,date_dim\n where d_year = 2000\n and d_moy = 3\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer))\n limit 100\"\"\",\n \"q23b\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (2000,2000 + 1,2000 + 2,2000 + 3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\n from max_store_sales))\n select c_last_name,c_first_name,sales\n from (select c_last_name,c_first_name,sum(cs_quantity*cs_list_price) sales\n from catalog_sales\n ,customer\n ,date_dim\n where d_year = 2000\n and d_moy = 3\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and cs_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name\n union all\n select c_last_name,c_first_name,sum(ws_quantity*ws_list_price) sales\n from web_sales\n ,customer\n ,date_dim\n where d_year = 2000\n and d_moy = 3\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and ws_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name)\n order by c_last_name,c_first_name,sales\n limit 100\"\"\",\n \"q24a\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_sales_price) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\nand s_market_id=10\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'snow'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q24b\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_sales_price) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\n and s_market_id = 10\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'chiffon'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q25\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,sum(ss_net_profit) as store_sales_profit\n ,sum(sr_net_loss) as store_returns_loss\n ,sum(cs_net_profit) as catalog_sales_profit\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 2000\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 10\n and d2.d_year = 2000\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_moy between 4 and 10\n and d3.d_year = 2000\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q26\" ->\n \"\"\"\nselect i_item_id,\n avg(cs_quantity) agg1,\n avg(cs_list_price) agg2,\n avg(cs_coupon_amt) agg3,\n avg(cs_sales_price) agg4\n from catalog_sales, customer_demographics, date_dim, item, promotion\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd_demo_sk and\n cs_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'S' and\n cd_education_status = 'College' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 1998\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q27\" ->\n \"\"\"\nselect i_item_id,\n s_state, grouping(s_state) g_state,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, store, item\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_store_sk = s_store_sk and\n ss_cdemo_sk = cd_demo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'U' and\n cd_education_status = '2 yr Degree' and\n d_year = 2000 and\n s_state in ('AL','IN', 'SC', 'NY', 'OH', 'FL')\n group by rollup (i_item_id, s_state)\n order by i_item_id\n ,s_state\n limit 100\"\"\",\n \"q28\" ->\n \"\"\"\nselect *\nfrom (select avg(ss_list_price) B1_LP\n ,count(ss_list_price) B1_CNT\n ,count(distinct ss_list_price) B1_CNTD\n from store_sales\n where ss_quantity between 0 and 5\n and (ss_list_price between 73 and 73+10\n or ss_coupon_amt between 7826 and 7826+1000\n or ss_wholesale_cost between 70 and 70+20)) B1,\n (select avg(ss_list_price) B2_LP\n ,count(ss_list_price) B2_CNT\n ,count(distinct ss_list_price) B2_CNTD\n from store_sales\n where ss_quantity between 6 and 10\n and (ss_list_price between 152 and 152+10\n or ss_coupon_amt between 2196 and 2196+1000\n or ss_wholesale_cost between 56 and 56+20)) B2,\n (select avg(ss_list_price) B3_LP\n ,count(ss_list_price) B3_CNT\n ,count(distinct ss_list_price) B3_CNTD\n from store_sales\n where ss_quantity between 11 and 15\n and (ss_list_price between 53 and 53+10\n or ss_coupon_amt between 3430 and 3430+1000\n or ss_wholesale_cost between 13 and 13+20)) B3,\n (select avg(ss_list_price) B4_LP\n ,count(ss_list_price) B4_CNT\n ,count(distinct ss_list_price) B4_CNTD\n from store_sales\n where ss_quantity between 16 and 20\n and (ss_list_price between 182 and 182+10\n or ss_coupon_amt between 3262 and 3262+1000\n or ss_wholesale_cost between 20 and 20+20)) B4,\n (select avg(ss_list_price) B5_LP\n ,count(ss_list_price) B5_CNT\n ,count(distinct ss_list_price) B5_CNTD\n from store_sales\n where ss_quantity between 21 and 25\n and (ss_list_price between 85 and 85+10\n or ss_coupon_amt between 3310 and 3310+1000\n or ss_wholesale_cost between 37 and 37+20)) B5,\n (select avg(ss_list_price) B6_LP\n ,count(ss_list_price) B6_CNT\n ,count(distinct ss_list_price) B6_CNTD\n from store_sales\n where ss_quantity between 26 and 30\n and (ss_list_price between 180 and 180+10\n or ss_coupon_amt between 12592 and 12592+1000\n or ss_wholesale_cost between 22 and 22+20)) B6\nlimit 100\"\"\",\n \"q29\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,stddev_samp(ss_quantity) as store_sales_quantity\n ,stddev_samp(sr_return_quantity) as store_returns_quantity\n ,stddev_samp(cs_quantity) as catalog_sales_quantity\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 1998\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 4 + 3\n and d2.d_year = 1998\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_year in (1998,1998+1,1998+2)\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q30\" ->\n \"\"\"\nwith customer_total_return as\n (select wr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(wr_return_amt) as ctr_total_return\n from web_returns\n ,date_dim\n ,customer_address\n where wr_returned_date_sk = d_date_sk\n and d_year =2000\n and wr_returning_addr_sk = ca_address_sk\n group by wr_returning_customer_sk\n ,ca_state)\n select c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'GA'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\nlimit 100\"\"\",\n \"q31\" ->\n \"\"\"\nwith ss as\n (select ca_county,d_qoy, d_year,sum(ss_ext_sales_price) as store_sales\n from store_sales,date_dim,customer_address\n where ss_sold_date_sk = d_date_sk\n and ss_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year),\n ws as\n (select ca_county,d_qoy, d_year,sum(ws_ext_sales_price) as web_sales\n from web_sales,date_dim,customer_address\n where ws_sold_date_sk = d_date_sk\n and ws_bill_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year)\n select\n ss1.ca_county\n ,ss1.d_year\n ,ws2.web_sales/ws1.web_sales web_q1_q2_increase\n ,ss2.store_sales/ss1.store_sales store_q1_q2_increase\n ,ws3.web_sales/ws2.web_sales web_q2_q3_increase\n ,ss3.store_sales/ss2.store_sales store_q2_q3_increase\n from\n ss ss1\n ,ss ss2\n ,ss ss3\n ,ws ws1\n ,ws ws2\n ,ws ws3\n where\n ss1.d_qoy = 1\n and ss1.d_year = 1999\n and ss1.ca_county = ss2.ca_county\n and ss2.d_qoy = 2\n and ss2.d_year = 1999\n and ss2.ca_county = ss3.ca_county\n and ss3.d_qoy = 3\n and ss3.d_year = 1999\n and ss1.ca_county = ws1.ca_county\n and ws1.d_qoy = 1\n and ws1.d_year = 1999\n and ws1.ca_county = ws2.ca_county\n and ws2.d_qoy = 2\n and ws2.d_year = 1999\n and ws1.ca_county = ws3.ca_county\n and ws3.d_qoy = 3\n and ws3.d_year =1999\n and case when ws1.web_sales > 0 then ws2.web_sales/ws1.web_sales else null end\n > case when ss1.store_sales > 0 then ss2.store_sales/ss1.store_sales else null end\n and case when ws2.web_sales > 0 then ws3.web_sales/ws2.web_sales else null end\n > case when ss2.store_sales > 0 then ss3.store_sales/ss2.store_sales else null end\n order by ss1.d_year\"\"\",\n \"q32\" ->\n \"\"\"\nselect sum(cs_ext_discount_amt) as `excess discount amount`\nfrom\n catalog_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 66\nand i_item_sk = cs_item_sk\nand d_date between '2002-03-29' and\n (cast('2002-03-29' as date) + INTERVAL 90 days)\nand d_date_sk = cs_sold_date_sk\nand cs_ext_discount_amt\n > (\n select\n 1.3 * avg(cs_ext_discount_amt)\n from\n catalog_sales\n ,date_dim\n where\n cs_item_sk = i_item_sk\n and d_date between '2002-03-29' and\n (cast('2002-03-29' as date) + INTERVAL 90 days)\n and d_date_sk = cs_sold_date_sk\n )\nlimit 100\"\"\",\n \"q33\" ->\n \"\"\"\nwith ss as (\n select\n i_manufact_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Home'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 5\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id),\n cs as (\n select\n i_manufact_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Home'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 5\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id),\n ws as (\n select\n i_manufact_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Home'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 5\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id)\n select i_manufact_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_manufact_id\n order by total_sales\nlimit 100\"\"\",\n \"q34\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (date_dim.d_dom between 1 and 3 or date_dim.d_dom between 25 and 28)\n and (household_demographics.hd_buy_potential = '>10000' or\n household_demographics.hd_buy_potential = 'Unknown')\n and household_demographics.hd_vehicle_count > 0\n and (case when household_demographics.hd_vehicle_count > 0\n\tthen household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count\n\telse null\n\tend) > 1.2\n and date_dim.d_year in (2000,2000+1,2000+2)\n and store.s_county in ('Salem County','Terrell County','Arthur County','Oglethorpe County',\n 'Lunenburg County','Perry County','Halifax County','Sumner County')\n group by ss_ticket_number,ss_customer_sk) dn,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 15 and 20\n order by c_last_name,c_first_name,c_salutation,c_preferred_cust_flag desc, ss_ticket_number\"\"\",\n \"q35\" ->\n \"\"\"\nselect\n ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n count(*) cnt1,\n avg(cd_dep_count),\n min(cd_dep_count),\n stddev_samp(cd_dep_count),\n cd_dep_employed_count,\n count(*) cnt2,\n avg(cd_dep_employed_count),\n min(cd_dep_employed_count),\n stddev_samp(cd_dep_employed_count),\n cd_dep_college_count,\n count(*) cnt3,\n avg(cd_dep_college_count),\n min(cd_dep_college_count),\n stddev_samp(cd_dep_college_count)\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_qoy < 4) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_qoy < 4) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_qoy < 4))\n group by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n limit 100\"\"\",\n \"q36\" ->\n \"\"\"\nselect\n sum(ss_net_profit)/sum(ss_ext_sales_price) as gross_margin\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ss_net_profit)/sum(ss_ext_sales_price) asc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,item\n ,store\n where\n d1.d_year = 1999\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and s_state in ('IN','AL','MI','MN',\n 'TN','LA','FL','NM')\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then i_category end\n ,rank_within_parent\n limit 100\"\"\",\n \"q37\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, catalog_sales\n where i_current_price between 39 and 39 + 30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2001-01-16' as date) and (cast('2001-01-16' as date) + interval 60 days)\n and i_manufact_id in (765,886,889,728)\n and inv_quantity_on_hand between 100 and 500\n and cs_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q38\" ->\n \"\"\"\nselect count(*) from (\n select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1186 and 1186 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1186 and 1186 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1186 and 1186 + 11\n) hot_cust\nlimit 100\"\"\",\n \"q39a\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =2000\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=2\n and inv2.d_moy=2+1\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q39b\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =2000\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=2\n and inv2.d_moy=2+1\n and inv1.cov > 1.5\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q40\" ->\n \"\"\"\nselect\n w_state\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('2000-03-18' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_before\n ,sum(case when (cast(d_date as date) >= cast ('2000-03-18' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_after\n from\n catalog_sales left outer join catalog_returns on\n (cs_order_number = cr_order_number\n and cs_item_sk = cr_item_sk)\n ,warehouse\n ,item\n ,date_dim\n where\n i_current_price between 0.99 and 1.49\n and i_item_sk = cs_item_sk\n and cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and d_date between (cast ('2000-03-18' as date) - INTERVAL 30 days)\n and (cast ('2000-03-18' as date) + INTERVAL 30 days)\n group by\n w_state,i_item_id\n order by w_state,i_item_id\nlimit 100\"\"\",\n \"q41\" ->\n \"\"\"\nselect distinct(i_product_name)\n from item i1\n where i_manufact_id between 970 and 970+40\n and (select count(*) as item_cnt\n from item\n where (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'frosted' or i_color = 'rose') and\n (i_units = 'Lb' or i_units = 'Gross') and\n (i_size = 'medium' or i_size = 'large')\n ) or\n (i_category = 'Women' and\n (i_color = 'chocolate' or i_color = 'black') and\n (i_units = 'Box' or i_units = 'Dram') and\n (i_size = 'economy' or i_size = 'petite')\n ) or\n (i_category = 'Men' and\n (i_color = 'slate' or i_color = 'magenta') and\n (i_units = 'Carton' or i_units = 'Bundle') and\n (i_size = 'N/A' or i_size = 'small')\n ) or\n (i_category = 'Men' and\n (i_color = 'cornflower' or i_color = 'firebrick') and\n (i_units = 'Pound' or i_units = 'Oz') and\n (i_size = 'medium' or i_size = 'large')\n ))) or\n (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'almond' or i_color = 'steel') and\n (i_units = 'Tsp' or i_units = 'Case') and\n (i_size = 'medium' or i_size = 'large')\n ) or\n (i_category = 'Women' and\n (i_color = 'purple' or i_color = 'aquamarine') and\n (i_units = 'Bunch' or i_units = 'Gram') and\n (i_size = 'economy' or i_size = 'petite')\n ) or\n (i_category = 'Men' and\n (i_color = 'lavender' or i_color = 'papaya') and\n (i_units = 'Pallet' or i_units = 'Cup') and\n (i_size = 'N/A' or i_size = 'small')\n ) or\n (i_category = 'Men' and\n (i_color = 'maroon' or i_color = 'cyan') and\n (i_units = 'Each' or i_units = 'N/A') and\n (i_size = 'medium' or i_size = 'large')\n )))) > 0\n order by i_product_name\n limit 100\"\"\",\n \"q42\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_category_id\n \t,item.i_category\n \t,sum(ss_ext_sales_price)\n from \tdate_dim dt\n \t,store_sales\n \t,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n \tand store_sales.ss_item_sk = item.i_item_sk\n \tand item.i_manager_id = 1\n \tand dt.d_moy=12\n \tand dt.d_year=1998\n group by \tdt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\n order by sum(ss_ext_sales_price) desc,dt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\nlimit 100 \"\"\",\n \"q43\" ->\n \"\"\"\nselect s_store_name, s_store_id,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from date_dim, store_sales, store\n where d_date_sk = ss_sold_date_sk and\n s_store_sk = ss_store_sk and\n s_gmt_offset = -6 and\n d_year = 2001\n group by s_store_name, s_store_id\n order by s_store_name, s_store_id,sun_sales,mon_sales,tue_sales,wed_sales,thu_sales,fri_sales,sat_sales\n limit 100\"\"\",\n \"q44\" ->\n \"\"\"\nselect asceding.rnk, i1.i_product_name best_performing, i2.i_product_name worst_performing\nfrom(select *\n from (select item_sk,rank() over (order by rank_col asc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 366\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 366\n and ss_cdemo_sk is null\n group by ss_store_sk))V1)V11\n where rnk < 11) asceding,\n (select *\n from (select item_sk,rank() over (order by rank_col desc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 366\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 366\n and ss_cdemo_sk is null\n group by ss_store_sk))V2)V21\n where rnk < 11) descending,\nitem i1,\nitem i2\nwhere asceding.rnk = descending.rnk\n and i1.i_item_sk=asceding.item_sk\n and i2.i_item_sk=descending.item_sk\norder by asceding.rnk\nlimit 100\"\"\",\n \"q45\" ->\n \"\"\"\nselect ca_zip, ca_county, sum(ws_sales_price)\n from web_sales, customer, customer_address, date_dim, item\n where ws_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ws_item_sk = i_item_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475', '85392', '85460', '80348', '81792')\n \t or\n \t i_item_id in (select i_item_id\n from item\n where i_item_sk in (2, 3, 5, 7, 11, 13, 17, 19, 23, 29)\n )\n \t )\n \tand ws_sold_date_sk = d_date_sk\n \tand d_qoy = 1 and d_year = 1998\n group by ca_zip, ca_county\n order by ca_zip, ca_county\n limit 100\"\"\",\n \"q46\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and (household_demographics.hd_dep_count = 0 or\n household_demographics.hd_vehicle_count= 1)\n and date_dim.d_dow in (6,0)\n and date_dim.d_year in (2000,2000+1,2000+2)\n and store.s_city in ('Five Forks','Oakland','Fairview','Winchester','Farmington')\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,ca_city) dn,customer,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n limit 100\"\"\",\n \"q47\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand,\n s_store_name, s_company_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n s_store_name, s_company_name\n order by d_year, d_moy) rn\n from item, store_sales, date_dim, store\n where ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n (\n d_year = 1999 or\n ( d_year = 1999-1 and d_moy =12) or\n ( d_year = 1999+1 and d_moy =1)\n )\n group by i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy),\n v2 as(\n select v1.s_store_name\n ,v1.d_year, v1.d_moy\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1.s_store_name = v1_lag.s_store_name and\n v1.s_store_name = v1_lead.s_store_name and\n v1.s_company_name = v1_lag.s_company_name and\n v1.s_company_name = v1_lead.s_company_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 1999 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, sum_sales\n limit 100\"\"\",\n \"q48\" ->\n \"\"\"\nselect sum (ss_quantity)\n from store_sales, store, customer_demographics, customer_address, date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 1998\n and\n (\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'M'\n and\n cd_education_status = 'Unknown'\n and\n ss_sales_price between 100.00 and 150.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'W'\n and\n cd_education_status = 'College'\n and\n ss_sales_price between 50.00 and 100.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'D'\n and\n cd_education_status = 'Primary'\n and\n ss_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('MI', 'GA', 'NH')\n and ss_net_profit between 0 and 2000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('TX', 'KY', 'SD')\n and ss_net_profit between 150 and 3000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('NY', 'OH', 'FL')\n and ss_net_profit between 50 and 25000\n )\n )\"\"\",\n \"q49\" ->\n \"\"\"\nselect channel, item, return_ratio, return_rank, currency_rank from\n (select\n 'web' as channel\n ,web.item\n ,web.return_ratio\n ,web.return_rank\n ,web.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect ws.ws_item_sk as item\n \t\t,(cast(sum(coalesce(wr.wr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(wr.wr_return_amt,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\t web_sales ws left outer join web_returns wr\n \t\t\ton (ws.ws_order_number = wr.wr_order_number and\n \t\t\tws.ws_item_sk = wr.wr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\twr.wr_return_amt > 10000\n \t\t\tand ws.ws_net_profit > 1\n and ws.ws_net_paid > 0\n and ws.ws_quantity > 0\n and ws_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 12\n \t\tgroup by ws.ws_item_sk\n \t) in_web\n ) web\n where\n (\n web.return_rank <= 10\n or\n web.currency_rank <= 10\n )\n union\n select\n 'catalog' as channel\n ,catalog.item\n ,catalog.return_ratio\n ,catalog.return_rank\n ,catalog.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect\n \t\tcs.cs_item_sk as item\n \t\t,(cast(sum(coalesce(cr.cr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(cr.cr_return_amount,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tcatalog_sales cs left outer join catalog_returns cr\n \t\t\ton (cs.cs_order_number = cr.cr_order_number and\n \t\t\tcs.cs_item_sk = cr.cr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tcr.cr_return_amount > 10000\n \t\t\tand cs.cs_net_profit > 1\n and cs.cs_net_paid > 0\n and cs.cs_quantity > 0\n and cs_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 12\n group by cs.cs_item_sk\n \t) in_cat\n ) catalog\n where\n (\n catalog.return_rank <= 10\n or\n catalog.currency_rank <=10\n )\n union\n select\n 'store' as channel\n ,store.item\n ,store.return_ratio\n ,store.return_rank\n ,store.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect sts.ss_item_sk as item\n \t\t,(cast(sum(coalesce(sr.sr_return_quantity,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(sr.sr_return_amt,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tstore_sales sts left outer join store_returns sr\n \t\t\ton (sts.ss_ticket_number = sr.sr_ticket_number and sts.ss_item_sk = sr.sr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tsr.sr_return_amt > 10000\n \t\t\tand sts.ss_net_profit > 1\n and sts.ss_net_paid > 0\n and sts.ss_quantity > 0\n and ss_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 12\n \t\tgroup by sts.ss_item_sk\n \t) in_store\n ) store\n where (\n store.return_rank <= 10\n or\n store.currency_rank <= 10\n )\n )\n order by 1,4,5,2\n limit 100\"\"\",\n \"q50\" ->\n \"\"\"\nselect\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 30) and\n (sr_returned_date_sk - ss_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 60) and\n (sr_returned_date_sk - ss_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 90) and\n (sr_returned_date_sk - ss_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n store_sales\n ,store_returns\n ,store\n ,date_dim d1\n ,date_dim d2\nwhere\n d2.d_year = 1998\nand d2.d_moy = 9\nand ss_ticket_number = sr_ticket_number\nand ss_item_sk = sr_item_sk\nand ss_sold_date_sk = d1.d_date_sk\nand sr_returned_date_sk = d2.d_date_sk\nand ss_customer_sk = sr_customer_sk\nand ss_store_sk = s_store_sk\ngroup by\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\norder by s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\nlimit 100\"\"\",\n \"q51\" ->\n \"\"\"\nWITH web_v1 as (\nselect\n ws_item_sk item_sk, d_date,\n sum(sum(ws_sales_price))\n over (partition by ws_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom web_sales\n ,date_dim\nwhere ws_sold_date_sk=d_date_sk\n and d_month_seq between 1214 and 1214+11\n and ws_item_sk is not NULL\ngroup by ws_item_sk, d_date),\nstore_v1 as (\nselect\n ss_item_sk item_sk, d_date,\n sum(sum(ss_sales_price))\n over (partition by ss_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom store_sales\n ,date_dim\nwhere ss_sold_date_sk=d_date_sk\n and d_month_seq between 1214 and 1214+11\n and ss_item_sk is not NULL\ngroup by ss_item_sk, d_date)\n select *\nfrom (select item_sk\n ,d_date\n ,web_sales\n ,store_sales\n ,max(web_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) web_cumulative\n ,max(store_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) store_cumulative\n from (select case when web.item_sk is not null then web.item_sk else store.item_sk end item_sk\n ,case when web.d_date is not null then web.d_date else store.d_date end d_date\n ,web.cume_sales web_sales\n ,store.cume_sales store_sales\n from web_v1 web full outer join store_v1 store on (web.item_sk = store.item_sk\n and web.d_date = store.d_date)\n )x )y\nwhere web_cumulative > store_cumulative\norder by item_sk\n ,d_date\nlimit 100\"\"\",\n \"q52\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_brand_id brand_id\n \t,item.i_brand brand\n \t,sum(ss_ext_sales_price) ext_price\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manager_id = 1\n and dt.d_moy=12\n and dt.d_year=2000\n group by dt.d_year\n \t,item.i_brand\n \t,item.i_brand_id\n order by dt.d_year\n \t,ext_price desc\n \t,brand_id\nlimit 100 \"\"\",\n \"q53\" ->\n \"\"\"\nselect * from\n(select i_manufact_id,\nsum(ss_sales_price) sum_sales,\navg(sum(ss_sales_price)) over (partition by i_manufact_id) avg_quarterly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\nss_sold_date_sk = d_date_sk and\nss_store_sk = s_store_sk and\nd_month_seq in (1212,1212+1,1212+2,1212+3,1212+4,1212+5,1212+6,1212+7,1212+8,1212+9,1212+10,1212+11) and\n((i_category in ('Books','Children','Electronics') and\ni_class in ('personal','portable','reference','self-help') and\ni_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t'exportiunivamalg #9','scholaramalgamalg #9'))\nor(i_category in ('Women','Music','Men') and\ni_class in ('accessories','classical','fragrances','pants') and\ni_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t'importoamalg #1')))\ngroup by i_manufact_id, d_qoy ) tmp1\nwhere case when avg_quarterly_sales > 0\n\tthen abs (sum_sales - avg_quarterly_sales)/ avg_quarterly_sales\n\telse null end > 0.1\norder by avg_quarterly_sales,\n\t sum_sales,\n\t i_manufact_id\nlimit 100\"\"\",\n \"q54\" ->\n \"\"\"\nwith my_customers as (\n select distinct c_customer_sk\n , c_current_addr_sk\n from\n ( select cs_sold_date_sk sold_date_sk,\n cs_bill_customer_sk customer_sk,\n cs_item_sk item_sk\n from catalog_sales\n union all\n select ws_sold_date_sk sold_date_sk,\n ws_bill_customer_sk customer_sk,\n ws_item_sk item_sk\n from web_sales\n ) cs_or_ws_sales,\n item,\n date_dim,\n customer\n where sold_date_sk = d_date_sk\n and item_sk = i_item_sk\n and i_category = 'Books'\n and i_class = 'business'\n and c_customer_sk = cs_or_ws_sales.customer_sk\n and d_moy = 2\n and d_year = 2000\n )\n , my_revenue as (\n select c_customer_sk,\n sum(ss_ext_sales_price) as revenue\n from my_customers,\n store_sales,\n customer_address,\n store,\n date_dim\n where c_current_addr_sk = ca_address_sk\n and ca_county = s_county\n and ca_state = s_state\n and ss_sold_date_sk = d_date_sk\n and c_customer_sk = ss_customer_sk\n and d_month_seq between (select distinct d_month_seq+1\n from date_dim where d_year = 2000 and d_moy = 2)\n and (select distinct d_month_seq+3\n from date_dim where d_year = 2000 and d_moy = 2)\n group by c_customer_sk\n )\n , segments as\n (select cast((revenue/50) as int) as segment\n from my_revenue\n )\n select segment, count(*) as num_customers, segment*50 as segment_base\n from segments\n group by segment\n order by segment, num_customers\n limit 100\"\"\",\n \"q55\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item\n where d_date_sk = ss_sold_date_sk\n \tand ss_item_sk = i_item_sk\n \tand i_manager_id=13\n \tand d_moy=11\n \tand d_year=1999\n group by i_brand, i_brand_id\n order by ext_price desc, i_brand_id\nlimit 100 \"\"\",\n \"q56\" ->\n \"\"\"\nwith ss as (\n select i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('chiffon','smoke','lace'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 2001\n and d_moy = 5\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_item_id),\n cs as (\n select i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('chiffon','smoke','lace'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 2001\n and d_moy = 5\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_item_id),\n ws as (\n select i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('chiffon','smoke','lace'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 2001\n and d_moy = 5\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_item_id)\n select i_item_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by total_sales,\n i_item_id\n limit 100\"\"\",\n \"q57\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n cc_name,\n d_year, d_moy,\n sum(cs_sales_price) sum_sales,\n avg(sum(cs_sales_price)) over\n (partition by i_category, i_brand,\n cc_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n cc_name\n order by d_year, d_moy) rn\n from item, catalog_sales, date_dim, call_center\n where cs_item_sk = i_item_sk and\n cs_sold_date_sk = d_date_sk and\n cc_call_center_sk= cs_call_center_sk and\n (\n d_year = 1999 or\n ( d_year = 1999-1 and d_moy =12) or\n ( d_year = 1999+1 and d_moy =1)\n )\n group by i_category, i_brand,\n cc_name , d_year, d_moy),\n v2 as(\n select v1.i_category, v1.i_brand\n ,v1.d_year, v1.d_moy\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1. cc_name = v1_lag. cc_name and\n v1. cc_name = v1_lead. cc_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 1999 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, avg_monthly_sales\n limit 100\"\"\",\n \"q58\" ->\n \"\"\"\nwith ss_items as\n (select i_item_id item_id\n ,sum(ss_ext_sales_price) ss_item_rev\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '1998-02-21'))\n and ss_sold_date_sk = d_date_sk\n group by i_item_id),\n cs_items as\n (select i_item_id item_id\n ,sum(cs_ext_sales_price) cs_item_rev\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '1998-02-21'))\n and cs_sold_date_sk = d_date_sk\n group by i_item_id),\n ws_items as\n (select i_item_id item_id\n ,sum(ws_ext_sales_price) ws_item_rev\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq =(select d_week_seq\n from date_dim\n where d_date = '1998-02-21'))\n and ws_sold_date_sk = d_date_sk\n group by i_item_id)\n select ss_items.item_id\n ,ss_item_rev\n ,ss_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ss_dev\n ,cs_item_rev\n ,cs_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 cs_dev\n ,ws_item_rev\n ,ws_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ws_dev\n ,(ss_item_rev+cs_item_rev+ws_item_rev)/3 average\n from ss_items,cs_items,ws_items\n where ss_items.item_id=cs_items.item_id\n and ss_items.item_id=ws_items.item_id\n and ss_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n and ss_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and cs_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and cs_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and ws_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and ws_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n order by item_id\n ,ss_item_rev\n limit 100\"\"\",\n \"q59\" ->\n \"\"\"\nwith wss as\n (select d_week_seq,\n ss_store_sk,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n group by d_week_seq,ss_store_sk\n )\n select s_store_name1,s_store_id1,d_week_seq1\n ,sun_sales1/sun_sales2,mon_sales1/mon_sales2\n ,tue_sales1/tue_sales2,wed_sales1/wed_sales2,thu_sales1/thu_sales2\n ,fri_sales1/fri_sales2,sat_sales1/sat_sales2\n from\n (select s_store_name s_store_name1,wss.d_week_seq d_week_seq1\n ,s_store_id s_store_id1,sun_sales sun_sales1\n ,mon_sales mon_sales1,tue_sales tue_sales1\n ,wed_sales wed_sales1,thu_sales thu_sales1\n ,fri_sales fri_sales1,sat_sales sat_sales1\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1205 and 1205 + 11) y,\n (select s_store_name s_store_name2,wss.d_week_seq d_week_seq2\n ,s_store_id s_store_id2,sun_sales sun_sales2\n ,mon_sales mon_sales2,tue_sales tue_sales2\n ,wed_sales wed_sales2,thu_sales thu_sales2\n ,fri_sales fri_sales2,sat_sales sat_sales2\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1205+ 12 and 1205 + 23) x\n where s_store_id1=s_store_id2\n and d_week_seq1=d_week_seq2-52\n order by s_store_name1,s_store_id1,d_week_seq1\nlimit 100\"\"\",\n \"q60\" ->\n \"\"\"\nwith ss as (\n select\n i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Children'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 10\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n cs as (\n select\n i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Children'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 10\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n ws as (\n select\n i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Children'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 10\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id)\n select\n i_item_id\n,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by i_item_id\n ,total_sales\n limit 100\"\"\",\n \"q61\" ->\n \"\"\"\nselect promotions,total,cast(promotions as decimal(15,4))/cast(total as decimal(15,4))*100\nfrom\n (select sum(ss_ext_sales_price) promotions\n from store_sales\n ,store\n ,promotion\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_promo_sk = p_promo_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -6\n and i_category = 'Sports'\n and (p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y')\n and s_gmt_offset = -6\n and d_year = 2001\n and d_moy = 12) promotional_sales,\n (select sum(ss_ext_sales_price) total\n from store_sales\n ,store\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -6\n and i_category = 'Sports'\n and s_gmt_offset = -6\n and d_year = 2001\n and d_moy = 12) all_sales\norder by promotions, total\nlimit 100\"\"\",\n \"q62\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 30) and\n (ws_ship_date_sk - ws_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 60) and\n (ws_ship_date_sk - ws_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 90) and\n (ws_ship_date_sk - ws_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n web_sales\n ,warehouse\n ,ship_mode\n ,web_site\n ,date_dim\nwhere\n d_month_seq between 1215 and 1215 + 11\nand ws_ship_date_sk = d_date_sk\nand ws_warehouse_sk = w_warehouse_sk\nand ws_ship_mode_sk = sm_ship_mode_sk\nand ws_web_site_sk = web_site_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\nlimit 100\"\"\",\n \"q63\" ->\n \"\"\"\nselect *\nfrom (select i_manager_id\n ,sum(ss_sales_price) sum_sales\n ,avg(sum(ss_sales_price)) over (partition by i_manager_id) avg_monthly_sales\n from item\n ,store_sales\n ,date_dim\n ,store\n where ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and d_month_seq in (1211,1211+1,1211+2,1211+3,1211+4,1211+5,1211+6,1211+7,1211+8,1211+9,1211+10,1211+11)\n and (( i_category in ('Books','Children','Electronics')\n and i_class in ('personal','portable','reference','self-help')\n and i_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t 'exportiunivamalg #9','scholaramalgamalg #9'))\n or( i_category in ('Women','Music','Men')\n and i_class in ('accessories','classical','fragrances','pants')\n and i_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t 'importoamalg #1')))\ngroup by i_manager_id, d_moy) tmp1\nwhere case when avg_monthly_sales > 0 then abs (sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\norder by i_manager_id\n ,avg_monthly_sales\n ,sum_sales\nlimit 100\"\"\",\n \"q64\" ->\n \"\"\"\nwith cs_ui as\n (select cs_item_sk\n ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund\n from catalog_sales\n ,catalog_returns\n where cs_item_sk = cr_item_sk\n and cs_order_number = cr_order_number\n group by cs_item_sk\n having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit)),\ncross_sales as\n (select i_product_name product_name\n ,i_item_sk item_sk\n ,s_store_name store_name\n ,s_zip store_zip\n ,ad1.ca_street_number b_street_number\n ,ad1.ca_street_name b_street_name\n ,ad1.ca_city b_city\n ,ad1.ca_zip b_zip\n ,ad2.ca_street_number c_street_number\n ,ad2.ca_street_name c_street_name\n ,ad2.ca_city c_city\n ,ad2.ca_zip c_zip\n ,d1.d_year as syear\n ,d2.d_year as fsyear\n ,d3.d_year s2year\n ,count(*) cnt\n ,sum(ss_wholesale_cost) s1\n ,sum(ss_list_price) s2\n ,sum(ss_coupon_amt) s3\n FROM store_sales\n ,store_returns\n ,cs_ui\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,customer\n ,customer_demographics cd1\n ,customer_demographics cd2\n ,promotion\n ,household_demographics hd1\n ,household_demographics hd2\n ,customer_address ad1\n ,customer_address ad2\n ,income_band ib1\n ,income_band ib2\n ,item\n WHERE ss_store_sk = s_store_sk AND\n ss_sold_date_sk = d1.d_date_sk AND\n ss_customer_sk = c_customer_sk AND\n ss_cdemo_sk= cd1.cd_demo_sk AND\n ss_hdemo_sk = hd1.hd_demo_sk AND\n ss_addr_sk = ad1.ca_address_sk and\n ss_item_sk = i_item_sk and\n ss_item_sk = sr_item_sk and\n ss_ticket_number = sr_ticket_number and\n ss_item_sk = cs_ui.cs_item_sk and\n c_current_cdemo_sk = cd2.cd_demo_sk AND\n c_current_hdemo_sk = hd2.hd_demo_sk AND\n c_current_addr_sk = ad2.ca_address_sk and\n c_first_sales_date_sk = d2.d_date_sk and\n c_first_shipto_date_sk = d3.d_date_sk and\n ss_promo_sk = p_promo_sk and\n hd1.hd_income_band_sk = ib1.ib_income_band_sk and\n hd2.hd_income_band_sk = ib2.ib_income_band_sk and\n cd1.cd_marital_status <> cd2.cd_marital_status and\n i_color in ('azure','gainsboro','misty','blush','hot','lemon') and\n i_current_price between 80 and 80 + 10 and\n i_current_price between 80 + 1 and 80 + 15\ngroup by i_product_name\n ,i_item_sk\n ,s_store_name\n ,s_zip\n ,ad1.ca_street_number\n ,ad1.ca_street_name\n ,ad1.ca_city\n ,ad1.ca_zip\n ,ad2.ca_street_number\n ,ad2.ca_street_name\n ,ad2.ca_city\n ,ad2.ca_zip\n ,d1.d_year\n ,d2.d_year\n ,d3.d_year\n)\nselect cs1.product_name\n ,cs1.store_name\n ,cs1.store_zip\n ,cs1.b_street_number\n ,cs1.b_street_name\n ,cs1.b_city\n ,cs1.b_zip\n ,cs1.c_street_number\n ,cs1.c_street_name\n ,cs1.c_city\n ,cs1.c_zip\n ,cs1.syear\n ,cs1.cnt\n ,cs1.s1 as s11\n ,cs1.s2 as s21\n ,cs1.s3 as s31\n ,cs2.s1 as s12\n ,cs2.s2 as s22\n ,cs2.s3 as s32\n ,cs2.syear\n ,cs2.cnt\nfrom cross_sales cs1,cross_sales cs2\nwhere cs1.item_sk=cs2.item_sk and\n cs1.syear = 1999 and\n cs2.syear = 1999 + 1 and\n cs2.cnt <= cs1.cnt and\n cs1.store_name = cs2.store_name and\n cs1.store_zip = cs2.store_zip\norder by cs1.product_name\n ,cs1.store_name\n ,cs2.cnt\n ,cs1.s1\n ,cs2.s1\"\"\",\n \"q65\" ->\n \"\"\"\nselect\n\ts_store_name,\n\ti_item_desc,\n\tsc.revenue,\n\ti_current_price,\n\ti_wholesale_cost,\n\ti_brand\n from store, item,\n (select ss_store_sk, avg(revenue) as ave\n \tfrom\n \t (select ss_store_sk, ss_item_sk,\n \t\t sum(ss_sales_price) as revenue\n \t\tfrom store_sales, date_dim\n \t\twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1186 and 1186+11\n \t\tgroup by ss_store_sk, ss_item_sk) sa\n \tgroup by ss_store_sk) sb,\n (select ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue\n \tfrom store_sales, date_dim\n \twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1186 and 1186+11\n \tgroup by ss_store_sk, ss_item_sk) sc\n where sb.ss_store_sk = sc.ss_store_sk and\n sc.revenue <= 0.1 * sb.ave and\n s_store_sk = sc.ss_store_sk and\n i_item_sk = sc.ss_item_sk\n order by s_store_name, i_item_desc\nlimit 100\"\"\",\n \"q66\" ->\n \"\"\"\nselect\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,ship_carriers\n ,year\n \t,sum(jan_sales) as jan_sales\n \t,sum(feb_sales) as feb_sales\n \t,sum(mar_sales) as mar_sales\n \t,sum(apr_sales) as apr_sales\n \t,sum(may_sales) as may_sales\n \t,sum(jun_sales) as jun_sales\n \t,sum(jul_sales) as jul_sales\n \t,sum(aug_sales) as aug_sales\n \t,sum(sep_sales) as sep_sales\n \t,sum(oct_sales) as oct_sales\n \t,sum(nov_sales) as nov_sales\n \t,sum(dec_sales) as dec_sales\n \t,sum(jan_sales/w_warehouse_sq_ft) as jan_sales_per_sq_foot\n \t,sum(feb_sales/w_warehouse_sq_ft) as feb_sales_per_sq_foot\n \t,sum(mar_sales/w_warehouse_sq_ft) as mar_sales_per_sq_foot\n \t,sum(apr_sales/w_warehouse_sq_ft) as apr_sales_per_sq_foot\n \t,sum(may_sales/w_warehouse_sq_ft) as may_sales_per_sq_foot\n \t,sum(jun_sales/w_warehouse_sq_ft) as jun_sales_per_sq_foot\n \t,sum(jul_sales/w_warehouse_sq_ft) as jul_sales_per_sq_foot\n \t,sum(aug_sales/w_warehouse_sq_ft) as aug_sales_per_sq_foot\n \t,sum(sep_sales/w_warehouse_sq_ft) as sep_sales_per_sq_foot\n \t,sum(oct_sales/w_warehouse_sq_ft) as oct_sales_per_sq_foot\n \t,sum(nov_sales/w_warehouse_sq_ft) as nov_sales_per_sq_foot\n \t,sum(dec_sales/w_warehouse_sq_ft) as dec_sales_per_sq_foot\n \t,sum(jan_net) as jan_net\n \t,sum(feb_net) as feb_net\n \t,sum(mar_net) as mar_net\n \t,sum(apr_net) as apr_net\n \t,sum(may_net) as may_net\n \t,sum(jun_net) as jun_net\n \t,sum(jul_net) as jul_net\n \t,sum(aug_net) as aug_net\n \t,sum(sep_net) as sep_net\n \t,sum(oct_net) as oct_net\n \t,sum(nov_net) as nov_net\n \t,sum(dec_net) as dec_net\n from (\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'MSC' || ',' || 'GERMA' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen ws_sales_price* ws_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen ws_sales_price* ws_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen ws_sales_price* ws_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen ws_sales_price* ws_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen ws_sales_price* ws_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen ws_sales_price* ws_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen ws_sales_price* ws_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen ws_sales_price* ws_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen ws_sales_price* ws_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen ws_sales_price* ws_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen ws_sales_price* ws_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen ws_sales_price* ws_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen ws_net_paid_inc_ship_tax * ws_quantity else 0 end) as dec_net\n from\n web_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n ws_warehouse_sk = w_warehouse_sk\n and ws_sold_date_sk = d_date_sk\n and ws_sold_time_sk = t_time_sk\n \tand ws_ship_mode_sk = sm_ship_mode_sk\n and d_year = 2001\n \tand t_time between 9453 and 9453+28800\n \tand sm_carrier in ('MSC','GERMA')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n union all\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'MSC' || ',' || 'GERMA' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen cs_net_paid_inc_ship * cs_quantity else 0 end) as dec_net\n from\n catalog_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and cs_sold_time_sk = t_time_sk\n \tand cs_ship_mode_sk = sm_ship_mode_sk\n and d_year = 2001\n \tand t_time between 9453 AND 9453+28800\n \tand sm_carrier in ('MSC','GERMA')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n ) x\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,ship_carriers\n ,year\n order by w_warehouse_name\n limit 100\"\"\",\n \"q67\" ->\n \"\"\"\nselect *\nfrom (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rank() over (partition by i_category order by sumsales desc) rk\n from (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sum(coalesce(ss_sales_price*ss_quantity,0)) sumsales\n from store_sales\n ,date_dim\n ,store\n ,item\n where ss_sold_date_sk=d_date_sk\n and ss_item_sk=i_item_sk\n and ss_store_sk = s_store_sk\n and d_month_seq between 1185 and 1185+11\n group by rollup(i_category, i_class, i_brand, i_product_name, d_year, d_qoy, d_moy,s_store_id))dw1) dw2\nwhere rk <= 100\norder by i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rk\nlimit 100\"\"\",\n \"q68\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,extended_price\n ,extended_tax\n ,list_price\n from (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_ext_sales_price) extended_price\n ,sum(ss_ext_list_price) list_price\n ,sum(ss_ext_tax) extended_tax\n from store_sales\n ,date_dim\n ,store\n ,household_demographics\n ,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_dep_count = 4 or\n household_demographics.hd_vehicle_count= 0)\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_city in ('Pleasant Hill','Bethel')\n group by ss_ticket_number\n ,ss_customer_sk\n ,ss_addr_sk,ca_city) dn\n ,customer\n ,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,ss_ticket_number\n limit 100\"\"\",\n \"q69\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_state in ('MO','MN','AZ') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2003 and\n d_moy between 2 and 2+2) and\n (not exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2003 and\n d_moy between 2 and 2+2) and\n not exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2003 and\n d_moy between 2 and 2+2))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n limit 100\"\"\",\n \"q70\" ->\n \"\"\"\nselect\n sum(ss_net_profit) as total_sum\n ,s_state\n ,s_county\n ,grouping(s_state)+grouping(s_county) as lochierarchy\n ,rank() over (\n \tpartition by grouping(s_state)+grouping(s_county),\n \tcase when grouping(s_county) = 0 then s_state end\n \torder by sum(ss_net_profit) desc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,store\n where\n d1.d_month_seq between 1218 and 1218+11\n and d1.d_date_sk = ss_sold_date_sk\n and s_store_sk = ss_store_sk\n and s_state in\n ( select s_state\n from (select s_state as s_state,\n \t\t\t rank() over ( partition by s_state order by sum(ss_net_profit) desc) as ranking\n from store_sales, store, date_dim\n where d_month_seq between 1218 and 1218+11\n \t\t\t and d_date_sk = ss_sold_date_sk\n \t\t\t and s_store_sk = ss_store_sk\n group by s_state\n ) tmp1\n where ranking <= 5\n )\n group by rollup(s_state,s_county)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then s_state end\n ,rank_within_parent\n limit 100\"\"\",\n \"q71\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,t_hour,t_minute,\n \tsum(ext_price) ext_price\n from item, (select ws_ext_sales_price as ext_price,\n ws_sold_date_sk as sold_date_sk,\n ws_item_sk as sold_item_sk,\n ws_sold_time_sk as time_sk\n from web_sales,date_dim\n where d_date_sk = ws_sold_date_sk\n and d_moy=12\n and d_year=2000\n union all\n select cs_ext_sales_price as ext_price,\n cs_sold_date_sk as sold_date_sk,\n cs_item_sk as sold_item_sk,\n cs_sold_time_sk as time_sk\n from catalog_sales,date_dim\n where d_date_sk = cs_sold_date_sk\n and d_moy=12\n and d_year=2000\n union all\n select ss_ext_sales_price as ext_price,\n ss_sold_date_sk as sold_date_sk,\n ss_item_sk as sold_item_sk,\n ss_sold_time_sk as time_sk\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n and d_moy=12\n and d_year=2000\n ) tmp,time_dim\n where\n sold_item_sk = i_item_sk\n and i_manager_id=1\n and time_sk = t_time_sk\n and (t_meal_time = 'breakfast' or t_meal_time = 'dinner')\n group by i_brand, i_brand_id,t_hour,t_minute\n order by ext_price desc, i_brand_id\n \"\"\",\n \"q72\" ->\n \"\"\"\nselect i_item_desc\n ,w_warehouse_name\n ,d1.d_week_seq\n ,sum(case when p_promo_sk is null then 1 else 0 end) no_promo\n ,sum(case when p_promo_sk is not null then 1 else 0 end) promo\n ,count(*) total_cnt\nfrom catalog_sales\njoin inventory on (cs_item_sk = inv_item_sk)\njoin warehouse on (w_warehouse_sk=inv_warehouse_sk)\njoin item on (i_item_sk = cs_item_sk)\njoin customer_demographics on (cs_bill_cdemo_sk = cd_demo_sk)\njoin household_demographics on (cs_bill_hdemo_sk = hd_demo_sk)\njoin date_dim d1 on (cs_sold_date_sk = d1.d_date_sk)\njoin date_dim d2 on (inv_date_sk = d2.d_date_sk)\njoin date_dim d3 on (cs_ship_date_sk = d3.d_date_sk)\nleft outer join promotion on (cs_promo_sk=p_promo_sk)\nleft outer join catalog_returns on (cr_item_sk = cs_item_sk and cr_order_number = cs_order_number)\nwhere d1.d_week_seq = d2.d_week_seq\n and inv_quantity_on_hand < cs_quantity\n and d3.d_date > d1.d_date + interval 5 days\n and hd_buy_potential = '1001-5000'\n and d1.d_year = 2000\n and cd_marital_status = 'D'\ngroup by i_item_desc,w_warehouse_name,d1.d_week_seq\norder by total_cnt desc, i_item_desc, w_warehouse_name, d_week_seq\nlimit 100\"\"\",\n \"q73\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_buy_potential = '>10000' or\n household_demographics.hd_buy_potential = '5001-10000')\n and household_demographics.hd_vehicle_count > 0\n and case when household_demographics.hd_vehicle_count > 0 then\n household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count else null end > 1\n and date_dim.d_year in (2000,2000+1,2000+2)\n and store.s_county in ('Lea County','Furnas County','Pennington County','Bronx County')\n group by ss_ticket_number,ss_customer_sk) dj,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 1 and 5\n order by cnt desc, c_last_name asc\"\"\",\n \"q74\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,sum(ss_net_paid) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (1998,1998+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,sum(ws_net_paid) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n and d_year in (1998,1998+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n )\n select\n t_s_secyear.customer_id, t_s_secyear.customer_first_name, t_s_secyear.customer_last_name\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.year = 1998\n and t_s_secyear.year = 1998+1\n and t_w_firstyear.year = 1998\n and t_w_secyear.year = 1998+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n order by 3,1,2\nlimit 100\"\"\",\n \"q75\" ->\n \"\"\"\nWITH all_sales AS (\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,SUM(sales_cnt) AS sales_cnt\n ,SUM(sales_amt) AS sales_amt\n FROM (SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,cs_quantity - COALESCE(cr_return_quantity,0) AS sales_cnt\n ,cs_ext_sales_price - COALESCE(cr_return_amount,0.0) AS sales_amt\n FROM catalog_sales JOIN item ON i_item_sk=cs_item_sk\n JOIN date_dim ON d_date_sk=cs_sold_date_sk\n LEFT JOIN catalog_returns ON (cs_order_number=cr_order_number\n AND cs_item_sk=cr_item_sk)\n WHERE i_category='Sports'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ss_quantity - COALESCE(sr_return_quantity,0) AS sales_cnt\n ,ss_ext_sales_price - COALESCE(sr_return_amt,0.0) AS sales_amt\n FROM store_sales JOIN item ON i_item_sk=ss_item_sk\n JOIN date_dim ON d_date_sk=ss_sold_date_sk\n LEFT JOIN store_returns ON (ss_ticket_number=sr_ticket_number\n AND ss_item_sk=sr_item_sk)\n WHERE i_category='Sports'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ws_quantity - COALESCE(wr_return_quantity,0) AS sales_cnt\n ,ws_ext_sales_price - COALESCE(wr_return_amt,0.0) AS sales_amt\n FROM web_sales JOIN item ON i_item_sk=ws_item_sk\n JOIN date_dim ON d_date_sk=ws_sold_date_sk\n LEFT JOIN web_returns ON (ws_order_number=wr_order_number\n AND ws_item_sk=wr_item_sk)\n WHERE i_category='Sports') sales_detail\n GROUP BY d_year, i_brand_id, i_class_id, i_category_id, i_manufact_id)\n SELECT prev_yr.d_year AS prev_year\n ,curr_yr.d_year AS year\n ,curr_yr.i_brand_id\n ,curr_yr.i_class_id\n ,curr_yr.i_category_id\n ,curr_yr.i_manufact_id\n ,prev_yr.sales_cnt AS prev_yr_cnt\n ,curr_yr.sales_cnt AS curr_yr_cnt\n ,curr_yr.sales_cnt-prev_yr.sales_cnt AS sales_cnt_diff\n ,curr_yr.sales_amt-prev_yr.sales_amt AS sales_amt_diff\n FROM all_sales curr_yr, all_sales prev_yr\n WHERE curr_yr.i_brand_id=prev_yr.i_brand_id\n AND curr_yr.i_class_id=prev_yr.i_class_id\n AND curr_yr.i_category_id=prev_yr.i_category_id\n AND curr_yr.i_manufact_id=prev_yr.i_manufact_id\n AND curr_yr.d_year=2001\n AND prev_yr.d_year=2001-1\n AND CAST(curr_yr.sales_cnt AS DECIMAL(17,2))/CAST(prev_yr.sales_cnt AS DECIMAL(17,2))<0.9\n ORDER BY sales_cnt_diff,sales_amt_diff\n limit 100\"\"\",\n \"q76\" ->\n \"\"\"\nselect channel, col_name, d_year, d_qoy, i_category, COUNT(*) sales_cnt, SUM(ext_sales_price) sales_amt FROM (\n SELECT 'store' as channel, 'ss_customer_sk' col_name, d_year, d_qoy, i_category, ss_ext_sales_price ext_sales_price\n FROM store_sales, item, date_dim\n WHERE ss_customer_sk IS NULL\n AND ss_sold_date_sk=d_date_sk\n AND ss_item_sk=i_item_sk\n UNION ALL\n SELECT 'web' as channel, 'ws_ship_addr_sk' col_name, d_year, d_qoy, i_category, ws_ext_sales_price ext_sales_price\n FROM web_sales, item, date_dim\n WHERE ws_ship_addr_sk IS NULL\n AND ws_sold_date_sk=d_date_sk\n AND ws_item_sk=i_item_sk\n UNION ALL\n SELECT 'catalog' as channel, 'cs_ship_mode_sk' col_name, d_year, d_qoy, i_category, cs_ext_sales_price ext_sales_price\n FROM catalog_sales, item, date_dim\n WHERE cs_ship_mode_sk IS NULL\n AND cs_sold_date_sk=d_date_sk\n AND cs_item_sk=i_item_sk) foo\nGROUP BY channel, col_name, d_year, d_qoy, i_category\nORDER BY channel, col_name, d_year, d_qoy, i_category\nlimit 100\"\"\",\n \"q77\" ->\n \"\"\"\nwith ss as\n (select s_store_sk,\n sum(ss_ext_sales_price) as sales,\n sum(ss_net_profit) as profit\n from store_sales,\n date_dim,\n store\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2000-08-16' as date)\n and (cast('2000-08-16' as date) + INTERVAL 30 days)\n and ss_store_sk = s_store_sk\n group by s_store_sk)\n ,\n sr as\n (select s_store_sk,\n sum(sr_return_amt) as returns,\n sum(sr_net_loss) as profit_loss\n from store_returns,\n date_dim,\n store\n where sr_returned_date_sk = d_date_sk\n and d_date between cast('2000-08-16' as date)\n and (cast('2000-08-16' as date) + INTERVAL 30 days)\n and sr_store_sk = s_store_sk\n group by s_store_sk),\n cs as\n (select cs_call_center_sk,\n sum(cs_ext_sales_price) as sales,\n sum(cs_net_profit) as profit\n from catalog_sales,\n date_dim\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2000-08-16' as date)\n and (cast('2000-08-16' as date) + INTERVAL 30 days)\n group by cs_call_center_sk\n ),\n cr as\n (select cr_call_center_sk,\n sum(cr_return_amount) as returns,\n sum(cr_net_loss) as profit_loss\n from catalog_returns,\n date_dim\n where cr_returned_date_sk = d_date_sk\n and d_date between cast('2000-08-16' as date)\n and (cast('2000-08-16' as date) + INTERVAL 30 days)\n group by cr_call_center_sk\n ),\n ws as\n ( select wp_web_page_sk,\n sum(ws_ext_sales_price) as sales,\n sum(ws_net_profit) as profit\n from web_sales,\n date_dim,\n web_page\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2000-08-16' as date)\n and (cast('2000-08-16' as date) + INTERVAL 30 days)\n and ws_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk),\n wr as\n (select wp_web_page_sk,\n sum(wr_return_amt) as returns,\n sum(wr_net_loss) as profit_loss\n from web_returns,\n date_dim,\n web_page\n where wr_returned_date_sk = d_date_sk\n and d_date between cast('2000-08-16' as date)\n and (cast('2000-08-16' as date) + INTERVAL 30 days)\n and wr_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , ss.s_store_sk as id\n , sales\n , coalesce(returns, 0) as returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ss left join sr\n on ss.s_store_sk = sr.s_store_sk\n union all\n select 'catalog channel' as channel\n , cs_call_center_sk as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from cs\n , cr\n union all\n select 'web channel' as channel\n , ws.wp_web_page_sk as id\n , sales\n , coalesce(returns, 0) returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ws left join wr\n on ws.wp_web_page_sk = wr.wp_web_page_sk\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q78\" ->\n \"\"\"\nwith ws as\n (select d_year AS ws_sold_year, ws_item_sk,\n ws_bill_customer_sk ws_customer_sk,\n sum(ws_quantity) ws_qty,\n sum(ws_wholesale_cost) ws_wc,\n sum(ws_sales_price) ws_sp\n from web_sales\n left join web_returns on wr_order_number=ws_order_number and ws_item_sk=wr_item_sk\n join date_dim on ws_sold_date_sk = d_date_sk\n where wr_order_number is null\n group by d_year, ws_item_sk, ws_bill_customer_sk\n ),\ncs as\n (select d_year AS cs_sold_year, cs_item_sk,\n cs_bill_customer_sk cs_customer_sk,\n sum(cs_quantity) cs_qty,\n sum(cs_wholesale_cost) cs_wc,\n sum(cs_sales_price) cs_sp\n from catalog_sales\n left join catalog_returns on cr_order_number=cs_order_number and cs_item_sk=cr_item_sk\n join date_dim on cs_sold_date_sk = d_date_sk\n where cr_order_number is null\n group by d_year, cs_item_sk, cs_bill_customer_sk\n ),\nss as\n (select d_year AS ss_sold_year, ss_item_sk,\n ss_customer_sk,\n sum(ss_quantity) ss_qty,\n sum(ss_wholesale_cost) ss_wc,\n sum(ss_sales_price) ss_sp\n from store_sales\n left join store_returns on sr_ticket_number=ss_ticket_number and ss_item_sk=sr_item_sk\n join date_dim on ss_sold_date_sk = d_date_sk\n where sr_ticket_number is null\n group by d_year, ss_item_sk, ss_customer_sk\n )\n select\nss_customer_sk,\nround(ss_qty/(coalesce(ws_qty,0)+coalesce(cs_qty,0)),2) ratio,\nss_qty store_qty, ss_wc store_wholesale_cost, ss_sp store_sales_price,\ncoalesce(ws_qty,0)+coalesce(cs_qty,0) other_chan_qty,\ncoalesce(ws_wc,0)+coalesce(cs_wc,0) other_chan_wholesale_cost,\ncoalesce(ws_sp,0)+coalesce(cs_sp,0) other_chan_sales_price\nfrom ss\nleft join ws on (ws_sold_year=ss_sold_year and ws_item_sk=ss_item_sk and ws_customer_sk=ss_customer_sk)\nleft join cs on (cs_sold_year=ss_sold_year and cs_item_sk=ss_item_sk and cs_customer_sk=ss_customer_sk)\nwhere (coalesce(ws_qty,0)>0 or coalesce(cs_qty, 0)>0) and ss_sold_year=2001\norder by\n ss_customer_sk,\n ss_qty desc, ss_wc desc, ss_sp desc,\n other_chan_qty,\n other_chan_wholesale_cost,\n other_chan_sales_price,\n ratio\nlimit 100\"\"\",\n \"q79\" ->\n \"\"\"\nselect\n c_last_name,c_first_name,substr(s_city,1,30),ss_ticket_number,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,store.s_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (household_demographics.hd_dep_count = 0 or household_demographics.hd_vehicle_count > 3)\n and date_dim.d_dow = 1\n and date_dim.d_year in (1998,1998+1,1998+2)\n and store.s_number_employees between 200 and 295\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,store.s_city) ms,customer\n where ss_customer_sk = c_customer_sk\n order by c_last_name,c_first_name,substr(s_city,1,30), profit\nlimit 100\"\"\",\n \"q80\" ->\n \"\"\"\nwith ssr as\n (select s_store_id as store_id,\n sum(ss_ext_sales_price) as sales,\n sum(coalesce(sr_return_amt, 0)) as returns,\n sum(ss_net_profit - coalesce(sr_net_loss, 0)) as profit\n from store_sales left outer join store_returns on\n (ss_item_sk = sr_item_sk and ss_ticket_number = sr_ticket_number),\n date_dim,\n store,\n item,\n promotion\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2002-08-06' as date)\n and (cast('2002-08-06' as date) + INTERVAL 60 days)\n and ss_store_sk = s_store_sk\n and ss_item_sk = i_item_sk\n and i_current_price > 50\n and ss_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id as catalog_page_id,\n sum(cs_ext_sales_price) as sales,\n sum(coalesce(cr_return_amount, 0)) as returns,\n sum(cs_net_profit - coalesce(cr_net_loss, 0)) as profit\n from catalog_sales left outer join catalog_returns on\n (cs_item_sk = cr_item_sk and cs_order_number = cr_order_number),\n date_dim,\n catalog_page,\n item,\n promotion\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2002-08-06' as date)\n and (cast('2002-08-06' as date) + INTERVAL 60 days)\n and cs_catalog_page_sk = cp_catalog_page_sk\n and cs_item_sk = i_item_sk\n and i_current_price > 50\n and cs_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(ws_ext_sales_price) as sales,\n sum(coalesce(wr_return_amt, 0)) as returns,\n sum(ws_net_profit - coalesce(wr_net_loss, 0)) as profit\n from web_sales left outer join web_returns on\n (ws_item_sk = wr_item_sk and ws_order_number = wr_order_number),\n date_dim,\n web_site,\n item,\n promotion\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2002-08-06' as date)\n and (cast('2002-08-06' as date) + INTERVAL 60 days)\n and ws_web_site_sk = web_site_sk\n and ws_item_sk = i_item_sk\n and i_current_price > 50\n and ws_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || store_id as id\n , sales\n , returns\n , profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || catalog_page_id as id\n , sales\n , returns\n , profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q81\" ->\n \"\"\"\nwith customer_total_return as\n (select cr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(cr_return_amt_inc_tax) as ctr_total_return\n from catalog_returns\n ,date_dim\n ,customer_address\n where cr_returned_date_sk = d_date_sk\n and d_year =1998\n and cr_returning_addr_sk = ca_address_sk\n group by cr_returning_customer_sk\n ,ca_state )\n select c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'TX'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n limit 100\"\"\",\n \"q82\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, store_sales\n where i_current_price between 49 and 49+30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2001-01-28' as date) and (cast('2001-01-28' as date) + INTERVAL 60 days)\n and i_manufact_id in (80,675,292,17)\n and inv_quantity_on_hand between 100 and 500\n and ss_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q83\" ->\n \"\"\"\nwith sr_items as\n (select i_item_id item_id,\n sum(sr_return_quantity) sr_item_qty\n from store_returns,\n item,\n date_dim\n where sr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('2000-06-17','2000-08-22','2000-11-17')))\n and sr_returned_date_sk = d_date_sk\n group by i_item_id),\n cr_items as\n (select i_item_id item_id,\n sum(cr_return_quantity) cr_item_qty\n from catalog_returns,\n item,\n date_dim\n where cr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('2000-06-17','2000-08-22','2000-11-17')))\n and cr_returned_date_sk = d_date_sk\n group by i_item_id),\n wr_items as\n (select i_item_id item_id,\n sum(wr_return_quantity) wr_item_qty\n from web_returns,\n item,\n date_dim\n where wr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t\twhere d_date in ('2000-06-17','2000-08-22','2000-11-17')))\n and wr_returned_date_sk = d_date_sk\n group by i_item_id)\n select sr_items.item_id\n ,sr_item_qty\n ,sr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 sr_dev\n ,cr_item_qty\n ,cr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 cr_dev\n ,wr_item_qty\n ,wr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 wr_dev\n ,(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 average\n from sr_items\n ,cr_items\n ,wr_items\n where sr_items.item_id=cr_items.item_id\n and sr_items.item_id=wr_items.item_id\n order by sr_items.item_id\n ,sr_item_qty\n limit 100\"\"\",\n \"q84\" ->\n \"\"\"\nselect c_customer_id as customer_id\n , coalesce(c_last_name,'') || ', ' || coalesce(c_first_name,'') as customername\n from customer\n ,customer_address\n ,customer_demographics\n ,household_demographics\n ,income_band\n ,store_returns\n where ca_city\t = 'Hopewell'\n and c_current_addr_sk = ca_address_sk\n and ib_lower_bound >= 37855\n and ib_upper_bound <= 37855 + 50000\n and ib_income_band_sk = hd_income_band_sk\n and cd_demo_sk = c_current_cdemo_sk\n and hd_demo_sk = c_current_hdemo_sk\n and sr_cdemo_sk = cd_demo_sk\n order by c_customer_id\n limit 100\"\"\",\n \"q85\" ->\n \"\"\"\nselect substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\n from web_sales, web_returns, web_page, customer_demographics cd1,\n customer_demographics cd2, customer_address, date_dim, reason\n where ws_web_page_sk = wp_web_page_sk\n and ws_item_sk = wr_item_sk\n and ws_order_number = wr_order_number\n and ws_sold_date_sk = d_date_sk and d_year = 2001\n and cd1.cd_demo_sk = wr_refunded_cdemo_sk\n and cd2.cd_demo_sk = wr_returning_cdemo_sk\n and ca_address_sk = wr_refunded_addr_sk\n and r_reason_sk = wr_reason_sk\n and\n (\n (\n cd1.cd_marital_status = 'M'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = '4 yr Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 100.00 and 150.00\n )\n or\n (\n cd1.cd_marital_status = 'S'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'College'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 50.00 and 100.00\n )\n or\n (\n cd1.cd_marital_status = 'D'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'Secondary'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ca_country = 'United States'\n and\n ca_state in ('TX', 'VA', 'CA')\n and ws_net_profit between 100 and 200\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('AR', 'NE', 'MO')\n and ws_net_profit between 150 and 300\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('IA', 'MS', 'WA')\n and ws_net_profit between 50 and 250\n )\n )\ngroup by r_reason_desc\norder by substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\nlimit 100\"\"\",\n \"q86\" ->\n \"\"\"\nselect\n sum(ws_net_paid) as total_sum\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ws_net_paid) desc) as rank_within_parent\n from\n web_sales\n ,date_dim d1\n ,item\n where\n d1.d_month_seq between 1215 and 1215+11\n and d1.d_date_sk = ws_sold_date_sk\n and i_item_sk = ws_item_sk\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc,\n case when lochierarchy = 0 then i_category end,\n rank_within_parent\n limit 100\"\"\",\n \"q87\" ->\n \"\"\"\nselect count(*)\nfrom ((select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1221 and 1221+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1221 and 1221+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1221 and 1221+11)\n) cool_cust\"\"\",\n \"q88\" ->\n \"\"\"\nselect *\nfrom\n (select count(*) h8_30_to_9\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 8\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s1,\n (select count(*) h9_to_9_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s2,\n (select count(*) h9_30_to_10\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s3,\n (select count(*) h10_to_10_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s4,\n (select count(*) h10_30_to_11\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s5,\n (select count(*) h11_to_11_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s6,\n (select count(*) h11_30_to_12\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s7,\n (select count(*) h12_to_12_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 12\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s8\"\"\",\n \"q89\" ->\n \"\"\"\nselect *\nfrom(\nselect i_category, i_class, i_brand,\n s_store_name, s_company_name,\n d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand, s_store_name, s_company_name)\n avg_monthly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n d_year in (2000) and\n ((i_category in ('Home','Music','Books') and\n i_class in ('glassware','classical','fiction')\n )\n or (i_category in ('Jewelry','Sports','Women') and\n i_class in ('semi-precious','baseball','dresses')\n ))\ngroup by i_category, i_class, i_brand,\n s_store_name, s_company_name, d_moy) tmp1\nwhere case when (avg_monthly_sales <> 0) then (abs(sum_sales - avg_monthly_sales) / avg_monthly_sales) else null end > 0.1\norder by sum_sales - avg_monthly_sales, s_store_name\nlimit 100\"\"\",\n \"q90\" ->\n \"\"\"\nselect cast(amc as decimal(15,4))/cast(pmc as decimal(15,4)) am_pm_ratio\n from ( select count(*) amc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 9 and 9+1\n and household_demographics.hd_dep_count = 3\n and web_page.wp_char_count between 5000 and 5200) at,\n ( select count(*) pmc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 16 and 16+1\n and household_demographics.hd_dep_count = 3\n and web_page.wp_char_count between 5000 and 5200) pt\n order by am_pm_ratio\n limit 100\"\"\",\n \"q91\" ->\n \"\"\"\nselect\n cc_call_center_id Call_Center,\n cc_name Call_Center_Name,\n cc_manager Manager,\n sum(cr_net_loss) Returns_Loss\nfrom\n call_center,\n catalog_returns,\n date_dim,\n customer,\n customer_address,\n customer_demographics,\n household_demographics\nwhere\n cr_call_center_sk = cc_call_center_sk\nand cr_returned_date_sk = d_date_sk\nand cr_returning_customer_sk= c_customer_sk\nand cd_demo_sk = c_current_cdemo_sk\nand hd_demo_sk = c_current_hdemo_sk\nand ca_address_sk = c_current_addr_sk\nand d_year = 2000\nand d_moy = 12\nand ( (cd_marital_status = 'M' and cd_education_status = 'Unknown')\n or(cd_marital_status = 'W' and cd_education_status = 'Advanced Degree'))\nand hd_buy_potential like 'Unknown%'\nand ca_gmt_offset = -7\ngroup by cc_call_center_id,cc_name,cc_manager,cd_marital_status,cd_education_status\norder by sum(cr_net_loss) desc\"\"\",\n \"q92\" ->\n \"\"\"\nselect\n sum(ws_ext_discount_amt) as `Excess Discount Amount`\nfrom\n web_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 356\nand i_item_sk = ws_item_sk\nand d_date between '2001-03-12' and\n (cast('2001-03-12' as date) + INTERVAL 90 days)\nand d_date_sk = ws_sold_date_sk\nand ws_ext_discount_amt\n > (\n SELECT\n 1.3 * avg(ws_ext_discount_amt)\n FROM\n web_sales\n ,date_dim\n WHERE\n ws_item_sk = i_item_sk\n and d_date between '2001-03-12' and\n (cast('2001-03-12' as date) + INTERVAL 90 days)\n and d_date_sk = ws_sold_date_sk\n )\norder by sum(ws_ext_discount_amt)\nlimit 100\"\"\",\n \"q93\" ->\n \"\"\"\nselect ss_customer_sk\n ,sum(act_sales) sumsales\n from (select ss_item_sk\n ,ss_ticket_number\n ,ss_customer_sk\n ,case when sr_return_quantity is not null then (ss_quantity-sr_return_quantity)*ss_sales_price\n else (ss_quantity*ss_sales_price) end act_sales\n from store_sales left outer join store_returns on (sr_item_sk = ss_item_sk\n and sr_ticket_number = ss_ticket_number)\n ,reason\n where sr_reason_sk = r_reason_sk\n and r_reason_desc = 'reason 66') t\n group by ss_customer_sk\n order by sumsales, ss_customer_sk\nlimit 100\"\"\",\n \"q94\" ->\n \"\"\"\nselect\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '1999-4-01' and\n (cast('1999-4-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'NE'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand exists (select *\n from web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\nand not exists(select *\n from web_returns wr1\n where ws1.ws_order_number = wr1.wr_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q95\" ->\n \"\"\"\nwith ws_wh as\n(select ws1.ws_order_number,ws1.ws_warehouse_sk wh1,ws2.ws_warehouse_sk wh2\n from web_sales ws1,web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\n select\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2002-4-01' and\n (cast('2002-4-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'AL'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand ws1.ws_order_number in (select ws_order_number\n from ws_wh)\nand ws1.ws_order_number in (select wr_order_number\n from web_returns,ws_wh\n where wr_order_number = ws_wh.ws_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q96\" ->\n \"\"\"\nselect count(*)\nfrom store_sales\n ,household_demographics\n ,time_dim, store\nwhere ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 16\n and time_dim.t_minute >= 30\n and household_demographics.hd_dep_count = 6\n and store.s_store_name = 'ese'\norder by count(*)\nlimit 100\"\"\",\n \"q97\" ->\n \"\"\"\nwith ssci as (\nselect ss_customer_sk customer_sk\n ,ss_item_sk item_sk\nfrom store_sales,date_dim\nwhere ss_sold_date_sk = d_date_sk\n and d_month_seq between 1190 and 1190 + 11\ngroup by ss_customer_sk\n ,ss_item_sk),\ncsci as(\n select cs_bill_customer_sk customer_sk\n ,cs_item_sk item_sk\nfrom catalog_sales,date_dim\nwhere cs_sold_date_sk = d_date_sk\n and d_month_seq between 1190 and 1190 + 11\ngroup by cs_bill_customer_sk\n ,cs_item_sk)\n select sum(case when ssci.customer_sk is not null and csci.customer_sk is null then 1 else 0 end) store_only\n ,sum(case when ssci.customer_sk is null and csci.customer_sk is not null then 1 else 0 end) catalog_only\n ,sum(case when ssci.customer_sk is not null and csci.customer_sk is not null then 1 else 0 end) store_and_catalog\nfrom ssci full outer join csci on (ssci.customer_sk=csci.customer_sk\n and ssci.item_sk = csci.item_sk)\nlimit 100\"\"\",\n \"q98\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ss_ext_sales_price) as itemrevenue\n ,sum(ss_ext_sales_price)*100/sum(sum(ss_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tstore_sales\n \t,item\n \t,date_dim\nwhere\n\tss_item_sk = i_item_sk\n \tand i_category in ('Home', 'Sports', 'Men')\n \tand ss_sold_date_sk = d_date_sk\n\tand d_date between cast('2002-01-05' as date)\n\t\t\t\tand (cast('2002-01-05' as date) + interval 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\"\"\",\n \"q99\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 30) and\n (cs_ship_date_sk - cs_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 60) and\n (cs_ship_date_sk - cs_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 90) and\n (cs_ship_date_sk - cs_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n catalog_sales\n ,warehouse\n ,ship_mode\n ,call_center\n ,date_dim\nwhere\n d_month_seq between 1178 and 1178 + 11\nand cs_ship_date_sk = d_date_sk\nand cs_warehouse_sk = w_warehouse_sk\nand cs_ship_mode_sk = sm_ship_mode_sk\nand cs_call_center_sk = cc_call_center_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\nlimit 100\"\"\",\n \"q1\" ->\n \"\"\"\nwith customer_total_return as\n(select sr_customer_sk as ctr_customer_sk\n,sr_store_sk as ctr_store_sk\n,sum(SR_FEE) as ctr_total_return\nfrom store_returns\n,date_dim\nwhere sr_returned_date_sk = d_date_sk\nand d_year =2000\ngroup by sr_customer_sk\n,sr_store_sk)\n select c_customer_id\nfrom customer_total_return ctr1\n,store\n,customer\nwhere ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\nfrom customer_total_return ctr2\nwhere ctr1.ctr_store_sk = ctr2.ctr_store_sk)\nand s_store_sk = ctr1.ctr_store_sk\nand s_state = 'NY'\nand ctr1.ctr_customer_sk = c_customer_sk\norder by c_customer_id\nlimit 100\"\"\",\n \"q2\" ->\n \"\"\"\nwith wscs as\n (select sold_date_sk\n ,sales_price\n from (select ws_sold_date_sk sold_date_sk\n ,ws_ext_sales_price sales_price\n from web_sales\n union all\n select cs_sold_date_sk sold_date_sk\n ,cs_ext_sales_price sales_price\n from catalog_sales)),\n wswscs as\n (select d_week_seq,\n sum(case when (d_day_name='Sunday') then sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then sales_price else null end) sat_sales\n from wscs\n ,date_dim\n where d_date_sk = sold_date_sk\n group by d_week_seq)\n select d_week_seq1\n ,round(sun_sales1/sun_sales2,2)\n ,round(mon_sales1/mon_sales2,2)\n ,round(tue_sales1/tue_sales2,2)\n ,round(wed_sales1/wed_sales2,2)\n ,round(thu_sales1/thu_sales2,2)\n ,round(fri_sales1/fri_sales2,2)\n ,round(sat_sales1/sat_sales2,2)\n from\n (select wswscs.d_week_seq d_week_seq1\n ,sun_sales sun_sales1\n ,mon_sales mon_sales1\n ,tue_sales tue_sales1\n ,wed_sales wed_sales1\n ,thu_sales thu_sales1\n ,fri_sales fri_sales1\n ,sat_sales sat_sales1\n from wswscs,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998) y,\n (select wswscs.d_week_seq d_week_seq2\n ,sun_sales sun_sales2\n ,mon_sales mon_sales2\n ,tue_sales tue_sales2\n ,wed_sales wed_sales2\n ,thu_sales thu_sales2\n ,fri_sales fri_sales2\n ,sat_sales sat_sales2\n from wswscs\n ,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998+1) z\n where d_week_seq1=d_week_seq2-53\n order by d_week_seq1\"\"\",\n \"q3\" ->\n \"\"\"\nselect dt.d_year\n ,item.i_brand_id brand_id\n ,item.i_brand brand\n ,sum(ss_sales_price) sum_agg\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manufact_id = 816\n and dt.d_moy=11\n group by dt.d_year\n ,item.i_brand\n ,item.i_brand_id\n order by dt.d_year\n ,sum_agg desc\n ,brand_id\n limit 100\"\"\",\n \"q4\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(((ss_ext_list_price-ss_ext_wholesale_cost-ss_ext_discount_amt)+ss_ext_sales_price)/2) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((cs_ext_list_price-cs_ext_wholesale_cost-cs_ext_discount_amt)+cs_ext_sales_price)/2) ) year_total\n ,'c' sale_type\n from customer\n ,catalog_sales\n ,date_dim\n where c_customer_sk = cs_bill_customer_sk\n and cs_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\nunion all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((ws_ext_list_price-ws_ext_wholesale_cost-ws_ext_discount_amt)+ws_ext_sales_price)/2) ) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_c_firstyear\n ,year_total t_c_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_c_secyear.customer_id\n and t_s_firstyear.customer_id = t_c_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_c_firstyear.sale_type = 'c'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_c_secyear.sale_type = 'c'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 1999\n and t_s_secyear.dyear = 1999+1\n and t_c_firstyear.dyear = 1999\n and t_c_secyear.dyear = 1999+1\n and t_w_firstyear.dyear = 1999\n and t_w_secyear.dyear = 1999+1\n and t_s_firstyear.year_total > 0\n and t_c_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\nlimit 100\"\"\",\n \"q5\" ->\n \"\"\"\nwith ssr as\n (select s_store_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ss_store_sk as store_sk,\n ss_sold_date_sk as date_sk,\n ss_ext_sales_price as sales_price,\n ss_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from store_sales\n union all\n select sr_store_sk as store_sk,\n sr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n sr_return_amt as return_amt,\n sr_net_loss as net_loss\n from store_returns\n ) salesreturns,\n date_dim,\n store\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and store_sk = s_store_sk\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select cs_catalog_page_sk as page_sk,\n cs_sold_date_sk as date_sk,\n cs_ext_sales_price as sales_price,\n cs_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from catalog_sales\n union all\n select cr_catalog_page_sk as page_sk,\n cr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n cr_return_amount as return_amt,\n cr_net_loss as net_loss\n from catalog_returns\n ) salesreturns,\n date_dim,\n catalog_page\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and page_sk = cp_catalog_page_sk\n group by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ws_web_site_sk as wsr_web_site_sk,\n ws_sold_date_sk as date_sk,\n ws_ext_sales_price as sales_price,\n ws_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from web_sales\n union all\n select ws_web_site_sk as wsr_web_site_sk,\n wr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n wr_return_amt as return_amt,\n wr_net_loss as net_loss\n from web_returns left outer join web_sales on\n ( wr_item_sk = ws_item_sk\n and wr_order_number = ws_order_number)\n ) salesreturns,\n date_dim,\n web_site\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and wsr_web_site_sk = web_site_sk\n group by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || s_store_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || cp_catalog_page_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q6\" ->\n \"\"\"\nselect a.ca_state state, count(*) cnt\n from customer_address a\n ,customer c\n ,store_sales s\n ,date_dim d\n ,item i\n where a.ca_address_sk = c.c_current_addr_sk\n \tand c.c_customer_sk = s.ss_customer_sk\n \tand s.ss_sold_date_sk = d.d_date_sk\n \tand s.ss_item_sk = i.i_item_sk\n \tand d.d_month_seq =\n \t (select distinct (d_month_seq)\n \t from date_dim\n where d_year = 2002\n \t and d_moy = 3 )\n \tand i.i_current_price > 1.2 *\n (select avg(j.i_current_price)\n \t from item j\n \t where j.i_category = i.i_category)\n group by a.ca_state\n having count(*) >= 10\n order by cnt, a.ca_state\n limit 100\"\"\",\n \"q7\" ->\n \"\"\"\nselect i_item_id,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, item, promotion\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_cdemo_sk = cd_demo_sk and\n ss_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'W' and\n cd_education_status = 'College' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 2001\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q8\" ->\n \"\"\"\nselect s_store_name\n ,sum(ss_net_profit)\n from store_sales\n ,date_dim\n ,store,\n (select ca_zip\n from (\n SELECT substr(ca_zip,1,5) ca_zip\n FROM customer_address\n WHERE substr(ca_zip,1,5) IN (\n '47602','16704','35863','28577','83910','36201',\n '58412','48162','28055','41419','80332',\n '38607','77817','24891','16226','18410',\n '21231','59345','13918','51089','20317',\n '17167','54585','67881','78366','47770',\n '18360','51717','73108','14440','21800',\n '89338','45859','65501','34948','25973',\n '73219','25333','17291','10374','18829',\n '60736','82620','41351','52094','19326',\n '25214','54207','40936','21814','79077',\n '25178','75742','77454','30621','89193',\n '27369','41232','48567','83041','71948',\n '37119','68341','14073','16891','62878',\n '49130','19833','24286','27700','40979',\n '50412','81504','94835','84844','71954',\n '39503','57649','18434','24987','12350',\n '86379','27413','44529','98569','16515',\n '27287','24255','21094','16005','56436',\n '91110','68293','56455','54558','10298',\n '83647','32754','27052','51766','19444',\n '13869','45645','94791','57631','20712',\n '37788','41807','46507','21727','71836',\n '81070','50632','88086','63991','20244',\n '31655','51782','29818','63792','68605',\n '94898','36430','57025','20601','82080',\n '33869','22728','35834','29086','92645',\n '98584','98072','11652','78093','57553',\n '43830','71144','53565','18700','90209',\n '71256','38353','54364','28571','96560',\n '57839','56355','50679','45266','84680',\n '34306','34972','48530','30106','15371',\n '92380','84247','92292','68852','13338',\n '34594','82602','70073','98069','85066',\n '47289','11686','98862','26217','47529',\n '63294','51793','35926','24227','14196',\n '24594','32489','99060','49472','43432',\n '49211','14312','88137','47369','56877',\n '20534','81755','15794','12318','21060',\n '73134','41255','63073','81003','73873',\n '66057','51184','51195','45676','92696',\n '70450','90669','98338','25264','38919',\n '59226','58581','60298','17895','19489',\n '52301','80846','95464','68770','51634',\n '19988','18367','18421','11618','67975',\n '25494','41352','95430','15734','62585',\n '97173','33773','10425','75675','53535',\n '17879','41967','12197','67998','79658',\n '59130','72592','14851','43933','68101',\n '50636','25717','71286','24660','58058',\n '72991','95042','15543','33122','69280',\n '11912','59386','27642','65177','17672',\n '33467','64592','36335','54010','18767',\n '63193','42361','49254','33113','33159',\n '36479','59080','11855','81963','31016',\n '49140','29392','41836','32958','53163',\n '13844','73146','23952','65148','93498',\n '14530','46131','58454','13376','13378',\n '83986','12320','17193','59852','46081',\n '98533','52389','13086','68843','31013',\n '13261','60560','13443','45533','83583',\n '11489','58218','19753','22911','25115',\n '86709','27156','32669','13123','51933',\n '39214','41331','66943','14155','69998',\n '49101','70070','35076','14242','73021',\n '59494','15782','29752','37914','74686',\n '83086','34473','15751','81084','49230',\n '91894','60624','17819','28810','63180',\n '56224','39459','55233','75752','43639',\n '55349','86057','62361','50788','31830',\n '58062','18218','85761','60083','45484',\n '21204','90229','70041','41162','35390',\n '16364','39500','68908','26689','52868',\n '81335','40146','11340','61527','61794',\n '71997','30415','59004','29450','58117',\n '69952','33562','83833','27385','61860',\n '96435','48333','23065','32961','84919',\n '61997','99132','22815','56600','68730',\n '48017','95694','32919','88217','27116',\n '28239','58032','18884','16791','21343',\n '97462','18569','75660','15475')\n intersect\n select ca_zip\n from (SELECT substr(ca_zip,1,5) ca_zip,count(*) cnt\n FROM customer_address, customer\n WHERE ca_address_sk = c_current_addr_sk and\n c_preferred_cust_flag='Y'\n group by ca_zip\n having count(*) > 10)A1)A2) V1\n where ss_store_sk = s_store_sk\n and ss_sold_date_sk = d_date_sk\n and d_qoy = 2 and d_year = 1998\n and (substr(s_zip,1,2) = substr(V1.ca_zip,1,2))\n group by s_store_name\n order by s_store_name\n limit 100\"\"\",\n \"q9\" ->\n \"\"\"\nselect case when (select count(*)\n from store_sales\n where ss_quantity between 1 and 20) > 578972190\n then (select avg(ss_ext_list_price)\n from store_sales\n where ss_quantity between 1 and 20)\n else (select avg(ss_net_paid_inc_tax)\n from store_sales\n where ss_quantity between 1 and 20) end bucket1 ,\n case when (select count(*)\n from store_sales\n where ss_quantity between 21 and 40) > 536856786\n then (select avg(ss_ext_list_price)\n from store_sales\n where ss_quantity between 21 and 40)\n else (select avg(ss_net_paid_inc_tax)\n from store_sales\n where ss_quantity between 21 and 40) end bucket2,\n case when (select count(*)\n from store_sales\n where ss_quantity between 41 and 60) > 12733327\n then (select avg(ss_ext_list_price)\n from store_sales\n where ss_quantity between 41 and 60)\n else (select avg(ss_net_paid_inc_tax)\n from store_sales\n where ss_quantity between 41 and 60) end bucket3,\n case when (select count(*)\n from store_sales\n where ss_quantity between 61 and 80) > 205136171\n then (select avg(ss_ext_list_price)\n from store_sales\n where ss_quantity between 61 and 80)\n else (select avg(ss_net_paid_inc_tax)\n from store_sales\n where ss_quantity between 61 and 80) end bucket4,\n case when (select count(*)\n from store_sales\n where ss_quantity between 81 and 100) > 1192341092\n then (select avg(ss_ext_list_price)\n from store_sales\n where ss_quantity between 81 and 100)\n else (select avg(ss_net_paid_inc_tax)\n from store_sales\n where ss_quantity between 81 and 100) end bucket5\nfrom reason\nwhere r_reason_sk = 1\"\"\",\n \"q10\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3,\n cd_dep_count,\n count(*) cnt4,\n cd_dep_employed_count,\n count(*) cnt5,\n cd_dep_college_count,\n count(*) cnt6\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_county in ('Baltimore city','Stafford County','Greene County','Ballard County','Franklin County') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2000 and\n d_moy between 1 and 1+3) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2000 and\n d_moy between 1 ANd 1+3) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2000 and\n d_moy between 1 and 1+3))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\nlimit 100\"\"\",\n \"q11\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ss_ext_list_price-ss_ext_discount_amt) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ws_ext_list_price-ws_ext_discount_amt) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_preferred_cust_flag\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 2001\n and t_s_secyear.dyear = 2001+1\n and t_w_firstyear.dyear = 2001\n and t_w_secyear.dyear = 2001+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else 0.0 end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else 0.0 end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_preferred_cust_flag\nlimit 100\"\"\",\n \"q12\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ws_ext_sales_price) as itemrevenue\n ,sum(ws_ext_sales_price)*100/sum(sum(ws_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tweb_sales\n \t,item\n \t,date_dim\nwhere\n\tws_item_sk = i_item_sk\n \tand i_category in ('Children', 'Shoes', 'Women')\n \tand ws_sold_date_sk = d_date_sk\n\tand d_date between cast('1998-06-19' as date)\n\t\t\t\tand (cast('1998-06-19' as date) + INTERVAL 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q13\" ->\n \"\"\"\nselect avg(ss_quantity)\n ,avg(ss_ext_sales_price)\n ,avg(ss_ext_wholesale_cost)\n ,sum(ss_ext_wholesale_cost)\n from store_sales\n ,store\n ,customer_demographics\n ,household_demographics\n ,customer_address\n ,date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 2001\n and((ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'D'\n and cd_education_status = '2 yr Degree'\n and ss_sales_price between 100.00 and 150.00\n and hd_dep_count = 3\n )or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'U'\n and cd_education_status = 'College'\n and ss_sales_price between 50.00 and 100.00\n and hd_dep_count = 1\n ) or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'S'\n and cd_education_status = 'Primary'\n and ss_sales_price between 150.00 and 200.00\n and hd_dep_count = 1\n ))\n and((ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('GA', 'IN', 'NY')\n and ss_net_profit between 100 and 200\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('ND', 'WV', 'TX')\n and ss_net_profit between 150 and 300\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('KS', 'NC', 'NM')\n and ss_net_profit between 50 and 250\n ))\"\"\",\n \"q14a\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 1998 AND 1998 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 1998 AND 1998 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 1998 AND 1998 + 2)\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n (select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2) x)\n select channel, i_brand_id,i_class_id,i_category_id,sum(sales), sum(number_sales)\n from(\n select 'store' channel, i_brand_id,i_class_id\n ,i_category_id,sum(ss_quantity*ss_list_price) sales\n , count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1998+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)\n union all\n select 'catalog' channel, i_brand_id,i_class_id,i_category_id, sum(cs_quantity*cs_list_price) sales, count(*) number_sales\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk in (select ss_item_sk from cross_items)\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1998+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(cs_quantity*cs_list_price) > (select average_sales from avg_sales)\n union all\n select 'web' channel, i_brand_id,i_class_id,i_category_id, sum(ws_quantity*ws_list_price) sales , count(*) number_sales\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk in (select ss_item_sk from cross_items)\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1998+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ws_quantity*ws_list_price) > (select average_sales from avg_sales)\n ) y\n group by rollup (channel, i_brand_id,i_class_id,i_category_id)\n order by channel,i_brand_id,i_class_id,i_category_id\n limit 100\"\"\",\n \"q14b\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 1998 AND 1998 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 1998 AND 1998 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 1998 AND 1998 + 2) x\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n(select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 1998 and 1998 + 2) x)\n select this_year.channel ty_channel\n ,this_year.i_brand_id ty_brand\n ,this_year.i_class_id ty_class\n ,this_year.i_category_id ty_category\n ,this_year.sales ty_sales\n ,this_year.number_sales ty_number_sales\n ,last_year.channel ly_channel\n ,last_year.i_brand_id ly_brand\n ,last_year.i_class_id ly_class\n ,last_year.i_category_id ly_category\n ,last_year.sales ly_sales\n ,last_year.number_sales ly_number_sales\n from\n (select 'store' channel, i_brand_id,i_class_id,i_category_id\n ,sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 1998 + 1\n and d_moy = 12\n and d_dom = 17)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) this_year,\n (select 'store' channel, i_brand_id,i_class_id\n ,i_category_id, sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 1998\n and d_moy = 12\n and d_dom = 17)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) last_year\n where this_year.i_brand_id= last_year.i_brand_id\n and this_year.i_class_id = last_year.i_class_id\n and this_year.i_category_id = last_year.i_category_id\n order by this_year.channel, this_year.i_brand_id, this_year.i_class_id, this_year.i_category_id\n limit 100\"\"\",\n \"q15\" ->\n \"\"\"\nselect ca_zip\n ,sum(cs_sales_price)\n from catalog_sales\n ,customer\n ,customer_address\n ,date_dim\n where cs_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475',\n '85392', '85460', '80348', '81792')\n \t or ca_state in ('CA','WA','GA')\n \t or cs_sales_price > 500)\n \tand cs_sold_date_sk = d_date_sk\n \tand d_qoy = 1 and d_year = 2002\n group by ca_zip\n order by ca_zip\n limit 100\"\"\",\n \"q16\" ->\n \"\"\"\nselect\n count(distinct cs_order_number) as `order count`\n ,sum(cs_ext_ship_cost) as `total shipping cost`\n ,sum(cs_net_profit) as `total net profit`\nfrom\n catalog_sales cs1\n ,date_dim\n ,customer_address\n ,call_center\nwhere\n d_date between '2001-3-01' and\n (cast('2001-3-01' as date) + INTERVAL 60 days)\nand cs1.cs_ship_date_sk = d_date_sk\nand cs1.cs_ship_addr_sk = ca_address_sk\nand ca_state = 'PA'\nand cs1.cs_call_center_sk = cc_call_center_sk\nand cc_county in ('Luce County','Franklin Parish','Sierra County','Williamson County',\n 'Kittitas County'\n)\nand exists (select *\n from catalog_sales cs2\n where cs1.cs_order_number = cs2.cs_order_number\n and cs1.cs_warehouse_sk <> cs2.cs_warehouse_sk)\nand not exists(select *\n from catalog_returns cr1\n where cs1.cs_order_number = cr1.cr_order_number)\norder by count(distinct cs_order_number)\nlimit 100\"\"\",\n \"q17\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,s_state\n ,count(ss_quantity) as store_sales_quantitycount\n ,avg(ss_quantity) as store_sales_quantityave\n ,stddev_samp(ss_quantity) as store_sales_quantitystdev\n ,stddev_samp(ss_quantity)/avg(ss_quantity) as store_sales_quantitycov\n ,count(sr_return_quantity) as store_returns_quantitycount\n ,avg(sr_return_quantity) as store_returns_quantityave\n ,stddev_samp(sr_return_quantity) as store_returns_quantitystdev\n ,stddev_samp(sr_return_quantity)/avg(sr_return_quantity) as store_returns_quantitycov\n ,count(cs_quantity) as catalog_sales_quantitycount ,avg(cs_quantity) as catalog_sales_quantityave\n ,stddev_samp(cs_quantity) as catalog_sales_quantitystdev\n ,stddev_samp(cs_quantity)/avg(cs_quantity) as catalog_sales_quantitycov\n from store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where d1.d_quarter_name = '2001Q1'\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_quarter_name in ('2001Q1','2001Q2','2001Q3')\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_quarter_name in ('2001Q1','2001Q2','2001Q3')\n group by i_item_id\n ,i_item_desc\n ,s_state\n order by i_item_id\n ,i_item_desc\n ,s_state\nlimit 100\"\"\",\n \"q18\" ->\n \"\"\"\nselect i_item_id,\n ca_country,\n ca_state,\n ca_county,\n avg( cast(cs_quantity as decimal(12,2))) agg1,\n avg( cast(cs_list_price as decimal(12,2))) agg2,\n avg( cast(cs_coupon_amt as decimal(12,2))) agg3,\n avg( cast(cs_sales_price as decimal(12,2))) agg4,\n avg( cast(cs_net_profit as decimal(12,2))) agg5,\n avg( cast(c_birth_year as decimal(12,2))) agg6,\n avg( cast(cd1.cd_dep_count as decimal(12,2))) agg7\n from catalog_sales, customer_demographics cd1,\n customer_demographics cd2, customer, customer_address, date_dim, item\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd1.cd_demo_sk and\n cs_bill_customer_sk = c_customer_sk and\n cd1.cd_gender = 'M' and\n cd1.cd_education_status = 'Unknown' and\n c_current_cdemo_sk = cd2.cd_demo_sk and\n c_current_addr_sk = ca_address_sk and\n c_birth_month in (5,7,8,6,12,4) and\n d_year = 2000 and\n ca_state in ('MO','NY','ME'\n ,'MI','IA','OH','MS')\n group by rollup (i_item_id, ca_country, ca_state, ca_county)\n order by ca_country,\n ca_state,\n ca_county,\n\ti_item_id\n limit 100\"\"\",\n \"q19\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand, i_manufact_id, i_manufact,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item,customer,customer_address,store\n where d_date_sk = ss_sold_date_sk\n and ss_item_sk = i_item_sk\n and i_manager_id=55\n and d_moy=11\n and d_year=1998\n and ss_customer_sk = c_customer_sk\n and c_current_addr_sk = ca_address_sk\n and substr(ca_zip,1,5) <> substr(s_zip,1,5)\n and ss_store_sk = s_store_sk\n group by i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\n order by ext_price desc\n ,i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\nlimit 100 \"\"\",\n \"q20\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(cs_ext_sales_price) as itemrevenue\n ,sum(cs_ext_sales_price)*100/sum(sum(cs_ext_sales_price)) over\n (partition by i_class) as revenueratio\n from\tcatalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and i_category in ('Shoes', 'Electronics', 'Home')\n and cs_sold_date_sk = d_date_sk\n and d_date between cast('2000-05-15' as date)\n \t\t\t\tand (cast('2000-05-15' as date) + INTERVAL 30 days)\n group by i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n order by i_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q21\" ->\n \"\"\"\nselect *\n from(select w_warehouse_name\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('2002-02-15' as date))\n\t then inv_quantity_on_hand\n else 0 end) as inv_before\n ,sum(case when (cast(d_date as date) >= cast ('2002-02-15' as date))\n then inv_quantity_on_hand\n else 0 end) as inv_after\n from inventory\n ,warehouse\n ,item\n ,date_dim\n where i_current_price between 0.99 and 1.49\n and i_item_sk = inv_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_date between (cast ('2002-02-15' as date) - INTERVAL 30 days)\n and (cast ('2002-02-15' as date) + INTERVAL 30 days)\n group by w_warehouse_name, i_item_id) x\n where (case when inv_before > 0\n then inv_after / inv_before\n else null\n end) between 2.0/3.0 and 3.0/2.0\n order by w_warehouse_name\n ,i_item_id\n limit 100\"\"\",\n \"q22\" ->\n \"\"\"\nselect i_product_name\n ,i_brand\n ,i_class\n ,i_category\n ,avg(inv_quantity_on_hand) qoh\n from inventory\n ,date_dim\n ,item\n where inv_date_sk=d_date_sk\n and inv_item_sk=i_item_sk\n and d_month_seq between 1202 and 1202 + 11\n group by rollup(i_product_name\n ,i_brand\n ,i_class\n ,i_category)\norder by qoh, i_product_name, i_brand, i_class, i_category\nlimit 100\"\"\",\n \"q23a\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\nfrom\n max_store_sales))\n select sum(sales)\n from (select cs_quantity*cs_list_price sales\n from catalog_sales\n ,date_dim\n where d_year = 2000\n and d_moy = 4\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n union all\n select ws_quantity*ws_list_price sales\n from web_sales\n ,date_dim\n where d_year = 2000\n and d_moy = 4\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer))\n limit 100\"\"\",\n \"q23b\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (2000,2000 + 1,2000 + 2,2000 + 3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2000,2000+1,2000+2,2000+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\n from max_store_sales))\n select c_last_name,c_first_name,sales\n from (select c_last_name,c_first_name,sum(cs_quantity*cs_list_price) sales\n from catalog_sales\n ,customer\n ,date_dim\n where d_year = 2000\n and d_moy = 4\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and cs_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name\n union all\n select c_last_name,c_first_name,sum(ws_quantity*ws_list_price) sales\n from web_sales\n ,customer\n ,date_dim\n where d_year = 2000\n and d_moy = 4\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and ws_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name)\n order by c_last_name,c_first_name,sales\n limit 100\"\"\",\n \"q24a\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_net_paid_inc_tax) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\nand s_market_id=5\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'cyan'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q24b\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_net_paid_inc_tax) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\n and s_market_id = 5\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'ivory'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q25\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,stddev_samp(ss_net_profit) as store_sales_profit\n ,stddev_samp(sr_net_loss) as store_returns_loss\n ,stddev_samp(cs_net_profit) as catalog_sales_profit\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 2000\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 10\n and d2.d_year = 2000\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_moy between 4 and 10\n and d3.d_year = 2000\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q26\" ->\n \"\"\"\nselect i_item_id,\n avg(cs_quantity) agg1,\n avg(cs_list_price) agg2,\n avg(cs_coupon_amt) agg3,\n avg(cs_sales_price) agg4\n from catalog_sales, customer_demographics, date_dim, item, promotion\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd_demo_sk and\n cs_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'M' and\n cd_education_status = 'Unknown' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 2001\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q27\" ->\n \"\"\"\nselect i_item_id,\n s_state, grouping(s_state) g_state,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, store, item\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_store_sk = s_store_sk and\n ss_cdemo_sk = cd_demo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'D' and\n cd_education_status = '2 yr Degree' and\n d_year = 1999 and\n s_state in ('MI','WV', 'MI', 'NY', 'TN', 'MI')\n group by rollup (i_item_id, s_state)\n order by i_item_id\n ,s_state\n limit 100\"\"\",\n \"q28\" ->\n \"\"\"\nselect *\nfrom (select avg(ss_list_price) B1_LP\n ,count(ss_list_price) B1_CNT\n ,count(distinct ss_list_price) B1_CNTD\n from store_sales\n where ss_quantity between 0 and 5\n and (ss_list_price between 151 and 151+10\n or ss_coupon_amt between 4349 and 4349+1000\n or ss_wholesale_cost between 75 and 75+20)) B1,\n (select avg(ss_list_price) B2_LP\n ,count(ss_list_price) B2_CNT\n ,count(distinct ss_list_price) B2_CNTD\n from store_sales\n where ss_quantity between 6 and 10\n and (ss_list_price between 45 and 45+10\n or ss_coupon_amt between 12490 and 12490+1000\n or ss_wholesale_cost between 37 and 37+20)) B2,\n (select avg(ss_list_price) B3_LP\n ,count(ss_list_price) B3_CNT\n ,count(distinct ss_list_price) B3_CNTD\n from store_sales\n where ss_quantity between 11 and 15\n and (ss_list_price between 54 and 54+10\n or ss_coupon_amt between 13038 and 13038+1000\n or ss_wholesale_cost between 17 and 17+20)) B3,\n (select avg(ss_list_price) B4_LP\n ,count(ss_list_price) B4_CNT\n ,count(distinct ss_list_price) B4_CNTD\n from store_sales\n where ss_quantity between 16 and 20\n and (ss_list_price between 178 and 178+10\n or ss_coupon_amt between 10744 and 10744+1000\n or ss_wholesale_cost between 51 and 51+20)) B4,\n (select avg(ss_list_price) B5_LP\n ,count(ss_list_price) B5_CNT\n ,count(distinct ss_list_price) B5_CNTD\n from store_sales\n where ss_quantity between 21 and 25\n and (ss_list_price between 49 and 49+10\n or ss_coupon_amt between 8494 and 8494+1000\n or ss_wholesale_cost between 56 and 56+20)) B5,\n (select avg(ss_list_price) B6_LP\n ,count(ss_list_price) B6_CNT\n ,count(distinct ss_list_price) B6_CNTD\n from store_sales\n where ss_quantity between 26 and 30\n and (ss_list_price between 0 and 0+10\n or ss_coupon_amt between 17854 and 17854+1000\n or ss_wholesale_cost between 31 and 31+20)) B6\nlimit 100\"\"\",\n \"q29\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,max(ss_quantity) as store_sales_quantity\n ,max(sr_return_quantity) as store_returns_quantity\n ,max(cs_quantity) as catalog_sales_quantity\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 1999\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 4 + 3\n and d2.d_year = 1999\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_year in (1999,1999+1,1999+2)\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q30\" ->\n \"\"\"\nwith customer_total_return as\n (select wr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(wr_return_amt) as ctr_total_return\n from web_returns\n ,date_dim\n ,customer_address\n where wr_returned_date_sk = d_date_sk\n and d_year =2000\n and wr_returning_addr_sk = ca_address_sk\n group by wr_returning_customer_sk\n ,ca_state)\n select c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'MD'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\nlimit 100\"\"\",\n \"q31\" ->\n \"\"\"\nwith ss as\n (select ca_county,d_qoy, d_year,sum(ss_ext_sales_price) as store_sales\n from store_sales,date_dim,customer_address\n where ss_sold_date_sk = d_date_sk\n and ss_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year),\n ws as\n (select ca_county,d_qoy, d_year,sum(ws_ext_sales_price) as web_sales\n from web_sales,date_dim,customer_address\n where ws_sold_date_sk = d_date_sk\n and ws_bill_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year)\n select\n ss1.ca_county\n ,ss1.d_year\n ,ws2.web_sales/ws1.web_sales web_q1_q2_increase\n ,ss2.store_sales/ss1.store_sales store_q1_q2_increase\n ,ws3.web_sales/ws2.web_sales web_q2_q3_increase\n ,ss3.store_sales/ss2.store_sales store_q2_q3_increase\n from\n ss ss1\n ,ss ss2\n ,ss ss3\n ,ws ws1\n ,ws ws2\n ,ws ws3\n where\n ss1.d_qoy = 1\n and ss1.d_year = 1999\n and ss1.ca_county = ss2.ca_county\n and ss2.d_qoy = 2\n and ss2.d_year = 1999\n and ss2.ca_county = ss3.ca_county\n and ss3.d_qoy = 3\n and ss3.d_year = 1999\n and ss1.ca_county = ws1.ca_county\n and ws1.d_qoy = 1\n and ws1.d_year = 1999\n and ws1.ca_county = ws2.ca_county\n and ws2.d_qoy = 2\n and ws2.d_year = 1999\n and ws1.ca_county = ws3.ca_county\n and ws3.d_qoy = 3\n and ws3.d_year =1999\n and case when ws1.web_sales > 0 then ws2.web_sales/ws1.web_sales else null end\n > case when ss1.store_sales > 0 then ss2.store_sales/ss1.store_sales else null end\n and case when ws2.web_sales > 0 then ws3.web_sales/ws2.web_sales else null end\n > case when ss2.store_sales > 0 then ss3.store_sales/ss2.store_sales else null end\n order by store_q2_q3_increase\"\"\",\n \"q32\" ->\n \"\"\"\nselect sum(cs_ext_discount_amt) as `excess discount amount`\nfrom\n catalog_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 7\nand i_item_sk = cs_item_sk\nand d_date between '2000-01-21' and\n (cast('2000-01-21' as date) + INTERVAL 90 days)\nand d_date_sk = cs_sold_date_sk\nand cs_ext_discount_amt\n > (\n select\n 1.3 * avg(cs_ext_discount_amt)\n from\n catalog_sales\n ,date_dim\n where\n cs_item_sk = i_item_sk\n and d_date between '2000-01-21' and\n (cast('2000-01-21' as date) + INTERVAL 90 days)\n and d_date_sk = cs_sold_date_sk\n )\nlimit 100\"\"\",\n \"q33\" ->\n \"\"\"\nwith ss as (\n select\n i_manufact_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Books'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 6\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_manufact_id),\n cs as (\n select\n i_manufact_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Books'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 6\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_manufact_id),\n ws as (\n select\n i_manufact_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Books'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 6\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_manufact_id)\n select i_manufact_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_manufact_id\n order by total_sales\nlimit 100\"\"\",\n \"q34\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (date_dim.d_dom between 1 and 3 or date_dim.d_dom between 25 and 28)\n and (household_demographics.hd_buy_potential = '501-1000' or\n household_demographics.hd_buy_potential = '5001-10000')\n and household_demographics.hd_vehicle_count > 0\n and (case when household_demographics.hd_vehicle_count > 0\n\tthen household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count\n\telse null\n\tend) > 1.2\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_county in ('Levy County','Val Verde County','Porter County','Nowata County',\n 'Lincoln County','Brazos County','Franklin Parish','Pipestone County')\n group by ss_ticket_number,ss_customer_sk) dn,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 15 and 20\n order by c_last_name,c_first_name,c_salutation,c_preferred_cust_flag desc, ss_ticket_number\"\"\",\n \"q35\" ->\n \"\"\"\nselect\n ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n count(*) cnt1,\n sum(cd_dep_count),\n sum(cd_dep_count),\n sum(cd_dep_count),\n cd_dep_employed_count,\n count(*) cnt2,\n sum(cd_dep_employed_count),\n sum(cd_dep_employed_count),\n sum(cd_dep_employed_count),\n cd_dep_college_count,\n count(*) cnt3,\n sum(cd_dep_college_count),\n sum(cd_dep_college_count),\n sum(cd_dep_college_count)\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_qoy < 4) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_qoy < 4) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2001 and\n d_qoy < 4))\n group by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n limit 100\"\"\",\n \"q36\" ->\n \"\"\"\nselect\n sum(ss_net_profit)/sum(ss_ext_sales_price) as gross_margin\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ss_net_profit)/sum(ss_ext_sales_price) asc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,item\n ,store\n where\n d1.d_year = 1999\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and s_state in ('MO','AL','OH','WV',\n 'AL','MN','TN','WA')\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then i_category end\n ,rank_within_parent\n limit 100\"\"\",\n \"q37\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, catalog_sales\n where i_current_price between 57 and 57 + 30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2001-04-19' as date) and (cast('2001-04-19' as date) + interval 60 days)\n and i_manufact_id in (804,916,707,680)\n and inv_quantity_on_hand between 100 and 500\n and cs_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q38\" ->\n \"\"\"\nselect count(*) from (\n select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1189 and 1189 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1189 and 1189 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1189 and 1189 + 11\n) hot_cust\nlimit 100\"\"\",\n \"q39a\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =2000\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=3\n and inv2.d_moy=3+1\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q39b\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =2000\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=3\n and inv2.d_moy=3+1\n and inv1.cov > 1.5\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q40\" ->\n \"\"\"\nselect\n w_state\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('2000-04-09' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_before\n ,sum(case when (cast(d_date as date) >= cast ('2000-04-09' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_after\n from\n catalog_sales left outer join catalog_returns on\n (cs_order_number = cr_order_number\n and cs_item_sk = cr_item_sk)\n ,warehouse\n ,item\n ,date_dim\n where\n i_current_price between 0.99 and 1.49\n and i_item_sk = cs_item_sk\n and cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and d_date between (cast ('2000-04-09' as date) - INTERVAL 30 days)\n and (cast ('2000-04-09' as date) + INTERVAL 30 days)\n group by\n w_state,i_item_id\n order by w_state,i_item_id\nlimit 100\"\"\",\n \"q41\" ->\n \"\"\"\nselect distinct(i_product_name)\n from item i1\n where i_manufact_id between 917 and 917+40\n and (select count(*) as item_cnt\n from item\n where (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'antique' or i_color = 'pale') and\n (i_units = 'Tbl' or i_units = 'Case') and\n (i_size = 'small' or i_size = 'extra large')\n ) or\n (i_category = 'Women' and\n (i_color = 'snow' or i_color = 'lemon') and\n (i_units = 'Box' or i_units = 'Ounce') and\n (i_size = 'economy' or i_size = 'N/A')\n ) or\n (i_category = 'Men' and\n (i_color = 'green' or i_color = 'blue') and\n (i_units = 'Gross' or i_units = 'Ton') and\n (i_size = 'large' or i_size = 'petite')\n ) or\n (i_category = 'Men' and\n (i_color = 'cream' or i_color = 'frosted') and\n (i_units = 'Bundle' or i_units = 'Gram') and\n (i_size = 'small' or i_size = 'extra large')\n ))) or\n (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'orange' or i_color = 'spring') and\n (i_units = 'Lb' or i_units = 'Carton') and\n (i_size = 'small' or i_size = 'extra large')\n ) or\n (i_category = 'Women' and\n (i_color = 'lawn' or i_color = 'violet') and\n (i_units = 'Oz' or i_units = 'Cup') and\n (i_size = 'economy' or i_size = 'N/A')\n ) or\n (i_category = 'Men' and\n (i_color = 'navy' or i_color = 'linen') and\n (i_units = 'Pound' or i_units = 'Unknown') and\n (i_size = 'large' or i_size = 'petite')\n ) or\n (i_category = 'Men' and\n (i_color = 'almond' or i_color = 'olive') and\n (i_units = 'Pallet' or i_units = 'Bunch') and\n (i_size = 'small' or i_size = 'extra large')\n )))) > 0\n order by i_product_name\n limit 100\"\"\",\n \"q42\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_category_id\n \t,item.i_category\n \t,sum(ss_ext_sales_price)\n from \tdate_dim dt\n \t,store_sales\n \t,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n \tand store_sales.ss_item_sk = item.i_item_sk\n \tand item.i_manager_id = 1\n \tand dt.d_moy=11\n \tand dt.d_year=1998\n group by \tdt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\n order by sum(ss_ext_sales_price) desc,dt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\nlimit 100 \"\"\",\n \"q43\" ->\n \"\"\"\nselect s_store_name, s_store_id,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from date_dim, store_sales, store\n where d_date_sk = ss_sold_date_sk and\n s_store_sk = ss_store_sk and\n s_gmt_offset = -6 and\n d_year = 2000\n group by s_store_name, s_store_id\n order by s_store_name, s_store_id,sun_sales,mon_sales,tue_sales,wed_sales,thu_sales,fri_sales,sat_sales\n limit 100\"\"\",\n \"q44\" ->\n \"\"\"\nselect asceding.rnk, i1.i_product_name best_performing, i2.i_product_name worst_performing\nfrom(select *\n from (select item_sk,rank() over (order by rank_col asc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 731\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 731\n and ss_promo_sk is null\n group by ss_store_sk))V1)V11\n where rnk < 11) asceding,\n (select *\n from (select item_sk,rank() over (order by rank_col desc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 731\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 731\n and ss_promo_sk is null\n group by ss_store_sk))V2)V21\n where rnk < 11) descending,\nitem i1,\nitem i2\nwhere asceding.rnk = descending.rnk\n and i1.i_item_sk=asceding.item_sk\n and i2.i_item_sk=descending.item_sk\norder by asceding.rnk\nlimit 100\"\"\",\n \"q45\" ->\n \"\"\"\nselect ca_zip, ca_city, sum(ws_sales_price)\n from web_sales, customer, customer_address, date_dim, item\n where ws_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ws_item_sk = i_item_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475', '85392', '85460', '80348', '81792')\n \t or\n \t i_item_id in (select i_item_id\n from item\n where i_item_sk in (2, 3, 5, 7, 11, 13, 17, 19, 23, 29)\n )\n \t )\n \tand ws_sold_date_sk = d_date_sk\n \tand d_qoy = 1 and d_year = 2000\n group by ca_zip, ca_city\n order by ca_zip, ca_city\n limit 100\"\"\",\n \"q46\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and (household_demographics.hd_dep_count = 1 or\n household_demographics.hd_vehicle_count= 2)\n and date_dim.d_dow in (6,0)\n and date_dim.d_year in (2000,2000+1,2000+2)\n and store.s_city in ('Buena Vista','Friendship','Monroe','Oak Hill','Randolph')\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,ca_city) dn,customer,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n limit 100\"\"\",\n \"q47\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand,\n s_store_name, s_company_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n s_store_name, s_company_name\n order by d_year, d_moy) rn\n from item, store_sales, date_dim, store\n where ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n (\n d_year = 1999 or\n ( d_year = 1999-1 and d_moy =12) or\n ( d_year = 1999+1 and d_moy =1)\n )\n group by i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy),\n v2 as(\n select v1.i_category\n ,v1.d_year, v1.d_moy\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1.s_store_name = v1_lag.s_store_name and\n v1.s_store_name = v1_lead.s_store_name and\n v1.s_company_name = v1_lag.s_company_name and\n v1.s_company_name = v1_lead.s_company_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 1999 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, sum_sales\n limit 100\"\"\",\n \"q48\" ->\n \"\"\"\nselect sum (ss_quantity)\n from store_sales, store, customer_demographics, customer_address, date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 1999\n and\n (\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'S'\n and\n cd_education_status = 'Primary'\n and\n ss_sales_price between 100.00 and 150.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'D'\n and\n cd_education_status = 'College'\n and\n ss_sales_price between 50.00 and 100.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'U'\n and\n cd_education_status = '2 yr Degree'\n and\n ss_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('ND', 'NC', 'TX')\n and ss_net_profit between 0 and 2000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('VA', 'IA', 'AR')\n and ss_net_profit between 150 and 3000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('MA', 'FL', 'TN')\n and ss_net_profit between 50 and 25000\n )\n )\"\"\",\n \"q49\" ->\n \"\"\"\nselect channel, item, return_ratio, return_rank, currency_rank from\n (select\n 'web' as channel\n ,web.item\n ,web.return_ratio\n ,web.return_rank\n ,web.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect ws.ws_item_sk as item\n \t\t,(cast(sum(coalesce(wr.wr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(wr.wr_return_amt,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\t web_sales ws left outer join web_returns wr\n \t\t\ton (ws.ws_order_number = wr.wr_order_number and\n \t\t\tws.ws_item_sk = wr.wr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\twr.wr_return_amt > 10000\n \t\t\tand ws.ws_net_profit > 1\n and ws.ws_net_paid > 0\n and ws.ws_quantity > 0\n and ws_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 11\n \t\tgroup by ws.ws_item_sk\n \t) in_web\n ) web\n where\n (\n web.return_rank <= 10\n or\n web.currency_rank <= 10\n )\n union\n select\n 'catalog' as channel\n ,catalog.item\n ,catalog.return_ratio\n ,catalog.return_rank\n ,catalog.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect\n \t\tcs.cs_item_sk as item\n \t\t,(cast(sum(coalesce(cr.cr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(cr.cr_return_amount,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tcatalog_sales cs left outer join catalog_returns cr\n \t\t\ton (cs.cs_order_number = cr.cr_order_number and\n \t\t\tcs.cs_item_sk = cr.cr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tcr.cr_return_amount > 10000\n \t\t\tand cs.cs_net_profit > 1\n and cs.cs_net_paid > 0\n and cs.cs_quantity > 0\n and cs_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 11\n group by cs.cs_item_sk\n \t) in_cat\n ) catalog\n where\n (\n catalog.return_rank <= 10\n or\n catalog.currency_rank <=10\n )\n union\n select\n 'store' as channel\n ,store.item\n ,store.return_ratio\n ,store.return_rank\n ,store.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect sts.ss_item_sk as item\n \t\t,(cast(sum(coalesce(sr.sr_return_quantity,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(sr.sr_return_amt,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tstore_sales sts left outer join store_returns sr\n \t\t\ton (sts.ss_ticket_number = sr.sr_ticket_number and sts.ss_item_sk = sr.sr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tsr.sr_return_amt > 10000\n \t\t\tand sts.ss_net_profit > 1\n and sts.ss_net_paid > 0\n and sts.ss_quantity > 0\n and ss_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 11\n \t\tgroup by sts.ss_item_sk\n \t) in_store\n ) store\n where (\n store.return_rank <= 10\n or\n store.currency_rank <= 10\n )\n )\n order by 1,4,5,2\n limit 100\"\"\",\n \"q50\" ->\n \"\"\"\nselect\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 30) and\n (sr_returned_date_sk - ss_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 60) and\n (sr_returned_date_sk - ss_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 90) and\n (sr_returned_date_sk - ss_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n store_sales\n ,store_returns\n ,store\n ,date_dim d1\n ,date_dim d2\nwhere\n d2.d_year = 2002\nand d2.d_moy = 10\nand ss_ticket_number = sr_ticket_number\nand ss_item_sk = sr_item_sk\nand ss_sold_date_sk = d1.d_date_sk\nand sr_returned_date_sk = d2.d_date_sk\nand ss_customer_sk = sr_customer_sk\nand ss_store_sk = s_store_sk\ngroup by\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\norder by s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\nlimit 100\"\"\",\n \"q51\" ->\n \"\"\"\nWITH web_v1 as (\nselect\n ws_item_sk item_sk, d_date,\n sum(sum(ws_sales_price))\n over (partition by ws_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom web_sales\n ,date_dim\nwhere ws_sold_date_sk=d_date_sk\n and d_month_seq between 1213 and 1213+11\n and ws_item_sk is not NULL\ngroup by ws_item_sk, d_date),\nstore_v1 as (\nselect\n ss_item_sk item_sk, d_date,\n sum(sum(ss_sales_price))\n over (partition by ss_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom store_sales\n ,date_dim\nwhere ss_sold_date_sk=d_date_sk\n and d_month_seq between 1213 and 1213+11\n and ss_item_sk is not NULL\ngroup by ss_item_sk, d_date)\n select *\nfrom (select item_sk\n ,d_date\n ,web_sales\n ,store_sales\n ,max(web_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) web_cumulative\n ,max(store_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) store_cumulative\n from (select case when web.item_sk is not null then web.item_sk else store.item_sk end item_sk\n ,case when web.d_date is not null then web.d_date else store.d_date end d_date\n ,web.cume_sales web_sales\n ,store.cume_sales store_sales\n from web_v1 web full outer join store_v1 store on (web.item_sk = store.item_sk\n and web.d_date = store.d_date)\n )x )y\nwhere web_cumulative > store_cumulative\norder by item_sk\n ,d_date\nlimit 100\"\"\",\n \"q52\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_brand_id brand_id\n \t,item.i_brand brand\n \t,sum(ss_ext_sales_price) ext_price\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manager_id = 1\n and dt.d_moy=11\n and dt.d_year=1998\n group by dt.d_year\n \t,item.i_brand\n \t,item.i_brand_id\n order by dt.d_year\n \t,ext_price desc\n \t,brand_id\nlimit 100 \"\"\",\n \"q53\" ->\n \"\"\"\nselect * from\n(select i_manufact_id,\nsum(ss_sales_price) sum_sales,\navg(sum(ss_sales_price)) over (partition by i_manufact_id) avg_quarterly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\nss_sold_date_sk = d_date_sk and\nss_store_sk = s_store_sk and\nd_month_seq in (1219,1219+1,1219+2,1219+3,1219+4,1219+5,1219+6,1219+7,1219+8,1219+9,1219+10,1219+11) and\n((i_category in ('Books','Children','Electronics') and\ni_class in ('personal','portable','reference','self-help') and\ni_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t'exportiunivamalg #9','scholaramalgamalg #9'))\nor(i_category in ('Women','Music','Men') and\ni_class in ('accessories','classical','fragrances','pants') and\ni_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t'importoamalg #1')))\ngroup by i_manufact_id, d_qoy ) tmp1\nwhere case when avg_quarterly_sales > 0\n\tthen abs (sum_sales - avg_quarterly_sales)/ avg_quarterly_sales\n\telse null end > 0.1\norder by avg_quarterly_sales,\n\t sum_sales,\n\t i_manufact_id\nlimit 100\"\"\",\n \"q54\" ->\n \"\"\"\nwith my_customers as (\n select distinct c_customer_sk\n , c_current_addr_sk\n from\n ( select cs_sold_date_sk sold_date_sk,\n cs_bill_customer_sk customer_sk,\n cs_item_sk item_sk\n from catalog_sales\n union all\n select ws_sold_date_sk sold_date_sk,\n ws_bill_customer_sk customer_sk,\n ws_item_sk item_sk\n from web_sales\n ) cs_or_ws_sales,\n item,\n date_dim,\n customer\n where sold_date_sk = d_date_sk\n and item_sk = i_item_sk\n and i_category = 'Men'\n and i_class = 'shirts'\n and c_customer_sk = cs_or_ws_sales.customer_sk\n and d_moy = 2\n and d_year = 1999\n )\n , my_revenue as (\n select c_customer_sk,\n sum(ss_ext_sales_price) as revenue\n from my_customers,\n store_sales,\n customer_address,\n store,\n date_dim\n where c_current_addr_sk = ca_address_sk\n and ca_county = s_county\n and ca_state = s_state\n and ss_sold_date_sk = d_date_sk\n and c_customer_sk = ss_customer_sk\n and d_month_seq between (select distinct d_month_seq+1\n from date_dim where d_year = 1999 and d_moy = 2)\n and (select distinct d_month_seq+3\n from date_dim where d_year = 1999 and d_moy = 2)\n group by c_customer_sk\n )\n , segments as\n (select cast((revenue/50) as int) as segment\n from my_revenue\n )\n select segment, count(*) as num_customers, segment*50 as segment_base\n from segments\n group by segment\n order by segment, num_customers\n limit 100\"\"\",\n \"q55\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item\n where d_date_sk = ss_sold_date_sk\n \tand ss_item_sk = i_item_sk\n \tand i_manager_id=96\n \tand d_moy=11\n \tand d_year=2000\n group by i_brand, i_brand_id\n order by ext_price desc, i_brand_id\nlimit 100 \"\"\",\n \"q56\" ->\n \"\"\"\nwith ss as (\n select i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('antique','white','smoke'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 6\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_item_id),\n cs as (\n select i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('antique','white','smoke'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 6\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_item_id),\n ws as (\n select i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('antique','white','smoke'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 2000\n and d_moy = 6\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_item_id)\n select i_item_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by total_sales,\n i_item_id\n limit 100\"\"\",\n \"q57\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n cc_name,\n d_year, d_moy,\n sum(cs_sales_price) sum_sales,\n avg(sum(cs_sales_price)) over\n (partition by i_category, i_brand,\n cc_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n cc_name\n order by d_year, d_moy) rn\n from item, catalog_sales, date_dim, call_center\n where cs_item_sk = i_item_sk and\n cs_sold_date_sk = d_date_sk and\n cc_call_center_sk= cs_call_center_sk and\n (\n d_year = 2000 or\n ( d_year = 2000-1 and d_moy =12) or\n ( d_year = 2000+1 and d_moy =1)\n )\n group by i_category, i_brand,\n cc_name , d_year, d_moy),\n v2 as(\n select v1.i_category, v1.i_brand, v1.cc_name\n ,v1.d_year\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1. cc_name = v1_lag. cc_name and\n v1. cc_name = v1_lead. cc_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 2000 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, sum_sales\n limit 100\"\"\",\n \"q58\" ->\n \"\"\"\nwith ss_items as\n (select i_item_id item_id\n ,sum(ss_ext_sales_price) ss_item_rev\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '2001-01-27'))\n and ss_sold_date_sk = d_date_sk\n group by i_item_id),\n cs_items as\n (select i_item_id item_id\n ,sum(cs_ext_sales_price) cs_item_rev\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '2001-01-27'))\n and cs_sold_date_sk = d_date_sk\n group by i_item_id),\n ws_items as\n (select i_item_id item_id\n ,sum(ws_ext_sales_price) ws_item_rev\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq =(select d_week_seq\n from date_dim\n where d_date = '2001-01-27'))\n and ws_sold_date_sk = d_date_sk\n group by i_item_id)\n select ss_items.item_id\n ,ss_item_rev\n ,ss_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ss_dev\n ,cs_item_rev\n ,cs_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 cs_dev\n ,ws_item_rev\n ,ws_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ws_dev\n ,(ss_item_rev+cs_item_rev+ws_item_rev)/3 average\n from ss_items,cs_items,ws_items\n where ss_items.item_id=cs_items.item_id\n and ss_items.item_id=ws_items.item_id\n and ss_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n and ss_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and cs_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and cs_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and ws_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and ws_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n order by item_id\n ,ss_item_rev\n limit 100\"\"\",\n \"q59\" ->\n \"\"\"\nwith wss as\n (select d_week_seq,\n ss_store_sk,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n group by d_week_seq,ss_store_sk\n )\n select s_store_name1,s_store_id1,d_week_seq1\n ,sun_sales1/sun_sales2,mon_sales1/mon_sales2\n ,tue_sales1/tue_sales2,wed_sales1/wed_sales2,thu_sales1/thu_sales2\n ,fri_sales1/fri_sales2,sat_sales1/sat_sales2\n from\n (select s_store_name s_store_name1,wss.d_week_seq d_week_seq1\n ,s_store_id s_store_id1,sun_sales sun_sales1\n ,mon_sales mon_sales1,tue_sales tue_sales1\n ,wed_sales wed_sales1,thu_sales thu_sales1\n ,fri_sales fri_sales1,sat_sales sat_sales1\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1177 and 1177 + 11) y,\n (select s_store_name s_store_name2,wss.d_week_seq d_week_seq2\n ,s_store_id s_store_id2,sun_sales sun_sales2\n ,mon_sales mon_sales2,tue_sales tue_sales2\n ,wed_sales wed_sales2,thu_sales thu_sales2\n ,fri_sales fri_sales2,sat_sales sat_sales2\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1177+ 12 and 1177 + 23) x\n where s_store_id1=s_store_id2\n and d_week_seq1=d_week_seq2-52\n order by s_store_name1,s_store_id1,d_week_seq1\nlimit 100\"\"\",\n \"q60\" ->\n \"\"\"\nwith ss as (\n select\n i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Shoes'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 2002\n and d_moy = 8\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_item_id),\n cs as (\n select\n i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Shoes'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 2002\n and d_moy = 8\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_item_id),\n ws as (\n select\n i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Shoes'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 2002\n and d_moy = 8\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_item_id)\n select\n i_item_id\n,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by i_item_id\n ,total_sales\n limit 100\"\"\",\n \"q61\" ->\n \"\"\"\nselect promotions,total,cast(promotions as decimal(15,4))/cast(total as decimal(15,4))*100\nfrom\n (select sum(ss_ext_sales_price) promotions\n from store_sales\n ,store\n ,promotion\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_promo_sk = p_promo_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -6\n and i_category = 'Home'\n and (p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y')\n and s_gmt_offset = -6\n and d_year = 1999\n and d_moy = 12) promotional_sales,\n (select sum(ss_ext_sales_price) total\n from store_sales\n ,store\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -6\n and i_category = 'Home'\n and s_gmt_offset = -6\n and d_year = 1999\n and d_moy = 12) all_sales\norder by promotions, total\nlimit 100\"\"\",\n \"q62\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 30) and\n (ws_ship_date_sk - ws_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 60) and\n (ws_ship_date_sk - ws_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 90) and\n (ws_ship_date_sk - ws_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n web_sales\n ,warehouse\n ,ship_mode\n ,web_site\n ,date_dim\nwhere\n d_month_seq between 1191 and 1191 + 11\nand ws_ship_date_sk = d_date_sk\nand ws_warehouse_sk = w_warehouse_sk\nand ws_ship_mode_sk = sm_ship_mode_sk\nand ws_web_site_sk = web_site_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\nlimit 100\"\"\",\n \"q63\" ->\n \"\"\"\nselect *\nfrom (select i_manager_id\n ,sum(ss_sales_price) sum_sales\n ,avg(sum(ss_sales_price)) over (partition by i_manager_id) avg_monthly_sales\n from item\n ,store_sales\n ,date_dim\n ,store\n where ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and d_month_seq in (1193,1193+1,1193+2,1193+3,1193+4,1193+5,1193+6,1193+7,1193+8,1193+9,1193+10,1193+11)\n and (( i_category in ('Books','Children','Electronics')\n and i_class in ('personal','portable','reference','self-help')\n and i_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t 'exportiunivamalg #9','scholaramalgamalg #9'))\n or( i_category in ('Women','Music','Men')\n and i_class in ('accessories','classical','fragrances','pants')\n and i_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t 'importoamalg #1')))\ngroup by i_manager_id, d_moy) tmp1\nwhere case when avg_monthly_sales > 0 then abs (sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\norder by i_manager_id\n ,avg_monthly_sales\n ,sum_sales\nlimit 100\"\"\",\n \"q64\" ->\n \"\"\"\nwith cs_ui as\n (select cs_item_sk\n ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund\n from catalog_sales\n ,catalog_returns\n where cs_item_sk = cr_item_sk\n and cs_order_number = cr_order_number\n group by cs_item_sk\n having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit)),\ncross_sales as\n (select i_product_name product_name\n ,i_item_sk item_sk\n ,s_store_name store_name\n ,s_zip store_zip\n ,ad1.ca_street_number b_street_number\n ,ad1.ca_street_name b_street_name\n ,ad1.ca_city b_city\n ,ad1.ca_zip b_zip\n ,ad2.ca_street_number c_street_number\n ,ad2.ca_street_name c_street_name\n ,ad2.ca_city c_city\n ,ad2.ca_zip c_zip\n ,d1.d_year as syear\n ,d2.d_year as fsyear\n ,d3.d_year s2year\n ,count(*) cnt\n ,sum(ss_wholesale_cost) s1\n ,sum(ss_list_price) s2\n ,sum(ss_coupon_amt) s3\n FROM store_sales\n ,store_returns\n ,cs_ui\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,customer\n ,customer_demographics cd1\n ,customer_demographics cd2\n ,promotion\n ,household_demographics hd1\n ,household_demographics hd2\n ,customer_address ad1\n ,customer_address ad2\n ,income_band ib1\n ,income_band ib2\n ,item\n WHERE ss_store_sk = s_store_sk AND\n ss_sold_date_sk = d1.d_date_sk AND\n ss_customer_sk = c_customer_sk AND\n ss_cdemo_sk= cd1.cd_demo_sk AND\n ss_hdemo_sk = hd1.hd_demo_sk AND\n ss_addr_sk = ad1.ca_address_sk and\n ss_item_sk = i_item_sk and\n ss_item_sk = sr_item_sk and\n ss_ticket_number = sr_ticket_number and\n ss_item_sk = cs_ui.cs_item_sk and\n c_current_cdemo_sk = cd2.cd_demo_sk AND\n c_current_hdemo_sk = hd2.hd_demo_sk AND\n c_current_addr_sk = ad2.ca_address_sk and\n c_first_sales_date_sk = d2.d_date_sk and\n c_first_shipto_date_sk = d3.d_date_sk and\n ss_promo_sk = p_promo_sk and\n hd1.hd_income_band_sk = ib1.ib_income_band_sk and\n hd2.hd_income_band_sk = ib2.ib_income_band_sk and\n cd1.cd_marital_status <> cd2.cd_marital_status and\n i_color in ('orange','aquamarine','olive','linen','smoke','coral') and\n i_current_price between 74 and 74 + 10 and\n i_current_price between 74 + 1 and 74 + 15\ngroup by i_product_name\n ,i_item_sk\n ,s_store_name\n ,s_zip\n ,ad1.ca_street_number\n ,ad1.ca_street_name\n ,ad1.ca_city\n ,ad1.ca_zip\n ,ad2.ca_street_number\n ,ad2.ca_street_name\n ,ad2.ca_city\n ,ad2.ca_zip\n ,d1.d_year\n ,d2.d_year\n ,d3.d_year\n)\nselect cs1.product_name\n ,cs1.store_name\n ,cs1.store_zip\n ,cs1.b_street_number\n ,cs1.b_street_name\n ,cs1.b_city\n ,cs1.b_zip\n ,cs1.c_street_number\n ,cs1.c_street_name\n ,cs1.c_city\n ,cs1.c_zip\n ,cs1.syear\n ,cs1.cnt\n ,cs1.s1 as s11\n ,cs1.s2 as s21\n ,cs1.s3 as s31\n ,cs2.s1 as s12\n ,cs2.s2 as s22\n ,cs2.s3 as s32\n ,cs2.syear\n ,cs2.cnt\nfrom cross_sales cs1,cross_sales cs2\nwhere cs1.item_sk=cs2.item_sk and\n cs1.syear = 2001 and\n cs2.syear = 2001 + 1 and\n cs2.cnt <= cs1.cnt and\n cs1.store_name = cs2.store_name and\n cs1.store_zip = cs2.store_zip\norder by cs1.product_name\n ,cs1.store_name\n ,cs2.cnt\n ,cs1.s1\n ,cs2.s1\"\"\",\n \"q65\" ->\n \"\"\"\nselect\n\ts_store_name,\n\ti_item_desc,\n\tsc.revenue,\n\ti_current_price,\n\ti_wholesale_cost,\n\ti_brand\n from store, item,\n (select ss_store_sk, avg(revenue) as ave\n \tfrom\n \t (select ss_store_sk, ss_item_sk,\n \t\t sum(ss_sales_price) as revenue\n \t\tfrom store_sales, date_dim\n \t\twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1195 and 1195+11\n \t\tgroup by ss_store_sk, ss_item_sk) sa\n \tgroup by ss_store_sk) sb,\n (select ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue\n \tfrom store_sales, date_dim\n \twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1195 and 1195+11\n \tgroup by ss_store_sk, ss_item_sk) sc\n where sb.ss_store_sk = sc.ss_store_sk and\n sc.revenue <= 0.1 * sb.ave and\n s_store_sk = sc.ss_store_sk and\n i_item_sk = sc.ss_item_sk\n order by s_store_name, i_item_desc\nlimit 100\"\"\",\n \"q66\" ->\n \"\"\"\nselect\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,ship_carriers\n ,year\n \t,sum(jan_sales) as jan_sales\n \t,sum(feb_sales) as feb_sales\n \t,sum(mar_sales) as mar_sales\n \t,sum(apr_sales) as apr_sales\n \t,sum(may_sales) as may_sales\n \t,sum(jun_sales) as jun_sales\n \t,sum(jul_sales) as jul_sales\n \t,sum(aug_sales) as aug_sales\n \t,sum(sep_sales) as sep_sales\n \t,sum(oct_sales) as oct_sales\n \t,sum(nov_sales) as nov_sales\n \t,sum(dec_sales) as dec_sales\n \t,sum(jan_sales/w_warehouse_sq_ft) as jan_sales_per_sq_foot\n \t,sum(feb_sales/w_warehouse_sq_ft) as feb_sales_per_sq_foot\n \t,sum(mar_sales/w_warehouse_sq_ft) as mar_sales_per_sq_foot\n \t,sum(apr_sales/w_warehouse_sq_ft) as apr_sales_per_sq_foot\n \t,sum(may_sales/w_warehouse_sq_ft) as may_sales_per_sq_foot\n \t,sum(jun_sales/w_warehouse_sq_ft) as jun_sales_per_sq_foot\n \t,sum(jul_sales/w_warehouse_sq_ft) as jul_sales_per_sq_foot\n \t,sum(aug_sales/w_warehouse_sq_ft) as aug_sales_per_sq_foot\n \t,sum(sep_sales/w_warehouse_sq_ft) as sep_sales_per_sq_foot\n \t,sum(oct_sales/w_warehouse_sq_ft) as oct_sales_per_sq_foot\n \t,sum(nov_sales/w_warehouse_sq_ft) as nov_sales_per_sq_foot\n \t,sum(dec_sales/w_warehouse_sq_ft) as dec_sales_per_sq_foot\n \t,sum(jan_net) as jan_net\n \t,sum(feb_net) as feb_net\n \t,sum(mar_net) as mar_net\n \t,sum(apr_net) as apr_net\n \t,sum(may_net) as may_net\n \t,sum(jun_net) as jun_net\n \t,sum(jul_net) as jul_net\n \t,sum(aug_net) as aug_net\n \t,sum(sep_net) as sep_net\n \t,sum(oct_net) as oct_net\n \t,sum(nov_net) as nov_net\n \t,sum(dec_net) as dec_net\n from (\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'LATVIAN' || ',' || 'ALLIANCE' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen ws_net_paid_inc_ship * ws_quantity else 0 end) as dec_net\n from\n web_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n ws_warehouse_sk = w_warehouse_sk\n and ws_sold_date_sk = d_date_sk\n and ws_sold_time_sk = t_time_sk\n \tand ws_ship_mode_sk = sm_ship_mode_sk\n and d_year = 1998\n \tand t_time between 16224 and 16224+28800\n \tand sm_carrier in ('LATVIAN','ALLIANCE')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n union all\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'LATVIAN' || ',' || 'ALLIANCE' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen cs_ext_sales_price* cs_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as dec_net\n from\n catalog_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and cs_sold_time_sk = t_time_sk\n \tand cs_ship_mode_sk = sm_ship_mode_sk\n and d_year = 1998\n \tand t_time between 16224 AND 16224+28800\n \tand sm_carrier in ('LATVIAN','ALLIANCE')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n ) x\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,ship_carriers\n ,year\n order by w_warehouse_name\n limit 100\"\"\",\n \"q67\" ->\n \"\"\"\nselect *\nfrom (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rank() over (partition by i_category order by sumsales desc) rk\n from (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sum(coalesce(ss_sales_price*ss_quantity,0)) sumsales\n from store_sales\n ,date_dim\n ,store\n ,item\n where ss_sold_date_sk=d_date_sk\n and ss_item_sk=i_item_sk\n and ss_store_sk = s_store_sk\n and d_month_seq between 1203 and 1203+11\n group by rollup(i_category, i_class, i_brand, i_product_name, d_year, d_qoy, d_moy,s_store_id))dw1) dw2\nwhere rk <= 100\norder by i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rk\nlimit 100\"\"\",\n \"q68\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,extended_price\n ,extended_tax\n ,list_price\n from (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_ext_sales_price) extended_price\n ,sum(ss_ext_list_price) list_price\n ,sum(ss_ext_tax) extended_tax\n from store_sales\n ,date_dim\n ,store\n ,household_demographics\n ,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_dep_count = 3 or\n household_demographics.hd_vehicle_count= -1)\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_city in ('Jamestown','Pine Hill')\n group by ss_ticket_number\n ,ss_customer_sk\n ,ss_addr_sk,ca_city) dn\n ,customer\n ,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,ss_ticket_number\n limit 100\"\"\",\n \"q69\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_state in ('CA','MT','SD') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2003 and\n d_moy between 2 and 2+2) and\n (not exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2003 and\n d_moy between 2 and 2+2) and\n not exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2003 and\n d_moy between 2 and 2+2))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n limit 100\"\"\",\n \"q70\" ->\n \"\"\"\nselect\n sum(ss_net_profit) as total_sum\n ,s_state\n ,s_county\n ,grouping(s_state)+grouping(s_county) as lochierarchy\n ,rank() over (\n \tpartition by grouping(s_state)+grouping(s_county),\n \tcase when grouping(s_county) = 0 then s_state end\n \torder by sum(ss_net_profit) desc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,store\n where\n d1.d_month_seq between 1215 and 1215+11\n and d1.d_date_sk = ss_sold_date_sk\n and s_store_sk = ss_store_sk\n and s_state in\n ( select s_state\n from (select s_state as s_state,\n \t\t\t rank() over ( partition by s_state order by sum(ss_net_profit) desc) as ranking\n from store_sales, store, date_dim\n where d_month_seq between 1215 and 1215+11\n \t\t\t and d_date_sk = ss_sold_date_sk\n \t\t\t and s_store_sk = ss_store_sk\n group by s_state\n ) tmp1\n where ranking <= 5\n )\n group by rollup(s_state,s_county)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then s_state end\n ,rank_within_parent\n limit 100\"\"\",\n \"q71\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,t_hour,t_minute,\n \tsum(ext_price) ext_price\n from item, (select ws_ext_sales_price as ext_price,\n ws_sold_date_sk as sold_date_sk,\n ws_item_sk as sold_item_sk,\n ws_sold_time_sk as time_sk\n from web_sales,date_dim\n where d_date_sk = ws_sold_date_sk\n and d_moy=11\n and d_year=1998\n union all\n select cs_ext_sales_price as ext_price,\n cs_sold_date_sk as sold_date_sk,\n cs_item_sk as sold_item_sk,\n cs_sold_time_sk as time_sk\n from catalog_sales,date_dim\n where d_date_sk = cs_sold_date_sk\n and d_moy=11\n and d_year=1998\n union all\n select ss_ext_sales_price as ext_price,\n ss_sold_date_sk as sold_date_sk,\n ss_item_sk as sold_item_sk,\n ss_sold_time_sk as time_sk\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n and d_moy=11\n and d_year=1998\n ) tmp,time_dim\n where\n sold_item_sk = i_item_sk\n and i_manager_id=1\n and time_sk = t_time_sk\n and (t_meal_time = 'breakfast' or t_meal_time = 'dinner')\n group by i_brand, i_brand_id,t_hour,t_minute\n order by ext_price desc, i_brand_id\n \"\"\",\n \"q72\" ->\n \"\"\"\nselect i_item_desc\n ,w_warehouse_name\n ,d1.d_week_seq\n ,sum(case when p_promo_sk is null then 1 else 0 end) no_promo\n ,sum(case when p_promo_sk is not null then 1 else 0 end) promo\n ,count(*) total_cnt\nfrom catalog_sales\njoin inventory on (cs_item_sk = inv_item_sk)\njoin warehouse on (w_warehouse_sk=inv_warehouse_sk)\njoin item on (i_item_sk = cs_item_sk)\njoin customer_demographics on (cs_bill_cdemo_sk = cd_demo_sk)\njoin household_demographics on (cs_bill_hdemo_sk = hd_demo_sk)\njoin date_dim d1 on (cs_sold_date_sk = d1.d_date_sk)\njoin date_dim d2 on (inv_date_sk = d2.d_date_sk)\njoin date_dim d3 on (cs_ship_date_sk = d3.d_date_sk)\nleft outer join promotion on (cs_promo_sk=p_promo_sk)\nleft outer join catalog_returns on (cr_item_sk = cs_item_sk and cr_order_number = cs_order_number)\nwhere d1.d_week_seq = d2.d_week_seq\n and inv_quantity_on_hand < cs_quantity\n and d3.d_date > d1.d_date + interval 5 days\n and hd_buy_potential = '1001-5000'\n and d1.d_year = 1998\n and cd_marital_status = 'S'\ngroup by i_item_desc,w_warehouse_name,d1.d_week_seq\norder by total_cnt desc, i_item_desc, w_warehouse_name, d_week_seq\nlimit 100\"\"\",\n \"q73\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_buy_potential = '>10000' or\n household_demographics.hd_buy_potential = 'Unknown')\n and household_demographics.hd_vehicle_count > 0\n and case when household_demographics.hd_vehicle_count > 0 then\n household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count else null end > 1\n and date_dim.d_year in (1998,1998+1,1998+2)\n and store.s_county in ('Van Buren County','Terrell County','Belknap County','Kootenai County')\n group by ss_ticket_number,ss_customer_sk) dj,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 1 and 5\n order by cnt desc, c_last_name asc\"\"\",\n \"q74\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,max(ss_net_paid) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2001,2001+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,max(ws_net_paid) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n and d_year in (2001,2001+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n )\n select\n t_s_secyear.customer_id, t_s_secyear.customer_first_name, t_s_secyear.customer_last_name\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.year = 2001\n and t_s_secyear.year = 2001+1\n and t_w_firstyear.year = 2001\n and t_w_secyear.year = 2001+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n order by 2,3,1\nlimit 100\"\"\",\n \"q75\" ->\n \"\"\"\nWITH all_sales AS (\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,SUM(sales_cnt) AS sales_cnt\n ,SUM(sales_amt) AS sales_amt\n FROM (SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,cs_quantity - COALESCE(cr_return_quantity,0) AS sales_cnt\n ,cs_ext_sales_price - COALESCE(cr_return_amount,0.0) AS sales_amt\n FROM catalog_sales JOIN item ON i_item_sk=cs_item_sk\n JOIN date_dim ON d_date_sk=cs_sold_date_sk\n LEFT JOIN catalog_returns ON (cs_order_number=cr_order_number\n AND cs_item_sk=cr_item_sk)\n WHERE i_category='Music'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ss_quantity - COALESCE(sr_return_quantity,0) AS sales_cnt\n ,ss_ext_sales_price - COALESCE(sr_return_amt,0.0) AS sales_amt\n FROM store_sales JOIN item ON i_item_sk=ss_item_sk\n JOIN date_dim ON d_date_sk=ss_sold_date_sk\n LEFT JOIN store_returns ON (ss_ticket_number=sr_ticket_number\n AND ss_item_sk=sr_item_sk)\n WHERE i_category='Music'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ws_quantity - COALESCE(wr_return_quantity,0) AS sales_cnt\n ,ws_ext_sales_price - COALESCE(wr_return_amt,0.0) AS sales_amt\n FROM web_sales JOIN item ON i_item_sk=ws_item_sk\n JOIN date_dim ON d_date_sk=ws_sold_date_sk\n LEFT JOIN web_returns ON (ws_order_number=wr_order_number\n AND ws_item_sk=wr_item_sk)\n WHERE i_category='Music') sales_detail\n GROUP BY d_year, i_brand_id, i_class_id, i_category_id, i_manufact_id)\n SELECT prev_yr.d_year AS prev_year\n ,curr_yr.d_year AS year\n ,curr_yr.i_brand_id\n ,curr_yr.i_class_id\n ,curr_yr.i_category_id\n ,curr_yr.i_manufact_id\n ,prev_yr.sales_cnt AS prev_yr_cnt\n ,curr_yr.sales_cnt AS curr_yr_cnt\n ,curr_yr.sales_cnt-prev_yr.sales_cnt AS sales_cnt_diff\n ,curr_yr.sales_amt-prev_yr.sales_amt AS sales_amt_diff\n FROM all_sales curr_yr, all_sales prev_yr\n WHERE curr_yr.i_brand_id=prev_yr.i_brand_id\n AND curr_yr.i_class_id=prev_yr.i_class_id\n AND curr_yr.i_category_id=prev_yr.i_category_id\n AND curr_yr.i_manufact_id=prev_yr.i_manufact_id\n AND curr_yr.d_year=2001\n AND prev_yr.d_year=2001-1\n AND CAST(curr_yr.sales_cnt AS DECIMAL(17,2))/CAST(prev_yr.sales_cnt AS DECIMAL(17,2))<0.9\n ORDER BY sales_cnt_diff,sales_amt_diff\n limit 100\"\"\",\n \"q76\" ->\n \"\"\"\nselect channel, col_name, d_year, d_qoy, i_category, COUNT(*) sales_cnt, SUM(ext_sales_price) sales_amt FROM (\n SELECT 'store' as channel, 'ss_promo_sk' col_name, d_year, d_qoy, i_category, ss_ext_sales_price ext_sales_price\n FROM store_sales, item, date_dim\n WHERE ss_promo_sk IS NULL\n AND ss_sold_date_sk=d_date_sk\n AND ss_item_sk=i_item_sk\n UNION ALL\n SELECT 'web' as channel, 'ws_web_site_sk' col_name, d_year, d_qoy, i_category, ws_ext_sales_price ext_sales_price\n FROM web_sales, item, date_dim\n WHERE ws_web_site_sk IS NULL\n AND ws_sold_date_sk=d_date_sk\n AND ws_item_sk=i_item_sk\n UNION ALL\n SELECT 'catalog' as channel, 'cs_bill_addr_sk' col_name, d_year, d_qoy, i_category, cs_ext_sales_price ext_sales_price\n FROM catalog_sales, item, date_dim\n WHERE cs_bill_addr_sk IS NULL\n AND cs_sold_date_sk=d_date_sk\n AND cs_item_sk=i_item_sk) foo\nGROUP BY channel, col_name, d_year, d_qoy, i_category\nORDER BY channel, col_name, d_year, d_qoy, i_category\nlimit 100\"\"\",\n \"q77\" ->\n \"\"\"\nwith ss as\n (select s_store_sk,\n sum(ss_ext_sales_price) as sales,\n sum(ss_net_profit) as profit\n from store_sales,\n date_dim,\n store\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-22' as date)\n and (cast('2001-08-22' as date) + INTERVAL 30 days)\n and ss_store_sk = s_store_sk\n group by s_store_sk)\n ,\n sr as\n (select s_store_sk,\n sum(sr_return_amt) as returns,\n sum(sr_net_loss) as profit_loss\n from store_returns,\n date_dim,\n store\n where sr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-22' as date)\n and (cast('2001-08-22' as date) + INTERVAL 30 days)\n and sr_store_sk = s_store_sk\n group by s_store_sk),\n cs as\n (select cs_call_center_sk,\n sum(cs_ext_sales_price) as sales,\n sum(cs_net_profit) as profit\n from catalog_sales,\n date_dim\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-22' as date)\n and (cast('2001-08-22' as date) + INTERVAL 30 days)\n group by cs_call_center_sk\n ),\n cr as\n (select cr_call_center_sk,\n sum(cr_return_amount) as returns,\n sum(cr_net_loss) as profit_loss\n from catalog_returns,\n date_dim\n where cr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-22' as date)\n and (cast('2001-08-22' as date) + INTERVAL 30 days)\n group by cr_call_center_sk\n ),\n ws as\n ( select wp_web_page_sk,\n sum(ws_ext_sales_price) as sales,\n sum(ws_net_profit) as profit\n from web_sales,\n date_dim,\n web_page\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-22' as date)\n and (cast('2001-08-22' as date) + INTERVAL 30 days)\n and ws_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk),\n wr as\n (select wp_web_page_sk,\n sum(wr_return_amt) as returns,\n sum(wr_net_loss) as profit_loss\n from web_returns,\n date_dim,\n web_page\n where wr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-22' as date)\n and (cast('2001-08-22' as date) + INTERVAL 30 days)\n and wr_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , ss.s_store_sk as id\n , sales\n , coalesce(returns, 0) as returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ss left join sr\n on ss.s_store_sk = sr.s_store_sk\n union all\n select 'catalog channel' as channel\n , cs_call_center_sk as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from cs\n , cr\n union all\n select 'web channel' as channel\n , ws.wp_web_page_sk as id\n , sales\n , coalesce(returns, 0) returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ws left join wr\n on ws.wp_web_page_sk = wr.wp_web_page_sk\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q78\" ->\n \"\"\"\nwith ws as\n (select d_year AS ws_sold_year, ws_item_sk,\n ws_bill_customer_sk ws_customer_sk,\n sum(ws_quantity) ws_qty,\n sum(ws_wholesale_cost) ws_wc,\n sum(ws_sales_price) ws_sp\n from web_sales\n left join web_returns on wr_order_number=ws_order_number and ws_item_sk=wr_item_sk\n join date_dim on ws_sold_date_sk = d_date_sk\n where wr_order_number is null\n group by d_year, ws_item_sk, ws_bill_customer_sk\n ),\ncs as\n (select d_year AS cs_sold_year, cs_item_sk,\n cs_bill_customer_sk cs_customer_sk,\n sum(cs_quantity) cs_qty,\n sum(cs_wholesale_cost) cs_wc,\n sum(cs_sales_price) cs_sp\n from catalog_sales\n left join catalog_returns on cr_order_number=cs_order_number and cs_item_sk=cr_item_sk\n join date_dim on cs_sold_date_sk = d_date_sk\n where cr_order_number is null\n group by d_year, cs_item_sk, cs_bill_customer_sk\n ),\nss as\n (select d_year AS ss_sold_year, ss_item_sk,\n ss_customer_sk,\n sum(ss_quantity) ss_qty,\n sum(ss_wholesale_cost) ss_wc,\n sum(ss_sales_price) ss_sp\n from store_sales\n left join store_returns on sr_ticket_number=ss_ticket_number and ss_item_sk=sr_item_sk\n join date_dim on ss_sold_date_sk = d_date_sk\n where sr_ticket_number is null\n group by d_year, ss_item_sk, ss_customer_sk\n )\n select\nss_sold_year, ss_item_sk, ss_customer_sk,\nround(ss_qty/(coalesce(ws_qty,0)+coalesce(cs_qty,0)),2) ratio,\nss_qty store_qty, ss_wc store_wholesale_cost, ss_sp store_sales_price,\ncoalesce(ws_qty,0)+coalesce(cs_qty,0) other_chan_qty,\ncoalesce(ws_wc,0)+coalesce(cs_wc,0) other_chan_wholesale_cost,\ncoalesce(ws_sp,0)+coalesce(cs_sp,0) other_chan_sales_price\nfrom ss\nleft join ws on (ws_sold_year=ss_sold_year and ws_item_sk=ss_item_sk and ws_customer_sk=ss_customer_sk)\nleft join cs on (cs_sold_year=ss_sold_year and cs_item_sk=ss_item_sk and cs_customer_sk=ss_customer_sk)\nwhere (coalesce(ws_qty,0)>0 or coalesce(cs_qty, 0)>0) and ss_sold_year=2001\norder by\n ss_sold_year, ss_item_sk, ss_customer_sk,\n ss_qty desc, ss_wc desc, ss_sp desc,\n other_chan_qty,\n other_chan_wholesale_cost,\n other_chan_sales_price,\n ratio\nlimit 100\"\"\",\n \"q79\" ->\n \"\"\"\nselect\n c_last_name,c_first_name,substr(s_city,1,30),ss_ticket_number,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,store.s_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (household_demographics.hd_dep_count = 6 or household_demographics.hd_vehicle_count > -1)\n and date_dim.d_dow = 1\n and date_dim.d_year in (1998,1998+1,1998+2)\n and store.s_number_employees between 200 and 295\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,store.s_city) ms,customer\n where ss_customer_sk = c_customer_sk\n order by c_last_name,c_first_name,substr(s_city,1,30), profit\nlimit 100\"\"\",\n \"q80\" ->\n \"\"\"\nwith ssr as\n (select s_store_id as store_id,\n sum(ss_ext_sales_price) as sales,\n sum(coalesce(sr_return_amt, 0)) as returns,\n sum(ss_net_profit - coalesce(sr_net_loss, 0)) as profit\n from store_sales left outer join store_returns on\n (ss_item_sk = sr_item_sk and ss_ticket_number = sr_ticket_number),\n date_dim,\n store,\n item,\n promotion\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2000-08-25' as date)\n and (cast('2000-08-25' as date) + INTERVAL 60 days)\n and ss_store_sk = s_store_sk\n and ss_item_sk = i_item_sk\n and i_current_price > 50\n and ss_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id as catalog_page_id,\n sum(cs_ext_sales_price) as sales,\n sum(coalesce(cr_return_amount, 0)) as returns,\n sum(cs_net_profit - coalesce(cr_net_loss, 0)) as profit\n from catalog_sales left outer join catalog_returns on\n (cs_item_sk = cr_item_sk and cs_order_number = cr_order_number),\n date_dim,\n catalog_page,\n item,\n promotion\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2000-08-25' as date)\n and (cast('2000-08-25' as date) + INTERVAL 60 days)\n and cs_catalog_page_sk = cp_catalog_page_sk\n and cs_item_sk = i_item_sk\n and i_current_price > 50\n and cs_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(ws_ext_sales_price) as sales,\n sum(coalesce(wr_return_amt, 0)) as returns,\n sum(ws_net_profit - coalesce(wr_net_loss, 0)) as profit\n from web_sales left outer join web_returns on\n (ws_item_sk = wr_item_sk and ws_order_number = wr_order_number),\n date_dim,\n web_site,\n item,\n promotion\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2000-08-25' as date)\n and (cast('2000-08-25' as date) + INTERVAL 60 days)\n and ws_web_site_sk = web_site_sk\n and ws_item_sk = i_item_sk\n and i_current_price > 50\n and ws_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || store_id as id\n , sales\n , returns\n , profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || catalog_page_id as id\n , sales\n , returns\n , profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q81\" ->\n \"\"\"\nwith customer_total_return as\n (select cr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(cr_return_amt_inc_tax) as ctr_total_return\n from catalog_returns\n ,date_dim\n ,customer_address\n where cr_returned_date_sk = d_date_sk\n and d_year =2000\n and cr_returning_addr_sk = ca_address_sk\n group by cr_returning_customer_sk\n ,ca_state )\n select c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'SC'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n limit 100\"\"\",\n \"q82\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, store_sales\n where i_current_price between 6 and 6+30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2001-02-23' as date) and (cast('2001-02-23' as date) + INTERVAL 60 days)\n and i_manufact_id in (669,623,578,379)\n and inv_quantity_on_hand between 100 and 500\n and ss_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q83\" ->\n \"\"\"\nwith sr_items as\n (select i_item_id item_id,\n sum(sr_return_quantity) sr_item_qty\n from store_returns,\n item,\n date_dim\n where sr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('2001-01-15','2001-09-03','2001-11-17')))\n and sr_returned_date_sk = d_date_sk\n group by i_item_id),\n cr_items as\n (select i_item_id item_id,\n sum(cr_return_quantity) cr_item_qty\n from catalog_returns,\n item,\n date_dim\n where cr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('2001-01-15','2001-09-03','2001-11-17')))\n and cr_returned_date_sk = d_date_sk\n group by i_item_id),\n wr_items as\n (select i_item_id item_id,\n sum(wr_return_quantity) wr_item_qty\n from web_returns,\n item,\n date_dim\n where wr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t\twhere d_date in ('2001-01-15','2001-09-03','2001-11-17')))\n and wr_returned_date_sk = d_date_sk\n group by i_item_id)\n select sr_items.item_id\n ,sr_item_qty\n ,sr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 sr_dev\n ,cr_item_qty\n ,cr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 cr_dev\n ,wr_item_qty\n ,wr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 wr_dev\n ,(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 average\n from sr_items\n ,cr_items\n ,wr_items\n where sr_items.item_id=cr_items.item_id\n and sr_items.item_id=wr_items.item_id\n order by sr_items.item_id\n ,sr_item_qty\n limit 100\"\"\",\n \"q84\" ->\n \"\"\"\nselect c_customer_id as customer_id\n , coalesce(c_last_name,'') || ', ' || coalesce(c_first_name,'') as customername\n from customer\n ,customer_address\n ,customer_demographics\n ,household_demographics\n ,income_band\n ,store_returns\n where ca_city\t = 'Walnut Grove'\n and c_current_addr_sk = ca_address_sk\n and ib_lower_bound >= 53669\n and ib_upper_bound <= 53669 + 50000\n and ib_income_band_sk = hd_income_band_sk\n and cd_demo_sk = c_current_cdemo_sk\n and hd_demo_sk = c_current_hdemo_sk\n and sr_cdemo_sk = cd_demo_sk\n order by c_customer_id\n limit 100\"\"\",\n \"q85\" ->\n \"\"\"\nselect substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\n from web_sales, web_returns, web_page, customer_demographics cd1,\n customer_demographics cd2, customer_address, date_dim, reason\n where ws_web_page_sk = wp_web_page_sk\n and ws_item_sk = wr_item_sk\n and ws_order_number = wr_order_number\n and ws_sold_date_sk = d_date_sk and d_year = 2001\n and cd1.cd_demo_sk = wr_refunded_cdemo_sk\n and cd2.cd_demo_sk = wr_returning_cdemo_sk\n and ca_address_sk = wr_refunded_addr_sk\n and r_reason_sk = wr_reason_sk\n and\n (\n (\n cd1.cd_marital_status = 'S'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'Secondary'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 100.00 and 150.00\n )\n or\n (\n cd1.cd_marital_status = 'D'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'Advanced Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 50.00 and 100.00\n )\n or\n (\n cd1.cd_marital_status = 'W'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'Primary'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ca_country = 'United States'\n and\n ca_state in ('AZ', 'SD', 'TN')\n and ws_net_profit between 100 and 200\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('TX', 'GA', 'IA')\n and ws_net_profit between 150 and 300\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('WI', 'VT', 'AL')\n and ws_net_profit between 50 and 250\n )\n )\ngroup by r_reason_desc\norder by substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\nlimit 100\"\"\",\n \"q86\" ->\n \"\"\"\nselect\n sum(ws_net_paid) as total_sum\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ws_net_paid) desc) as rank_within_parent\n from\n web_sales\n ,date_dim d1\n ,item\n where\n d1.d_month_seq between 1195 and 1195+11\n and d1.d_date_sk = ws_sold_date_sk\n and i_item_sk = ws_item_sk\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc,\n case when lochierarchy = 0 then i_category end,\n rank_within_parent\n limit 100\"\"\",\n \"q87\" ->\n \"\"\"\nselect count(*)\nfrom ((select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1194 and 1194+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1194 and 1194+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1194 and 1194+11)\n) cool_cust\"\"\",\n \"q88\" ->\n \"\"\"\nselect *\nfrom\n (select count(*) h8_30_to_9\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 8\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s1,\n (select count(*) h9_to_9_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s2,\n (select count(*) h9_30_to_10\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s3,\n (select count(*) h10_to_10_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s4,\n (select count(*) h10_30_to_11\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s5,\n (select count(*) h11_to_11_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s6,\n (select count(*) h11_30_to_12\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s7,\n (select count(*) h12_to_12_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 12\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or\n (household_demographics.hd_dep_count = 4 and household_demographics.hd_vehicle_count<=4+2) or\n (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))\n and store.s_store_name = 'ese') s8\"\"\",\n \"q89\" ->\n \"\"\"\nselect *\nfrom(\nselect i_category, i_class, i_brand,\n s_store_name, s_company_name,\n d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand, s_store_name, s_company_name)\n avg_monthly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n d_year in (2000) and\n ((i_category in ('Home','Shoes','Electronics') and\n i_class in ('flatware','mens','televisions')\n )\n or (i_category in ('Women','Sports','Music') and\n i_class in ('maternity','camping','rock')\n ))\ngroup by i_category, i_class, i_brand,\n s_store_name, s_company_name, d_moy) tmp1\nwhere case when (avg_monthly_sales <> 0) then (abs(sum_sales - avg_monthly_sales) / avg_monthly_sales) else null end > 0.1\norder by sum_sales - avg_monthly_sales, s_store_name\nlimit 100\"\"\",\n \"q90\" ->\n \"\"\"\nselect cast(amc as decimal(15,4))/cast(pmc as decimal(15,4)) am_pm_ratio\n from ( select count(*) amc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 8 and 8+1\n and household_demographics.hd_dep_count = 4\n and web_page.wp_char_count between 5000 and 5200) at,\n ( select count(*) pmc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 20 and 20+1\n and household_demographics.hd_dep_count = 4\n and web_page.wp_char_count between 5000 and 5200) pt\n order by am_pm_ratio\n limit 100\"\"\",\n \"q91\" ->\n \"\"\"\nselect\n cc_call_center_id Call_Center,\n cc_name Call_Center_Name,\n cc_manager Manager,\n sum(cr_net_loss) Returns_Loss\nfrom\n call_center,\n catalog_returns,\n date_dim,\n customer,\n customer_address,\n customer_demographics,\n household_demographics\nwhere\n cr_call_center_sk = cc_call_center_sk\nand cr_returned_date_sk = d_date_sk\nand cr_returning_customer_sk= c_customer_sk\nand cd_demo_sk = c_current_cdemo_sk\nand hd_demo_sk = c_current_hdemo_sk\nand ca_address_sk = c_current_addr_sk\nand d_year = 2001\nand d_moy = 12\nand ( (cd_marital_status = 'M' and cd_education_status = 'Unknown')\n or(cd_marital_status = 'W' and cd_education_status = 'Advanced Degree'))\nand hd_buy_potential like 'Unknown%'\nand ca_gmt_offset = -6\ngroup by cc_call_center_id,cc_name,cc_manager,cd_marital_status,cd_education_status\norder by sum(cr_net_loss) desc\"\"\",\n \"q92\" ->\n \"\"\"\nselect\n sum(ws_ext_discount_amt) as `Excess Discount Amount`\nfrom\n web_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 7\nand i_item_sk = ws_item_sk\nand d_date between '2000-01-16' and\n (cast('2000-01-16' as date) + INTERVAL 90 days)\nand d_date_sk = ws_sold_date_sk\nand ws_ext_discount_amt\n > (\n SELECT\n 1.3 * avg(ws_ext_discount_amt)\n FROM\n web_sales\n ,date_dim\n WHERE\n ws_item_sk = i_item_sk\n and d_date between '2000-01-16' and\n (cast('2000-01-16' as date) + INTERVAL 90 days)\n and d_date_sk = ws_sold_date_sk\n )\norder by sum(ws_ext_discount_amt)\nlimit 100\"\"\",\n \"q93\" ->\n \"\"\"\nselect ss_customer_sk\n ,sum(act_sales) sumsales\n from (select ss_item_sk\n ,ss_ticket_number\n ,ss_customer_sk\n ,case when sr_return_quantity is not null then (ss_quantity-sr_return_quantity)*ss_sales_price\n else (ss_quantity*ss_sales_price) end act_sales\n from store_sales left outer join store_returns on (sr_item_sk = ss_item_sk\n and sr_ticket_number = ss_ticket_number)\n ,reason\n where sr_reason_sk = r_reason_sk\n and r_reason_desc = 'reason 24') t\n group by ss_customer_sk\n order by sumsales, ss_customer_sk\nlimit 100\"\"\",\n \"q94\" ->\n \"\"\"\nselect\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2001-2-01' and\n (cast('2001-2-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'VT'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand exists (select *\n from web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\nand not exists(select *\n from web_returns wr1\n where ws1.ws_order_number = wr1.wr_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q95\" ->\n \"\"\"\nwith ws_wh as\n(select ws1.ws_order_number,ws1.ws_warehouse_sk wh1,ws2.ws_warehouse_sk wh2\n from web_sales ws1,web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\n select\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2001-3-01' and\n (cast('2001-3-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'TN'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand ws1.ws_order_number in (select ws_order_number\n from ws_wh)\nand ws1.ws_order_number in (select wr_order_number\n from web_returns,ws_wh\n where wr_order_number = ws_wh.ws_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q96\" ->\n \"\"\"\nselect count(*)\nfrom store_sales\n ,household_demographics\n ,time_dim, store\nwhere ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 20\n and time_dim.t_minute >= 30\n and household_demographics.hd_dep_count = 6\n and store.s_store_name = 'ese'\norder by count(*)\nlimit 100\"\"\",\n \"q97\" ->\n \"\"\"\nwith ssci as (\nselect ss_customer_sk customer_sk\n ,ss_item_sk item_sk\nfrom store_sales,date_dim\nwhere ss_sold_date_sk = d_date_sk\n and d_month_seq between 1206 and 1206 + 11\ngroup by ss_customer_sk\n ,ss_item_sk),\ncsci as(\n select cs_bill_customer_sk customer_sk\n ,cs_item_sk item_sk\nfrom catalog_sales,date_dim\nwhere cs_sold_date_sk = d_date_sk\n and d_month_seq between 1206 and 1206 + 11\ngroup by cs_bill_customer_sk\n ,cs_item_sk)\n select sum(case when ssci.customer_sk is not null and csci.customer_sk is null then 1 else 0 end) store_only\n ,sum(case when ssci.customer_sk is null and csci.customer_sk is not null then 1 else 0 end) catalog_only\n ,sum(case when ssci.customer_sk is not null and csci.customer_sk is not null then 1 else 0 end) store_and_catalog\nfrom ssci full outer join csci on (ssci.customer_sk=csci.customer_sk\n and ssci.item_sk = csci.item_sk)\nlimit 100\"\"\",\n \"q98\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ss_ext_sales_price) as itemrevenue\n ,sum(ss_ext_sales_price)*100/sum(sum(ss_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tstore_sales\n \t,item\n \t,date_dim\nwhere\n\tss_item_sk = i_item_sk\n \tand i_category in ('Sports', 'Books', 'Electronics')\n \tand ss_sold_date_sk = d_date_sk\n\tand d_date between cast('2002-06-29' as date)\n\t\t\t\tand (cast('2002-06-29' as date) + interval 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\"\"\",\n \"q99\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 30) and\n (cs_ship_date_sk - cs_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 60) and\n (cs_ship_date_sk - cs_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 90) and\n (cs_ship_date_sk - cs_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n catalog_sales\n ,warehouse\n ,ship_mode\n ,call_center\n ,date_dim\nwhere\n d_month_seq between 1199 and 1199 + 11\nand cs_ship_date_sk = d_date_sk\nand cs_warehouse_sk = w_warehouse_sk\nand cs_ship_mode_sk = sm_ship_mode_sk\nand cs_call_center_sk = cc_call_center_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\nlimit 100\"\"\",\n \"q1\" ->\n \"\"\"\nwith customer_total_return as\n(select sr_customer_sk as ctr_customer_sk\n,sr_store_sk as ctr_store_sk\n,sum(SR_FEE) as ctr_total_return\nfrom store_returns\n,date_dim\nwhere sr_returned_date_sk = d_date_sk\nand d_year =2000\ngroup by sr_customer_sk\n,sr_store_sk)\n select c_customer_id\nfrom customer_total_return ctr1\n,store\n,customer\nwhere ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\nfrom customer_total_return ctr2\nwhere ctr1.ctr_store_sk = ctr2.ctr_store_sk)\nand s_store_sk = ctr1.ctr_store_sk\nand s_state = 'MO'\nand ctr1.ctr_customer_sk = c_customer_sk\norder by c_customer_id\nlimit 100\"\"\",\n \"q2\" ->\n \"\"\"\nwith wscs as\n (select sold_date_sk\n ,sales_price\n from (select ws_sold_date_sk sold_date_sk\n ,ws_ext_sales_price sales_price\n from web_sales\n union all\n select cs_sold_date_sk sold_date_sk\n ,cs_ext_sales_price sales_price\n from catalog_sales)),\n wswscs as\n (select d_week_seq,\n sum(case when (d_day_name='Sunday') then sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then sales_price else null end) sat_sales\n from wscs\n ,date_dim\n where d_date_sk = sold_date_sk\n group by d_week_seq)\n select d_week_seq1\n ,round(sun_sales1/sun_sales2,2)\n ,round(mon_sales1/mon_sales2,2)\n ,round(tue_sales1/tue_sales2,2)\n ,round(wed_sales1/wed_sales2,2)\n ,round(thu_sales1/thu_sales2,2)\n ,round(fri_sales1/fri_sales2,2)\n ,round(sat_sales1/sat_sales2,2)\n from\n (select wswscs.d_week_seq d_week_seq1\n ,sun_sales sun_sales1\n ,mon_sales mon_sales1\n ,tue_sales tue_sales1\n ,wed_sales wed_sales1\n ,thu_sales thu_sales1\n ,fri_sales fri_sales1\n ,sat_sales sat_sales1\n from wswscs,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998) y,\n (select wswscs.d_week_seq d_week_seq2\n ,sun_sales sun_sales2\n ,mon_sales mon_sales2\n ,tue_sales tue_sales2\n ,wed_sales wed_sales2\n ,thu_sales thu_sales2\n ,fri_sales fri_sales2\n ,sat_sales sat_sales2\n from wswscs\n ,date_dim\n where date_dim.d_week_seq = wswscs.d_week_seq and\n d_year = 1998+1) z\n where d_week_seq1=d_week_seq2-53\n order by d_week_seq1\"\"\",\n \"q3\" ->\n \"\"\"\nselect dt.d_year\n ,item.i_brand_id brand_id\n ,item.i_brand brand\n ,sum(ss_sales_price) sum_agg\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manufact_id = 816\n and dt.d_moy=11\n group by dt.d_year\n ,item.i_brand\n ,item.i_brand_id\n order by dt.d_year\n ,sum_agg desc\n ,brand_id\n limit 100\"\"\",\n \"q4\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(((ss_ext_list_price-ss_ext_wholesale_cost-ss_ext_discount_amt)+ss_ext_sales_price)/2) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((cs_ext_list_price-cs_ext_wholesale_cost-cs_ext_discount_amt)+cs_ext_sales_price)/2) ) year_total\n ,'c' sale_type\n from customer\n ,catalog_sales\n ,date_dim\n where c_customer_sk = cs_bill_customer_sk\n and cs_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\nunion all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum((((ws_ext_list_price-ws_ext_wholesale_cost-ws_ext_discount_amt)+ws_ext_sales_price)/2) ) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_c_firstyear\n ,year_total t_c_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_c_secyear.customer_id\n and t_s_firstyear.customer_id = t_c_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_c_firstyear.sale_type = 'c'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_c_secyear.sale_type = 'c'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 1999\n and t_s_secyear.dyear = 1999+1\n and t_c_firstyear.dyear = 1999\n and t_c_secyear.dyear = 1999+1\n and t_w_firstyear.dyear = 1999\n and t_w_secyear.dyear = 1999+1\n and t_s_firstyear.year_total > 0\n and t_c_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end\n > case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_birth_country\nlimit 100\"\"\",\n \"q5\" ->\n \"\"\"\nwith ssr as\n (select s_store_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ss_store_sk as store_sk,\n ss_sold_date_sk as date_sk,\n ss_ext_sales_price as sales_price,\n ss_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from store_sales\n union all\n select sr_store_sk as store_sk,\n sr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n sr_return_amt as return_amt,\n sr_net_loss as net_loss\n from store_returns\n ) salesreturns,\n date_dim,\n store\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and store_sk = s_store_sk\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select cs_catalog_page_sk as page_sk,\n cs_sold_date_sk as date_sk,\n cs_ext_sales_price as sales_price,\n cs_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from catalog_sales\n union all\n select cr_catalog_page_sk as page_sk,\n cr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n cr_return_amount as return_amt,\n cr_net_loss as net_loss\n from catalog_returns\n ) salesreturns,\n date_dim,\n catalog_page\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and page_sk = cp_catalog_page_sk\n group by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(sales_price) as sales,\n sum(profit) as profit,\n sum(return_amt) as returns,\n sum(net_loss) as profit_loss\n from\n ( select ws_web_site_sk as wsr_web_site_sk,\n ws_sold_date_sk as date_sk,\n ws_ext_sales_price as sales_price,\n ws_net_profit as profit,\n cast(0 as decimal(7,2)) as return_amt,\n cast(0 as decimal(7,2)) as net_loss\n from web_sales\n union all\n select ws_web_site_sk as wsr_web_site_sk,\n wr_returned_date_sk as date_sk,\n cast(0 as decimal(7,2)) as sales_price,\n cast(0 as decimal(7,2)) as profit,\n wr_return_amt as return_amt,\n wr_net_loss as net_loss\n from web_returns left outer join web_sales on\n ( wr_item_sk = ws_item_sk\n and wr_order_number = ws_order_number)\n ) salesreturns,\n date_dim,\n web_site\n where date_sk = d_date_sk\n and d_date between cast('2000-08-19' as date)\n and (cast('2000-08-19' as date) + INTERVAL 14 days)\n and wsr_web_site_sk = web_site_sk\n group by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || s_store_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || cp_catalog_page_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q6\" ->\n \"\"\"\nselect a.ca_state state, count(*) cnt\n from customer_address a\n ,customer c\n ,store_sales s\n ,date_dim d\n ,item i\n where a.ca_address_sk = c.c_current_addr_sk\n \tand c.c_customer_sk = s.ss_customer_sk\n \tand s.ss_sold_date_sk = d.d_date_sk\n \tand s.ss_item_sk = i.i_item_sk\n \tand d.d_month_seq =\n \t (select distinct (d_month_seq)\n \t from date_dim\n where d_year = 2002\n \t and d_moy = 3 )\n \tand i.i_current_price > 1.2 *\n (select avg(j.i_current_price)\n \t from item j\n \t where j.i_category = i.i_category)\n group by a.ca_state\n having count(*) >= 10\n order by cnt, a.ca_state\n limit 100\"\"\",\n \"q7\" ->\n \"\"\"\nselect i_item_id,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, item, promotion\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_cdemo_sk = cd_demo_sk and\n ss_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'W' and\n cd_education_status = 'College' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 2001\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q8\" ->\n \"\"\"\nselect s_store_name\n ,sum(ss_net_profit)\n from store_sales\n ,date_dim\n ,store,\n (select ca_zip\n from (\n SELECT substr(ca_zip,1,5) ca_zip\n FROM customer_address\n WHERE substr(ca_zip,1,5) IN (\n '47602','16704','35863','28577','83910','36201',\n '58412','48162','28055','41419','80332',\n '38607','77817','24891','16226','18410',\n '21231','59345','13918','51089','20317',\n '17167','54585','67881','78366','47770',\n '18360','51717','73108','14440','21800',\n '89338','45859','65501','34948','25973',\n '73219','25333','17291','10374','18829',\n '60736','82620','41351','52094','19326',\n '25214','54207','40936','21814','79077',\n '25178','75742','77454','30621','89193',\n '27369','41232','48567','83041','71948',\n '37119','68341','14073','16891','62878',\n '49130','19833','24286','27700','40979',\n '50412','81504','94835','84844','71954',\n '39503','57649','18434','24987','12350',\n '86379','27413','44529','98569','16515',\n '27287','24255','21094','16005','56436',\n '91110','68293','56455','54558','10298',\n '83647','32754','27052','51766','19444',\n '13869','45645','94791','57631','20712',\n '37788','41807','46507','21727','71836',\n '81070','50632','88086','63991','20244',\n '31655','51782','29818','63792','68605',\n '94898','36430','57025','20601','82080',\n '33869','22728','35834','29086','92645',\n '98584','98072','11652','78093','57553',\n '43830','71144','53565','18700','90209',\n '71256','38353','54364','28571','96560',\n '57839','56355','50679','45266','84680',\n '34306','34972','48530','30106','15371',\n '92380','84247','92292','68852','13338',\n '34594','82602','70073','98069','85066',\n '47289','11686','98862','26217','47529',\n '63294','51793','35926','24227','14196',\n '24594','32489','99060','49472','43432',\n '49211','14312','88137','47369','56877',\n '20534','81755','15794','12318','21060',\n '73134','41255','63073','81003','73873',\n '66057','51184','51195','45676','92696',\n '70450','90669','98338','25264','38919',\n '59226','58581','60298','17895','19489',\n '52301','80846','95464','68770','51634',\n '19988','18367','18421','11618','67975',\n '25494','41352','95430','15734','62585',\n '97173','33773','10425','75675','53535',\n '17879','41967','12197','67998','79658',\n '59130','72592','14851','43933','68101',\n '50636','25717','71286','24660','58058',\n '72991','95042','15543','33122','69280',\n '11912','59386','27642','65177','17672',\n '33467','64592','36335','54010','18767',\n '63193','42361','49254','33113','33159',\n '36479','59080','11855','81963','31016',\n '49140','29392','41836','32958','53163',\n '13844','73146','23952','65148','93498',\n '14530','46131','58454','13376','13378',\n '83986','12320','17193','59852','46081',\n '98533','52389','13086','68843','31013',\n '13261','60560','13443','45533','83583',\n '11489','58218','19753','22911','25115',\n '86709','27156','32669','13123','51933',\n '39214','41331','66943','14155','69998',\n '49101','70070','35076','14242','73021',\n '59494','15782','29752','37914','74686',\n '83086','34473','15751','81084','49230',\n '91894','60624','17819','28810','63180',\n '56224','39459','55233','75752','43639',\n '55349','86057','62361','50788','31830',\n '58062','18218','85761','60083','45484',\n '21204','90229','70041','41162','35390',\n '16364','39500','68908','26689','52868',\n '81335','40146','11340','61527','61794',\n '71997','30415','59004','29450','58117',\n '69952','33562','83833','27385','61860',\n '96435','48333','23065','32961','84919',\n '61997','99132','22815','56600','68730',\n '48017','95694','32919','88217','27116',\n '28239','58032','18884','16791','21343',\n '97462','18569','75660','15475')\n intersect\n select ca_zip\n from (SELECT substr(ca_zip,1,5) ca_zip,count(*) cnt\n FROM customer_address, customer\n WHERE ca_address_sk = c_current_addr_sk and\n c_preferred_cust_flag='Y'\n group by ca_zip\n having count(*) > 10)A1)A2) V1\n where ss_store_sk = s_store_sk\n and ss_sold_date_sk = d_date_sk\n and d_qoy = 2 and d_year = 1998\n and (substr(s_zip,1,2) = substr(V1.ca_zip,1,2))\n group by s_store_name\n order by s_store_name\n limit 100\"\"\",\n \"q9\" ->\n \"\"\"\nselect case when (select count(*)\n from store_sales\n where ss_quantity between 1 and 20) > 4502397049\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 1 and 20)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 1 and 20) end bucket1 ,\n case when (select count(*)\n from store_sales\n where ss_quantity between 21 and 40) > 4756228269\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 21 and 40)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 21 and 40) end bucket2,\n case when (select count(*)\n from store_sales\n where ss_quantity between 41 and 60) > 4101835064\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 41 and 60)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 41 and 60) end bucket3,\n case when (select count(*)\n from store_sales\n where ss_quantity between 61 and 80) > 4583261513\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 61 and 80)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 61 and 80) end bucket4,\n case when (select count(*)\n from store_sales\n where ss_quantity between 81 and 100) > 4208819283\n then (select avg(ss_ext_discount_amt)\n from store_sales\n where ss_quantity between 81 and 100)\n else (select avg(ss_net_profit)\n from store_sales\n where ss_quantity between 81 and 100) end bucket5\nfrom reason\nwhere r_reason_sk = 1\"\"\",\n \"q10\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3,\n cd_dep_count,\n count(*) cnt4,\n cd_dep_employed_count,\n count(*) cnt5,\n cd_dep_college_count,\n count(*) cnt6\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_county in ('Grady County','Marion County','Decatur County','Lyman County','Beaver County') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 1999 and\n d_moy between 2 and 2+3) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 1999 and\n d_moy between 2 ANd 2+3) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 1999 and\n d_moy between 2 and 2+3))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\nlimit 100\"\"\",\n \"q11\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ss_ext_list_price-ss_ext_discount_amt) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,c_preferred_cust_flag customer_preferred_cust_flag\n ,c_birth_country customer_birth_country\n ,c_login customer_login\n ,c_email_address customer_email_address\n ,d_year dyear\n ,sum(ws_ext_list_price-ws_ext_discount_amt) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,c_preferred_cust_flag\n ,c_birth_country\n ,c_login\n ,c_email_address\n ,d_year\n )\n select\n t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_preferred_cust_flag\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.dyear = 2001\n and t_s_secyear.dyear = 2001+1\n and t_w_firstyear.dyear = 2001\n and t_w_secyear.dyear = 2001+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else 0.0 end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else 0.0 end\n order by t_s_secyear.customer_id\n ,t_s_secyear.customer_first_name\n ,t_s_secyear.customer_last_name\n ,t_s_secyear.customer_preferred_cust_flag\nlimit 100\"\"\",\n \"q12\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ws_ext_sales_price) as itemrevenue\n ,sum(ws_ext_sales_price)*100/sum(sum(ws_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tweb_sales\n \t,item\n \t,date_dim\nwhere\n\tws_item_sk = i_item_sk\n \tand i_category in ('Children', 'Jewelry', 'Music')\n \tand ws_sold_date_sk = d_date_sk\n\tand d_date between cast('2001-05-11' as date)\n\t\t\t\tand (cast('2001-05-11' as date) + INTERVAL 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q13\" ->\n \"\"\"\nselect avg(ss_quantity)\n ,avg(ss_ext_sales_price)\n ,avg(ss_ext_wholesale_cost)\n ,sum(ss_ext_wholesale_cost)\n from store_sales\n ,store\n ,customer_demographics\n ,household_demographics\n ,customer_address\n ,date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 2001\n and((ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'M'\n and cd_education_status = 'Primary'\n and ss_sales_price between 100.00 and 150.00\n and hd_dep_count = 3\n )or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'S'\n and cd_education_status = '4 yr Degree'\n and ss_sales_price between 50.00 and 100.00\n and hd_dep_count = 1\n ) or\n (ss_hdemo_sk=hd_demo_sk\n and cd_demo_sk = ss_cdemo_sk\n and cd_marital_status = 'W'\n and cd_education_status = '2 yr Degree'\n and ss_sales_price between 150.00 and 200.00\n and hd_dep_count = 1\n ))\n and((ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('SC', 'WY', 'TX')\n and ss_net_profit between 100 and 200\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('NY', 'NE', 'GA')\n and ss_net_profit between 150 and 300\n ) or\n (ss_addr_sk = ca_address_sk\n and ca_country = 'United States'\n and ca_state in ('AL', 'AR', 'MI')\n and ss_net_profit between 50 and 250\n ))\"\"\",\n \"q14a\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 1999 AND 1999 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 1999 AND 1999 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 1999 AND 1999 + 2)\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n (select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 1999 and 1999 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 1999 and 1999 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 1999 and 1999 + 2) x)\n select channel, i_brand_id,i_class_id,i_category_id,sum(sales), sum(number_sales)\n from(\n select 'store' channel, i_brand_id,i_class_id\n ,i_category_id,sum(ss_quantity*ss_list_price) sales\n , count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1999+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)\n union all\n select 'catalog' channel, i_brand_id,i_class_id,i_category_id, sum(cs_quantity*cs_list_price) sales, count(*) number_sales\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk in (select ss_item_sk from cross_items)\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1999+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(cs_quantity*cs_list_price) > (select average_sales from avg_sales)\n union all\n select 'web' channel, i_brand_id,i_class_id,i_category_id, sum(ws_quantity*ws_list_price) sales , count(*) number_sales\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk in (select ss_item_sk from cross_items)\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1999+2\n and d_moy = 11\n group by i_brand_id,i_class_id,i_category_id\n having sum(ws_quantity*ws_list_price) > (select average_sales from avg_sales)\n ) y\n group by rollup (channel, i_brand_id,i_class_id,i_category_id)\n order by channel,i_brand_id,i_class_id,i_category_id\n limit 100\"\"\",\n \"q14b\" ->\n \"\"\"\nwith cross_items as\n (select i_item_sk ss_item_sk\n from item,\n (select iss.i_brand_id brand_id\n ,iss.i_class_id class_id\n ,iss.i_category_id category_id\n from store_sales\n ,item iss\n ,date_dim d1\n where ss_item_sk = iss.i_item_sk\n and ss_sold_date_sk = d1.d_date_sk\n and d1.d_year between 1999 AND 1999 + 2\n intersect\n select ics.i_brand_id\n ,ics.i_class_id\n ,ics.i_category_id\n from catalog_sales\n ,item ics\n ,date_dim d2\n where cs_item_sk = ics.i_item_sk\n and cs_sold_date_sk = d2.d_date_sk\n and d2.d_year between 1999 AND 1999 + 2\n intersect\n select iws.i_brand_id\n ,iws.i_class_id\n ,iws.i_category_id\n from web_sales\n ,item iws\n ,date_dim d3\n where ws_item_sk = iws.i_item_sk\n and ws_sold_date_sk = d3.d_date_sk\n and d3.d_year between 1999 AND 1999 + 2) x\n where i_brand_id = brand_id\n and i_class_id = class_id\n and i_category_id = category_id\n),\n avg_sales as\n(select avg(quantity*list_price) average_sales\n from (select ss_quantity quantity\n ,ss_list_price list_price\n from store_sales\n ,date_dim\n where ss_sold_date_sk = d_date_sk\n and d_year between 1999 and 1999 + 2\n union all\n select cs_quantity quantity\n ,cs_list_price list_price\n from catalog_sales\n ,date_dim\n where cs_sold_date_sk = d_date_sk\n and d_year between 1999 and 1999 + 2\n union all\n select ws_quantity quantity\n ,ws_list_price list_price\n from web_sales\n ,date_dim\n where ws_sold_date_sk = d_date_sk\n and d_year between 1999 and 1999 + 2) x)\n select this_year.channel ty_channel\n ,this_year.i_brand_id ty_brand\n ,this_year.i_class_id ty_class\n ,this_year.i_category_id ty_category\n ,this_year.sales ty_sales\n ,this_year.number_sales ty_number_sales\n ,last_year.channel ly_channel\n ,last_year.i_brand_id ly_brand\n ,last_year.i_class_id ly_class\n ,last_year.i_category_id ly_category\n ,last_year.sales ly_sales\n ,last_year.number_sales ly_number_sales\n from\n (select 'store' channel, i_brand_id,i_class_id,i_category_id\n ,sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 1999 + 1\n and d_moy = 12\n and d_dom = 5)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) this_year,\n (select 'store' channel, i_brand_id,i_class_id\n ,i_category_id, sum(ss_quantity*ss_list_price) sales, count(*) number_sales\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk in (select ss_item_sk from cross_items)\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_week_seq = (select d_week_seq\n from date_dim\n where d_year = 1999\n and d_moy = 12\n and d_dom = 5)\n group by i_brand_id,i_class_id,i_category_id\n having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) last_year\n where this_year.i_brand_id= last_year.i_brand_id\n and this_year.i_class_id = last_year.i_class_id\n and this_year.i_category_id = last_year.i_category_id\n order by this_year.channel, this_year.i_brand_id, this_year.i_class_id, this_year.i_category_id\n limit 100\"\"\",\n \"q15\" ->\n \"\"\"\nselect ca_zip\n ,sum(cs_sales_price)\n from catalog_sales\n ,customer\n ,customer_address\n ,date_dim\n where cs_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475',\n '85392', '85460', '80348', '81792')\n \t or ca_state in ('CA','WA','GA')\n \t or cs_sales_price > 500)\n \tand cs_sold_date_sk = d_date_sk\n \tand d_qoy = 1 and d_year = 1998\n group by ca_zip\n order by ca_zip\n limit 100\"\"\",\n \"q16\" ->\n \"\"\"\nselect\n count(distinct cs_order_number) as `order count`\n ,sum(cs_ext_ship_cost) as `total shipping cost`\n ,sum(cs_net_profit) as `total net profit`\nfrom\n catalog_sales cs1\n ,date_dim\n ,customer_address\n ,call_center\nwhere\n d_date between '2000-3-01' and\n (cast('2000-3-01' as date) + INTERVAL 60 days)\nand cs1.cs_ship_date_sk = d_date_sk\nand cs1.cs_ship_addr_sk = ca_address_sk\nand ca_state = 'IA'\nand cs1.cs_call_center_sk = cc_call_center_sk\nand cc_county in ('Luce County','Wadena County','Jefferson Davis Parish','Daviess County',\n 'Williamson County'\n)\nand exists (select *\n from catalog_sales cs2\n where cs1.cs_order_number = cs2.cs_order_number\n and cs1.cs_warehouse_sk <> cs2.cs_warehouse_sk)\nand not exists(select *\n from catalog_returns cr1\n where cs1.cs_order_number = cr1.cr_order_number)\norder by count(distinct cs_order_number)\nlimit 100\"\"\",\n \"q17\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,s_state\n ,count(ss_quantity) as store_sales_quantitycount\n ,avg(ss_quantity) as store_sales_quantityave\n ,stddev_samp(ss_quantity) as store_sales_quantitystdev\n ,stddev_samp(ss_quantity)/avg(ss_quantity) as store_sales_quantitycov\n ,count(sr_return_quantity) as store_returns_quantitycount\n ,avg(sr_return_quantity) as store_returns_quantityave\n ,stddev_samp(sr_return_quantity) as store_returns_quantitystdev\n ,stddev_samp(sr_return_quantity)/avg(sr_return_quantity) as store_returns_quantitycov\n ,count(cs_quantity) as catalog_sales_quantitycount ,avg(cs_quantity) as catalog_sales_quantityave\n ,stddev_samp(cs_quantity) as catalog_sales_quantitystdev\n ,stddev_samp(cs_quantity)/avg(cs_quantity) as catalog_sales_quantitycov\n from store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where d1.d_quarter_name = '1999Q1'\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_quarter_name in ('1999Q1','1999Q2','1999Q3')\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_quarter_name in ('1999Q1','1999Q2','1999Q3')\n group by i_item_id\n ,i_item_desc\n ,s_state\n order by i_item_id\n ,i_item_desc\n ,s_state\nlimit 100\"\"\",\n \"q18\" ->\n \"\"\"\nselect i_item_id,\n ca_country,\n ca_state,\n ca_county,\n avg( cast(cs_quantity as decimal(12,2))) agg1,\n avg( cast(cs_list_price as decimal(12,2))) agg2,\n avg( cast(cs_coupon_amt as decimal(12,2))) agg3,\n avg( cast(cs_sales_price as decimal(12,2))) agg4,\n avg( cast(cs_net_profit as decimal(12,2))) agg5,\n avg( cast(c_birth_year as decimal(12,2))) agg6,\n avg( cast(cd1.cd_dep_count as decimal(12,2))) agg7\n from catalog_sales, customer_demographics cd1,\n customer_demographics cd2, customer, customer_address, date_dim, item\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd1.cd_demo_sk and\n cs_bill_customer_sk = c_customer_sk and\n cd1.cd_gender = 'F' and\n cd1.cd_education_status = 'Unknown' and\n c_current_cdemo_sk = cd2.cd_demo_sk and\n c_current_addr_sk = ca_address_sk and\n c_birth_month in (4,8,12,10,11,9) and\n d_year = 2001 and\n ca_state in ('AR','IA','TX'\n ,'KS','LA','NC','SD')\n group by rollup (i_item_id, ca_country, ca_state, ca_county)\n order by ca_country,\n ca_state,\n ca_county,\n\ti_item_id\n limit 100\"\"\",\n \"q19\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand, i_manufact_id, i_manufact,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item,customer,customer_address,store\n where d_date_sk = ss_sold_date_sk\n and ss_item_sk = i_item_sk\n and i_manager_id=63\n and d_moy=11\n and d_year=2002\n and ss_customer_sk = c_customer_sk\n and c_current_addr_sk = ca_address_sk\n and substr(ca_zip,1,5) <> substr(s_zip,1,5)\n and ss_store_sk = s_store_sk\n group by i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\n order by ext_price desc\n ,i_brand\n ,i_brand_id\n ,i_manufact_id\n ,i_manufact\nlimit 100 \"\"\",\n \"q20\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(cs_ext_sales_price) as itemrevenue\n ,sum(cs_ext_sales_price)*100/sum(sum(cs_ext_sales_price)) over\n (partition by i_class) as revenueratio\n from\tcatalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and i_category in ('Electronics', 'Children', 'Home')\n and cs_sold_date_sk = d_date_sk\n and d_date between cast('2002-03-19' as date)\n \t\t\t\tand (cast('2002-03-19' as date) + INTERVAL 30 days)\n group by i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n order by i_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\nlimit 100\"\"\",\n \"q21\" ->\n \"\"\"\nselect *\n from(select w_warehouse_name\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('1999-04-12' as date))\n\t then inv_quantity_on_hand\n else 0 end) as inv_before\n ,sum(case when (cast(d_date as date) >= cast ('1999-04-12' as date))\n then inv_quantity_on_hand\n else 0 end) as inv_after\n from inventory\n ,warehouse\n ,item\n ,date_dim\n where i_current_price between 0.99 and 1.49\n and i_item_sk = inv_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_date between (cast ('1999-04-12' as date) - INTERVAL 30 days)\n and (cast ('1999-04-12' as date) + INTERVAL 30 days)\n group by w_warehouse_name, i_item_id) x\n where (case when inv_before > 0\n then inv_after / inv_before\n else null\n end) between 2.0/3.0 and 3.0/2.0\n order by w_warehouse_name\n ,i_item_id\n limit 100\"\"\",\n \"q22\" ->\n \"\"\"\nselect i_product_name\n ,i_brand\n ,i_class\n ,i_category\n ,avg(inv_quantity_on_hand) qoh\n from inventory\n ,date_dim\n ,item\n where inv_date_sk=d_date_sk\n and inv_item_sk=i_item_sk\n and d_month_seq between 1188 and 1188 + 11\n group by rollup(i_product_name\n ,i_brand\n ,i_class\n ,i_category)\norder by qoh, i_product_name, i_brand, i_class, i_category\nlimit 100\"\"\",\n \"q23a\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (1998,1998+1,1998+2,1998+3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (1998,1998+1,1998+2,1998+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\nfrom\n max_store_sales))\n select sum(sales)\n from (select cs_quantity*cs_list_price sales\n from catalog_sales\n ,date_dim\n where d_year = 1998\n and d_moy = 7\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n union all\n select ws_quantity*ws_list_price sales\n from web_sales\n ,date_dim\n where d_year = 1998\n and d_moy = 7\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer))\n limit 100\"\"\",\n \"q23b\" ->\n \"\"\"\nwith frequent_ss_items as\n (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt\n from store_sales\n ,date_dim\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_item_sk = i_item_sk\n and d_year in (1998,1998 + 1,1998 + 2,1998 + 3)\n group by substr(i_item_desc,1,30),i_item_sk,d_date\n having count(*) >4),\n max_store_sales as\n (select max(csales) tpcds_cmax\n from (select c_customer_sk,sum(ss_quantity*ss_sales_price) csales\n from store_sales\n ,customer\n ,date_dim\n where ss_customer_sk = c_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (1998,1998+1,1998+2,1998+3)\n group by c_customer_sk)),\n best_ss_customer as\n (select c_customer_sk,sum(ss_quantity*ss_sales_price) ssales\n from store_sales\n ,customer\n where ss_customer_sk = c_customer_sk\n group by c_customer_sk\n having sum(ss_quantity*ss_sales_price) > (95/100.0) * (select\n *\n from max_store_sales))\n select c_last_name,c_first_name,sales\n from (select c_last_name,c_first_name,sum(cs_quantity*cs_list_price) sales\n from catalog_sales\n ,customer\n ,date_dim\n where d_year = 1998\n and d_moy = 7\n and cs_sold_date_sk = d_date_sk\n and cs_item_sk in (select item_sk from frequent_ss_items)\n and cs_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and cs_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name\n union all\n select c_last_name,c_first_name,sum(ws_quantity*ws_list_price) sales\n from web_sales\n ,customer\n ,date_dim\n where d_year = 1998\n and d_moy = 7\n and ws_sold_date_sk = d_date_sk\n and ws_item_sk in (select item_sk from frequent_ss_items)\n and ws_bill_customer_sk in (select c_customer_sk from best_ss_customer)\n and ws_bill_customer_sk = c_customer_sk\n group by c_last_name,c_first_name)\n order by c_last_name,c_first_name,sales\n limit 100\"\"\",\n \"q24a\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_sales_price) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\nand s_market_id=7\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'goldenrod'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q24b\" ->\n \"\"\"\nwith ssales as\n(select c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size\n ,sum(ss_sales_price) netpaid\nfrom store_sales\n ,store_returns\n ,store\n ,item\n ,customer\n ,customer_address\nwhere ss_ticket_number = sr_ticket_number\n and ss_item_sk = sr_item_sk\n and ss_customer_sk = c_customer_sk\n and ss_item_sk = i_item_sk\n and ss_store_sk = s_store_sk\n and c_current_addr_sk = ca_address_sk\n and c_birth_country <> upper(ca_country)\n and s_zip = ca_zip\n and s_market_id = 7\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\n ,ca_state\n ,s_state\n ,i_color\n ,i_current_price\n ,i_manager_id\n ,i_units\n ,i_size)\nselect c_last_name\n ,c_first_name\n ,s_store_name\n ,sum(netpaid) paid\nfrom ssales\nwhere i_color = 'magenta'\ngroup by c_last_name\n ,c_first_name\n ,s_store_name\nhaving sum(netpaid) > (select 0.05*avg(netpaid)\n from ssales)\norder by c_last_name\n ,c_first_name\n ,s_store_name\"\"\",\n \"q25\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,min(ss_net_profit) as store_sales_profit\n ,min(sr_net_loss) as store_returns_loss\n ,min(cs_net_profit) as catalog_sales_profit\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 2002\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 10\n and d2.d_year = 2002\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_moy between 4 and 10\n and d3.d_year = 2002\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q26\" ->\n \"\"\"\nselect i_item_id,\n avg(cs_quantity) agg1,\n avg(cs_list_price) agg2,\n avg(cs_coupon_amt) agg3,\n avg(cs_sales_price) agg4\n from catalog_sales, customer_demographics, date_dim, item, promotion\n where cs_sold_date_sk = d_date_sk and\n cs_item_sk = i_item_sk and\n cs_bill_cdemo_sk = cd_demo_sk and\n cs_promo_sk = p_promo_sk and\n cd_gender = 'F' and\n cd_marital_status = 'M' and\n cd_education_status = '4 yr Degree' and\n (p_channel_email = 'N' or p_channel_event = 'N') and\n d_year = 1998\n group by i_item_id\n order by i_item_id\n limit 100\"\"\",\n \"q27\" ->\n \"\"\"\nselect i_item_id,\n s_state, grouping(s_state) g_state,\n avg(ss_quantity) agg1,\n avg(ss_list_price) agg2,\n avg(ss_coupon_amt) agg3,\n avg(ss_sales_price) agg4\n from store_sales, customer_demographics, date_dim, store, item\n where ss_sold_date_sk = d_date_sk and\n ss_item_sk = i_item_sk and\n ss_store_sk = s_store_sk and\n ss_cdemo_sk = cd_demo_sk and\n cd_gender = 'M' and\n cd_marital_status = 'M' and\n cd_education_status = 'Secondary' and\n d_year = 1999 and\n s_state in ('AL','FL', 'TX', 'NM', 'MI', 'GA')\n group by rollup (i_item_id, s_state)\n order by i_item_id\n ,s_state\n limit 100\"\"\",\n \"q28\" ->\n \"\"\"\nselect *\nfrom (select avg(ss_list_price) B1_LP\n ,count(ss_list_price) B1_CNT\n ,count(distinct ss_list_price) B1_CNTD\n from store_sales\n where ss_quantity between 0 and 5\n and (ss_list_price between 74 and 74+10\n or ss_coupon_amt between 2949 and 2949+1000\n or ss_wholesale_cost between 49 and 49+20)) B1,\n (select avg(ss_list_price) B2_LP\n ,count(ss_list_price) B2_CNT\n ,count(distinct ss_list_price) B2_CNTD\n from store_sales\n where ss_quantity between 6 and 10\n and (ss_list_price between 136 and 136+10\n or ss_coupon_amt between 10027 and 10027+1000\n or ss_wholesale_cost between 53 and 53+20)) B2,\n (select avg(ss_list_price) B3_LP\n ,count(ss_list_price) B3_CNT\n ,count(distinct ss_list_price) B3_CNTD\n from store_sales\n where ss_quantity between 11 and 15\n and (ss_list_price between 73 and 73+10\n or ss_coupon_amt between 1451 and 1451+1000\n or ss_wholesale_cost between 78 and 78+20)) B3,\n (select avg(ss_list_price) B4_LP\n ,count(ss_list_price) B4_CNT\n ,count(distinct ss_list_price) B4_CNTD\n from store_sales\n where ss_quantity between 16 and 20\n and (ss_list_price between 87 and 87+10\n or ss_coupon_amt between 17007 and 17007+1000\n or ss_wholesale_cost between 55 and 55+20)) B4,\n (select avg(ss_list_price) B5_LP\n ,count(ss_list_price) B5_CNT\n ,count(distinct ss_list_price) B5_CNTD\n from store_sales\n where ss_quantity between 21 and 25\n and (ss_list_price between 112 and 112+10\n or ss_coupon_amt between 17243 and 17243+1000\n or ss_wholesale_cost between 2 and 2+20)) B5,\n (select avg(ss_list_price) B6_LP\n ,count(ss_list_price) B6_CNT\n ,count(distinct ss_list_price) B6_CNTD\n from store_sales\n where ss_quantity between 26 and 30\n and (ss_list_price between 119 and 119+10\n or ss_coupon_amt between 4954 and 4954+1000\n or ss_wholesale_cost between 22 and 22+20)) B6\nlimit 100\"\"\",\n \"q29\" ->\n \"\"\"\nselect\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n ,stddev_samp(ss_quantity) as store_sales_quantity\n ,stddev_samp(sr_return_quantity) as store_returns_quantity\n ,stddev_samp(cs_quantity) as catalog_sales_quantity\n from\n store_sales\n ,store_returns\n ,catalog_sales\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,item\n where\n d1.d_moy = 4\n and d1.d_year = 2000\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and ss_customer_sk = sr_customer_sk\n and ss_item_sk = sr_item_sk\n and ss_ticket_number = sr_ticket_number\n and sr_returned_date_sk = d2.d_date_sk\n and d2.d_moy between 4 and 4 + 3\n and d2.d_year = 2000\n and sr_customer_sk = cs_bill_customer_sk\n and sr_item_sk = cs_item_sk\n and cs_sold_date_sk = d3.d_date_sk\n and d3.d_year in (2000,2000+1,2000+2)\n group by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n order by\n i_item_id\n ,i_item_desc\n ,s_store_id\n ,s_store_name\n limit 100\"\"\",\n \"q30\" ->\n \"\"\"\nwith customer_total_return as\n (select wr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(wr_return_amt) as ctr_total_return\n from web_returns\n ,date_dim\n ,customer_address\n where wr_returned_date_sk = d_date_sk\n and d_year =2001\n and wr_returning_addr_sk = ca_address_sk\n group by wr_returning_customer_sk\n ,ca_state)\n select c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'MI'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,c_preferred_cust_flag\n ,c_birth_day,c_birth_month,c_birth_year,c_birth_country,c_login,c_email_address\n ,c_last_review_date,ctr_total_return\nlimit 100\"\"\",\n \"q31\" ->\n \"\"\"\nwith ss as\n (select ca_county,d_qoy, d_year,sum(ss_ext_sales_price) as store_sales\n from store_sales,date_dim,customer_address\n where ss_sold_date_sk = d_date_sk\n and ss_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year),\n ws as\n (select ca_county,d_qoy, d_year,sum(ws_ext_sales_price) as web_sales\n from web_sales,date_dim,customer_address\n where ws_sold_date_sk = d_date_sk\n and ws_bill_addr_sk=ca_address_sk\n group by ca_county,d_qoy, d_year)\n select\n ss1.ca_county\n ,ss1.d_year\n ,ws2.web_sales/ws1.web_sales web_q1_q2_increase\n ,ss2.store_sales/ss1.store_sales store_q1_q2_increase\n ,ws3.web_sales/ws2.web_sales web_q2_q3_increase\n ,ss3.store_sales/ss2.store_sales store_q2_q3_increase\n from\n ss ss1\n ,ss ss2\n ,ss ss3\n ,ws ws1\n ,ws ws2\n ,ws ws3\n where\n ss1.d_qoy = 1\n and ss1.d_year = 2000\n and ss1.ca_county = ss2.ca_county\n and ss2.d_qoy = 2\n and ss2.d_year = 2000\n and ss2.ca_county = ss3.ca_county\n and ss3.d_qoy = 3\n and ss3.d_year = 2000\n and ss1.ca_county = ws1.ca_county\n and ws1.d_qoy = 1\n and ws1.d_year = 2000\n and ws1.ca_county = ws2.ca_county\n and ws2.d_qoy = 2\n and ws2.d_year = 2000\n and ws1.ca_county = ws3.ca_county\n and ws3.d_qoy = 3\n and ws3.d_year =2000\n and case when ws1.web_sales > 0 then ws2.web_sales/ws1.web_sales else null end\n > case when ss1.store_sales > 0 then ss2.store_sales/ss1.store_sales else null end\n and case when ws2.web_sales > 0 then ws3.web_sales/ws2.web_sales else null end\n > case when ss2.store_sales > 0 then ss3.store_sales/ss2.store_sales else null end\n order by store_q1_q2_increase\"\"\",\n \"q32\" ->\n \"\"\"\nselect sum(cs_ext_discount_amt) as `excess discount amount`\nfrom\n catalog_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 490\nand i_item_sk = cs_item_sk\nand d_date between '1999-01-27' and\n (cast('1999-01-27' as date) + INTERVAL 90 days)\nand d_date_sk = cs_sold_date_sk\nand cs_ext_discount_amt\n > (\n select\n 1.3 * avg(cs_ext_discount_amt)\n from\n catalog_sales\n ,date_dim\n where\n cs_item_sk = i_item_sk\n and d_date between '1999-01-27' and\n (cast('1999-01-27' as date) + INTERVAL 90 days)\n and d_date_sk = cs_sold_date_sk\n )\nlimit 100\"\"\",\n \"q33\" ->\n \"\"\"\nwith ss as (\n select\n i_manufact_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Electronics'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 2001\n and d_moy = 1\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id),\n cs as (\n select\n i_manufact_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Electronics'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 2001\n and d_moy = 1\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id),\n ws as (\n select\n i_manufact_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_manufact_id in (select\n i_manufact_id\nfrom\n item\nwhere i_category in ('Electronics'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 2001\n and d_moy = 1\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -6\n group by i_manufact_id)\n select i_manufact_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_manufact_id\n order by total_sales\nlimit 100\"\"\",\n \"q34\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (date_dim.d_dom between 1 and 3 or date_dim.d_dom between 25 and 28)\n and (household_demographics.hd_buy_potential = '1001-5000' or\n household_demographics.hd_buy_potential = 'Unknown')\n and household_demographics.hd_vehicle_count > 0\n and (case when household_demographics.hd_vehicle_count > 0\n\tthen household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count\n\telse null\n\tend) > 1.2\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_county in ('Nez Perce County','Murray County','Surry County','Calhoun County',\n 'Wilkinson County','Brown County','Wallace County','Carter County')\n group by ss_ticket_number,ss_customer_sk) dn,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 15 and 20\n order by c_last_name,c_first_name,c_salutation,c_preferred_cust_flag desc, ss_ticket_number\"\"\",\n \"q35\" ->\n \"\"\"\nselect\n ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n count(*) cnt1,\n stddev_samp(cd_dep_count),\n sum(cd_dep_count),\n min(cd_dep_count),\n cd_dep_employed_count,\n count(*) cnt2,\n stddev_samp(cd_dep_employed_count),\n sum(cd_dep_employed_count),\n min(cd_dep_employed_count),\n cd_dep_college_count,\n count(*) cnt3,\n stddev_samp(cd_dep_college_count),\n sum(cd_dep_college_count),\n min(cd_dep_college_count)\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 2002 and\n d_qoy < 4) and\n (exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 2002 and\n d_qoy < 4) or\n exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 2002 and\n d_qoy < 4))\n group by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n order by ca_state,\n cd_gender,\n cd_marital_status,\n cd_dep_count,\n cd_dep_employed_count,\n cd_dep_college_count\n limit 100\"\"\",\n \"q36\" ->\n \"\"\"\nselect\n sum(ss_net_profit)/sum(ss_ext_sales_price) as gross_margin\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ss_net_profit)/sum(ss_ext_sales_price) asc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,item\n ,store\n where\n d1.d_year = 2000\n and d1.d_date_sk = ss_sold_date_sk\n and i_item_sk = ss_item_sk\n and s_store_sk = ss_store_sk\n and s_state in ('MN','TX','TX','IN',\n 'CA','LA','NM','TX')\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then i_category end\n ,rank_within_parent\n limit 100\"\"\",\n \"q37\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, catalog_sales\n where i_current_price between 16 and 16 + 30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2002-06-05' as date) and (cast('2002-06-05' as date) + interval 60 days)\n and i_manufact_id in (841,790,796,739)\n and inv_quantity_on_hand between 100 and 500\n and cs_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q38\" ->\n \"\"\"\nselect count(*) from (\n select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1203 and 1203 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1203 and 1203 + 11\n intersect\n select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1203 and 1203 + 11\n) hot_cust\nlimit 100\"\"\",\n \"q39a\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =1999\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=3\n and inv2.d_moy=3+1\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q39b\" ->\n \"\"\"\nwith inv as\n(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stdev,mean, case mean when 0 then null else stdev/mean end cov\n from(select w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy\n ,stddev_samp(inv_quantity_on_hand) stdev,avg(inv_quantity_on_hand) mean\n from inventory\n ,item\n ,warehouse\n ,date_dim\n where inv_item_sk = i_item_sk\n and inv_warehouse_sk = w_warehouse_sk\n and inv_date_sk = d_date_sk\n and d_year =1999\n group by w_warehouse_name,w_warehouse_sk,i_item_sk,d_moy) foo\n where case mean when 0 then 0 else stdev/mean end > 1)\nselect inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean, inv1.cov\n ,inv2.w_warehouse_sk,inv2.i_item_sk,inv2.d_moy,inv2.mean, inv2.cov\nfrom inv inv1,inv inv2\nwhere inv1.i_item_sk = inv2.i_item_sk\n and inv1.w_warehouse_sk = inv2.w_warehouse_sk\n and inv1.d_moy=3\n and inv2.d_moy=3+1\n and inv1.cov > 1.5\norder by inv1.w_warehouse_sk,inv1.i_item_sk,inv1.d_moy,inv1.mean,inv1.cov\n ,inv2.d_moy,inv2.mean, inv2.cov\"\"\",\n \"q40\" ->\n \"\"\"\nselect\n w_state\n ,i_item_id\n ,sum(case when (cast(d_date as date) < cast ('1999-04-27' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_before\n ,sum(case when (cast(d_date as date) >= cast ('1999-04-27' as date))\n \t\tthen cs_sales_price - coalesce(cr_refunded_cash,0) else 0 end) as sales_after\n from\n catalog_sales left outer join catalog_returns on\n (cs_order_number = cr_order_number\n and cs_item_sk = cr_item_sk)\n ,warehouse\n ,item\n ,date_dim\n where\n i_current_price between 0.99 and 1.49\n and i_item_sk = cs_item_sk\n and cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and d_date between (cast ('1999-04-27' as date) - INTERVAL 30 days)\n and (cast ('1999-04-27' as date) + INTERVAL 30 days)\n group by\n w_state,i_item_id\n order by w_state,i_item_id\nlimit 100\"\"\",\n \"q41\" ->\n \"\"\"\nselect distinct(i_product_name)\n from item i1\n where i_manufact_id between 841 and 841+40\n and (select count(*) as item_cnt\n from item\n where (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'bisque' or i_color = 'khaki') and\n (i_units = 'Carton' or i_units = 'Box') and\n (i_size = 'large' or i_size = 'extra large')\n ) or\n (i_category = 'Women' and\n (i_color = 'antique' or i_color = 'sandy') and\n (i_units = 'Pallet' or i_units = 'Cup') and\n (i_size = 'petite' or i_size = 'small')\n ) or\n (i_category = 'Men' and\n (i_color = 'forest' or i_color = 'brown') and\n (i_units = 'Dram' or i_units = 'Ton') and\n (i_size = 'economy' or i_size = 'medium')\n ) or\n (i_category = 'Men' and\n (i_color = 'chartreuse' or i_color = 'light') and\n (i_units = 'Pound' or i_units = 'Dozen') and\n (i_size = 'large' or i_size = 'extra large')\n ))) or\n (i_manufact = i1.i_manufact and\n ((i_category = 'Women' and\n (i_color = 'turquoise' or i_color = 'chocolate') and\n (i_units = 'Bundle' or i_units = 'Unknown') and\n (i_size = 'large' or i_size = 'extra large')\n ) or\n (i_category = 'Women' and\n (i_color = 'maroon' or i_color = 'pale') and\n (i_units = 'Each' or i_units = 'Tbl') and\n (i_size = 'petite' or i_size = 'small')\n ) or\n (i_category = 'Men' and\n (i_color = 'almond' or i_color = 'floral') and\n (i_units = 'Gross' or i_units = 'N/A') and\n (i_size = 'economy' or i_size = 'medium')\n ) or\n (i_category = 'Men' and\n (i_color = 'drab' or i_color = 'plum') and\n (i_units = 'Bunch' or i_units = 'Case') and\n (i_size = 'large' or i_size = 'extra large')\n )))) > 0\n order by i_product_name\n limit 100\"\"\",\n \"q42\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_category_id\n \t,item.i_category\n \t,sum(ss_ext_sales_price)\n from \tdate_dim dt\n \t,store_sales\n \t,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n \tand store_sales.ss_item_sk = item.i_item_sk\n \tand item.i_manager_id = 1\n \tand dt.d_moy=11\n \tand dt.d_year=2002\n group by \tdt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\n order by sum(ss_ext_sales_price) desc,dt.d_year\n \t\t,item.i_category_id\n \t\t,item.i_category\nlimit 100 \"\"\",\n \"q43\" ->\n \"\"\"\nselect s_store_name, s_store_id,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from date_dim, store_sales, store\n where d_date_sk = ss_sold_date_sk and\n s_store_sk = ss_store_sk and\n s_gmt_offset = -5 and\n d_year = 2002\n group by s_store_name, s_store_id\n order by s_store_name, s_store_id,sun_sales,mon_sales,tue_sales,wed_sales,thu_sales,fri_sales,sat_sales\n limit 100\"\"\",\n \"q44\" ->\n \"\"\"\nselect asceding.rnk, i1.i_product_name best_performing, i2.i_product_name worst_performing\nfrom(select *\n from (select item_sk,rank() over (order by rank_col asc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 709\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 709\n and ss_addr_sk is null\n group by ss_store_sk))V1)V11\n where rnk < 11) asceding,\n (select *\n from (select item_sk,rank() over (order by rank_col desc) rnk\n from (select ss_item_sk item_sk,avg(ss_net_profit) rank_col\n from store_sales ss1\n where ss_store_sk = 709\n group by ss_item_sk\n having avg(ss_net_profit) > 0.9*(select avg(ss_net_profit) rank_col\n from store_sales\n where ss_store_sk = 709\n and ss_addr_sk is null\n group by ss_store_sk))V2)V21\n where rnk < 11) descending,\nitem i1,\nitem i2\nwhere asceding.rnk = descending.rnk\n and i1.i_item_sk=asceding.item_sk\n and i2.i_item_sk=descending.item_sk\norder by asceding.rnk\nlimit 100\"\"\",\n \"q45\" ->\n \"\"\"\nselect ca_zip, ca_state, sum(ws_sales_price)\n from web_sales, customer, customer_address, date_dim, item\n where ws_bill_customer_sk = c_customer_sk\n \tand c_current_addr_sk = ca_address_sk\n \tand ws_item_sk = i_item_sk\n \tand ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475', '85392', '85460', '80348', '81792')\n \t or\n \t i_item_id in (select i_item_id\n from item\n where i_item_sk in (2, 3, 5, 7, 11, 13, 17, 19, 23, 29)\n )\n \t )\n \tand ws_sold_date_sk = d_date_sk\n \tand d_qoy = 2 and d_year = 2002\n group by ca_zip, ca_state\n order by ca_zip, ca_state\n limit 100\"\"\",\n \"q46\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and (household_demographics.hd_dep_count = 0 or\n household_demographics.hd_vehicle_count= 1)\n and date_dim.d_dow in (6,0)\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_city in ('Johnson','Norwood','Cambridge','Klondike','Rock Hill')\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,ca_city) dn,customer,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n limit 100\"\"\",\n \"q47\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand,\n s_store_name, s_company_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n s_store_name, s_company_name\n order by d_year, d_moy) rn\n from item, store_sales, date_dim, store\n where ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n (\n d_year = 2001 or\n ( d_year = 2001-1 and d_moy =12) or\n ( d_year = 2001+1 and d_moy =1)\n )\n group by i_category, i_brand,\n s_store_name, s_company_name,\n d_year, d_moy),\n v2 as(\n select v1.i_category, v1.i_brand, v1.s_store_name, v1.s_company_name\n ,v1.d_year, v1.d_moy\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1.s_store_name = v1_lag.s_store_name and\n v1.s_store_name = v1_lead.s_store_name and\n v1.s_company_name = v1_lag.s_company_name and\n v1.s_company_name = v1_lead.s_company_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 2001 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, psum\n limit 100\"\"\",\n \"q48\" ->\n \"\"\"\nselect sum (ss_quantity)\n from store_sales, store, customer_demographics, customer_address, date_dim\n where s_store_sk = ss_store_sk\n and ss_sold_date_sk = d_date_sk and d_year = 2000\n and\n (\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'U'\n and\n cd_education_status = '2 yr Degree'\n and\n ss_sales_price between 100.00 and 150.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'S'\n and\n cd_education_status = 'Primary'\n and\n ss_sales_price between 50.00 and 100.00\n )\n or\n (\n cd_demo_sk = ss_cdemo_sk\n and\n cd_marital_status = 'W'\n and\n cd_education_status = '4 yr Degree'\n and\n ss_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('MT', 'OH', 'GA')\n and ss_net_profit between 0 and 2000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('WV', 'AZ', 'NM')\n and ss_net_profit between 150 and 3000\n )\n or\n (ss_addr_sk = ca_address_sk\n and\n ca_country = 'United States'\n and\n ca_state in ('NY', 'PA', 'KY')\n and ss_net_profit between 50 and 25000\n )\n )\"\"\",\n \"q49\" ->\n \"\"\"\nselect channel, item, return_ratio, return_rank, currency_rank from\n (select\n 'web' as channel\n ,web.item\n ,web.return_ratio\n ,web.return_rank\n ,web.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect ws.ws_item_sk as item\n \t\t,(cast(sum(coalesce(wr.wr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(wr.wr_return_amt,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(ws.ws_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\t web_sales ws left outer join web_returns wr\n \t\t\ton (ws.ws_order_number = wr.wr_order_number and\n \t\t\tws.ws_item_sk = wr.wr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\twr.wr_return_amt > 10000\n \t\t\tand ws.ws_net_profit > 1\n and ws.ws_net_paid > 0\n and ws.ws_quantity > 0\n and ws_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 11\n \t\tgroup by ws.ws_item_sk\n \t) in_web\n ) web\n where\n (\n web.return_rank <= 10\n or\n web.currency_rank <= 10\n )\n union\n select\n 'catalog' as channel\n ,catalog.item\n ,catalog.return_ratio\n ,catalog.return_rank\n ,catalog.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect\n \t\tcs.cs_item_sk as item\n \t\t,(cast(sum(coalesce(cr.cr_return_quantity,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(cr.cr_return_amount,0)) as decimal(15,4))/\n \t\tcast(sum(coalesce(cs.cs_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tcatalog_sales cs left outer join catalog_returns cr\n \t\t\ton (cs.cs_order_number = cr.cr_order_number and\n \t\t\tcs.cs_item_sk = cr.cr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tcr.cr_return_amount > 10000\n \t\t\tand cs.cs_net_profit > 1\n and cs.cs_net_paid > 0\n and cs.cs_quantity > 0\n and cs_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 11\n group by cs.cs_item_sk\n \t) in_cat\n ) catalog\n where\n (\n catalog.return_rank <= 10\n or\n catalog.currency_rank <=10\n )\n union\n select\n 'store' as channel\n ,store.item\n ,store.return_ratio\n ,store.return_rank\n ,store.currency_rank\n from (\n \tselect\n \t item\n \t,return_ratio\n \t,currency_ratio\n \t,rank() over (order by return_ratio) as return_rank\n \t,rank() over (order by currency_ratio) as currency_rank\n \tfrom\n \t(\tselect sts.ss_item_sk as item\n \t\t,(cast(sum(coalesce(sr.sr_return_quantity,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_quantity,0)) as decimal(15,4) )) as return_ratio\n \t\t,(cast(sum(coalesce(sr.sr_return_amt,0)) as decimal(15,4))/cast(sum(coalesce(sts.ss_net_paid,0)) as decimal(15,4) )) as currency_ratio\n \t\tfrom\n \t\tstore_sales sts left outer join store_returns sr\n \t\t\ton (sts.ss_ticket_number = sr.sr_ticket_number and sts.ss_item_sk = sr.sr_item_sk)\n ,date_dim\n \t\twhere\n \t\t\tsr.sr_return_amt > 10000\n \t\t\tand sts.ss_net_profit > 1\n and sts.ss_net_paid > 0\n and sts.ss_quantity > 0\n and ss_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 11\n \t\tgroup by sts.ss_item_sk\n \t) in_store\n ) store\n where (\n store.return_rank <= 10\n or\n store.currency_rank <= 10\n )\n )\n order by 1,4,5,2\n limit 100\"\"\",\n \"q50\" ->\n \"\"\"\nselect\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 30) and\n (sr_returned_date_sk - ss_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 60) and\n (sr_returned_date_sk - ss_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 90) and\n (sr_returned_date_sk - ss_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n store_sales\n ,store_returns\n ,store\n ,date_dim d1\n ,date_dim d2\nwhere\n d2.d_year = 2000\nand d2.d_moy = 9\nand ss_ticket_number = sr_ticket_number\nand ss_item_sk = sr_item_sk\nand ss_sold_date_sk = d1.d_date_sk\nand sr_returned_date_sk = d2.d_date_sk\nand ss_customer_sk = sr_customer_sk\nand ss_store_sk = s_store_sk\ngroup by\n s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\norder by s_store_name\n ,s_company_id\n ,s_street_number\n ,s_street_name\n ,s_street_type\n ,s_suite_number\n ,s_city\n ,s_county\n ,s_state\n ,s_zip\nlimit 100\"\"\",\n \"q51\" ->\n \"\"\"\nWITH web_v1 as (\nselect\n ws_item_sk item_sk, d_date,\n sum(sum(ws_sales_price))\n over (partition by ws_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom web_sales\n ,date_dim\nwhere ws_sold_date_sk=d_date_sk\n and d_month_seq between 1177 and 1177+11\n and ws_item_sk is not NULL\ngroup by ws_item_sk, d_date),\nstore_v1 as (\nselect\n ss_item_sk item_sk, d_date,\n sum(sum(ss_sales_price))\n over (partition by ss_item_sk order by d_date rows between unbounded preceding and current row) cume_sales\nfrom store_sales\n ,date_dim\nwhere ss_sold_date_sk=d_date_sk\n and d_month_seq between 1177 and 1177+11\n and ss_item_sk is not NULL\ngroup by ss_item_sk, d_date)\n select *\nfrom (select item_sk\n ,d_date\n ,web_sales\n ,store_sales\n ,max(web_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) web_cumulative\n ,max(store_sales)\n over (partition by item_sk order by d_date rows between unbounded preceding and current row) store_cumulative\n from (select case when web.item_sk is not null then web.item_sk else store.item_sk end item_sk\n ,case when web.d_date is not null then web.d_date else store.d_date end d_date\n ,web.cume_sales web_sales\n ,store.cume_sales store_sales\n from web_v1 web full outer join store_v1 store on (web.item_sk = store.item_sk\n and web.d_date = store.d_date)\n )x )y\nwhere web_cumulative > store_cumulative\norder by item_sk\n ,d_date\nlimit 100\"\"\",\n \"q52\" ->\n \"\"\"\nselect dt.d_year\n \t,item.i_brand_id brand_id\n \t,item.i_brand brand\n \t,sum(ss_ext_sales_price) ext_price\n from date_dim dt\n ,store_sales\n ,item\n where dt.d_date_sk = store_sales.ss_sold_date_sk\n and store_sales.ss_item_sk = item.i_item_sk\n and item.i_manager_id = 1\n and dt.d_moy=12\n and dt.d_year=2001\n group by dt.d_year\n \t,item.i_brand\n \t,item.i_brand_id\n order by dt.d_year\n \t,ext_price desc\n \t,brand_id\nlimit 100 \"\"\",\n \"q53\" ->\n \"\"\"\nselect * from\n(select i_manufact_id,\nsum(ss_sales_price) sum_sales,\navg(sum(ss_sales_price)) over (partition by i_manufact_id) avg_quarterly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\nss_sold_date_sk = d_date_sk and\nss_store_sk = s_store_sk and\nd_month_seq in (1188,1188+1,1188+2,1188+3,1188+4,1188+5,1188+6,1188+7,1188+8,1188+9,1188+10,1188+11) and\n((i_category in ('Books','Children','Electronics') and\ni_class in ('personal','portable','reference','self-help') and\ni_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t'exportiunivamalg #9','scholaramalgamalg #9'))\nor(i_category in ('Women','Music','Men') and\ni_class in ('accessories','classical','fragrances','pants') and\ni_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t'importoamalg #1')))\ngroup by i_manufact_id, d_qoy ) tmp1\nwhere case when avg_quarterly_sales > 0\n\tthen abs (sum_sales - avg_quarterly_sales)/ avg_quarterly_sales\n\telse null end > 0.1\norder by avg_quarterly_sales,\n\t sum_sales,\n\t i_manufact_id\nlimit 100\"\"\",\n \"q54\" ->\n \"\"\"\nwith my_customers as (\n select distinct c_customer_sk\n , c_current_addr_sk\n from\n ( select cs_sold_date_sk sold_date_sk,\n cs_bill_customer_sk customer_sk,\n cs_item_sk item_sk\n from catalog_sales\n union all\n select ws_sold_date_sk sold_date_sk,\n ws_bill_customer_sk customer_sk,\n ws_item_sk item_sk\n from web_sales\n ) cs_or_ws_sales,\n item,\n date_dim,\n customer\n where sold_date_sk = d_date_sk\n and item_sk = i_item_sk\n and i_category = 'Men'\n and i_class = 'pants'\n and c_customer_sk = cs_or_ws_sales.customer_sk\n and d_moy = 5\n and d_year = 2002\n )\n , my_revenue as (\n select c_customer_sk,\n sum(ss_ext_sales_price) as revenue\n from my_customers,\n store_sales,\n customer_address,\n store,\n date_dim\n where c_current_addr_sk = ca_address_sk\n and ca_county = s_county\n and ca_state = s_state\n and ss_sold_date_sk = d_date_sk\n and c_customer_sk = ss_customer_sk\n and d_month_seq between (select distinct d_month_seq+1\n from date_dim where d_year = 2002 and d_moy = 5)\n and (select distinct d_month_seq+3\n from date_dim where d_year = 2002 and d_moy = 5)\n group by c_customer_sk\n )\n , segments as\n (select cast((revenue/50) as int) as segment\n from my_revenue\n )\n select segment, count(*) as num_customers, segment*50 as segment_base\n from segments\n group by segment\n order by segment, num_customers\n limit 100\"\"\",\n \"q55\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,\n \tsum(ss_ext_sales_price) ext_price\n from date_dim, store_sales, item\n where d_date_sk = ss_sold_date_sk\n \tand ss_item_sk = i_item_sk\n \tand i_manager_id=67\n \tand d_moy=11\n \tand d_year=2001\n group by i_brand, i_brand_id\n order by ext_price desc, i_brand_id\nlimit 100 \"\"\",\n \"q56\" ->\n \"\"\"\nwith ss as (\n select i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('blanched','spring','seashell'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 6\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_item_id),\n cs as (\n select i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('blanched','spring','seashell'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 6\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_item_id),\n ws as (\n select i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom item\nwhere i_color in ('blanched','spring','seashell'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1999\n and d_moy = 6\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -7\n group by i_item_id)\n select i_item_id ,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by total_sales,\n i_item_id\n limit 100\"\"\",\n \"q57\" ->\n \"\"\"\nwith v1 as(\n select i_category, i_brand,\n cc_name,\n d_year, d_moy,\n sum(cs_sales_price) sum_sales,\n avg(sum(cs_sales_price)) over\n (partition by i_category, i_brand,\n cc_name, d_year)\n avg_monthly_sales,\n rank() over\n (partition by i_category, i_brand,\n cc_name\n order by d_year, d_moy) rn\n from item, catalog_sales, date_dim, call_center\n where cs_item_sk = i_item_sk and\n cs_sold_date_sk = d_date_sk and\n cc_call_center_sk= cs_call_center_sk and\n (\n d_year = 2000 or\n ( d_year = 2000-1 and d_moy =12) or\n ( d_year = 2000+1 and d_moy =1)\n )\n group by i_category, i_brand,\n cc_name , d_year, d_moy),\n v2 as(\n select v1.i_category, v1.i_brand\n ,v1.d_year\n ,v1.avg_monthly_sales\n ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum\n from v1, v1 v1_lag, v1 v1_lead\n where v1.i_category = v1_lag.i_category and\n v1.i_category = v1_lead.i_category and\n v1.i_brand = v1_lag.i_brand and\n v1.i_brand = v1_lead.i_brand and\n v1. cc_name = v1_lag. cc_name and\n v1. cc_name = v1_lead. cc_name and\n v1.rn = v1_lag.rn + 1 and\n v1.rn = v1_lead.rn - 1)\n select *\n from v2\n where d_year = 2000 and\n avg_monthly_sales > 0 and\n case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\n order by sum_sales - avg_monthly_sales, sum_sales\n limit 100\"\"\",\n \"q58\" ->\n \"\"\"\nwith ss_items as\n (select i_item_id item_id\n ,sum(ss_ext_sales_price) ss_item_rev\n from store_sales\n ,item\n ,date_dim\n where ss_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '2000-05-24'))\n and ss_sold_date_sk = d_date_sk\n group by i_item_id),\n cs_items as\n (select i_item_id item_id\n ,sum(cs_ext_sales_price) cs_item_rev\n from catalog_sales\n ,item\n ,date_dim\n where cs_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq = (select d_week_seq\n from date_dim\n where d_date = '2000-05-24'))\n and cs_sold_date_sk = d_date_sk\n group by i_item_id),\n ws_items as\n (select i_item_id item_id\n ,sum(ws_ext_sales_price) ws_item_rev\n from web_sales\n ,item\n ,date_dim\n where ws_item_sk = i_item_sk\n and d_date in (select d_date\n from date_dim\n where d_week_seq =(select d_week_seq\n from date_dim\n where d_date = '2000-05-24'))\n and ws_sold_date_sk = d_date_sk\n group by i_item_id)\n select ss_items.item_id\n ,ss_item_rev\n ,ss_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ss_dev\n ,cs_item_rev\n ,cs_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 cs_dev\n ,ws_item_rev\n ,ws_item_rev/((ss_item_rev+cs_item_rev+ws_item_rev)/3) * 100 ws_dev\n ,(ss_item_rev+cs_item_rev+ws_item_rev)/3 average\n from ss_items,cs_items,ws_items\n where ss_items.item_id=cs_items.item_id\n and ss_items.item_id=ws_items.item_id\n and ss_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n and ss_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and cs_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and cs_item_rev between 0.9 * ws_item_rev and 1.1 * ws_item_rev\n and ws_item_rev between 0.9 * ss_item_rev and 1.1 * ss_item_rev\n and ws_item_rev between 0.9 * cs_item_rev and 1.1 * cs_item_rev\n order by item_id\n ,ss_item_rev\n limit 100\"\"\",\n \"q59\" ->\n \"\"\"\nwith wss as\n (select d_week_seq,\n ss_store_sk,\n sum(case when (d_day_name='Sunday') then ss_sales_price else null end) sun_sales,\n sum(case when (d_day_name='Monday') then ss_sales_price else null end) mon_sales,\n sum(case when (d_day_name='Tuesday') then ss_sales_price else null end) tue_sales,\n sum(case when (d_day_name='Wednesday') then ss_sales_price else null end) wed_sales,\n sum(case when (d_day_name='Thursday') then ss_sales_price else null end) thu_sales,\n sum(case when (d_day_name='Friday') then ss_sales_price else null end) fri_sales,\n sum(case when (d_day_name='Saturday') then ss_sales_price else null end) sat_sales\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n group by d_week_seq,ss_store_sk\n )\n select s_store_name1,s_store_id1,d_week_seq1\n ,sun_sales1/sun_sales2,mon_sales1/mon_sales2\n ,tue_sales1/tue_sales2,wed_sales1/wed_sales2,thu_sales1/thu_sales2\n ,fri_sales1/fri_sales2,sat_sales1/sat_sales2\n from\n (select s_store_name s_store_name1,wss.d_week_seq d_week_seq1\n ,s_store_id s_store_id1,sun_sales sun_sales1\n ,mon_sales mon_sales1,tue_sales tue_sales1\n ,wed_sales wed_sales1,thu_sales thu_sales1\n ,fri_sales fri_sales1,sat_sales sat_sales1\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1197 and 1197 + 11) y,\n (select s_store_name s_store_name2,wss.d_week_seq d_week_seq2\n ,s_store_id s_store_id2,sun_sales sun_sales2\n ,mon_sales mon_sales2,tue_sales tue_sales2\n ,wed_sales wed_sales2,thu_sales thu_sales2\n ,fri_sales fri_sales2,sat_sales sat_sales2\n from wss,store,date_dim d\n where d.d_week_seq = wss.d_week_seq and\n ss_store_sk = s_store_sk and\n d_month_seq between 1197+ 12 and 1197 + 23) x\n where s_store_id1=s_store_id2\n and d_week_seq1=d_week_seq2-52\n order by s_store_name1,s_store_id1,d_week_seq1\nlimit 100\"\"\",\n \"q60\" ->\n \"\"\"\nwith ss as (\n select\n i_item_id,sum(ss_ext_sales_price) total_sales\n from\n \tstore_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Shoes'))\n and ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 10\n and ss_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n cs as (\n select\n i_item_id,sum(cs_ext_sales_price) total_sales\n from\n \tcatalog_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Shoes'))\n and cs_item_sk = i_item_sk\n and cs_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 10\n and cs_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id),\n ws as (\n select\n i_item_id,sum(ws_ext_sales_price) total_sales\n from\n \tweb_sales,\n \tdate_dim,\n customer_address,\n item\n where\n i_item_id in (select\n i_item_id\nfrom\n item\nwhere i_category in ('Shoes'))\n and ws_item_sk = i_item_sk\n and ws_sold_date_sk = d_date_sk\n and d_year = 1998\n and d_moy = 10\n and ws_bill_addr_sk = ca_address_sk\n and ca_gmt_offset = -5\n group by i_item_id)\n select\n i_item_id\n,sum(total_sales) total_sales\n from (select * from ss\n union all\n select * from cs\n union all\n select * from ws) tmp1\n group by i_item_id\n order by i_item_id\n ,total_sales\n limit 100\"\"\",\n \"q61\" ->\n \"\"\"\nselect promotions,total,cast(promotions as decimal(15,4))/cast(total as decimal(15,4))*100\nfrom\n (select sum(ss_ext_sales_price) promotions\n from store_sales\n ,store\n ,promotion\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_promo_sk = p_promo_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -7\n and i_category = 'Jewelry'\n and (p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y')\n and s_gmt_offset = -7\n and d_year = 2002\n and d_moy = 11) promotional_sales,\n (select sum(ss_ext_sales_price) total\n from store_sales\n ,store\n ,date_dim\n ,customer\n ,customer_address\n ,item\n where ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and ss_customer_sk= c_customer_sk\n and ca_address_sk = c_current_addr_sk\n and ss_item_sk = i_item_sk\n and ca_gmt_offset = -7\n and i_category = 'Jewelry'\n and s_gmt_offset = -7\n and d_year = 2002\n and d_moy = 11) all_sales\norder by promotions, total\nlimit 100\"\"\",\n \"q62\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 30) and\n (ws_ship_date_sk - ws_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 60) and\n (ws_ship_date_sk - ws_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 90) and\n (ws_ship_date_sk - ws_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n web_sales\n ,warehouse\n ,ship_mode\n ,web_site\n ,date_dim\nwhere\n d_month_seq between 1194 and 1194 + 11\nand ws_ship_date_sk = d_date_sk\nand ws_warehouse_sk = w_warehouse_sk\nand ws_ship_mode_sk = sm_ship_mode_sk\nand ws_web_site_sk = web_site_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,web_name\nlimit 100\"\"\",\n \"q63\" ->\n \"\"\"\nselect *\nfrom (select i_manager_id\n ,sum(ss_sales_price) sum_sales\n ,avg(sum(ss_sales_price)) over (partition by i_manager_id) avg_monthly_sales\n from item\n ,store_sales\n ,date_dim\n ,store\n where ss_item_sk = i_item_sk\n and ss_sold_date_sk = d_date_sk\n and ss_store_sk = s_store_sk\n and d_month_seq in (1222,1222+1,1222+2,1222+3,1222+4,1222+5,1222+6,1222+7,1222+8,1222+9,1222+10,1222+11)\n and (( i_category in ('Books','Children','Electronics')\n and i_class in ('personal','portable','reference','self-help')\n and i_brand in ('scholaramalgamalg #14','scholaramalgamalg #7',\n\t\t 'exportiunivamalg #9','scholaramalgamalg #9'))\n or( i_category in ('Women','Music','Men')\n and i_class in ('accessories','classical','fragrances','pants')\n and i_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1',\n\t\t 'importoamalg #1')))\ngroup by i_manager_id, d_moy) tmp1\nwhere case when avg_monthly_sales > 0 then abs (sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1\norder by i_manager_id\n ,avg_monthly_sales\n ,sum_sales\nlimit 100\"\"\",\n \"q64\" ->\n \"\"\"\nwith cs_ui as\n (select cs_item_sk\n ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund\n from catalog_sales\n ,catalog_returns\n where cs_item_sk = cr_item_sk\n and cs_order_number = cr_order_number\n group by cs_item_sk\n having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit)),\ncross_sales as\n (select i_product_name product_name\n ,i_item_sk item_sk\n ,s_store_name store_name\n ,s_zip store_zip\n ,ad1.ca_street_number b_street_number\n ,ad1.ca_street_name b_street_name\n ,ad1.ca_city b_city\n ,ad1.ca_zip b_zip\n ,ad2.ca_street_number c_street_number\n ,ad2.ca_street_name c_street_name\n ,ad2.ca_city c_city\n ,ad2.ca_zip c_zip\n ,d1.d_year as syear\n ,d2.d_year as fsyear\n ,d3.d_year s2year\n ,count(*) cnt\n ,sum(ss_wholesale_cost) s1\n ,sum(ss_list_price) s2\n ,sum(ss_coupon_amt) s3\n FROM store_sales\n ,store_returns\n ,cs_ui\n ,date_dim d1\n ,date_dim d2\n ,date_dim d3\n ,store\n ,customer\n ,customer_demographics cd1\n ,customer_demographics cd2\n ,promotion\n ,household_demographics hd1\n ,household_demographics hd2\n ,customer_address ad1\n ,customer_address ad2\n ,income_band ib1\n ,income_band ib2\n ,item\n WHERE ss_store_sk = s_store_sk AND\n ss_sold_date_sk = d1.d_date_sk AND\n ss_customer_sk = c_customer_sk AND\n ss_cdemo_sk= cd1.cd_demo_sk AND\n ss_hdemo_sk = hd1.hd_demo_sk AND\n ss_addr_sk = ad1.ca_address_sk and\n ss_item_sk = i_item_sk and\n ss_item_sk = sr_item_sk and\n ss_ticket_number = sr_ticket_number and\n ss_item_sk = cs_ui.cs_item_sk and\n c_current_cdemo_sk = cd2.cd_demo_sk AND\n c_current_hdemo_sk = hd2.hd_demo_sk AND\n c_current_addr_sk = ad2.ca_address_sk and\n c_first_sales_date_sk = d2.d_date_sk and\n c_first_shipto_date_sk = d3.d_date_sk and\n ss_promo_sk = p_promo_sk and\n hd1.hd_income_band_sk = ib1.ib_income_band_sk and\n hd2.hd_income_band_sk = ib2.ib_income_band_sk and\n cd1.cd_marital_status <> cd2.cd_marital_status and\n i_color in ('ivory','purple','almond','bisque','lawn','azure') and\n i_current_price between 60 and 60 + 10 and\n i_current_price between 60 + 1 and 60 + 15\ngroup by i_product_name\n ,i_item_sk\n ,s_store_name\n ,s_zip\n ,ad1.ca_street_number\n ,ad1.ca_street_name\n ,ad1.ca_city\n ,ad1.ca_zip\n ,ad2.ca_street_number\n ,ad2.ca_street_name\n ,ad2.ca_city\n ,ad2.ca_zip\n ,d1.d_year\n ,d2.d_year\n ,d3.d_year\n)\nselect cs1.product_name\n ,cs1.store_name\n ,cs1.store_zip\n ,cs1.b_street_number\n ,cs1.b_street_name\n ,cs1.b_city\n ,cs1.b_zip\n ,cs1.c_street_number\n ,cs1.c_street_name\n ,cs1.c_city\n ,cs1.c_zip\n ,cs1.syear\n ,cs1.cnt\n ,cs1.s1 as s11\n ,cs1.s2 as s21\n ,cs1.s3 as s31\n ,cs2.s1 as s12\n ,cs2.s2 as s22\n ,cs2.s3 as s32\n ,cs2.syear\n ,cs2.cnt\nfrom cross_sales cs1,cross_sales cs2\nwhere cs1.item_sk=cs2.item_sk and\n cs1.syear = 2001 and\n cs2.syear = 2001 + 1 and\n cs2.cnt <= cs1.cnt and\n cs1.store_name = cs2.store_name and\n cs1.store_zip = cs2.store_zip\norder by cs1.product_name\n ,cs1.store_name\n ,cs2.cnt\n ,cs1.s1\n ,cs2.s1\"\"\",\n \"q65\" ->\n \"\"\"\nselect\n\ts_store_name,\n\ti_item_desc,\n\tsc.revenue,\n\ti_current_price,\n\ti_wholesale_cost,\n\ti_brand\n from store, item,\n (select ss_store_sk, avg(revenue) as ave\n \tfrom\n \t (select ss_store_sk, ss_item_sk,\n \t\t sum(ss_sales_price) as revenue\n \t\tfrom store_sales, date_dim\n \t\twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1185 and 1185+11\n \t\tgroup by ss_store_sk, ss_item_sk) sa\n \tgroup by ss_store_sk) sb,\n (select ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue\n \tfrom store_sales, date_dim\n \twhere ss_sold_date_sk = d_date_sk and d_month_seq between 1185 and 1185+11\n \tgroup by ss_store_sk, ss_item_sk) sc\n where sb.ss_store_sk = sc.ss_store_sk and\n sc.revenue <= 0.1 * sb.ave and\n s_store_sk = sc.ss_store_sk and\n i_item_sk = sc.ss_item_sk\n order by s_store_name, i_item_desc\nlimit 100\"\"\",\n \"q66\" ->\n \"\"\"\nselect\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,ship_carriers\n ,year\n \t,sum(jan_sales) as jan_sales\n \t,sum(feb_sales) as feb_sales\n \t,sum(mar_sales) as mar_sales\n \t,sum(apr_sales) as apr_sales\n \t,sum(may_sales) as may_sales\n \t,sum(jun_sales) as jun_sales\n \t,sum(jul_sales) as jul_sales\n \t,sum(aug_sales) as aug_sales\n \t,sum(sep_sales) as sep_sales\n \t,sum(oct_sales) as oct_sales\n \t,sum(nov_sales) as nov_sales\n \t,sum(dec_sales) as dec_sales\n \t,sum(jan_sales/w_warehouse_sq_ft) as jan_sales_per_sq_foot\n \t,sum(feb_sales/w_warehouse_sq_ft) as feb_sales_per_sq_foot\n \t,sum(mar_sales/w_warehouse_sq_ft) as mar_sales_per_sq_foot\n \t,sum(apr_sales/w_warehouse_sq_ft) as apr_sales_per_sq_foot\n \t,sum(may_sales/w_warehouse_sq_ft) as may_sales_per_sq_foot\n \t,sum(jun_sales/w_warehouse_sq_ft) as jun_sales_per_sq_foot\n \t,sum(jul_sales/w_warehouse_sq_ft) as jul_sales_per_sq_foot\n \t,sum(aug_sales/w_warehouse_sq_ft) as aug_sales_per_sq_foot\n \t,sum(sep_sales/w_warehouse_sq_ft) as sep_sales_per_sq_foot\n \t,sum(oct_sales/w_warehouse_sq_ft) as oct_sales_per_sq_foot\n \t,sum(nov_sales/w_warehouse_sq_ft) as nov_sales_per_sq_foot\n \t,sum(dec_sales/w_warehouse_sq_ft) as dec_sales_per_sq_foot\n \t,sum(jan_net) as jan_net\n \t,sum(feb_net) as feb_net\n \t,sum(mar_net) as mar_net\n \t,sum(apr_net) as apr_net\n \t,sum(may_net) as may_net\n \t,sum(jun_net) as jun_net\n \t,sum(jul_net) as jul_net\n \t,sum(aug_net) as aug_net\n \t,sum(sep_net) as sep_net\n \t,sum(oct_net) as oct_net\n \t,sum(nov_net) as nov_net\n \t,sum(dec_net) as dec_net\n from (\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'FEDEX' || ',' || 'MSC' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen ws_ext_list_price* ws_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen ws_net_profit * ws_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen ws_net_profit * ws_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen ws_net_profit * ws_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen ws_net_profit * ws_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen ws_net_profit * ws_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen ws_net_profit * ws_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen ws_net_profit * ws_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen ws_net_profit * ws_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen ws_net_profit * ws_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen ws_net_profit * ws_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen ws_net_profit * ws_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen ws_net_profit * ws_quantity else 0 end) as dec_net\n from\n web_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n ws_warehouse_sk = w_warehouse_sk\n and ws_sold_date_sk = d_date_sk\n and ws_sold_time_sk = t_time_sk\n \tand ws_ship_mode_sk = sm_ship_mode_sk\n and d_year = 2002\n \tand t_time between 2662 and 2662+28800\n \tand sm_carrier in ('FEDEX','MSC')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n union all\n select\n \tw_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,'FEDEX' || ',' || 'MSC' as ship_carriers\n ,d_year as year\n \t,sum(case when d_moy = 1\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jan_sales\n \t,sum(case when d_moy = 2\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as feb_sales\n \t,sum(case when d_moy = 3\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as mar_sales\n \t,sum(case when d_moy = 4\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as apr_sales\n \t,sum(case when d_moy = 5\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as may_sales\n \t,sum(case when d_moy = 6\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jun_sales\n \t,sum(case when d_moy = 7\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as jul_sales\n \t,sum(case when d_moy = 8\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as aug_sales\n \t,sum(case when d_moy = 9\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as sep_sales\n \t,sum(case when d_moy = 10\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as oct_sales\n \t,sum(case when d_moy = 11\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as nov_sales\n \t,sum(case when d_moy = 12\n \t\tthen cs_ext_list_price* cs_quantity else 0 end) as dec_sales\n \t,sum(case when d_moy = 1\n \t\tthen cs_net_profit * cs_quantity else 0 end) as jan_net\n \t,sum(case when d_moy = 2\n \t\tthen cs_net_profit * cs_quantity else 0 end) as feb_net\n \t,sum(case when d_moy = 3\n \t\tthen cs_net_profit * cs_quantity else 0 end) as mar_net\n \t,sum(case when d_moy = 4\n \t\tthen cs_net_profit * cs_quantity else 0 end) as apr_net\n \t,sum(case when d_moy = 5\n \t\tthen cs_net_profit * cs_quantity else 0 end) as may_net\n \t,sum(case when d_moy = 6\n \t\tthen cs_net_profit * cs_quantity else 0 end) as jun_net\n \t,sum(case when d_moy = 7\n \t\tthen cs_net_profit * cs_quantity else 0 end) as jul_net\n \t,sum(case when d_moy = 8\n \t\tthen cs_net_profit * cs_quantity else 0 end) as aug_net\n \t,sum(case when d_moy = 9\n \t\tthen cs_net_profit * cs_quantity else 0 end) as sep_net\n \t,sum(case when d_moy = 10\n \t\tthen cs_net_profit * cs_quantity else 0 end) as oct_net\n \t,sum(case when d_moy = 11\n \t\tthen cs_net_profit * cs_quantity else 0 end) as nov_net\n \t,sum(case when d_moy = 12\n \t\tthen cs_net_profit * cs_quantity else 0 end) as dec_net\n from\n catalog_sales\n ,warehouse\n ,date_dim\n ,time_dim\n \t ,ship_mode\n where\n cs_warehouse_sk = w_warehouse_sk\n and cs_sold_date_sk = d_date_sk\n and cs_sold_time_sk = t_time_sk\n \tand cs_ship_mode_sk = sm_ship_mode_sk\n and d_year = 2002\n \tand t_time between 2662 AND 2662+28800\n \tand sm_carrier in ('FEDEX','MSC')\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n ,d_year\n ) x\n group by\n w_warehouse_name\n \t,w_warehouse_sq_ft\n \t,w_city\n \t,w_county\n \t,w_state\n \t,w_country\n \t,ship_carriers\n ,year\n order by w_warehouse_name\n limit 100\"\"\",\n \"q67\" ->\n \"\"\"\nselect *\nfrom (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rank() over (partition by i_category order by sumsales desc) rk\n from (select i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sum(coalesce(ss_sales_price*ss_quantity,0)) sumsales\n from store_sales\n ,date_dim\n ,store\n ,item\n where ss_sold_date_sk=d_date_sk\n and ss_item_sk=i_item_sk\n and ss_store_sk = s_store_sk\n and d_month_seq between 1177 and 1177+11\n group by rollup(i_category, i_class, i_brand, i_product_name, d_year, d_qoy, d_moy,s_store_id))dw1) dw2\nwhere rk <= 100\norder by i_category\n ,i_class\n ,i_brand\n ,i_product_name\n ,d_year\n ,d_qoy\n ,d_moy\n ,s_store_id\n ,sumsales\n ,rk\nlimit 100\"\"\",\n \"q68\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,ca_city\n ,bought_city\n ,ss_ticket_number\n ,extended_price\n ,extended_tax\n ,list_price\n from (select ss_ticket_number\n ,ss_customer_sk\n ,ca_city bought_city\n ,sum(ss_ext_sales_price) extended_price\n ,sum(ss_ext_list_price) list_price\n ,sum(ss_ext_tax) extended_tax\n from store_sales\n ,date_dim\n ,store\n ,household_demographics\n ,customer_address\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and store_sales.ss_addr_sk = customer_address.ca_address_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_dep_count = 5 or\n household_demographics.hd_vehicle_count= 4)\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_city in ('Lodi','Richmond')\n group by ss_ticket_number\n ,ss_customer_sk\n ,ss_addr_sk,ca_city) dn\n ,customer\n ,customer_address current_addr\n where ss_customer_sk = c_customer_sk\n and customer.c_current_addr_sk = current_addr.ca_address_sk\n and current_addr.ca_city <> bought_city\n order by c_last_name\n ,ss_ticket_number\n limit 100\"\"\",\n \"q69\" ->\n \"\"\"\nselect\n cd_gender,\n cd_marital_status,\n cd_education_status,\n count(*) cnt1,\n cd_purchase_estimate,\n count(*) cnt2,\n cd_credit_rating,\n count(*) cnt3\n from\n customer c,customer_address ca,customer_demographics\n where\n c.c_current_addr_sk = ca.ca_address_sk and\n ca_state in ('IL','FL','SD') and\n cd_demo_sk = c.c_current_cdemo_sk and\n exists (select *\n from store_sales,date_dim\n where c.c_customer_sk = ss_customer_sk and\n ss_sold_date_sk = d_date_sk and\n d_year = 1999 and\n d_moy between 1 and 1+2) and\n (not exists (select *\n from web_sales,date_dim\n where c.c_customer_sk = ws_bill_customer_sk and\n ws_sold_date_sk = d_date_sk and\n d_year = 1999 and\n d_moy between 1 and 1+2) and\n not exists (select *\n from catalog_sales,date_dim\n where c.c_customer_sk = cs_ship_customer_sk and\n cs_sold_date_sk = d_date_sk and\n d_year = 1999 and\n d_moy between 1 and 1+2))\n group by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n order by cd_gender,\n cd_marital_status,\n cd_education_status,\n cd_purchase_estimate,\n cd_credit_rating\n limit 100\"\"\",\n \"q70\" ->\n \"\"\"\nselect\n sum(ss_net_profit) as total_sum\n ,s_state\n ,s_county\n ,grouping(s_state)+grouping(s_county) as lochierarchy\n ,rank() over (\n \tpartition by grouping(s_state)+grouping(s_county),\n \tcase when grouping(s_county) = 0 then s_state end\n \torder by sum(ss_net_profit) desc) as rank_within_parent\n from\n store_sales\n ,date_dim d1\n ,store\n where\n d1.d_month_seq between 1206 and 1206+11\n and d1.d_date_sk = ss_sold_date_sk\n and s_store_sk = ss_store_sk\n and s_state in\n ( select s_state\n from (select s_state as s_state,\n \t\t\t rank() over ( partition by s_state order by sum(ss_net_profit) desc) as ranking\n from store_sales, store, date_dim\n where d_month_seq between 1206 and 1206+11\n \t\t\t and d_date_sk = ss_sold_date_sk\n \t\t\t and s_store_sk = ss_store_sk\n group by s_state\n ) tmp1\n where ranking <= 5\n )\n group by rollup(s_state,s_county)\n order by\n lochierarchy desc\n ,case when lochierarchy = 0 then s_state end\n ,rank_within_parent\n limit 100\"\"\",\n \"q71\" ->\n \"\"\"\nselect i_brand_id brand_id, i_brand brand,t_hour,t_minute,\n \tsum(ext_price) ext_price\n from item, (select ws_ext_sales_price as ext_price,\n ws_sold_date_sk as sold_date_sk,\n ws_item_sk as sold_item_sk,\n ws_sold_time_sk as time_sk\n from web_sales,date_dim\n where d_date_sk = ws_sold_date_sk\n and d_moy=11\n and d_year=1999\n union all\n select cs_ext_sales_price as ext_price,\n cs_sold_date_sk as sold_date_sk,\n cs_item_sk as sold_item_sk,\n cs_sold_time_sk as time_sk\n from catalog_sales,date_dim\n where d_date_sk = cs_sold_date_sk\n and d_moy=11\n and d_year=1999\n union all\n select ss_ext_sales_price as ext_price,\n ss_sold_date_sk as sold_date_sk,\n ss_item_sk as sold_item_sk,\n ss_sold_time_sk as time_sk\n from store_sales,date_dim\n where d_date_sk = ss_sold_date_sk\n and d_moy=11\n and d_year=1999\n ) tmp,time_dim\n where\n sold_item_sk = i_item_sk\n and i_manager_id=1\n and time_sk = t_time_sk\n and (t_meal_time = 'breakfast' or t_meal_time = 'dinner')\n group by i_brand, i_brand_id,t_hour,t_minute\n order by ext_price desc, i_brand_id\n \"\"\",\n \"q72\" ->\n \"\"\"\nselect i_item_desc\n ,w_warehouse_name\n ,d1.d_week_seq\n ,sum(case when p_promo_sk is null then 1 else 0 end) no_promo\n ,sum(case when p_promo_sk is not null then 1 else 0 end) promo\n ,count(*) total_cnt\nfrom catalog_sales\njoin inventory on (cs_item_sk = inv_item_sk)\njoin warehouse on (w_warehouse_sk=inv_warehouse_sk)\njoin item on (i_item_sk = cs_item_sk)\njoin customer_demographics on (cs_bill_cdemo_sk = cd_demo_sk)\njoin household_demographics on (cs_bill_hdemo_sk = hd_demo_sk)\njoin date_dim d1 on (cs_sold_date_sk = d1.d_date_sk)\njoin date_dim d2 on (inv_date_sk = d2.d_date_sk)\njoin date_dim d3 on (cs_ship_date_sk = d3.d_date_sk)\nleft outer join promotion on (cs_promo_sk=p_promo_sk)\nleft outer join catalog_returns on (cr_item_sk = cs_item_sk and cr_order_number = cs_order_number)\nwhere d1.d_week_seq = d2.d_week_seq\n and inv_quantity_on_hand < cs_quantity\n and d3.d_date > d1.d_date + interval 5 days\n and hd_buy_potential = '1001-5000'\n and d1.d_year = 2000\n and cd_marital_status = 'S'\ngroup by i_item_desc,w_warehouse_name,d1.d_week_seq\norder by total_cnt desc, i_item_desc, w_warehouse_name, d_week_seq\nlimit 100\"\"\",\n \"q73\" ->\n \"\"\"\nselect c_last_name\n ,c_first_name\n ,c_salutation\n ,c_preferred_cust_flag\n ,ss_ticket_number\n ,cnt from\n (select ss_ticket_number\n ,ss_customer_sk\n ,count(*) cnt\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and date_dim.d_dom between 1 and 2\n and (household_demographics.hd_buy_potential = '1001-5000' or\n household_demographics.hd_buy_potential = 'Unknown')\n and household_demographics.hd_vehicle_count > 0\n and case when household_demographics.hd_vehicle_count > 0 then\n household_demographics.hd_dep_count/ household_demographics.hd_vehicle_count else null end > 1\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_county in ('Humboldt County','Hickman County','Galax city','Abbeville County')\n group by ss_ticket_number,ss_customer_sk) dj,customer\n where ss_customer_sk = c_customer_sk\n and cnt between 1 and 5\n order by cnt desc, c_last_name asc\"\"\",\n \"q74\" ->\n \"\"\"\nwith year_total as (\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,max(ss_net_paid) year_total\n ,'s' sale_type\n from customer\n ,store_sales\n ,date_dim\n where c_customer_sk = ss_customer_sk\n and ss_sold_date_sk = d_date_sk\n and d_year in (2001,2001+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n union all\n select c_customer_id customer_id\n ,c_first_name customer_first_name\n ,c_last_name customer_last_name\n ,d_year as year\n ,max(ws_net_paid) year_total\n ,'w' sale_type\n from customer\n ,web_sales\n ,date_dim\n where c_customer_sk = ws_bill_customer_sk\n and ws_sold_date_sk = d_date_sk\n and d_year in (2001,2001+1)\n group by c_customer_id\n ,c_first_name\n ,c_last_name\n ,d_year\n )\n select\n t_s_secyear.customer_id, t_s_secyear.customer_first_name, t_s_secyear.customer_last_name\n from year_total t_s_firstyear\n ,year_total t_s_secyear\n ,year_total t_w_firstyear\n ,year_total t_w_secyear\n where t_s_secyear.customer_id = t_s_firstyear.customer_id\n and t_s_firstyear.customer_id = t_w_secyear.customer_id\n and t_s_firstyear.customer_id = t_w_firstyear.customer_id\n and t_s_firstyear.sale_type = 's'\n and t_w_firstyear.sale_type = 'w'\n and t_s_secyear.sale_type = 's'\n and t_w_secyear.sale_type = 'w'\n and t_s_firstyear.year = 2001\n and t_s_secyear.year = 2001+1\n and t_w_firstyear.year = 2001\n and t_w_secyear.year = 2001+1\n and t_s_firstyear.year_total > 0\n and t_w_firstyear.year_total > 0\n and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end\n > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end\n order by 3,1,2\nlimit 100\"\"\",\n \"q75\" ->\n \"\"\"\nWITH all_sales AS (\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,SUM(sales_cnt) AS sales_cnt\n ,SUM(sales_amt) AS sales_amt\n FROM (SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,cs_quantity - COALESCE(cr_return_quantity,0) AS sales_cnt\n ,cs_ext_sales_price - COALESCE(cr_return_amount,0.0) AS sales_amt\n FROM catalog_sales JOIN item ON i_item_sk=cs_item_sk\n JOIN date_dim ON d_date_sk=cs_sold_date_sk\n LEFT JOIN catalog_returns ON (cs_order_number=cr_order_number\n AND cs_item_sk=cr_item_sk)\n WHERE i_category='Books'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ss_quantity - COALESCE(sr_return_quantity,0) AS sales_cnt\n ,ss_ext_sales_price - COALESCE(sr_return_amt,0.0) AS sales_amt\n FROM store_sales JOIN item ON i_item_sk=ss_item_sk\n JOIN date_dim ON d_date_sk=ss_sold_date_sk\n LEFT JOIN store_returns ON (ss_ticket_number=sr_ticket_number\n AND ss_item_sk=sr_item_sk)\n WHERE i_category='Books'\n UNION\n SELECT d_year\n ,i_brand_id\n ,i_class_id\n ,i_category_id\n ,i_manufact_id\n ,ws_quantity - COALESCE(wr_return_quantity,0) AS sales_cnt\n ,ws_ext_sales_price - COALESCE(wr_return_amt,0.0) AS sales_amt\n FROM web_sales JOIN item ON i_item_sk=ws_item_sk\n JOIN date_dim ON d_date_sk=ws_sold_date_sk\n LEFT JOIN web_returns ON (ws_order_number=wr_order_number\n AND ws_item_sk=wr_item_sk)\n WHERE i_category='Books') sales_detail\n GROUP BY d_year, i_brand_id, i_class_id, i_category_id, i_manufact_id)\n SELECT prev_yr.d_year AS prev_year\n ,curr_yr.d_year AS year\n ,curr_yr.i_brand_id\n ,curr_yr.i_class_id\n ,curr_yr.i_category_id\n ,curr_yr.i_manufact_id\n ,prev_yr.sales_cnt AS prev_yr_cnt\n ,curr_yr.sales_cnt AS curr_yr_cnt\n ,curr_yr.sales_cnt-prev_yr.sales_cnt AS sales_cnt_diff\n ,curr_yr.sales_amt-prev_yr.sales_amt AS sales_amt_diff\n FROM all_sales curr_yr, all_sales prev_yr\n WHERE curr_yr.i_brand_id=prev_yr.i_brand_id\n AND curr_yr.i_class_id=prev_yr.i_class_id\n AND curr_yr.i_category_id=prev_yr.i_category_id\n AND curr_yr.i_manufact_id=prev_yr.i_manufact_id\n AND curr_yr.d_year=2001\n AND prev_yr.d_year=2001-1\n AND CAST(curr_yr.sales_cnt AS DECIMAL(17,2))/CAST(prev_yr.sales_cnt AS DECIMAL(17,2))<0.9\n ORDER BY sales_cnt_diff,sales_amt_diff\n limit 100\"\"\",\n \"q76\" ->\n \"\"\"\nselect channel, col_name, d_year, d_qoy, i_category, COUNT(*) sales_cnt, SUM(ext_sales_price) sales_amt FROM (\n SELECT 'store' as channel, 'ss_promo_sk' col_name, d_year, d_qoy, i_category, ss_ext_sales_price ext_sales_price\n FROM store_sales, item, date_dim\n WHERE ss_promo_sk IS NULL\n AND ss_sold_date_sk=d_date_sk\n AND ss_item_sk=i_item_sk\n UNION ALL\n SELECT 'web' as channel, 'ws_ship_addr_sk' col_name, d_year, d_qoy, i_category, ws_ext_sales_price ext_sales_price\n FROM web_sales, item, date_dim\n WHERE ws_ship_addr_sk IS NULL\n AND ws_sold_date_sk=d_date_sk\n AND ws_item_sk=i_item_sk\n UNION ALL\n SELECT 'catalog' as channel, 'cs_ship_customer_sk' col_name, d_year, d_qoy, i_category, cs_ext_sales_price ext_sales_price\n FROM catalog_sales, item, date_dim\n WHERE cs_ship_customer_sk IS NULL\n AND cs_sold_date_sk=d_date_sk\n AND cs_item_sk=i_item_sk) foo\nGROUP BY channel, col_name, d_year, d_qoy, i_category\nORDER BY channel, col_name, d_year, d_qoy, i_category\nlimit 100\"\"\",\n \"q77\" ->\n \"\"\"\nwith ss as\n (select s_store_sk,\n sum(ss_ext_sales_price) as sales,\n sum(ss_net_profit) as profit\n from store_sales,\n date_dim,\n store\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-16' as date)\n and (cast('2001-08-16' as date) + INTERVAL 30 days)\n and ss_store_sk = s_store_sk\n group by s_store_sk)\n ,\n sr as\n (select s_store_sk,\n sum(sr_return_amt) as returns,\n sum(sr_net_loss) as profit_loss\n from store_returns,\n date_dim,\n store\n where sr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-16' as date)\n and (cast('2001-08-16' as date) + INTERVAL 30 days)\n and sr_store_sk = s_store_sk\n group by s_store_sk),\n cs as\n (select cs_call_center_sk,\n sum(cs_ext_sales_price) as sales,\n sum(cs_net_profit) as profit\n from catalog_sales,\n date_dim\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-16' as date)\n and (cast('2001-08-16' as date) + INTERVAL 30 days)\n group by cs_call_center_sk\n ),\n cr as\n (select cr_call_center_sk,\n sum(cr_return_amount) as returns,\n sum(cr_net_loss) as profit_loss\n from catalog_returns,\n date_dim\n where cr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-16' as date)\n and (cast('2001-08-16' as date) + INTERVAL 30 days)\n group by cr_call_center_sk\n ),\n ws as\n ( select wp_web_page_sk,\n sum(ws_ext_sales_price) as sales,\n sum(ws_net_profit) as profit\n from web_sales,\n date_dim,\n web_page\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-16' as date)\n and (cast('2001-08-16' as date) + INTERVAL 30 days)\n and ws_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk),\n wr as\n (select wp_web_page_sk,\n sum(wr_return_amt) as returns,\n sum(wr_net_loss) as profit_loss\n from web_returns,\n date_dim,\n web_page\n where wr_returned_date_sk = d_date_sk\n and d_date between cast('2001-08-16' as date)\n and (cast('2001-08-16' as date) + INTERVAL 30 days)\n and wr_web_page_sk = wp_web_page_sk\n group by wp_web_page_sk)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , ss.s_store_sk as id\n , sales\n , coalesce(returns, 0) as returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ss left join sr\n on ss.s_store_sk = sr.s_store_sk\n union all\n select 'catalog channel' as channel\n , cs_call_center_sk as id\n , sales\n , returns\n , (profit - profit_loss) as profit\n from cs\n , cr\n union all\n select 'web channel' as channel\n , ws.wp_web_page_sk as id\n , sales\n , coalesce(returns, 0) returns\n , (profit - coalesce(profit_loss,0)) as profit\n from ws left join wr\n on ws.wp_web_page_sk = wr.wp_web_page_sk\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q78\" ->\n \"\"\"\nwith ws as\n (select d_year AS ws_sold_year, ws_item_sk,\n ws_bill_customer_sk ws_customer_sk,\n sum(ws_quantity) ws_qty,\n sum(ws_wholesale_cost) ws_wc,\n sum(ws_sales_price) ws_sp\n from web_sales\n left join web_returns on wr_order_number=ws_order_number and ws_item_sk=wr_item_sk\n join date_dim on ws_sold_date_sk = d_date_sk\n where wr_order_number is null\n group by d_year, ws_item_sk, ws_bill_customer_sk\n ),\ncs as\n (select d_year AS cs_sold_year, cs_item_sk,\n cs_bill_customer_sk cs_customer_sk,\n sum(cs_quantity) cs_qty,\n sum(cs_wholesale_cost) cs_wc,\n sum(cs_sales_price) cs_sp\n from catalog_sales\n left join catalog_returns on cr_order_number=cs_order_number and cs_item_sk=cr_item_sk\n join date_dim on cs_sold_date_sk = d_date_sk\n where cr_order_number is null\n group by d_year, cs_item_sk, cs_bill_customer_sk\n ),\nss as\n (select d_year AS ss_sold_year, ss_item_sk,\n ss_customer_sk,\n sum(ss_quantity) ss_qty,\n sum(ss_wholesale_cost) ss_wc,\n sum(ss_sales_price) ss_sp\n from store_sales\n left join store_returns on sr_ticket_number=ss_ticket_number and ss_item_sk=sr_item_sk\n join date_dim on ss_sold_date_sk = d_date_sk\n where sr_ticket_number is null\n group by d_year, ss_item_sk, ss_customer_sk\n )\n select\nss_item_sk,\nround(ss_qty/(coalesce(ws_qty,0)+coalesce(cs_qty,0)),2) ratio,\nss_qty store_qty, ss_wc store_wholesale_cost, ss_sp store_sales_price,\ncoalesce(ws_qty,0)+coalesce(cs_qty,0) other_chan_qty,\ncoalesce(ws_wc,0)+coalesce(cs_wc,0) other_chan_wholesale_cost,\ncoalesce(ws_sp,0)+coalesce(cs_sp,0) other_chan_sales_price\nfrom ss\nleft join ws on (ws_sold_year=ss_sold_year and ws_item_sk=ss_item_sk and ws_customer_sk=ss_customer_sk)\nleft join cs on (cs_sold_year=ss_sold_year and cs_item_sk=ss_item_sk and cs_customer_sk=ss_customer_sk)\nwhere (coalesce(ws_qty,0)>0 or coalesce(cs_qty, 0)>0) and ss_sold_year=2000\norder by\n ss_item_sk,\n ss_qty desc, ss_wc desc, ss_sp desc,\n other_chan_qty,\n other_chan_wholesale_cost,\n other_chan_sales_price,\n ratio\nlimit 100\"\"\",\n \"q79\" ->\n \"\"\"\nselect\n c_last_name,c_first_name,substr(s_city,1,30),ss_ticket_number,amt,profit\n from\n (select ss_ticket_number\n ,ss_customer_sk\n ,store.s_city\n ,sum(ss_coupon_amt) amt\n ,sum(ss_net_profit) profit\n from store_sales,date_dim,store,household_demographics\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_store_sk = store.s_store_sk\n and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk\n and (household_demographics.hd_dep_count = 5 or household_demographics.hd_vehicle_count > -1)\n and date_dim.d_dow = 1\n and date_dim.d_year in (1999,1999+1,1999+2)\n and store.s_number_employees between 200 and 295\n group by ss_ticket_number,ss_customer_sk,ss_addr_sk,store.s_city) ms,customer\n where ss_customer_sk = c_customer_sk\n order by c_last_name,c_first_name,substr(s_city,1,30), profit\nlimit 100\"\"\",\n \"q80\" ->\n \"\"\"\nwith ssr as\n (select s_store_id as store_id,\n sum(ss_ext_sales_price) as sales,\n sum(coalesce(sr_return_amt, 0)) as returns,\n sum(ss_net_profit - coalesce(sr_net_loss, 0)) as profit\n from store_sales left outer join store_returns on\n (ss_item_sk = sr_item_sk and ss_ticket_number = sr_ticket_number),\n date_dim,\n store,\n item,\n promotion\n where ss_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-19' as date)\n and (cast('2001-08-19' as date) + INTERVAL 60 days)\n and ss_store_sk = s_store_sk\n and ss_item_sk = i_item_sk\n and i_current_price > 50\n and ss_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\n group by s_store_id)\n ,\n csr as\n (select cp_catalog_page_id as catalog_page_id,\n sum(cs_ext_sales_price) as sales,\n sum(coalesce(cr_return_amount, 0)) as returns,\n sum(cs_net_profit - coalesce(cr_net_loss, 0)) as profit\n from catalog_sales left outer join catalog_returns on\n (cs_item_sk = cr_item_sk and cs_order_number = cr_order_number),\n date_dim,\n catalog_page,\n item,\n promotion\n where cs_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-19' as date)\n and (cast('2001-08-19' as date) + INTERVAL 60 days)\n and cs_catalog_page_sk = cp_catalog_page_sk\n and cs_item_sk = i_item_sk\n and i_current_price > 50\n and cs_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by cp_catalog_page_id)\n ,\n wsr as\n (select web_site_id,\n sum(ws_ext_sales_price) as sales,\n sum(coalesce(wr_return_amt, 0)) as returns,\n sum(ws_net_profit - coalesce(wr_net_loss, 0)) as profit\n from web_sales left outer join web_returns on\n (ws_item_sk = wr_item_sk and ws_order_number = wr_order_number),\n date_dim,\n web_site,\n item,\n promotion\n where ws_sold_date_sk = d_date_sk\n and d_date between cast('2001-08-19' as date)\n and (cast('2001-08-19' as date) + INTERVAL 60 days)\n and ws_web_site_sk = web_site_sk\n and ws_item_sk = i_item_sk\n and i_current_price > 50\n and ws_promo_sk = p_promo_sk\n and p_channel_tv = 'N'\ngroup by web_site_id)\n select channel\n , id\n , sum(sales) as sales\n , sum(returns) as returns\n , sum(profit) as profit\n from\n (select 'store channel' as channel\n , 'store' || store_id as id\n , sales\n , returns\n , profit\n from ssr\n union all\n select 'catalog channel' as channel\n , 'catalog_page' || catalog_page_id as id\n , sales\n , returns\n , profit\n from csr\n union all\n select 'web channel' as channel\n , 'web_site' || web_site_id as id\n , sales\n , returns\n , profit\n from wsr\n ) x\n group by rollup (channel, id)\n order by channel\n ,id\n limit 100\"\"\",\n \"q81\" ->\n \"\"\"\nwith customer_total_return as\n (select cr_returning_customer_sk as ctr_customer_sk\n ,ca_state as ctr_state,\n \tsum(cr_return_amt_inc_tax) as ctr_total_return\n from catalog_returns\n ,date_dim\n ,customer_address\n where cr_returned_date_sk = d_date_sk\n and d_year =1999\n and cr_returning_addr_sk = ca_address_sk\n group by cr_returning_customer_sk\n ,ca_state )\n select c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n from customer_total_return ctr1\n ,customer_address\n ,customer\n where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2\n \t\t\t from customer_total_return ctr2\n \t where ctr1.ctr_state = ctr2.ctr_state)\n and ca_address_sk = c_current_addr_sk\n and ca_state = 'MO'\n and ctr1.ctr_customer_sk = c_customer_sk\n order by c_customer_id,c_salutation,c_first_name,c_last_name,ca_street_number,ca_street_name\n ,ca_street_type,ca_suite_number,ca_city,ca_county,ca_state,ca_zip,ca_country,ca_gmt_offset\n ,ca_location_type,ctr_total_return\n limit 100\"\"\",\n \"q82\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_current_price\n from item, inventory, date_dim, store_sales\n where i_current_price between 68 and 68+30\n and inv_item_sk = i_item_sk\n and d_date_sk=inv_date_sk\n and d_date between cast('2002-05-08' as date) and (cast('2002-05-08' as date) + INTERVAL 60 days)\n and i_manufact_id in (562,370,230,182)\n and inv_quantity_on_hand between 100 and 500\n and ss_item_sk = i_item_sk\n group by i_item_id,i_item_desc,i_current_price\n order by i_item_id\n limit 100\"\"\",\n \"q83\" ->\n \"\"\"\nwith sr_items as\n (select i_item_id item_id,\n sum(sr_return_quantity) sr_item_qty\n from store_returns,\n item,\n date_dim\n where sr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('2000-02-20','2000-10-08','2000-11-04')))\n and sr_returned_date_sk = d_date_sk\n group by i_item_id),\n cr_items as\n (select i_item_id item_id,\n sum(cr_return_quantity) cr_item_qty\n from catalog_returns,\n item,\n date_dim\n where cr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t where d_date in ('2000-02-20','2000-10-08','2000-11-04')))\n and cr_returned_date_sk = d_date_sk\n group by i_item_id),\n wr_items as\n (select i_item_id item_id,\n sum(wr_return_quantity) wr_item_qty\n from web_returns,\n item,\n date_dim\n where wr_item_sk = i_item_sk\n and d_date in\n\t(select d_date\n\tfrom date_dim\n\twhere d_week_seq in\n\t\t(select d_week_seq\n\t\tfrom date_dim\n\t\twhere d_date in ('2000-02-20','2000-10-08','2000-11-04')))\n and wr_returned_date_sk = d_date_sk\n group by i_item_id)\n select sr_items.item_id\n ,sr_item_qty\n ,sr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 sr_dev\n ,cr_item_qty\n ,cr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 cr_dev\n ,wr_item_qty\n ,wr_item_qty/(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 * 100 wr_dev\n ,(sr_item_qty+cr_item_qty+wr_item_qty)/3.0 average\n from sr_items\n ,cr_items\n ,wr_items\n where sr_items.item_id=cr_items.item_id\n and sr_items.item_id=wr_items.item_id\n order by sr_items.item_id\n ,sr_item_qty\n limit 100\"\"\",\n \"q84\" ->\n \"\"\"\nselect c_customer_id as customer_id\n , coalesce(c_last_name,'') || ', ' || coalesce(c_first_name,'') as customername\n from customer\n ,customer_address\n ,customer_demographics\n ,household_demographics\n ,income_band\n ,store_returns\n where ca_city\t = 'Buena Vista'\n and c_current_addr_sk = ca_address_sk\n and ib_lower_bound >= 49786\n and ib_upper_bound <= 49786 + 50000\n and ib_income_band_sk = hd_income_band_sk\n and cd_demo_sk = c_current_cdemo_sk\n and hd_demo_sk = c_current_hdemo_sk\n and sr_cdemo_sk = cd_demo_sk\n order by c_customer_id\n limit 100\"\"\",\n \"q85\" ->\n \"\"\"\nselect substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\n from web_sales, web_returns, web_page, customer_demographics cd1,\n customer_demographics cd2, customer_address, date_dim, reason\n where ws_web_page_sk = wp_web_page_sk\n and ws_item_sk = wr_item_sk\n and ws_order_number = wr_order_number\n and ws_sold_date_sk = d_date_sk and d_year = 2001\n and cd1.cd_demo_sk = wr_refunded_cdemo_sk\n and cd2.cd_demo_sk = wr_returning_cdemo_sk\n and ca_address_sk = wr_refunded_addr_sk\n and r_reason_sk = wr_reason_sk\n and\n (\n (\n cd1.cd_marital_status = 'D'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = '4 yr Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 100.00 and 150.00\n )\n or\n (\n cd1.cd_marital_status = 'M'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = 'Primary'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 50.00 and 100.00\n )\n or\n (\n cd1.cd_marital_status = 'U'\n and\n cd1.cd_marital_status = cd2.cd_marital_status\n and\n cd1.cd_education_status = '2 yr Degree'\n and\n cd1.cd_education_status = cd2.cd_education_status\n and\n ws_sales_price between 150.00 and 200.00\n )\n )\n and\n (\n (\n ca_country = 'United States'\n and\n ca_state in ('IA', 'ND', 'FL')\n and ws_net_profit between 100 and 200\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('OH', 'MS', 'VA')\n and ws_net_profit between 150 and 300\n )\n or\n (\n ca_country = 'United States'\n and\n ca_state in ('MN', 'LA', 'TX')\n and ws_net_profit between 50 and 250\n )\n )\ngroup by r_reason_desc\norder by substr(r_reason_desc,1,20)\n ,avg(ws_quantity)\n ,avg(wr_refunded_cash)\n ,avg(wr_fee)\nlimit 100\"\"\",\n \"q86\" ->\n \"\"\"\nselect\n sum(ws_net_paid) as total_sum\n ,i_category\n ,i_class\n ,grouping(i_category)+grouping(i_class) as lochierarchy\n ,rank() over (\n \tpartition by grouping(i_category)+grouping(i_class),\n \tcase when grouping(i_class) = 0 then i_category end\n \torder by sum(ws_net_paid) desc) as rank_within_parent\n from\n web_sales\n ,date_dim d1\n ,item\n where\n d1.d_month_seq between 1217 and 1217+11\n and d1.d_date_sk = ws_sold_date_sk\n and i_item_sk = ws_item_sk\n group by rollup(i_category,i_class)\n order by\n lochierarchy desc,\n case when lochierarchy = 0 then i_category end,\n rank_within_parent\n limit 100\"\"\",\n \"q87\" ->\n \"\"\"\nselect count(*)\nfrom ((select distinct c_last_name, c_first_name, d_date\n from store_sales, date_dim, customer\n where store_sales.ss_sold_date_sk = date_dim.d_date_sk\n and store_sales.ss_customer_sk = customer.c_customer_sk\n and d_month_seq between 1224 and 1224+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from catalog_sales, date_dim, customer\n where catalog_sales.cs_sold_date_sk = date_dim.d_date_sk\n and catalog_sales.cs_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1224 and 1224+11)\n except\n (select distinct c_last_name, c_first_name, d_date\n from web_sales, date_dim, customer\n where web_sales.ws_sold_date_sk = date_dim.d_date_sk\n and web_sales.ws_bill_customer_sk = customer.c_customer_sk\n and d_month_seq between 1224 and 1224+11)\n) cool_cust\"\"\",\n \"q88\" ->\n \"\"\"\nselect *\nfrom\n (select count(*) h8_30_to_9\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 8\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s1,\n (select count(*) h9_to_9_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s2,\n (select count(*) h9_30_to_10\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 9\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s3,\n (select count(*) h10_to_10_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s4,\n (select count(*) h10_30_to_11\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 10\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s5,\n (select count(*) h11_to_11_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s6,\n (select count(*) h11_30_to_12\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 11\n and time_dim.t_minute >= 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s7,\n (select count(*) h12_to_12_30\n from store_sales, household_demographics , time_dim, store\n where ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 12\n and time_dim.t_minute < 30\n and ((household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or\n (household_demographics.hd_dep_count = 2 and household_demographics.hd_vehicle_count<=2+2) or\n (household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2))\n and store.s_store_name = 'ese') s8\"\"\",\n \"q89\" ->\n \"\"\"\nselect *\nfrom(\nselect i_category, i_class, i_brand,\n s_store_name, s_company_name,\n d_moy,\n sum(ss_sales_price) sum_sales,\n avg(sum(ss_sales_price)) over\n (partition by i_category, i_brand, s_store_name, s_company_name)\n avg_monthly_sales\nfrom item, store_sales, date_dim, store\nwhere ss_item_sk = i_item_sk and\n ss_sold_date_sk = d_date_sk and\n ss_store_sk = s_store_sk and\n d_year in (2001) and\n ((i_category in ('Children','Home','Women') and\n i_class in ('toddlers','flatware','fragrances')\n )\n or (i_category in ('Music','Electronics','Shoes') and\n i_class in ('country','dvd/vcr players','mens')\n ))\ngroup by i_category, i_class, i_brand,\n s_store_name, s_company_name, d_moy) tmp1\nwhere case when (avg_monthly_sales <> 0) then (abs(sum_sales - avg_monthly_sales) / avg_monthly_sales) else null end > 0.1\norder by sum_sales - avg_monthly_sales, s_store_name\nlimit 100\"\"\",\n \"q90\" ->\n \"\"\"\nselect cast(amc as decimal(15,4))/cast(pmc as decimal(15,4)) am_pm_ratio\n from ( select count(*) amc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 7 and 7+1\n and household_demographics.hd_dep_count = 1\n and web_page.wp_char_count between 5000 and 5200) at,\n ( select count(*) pmc\n from web_sales, household_demographics , time_dim, web_page\n where ws_sold_time_sk = time_dim.t_time_sk\n and ws_ship_hdemo_sk = household_demographics.hd_demo_sk\n and ws_web_page_sk = web_page.wp_web_page_sk\n and time_dim.t_hour between 20 and 20+1\n and household_demographics.hd_dep_count = 1\n and web_page.wp_char_count between 5000 and 5200) pt\n order by am_pm_ratio\n limit 100\"\"\",\n \"q91\" ->\n \"\"\"\nselect\n cc_call_center_id Call_Center,\n cc_name Call_Center_Name,\n cc_manager Manager,\n sum(cr_net_loss) Returns_Loss\nfrom\n call_center,\n catalog_returns,\n date_dim,\n customer,\n customer_address,\n customer_demographics,\n household_demographics\nwhere\n cr_call_center_sk = cc_call_center_sk\nand cr_returned_date_sk = d_date_sk\nand cr_returning_customer_sk= c_customer_sk\nand cd_demo_sk = c_current_cdemo_sk\nand hd_demo_sk = c_current_hdemo_sk\nand ca_address_sk = c_current_addr_sk\nand d_year = 1998\nand d_moy = 12\nand ( (cd_marital_status = 'M' and cd_education_status = 'Unknown')\n or(cd_marital_status = 'W' and cd_education_status = 'Advanced Degree'))\nand hd_buy_potential like 'Unknown%'\nand ca_gmt_offset = -6\ngroup by cc_call_center_id,cc_name,cc_manager,cd_marital_status,cd_education_status\norder by sum(cr_net_loss) desc\"\"\",\n \"q92\" ->\n \"\"\"\nselect\n sum(ws_ext_discount_amt) as `Excess Discount Amount`\nfrom\n web_sales\n ,item\n ,date_dim\nwhere\ni_manufact_id = 172\nand i_item_sk = ws_item_sk\nand d_date between '1999-01-12' and\n (cast('1999-01-12' as date) + INTERVAL 90 days)\nand d_date_sk = ws_sold_date_sk\nand ws_ext_discount_amt\n > (\n SELECT\n 1.3 * avg(ws_ext_discount_amt)\n FROM\n web_sales\n ,date_dim\n WHERE\n ws_item_sk = i_item_sk\n and d_date between '1999-01-12' and\n (cast('1999-01-12' as date) + INTERVAL 90 days)\n and d_date_sk = ws_sold_date_sk\n )\norder by sum(ws_ext_discount_amt)\nlimit 100\"\"\",\n \"q93\" ->\n \"\"\"\nselect ss_customer_sk\n ,sum(act_sales) sumsales\n from (select ss_item_sk\n ,ss_ticket_number\n ,ss_customer_sk\n ,case when sr_return_quantity is not null then (ss_quantity-sr_return_quantity)*ss_sales_price\n else (ss_quantity*ss_sales_price) end act_sales\n from store_sales left outer join store_returns on (sr_item_sk = ss_item_sk\n and sr_ticket_number = ss_ticket_number)\n ,reason\n where sr_reason_sk = r_reason_sk\n and r_reason_desc = 'reason 58') t\n group by ss_customer_sk\n order by sumsales, ss_customer_sk\nlimit 100\"\"\",\n \"q94\" ->\n \"\"\"\nselect\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2002-3-01' and\n (cast('2002-3-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'GA'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand exists (select *\n from web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\nand not exists(select *\n from web_returns wr1\n where ws1.ws_order_number = wr1.wr_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q95\" ->\n \"\"\"\nwith ws_wh as\n(select ws1.ws_order_number,ws1.ws_warehouse_sk wh1,ws2.ws_warehouse_sk wh2\n from web_sales ws1,web_sales ws2\n where ws1.ws_order_number = ws2.ws_order_number\n and ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)\n select\n count(distinct ws_order_number) as `order count`\n ,sum(ws_ext_ship_cost) as `total shipping cost`\n ,sum(ws_net_profit) as `total net profit`\nfrom\n web_sales ws1\n ,date_dim\n ,customer_address\n ,web_site\nwhere\n d_date between '2001-3-01' and\n (cast('2001-3-01' as date) + INTERVAL 60 days)\nand ws1.ws_ship_date_sk = d_date_sk\nand ws1.ws_ship_addr_sk = ca_address_sk\nand ca_state = 'NE'\nand ws1.ws_web_site_sk = web_site_sk\nand web_company_name = 'pri'\nand ws1.ws_order_number in (select ws_order_number\n from ws_wh)\nand ws1.ws_order_number in (select wr_order_number\n from web_returns,ws_wh\n where wr_order_number = ws_wh.ws_order_number)\norder by count(distinct ws_order_number)\nlimit 100\"\"\",\n \"q96\" ->\n \"\"\"\nselect count(*)\nfrom store_sales\n ,household_demographics\n ,time_dim, store\nwhere ss_sold_time_sk = time_dim.t_time_sk\n and ss_hdemo_sk = household_demographics.hd_demo_sk\n and ss_store_sk = s_store_sk\n and time_dim.t_hour = 16\n and time_dim.t_minute >= 30\n and household_demographics.hd_dep_count = 0\n and store.s_store_name = 'ese'\norder by count(*)\nlimit 100\"\"\",\n \"q97\" ->\n \"\"\"\nwith ssci as (\nselect ss_customer_sk customer_sk\n ,ss_item_sk item_sk\nfrom store_sales,date_dim\nwhere ss_sold_date_sk = d_date_sk\n and d_month_seq between 1219 and 1219 + 11\ngroup by ss_customer_sk\n ,ss_item_sk),\ncsci as(\n select cs_bill_customer_sk customer_sk\n ,cs_item_sk item_sk\nfrom catalog_sales,date_dim\nwhere cs_sold_date_sk = d_date_sk\n and d_month_seq between 1219 and 1219 + 11\ngroup by cs_bill_customer_sk\n ,cs_item_sk)\n select sum(case when ssci.customer_sk is not null and csci.customer_sk is null then 1 else 0 end) store_only\n ,sum(case when ssci.customer_sk is null and csci.customer_sk is not null then 1 else 0 end) catalog_only\n ,sum(case when ssci.customer_sk is not null and csci.customer_sk is not null then 1 else 0 end) store_and_catalog\nfrom ssci full outer join csci on (ssci.customer_sk=csci.customer_sk\n and ssci.item_sk = csci.item_sk)\nlimit 100\"\"\",\n \"q98\" ->\n \"\"\"\nselect i_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\n ,sum(ss_ext_sales_price) as itemrevenue\n ,sum(ss_ext_sales_price)*100/sum(sum(ss_ext_sales_price)) over\n (partition by i_class) as revenueratio\nfrom\n\tstore_sales\n \t,item\n \t,date_dim\nwhere\n\tss_item_sk = i_item_sk\n \tand i_category in ('Books', 'Children', 'Sports')\n \tand ss_sold_date_sk = d_date_sk\n\tand d_date between cast('2001-03-10' as date)\n\t\t\t\tand (cast('2001-03-10' as date) + interval 30 days)\ngroup by\n\ti_item_id\n ,i_item_desc\n ,i_category\n ,i_class\n ,i_current_price\norder by\n\ti_category\n ,i_class\n ,i_item_id\n ,i_item_desc\n ,revenueratio\"\"\",\n \"q99\" ->\n \"\"\"\nselect\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 30) and\n (cs_ship_date_sk - cs_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 60) and\n (cs_ship_date_sk - cs_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 90) and\n (cs_ship_date_sk - cs_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days`\n ,sum(case when (cs_ship_date_sk - cs_sold_date_sk > 120) then 1 else 0 end) as `>120 days`\nfrom\n catalog_sales\n ,warehouse\n ,ship_mode\n ,call_center\n ,date_dim\nwhere\n d_month_seq between 1205 and 1205 + 11\nand cs_ship_date_sk = d_date_sk\nand cs_warehouse_sk = w_warehouse_sk\nand cs_ship_mode_sk = sm_ship_mode_sk\nand cs_call_center_sk = cc_call_center_sk\ngroup by\n substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\norder by substr(w_warehouse_name,1,20)\n ,sm_type\n ,cc_name\nlimit 100\"\"\"\n )\n}" }, { "path": "benchmarks/src/main/scala/benchmark/TPCDSDataLoad.scala", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage benchmark\n\ncase class TPCDSDataLoadConf(\n protected val format: Option[String] = None,\n scaleInGB: Int = 0,\n userDefinedDbName: Option[String] = None,\n sourcePath: Option[String] = None,\n benchmarkPath: Option[String] = None,\n excludeNulls: Boolean = true) extends TPCDSConf\n\nobject TPCDSDataLoadConf {\n import scopt.OParser\n private val builder = OParser.builder[TPCDSDataLoadConf]\n private val argParser = {\n import builder._\n OParser.sequence(\n programName(\"TPC-DS Data Load\"),\n opt[String](\"format\")\n .required()\n .action((x, c) => c.copy(format = Some(x)))\n .text(\"file format to use\"),\n opt[String](\"scale-in-gb\")\n .required()\n .valueName(\"\")\n .action((x, c) => c.copy(scaleInGB = x.toInt))\n .text(\"Scale factor of the TPCDS benchmark\"),\n opt[String](\"benchmark-path\")\n .required()\n .valueName(\"\")\n .action((x, c) => c.copy(benchmarkPath = Some(x)))\n .text(\"Cloud storage path to be used for creating table and generating reports\"),\n opt[String](\"db-name\")\n .optional()\n .valueName(\"\")\n .action((x, c) => c.copy(userDefinedDbName = Some(x)))\n .text(\"Name of the target database to create with TPC-DS tables in necessary format\"),\n opt[String](\"source-path\")\n .optional()\n .valueName(\"\")\n .action((x, c) => c.copy(sourcePath = Some(x)))\n .text(\"The location of the TPC-DS raw input data\"),\n opt[String](\"exclude-nulls\")\n .optional()\n .valueName(\"true/false\")\n .action((x, c) => c.copy(excludeNulls = x.toBoolean))\n .text(\"Whether to remove null primary keys when loading data, default = false\"),\n )\n }\n\n def parse(args: Array[String]): Option[TPCDSDataLoadConf] = {\n OParser.parse(argParser, args, TPCDSDataLoadConf())\n }\n}\n\nclass TPCDSDataLoad(conf: TPCDSDataLoadConf) extends Benchmark(conf) {\n import TPCDSDataLoad._\n\n def runInternal(): Unit = {\n val dbName = conf.dbName\n val dbLocation = conf.dbLocation(dbName, suffix=benchmarkId.replace(\"-\", \"_\"))\n val dbCatalog = \"spark_catalog\"\n\n val partitionTables = true\n val primaryKeys = true\n\n val sourceFormat = \"parquet\"\n require(conf.scaleInGB > 0)\n require(Seq(1, 3000).contains(conf.scaleInGB), \"\")\n val sourceLocation = conf.sourcePath.getOrElse {\n s\"s3://devrel-delta-datasets/tpcds-2.13/tpcds_sf${conf.scaleInGB}_parquet/\"\n }\n\n runQuery(s\"DROP DATABASE IF EXISTS ${dbName} CASCADE\", s\"drop-database\")\n runQuery(s\"CREATE DATABASE IF NOT EXISTS ${dbName}\", s\"create-database\")\n\n // Iterate through all the source tables\n tableNamesTpcds.foreach { tableName =>\n val sourceTableLocation = s\"${sourceLocation}/${tableName}/\"\n val targetLocation = s\"${dbLocation}/${tableName}/\"\n val fullTableName = s\"`$dbName`.`$tableName`\"\n log(s\"Generating $tableName at $dbLocation/$tableName\")\n val partitionedBy =\n if (!partitionTables || tablePartitionKeys(tableName)(0).isEmpty) \"\"\n else \"PARTITIONED BY \" + tablePartitionKeys(tableName).mkString(\"(\", \", \", \")\")\n\n // Excluding nulls automatically when n\n val excludeNulls =\n if (!partitionTables || tablePartitionKeys(tableName)(0).isEmpty) \"\"\n else \"WHERE \" + tablePartitionKeys(tableName)(0) + \" IS NOT NULL\"\n\n var tableOptions = \"\"\n runQuery(s\"DROP TABLE IF EXISTS $fullTableName\", s\"drop-table-$tableName\")\n\n runQuery(s\"\"\"CREATE TABLE $fullTableName\n USING ${conf.formatName}\n $partitionedBy $tableOptions\n LOCATION '$targetLocation'\n SELECT * FROM `${sourceFormat}`.`$sourceTableLocation` $excludeNulls\n \"\"\", s\"create-table-$tableName\", ignoreError = true)\n\n val sourceCount =\n spark.sql(s\"SELECT * FROM `${sourceFormat}`.`$sourceTableLocation` ${excludeNulls}\").count()\n val targetCount = spark.table(fullTableName).count()\n assert(targetCount == sourceCount,\n s\"Row count mismatch: source table = $sourceCount, target $fullTableName = $targetCount\")\n }\n log(s\"====== Created all tables in database ${dbName} at '${dbLocation}' =======\")\n\n runQuery(s\"USE ${dbCatalog}.${dbName};\")\n runQuery(\"SHOW TABLES\", printRows = true)\n\n }\n}\nobject TPCDSDataLoad {\n def main(args: Array[String]): Unit = {\n TPCDSDataLoadConf.parse(args).foreach { conf =>\n new TPCDSDataLoad(conf).run()\n }\n }\n\n val tableNamesTpcds = Seq(\n // with partitions\n \"inventory\", \"catalog_returns\", \"catalog_sales\", \"store_returns\", \"web_returns\", \"web_sales\", \"store_sales\",\n // no partitions\n \"call_center\", \"catalog_page\", \"customer_address\", \"customer_demographics\", \"customer\", \"date_dim\",\n \"household_demographics\", \"income_band\", \"item\", \"promotion\", \"reason\", \"ship_mode\", \"store\", \"time_dim\",\n \"warehouse\", \"web_page\", \"web_site\"\n ).sorted\n\n\n val tableColumnSchemas = Map(\n \"dbgen_version\" -> \"\"\"\n dv_version varchar(16) ,\n dv_create_date date ,\n dv_create_time time ,\n dv_cmdline_args varchar(200)\n\"\"\",\n \"call_center\" -> \"\"\"\n cc_call_center_sk integer not null,\n cc_call_center_id char(16) not null,\n cc_rec_start_date date ,\n cc_rec_end_date date ,\n cc_closed_date_sk integer ,\n cc_open_date_sk integer ,\n cc_name varchar(50) ,\n cc_class varchar(50) ,\n cc_employees integer ,\n cc_sq_ft integer ,\n cc_hours char(20) ,\n cc_manager varchar(40) ,\n cc_mkt_id integer ,\n cc_mkt_class char(50) ,\n cc_mkt_desc varchar(100) ,\n cc_market_manager varchar(40) ,\n cc_division integer ,\n cc_division_name varchar(50) ,\n cc_company integer ,\n cc_company_name char(50) ,\n cc_street_number char(10) ,\n cc_street_name varchar(60) ,\n cc_street_type char(15) ,\n cc_suite_number char(10) ,\n cc_city varchar(60) ,\n cc_county varchar(30) ,\n cc_state char(2) ,\n cc_zip char(10) ,\n cc_country varchar(20) ,\n cc_gmt_offset decimal(5,2) ,\n cc_tax_percentage decimal(5,2)\n\"\"\",\n \"catalog_page\" -> \"\"\"\n cp_catalog_page_sk integer not null,\n cp_catalog_page_id char(16) not null,\n cp_start_date_sk integer ,\n cp_end_date_sk integer ,\n cp_department varchar(50) ,\n cp_catalog_number integer ,\n cp_catalog_page_number integer ,\n cp_description varchar(100) ,\n cp_type varchar(100)\n\"\"\",\n \"catalog_returns\" -> \"\"\"\n cr_returned_date_sk integer ,\n cr_returned_time_sk integer ,\n cr_item_sk integer not null,\n cr_refunded_customer_sk integer ,\n cr_refunded_cdemo_sk integer ,\n cr_refunded_hdemo_sk integer ,\n cr_refunded_addr_sk integer ,\n cr_returning_customer_sk integer ,\n cr_returning_cdemo_sk integer ,\n cr_returning_hdemo_sk integer ,\n cr_returning_addr_sk integer ,\n cr_call_center_sk integer ,\n cr_catalog_page_sk integer ,\n cr_ship_mode_sk integer ,\n cr_warehouse_sk integer ,\n cr_reason_sk integer ,\n cr_order_number bigint not null,\n cr_return_quantity integer ,\n cr_return_amount decimal(7,2) ,\n cr_return_tax decimal(7,2) ,\n cr_return_amt_inc_tax decimal(7,2) ,\n cr_fee decimal(7,2) ,\n cr_return_ship_cost decimal(7,2) ,\n cr_refunded_cash decimal(7,2) ,\n cr_reversed_charge decimal(7,2) ,\n cr_store_credit decimal(7,2) ,\n cr_net_loss decimal(7,2)\n\"\"\",\n \"catalog_sales\" -> \"\"\"\n cs_sold_date_sk integer ,\n cs_sold_time_sk integer ,\n cs_ship_date_sk integer ,\n cs_bill_customer_sk integer ,\n cs_bill_cdemo_sk integer ,\n cs_bill_hdemo_sk integer ,\n cs_bill_addr_sk integer ,\n cs_ship_customer_sk integer ,\n cs_ship_cdemo_sk integer ,\n cs_ship_hdemo_sk integer ,\n cs_ship_addr_sk integer ,\n cs_call_center_sk integer ,\n cs_catalog_page_sk integer ,\n cs_ship_mode_sk integer ,\n cs_warehouse_sk integer ,\n cs_item_sk integer not null,\n cs_promo_sk integer ,\n cs_order_number bigint not null,\n cs_quantity integer ,\n cs_wholesale_cost decimal(7,2) ,\n cs_list_price decimal(7,2) ,\n cs_sales_price decimal(7,2) ,\n cs_ext_discount_amt decimal(7,2) ,\n cs_ext_sales_price decimal(7,2) ,\n cs_ext_wholesale_cost decimal(7,2) ,\n cs_ext_list_price decimal(7,2) ,\n cs_ext_tax decimal(7,2) ,\n cs_coupon_amt decimal(7,2) ,\n cs_ext_ship_cost decimal(7,2) ,\n cs_net_paid decimal(7,2) ,\n cs_net_paid_inc_tax decimal(7,2) ,\n cs_net_paid_inc_ship decimal(7,2) ,\n cs_net_paid_inc_ship_tax decimal(7,2) ,\n cs_net_profit decimal(7,2)\n\"\"\",\n \"customer\" -> \"\"\"\n c_customer_sk integer not null,\n c_customer_id char(16) not null,\n c_current_cdemo_sk integer ,\n c_current_hdemo_sk integer ,\n c_current_addr_sk integer ,\n c_first_shipto_date_sk integer ,\n c_first_sales_date_sk integer ,\n c_salutation char(10) ,\n c_first_name char(20) ,\n c_last_name char(30) ,\n c_preferred_cust_flag char(1) ,\n c_birth_day integer ,\n c_birth_month integer ,\n c_birth_year integer ,\n c_birth_country varchar(20) ,\n c_login char(13) ,\n c_email_address char(50) ,\n c_last_review_date_sk integer\n\"\"\",\n \"customer_address\" -> \"\"\"\n ca_address_sk integer not null,\n ca_address_id char(16) not null,\n ca_street_number char(10) ,\n ca_street_name varchar(60) ,\n ca_street_type char(15) ,\n ca_suite_number char(10) ,\n ca_city varchar(60) ,\n ca_county varchar(30) ,\n ca_state char(2) ,\n ca_zip char(10) ,\n ca_country varchar(20) ,\n ca_gmt_offset decimal(5,2) ,\n ca_location_type char(20)\n\"\"\",\n \"customer_demographics\" -> \"\"\"\n cd_demo_sk integer not null,\n cd_gender char(1) ,\n cd_marital_status char(1) ,\n cd_education_status char(20) ,\n cd_purchase_estimate integer ,\n cd_credit_rating char(10) ,\n cd_dep_count integer ,\n cd_dep_employed_count integer ,\n cd_dep_college_count integer\n\"\"\",\n \"date_dim\" -> \"\"\"\n d_date_sk integer not null,\n d_date_id char(16) not null,\n d_date date ,\n d_month_seq integer ,\n d_week_seq integer ,\n d_quarter_seq integer ,\n d_year integer ,\n d_dow integer ,\n d_moy integer ,\n d_dom integer ,\n d_qoy integer ,\n d_fy_year integer ,\n d_fy_quarter_seq integer ,\n d_fy_week_seq integer ,\n d_day_name char(9) ,\n d_quarter_name char(6) ,\n d_holiday char(1) ,\n d_weekend char(1) ,\n d_following_holiday char(1) ,\n d_first_dom integer ,\n d_last_dom integer ,\n d_same_day_ly integer ,\n d_same_day_lq integer ,\n d_current_day char(1) ,\n d_current_week char(1) ,\n d_current_month char(1) ,\n d_current_quarter char(1) ,\n d_current_year char(1)\n\"\"\",\n \"household_demographics\" -> \"\"\"\n hd_demo_sk integer not null,\n hd_income_band_sk integer ,\n hd_buy_potential char(15) ,\n hd_dep_count integer ,\n hd_vehicle_count integer\n\"\"\",\n\n \"income_band\" -> \"\"\"\n ib_income_band_sk integer not null,\n ib_lower_bound integer ,\n ib_upper_bound integer\n\"\"\",\n \"inventory\" -> \"\"\"\n inv_date_sk integer not null,\n inv_item_sk integer not null,\n inv_warehouse_sk integer not null,\n inv_quantity_on_hand integer\n\"\"\",\n \"item\" -> \"\"\"\n i_item_sk integer not null,\n i_item_id char(16) not null,\n i_rec_start_date date ,\n i_rec_end_date date ,\n i_item_desc varchar(200) ,\n i_current_price decimal(7,2) ,\n i_wholesale_cost decimal(7,2) ,\n i_brand_id integer ,\n i_brand char(50) ,\n i_class_id integer ,\n i_class char(50) ,\n i_category_id integer ,\n i_category char(50) ,\n i_manufact_id integer ,\n i_manufact char(50) ,\n i_size char(20) ,\n i_formulation char(20) ,\n i_color char(20) ,\n i_units char(10) ,\n i_container char(10) ,\n i_manager_id integer ,\n i_product_name char(50)\n\"\"\",\n \"promotion\" -> \"\"\"\n p_promo_sk integer not null,\n p_promo_id char(16) not null,\n p_start_date_sk integer ,\n p_end_date_sk integer ,\n p_item_sk integer ,\n p_cost decimal(15,2) ,\n p_response_target integer ,\n p_promo_name char(50) ,\n p_channel_dmail char(1) ,\n p_channel_email char(1) ,\n p_channel_catalog char(1) ,\n p_channel_tv char(1) ,\n p_channel_radio char(1) ,\n p_channel_press char(1) ,\n p_channel_event char(1) ,\n p_channel_demo char(1) ,\n p_channel_details varchar(100) ,\n p_purpose char(15) ,\n p_discount_active char(1)\n\"\"\",\n \"reason\" -> \"\"\"\n r_reason_sk integer not null,\n r_reason_id char(16) not null,\n r_reason_desc char(100)\n\"\"\",\n \"ship_mode\" -> \"\"\"\n sm_ship_mode_sk integer not null,\n sm_ship_mode_id char(16) not null,\n sm_type char(30) ,\n sm_code char(10) ,\n sm_carrier char(20) ,\n sm_contract char(20)\n\"\"\",\n \"store\" -> \"\"\"\n s_store_sk integer not null,\n s_store_id char(16) not null,\n s_rec_start_date date ,\n s_rec_end_date date ,\n s_closed_date_sk integer ,\n s_store_name varchar(50) ,\n s_number_employees integer ,\n s_floor_space integer ,\n s_hours char(20) ,\n s_manager varchar(40) ,\n s_market_id integer ,\n s_geography_class varchar(100) ,\n s_market_desc varchar(100) ,\n s_market_manager varchar(40) ,\n s_division_id integer ,\n s_division_name varchar(50) ,\n s_company_id integer ,\n s_company_name varchar(50) ,\n s_street_number varchar(10) ,\n s_street_name varchar(60) ,\n s_street_type char(15) ,\n s_suite_number char(10) ,\n s_city varchar(60) ,\n s_county varchar(30) ,\n s_state char(2) ,\n s_zip char(10) ,\n s_country varchar(20) ,\n s_gmt_offset decimal(5,2) ,\n s_tax_precentage decimal(5,2)\n\"\"\",\n \"store_returns\" -> \"\"\"\n sr_returned_date_sk integer ,\n sr_return_time_sk integer ,\n sr_item_sk integer not null,\n sr_customer_sk integer ,\n sr_cdemo_sk integer ,\n sr_hdemo_sk integer ,\n sr_addr_sk integer ,\n sr_store_sk integer ,\n sr_reason_sk integer ,\n sr_ticket_number bigint not null,\n sr_return_quantity integer ,\n sr_return_amt decimal(7,2) ,\n sr_return_tax decimal(7,2) ,\n sr_return_amt_inc_tax decimal(7,2) ,\n sr_fee decimal(7,2) ,\n sr_return_ship_cost decimal(7,2) ,\n sr_refunded_cash decimal(7,2) ,\n sr_reversed_charge decimal(7,2) ,\n sr_store_credit decimal(7,2) ,\n sr_net_loss decimal(7,2)\n\"\"\",\n\n \"store_sales\" -> \"\"\"\n ss_sold_date_sk integer ,\n ss_sold_time_sk integer ,\n ss_item_sk integer not null,\n ss_customer_sk integer ,\n ss_cdemo_sk integer ,\n ss_hdemo_sk integer ,\n ss_addr_sk integer ,\n ss_store_sk integer ,\n ss_promo_sk integer ,\n ss_ticket_number bigint not null,\n ss_quantity integer ,\n ss_wholesale_cost decimal(7,2) ,\n ss_list_price decimal(7,2) ,\n ss_sales_price decimal(7,2) ,\n ss_ext_discount_amt decimal(7,2) ,\n ss_ext_sales_price decimal(7,2) ,\n ss_ext_wholesale_cost decimal(7,2) ,\n ss_ext_list_price decimal(7,2) ,\n ss_ext_tax decimal(7,2) ,\n ss_coupon_amt decimal(7,2) ,\n ss_net_paid decimal(7,2) ,\n ss_net_paid_inc_tax decimal(7,2) ,\n ss_net_profit decimal(7,2)\n\"\"\",\n \"time_dim\" -> \"\"\"\n t_time_sk integer not null,\n t_time_id char(16) not null,\n t_time integer ,\n t_hour integer ,\n t_minute integer ,\n t_second integer ,\n t_am_pm char(2) ,\n t_shift char(20) ,\n t_sub_shift char(20) ,\n t_meal_time char(20)\n\"\"\",\n \"warehouse\" -> \"\"\"\n w_warehouse_sk integer not null,\n w_warehouse_id char(16) not null,\n w_warehouse_name varchar(20) ,\n w_warehouse_sq_ft integer ,\n w_street_number char(10) ,\n w_street_name varchar(60) ,\n w_street_type char(15) ,\n w_suite_number char(10) ,\n w_city varchar(60) ,\n w_county varchar(30) ,\n w_state char(2) ,\n w_zip char(10) ,\n w_country varchar(20) ,\n w_gmt_offset decimal(5,2)\n\"\"\",\n \"web_page\" -> \"\"\"\n wp_web_page_sk integer not null,\n wp_web_page_id char(16) not null,\n wp_rec_start_date date ,\n wp_rec_end_date date ,\n wp_creation_date_sk integer ,\n wp_access_date_sk integer ,\n wp_autogen_flag char(1) ,\n wp_customer_sk integer ,\n wp_url varchar(100) ,\n wp_type char(50) ,\n wp_char_count integer ,\n wp_link_count integer ,\n wp_image_count integer ,\n wp_max_ad_count integer\n\"\"\",\n \"web_returns\" -> \"\"\"\n wr_returned_date_sk integer ,\n wr_returned_time_sk integer ,\n wr_item_sk integer not null,\n wr_refunded_customer_sk integer ,\n wr_refunded_cdemo_sk integer ,\n wr_refunded_hdemo_sk integer ,\n wr_refunded_addr_sk integer ,\n wr_returning_customer_sk integer ,\n wr_returning_cdemo_sk integer ,\n wr_returning_hdemo_sk integer ,\n wr_returning_addr_sk integer ,\n wr_web_page_sk integer ,\n wr_reason_sk integer ,\n wr_order_number bigint not null,\n wr_return_quantity integer ,\n wr_return_amt decimal(7,2) ,\n wr_return_tax decimal(7,2) ,\n wr_return_amt_inc_tax decimal(7,2) ,\n wr_fee decimal(7,2) ,\n wr_return_ship_cost decimal(7,2) ,\n wr_refunded_cash decimal(7,2) ,\n wr_reversed_charge decimal(7,2) ,\n wr_account_credit decimal(7,2) ,\n wr_net_loss decimal(7,2)\n\"\"\",\n \"web_sales\" -> \"\"\"\n ws_sold_date_sk integer ,\n ws_sold_time_sk integer ,\n ws_ship_date_sk integer ,\n ws_item_sk integer not null,\n ws_bill_customer_sk integer ,\n ws_bill_cdemo_sk integer ,\n ws_bill_hdemo_sk integer ,\n ws_bill_addr_sk integer ,\n ws_ship_customer_sk integer ,\n ws_ship_cdemo_sk integer ,\n ws_ship_hdemo_sk integer ,\n ws_ship_addr_sk integer ,\n ws_web_page_sk integer ,\n ws_web_site_sk integer ,\n ws_ship_mode_sk integer ,\n ws_warehouse_sk integer ,\n ws_promo_sk integer ,\n ws_order_number bigint not null,\n ws_quantity integer ,\n ws_wholesale_cost decimal(7,2) ,\n ws_list_price decimal(7,2) ,\n ws_sales_price decimal(7,2) ,\n ws_ext_discount_amt decimal(7,2) ,\n ws_ext_sales_price decimal(7,2) ,\n ws_ext_wholesale_cost decimal(7,2) ,\n ws_ext_list_price decimal(7,2) ,\n ws_ext_tax decimal(7,2) ,\n ws_coupon_amt decimal(7,2) ,\n ws_ext_ship_cost decimal(7,2) ,\n ws_net_paid decimal(7,2) ,\n ws_net_paid_inc_tax decimal(7,2) ,\n ws_net_paid_inc_ship decimal(7,2) ,\n ws_net_paid_inc_ship_tax decimal(7,2) ,\n ws_net_profit decimal(7,2)\n\"\"\",\n \"web_site\" -> \"\"\"\n web_site_sk integer not null,\n web_site_id char(16) not null,\n web_rec_start_date date ,\n web_rec_end_date date ,\n web_name varchar(50) ,\n web_open_date_sk integer ,\n web_close_date_sk integer ,\n web_class varchar(50) ,\n web_manager varchar(40) ,\n web_mkt_id integer ,\n web_mkt_class varchar(50) ,\n web_mkt_desc varchar(100) ,\n web_market_manager varchar(40) ,\n web_company_id integer ,\n web_company_name char(50) ,\n web_street_number char(10) ,\n web_street_name varchar(60) ,\n web_street_type char(15) ,\n web_suite_number char(10) ,\n web_city varchar(60) ,\n web_county varchar(30) ,\n web_state char(2) ,\n web_zip char(10) ,\n web_country varchar(20) ,\n web_gmt_offset decimal(5,2) ,\n web_tax_percentage decimal(5,2)\n\"\"\"\n )\n\n val tablePrimaryKeys = Map(\n \"dbgen_version\" -> Seq(\"\"),\n \"call_center\" -> Seq(\"cc_call_center_sk\"),\n \"catalog_page\" -> Seq(\"cp_catalog_page_sk\"),\n \"catalog_returns\" -> Seq(\"cr_item_sk\", \"cr_order_number\"),\n \"catalog_sales\" -> Seq(\"cs_item_sk\", \"cs_order_number\"),\n \"customer\" -> Seq(\"c_customer_sk\"),\n \"customer_address\" -> Seq(\"ca_address_sk\"),\n \"customer_demographics\" -> Seq(\"cd_demo_sk\"),\n \"date_dim\" -> Seq(\"d_date_sk\"),\n \"household_demographics\" -> Seq(\"hd_demo_sk\"),\n \"income_band\" -> Seq(\"ib_income_band_sk\"),\n \"inventory\" -> Seq(\"inv_date_sk\", \"inv_item_sk\", \"inv_warehouse_sk\"),\n \"item\" -> Seq(\"i_item_sk\"),\n \"promotion\" -> Seq(\"p_promo_sk\"),\n \"reason\" -> Seq(\"r_reason_sk\"),\n \"ship_mode\" -> Seq(\"sm_ship_mode_sk\"),\n \"store\" -> Seq(\"s_store_sk\"),\n \"store_returns\" -> Seq(\"sr_item_sk\", \"sr_ticket_number\"),\n \"store_sales\" -> Seq(\"ss_item_sk\", \"ss_ticket_number\"),\n \"time_dim\" -> Seq(\"t_time_sk\"),\n \"warehouse\" -> Seq(\"w_warehouse_sk\"),\n \"web_page\" -> Seq(\"wp_web_page_sk\"),\n \"web_returns\" -> Seq(\"wr_item_sk\", \"wr_order_number\"),\n \"web_sales\" -> Seq(\"ws_item_sk\", \"ws_order_number\"),\n \"web_site\" -> Seq(\"web_site_sk\")\n )\n\n\n val tablePartitionKeys = Map(\n \"dbgen_version\" -> Seq(\"\"),\n \"call_center\" -> Seq(\"\"),\n \"catalog_page\" -> Seq(\"\"),\n \"catalog_returns\" -> Seq(\"cr_returned_date_sk\"),\n \"catalog_sales\" -> Seq(\"cs_sold_date_sk\"),\n \"customer\" -> Seq(\"\"),\n \"customer_address\" -> Seq(\"\"),\n \"customer_demographics\" -> Seq(\"\"),\n \"date_dim\" -> Seq(\"\"),\n \"household_demographics\" -> Seq(\"\"),\n \"income_band\" -> Seq(\"\"),\n \"inventory\" -> Seq(\"inv_date_sk\"),\n \"item\" -> Seq(\"\"),\n \"promotion\" -> Seq(\"\"),\n \"reason\" -> Seq(\"\"),\n \"ship_mode\" -> Seq(\"\"),\n \"store\" -> Seq(\"\"),\n \"store_returns\" -> Seq(\"sr_returned_date_sk\"),\n \"store_sales\" -> Seq(\"ss_sold_date_sk\"),\n \"time_dim\" -> Seq(\"\"),\n \"warehouse\" -> Seq(\"\"),\n \"web_page\" -> Seq(\"\"),\n \"web_returns\" -> Seq(\"wr_returned_date_sk\"),\n \"web_sales\" -> Seq(\"ws_sold_date_sk\"),\n \"web_site\" -> Seq(\"\")\n )\n}\n" }, { "path": "benchmarks/src/main/scala/benchmark/TestBenchmark.scala", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\npackage benchmark\n\ncase class TestBenchmarkConf(\n dbName: Option[String] = None,\n benchmarkPath: Option[String] = None) extends BenchmarkConf\n\nobject TestBenchmarkConf {\n import scopt.OParser\n private val builder = OParser.builder[TestBenchmarkConf]\n private val argParser = {\n import builder._\n OParser.sequence(\n programName(\"Test Benchmark\"),\n opt[String](\"test-param\")\n .required()\n .action((x, c) => c) // ignore\n .text(\"Name of the target database to create with TPC-DS tables in necessary format\"),\n opt[String](\"benchmark-path\")\n .optional()\n .action((x, c) => c.copy(benchmarkPath = Some(x)))\n .text(\"Cloud path to be used for creating table and generating reports\"),\n opt[String](\"db-name\")\n .optional()\n .action((x, c) => c.copy(dbName = Some(x)))\n .text(\"Name of the test database to create\")\n )\n }\n\n def parse(args: Array[String]): Option[TestBenchmarkConf] = {\n OParser.parse(argParser, args, TestBenchmarkConf())\n }\n}\n\nclass TestBenchmark(conf: TestBenchmarkConf) extends Benchmark(conf) {\n def runInternal(): Unit = {\n // Test Spark SQL\n runQuery(\"SELECT 1 AS X\", \"sql-test\")\n if (conf.benchmarkPath.isEmpty) {\n log(\"Skipping the delta read / write test as benchmark path has not been provided\")\n return\n }\n\n val dbName = conf.dbName.getOrElse(benchmarkId.replaceAll(\"-\", \"_\"))\n val dbLocation = conf.dbLocation(dbName)\n\n // Run database management tests\n runQuery(\"SHOW DATABASES\", \"db-list-test\")\n runQuery(s\"\"\"CREATE DATABASE IF NOT EXISTS $dbName LOCATION \"$dbLocation\" \"\"\", \"db-create-test\")\n runQuery(s\"USE $dbName\", \"db-use-test\")\n\n // Run table tests\n val tableName = \"test\"\n runQuery(s\"DROP TABLE IF EXISTS $tableName\", \"table-drop-test\")\n runQuery(s\"CREATE TABLE $tableName USING delta SELECT 1 AS x\", \"table-create-test\")\n runQuery(s\"SELECT * FROM $tableName\", \"table-query-test\")\n }\n}\n\nobject TestBenchmark {\n def main(args: Array[String]): Unit = {\n println(\"All command line args = \" + args.toSeq)\n TestBenchmarkConf.parse(args).foreach { conf =>\n new TestBenchmark(conf).run()\n }\n }\n}\n" }, { "path": "benchmarks/src/main/scala/org/apache/spark/SparkUtils.scala", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage org.apache.spark\n\nimport benchmark.SparkEnvironmentInfo\n\nimport org.apache.spark.util.Utils\n\nobject SparkUtils {\n def getEnvironmentInfo(sc: SparkContext): SparkEnvironmentInfo = {\n val info = sc.statusStore.environmentInfo()\n val sparkBuildInfo = Map(\n \"sparkBuildBranch\" -> SPARK_BRANCH,\n \"sparkBuildVersion\" -> SPARK_VERSION,\n \"sparkBuildDate\" -> SPARK_BUILD_DATE,\n \"sparkBuildUser\" -> SPARK_BUILD_USER,\n \"sparkBuildRevision\" -> SPARK_REVISION\n )\n\n SparkEnvironmentInfo(\n sparkBuildInfo = sparkBuildInfo,\n runtimeInfo = caseClassToMap(info.runtime),\n sparkProps = Utils.redact(sc.conf, info.sparkProperties).toMap,\n hadoopProps = Utils.redact(sc.conf, info.hadoopProperties).toMap\n .filterKeys(k => !k.startsWith(\"mapred\") && !k.startsWith(\"yarn\")),\n systemProps = Utils.redact(sc.conf, info.systemProperties).toMap,\n classpathEntries = info.classpathEntries.toMap\n )\n }\n\n def caseClassToMap(obj: Object): Map[String, String] = {\n obj.getClass.getDeclaredFields.flatMap { f =>\n f.setAccessible(true)\n val valueOption = f.get(obj) match {\n case o: Option[_] => o.map(_.toString)\n case s => Some(s.toString)\n }\n valueOption.map(value => f.getName -> value)\n }.toMap\n }\n\n def median(sizes: Array[Long], alreadySorted: Boolean): Long = Utils.median(sizes, alreadySorted)\n}\n" }, { "path": "build/sbt", "content": "#!/usr/bin/env bash\n\n#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n#\n# This file contains code from the Apache Spark project (original license above).\n# It contains modifications, which are licensed as follows:\n#\n\n#\n# Copyright (2021) The Delta Lake Project Authors.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n\n# When creating new tests for Spark SQL Hive, the HADOOP_CLASSPATH must contain the hive jars so\n# that we can run Hive to generate the golden answer. This is not required for normal development\n# or testing.\nif [ -n \"$HIVE_HOME\" ]; then\n for i in \"$HIVE_HOME\"/lib/*\n do HADOOP_CLASSPATH=\"$HADOOP_CLASSPATH:$i\"\n done\n export HADOOP_CLASSPATH\nfi\n\nrealpath () {\n(\n TARGET_FILE=\"$1\"\n\n cd \"$(dirname \"$TARGET_FILE\")\"\n TARGET_FILE=\"$(basename \"$TARGET_FILE\")\"\n\n COUNT=0\n while [ -L \"$TARGET_FILE\" -a $COUNT -lt 100 ]\n do\n TARGET_FILE=\"$(readlink \"$TARGET_FILE\")\"\n cd $(dirname \"$TARGET_FILE\")\n TARGET_FILE=\"$(basename $TARGET_FILE)\"\n COUNT=$(($COUNT + 1))\n done\n\n echo \"$(pwd -P)/\"$TARGET_FILE\"\"\n)\n}\n\n# Make Jenkins use Google Mirror first as Maven Central may ban us\nSBT_REPOSITORIES_CONFIG=\"$(dirname \"$(realpath \"$0\")\")/sbt-config/repositories\"\nexport SBT_OPTS=\"-Dsbt.override.build.repos=true -Dsbt.repository.config=$SBT_REPOSITORIES_CONFIG\"\n\n. \"$(dirname \"$(realpath \"$0\")\")\"/sbt-launch-lib.bash\n\n\ndeclare -r noshare_opts=\"-Dsbt.global.base=project/.sbtboot -Dsbt.boot.directory=project/.boot -Dsbt.ivy.home=project/.ivy\"\ndeclare -r sbt_opts_file=\".sbtopts\"\ndeclare -r etc_sbt_opts_file=\"/etc/sbt/sbtopts\"\n\nusage() {\n cat < path to global settings/plugins directory (default: ~/.sbt)\n -sbt-boot path to shared boot directory (default: ~/.sbt/boot in 0.11 series)\n -ivy path to local Ivy repository (default: ~/.ivy2)\n -mem set memory options (default: $sbt_mem, which is $(get_mem_opts $sbt_mem))\n -no-share use all local caches; no sharing\n -no-global uses global caches, but does not use global ~/.sbt directory.\n -jvm-debug Turn on JVM debugging, open at the given port.\n -batch Disable interactive mode\n\n # sbt version (default: from project/build.properties if present, else latest release)\n -sbt-version use the specified version of sbt\n -sbt-jar use the specified jar as the sbt launcher\n -sbt-rc use an RC version of sbt\n -sbt-snapshot use a snapshot version of sbt\n\n # java version (default: java from PATH, currently $(java -version 2>&1 | grep version))\n -java-home alternate JAVA_HOME\n\n # jvm options and output control\n JAVA_OPTS environment variable, if unset uses \"$java_opts\"\n SBT_OPTS environment variable, if unset uses \"$default_sbt_opts\"\n .sbtopts if this file exists in the current directory, it is\n prepended to the runner args\n /etc/sbt/sbtopts if this file exists, it is prepended to the runner args\n -Dkey=val pass -Dkey=val directly to the java runtime\n -J-X pass option -X directly to the java runtime\n (-J is stripped)\n -S-X add -X to sbt's scalacOptions (-S is stripped)\n -PmavenProfiles Enable a maven profile for the build.\n\nIn the case of duplicated or conflicting options, the order above\nshows precedence: JAVA_OPTS lowest, command line options highest.\nEOM\n}\n\nprocess_my_args () {\n while [[ $# -gt 0 ]]; do\n case \"$1\" in\n -no-colors) addJava \"-Dsbt.log.noformat=true\" && shift ;;\n -no-share) addJava \"$noshare_opts\" && shift ;;\n -no-global) addJava \"-Dsbt.global.base=$(pwd)/project/.sbtboot\" && shift ;;\n -sbt-boot) require_arg path \"$1\" \"$2\" && addJava \"-Dsbt.boot.directory=$2\" && shift 2 ;;\n -sbt-dir) require_arg path \"$1\" \"$2\" && addJava \"-Dsbt.global.base=$2\" && shift 2 ;;\n -debug-inc) addJava \"-Dxsbt.inc.debug=true\" && shift ;;\n -batch) exec /dev/null)\n if [[ ! $? ]]; then\n saved_stty=\"\"\n fi\n}\n\nsaveSttySettings\ntrap onExit INT\n\nrun \"$@\"\n\nexit_status=$?\nonExit\n" }, { "path": "build/sbt-config/repositories", "content": "[repositories]\n local\n local-preloaded-ivy: file:///${sbt.preloaded-${sbt.global.base-${user.home}/.sbt}/preloaded/}, [organization]/[module]/[revision]/[type]s/[artifact](-[classifier]).[ext]\n local-preloaded: file:///${sbt.preloaded-${sbt.global.base-${user.home}/.sbt}/preloaded/}\n mulesoft: https://repository.mulesoft.org/nexus/content/groups/public/\n gcs-maven-central-mirror: https://maven-central.storage-download.googleapis.com/maven2/\n maven-central\n typesafe-ivy-releases: https://repo.typesafe.com/typesafe/ivy-releases/, [organization]/[module]/[revision]/[type]s/[artifact](-[classifier]).[ext], bootOnly\n sbt-ivy-snapshots: https://repo.scala-sbt.org/scalasbt/ivy-snapshots/, [organization]/[module]/[revision]/[type]s/[artifact](-[classifier]).[ext], bootOnly\n sbt-plugin-releases: https://repo.scala-sbt.org/scalasbt/sbt-plugin-releases/, [organization]/[module]/(scala_[scalaVersion]/)(sbt_[sbtVersion]/)[revision]/[type]s/[artifact](-[classifier]).[ext]\n bintray-typesafe-sbt-plugin-releases: https://dl.bintray.com/typesafe/sbt-plugins/, [organization]/[module]/(scala_[scalaVersion]/)(sbt_[sbtVersion]/)[revision]/[type]s/[artifact](-[classifier]).[ext]\n repos-spark-packages: https://repos.spark-packages.org\n typesafe-releases: https://repo.typesafe.com/typesafe/releases/\n apache-snapshot: https://repository.apache.org/content/groups/snapshots/\n jitpack: https://jitpack.io\n" }, { "path": "build/sbt-launch-lib.bash", "content": "#!/usr/bin/env bash\n#\n\n# A library to simplify using the SBT launcher from other packages.\n# Note: This should be used by tools like giter8/conscript etc.\n\n# TODO - Should we merge the main SBT script with this library?\n\nif test -z \"$HOME\"; then\n declare -r script_dir=\"$(dirname \"$script_path\")\"\nelse\n declare -r script_dir=\"$HOME/.sbt\"\nfi\n\ndeclare -a residual_args\ndeclare -a java_args\ndeclare -a scalac_args\ndeclare -a sbt_commands\ndeclare -a maven_profiles\n\nif test -x \"$JAVA_HOME/bin/java\"; then\n echo -e \"Using $JAVA_HOME as default JAVA_HOME.\"\n echo \"Note, this will be overridden by -java-home if it is set.\"\n declare java_cmd=\"$JAVA_HOME/bin/java\"\nelse\n declare java_cmd=java\nfi\n\nechoerr () {\n echo 1>&2 \"$@\"\n}\nvlog () {\n [[ $verbose || $debug ]] && echoerr \"$@\"\n}\ndlog () {\n [[ $debug ]] && echoerr \"$@\"\n}\n\ndownload_sbt () {\n local url=$1\n local output=$2\n local temp_file=\"${output}.part\"\n\n if [ $(command -v curl) ]; then\n curl --fail --location --silent ${url} > \"${temp_file}\" &&\\\n mv \"${temp_file}\" \"${output}\"\n elif [ $(command -v wget) ]; then\n wget --quiet ${url} -O \"${temp_file}\" &&\\\n mv \"${temp_file}\" \"${output}\"\n else\n printf \"You do not have curl or wget installed, unable to downlaod ${url}\\n\"\n exit -1\n fi\n}\n\n\nacquire_sbt_jar () {\n SBT_VERSION=`awk -F \"=\" '/sbt\\.version/ {print $2}' ./project/build.properties`\n\n # Set primary and fallback URLs\n if [[ \"${SBT_VERSION}\" == \"0.13.18\" ]] && [[ -n \"${SBT_MIRROR_JAR_URL}\" ]]; then\n URL1=\"${SBT_MIRROR_JAR_URL}\"\n elif [[ \"${SBT_VERSION}\" == \"1.5.5\" ]] && [[ -n \"${SBT_1_5_5_MIRROR_JAR_URL}\" ]]; then\n URL1=\"${SBT_1_5_5_MIRROR_JAR_URL}\"\n else\n URL1=${DEFAULT_ARTIFACT_REPOSITORY:-https://maven-central.storage-download.googleapis.com/maven2/}org/scala-sbt/sbt-launch/${SBT_VERSION}/sbt-launch-${SBT_VERSION}.jar\n fi\n BACKUP_URL=\"https://repo1.maven.org/maven2/org/scala-sbt/sbt-launch/${SBT_VERSION}/sbt-launch-${SBT_VERSION}.jar\"\n\n JAR=build/sbt-launch-${SBT_VERSION}.jar\n sbt_jar=$JAR\n\n if [[ ! -f \"$sbt_jar\" ]]; then\n printf 'Attempting to fetch sbt from %s\\n' \"${URL1}\"\n download_sbt \"${URL1}\" \"${JAR}\"\n\n if [[ ! -f \"${JAR}\" ]]; then\n printf 'Download from %s failed. Retrying from %s\\n' \"${URL1}\" \"${BACKUP_URL}\"\n download_sbt \"${BACKUP_URL}\" \"${JAR}\"\n fi\n\n if [[ ! -f \"${JAR}\" ]]; then\n printf \"Failed to download sbt. Please install sbt manually from https://www.scala-sbt.org/\\n\"\n exit 1\n fi\n printf \"Launching sbt from ${JAR}\\n\"\n fi\n}\n\nexecRunner () {\n # print the arguments one to a line, quoting any containing spaces\n [[ $verbose || $debug ]] && echo \"# Executing command line:\" && {\n for arg; do\n if printf \"%s\\n\" \"$arg\" | grep -q ' '; then\n printf \"\\\"%s\\\"\\n\" \"$arg\"\n else\n printf \"%s\\n\" \"$arg\"\n fi\n done\n echo \"\"\n }\n\n \"$@\"\n}\n\naddJava () {\n dlog \"[addJava] arg = '$1'\"\n java_args=( \"${java_args[@]}\" \"$1\" )\n}\n\nenableProfile () {\n dlog \"[enableProfile] arg = '$1'\"\n maven_profiles=( \"${maven_profiles[@]}\" \"$1\" )\n export SBT_MAVEN_PROFILES=\"${maven_profiles[@]}\"\n}\n\naddSbt () {\n dlog \"[addSbt] arg = '$1'\"\n sbt_commands=( \"${sbt_commands[@]}\" \"$1\" )\n}\naddResidual () {\n dlog \"[residual] arg = '$1'\"\n residual_args=( \"${residual_args[@]}\" \"$1\" )\n}\naddDebugger () {\n addJava \"-agentlib:jdwp=transport=dt_socket,server=y,suspend=n,address=$1\"\n}\n\n# a ham-fisted attempt to move some memory settings in concert\n# so they need not be dicked around with individually.\nget_mem_opts () {\n local mem=${1:-1000}\n local perm=$(( $mem / 4 ))\n (( $perm > 256 )) || perm=256\n (( $perm < 4096 )) || perm=4096\n local codecache=$(( $perm / 2 ))\n\n echo \"-Xms${mem}m -Xmx${mem}m -XX:ReservedCodeCacheSize=${codecache}m\"\n}\n\nrequire_arg () {\n local type=\"$1\"\n local opt=\"$2\"\n local arg=\"$3\"\n if [[ -z \"$arg\" ]] || [[ \"${arg:0:1}\" == \"-\" ]]; then\n echo \"$opt requires <$type> argument\" 1>&2\n exit 1\n fi\n}\n\nis_function_defined() {\n declare -f \"$1\" > /dev/null\n}\n\nprocess_args () {\n while [[ $# -gt 0 ]]; do\n case \"$1\" in\n -h|-help) usage; exit 1 ;;\n -v|-verbose) verbose=1 && shift ;;\n -d|-debug) debug=1 && shift ;;\n\n -ivy) require_arg path \"$1\" \"$2\" && addJava \"-Dsbt.ivy.home=$2\" && shift 2 ;;\n -mem) require_arg integer \"$1\" \"$2\" && sbt_mem=\"$2\" && shift 2 ;;\n -jvm-debug) require_arg port \"$1\" \"$2\" && addDebugger $2 && shift 2 ;;\n -batch) exec = 17) {\n Seq( // For Java 17 +\n \"--add-opens=java.base/java.nio=ALL-UNNAMED\",\n \"--add-opens=java.base/java.lang=ALL-UNNAMED\",\n \"--add-opens=java.base/java.net=ALL-UNNAMED\",\n \"--add-opens=java.base/sun.nio.ch=ALL-UNNAMED\",\n \"--add-opens=java.base/sun.util.calendar=ALL-UNNAMED\"\n )\n } else {\n Seq.empty\n }\n },\n\n testOptions += Tests.Argument(\"-oF\"),\n\n // Unidoc settings: by default dont document any source file\n unidocSourceFilePatterns := Nil,\n)\n\n////////////////////////////\n// START: Code Formatting //\n////////////////////////////\n\n/** Enforce java code style on compile. */\ndef javafmtCheckSettings(): Seq[Def.Setting[Task[CompileAnalysis]]] = Seq(\n (Compile / compile) := ((Compile / compile) dependsOn (Compile / javafmtCheckAll)).value\n)\n\n/** Enforce scala code style on compile. */\ndef scalafmtCheckSettings(): Seq[Def.Setting[Task[CompileAnalysis]]] = Seq(\n (Compile / compile) := ((Compile / compile) dependsOn (Compile / scalafmtCheckAll)).value,\n)\n\n// TODO: define fmtAll and fmtCheckAll tasks that run both scala and java fmts/checks\n\n//////////////////////////\n// END: Code Formatting //\n//////////////////////////\n\n/**\n * Note: we cannot access sparkVersion.value here, since that can only be used within a task or\n * setting macro.\n */\ndef runTaskOnlyOnSparkMaster[T](\n task: sbt.TaskKey[T],\n taskName: String,\n projectName: String,\n emptyValue: => T): Def.Initialize[Task[T]] = {\n if (CrossSparkVersions.getSparkVersionSpec().isMaster) {\n Def.task(task.value)\n } else {\n Def.task {\n // scalastyle:off println\n val masterVersion = SparkVersionSpec.MASTER.map(_.fullVersion).getOrElse(\"(no master version configured)\")\n println(s\"Project $projectName: Skipping `$taskName` as Spark version \" +\n s\"${CrossSparkVersions.getSparkVersion()} does not equal $masterVersion.\")\n // scalastyle:on println\n emptyValue\n }\n }\n}\n\nlazy val connectCommon = (project in file(\"spark-connect/common\"))\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings(\n name := \"delta-connect-common\",\n commonSettings,\n CrossSparkVersions.sparkDependentSettings(sparkVersion),\n releaseSettings,\n // Export as JAR instead of classes directory. This ensures protobuf-generated classes\n // (e.g., io.delta.connect.proto.DeltaCommand) are available as a JAR file in fullClasspath,\n // which can be symlinked and picked up by Spark Submit's jars/* wildcard in connectClient tests.\n exportJars := true,\n libraryDependencies ++= Seq(\n \"io.grpc\" % \"protoc-gen-grpc-java\" % grpcVersion asProtocPlugin(),\n \"io.grpc\" % \"grpc-protobuf\" % grpcVersion,\n \"io.grpc\" % \"grpc-stub\" % grpcVersion,\n \"com.google.protobuf\" % \"protobuf-java\" % protoVersion % \"protobuf\",\n \"javax.annotation\" % \"javax.annotation-api\" % \"1.3.2\",\n\n \"org.apache.spark\" %% \"spark-connect-common\" % sparkVersion.value % \"provided\",\n ),\n PB.protocVersion := protoVersion,\n Compile / PB.targets := Seq(\n PB.gens.java -> (Compile / sourceManaged).value,\n PB.gens.plugin(\"grpc-java\") -> (Compile / sourceManaged).value\n )\n )\n\nlazy val connectClient = (project in file(\"spark-connect/client\"))\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .dependsOn(connectCommon % \"compile->compile;test->test;provided->provided\")\n .settings(\n name := \"delta-connect-client\",\n commonSettings,\n releaseSettings,\n CrossSparkVersions.sparkDependentSettings(sparkVersion),\n libraryDependencies ++= Seq(\n \"com.google.protobuf\" % \"protobuf-java\" % protoVersion % \"protobuf\",\n \"org.apache.spark\" %% \"spark-connect-client-jvm\" % sparkVersion.value % \"provided\",\n\n // Test deps\n \"org.scalatest\" %% \"scalatest\" % scalaTestVersion % \"test\",\n \"org.apache.spark\" %% \"spark-connect-client-jvm\" % sparkVersion.value % \"test\" classifier \"tests\"\n ),\n (Test / javaOptions) += {\n // Create a (mini) Spark Distribution based on the server classpath.\n val serverClassPath = (connectServer / Compile / fullClasspath).value\n val distributionDir = crossTarget.value / \"test-dist\"\n val jarsDir = distributionDir / \"jars\"\n\n if (!distributionDir.exists()) {\n IO.createDirectory(jarsDir)\n // Create symlinks for all dependencies (filter to only JAR files)\n serverClassPath.distinct.filter(_.data.isFile).foreach { entry =>\n val jarFile = entry.data.toPath\n val linkedJarFile = jarsDir / entry.data.getName\n if (!java.nio.file.Files.exists(linkedJarFile.toPath)) {\n Files.createSymbolicLink(linkedJarFile.toPath, jarFile)\n }\n }\n // Create a symlink for the log4j properties\n val confDir = distributionDir / \"conf\"\n IO.createDirectory(confDir)\n val log4jProps = (sparkV1 / Test / resourceDirectory).value / \"log4j2.properties\"\n val linkedLog4jProps = confDir / \"log4j2.properties\"\n if (!java.nio.file.Files.exists(linkedLog4jProps.toPath)) {\n Files.createSymbolicLink(linkedLog4jProps.toPath, log4jProps.toPath)\n }\n }\n // Return the location of the distribution directory.\n \"-Ddelta.spark.home=\" + distributionDir\n },\n // Required for testing addFeatureSupport/dropFeatureSupport.\n Test / envVars += (\"DELTA_TESTING\", \"1\")\n )\n\nlazy val connectServer = (project in file(\"spark-connect/server\"))\n .dependsOn(connectCommon % \"compile->compile;test->test;provided->provided\")\n .dependsOn(spark % \"compile->compile;test->test;provided->provided\")\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings(\n name := \"delta-connect-server\",\n commonSettings,\n releaseSettings,\n CrossSparkVersions.sparkDependentSettings(sparkVersion),\n // Export as JAR instead of classes directory. Required for connectClient test setup so that\n // classes like SimpleDeltaConnectService are available as a JAR file that can be symlinked\n // and picked up by Spark Submit's jars/* wildcard. Also prevents classpath conflicts.\n exportJars := true,\n assembly / assemblyMergeStrategy := {\n // Discard module-info.class files from Java 9+ modules and multi-release JARs\n case \"module-info.class\" => MergeStrategy.discard\n case PathList(\"META-INF\", \"versions\", _, \"module-info.class\") => MergeStrategy.discard\n case x =>\n val oldStrategy = (assembly / assemblyMergeStrategy).value\n oldStrategy(x)\n },\n libraryDependencies ++= Seq(\n \"com.google.protobuf\" % \"protobuf-java\" % protoVersion % \"protobuf\",\n\n \"org.apache.spark\" %% \"spark-hive\" % sparkVersion.value % \"provided\",\n \"org.apache.spark\" %% \"spark-sql\" % sparkVersion.value % \"provided\",\n \"org.apache.spark\" %% \"spark-core\" % sparkVersion.value % \"provided\",\n \"org.apache.spark\" %% \"spark-catalyst\" % sparkVersion.value % \"provided\",\n \"org.apache.spark\" %% \"spark-connect\" % sparkVersion.value % \"provided\",\n\n \"org.apache.spark\" %% \"spark-catalyst\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-core\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-sql\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-hive\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-connect\" % sparkVersion.value % \"test\" classifier \"tests\",\n ),\n excludeDependencies ++= Seq(\n // Exclude connect common because a properly shaded version of it is included in the\n // spark-connect jar. Including it causes classpath problems.\n ExclusionRule(\"org.apache.spark\", \"spark-connect-common_2.13\"),\n // Exclude connect shims because we have spark-core on the classpath. The shims are only\n // needed for the client. Including it causes classpath problems.\n ExclusionRule(\"org.apache.spark\", \"spark-connect-shims_2.13\")\n ),\n // Required for testing addFeatureSupport/dropFeatureSupport.\n Test / envVars += (\"DELTA_TESTING\", \"1\"),\n // Force Spark to bind to localhost to avoid network issues\n Test / envVars += (\"SPARK_LOCAL_IP\", \"127.0.0.1\")\n )\n\nlazy val deltaSuiteGenerator = (project in file(\"spark/delta-suite-generator\"))\n .disablePlugins(ScalafmtPlugin)\n .settings (\n name := \"delta-suite-generator\",\n commonSettings,\n scalaStyleSettings,\n skipReleaseSettings, // Internal module - not published to Maven\n libraryDependencies ++= Seq(\n \"org.scala-lang.modules\" %% \"scala-collection-compat\" % \"2.11.0\",\n \"org.scalameta\" %% \"scalameta\" % \"4.13.5\",\n \"org.scalameta\" %% \"scalafmt-core\" % \"3.9.6\",\n \"commons-cli\" % \"commons-cli\" % \"1.9.0\",\n \"commons-codec\" % \"commons-codec\" % \"1.17.2\",\n \"org.scalatest\" %% \"scalatest\" % scalaTestVersion % \"test\",\n ),\n Compile / mainClass := Some(\"io.delta.suitegenerator.ModularSuiteGenerator\"),\n Test / baseDirectory := (ThisBuild / baseDirectory).value,\n )\n\n// ============================================================\n// Spark Module 1: sparkV1 (prod code only, no tests)\n// ============================================================\nlazy val sparkV1 = (project in file(\"spark\"))\n .dependsOn(storage)\n .enablePlugins(Antlr4Plugin)\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings (\n name := \"delta-spark-v1\",\n commonSettings,\n scalaStyleSettings,\n skipReleaseSettings, // Internal module - not published to Maven\n CrossSparkVersions.sparkDependentSettings(sparkVersion),\n\n // Export as JAR instead of classes directory. This prevents dependent projects\n // (e.g., connectServer) from seeing multiple 'classes' directories with the same\n // name in their classpath, which would cause FileAlreadyExistsException.\n exportJars := true,\n\n // Tests are compiled in the final 'spark' module to avoid circular dependencies\n Test / sources := Seq.empty,\n Test / resources := Seq.empty,\n\n libraryDependencies ++= Seq(\n // Adding test classifier seems to break transitive resolution of the core dependencies\n \"org.apache.spark\" %% \"spark-hive\" % sparkVersion.value % \"provided\",\n \"org.apache.spark\" %% \"spark-sql\" % sparkVersion.value % \"provided\",\n \"org.apache.spark\" %% \"spark-core\" % sparkVersion.value % \"provided\",\n \"org.apache.spark\" %% \"spark-catalyst\" % sparkVersion.value % \"provided\",\n // For DynamoDBCommitStore\n \"com.amazonaws\" % \"aws-java-sdk\" % \"1.12.262\" % \"provided\",\n\n // Test deps\n \"org.scalatest\" %% \"scalatest\" % scalaTestVersion % \"test\",\n \"org.scalatestplus\" %% \"scalacheck-1-15\" % \"3.2.9.0\" % \"test\",\n \"junit\" % \"junit\" % \"4.13.2\" % \"test\",\n \"com.novocode\" % \"junit-interface\" % \"0.11\" % \"test\",\n \"org.apache.spark\" %% \"spark-catalyst\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-core\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-sql\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-hive\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.mockito\" % \"mockito-inline\" % \"4.11.0\" % \"test\",\n ),\n Compile / packageBin / mappings := (Compile / packageBin / mappings).value ++\n listPythonFiles(baseDirectory.value.getParentFile / \"python\"),\n Antlr4 / antlr4PackageName := Some(\"io.delta.sql.parser\"),\n Antlr4 / antlr4GenListener := true,\n Antlr4 / antlr4GenVisitor := true,\n\n // Introduced in https://github.com/delta-io/delta/commit/d2990624d34b6b86fa5cf230e00a89b095fde254\n //\n // Hack to avoid errors related to missing repo-root/target/scala-2.13/classes/\n // In multi-module sbt projects, some dependencies may attempt to locate this directory\n // at the repository root, causing build failures if it doesn't exist.\n createTargetClassesDir := {\n val dir = baseDirectory.value.getParentFile / \"target\" / \"scala-2.13\" / \"classes\"\n Files.createDirectories(dir.toPath)\n },\n // Generate Python version.py file with hardcoded version.\n // This file is committed to git and auto-updated during build.\n generatePythonVersion := {\n val versionValue = version.value\n // Trim -SNAPSHOT suffix to get PyPI-compatible version (like setup.py does)\n val trimmedVersion = versionValue.split(\"-SNAPSHOT\")(0)\n val versionFile = baseDirectory.value.getParentFile / \"python\" / \"delta\" / \"version.py\"\n val content =\n s\"\"\"#\n |# Copyright (2026) The Delta Lake Project Authors.\n |#\n |# Licensed under the Apache License, Version 2.0 (the \"License\");\n |# you may not use this file except in compliance with the License.\n |# You may obtain a copy of the License at\n |#\n |# http://www.apache.org/licenses/LICENSE-2.0\n |#\n |# Unless required by applicable law or agreed to in writing, software\n |# distributed under the License is distributed on an \"AS IS\" BASIS,\n |# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n |# See the License for the specific language governing permissions and\n |# limitations under the License.\n |#\n |\n |# This file is auto-generated by the build.sbt generatePythonVersion task.\n |# Do not edit manually - edit version.sbt instead and run:\n |# build/sbt sparkV1/generatePythonVersion\n |\n |__version__ = \"$trimmedVersion\"\n |\"\"\".stripMargin\n IO.write(versionFile, content)\n versionFile\n },\n // Hook both createTargetClassesDir and generatePythonVersion into compile task\n Compile / compile := ((Compile / compile) dependsOn createTargetClassesDir dependsOn generatePythonVersion).value,\n // Generate the package object to provide the version information in runtime.\n Compile / sourceGenerators += Def.task {\n val file = (Compile / sourceManaged).value / \"io\" / \"delta\" / \"package.scala\"\n IO.write(file,\n s\"\"\"package io\n |\n |package object delta {\n | val VERSION = \"${version.value}\"\n |}\n |\"\"\".stripMargin)\n Seq(file)\n },\n )\n\n// ============================================================\n// Spark Module 2: sparkV1Filtered (v1 without DeltaLog for v2 dependency)\n// This filtered version of sparkV1 is needed because sparkV2 (spark/v2) depends on some\n// V1 classes for utilities and common functionality, but must NOT have access to DeltaLog,\n// Snapshot, OptimisticTransaction, or actions that belongs to core V1 delta libraries.\n// We should use Kernel as the Delta implementation.\n// ============================================================\nlazy val sparkV1Filtered = (project in file(\"spark-v1-filtered\"))\n .dependsOn(sparkV1)\n .dependsOn(storage)\n .settings(\n name := \"delta-spark-v1-filtered\",\n commonSettings,\n skipReleaseSettings, // Internal module - not published to Maven\n exportJars := true, // Export as JAR to avoid classpath conflicts\n\n // No source code - just repackage sparkV1 without DeltaLog classes\n Compile / sources := Seq.empty,\n Test / sources := Seq.empty,\n\n // Repackage sparkV1 jar but exclude DeltaLog and related classes\n Compile / packageBin / mappings := {\n val v1Mappings = (sparkV1 / Compile / packageBin / mappings).value\n\n // Filter out DeltaLog, Snapshot, OptimisticTransaction, and actions.scala classes\n v1Mappings.filterNot { case (file, path) =>\n path.contains(\"org/apache/spark/sql/delta/DeltaLog\") ||\n path.contains(\"org/apache/spark/sql/delta/Snapshot\") ||\n path.contains(\"org/apache/spark/sql/delta/OptimisticTransaction\") ||\n path.contains(\"org/apache/spark/sql/delta/actions/actions\")\n }\n },\n )\n\n// ============================================================\n// Spark Module 3: sparkV2 (Kernel-based DSv2 connector, depends on v1-filtered)\n// ============================================================\nlazy val sparkV2 = (project in file(\"spark/v2\"))\n .dependsOn(sparkV1Filtered)\n .dependsOn(kernelDefaults)\n .dependsOn(kernelUnityCatalog % \"compile->compile;test->test\")\n .dependsOn(goldenTables % \"test\")\n .settings(\n name := \"delta-spark-v2\",\n commonSettings,\n javafmtCheckSettings,\n skipReleaseSettings, // Internal module - not published to Maven\n CrossSparkVersions.sparkDependentSettings(sparkVersion),\n exportJars := true, // Export as JAR to avoid classpath conflicts\n\n Test / javaOptions ++= Seq(\"-ea\"),\n // make sure shaded kernel-api jar exists before compiling/testing\n Compile / compile := (Compile / compile)\n .dependsOn(kernelApi / Compile / packageBin).value,\n Test / test := (Test / test)\n .dependsOn(kernelApi / Compile / packageBin).value,\n Test / unmanagedJars += (kernelApi / Test / packageBin).value,\n Compile / unmanagedJars ++= Seq(\n (kernelApi / Compile / packageBin).value\n ),\n libraryDependencies ++= Seq(\n \"org.apache.spark\" %% \"spark-sql\" % sparkVersion.value % \"provided\",\n \"org.apache.spark\" %% \"spark-core\" % sparkVersion.value % \"provided\",\n \"org.apache.spark\" %% \"spark-catalyst\" % sparkVersion.value % \"provided\",\n\n // Test dependencies\n \"org.junit.jupiter\" % \"junit-jupiter-api\" % \"5.11.4\" % \"test\",\n \"org.junit.jupiter\" % \"junit-jupiter-engine\" % \"5.11.4\" % \"test\",\n \"org.junit.jupiter\" % \"junit-jupiter-params\" % \"5.11.4\" % \"test\",\n \"com.github.sbt.junit\" % \"jupiter-interface\" % \"0.17.0\" % \"test\",\n // Spark test classes for Scala/Java test utilities\n \"org.apache.spark\" %% \"spark-catalyst\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-core\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-sql\" % sparkVersion.value % \"test\" classifier \"tests\",\n // ScalaTest for test utilities (needed by Spark test classes)\n \"org.scalatest\" %% \"scalatest\" % scalaTestVersion % \"test\"\n ),\n Test / testOptions += Tests.Argument(TestFrameworks.JUnit, \"-v\", \"-a\"),\n TestParallelization.settings\n )\n\n\n// ============================================================\n// Spark Module 4: delta-spark (final published module - unified v1+v2)\n// ============================================================\nlazy val spark = (project in file(\"spark-unified\"))\n .dependsOn(sparkV1)\n .dependsOn(sparkV2)\n .dependsOn(storage)\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings (\n name := \"delta-spark\",\n commonSettings,\n scalaStyleSettings,\n sparkMimaSettings,\n releaseSettings, // Published to Maven as delta-spark.jar\n\n // Set Test baseDirectory before sparkDependentSettings() so it uses the correct directory\n Test / baseDirectory := (sparkV1 / baseDirectory).value,\n\n // Test sources from spark/ directory (sparkV1's directory) AND spark-unified's own directory\n // MUST be set BEFORE crossSparkSettings() to avoid overwriting version-specific directories\n Test / unmanagedSourceDirectories := {\n val sparkDir = (sparkV1 / baseDirectory).value\n val unifiedDir = baseDirectory.value\n Seq(\n sparkDir / \"src\" / \"test\" / \"scala\",\n sparkDir / \"src\" / \"test\" / \"java\",\n unifiedDir / \"src\" / \"test\" / \"scala\",\n unifiedDir / \"src\" / \"test\" / \"java\"\n )\n },\n Test / unmanagedResourceDirectories := Seq(\n (sparkV1 / baseDirectory).value / \"src\" / \"test\" / \"resources\",\n baseDirectory.value / \"src\" / \"test\" / \"resources\"\n ),\n\n CrossSparkVersions.sparkDependentSettings(sparkVersion),\n\n // MiMa should use the generated JAR (not classDirectory) because we merge classes at package time\n mimaCurrentClassfiles := (Compile / packageBin).value,\n\n // Export as JAR to dependent projects (e.g., connectServer, connectClient).\n // This prevents classpath conflicts from internal module 'classes' directories.\n exportJars := true,\n\n // Internal module artifact names to exclude from published POM\n internalModuleNames := Set(\"delta-spark-v1\", \"delta-spark-v1-shaded\", \"delta-spark-v2\"),\n\n // Merge classes from internal modules (v1, v2) into final JAR\n // kernel modules are kept as separate JARs and listed as dependencies in POM\n Compile / packageBin / mappings ++= {\n val log = streams.value.log\n\n // Collect mappings from internal modules\n val v1Mappings = (sparkV1 / Compile / packageBin / mappings).value\n val v2Mappings = (sparkV2 / Compile / packageBin / mappings).value\n\n // Include Python files (from spark/ directory)\n val pythonMappings = listPythonFiles(baseDirectory.value.getParentFile / \"python\")\n\n // Combine all mappings\n val allMappings = v1Mappings ++ v2Mappings ++ pythonMappings\n\n // Detect duplicate class files\n val classFiles = allMappings.filter(_._2.endsWith(\".class\"))\n val duplicates = classFiles.groupBy(_._2).filter(_._2.size > 1)\n\n if (duplicates.nonEmpty) {\n log.error(s\"Found ${duplicates.size} duplicate class(es) in packageBin mappings:\")\n duplicates.foreach { case (className, entries) =>\n log.error(s\" - $className:\")\n entries.foreach { case (file, path) => log.error(s\" from: $file\") }\n }\n sys.error(\"Duplicate classes found. This indicates overlapping code between sparkV1, sparkV2, and storage modules.\")\n }\n\n allMappings.distinct\n },\n\n // Exclude internal modules from published POM and add kernel dependencies.\n // Kernel modules are transitive through sparkV2 (an internal module), so they\n // are lost when sparkV2 is filtered out. We re-add them explicitly here.\n pomPostProcess := { node =>\n val internalModules = internalModuleNames.value\n val ver = version.value\n import scala.xml._\n import scala.xml.transform._\n\n def kernelDependencyNode(artifactId: String): Elem = {\n \n io.delta\n {artifactId}\n {ver}\n \n }\n\n val kernelDeps = Seq(\n kernelDependencyNode(\"delta-kernel-api\"),\n kernelDependencyNode(\"delta-kernel-defaults\"),\n kernelDependencyNode(\"delta-kernel-unitycatalog\")\n )\n\n new RuleTransformer(new RewriteRule {\n override def transform(n: Node): Seq[Node] = n match {\n case e: Elem if e.label == \"dependencies\" =>\n val filtered = e.child.filter {\n case child: Elem if child.label == \"dependency\" =>\n val artifactId = (child \\ \"artifactId\").text\n !internalModules.exists(module => artifactId.startsWith(module))\n case _ => true\n }\n Seq(e.copy(child = filtered ++ kernelDeps))\n case _ => Seq(n)\n }\n }).transform(node).head\n },\n\n pomIncludeRepository := { _ => false },\n\n // Filter internal modules from project dependencies\n // This works together with pomPostProcess to ensure internal modules\n // (sparkV1, sparkV2, sparkV1Filtered) are not listed as dependencies in POM\n projectDependencies := {\n val internalModules = internalModuleNames.value\n projectDependencies.value.filterNot(dep => internalModules.contains(dep.name))\n },\n\n libraryDependencies ++= Seq(\n \"org.apache.spark\" %% \"spark-hive\" % sparkVersion.value % \"provided\",\n \"org.apache.spark\" %% \"spark-sql\" % sparkVersion.value % \"provided\",\n \"org.apache.spark\" %% \"spark-core\" % sparkVersion.value % \"provided\",\n \"org.apache.spark\" %% \"spark-catalyst\" % sparkVersion.value % \"provided\",\n \"com.amazonaws\" % \"aws-java-sdk\" % \"1.12.262\" % \"provided\",\n\n \"org.scalatest\" %% \"scalatest\" % scalaTestVersion % \"test\",\n \"org.scalatestplus\" %% \"scalacheck-1-15\" % \"3.2.9.0\" % \"test\",\n \"junit\" % \"junit\" % \"4.13.2\" % \"test\",\n \"com.novocode\" % \"junit-interface\" % \"0.11\" % \"test\",\n \"org.apache.spark\" %% \"spark-catalyst\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-core\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-sql\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-hive\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.mockito\" % \"mockito-inline\" % \"4.11.0\" % \"test\",\n ),\n\n Test / testOptions += Tests.Argument(\"-oDF\"),\n Test / testOptions += Tests.Argument(TestFrameworks.JUnit, \"-v\", \"-a\"),\n\n // Don't execute in parallel since we can't have multiple Sparks in the same JVM\n Test / parallelExecution := false,\n\n javaOptions += \"-Xmx1024m\",\n\n // Configurations to speed up tests and reduce memory footprint\n Test / javaOptions ++= Seq(\n \"-Dspark.ui.enabled=false\",\n \"-Dspark.ui.showConsoleProgress=false\",\n \"-Dspark.databricks.delta.snapshotPartitions=2\",\n \"-Dspark.sql.shuffle.partitions=5\",\n \"-Ddelta.log.cacheSize=3\",\n \"-Dspark.databricks.delta.delta.log.cacheSize=3\",\n \"-Dspark.sql.sources.parallelPartitionDiscovery.parallelism=5\",\n \"-Xmx1024m\"\n ),\n\n // Required for testing table features see https://github.com/delta-io/delta/issues/1602\n Test / envVars += (\"DELTA_TESTING\", \"1\"),\n\n TestParallelization.settings,\n )\n .configureUnidoc(\n generatedJavaDoc = CrossSparkVersions.getSparkVersionSpec().generateDocs,\n generateScalaDoc = CrossSparkVersions.getSparkVersionSpec().generateDocs,\n // spark-connect has classes with the same name as spark-core, this causes compilation issues\n // with unidoc since it concatenates the classpaths from all modules\n // ==> thus we exclude such sources\n // (mostly) relevant github issue: https://github.com/sbt/sbt-unidoc/issues/77\n classPathToSkip = \"spark-connect\"\n )\n\nlazy val contribs = (project in file(\"contribs\"))\n .dependsOn(spark % \"compile->compile;test->test;provided->provided\")\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings (\n name := \"delta-contribs\",\n commonSettings,\n scalaStyleSettings,\n releaseSettings,\n // Set sparkVersion directly (not sparkDependentModuleName) so that\n // runOnlyForReleasableSparkModules discovers this module, but without adding a Spark\n // suffix to the artifact name. delta-contribs is only published as delta-contribs_2.13.\n sparkVersion := CrossSparkVersions.getSparkVersion(),\n Compile / packageBin / mappings := (Compile / packageBin / mappings).value ++\n listPythonFiles(baseDirectory.value.getParentFile / \"python\"),\n\n Test / testOptions += Tests.Argument(\"-oDF\"),\n Test / testOptions += Tests.Argument(TestFrameworks.JUnit, \"-v\", \"-a\"),\n\n // Don't execute in parallel since we can't have multiple Sparks in the same JVM\n Test / parallelExecution := false,\n\n javaOptions += \"-Xmx1024m\",\n\n // Configurations to speed up tests and reduce memory footprint\n Test / javaOptions ++= Seq(\n \"-Dspark.ui.enabled=false\",\n \"-Dspark.ui.showConsoleProgress=false\",\n \"-Dspark.databricks.delta.snapshotPartitions=2\",\n \"-Dspark.sql.shuffle.partitions=5\",\n \"-Ddelta.log.cacheSize=3\",\n \"-Dspark.databricks.delta.delta.log.cacheSize=3\",\n \"-Dspark.sql.sources.parallelPartitionDiscovery.parallelism=5\",\n \"-Xmx1024m\"\n ),\n\n // Introduced in https://github.com/delta-io/delta/commit/d2990624d34b6b86fa5cf230e00a89b095fde254\n //\n // Hack to avoid errors related to missing repo-root/target/scala-2.13/classes/\n // In multi-module sbt projects, some dependencies may attempt to locate this directory\n // at the repository root, causing build failures if it doesn't exist.\n createTargetClassesDir := {\n val dir = baseDirectory.value.getParentFile / \"target\" / \"scala-2.13\" / \"classes\"\n Files.createDirectories(dir.toPath)\n },\n Compile / compile := ((Compile / compile) dependsOn createTargetClassesDir).value,\n TestParallelization.settings\n ).configureUnidoc()\n\n\nval unityCatalogVersion = \"0.4.0\"\nval sparkUnityCatalogJacksonVersion = \"2.15.4\" // We are using Spark 4.0's Jackson version 2.15.x, to override Unity Catalog 0.3.0's version 2.18.x\n\nlazy val sparkUnityCatalog = (project in file(\"spark/unitycatalog\"))\n .dependsOn(spark % \"compile->compile;test->test;provided->provided\")\n .disablePlugins(ScalafmtPlugin)\n .settings(\n name := \"delta-spark-unitycatalog\",\n commonSettings,\n skipReleaseSettings,\n javafmtCheckSettings(),\n CrossSparkVersions.sparkDependentSettings(sparkVersion),\n\n // This is a test-only module - no production sources\n Compile / sources := Seq.empty,\n\n // Ensure Java sources are picked up\n Test / unmanagedSourceDirectories += baseDirectory.value / \"src\" / \"test\" / \"java\",\n\n Test / javaOptions ++= Seq(\"-ea\"),\n\n // Don't execute in parallel since we can't have multiple Sparks in the same JVM\n Test / parallelExecution := false,\n\n // Force ALL Jackson dependencies to match Spark's Jackson version\n // This overrides Jackson from Unity Catalog's transitive dependencies (e.g., Armeria)\n dependencyOverrides ++= Seq(\n \"com.fasterxml.jackson.core\" % \"jackson-core\" % sparkUnityCatalogJacksonVersion,\n \"com.fasterxml.jackson.core\" % \"jackson-annotations\" % sparkUnityCatalogJacksonVersion,\n \"com.fasterxml.jackson.core\" % \"jackson-databind\" % sparkUnityCatalogJacksonVersion,\n \"com.fasterxml.jackson.module\" %% \"jackson-module-scala\" % sparkUnityCatalogJacksonVersion,\n \"com.fasterxml.jackson.dataformat\" % \"jackson-dataformat-yaml\" % sparkUnityCatalogJacksonVersion,\n \"com.fasterxml.jackson.datatype\" % \"jackson-datatype-jsr310\" % sparkUnityCatalogJacksonVersion,\n \"com.fasterxml.jackson.datatype\" % \"jackson-datatype-jdk8\" % sparkUnityCatalogJacksonVersion\n ),\n\n libraryDependencies ++= Seq(\n \"org.assertj\" % \"assertj-core\" % \"3.26.3\" % \"test\",\n // JUnit 5 test dependencies\n \"org.junit.jupiter\" % \"junit-jupiter-api\" % \"5.11.4\" % \"test\",\n \"org.junit.jupiter\" % \"junit-jupiter-engine\" % \"5.11.4\" % \"test\",\n \"org.junit.jupiter\" % \"junit-jupiter-params\" % \"5.11.4\" % \"test\",\n \"com.github.sbt.junit\" % \"jupiter-interface\" % \"0.17.0\" % \"test\",\n // Lombok for generating boilerplate code\n \"org.projectlombok\" % \"lombok\" % \"1.18.34\" % \"test\",\n\n // Unity Catalog dependencies - exclude Jackson to use Spark's Jackson 2.15.x\n \"io.unitycatalog\" %% \"unitycatalog-spark\" % unityCatalogVersion % \"test\" excludeAll(\n ExclusionRule(organization = \"com.fasterxml.jackson.core\"),\n ExclusionRule(organization = \"com.fasterxml.jackson.module\"),\n ExclusionRule(organization = \"com.fasterxml.jackson.datatype\"),\n ExclusionRule(organization = \"com.fasterxml.jackson.dataformat\")\n ),\n \"io.unitycatalog\" % \"unitycatalog-server\" % unityCatalogVersion % \"test\" excludeAll(\n ExclusionRule(organization = \"com.fasterxml.jackson.core\"),\n ExclusionRule(organization = \"com.fasterxml.jackson.module\"),\n ExclusionRule(organization = \"com.fasterxml.jackson.datatype\"),\n ExclusionRule(organization = \"com.fasterxml.jackson.dataformat\")\n ),\n\n // Spark test dependencies\n \"org.apache.spark\" %% \"spark-sql\" % sparkVersion.value % \"test\",\n \"org.apache.spark\" %% \"spark-catalyst\" % sparkVersion.value % \"test\",\n \"org.apache.spark\" %% \"spark-core\" % sparkVersion.value % \"test\",\n ),\n\n // Conditionally add hadoop-aws dependency only when UC_REMOTE=true\n // Please see: https://github.com/delta-io/delta/issues/5624#issuecomment-3673383736\n // Once we release the relocated unitycatalog-server, we can remove this.\n libraryDependencies ++= {\n if (sys.env.get(\"UC_REMOTE\").contains(\"true\")) {\n Seq(\n \"org.apache.hadoop\" % \"hadoop-aws\" % hadoopVersion % \"test\",\n \"org.apache.hadoop\" % \"hadoop-common\" % hadoopVersion % \"test\",\n \"org.apache.hadoop\" % \"hadoop-client-api\" % hadoopVersion % \"test\",\n \"org.apache.hadoop\" % \"hadoop-client-runtime\" % hadoopVersion % \"test\"\n )\n } else {\n Seq.empty\n }\n },\n\n Test / testOptions += Tests.Argument(\"-oDF\"),\n Test / testOptions += Tests.Argument(TestFrameworks.JUnit, \"-v\", \"-a\")\n )\n\nlazy val sharing = (project in file(\"sharing\"))\n .dependsOn(spark % \"compile->compile;test->test;provided->provided\")\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings(\n name := \"delta-sharing-spark\",\n commonSettings,\n scalaStyleSettings,\n releaseSettings,\n CrossSparkVersions.sparkDependentSettings(sparkVersion),\n Test / javaOptions ++= Seq(\"-ea\"),\n libraryDependencies ++= Seq(\n \"org.apache.spark\" %% \"spark-sql\" % sparkVersion.value % \"provided\",\n\n \"io.delta\" %% \"delta-sharing-client\" % \"1.3.10\",\n\n // Test deps\n \"org.scalatest\" %% \"scalatest\" % scalaTestVersion % \"test\",\n \"org.scalatestplus\" %% \"scalacheck-1-15\" % \"3.2.9.0\" % \"test\",\n \"junit\" % \"junit\" % \"4.13.2\" % \"test\",\n \"com.novocode\" % \"junit-interface\" % \"0.11\" % \"test\",\n \"org.apache.spark\" %% \"spark-catalyst\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-core\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-sql\" % sparkVersion.value % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-hive\" % sparkVersion.value % \"test\" classifier \"tests\",\n ),\n TestParallelization.settings\n ).configureUnidoc()\n\nlazy val kernelApi = (project in file(\"kernel/kernel-api\"))\n .enablePlugins(ScalafmtPlugin)\n .settings(\n name := \"delta-kernel-api\",\n commonSettings,\n scalaStyleSettings,\n javaOnlyReleaseSettings,\n javafmtCheckSettings,\n scalafmtCheckSettings,\n\n // Use unique classDirectory name to avoid conflicts in connectClient test setup\n // This allows connectClient to create symlinks without FileAlreadyExistsException\n Compile / classDirectory := target.value / \"scala-2.13\" / \"kernel-api-classes\",\n\n Test / javaOptions ++= Seq(\"-ea\"),\n\n // Also publish a test-jar (classifier = \"tests\") so consumers (e.g. kernelDefault)\n // can depend on test utilities via a published artifact instead of depending on raw class directories.\n Test / publishArtifact := true,\n Test / packageBin / artifactClassifier := Some(\"tests\"),\n libraryDependencies ++= Seq(\n \"org.roaringbitmap\" % \"RoaringBitmap\" % \"0.9.25\",\n \"org.slf4j\" % \"slf4j-api\" % \"1.7.36\",\n\n \"com.fasterxml.jackson.core\" % \"jackson-databind\" % \"2.13.5\",\n \"com.fasterxml.jackson.core\" % \"jackson-core\" % \"2.13.5\",\n \"com.fasterxml.jackson.core\" % \"jackson-annotations\" % \"2.13.5\",\n \"com.fasterxml.jackson.datatype\" % \"jackson-datatype-jdk8\" % \"2.13.5\",\n\n // JSR-305 annotations for @Nullable\n \"com.google.code.findbugs\" % \"jsr305\" % \"3.0.2\",\n\n \"org.scalatest\" %% \"scalatest\" % scalaTestVersion % \"test\",\n \"junit\" % \"junit\" % \"4.13.2\" % \"test\",\n \"com.novocode\" % \"junit-interface\" % \"0.11\" % \"test\",\n \"org.apache.logging.log4j\" % \"log4j-slf4j-impl\" % \"2.25.3\" % \"test\",\n \"org.apache.logging.log4j\" % \"log4j-core\" % \"2.25.3\" % \"test\",\n \"org.assertj\" % \"assertj-core\" % \"3.26.3\" % \"test\",\n // JMH dependencies allow writing micro-benchmarks for testing performance of components.\n // JMH has framework to define benchmarks and takes care of many common functionalities\n // such as warm runs, cold runs, defining benchmark parameter variables etc.\n \"org.openjdk.jmh\" % \"jmh-core\" % \"1.37\" % \"test\",\n \"org.openjdk.jmh\" % \"jmh-generator-annprocess\" % \"1.37\" % \"test\"\n ),\n // Shade jackson libraries so that connector developers don't have to worry\n // about jackson version conflicts.\n Compile / packageBin := assembly.value,\n assembly / assemblyJarName := s\"${name.value}-${version.value}.jar\",\n assembly / logLevel := Level.Info,\n assembly / test := {},\n assembly / assemblyExcludedJars := {\n val cp = (assembly / fullClasspath).value\n val allowedPrefixes = Set(\"META_INF\", \"io\", \"jackson\")\n cp.filter { f =>\n !allowedPrefixes.exists(prefix => f.data.getName.startsWith(prefix))\n }\n },\n assembly / assemblyShadeRules := Seq(\n ShadeRule.rename(\"com.fasterxml.jackson.**\" -> \"io.delta.kernel.shaded.com.fasterxml.jackson.@1\").inAll\n ),\n assembly / assemblyMergeStrategy := {\n // Discard `module-info.class` to fix the `different file contents found` error.\n // TODO Upgrade SBT to 1.5 which will do this automatically\n case \"module-info.class\" => MergeStrategy.discard\n case PathList(\"META-INF\", \"services\", xs @ _*) => MergeStrategy.discard\n case x =>\n val oldStrategy = (assembly / assemblyMergeStrategy).value\n oldStrategy(x)\n },\n // Generate the package object to provide the version information in runtime.\n Compile / sourceGenerators += Def.task {\n val file = (Compile / sourceManaged).value / \"io\" / \"delta\" / \"kernel\" / \"Meta.java\"\n IO.write(file,\n s\"\"\"/*\n | * Copyright (2024) The Delta Lake Project Authors.\n | *\n | * Licensed under the Apache License, Version 2.0 (the \"License\");\n | * you may not use this file except in compliance with the License.\n | * You may obtain a copy of the License at\n | *\n | * http://www.apache.org/licenses/LICENSE-2.0\n | *\n | * Unless required by applicable law or agreed to in writing, software\n | * distributed under the License is distributed on an \"AS IS\" BASIS,\n | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n | * See the License for the specific language governing permissions and\n | * limitations under the License.\n | */\n |package io.delta.kernel;\n |\n |public final class Meta {\n | public static final String KERNEL_VERSION = \"${version.value}\";\n |}\n |\"\"\".stripMargin)\n Seq(file)\n },\n MultiShardMultiJVMTestParallelization.settings,\n javaCheckstyleSettings(\"dev/kernel-checkstyle.xml\"),\n // Unidoc settings\n unidocSourceFilePatterns := Seq(SourceFilePattern(\"io/delta/kernel/\")),\n ).configureUnidoc(docTitle = \"Delta Kernel\")\n\nlazy val kernelDefaults = (project in file(\"kernel/kernel-defaults\"))\n .enablePlugins(ScalafmtPlugin)\n .dependsOn(storage)\n .dependsOn(storage % \"test->test\") // Required for InMemoryCommitCoordinator for tests\n .dependsOn(goldenTables % \"test\")\n .settings(\n name := \"delta-kernel-defaults\",\n commonSettings,\n scalaStyleSettings,\n javaOnlyReleaseSettings,\n javafmtCheckSettings,\n scalafmtCheckSettings,\n\n // Use unique classDirectory name to avoid conflicts in connectClient test setup\n // This allows connectClient to create symlinks without FileAlreadyExistsException\n Compile / classDirectory := target.value / \"scala-2.13\" / \"kernel-defaults-classes\",\n\n Test / javaOptions ++= Seq(\"-ea\"),\n // This allows generating tables with unsupported test table features in delta-spark\n Test / envVars += (\"DELTA_TESTING\", \"1\"),\n\n // Put the shaded kernel-api JAR on the classpath (compile & test)\n Compile / unmanagedJars += (kernelApi / Compile / packageBin).value,\n Test / unmanagedJars += (kernelApi / Compile / packageBin).value,\n\n // Make sure the shaded JAR is produced before we compile/run tests\n Compile / compile := (Compile / compile).dependsOn(kernelApi / Compile / packageBin).value,\n Test / test := (Test / test).dependsOn(kernelApi / Compile / packageBin).value,\n Test / unmanagedJars += (kernelApi / Test / packageBin).value,\n\n libraryDependencies ++= Seq(\n \"org.assertj\" % \"assertj-core\" % \"3.26.3\" % Test,\n \"org.apache.hadoop\" % \"hadoop-client-runtime\" % hadoopVersion,\n \"com.fasterxml.jackson.core\" % \"jackson-databind\" % \"2.13.5\",\n \"com.fasterxml.jackson.datatype\" % \"jackson-datatype-jdk8\" % \"2.13.5\",\n \"org.apache.parquet\" % \"parquet-hadoop\" % \"1.12.3\",\n\n \"org.scalatest\" %% \"scalatest\" % scalaTestVersion % \"test\",\n \"junit\" % \"junit\" % \"4.13.2\" % \"test\",\n \"commons-io\" % \"commons-io\" % \"2.8.0\" % \"test\",\n \"com.novocode\" % \"junit-interface\" % \"0.11\" % \"test\",\n \"org.apache.logging.log4j\" % \"log4j-slf4j-impl\" % \"2.25.3\" % \"test\",\n \"org.apache.logging.log4j\" % \"log4j-core\" % \"2.25.3\" % \"test\",\n // JMH dependencies allow writing micro-benchmarks for testing performance of components.\n // JMH has framework to define benchmarks and takes care of many common functionalities\n // such as warm runs, cold runs, defining benchmark parameter variables etc.\n \"org.openjdk.jmh\" % \"jmh-core\" % \"1.37\" % \"test\",\n \"org.openjdk.jmh\" % \"jmh-generator-annprocess\" % \"1.37\" % \"test\",\n\n // The delta-spark and spark dependencies are mainly used for catalog-based table creation.\n // Instead of using the latest snapshot, those are fine to use the released 4.0.0.\n \"io.delta\" %% \"delta-spark\" % \"4.0.0\" % \"test\",\n \"org.apache.spark\" %% \"spark-hive\" % sparkVersionForKernelTest % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-sql\" % sparkVersionForKernelTest % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-core\" % sparkVersionForKernelTest % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-catalyst\" % sparkVersionForKernelTest % \"test\" classifier \"tests\",\n ),\n MultiShardMultiJVMTestParallelization.settings,\n javaCheckstyleSettings(\"dev/kernel-checkstyle.xml\"),\n // Unidoc settings\n unidocSourceFilePatterns += SourceFilePattern(\"io/delta/kernel/\"),\n ).configureUnidoc(docTitle = \"Delta Kernel Defaults\")\n\nlazy val kernelBenchmarks = (project in file(\"kernel/kernel-benchmarks\"))\n .enablePlugins(ScalafmtPlugin)\n .dependsOn(kernelDefaults % \"test->test\")\n .dependsOn(kernelApi % \"test->test\")\n .dependsOn(storage % \"test->test\")\n .dependsOn(kernelUnityCatalog % \"test->test\")\n .settings(\n name := \"delta-kernel-benchmarks\",\n commonSettings,\n skipReleaseSettings,\n exportJars := false,\n javafmtCheckSettings,\n scalafmtCheckSettings,\n \n libraryDependencies ++= Seq(\n \"org.openjdk.jmh\" % \"jmh-core\" % \"1.37\" % \"test\",\n \"org.openjdk.jmh\" % \"jmh-generator-annprocess\" % \"1.37\" % \"test\",\n ),\n )\n\nlazy val kernelUnityCatalog = (project in file(\"kernel/unitycatalog\"))\n .enablePlugins(ScalafmtPlugin)\n .dependsOn(kernelDefaults % \"test->test\")\n .dependsOn(storage)\n .settings (\n name := \"delta-kernel-unitycatalog\",\n commonSettings,\n javaOnlyReleaseSettings,\n javafmtCheckSettings,\n javaCheckstyleSettings(\"dev/kernel-checkstyle.xml\"),\n scalaStyleSettings,\n scalafmtCheckSettings,\n\n // Put the shaded kernel-api JAR on the classpath (compile & test)\n Compile / unmanagedJars += (kernelApi / Compile / packageBin).value,\n Test / unmanagedJars += (kernelApi / Compile / packageBin).value,\n\n // Make sure the shaded JAR is produced before we compile/run tests\n Compile / compile := (Compile / compile).dependsOn(kernelApi / Compile / packageBin).value,\n Test / test := (Test / test).dependsOn(kernelApi / Compile / packageBin).value,\n Test / unmanagedJars += (kernelApi / Test / packageBin).value,\n\n libraryDependencies ++= Seq(\n \"org.apache.hadoop\" % \"hadoop-common\" % hadoopVersion % \"provided\",\n \"org.scalatest\" %% \"scalatest\" % scalaTestVersion % \"test\",\n \"org.apache.logging.log4j\" % \"log4j-slf4j-impl\" % \"2.25.3\" % \"test\",\n \"org.apache.logging.log4j\" % \"log4j-core\" % \"2.25.3\" % \"test\",\n ),\n unidocSourceFilePatterns += SourceFilePattern(\"src/main/java/io/delta/unity/\"),\n ).configureUnidoc()\n\n// TODO javastyle tests\n// TODO unidoc\n// TODO(scott): figure out a better way to include tests in this project\nlazy val storage = (project in file(\"storage\"))\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings (\n name := \"delta-storage\",\n commonSettings,\n exportJars := true,\n javaOnlyReleaseSettings,\n libraryDependencies ++= Seq(\n // User can provide any 2.x or 3.x version. We don't use any new fancy APIs. Watch out for\n // versions with known vulnerabilities.\n \"org.apache.hadoop\" % \"hadoop-common\" % hadoopVersion % \"provided\",\n\n // Note that the org.apache.hadoop.fs.s3a.Listing::createFileStatusListingIterator 3.3.1 API\n // is not compatible with 3.3.2.\n \"org.apache.hadoop\" % \"hadoop-aws\" % hadoopVersion % \"provided\",\n \"io.unitycatalog\" % \"unitycatalog-client\" % unityCatalogVersion excludeAll(\n ExclusionRule(organization = \"org.openapitools\"),\n ExclusionRule(organization = \"com.fasterxml.jackson.core\"),\n ExclusionRule(organization = \"com.fasterxml.jackson.module\"),\n ExclusionRule(organization = \"com.fasterxml.jackson.datatype\"),\n ExclusionRule(organization = \"com.fasterxml.jackson.dataformat\")\n ),\n\n // Test Deps\n \"org.scalatest\" %% \"scalatest\" % scalaTestVersion % \"test\",\n // Jackson datatype module needed for UC SDK tests (excluded from main compile scope)\n \"com.fasterxml.jackson.datatype\" % \"jackson-datatype-jsr310\" % \"2.15.4\" % \"test\",\n ),\n\n // Unidoc settings\n unidocSourceFilePatterns += SourceFilePattern(\"/LogStore.java\", \"/CloseableIterator.java\"),\n TestParallelization.settings\n ).configureUnidoc()\n\nlazy val storageS3DynamoDB = (project in file(\"storage-s3-dynamodb\"))\n .dependsOn(storage % \"compile->compile;test->test;provided->provided\")\n .dependsOn(spark % \"test->test\")\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings (\n name := \"delta-storage-s3-dynamodb\",\n commonSettings,\n javaOnlyReleaseSettings,\n\n // uncomment only when testing FailingS3DynamoDBLogStore. this will include test sources in\n // a separate test jar.\n // Test / publishArtifact := true,\n\n libraryDependencies ++= Seq(\n \"com.amazonaws\" % \"aws-java-sdk\" % \"1.12.262\" % \"provided\",\n\n // Test Deps\n \"org.apache.hadoop\" % \"hadoop-aws\" % hadoopVersion % \"test\", // RemoteFileChangedException\n ),\n TestParallelization.settings\n ).configureUnidoc()\n\nval icebergSparkRuntimeArtifactName = {\n val currentSparkVersion = CrossSparkVersions.getSparkVersion()\n val (expMaj, expMin, _) = getMajorMinorPatch(currentSparkVersion)\n s\"iceberg-spark-runtime-$expMaj.$expMin\"\n}\n\nlazy val testDeltaIcebergJar = (project in file(\"testDeltaIcebergJar\"))\n // delta-iceberg depends on delta-spark! So, we need to include it during our test.\n .dependsOn(spark % \"test\")\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings(\n name := \"test-delta-iceberg-jar\",\n commonSettings,\n skipReleaseSettings,\n exportJars := true,\n Compile / unmanagedJars += (iceberg / assembly).value,\n libraryDependencies ++= Seq(\n \"org.apache.hadoop\" % \"hadoop-client\" % hadoopVersion,\n \"org.scalatest\" %% \"scalatest\" % scalaTestVersion % \"test\",\n \"org.apache.spark\" %% \"spark-core\" % defaultSparkVersion % \"test\"\n )\n )\n\nval deltaIcebergSparkIncludePrefixes = Seq(\n // We want everything from this package\n \"org/apache/spark/sql/delta/icebergShaded\",\n // Server-side planning support\n \"org/apache/spark/sql/delta/serverSidePlanning\",\n\n // We only want the files in this project from this package. e.g. we want to exclude\n // org/apache/spark/sql/delta/commands/convert/ConvertTargetFile.class (from delta-spark project).\n \"org/apache/spark/sql/delta/commands/convert/IcebergFileManifest\",\n \"org/apache/spark/sql/delta/commands/convert/IcebergSchemaUtils\",\n \"org/apache/spark/sql/delta/commands/convert/IcebergTable\"\n)\n\n// Build using: build/sbt clean icebergShaded/compile iceberg/compile\n// It will fail the first time, just re-run it.\n// scalastyle:off println\nlazy val iceberg = (project in file(\"iceberg\"))\n .dependsOn(spark % \"compile->compile;test->test;provided->provided\")\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings (\n name := \"delta-iceberg\",\n commonSettings,\n scalaStyleSettings,\n releaseSettings,\n // Set sparkVersion directly (not sparkDependentModuleName) so that\n // runOnlyForReleasableSparkModules discovers this module, but without adding a Spark\n // suffix to the artifact name. delta-iceberg is only published as delta-iceberg_2.13.\n sparkVersion := CrossSparkVersions.getSparkVersion(),\n libraryDependencies ++= {\n if (supportIceberg) {\n Seq(\n // Fix Iceberg's legacy java.lang.NoClassDefFoundError: scala/jdk/CollectionConverters$ error\n // due to legacy scala.\n \"org.scala-lang.modules\" %% \"scala-collection-compat\" % \"2.1.1\",\n \"com.github.ben-manes.caffeine\" % \"caffeine\" % \"2.9.3\",\n \"com.jolbox\" % \"bonecp\" % \"0.8.0.RELEASE\" % \"test\",\n \"org.eclipse.jetty\" % \"jetty-server\" % \"11.0.26\" % \"test\",\n \"org.eclipse.jetty\" % \"jetty-servlet\" % \"11.0.26\" % \"test\",\n \"org.xerial\" % \"sqlite-jdbc\" % \"3.45.0.0\" % \"test\",\n \"org.apache.httpcomponents.core5\" % \"httpcore5\" % \"5.2.4\" % \"test\",\n \"org.apache.httpcomponents.client5\" % \"httpclient5\" % \"5.3.1\" % \"test\",\n \"org.apache.iceberg\" %% icebergSparkRuntimeArtifactName % \"1.10.0\" % \"provided\",\n // For FixedGcsAccessTokenProvider (GCS server-side planning credentials)\n \"com.google.cloud.bigdataoss\" % \"util-hadoop\" % \"hadoop3-2.2.26\" % \"provided\"\n )\n } else {\n Seq.empty\n }\n },\n // Skip compilation and publishing when supportIceberg is false\n Compile / skip := !supportIceberg,\n Test / skip := !supportIceberg,\n publish / skip := !supportIceberg,\n publishLocal / skip := !supportIceberg,\n publishM2 / skip := !supportIceberg,\n Compile / unmanagedJars += (icebergShaded / assembly).value,\n // Generate the assembly JAR as the package JAR\n Compile / packageBin := assembly.value,\n Compile / scalacOptions += \"-nowarn\",\n Test / unmanagedJars += (icebergTestsShaded / assembly).value,\n Test / scalacOptions += \"-nowarn\",\n assembly / assemblyJarName := {\n s\"${moduleName.value}_${scalaBinaryVersion.value}-${version.value}.jar\"\n },\n assembly / logLevel := Level.Info,\n assembly / test := {},\n assembly / assemblyExcludedJars := {\n // Note: the input here is only `libraryDependencies` jars, not `.dependsOn(_)` jars.\n val allowedJars = Seq(\n s\"iceberg-shaded_${scalaBinaryVersion.value}-${version.value}.jar\",\n s\"scala-library-${scala213}.jar\",\n s\"scala-collection-compat_${scalaBinaryVersion.value}-2.1.1.jar\",\n \"caffeine-2.9.3.jar\",\n // Note: We are excluding\n // - antlr4-runtime-4.9.3.jar\n // - checker-qual-3.19.0.jar\n // - error_prone_annotations-2.10.0.jar\n )\n val cp = (assembly / fullClasspath).value\n\n // Return `true` when we want the jar `f` to be excluded from the assembly jar\n cp.filter { f =>\n val doExclude = !allowedJars.contains(f.data.getName)\n println(s\"Excluding jar: ${f.data.getName} ? $doExclude\")\n doExclude\n }\n },\n assembly / assemblyMergeStrategy := {\n // Project iceberg `dependsOn` spark and accidentally brings in it, along with its\n // compile-time dependencies (like delta-storage). We want these excluded from the\n // delta-iceberg jar.\n case PathList(\"io\", \"delta\", xs @ _*) =>\n // - delta-storage will bring in classes: io/delta/storage\n // - delta-spark will bring in classes: io/delta/exceptions/, io/delta/implicits,\n // io/delta/package, io/delta/sql, io/delta/tables,\n MergeStrategy.discard\n case PathList(\"com\", \"databricks\", xs @ _*) =>\n // delta-spark will bring in com/databricks/spark/util\n MergeStrategy.discard\n case PathList(\"org\", \"apache\", \"spark\", xs @ _*)\n if !deltaIcebergSparkIncludePrefixes.exists { prefix =>\n s\"org/apache/spark/${xs.mkString(\"/\")}\".startsWith(prefix) } =>\n MergeStrategy.discard\n case PathList(\"scoverage\", xs @ _*) =>\n MergeStrategy.discard\n case x =>\n (assembly / assemblyMergeStrategy).value(x)\n },\n assemblyPackageScala / assembleArtifact := false\n )\n// scalastyle:on println\n\nval icebergShadedVersion = \"1.10.1\"\nlazy val icebergShaded = (project in file(\"icebergShaded\"))\n .dependsOn(spark % \"provided\")\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings (\n name := \"iceberg-shaded\",\n commonSettings,\n skipReleaseSettings,\n // must exclude all dependencies from Iceberg that delta-spark includes\n libraryDependencies ++= Seq(\n // Fix Iceberg's legacy java.lang.NoClassDefFoundError: scala/jdk/CollectionConverters$ error\n // due to legacy scala.\n \"org.scala-lang.modules\" %% \"scala-collection-compat\" % \"2.1.1\" % \"provided\",\n \"org.apache.iceberg\" % \"iceberg-core\" % icebergShadedVersion excludeAll (\n icebergExclusionRules: _*\n ),\n \"org.apache.iceberg\" % \"iceberg-hive-metastore\" % icebergShadedVersion excludeAll (\n icebergExclusionRules: _*\n ),\n // the hadoop client and hive metastore versions come from this file in the\n // iceberg repo of icebergShadedVersion: iceberg/gradle/libs.versions.toml\n \"org.apache.hadoop\" % \"hadoop-client\" % \"2.7.3\" % \"provided\" excludeAll (\n hadoopClientExclusionRules: _*\n ),\n \"org.apache.hive\" % \"hive-metastore\" % \"2.3.8\" % \"provided\" excludeAll (\n hiveMetastoreExclusionRules: _*\n )\n ),\n // Generated shaded Iceberg JARs\n Compile / packageBin := assembly.value,\n assembly / assemblyJarName := s\"${name.value}_${scalaBinaryVersion.value}-${version.value}.jar\",\n assembly / logLevel := Level.Info,\n assembly / test := {},\n assembly / assemblyShadeRules := Seq(\n ShadeRule.rename(\"org.apache.iceberg.**\" -> \"shadedForDelta.@0\").inAll\n ),\n assembly / assemblyExcludedJars := {\n val cp = (assembly / fullClasspath).value\n cp.filter { jar =>\n val doExclude = jar.data.getName.contains(\"jackson-annotations\") ||\n jar.data.getName.contains(\"RoaringBitmap\")\n doExclude\n }\n },\n // all following clases have Delta customized implementation under icebergShaded/src and thus\n // require them to be 'first' to replace the class from iceberg jar\n assembly / assemblyMergeStrategy := updateMergeStrategy((assembly / assemblyMergeStrategy).value),\n assemblyPackageScala / assembleArtifact := false,\n )\n\nlazy val icebergTestsShaded = (project in file(\"icebergTestsShaded\"))\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings (\n name := \"iceberg-tests-shaded\",\n commonSettings,\n skipReleaseSettings,\n // must exclude all dependencies from Iceberg that delta-spark includes\n libraryDependencies ++= Seq(\n \"org.apache.iceberg\" % \"iceberg-core\" % icebergShadedVersion classifier \"tests\" excludeAll (\n icebergExclusionRules: _*\n ),\n ),\n // Generated shaded Iceberg JARs\n Compile / packageBin := assembly.value,\n assembly / assemblyJarName := s\"${name.value}_${scalaBinaryVersion.value}-${version.value}.jar\",\n assembly / logLevel := Level.Info,\n assembly / test := {},\n assembly / assemblyShadeRules := Seq(\n ShadeRule.rename(\"org.apache.iceberg.**\" -> \"shadedForDelta.@0\").inAll\n ),\n assembly / assemblyExcludedJars := {\n val cp = (fullClasspath in assembly).value\n cp.filter { jar =>\n val doExclude = jar.data.getName.contains(\"jackson-annotations\") ||\n jar.data.getName.contains(\"RoaringBitmap\")\n doExclude\n }\n },\n assembly / assemblyMergeStrategy := updateMergeStrategy((assembly / assemblyMergeStrategy).value),\n assemblyPackageScala / assembleArtifact := false,\n )\n\n\nlazy val hudi = (project in file(\"hudi\"))\n .dependsOn(spark % \"compile->compile;test->test;provided->provided\")\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings (\n name := \"delta-hudi\",\n commonSettings,\n scalaStyleSettings,\n releaseSettings,\n // Set sparkVersion directly (not sparkDependentModuleName) so that\n // runOnlyForReleasableSparkModules discovers this module, but without adding a Spark\n // suffix to the artifact name. delta-hudi is only published as delta-hudi_2.13.\n sparkVersion := CrossSparkVersions.getSparkVersion(),\n libraryDependencies ++= {\n if (supportHudi) {\n Seq(\n \"org.apache.hudi\" % \"hudi-java-client\" % \"0.15.0\" % \"compile\" excludeAll(\n ExclusionRule(organization = \"org.apache.hadoop\"),\n ExclusionRule(organization = \"org.apache.zookeeper\"),\n ),\n \"org.apache.spark\" %% \"spark-avro\" % sparkVersion.value % \"test\" excludeAll ExclusionRule(organization = \"org.apache.hadoop\"),\n \"org.apache.parquet\" % \"parquet-avro\" % \"1.12.3\" % \"compile\"\n )\n } else {\n Seq.empty\n }\n },\n // Skip compilation and publishing when supportHudi is false\n Compile / skip := !supportHudi,\n Test / skip := !supportHudi,\n publish / skip := !supportHudi,\n publishLocal / skip := !supportHudi,\n publishM2 / skip := !supportHudi,\n assembly / assemblyJarName := s\"${name.value}-assembly_${scalaBinaryVersion.value}-${version.value}.jar\",\n assembly / logLevel := Level.Info,\n assembly / test := {},\n assembly / assemblyMergeStrategy := {\n // Project hudi `dependsOn` spark and accidentally brings in it, along with its\n // compile-time dependencies (like delta-storage). We want these excluded from the\n // delta-hudi jar.\n case PathList(\"io\", \"delta\", xs @ _*) =>\n // - delta-storage will bring in classes: io/delta/storage\n // - delta-spark will bring in classes: io/delta/exceptions/, io/delta/implicits,\n // io/delta/package, io/delta/sql, io/delta/tables,\n MergeStrategy.discard\n case PathList(\"com\", \"databricks\", xs @ _*) =>\n // delta-spark will bring in com/databricks/spark/util\n MergeStrategy.discard\n case PathList(\"org\", \"apache\", \"spark\", \"sql\", \"delta\", \"hudi\", xs @ _*) =>\n MergeStrategy.first\n case PathList(\"org\", \"apache\", \"spark\", xs @ _*) =>\n MergeStrategy.discard\n // Discard `module-info.class` to fix the `different file contents found` error.\n // TODO Upgrade SBT to 1.5 which will do this automatically\n case \"module-info.class\" => MergeStrategy.discard\n // Discard unused `parquet.thrift` so that we don't conflict the file used by the user\n case \"parquet.thrift\" => MergeStrategy.discard\n // Hudi metadata writer requires this service file to be present on the classpath\n case \"META-INF/services/org.apache.hadoop.hbase.regionserver.MetricsRegionServerSourceFactory\" => MergeStrategy.first\n // Discard the jackson service configs that we don't need. These files are not shaded so\n // adding them may conflict with other jackson version used by the user.\n case PathList(\"META-INF\", \"services\", xs @ _*) => MergeStrategy.discard\n case x =>\n MergeStrategy.first\n },\n // Make the 'compile' invoke the 'assembly' task to generate the uber jar.\n Compile / packageBin := assembly.value,\n TestParallelization.settings\n )\n\nlazy val flink = (project in file(\"flink\"))\n// .dependsOn(kernelApi)\n .dependsOn(kernelDefaults)\n .dependsOn(kernelUnityCatalog)\n .settings(\n name := \"delta-flink\",\n commonSettings,\n skipReleaseSettings,\n javafmtCheckSettings(),\n publishArtifact := scalaBinaryVersion.value == \"2.12\", // only publish once\n autoScalaLibrary := false, // exclude scala-library from dependencies\n assembly / assemblyJarName := s\"delta-flink-$flinkVersion-${version.value}.jar\",\n assembly / assemblyMergeStrategy := {\n // Discard module-info.class files from Java 9+ modules and multi-release JARs\n case \"module-info.class\" => MergeStrategy.discard\n case \"parquet.thrift\" => MergeStrategy.discard\n case PathList(\"META-INF\", \"versions\", _, \"module-info.class\") => MergeStrategy.discard\n case PathList(\"mozilla\", \"public-suffix-list.txt\") => MergeStrategy.discard\n case x => MergeStrategy.first\n },\n assembly / assemblyExcludedJars := {\n val cp = (assembly / fullClasspath).value\n cp.filter { entry =>\n entry.data.getName.startsWith(\"bundle-\") &&\n entry.data.getName.endsWith(\".jar\")\n }\n },\n Compile / unmanagedJars += (kernelApi / Compile / packageBin).value,\n Test / unmanagedJars += (kernelApi / Compile / packageBin).value,\n\n // Make sure the shaded JAR is produced before we compile/run tests\n Compile / compile := (Compile / compile).dependsOn(kernelApi / Compile / packageBin).value,\n Test / test := (Test / test).dependsOn(kernelApi / Compile / packageBin).value,\n Test / unmanagedJars += (kernelApi / Test / packageBin).value,\n\n Test / publishArtifact := false,\n Test / javaOptions ++= Seq(\n \"--add-opens=java.base/java.util=ALL-UNNAMED\" // for Flink with Java 17.\n ),\n crossPaths := false,\n libraryDependencies ++= Seq(\n \"org.apache.flink\" % \"flink-core\" % flinkVersion % \"provided\",\n \"org.apache.flink\" % \"flink-table-common\" % flinkVersion % \"provided\",\n \"org.apache.flink\" % \"flink-streaming-java\" % flinkVersion % \"provided\",\n \"org.apache.flink\" % \"flink-table-api-java-bridge\" % flinkVersion % \"provided\",\n \"io.unitycatalog\" % \"unitycatalog-client\" % \"0.3.1\",\n \"org.apache.httpcomponents\" % \"httpclient\" % \"4.5.14\" % Runtime,\n \"dev.failsafe\" % \"failsafe\" % \"3.2.0\",\n \"com.github.ben-manes.caffeine\" % \"caffeine\" % \"3.1.8\",\n \"org.apache.hadoop\" % \"hadoop-aws\" % hadoopVersion,\n\n // Test dependencies\n \"org.junit.jupiter\" % \"junit-jupiter-api\" % \"5.11.4\" % \"test\",\n \"org.junit.jupiter\" % \"junit-jupiter-engine\" % \"5.11.4\" % \"test\",\n \"org.junit.jupiter\" % \"junit-jupiter-params\" % \"5.11.4\" % \"test\",\n \"com.github.sbt.junit\" % \"jupiter-interface\" % \"0.17.0\" % \"test\",\n \"org.apache.flink\" % \"flink-test-utils\" % flinkVersion % \"test\",\n \"org.apache.flink\" % \"flink-clients\" % flinkVersion % \"test\",\n \"org.apache.flink\" % \"flink-table-api-java-bridge\" % flinkVersion % Test,\n \"org.apache.flink\" % \"flink-table-planner-loader\" % flinkVersion % Test,\n \"org.apache.flink\" % \"flink-table-runtime\" % flinkVersion % Test,\n \"org.apache.flink\" % \"flink-test-utils-junit\" % flinkVersion % Test,\n \"org.slf4j\" % \"slf4j-log4j12\" % \"2.0.17\" % \"test\",\n \"com.github.tomakehurst\" % \"wiremock-jre8\" % \"2.35.0\" % Test\n ),\n // Use jupiter\n excludeDependencies ++= Seq(\n ExclusionRule(\"junit\", \"junit\"),\n ExclusionRule(\"org.junit.vintage\", \"junit-vintage-engine\")\n )\n )\n\n\nlazy val goldenTables = (project in file(\"connectors/golden-tables\"))\n .disablePlugins(JavaFormatterPlugin, ScalafmtPlugin)\n .settings(\n name := \"golden-tables\",\n commonSettings,\n skipReleaseSettings,\n libraryDependencies ++= Seq(\n // Test Dependencies\n \"org.scalatest\" %% \"scalatest\" % scalaTestVersion % \"test\",\n \"commons-io\" % \"commons-io\" % \"2.8.0\" % \"test\",\n\n \"io.delta\" %% \"delta-spark\" % \"3.3.2\" % \"test\",\n \"org.apache.spark\" %% \"spark-sql\" % defaultSparkVersion % \"test\",\n \"org.apache.spark\" %% \"spark-catalyst\" % defaultSparkVersion % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-core\" % defaultSparkVersion % \"test\" classifier \"tests\",\n \"org.apache.spark\" %% \"spark-sql\" % defaultSparkVersion % \"test\" classifier \"tests\"\n )\n )\n\n/**\n * Get list of python files and return the mapping between source files and target paths\n * in the generated package JAR.\n */\ndef listPythonFiles(pythonBase: File): Seq[(File, String)] = {\n val pythonExcludeDirs = pythonBase / \"lib\" :: pythonBase / \"doc\" :: pythonBase / \"bin\" :: Nil\n import scala.collection.JavaConverters._\n val pythonFiles = Files.walk(pythonBase.toPath).iterator().asScala\n .map { path => path.toFile() }\n .filter { file => file.getName.endsWith(\".py\") && ! file.getName.contains(\"test\") }\n .filter { file => ! pythonExcludeDirs.exists { base => IO.relativize(base, file).nonEmpty} }\n .toSeq\n\n pythonFiles pair Path.relativeTo(pythonBase)\n}\n\nThisBuild / parallelExecution := false\n\nval createTargetClassesDir = taskKey[Unit](\"create target classes dir\")\nval generatePythonVersion = taskKey[File](\"Generate Python version.py file\")\n\n/*\n ******************\n * Project groups *\n ******************\n */\n\n// Don't use these groups for any other projects\nlazy val sparkGroup = {\n val baseProjects = Seq(spark, sparkV1, sparkV1Filtered, sparkV2, contribs, sparkUnityCatalog, storage, storageS3DynamoDB, sharing, connectCommon, connectClient, connectServer)\n val allProjects = if (supportHudi) {\n baseProjects :+ hudi\n } else {\n baseProjects\n }\n\n Project(\"sparkGroup\", file(\"sparkGroup\"))\n .aggregate(allProjects.map(_.project): _*)\n .settings(\n // crossScalaVersions must be set to Nil on the aggregating project\n crossScalaVersions := Nil,\n publishArtifact := false,\n publish / skip := true,\n )\n}\n\nlazy val icebergGroup = {\n val allProjects = if (supportIceberg) {\n Seq(iceberg, testDeltaIcebergJar)\n } else {\n Seq.empty\n }\n\n Project(\"icebergGroup\", file(\"icebergGroup\"))\n .aggregate(allProjects.map(_.project): _*)\n .settings(\n // crossScalaVersions must be set to Nil on the aggregating project\n crossScalaVersions := Nil,\n publishArtifact := false,\n publish / skip := true,\n )\n}\n\nlazy val kernelGroup = project\n .aggregate(kernelApi, kernelDefaults, kernelBenchmarks)\n .settings(\n // crossScalaVersions must be set to Nil on the aggregating project\n crossScalaVersions := Nil,\n publishArtifact := false,\n publish / skip := true,\n unidocSourceFilePatterns := {\n (kernelApi / unidocSourceFilePatterns).value.scopeToProject(kernelApi) ++\n (kernelDefaults / unidocSourceFilePatterns).value.scopeToProject(kernelDefaults)\n }\n ).configureUnidoc(docTitle = \"Delta Kernel\")\n\nlazy val flinkGroup = project\n .aggregate(flink)\n .settings(\n // crossScalaVersions must be set to Nil on the aggregating project\n crossScalaVersions := Nil,\n publishArtifact := false,\n publish / skip := true,\n )\n\n/*\n ********************\n * Release settings *\n ********************\n */\nimport ReleaseTransformations._\n\nlazy val skipReleaseSettings = Seq(\n publishArtifact := false,\n publish / skip := true\n)\n\n\n// Release settings for artifact that contains only Java source code\nlazy val javaOnlyReleaseSettings = releaseSettings ++ Seq(\n // drop off Scala suffix from artifact names\n crossPaths := false,\n\n // we publish jars for each scalaVersion in crossScalaVersions. however, we only need to publish\n // one java jar. thus, only do so when the current scala version == default scala version\n publishArtifact := {\n val (expMaj, expMin, _) = getMajorMinorPatch(default_scala_version.value)\n s\"$expMaj.$expMin\" == scalaBinaryVersion.value\n },\n\n // exclude scala-library from dependencies in generated pom.xml\n autoScalaLibrary := false,\n)\n\nlazy val releaseSettings = Seq(\n publishMavenStyle := true,\n publishArtifact := true,\n Test / publishArtifact := false,\n releasePublishArtifactsAction := PgpKeys.publishSigned.value,\n releaseCrossBuild := true,\n pgpPassphrase := sys.env.get(\"PGP_PASSPHRASE\").map(_.toArray),\n\n // TODO: This isn't working yet ...\n sonatypeProfileName := \"io.delta\", // sonatype account domain name prefix / group ID\n credentials += Credentials(\n \"OSSRH Staging API Service\",\n \"ossrh-staging-api.central.sonatype.com\",\n sys.env.getOrElse(\"SONATYPE_USERNAME\", \"\"),\n sys.env.getOrElse(\"SONATYPE_PASSWORD\", \"\")\n ),\n credentials += Credentials(\n \"Sonatype Nexus Repository Manager\",\n \"central.sonatype.com\",\n sys.env.getOrElse(\"SONATYPE_USERNAME\", \"\"),\n sys.env.getOrElse(\"SONATYPE_PASSWORD\", \"\")\n ),\n publishTo := {\n val ossrhBase = \"https://ossrh-staging-api.central.sonatype.com/\"\n val centralSnapshots = \"https://central.sonatype.com/repository/maven-snapshots/\"\n if (isSnapshot.value) {\n Some(\"snapshots\" at centralSnapshots)\n } else {\n Some(\"releases\" at ossrhBase + \"service/local/staging/deploy/maven2\")\n }\n },\n licenses += (\"Apache-2.0\", url(\"http://www.apache.org/licenses/LICENSE-2.0\")),\n pomExtra :=\n https://delta.io/\n \n git@github.com:delta-io/delta.git\n scm:git:git@github.com:delta-io/delta.git\n \n \n \n marmbrus\n Michael Armbrust\n https://github.com/marmbrus\n \n \n brkyvz\n Burak Yavuz\n https://github.com/brkyvz\n \n \n jose-torres\n Jose Torres\n https://github.com/jose-torres\n \n \n liwensun\n Liwen Sun\n https://github.com/liwensun\n \n \n mukulmurthy\n Mukul Murthy\n https://github.com/mukulmurthy\n \n \n tdas\n Tathagata Das\n https://github.com/tdas\n \n \n zsxwing\n Shixiong Zhu\n https://github.com/zsxwing\n \n \n scottsand-db\n Scott Sandre\n https://github.com/scottsand-db\n \n \n windpiger\n Jun Song\n https://github.com/windpiger\n \n \n)\n\n// Looks like some of release settings should be set for the root project as well.\npublishArtifact := false // Don't release the root project\npublish / skip := true\npublishTo := Some(\"snapshots\" at \"https://central.sonatype.com/repository/maven-snapshots/\")\nreleaseCrossBuild := false // Don't use sbt-release's cross facility\nreleaseProcess := Seq[ReleaseStep](\n checkSnapshotDependencies,\n inquireVersions,\n runTest,\n setReleaseVersion,\n commitReleaseVersion,\n tagRelease\n) ++ CrossSparkVersions.crossSparkReleaseSteps(\"publishSigned\") ++ Seq[ReleaseStep](\n\n // Do NOT use `sonatypeBundleRelease` - it will actually release to Maven! We want to do that\n // manually.\n //\n // Do NOT use `sonatypePromote` - it will promote the closed staging repository (i.e. sync to\n // Maven central)\n //\n // See https://github.com/xerial/sbt-sonatype#publishing-your-artifact.\n //\n // - sonatypePrepare: Drop the existing staging repositories (if exist) and create a new staging\n // repository using sonatypeSessionName as a unique key\n // - sonatypeBundleUpload: Upload your local staging folder contents to a remote Sonatype\n // repository\n // - sonatypeClose: closes your staging repository at Sonatype. This step verifies Maven central\n // sync requirement, GPG-signature, javadoc and source code presence, pom.xml\n // settings, etc\n // TODO: this isn't working yet\n // releaseStepCommand(\"sonatypePrepare; sonatypeBundleUpload; sonatypeClose\"),\n setNextVersion,\n commitNextVersion\n)\n" }, { "path": "connectors/.gitignore", "content": "*#*#\n*.#*\n*.iml\n*.ipr\n*.iws\n*.pyc\n*.pyo\n*.swp\n*~\n.DS_Store\n.bsp\n.cache\n.classpath\n.ensime\n.ensime_cache/\n.ensime_lucene\n.generated-mima*\n.idea/\n.idea_modules/\n.project\n.pydevproject\n.scala_dependencies\n.settings\n*.pbix\n/lib/\nR-unit-tests.log\nR/unit-tests.out\nR/cran-check.out\nR/pkg/vignettes/sparkr-vignettes.html\nR/pkg/tests/fulltests/Rplots.pdf\nbuild/*.jar\nbuild/apache-maven*\nbuild/scala*\nbuild/zinc*\ncache\nconf/*.cmd\nconf/*.conf\nconf/*.properties\nconf/*.sh\nconf/*.xml\nconf/java-opts\nconf/slaves\ndependency-reduced-pom.xml\nderby.log\ndev/create-release/*final\ndev/create-release/*txt\ndev/pr-deps/\ndist/\ndocs/_site\ndocs/api\nsql/docs\nsql/site\nlib_managed/\nlint-r-report.log\nlog/\nlogs/\nout/\nproject/boot/\nproject/build/target/\nproject/plugins/lib_managed/\nproject/plugins/project/build.properties\nproject/plugins/src_managed/\nproject/plugins/target/\npython/lib/pyspark.zip\npython/deps\ndocs/python/_static/\ndocs/python/_templates/\ndocs/python/_build/\npython/test_coverage/coverage_data\npython/test_coverage/htmlcov\npython/pyspark/python\nreports/\nscalastyle-on-compile.generated.xml\nscalastyle-output.xml\nscalastyle.txt\nspark-*-bin-*.tgz\nspark-tests.log\nsrc_managed/\nstreaming-tests.log\ntarget/\nunit-tests.log\nwork/\ndocs/.jekyll-metadata\n\n# For Hive\nTempStatsStore/\nmetastore/\nmetastore_db/\nsql/hive-thriftserver/test_warehouses\nwarehouse/\nspark-warehouse/\n\n# For R session data\n.RData\n.RHistory\n.Rhistory\n*.Rproj\n*.Rproj.*\n\n.Rproj.user\n\n**/src/main/resources/js\n\n# For SBT\n.jvmopts\n\n# For VS\n/.vs\n/obj\n/bin\n" }, { "path": "connectors/README.md", "content": "Connectors projects are no longer maintained in the `master` branch and new releases due to migration to the Delta Kernel project. Projects will continue to be supported in maintanence mode from the `spark-3.5-support` branch.\n" }, { "path": "connectors/golden-tables/src/main/resources/golden/124-decimal-decode-bug/_delta_log/00000000000000000000.json", "content": "{\"commitInfo\":{\"timestamp\":1636689272898,\"operation\":\"WRITE\",\"operationParameters\":{\"mode\":\"ErrorIfExists\",\"partitionBy\":\"[]\"},\"isBlindAppend\":true,\"operationMetrics\":{\"numFiles\":\"2\",\"numOutputBytes\":\"844\",\"numOutputRows\":\"1\"}}}\n{\"protocol\":{\"minReaderVersion\":1,\"minWriterVersion\":2}}\n{\"metaData\":{\"id\":\"testId\",\"format\":{\"provider\":\"parquet\",\"options\":{}},\"schemaString\":\"{\\\"type\\\":\\\"struct\\\",\\\"fields\\\":[{\\\"name\\\":\\\"large_decimal\\\",\\\"type\\\":\\\"decimal(10,0)\\\",\\\"nullable\\\":true,\\\"metadata\\\":{}}]}\",\"partitionColumns\":[],\"configuration\":{},\"createdTime\":1636689270919}}\n{\"add\":{\"path\":\"part-00000-2abbde89-2d0f-465e-a2f0-3e84f1b84654-c000.snappy.parquet\",\"partitionValues\":{},\"size\":333,\"modificationTime\":1636689272000,\"dataChange\":true}}\n{\"add\":{\"path\":\"part-00001-5419c9a2-bb44-454f-a109-6e6c6f000a24-c000.snappy.parquet\",\"partitionValues\":{},\"size\":511,\"modificationTime\":1636689272000,\"dataChange\":true}}\n" }, { "path": "connectors/golden-tables/src/main/resources/golden/125-iterator-bug/_delta_log/00000000000000000000.json", "content": "{\"commitInfo\":{\"timestamp\":1633728454095,\"operation\":\"WRITE\",\"operationParameters\":{\"mode\":\"Append\",\"partitionBy\":\"[]\"},\"isBlindAppend\":true,\"operationMetrics\":{\"numFiles\":\"1\",\"numOutputBytes\":\"303\",\"numOutputRows\":\"0\"}}}\n{\"protocol\":{\"minReaderVersion\":1,\"minWriterVersion\":2}}\n{\"metaData\":{\"id\":\"testId\",\"format\":{\"provider\":\"parquet\",\"options\":{}},\"schemaString\":\"{\\\"type\\\":\\\"struct\\\",\\\"fields\\\":[{\\\"name\\\":\\\"col1\\\",\\\"type\\\":\\\"integer\\\",\\\"nullable\\\":true,\\\"metadata\\\":{}}]}\",\"partitionColumns\":[],\"configuration\":{},\"createdTime\":1633728453099}}\n{\"add\":{\"path\":\"part-00000-2a248db5-8f96-423c-a0f7-c503fe640c6a-c000.snappy.parquet\",\"partitionValues\":{},\"size\":303,\"modificationTime\":1633728454000,\"dataChange\":true}}\n" }, { "path": "connectors/golden-tables/src/main/resources/golden/125-iterator-bug/_delta_log/00000000000000000001.json", "content": "{\"commitInfo\":{\"timestamp\":1633728458439,\"operation\":\"WRITE\",\"operationParameters\":{\"mode\":\"Append\",\"partitionBy\":\"[]\"},\"readVersion\":0,\"isBlindAppend\":true,\"operationMetrics\":{\"numFiles\":\"1\",\"numOutputBytes\":\"433\",\"numOutputRows\":\"1\"}}}\n{\"add\":{\"path\":\"part-00000-15088d9b-5348-490b-933d-5bf9b7d0b223-c000.snappy.parquet\",\"partitionValues\":{},\"size\":433,\"modificationTime\":1633728458000,\"dataChange\":true}}\n" }, { "path": "connectors/golden-tables/src/main/resources/golden/125-iterator-bug/_delta_log/00000000000000000002.json", "content": "{\"commitInfo\":{\"timestamp\":1633728459288,\"operation\":\"WRITE\",\"operationParameters\":{\"mode\":\"Append\",\"partitionBy\":\"[]\"},\"readVersion\":1,\"isBlindAppend\":true,\"operationMetrics\":{\"numFiles\":\"1\",\"numOutputBytes\":\"433\",\"numOutputRows\":\"1\"}}}\n{\"add\":{\"path\":\"part-00000-c855206c-f42a-4b53-a526-08a9a957ad58-c000.snappy.parquet\",\"partitionValues\":{},\"size\":433,\"modificationTime\":1633728459000,\"dataChange\":true}}\n" }, { "path": "connectors/golden-tables/src/main/resources/golden/125-iterator-bug/_delta_log/00000000000000000003.json", "content": "{\"commitInfo\":{\"timestamp\":1633728460020,\"operation\":\"WRITE\",\"operationParameters\":{\"mode\":\"Append\",\"partitionBy\":\"[]\"},\"readVersion\":2,\"isBlindAppend\":true,\"operationMetrics\":{\"numFiles\":\"1\",\"numOutputBytes\":\"303\",\"numOutputRows\":\"0\"}}}\n{\"add\":{\"path\":\"part-00000-3f0f0396-41aa-4fa7-954a-c5b22f5b157a-c000.snappy.parquet\",\"partitionValues\":{},\"size\":303,\"modificationTime\":1633728460000,\"dataChange\":true}}\n" }, { "path": "connectors/golden-tables/src/main/resources/golden/125-iterator-bug/_delta_log/00000000000000000004.json", "content": "{\"commitInfo\":{\"timestamp\":1633728460726,\"operation\":\"WRITE\",\"operationParameters\":{\"mode\":\"Append\",\"partitionBy\":\"[]\"},\"readVersion\":3,\"isBlindAppend\":true,\"operationMetrics\":{\"numFiles\":\"1\",\"numOutputBytes\":\"433\",\"numOutputRows\":\"1\"}}}\n{\"add\":{\"path\":\"part-00000-c4738537-d851-4caa-9596-d543afa47196-c000.snappy.parquet\",\"partitionValues\":{},\"size\":433,\"modificationTime\":1633728460000,\"dataChange\":true}}\n" }, { "path": "connectors/golden-tables/src/main/resources/golden/125-iterator-bug/_delta_log/00000000000000000005.json", "content": "{\"commitInfo\":{\"timestamp\":1633728461405,\"operation\":\"WRITE\",\"operationParameters\":{\"mode\":\"Append\",\"partitionBy\":\"[]\"},\"readVersion\":4,\"isBlindAppend\":true,\"operationMetrics\":{\"numFiles\":\"1\",\"numOutputBytes\":\"303\",\"numOutputRows\":\"0\"}}}\n{\"add\":{\"path\":\"part-00000-f9490ff6-f374-4b40-9d76-22addae085d1-c000.snappy.parquet\",\"partitionValues\":{},\"size\":303,\"modificationTime\":1633728461000,\"dataChange\":true}}\n" }, { "path": "connectors/golden-tables/src/main/resources/golden/125-iterator-bug/_delta_log/00000000000000000006.json", "content": "{\"commitInfo\":{\"timestamp\":1633728462063,\"operation\":\"WRITE\",\"operationParameters\":{\"mode\":\"Append\",\"partitionBy\":\"[]\"},\"readVersion\":5,\"isBlindAppend\":true,\"operationMetrics\":{\"numFiles\":\"1\",\"numOutputBytes\":\"303\",\"numOutputRows\":\"0\"}}}\n{\"add\":{\"path\":\"part-00000-66d18d0c-8cab-4cfa-a2c6-7e90df860b5a-c000.snappy.parquet\",\"partitionValues\":{},\"size\":303,\"modificationTime\":1633728462000,\"dataChange\":true}}\n" }, { "path": "connectors/golden-tables/src/main/resources/golden/125-iterator-bug/_delta_log/00000000000000000007.json", "content": "{\"commitInfo\":{\"timestamp\":1633728462739,\"operation\":\"WRITE\",\"operationParameters\":{\"mode\":\"Append\",\"partitionBy\":\"[]\"},\"readVersion\":6,\"isBlindAppend\":true,\"operationMetrics\":{\"numFiles\":\"1\",\"numOutputBytes\":\"433\",\"numOutputRows\":\"1\"}}}\n{\"add\":{\"path\":\"part-00000-1b8ea57e-424b-4068-8d0e-707edf853376-c000.snappy.parquet\",\"partitionValues\":{},\"size\":433,\"modificationTime\":1633728462000,\"dataChange\":true}}\n" }, { "path": "connectors/golden-tables/src/main/resources/golden/125-iterator-bug/_delta_log/00000000000000000008.json", "content": "{\"commitInfo\":{\"timestamp\":1633728463394,\"operation\":\"WRITE\",\"operationParameters\":{\"mode\":\"Append\",\"partitionBy\":\"[]\"},\"readVersion\":7,\"isBlindAppend\":true,\"operationMetrics\":{\"numFiles\":\"1\",\"numOutputBytes\":\"303\",\"numOutputRows\":\"0\"}}}\n{\"add\":{\"path\":\"part-00000-93beced9-3a9d-4519-b31a-5602a972ffa4-c000.snappy.parquet\",\"partitionValues\":{},\"size\":303,\"modificationTime\":1633728463000,\"dataChange\":true}}\n" }, { "path": "connectors/golden-tables/src/main/resources/golden/125-iterator-bug/_delta_log/00000000000000000009.json", "content": 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"connectors/golden-tables/src/main/resources/golden/log-replay-dv-key-cases/_delta_log/00000000000000000003.json", "content": "{\"commitInfo\":{\"timestamp\":1697571690963,\"operation\":\"DELETE\",\"operationParameters\":{\"predicate\":\"[\\\"(id#3191L = 14)\\\"]\"},\"readVersion\":2,\"isolationLevel\":\"Serializable\",\"isBlindAppend\":false,\"operationMetrics\":{\"numRemovedFiles\":\"0\",\"numRemovedBytes\":\"0\",\"numCopiedRows\":\"0\",\"numDeletionVectorsAdded\":\"1\",\"numDeletionVectorsRemoved\":\"1\",\"numAddedChangeFiles\":\"0\",\"executionTimeMs\":\"2740\",\"numDeletionVectorsUpdated\":\"1\",\"numDeletedRows\":\"1\",\"scanTimeMs\":\"0\",\"numAddedFiles\":\"0\",\"numAddedBytes\":\"0\",\"rewriteTimeMs\":\"0\"},\"engineInfo\":\"Apache-Spark/3.5.0 Delta-Lake/3.0.0-SNAPSHOT\",\"txnId\":\"c2f503a1-043c-4bbc-af37-b6c2de878a01\"}}\n{\"add\":{\"path\":\"part-00000-90177277-75c2-48db-92a2-20dcba39fd06-c000.snappy.parquet\",\"partitionValues\":{},\"size\":765,\"modificationTime\":1697571663000,\"dataChange\":true,\"stats\":\"{\\\"numRecords\\\":50,\\\"minValues\\\":{\\\"id\\\":0},\\\"maxValues\\\":{\\\"id\\\":49},\\\"nullCount\\\":{\\\"id\\\":0},\\\"tightBounds\\\":false}\",\"deletionVector\":{\"storageType\":\"u\",\"pathOrInlineDv\":\"^jP?.\"\nval query = \"\"\"\n with jsonStrings as (\n select\n id,\n format_string('{\"key\": %s}', id) as jsonString\n from\n range(0, 100)\n )\n select\n id,\n parse_json(jsonString) as v,\n array(\n parse_json(jsonString),\n null,\n parse_json(jsonString),\n null,\n parse_json(jsonString)\n ) as array_of_variants,\n named_struct('v', parse_json(jsonString)) as struct_of_variants,\n map(\n cast(id as string),\n parse_json(jsonString),\n 'nullKey',\n null\n ) as map_of_variants,\n array(\n named_struct('v', parse_json(jsonString)),\n named_struct('v', null),\n null,\n named_struct(\n 'v',\n parse_json(jsonString)\n ),\n null,\n named_struct(\n 'v',\n parse_json(jsonString)\n )\n ) as array_of_struct_of_variants,\n named_struct(\n 'v',\n array(\n null,\n parse_json(jsonString)\n )\n ) as struct_of_array_of_variants\n from\n jsonStrings\n\"\"\"\n\nval writeToTableSql = s\"\"\"\n create or replace table $tableName\n USING DELTA TBLPROPERTIES (delta.checkpointInterval = 2)\n\"\"\"\n\nspark.sql(s\"${writeToTableSql}\\n${query}\")\n// Write two additional rows to create a checkpoint.\n(0 until 2).foreach { v =>\n spark\n .sql(query)\n .where(s\"id = $v\")\n .write\n .format(\"delta\")\n .mode(\"append\")\n .insertInto(tableName)\n}\n" }, { "path": "connectors/golden-tables/src/main/resources/golden/table-with-columnmapping-mode-id/_delta_log/00000000000000000000.json", "content": "{\"commitInfo\":{\"timestamp\":1723094980674,\"operation\":\"WRITE\",\"operationParameters\":{\"mode\":\"ErrorIfExists\",\"partitionBy\":\"[]\"},\"isolationLevel\":\"Serializable\",\"isBlindAppend\":true,\"operationMetrics\":{\"numFiles\":\"2\",\"numOutputRows\":\"6\",\"numOutputBytes\":\"35024\"},\"engineInfo\":\"Apache-Spark/3.5.0 Delta-Lake/3.3.0-SNAPSHOT\",\"txnId\":\"ad640169-85ab-4030-ada7-b70661040863\"}}\n{\"metaData\":{\"id\":\"6fb2dbf2-52a9-4632-8952-71c976b4bf77\",\"format\":{\"provider\":\"parquet\",\"options\":{}},\"schemaString\":\"{\\\"type\\\":\\\"struct\\\",\\\"fields\\\":[{\\\"name\\\":\\\"ByteType\\\",\\\"type\\\":\\\"byte\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":1,\\\"delta.columnMapping.physicalName\\\":\\\"col-91e8b36f-2559-4aa7-b451-d30f24d5bf28\\\"}},{\\\"name\\\":\\\"ShortType\\\",\\\"type\\\":\\\"short\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":2,\\\"delta.columnMapping.physicalName\\\":\\\"col-d1a51f0a-855a-4752-8a35-76f52cda06b9\\\"}},{\\\"name\\\":\\\"IntegerType\\\",\\\"type\\\":\\\"integer\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":3,\\\"delta.columnMapping.physicalName\\\":\\\"col-0aa7e907-848d-47b7-9805-e014c0a09d83\\\"}},{\\\"name\\\":\\\"LongType\\\",\\\"type\\\":\\\"long\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":4,\\\"delta.columnMapping.physicalName\\\":\\\"col-b55a2771-5f84-432a-9fe4-39e1ec1edfd7\\\"}},{\\\"name\\\":\\\"FloatType\\\",\\\"type\\\":\\\"float\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":5,\\\"delta.columnMapping.physicalName\\\":\\\"col-7722f52d-c253-4df9-96f4-a87a1dbef4dd\\\"}},{\\\"name\\\":\\\"DoubleType\\\",\\\"type\\\":\\\"double\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":6,\\\"delta.columnMapping.physicalName\\\":\\\"col-1dda278c-a501-4499-a580-f3dff1b79834\\\"}},{\\\"name\\\":\\\"decimal\\\",\\\"type\\\":\\\"decimal(10,2)\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":7,\\\"delta.columnMapping.physicalName\\\":\\\"col-30298583-3b87-4b4d-ab7c-f1102050f720\\\"}},{\\\"name\\\":\\\"BooleanType\\\",\\\"type\\\":\\\"boolean\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":8,\\\"delta.columnMapping.physicalName\\\":\\\"col-836e8d56-f73a-4203-9c0f-a50476468c2c\\\"}},{\\\"name\\\":\\\"StringType\\\",\\\"type\\\":\\\"string\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":9,\\\"delta.columnMapping.physicalName\\\":\\\"col-ca55bcbb-453c-433c-995f-80e0ccc13cde\\\"}},{\\\"name\\\":\\\"BinaryType\\\",\\\"type\\\":\\\"binary\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":10,\\\"delta.columnMapping.physicalName\\\":\\\"col-da9f099c-f6bc-459c-a740-b02f658221e2\\\"}},{\\\"name\\\":\\\"DateType\\\",\\\"type\\\":\\\"date\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":11,\\\"delta.columnMapping.physicalName\\\":\\\"col-7139e00c-abc4-4434-893d-1cf11d2b68bf\\\"}},{\\\"name\\\":\\\"TimestampType\\\",\\\"type\\\":\\\"timestamp\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":12,\\\"delta.columnMapping.physicalName\\\":\\\"col-292ce280-d844-4477-b078-87a64a6972d2\\\"}},{\\\"name\\\":\\\"nested_struct\\\",\\\"type\\\":{\\\"type\\\":\\\"struct\\\",\\\"fields\\\":[{\\\"name\\\":\\\"aa\\\",\\\"type\\\":\\\"string\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":14,\\\"delta.columnMapping.physicalName\\\":\\\"col-b47b5204-0100-46bc-92df-aa446f634191\\\"}},{\\\"name\\\":\\\"ac\\\",\\\"type\\\":{\\\"type\\\":\\\"struct\\\",\\\"fields\\\":[{\\\"name\\\":\\\"aca\\\",\\\"type\\\":\\\"integer\\\",\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":16,\\\"delta.columnMapping.physicalName\\\":\\\"col-75cd857b-0152-438b-a0b3-43db0d4daa55\\\"}}]},\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":15,\\\"delta.columnMapping.physicalName\\\":\\\"col-94e598c8-0480-4710-a288-fc332ed449de\\\"}}]},\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":13,\\\"delta.columnMapping.physicalName\\\":\\\"col-ee8dc621-b36b-4784-bdb5-26c5c364e254\\\"}},{\\\"name\\\":\\\"array_of_prims\\\",\\\"type\\\":{\\\"type\\\":\\\"array\\\",\\\"elementType\\\":\\\"integer\\\",\\\"containsNull\\\":true},\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":17,\\\"delta.columnMapping.physicalName\\\":\\\"col-720537b9-44e0-4829-a189-f3742da4f095\\\"}},{\\\"name\\\":\\\"array_of_arrays\\\",\\\"type\\\":{\\\"type\\\":\\\"array\\\",\\\"elementType\\\":{\\\"type\\\":\\\"array\\\",\\\"elementType\\\":\\\"integer\\\",\\\"containsNull\\\":true},\\\"containsNull\\\":true},\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.columnMapping.id\\\":18,\\\"delta.columnMapping.physicalName\\\":\\\"col-9f8d855c-cc39-426c-b579-f1b85b1b6991\\\"}},{\\\"name\\\":\\\"array_of_map_of_arrays\\\",\\\"type\\\":{\\\"type\\\":\\\"array\\\",\\\"elementType\\\":{\\\"type\\\":\\\"map\\\",\\\"keyType\\\":\\\"integer\\\",\\\"valueType\\\":{\\\"type\\\":\\\"array\\\",\\\"elementType\\\":\\\"integer\\\",\\\"containsNull\\\":true},\\\"valueContainsNull\\\":true},\\\"containsNull\\\":true},\\\"nullable\\\":true,\\\"metadata\\\":{\\\"delta.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}, { "path": "connectors/golden-tables/src/main/resources/golden/versions-not-contiguous/_delta_log/00000000000000000000.json", "content": "{\"commitInfo\":{\"timestamp\":1603723995084,\"operation\":\"Manual Update\",\"operationParameters\":{},\"isBlindAppend\":true}}\n{\"protocol\":{\"minReaderVersion\":1,\"minWriterVersion\":2}}\n{\"metaData\":{\"id\":\"a564e335-d717-4a71-a1eb-25541a0f8d15\",\"format\":{\"provider\":\"parquet\",\"options\":{}},\"partitionColumns\":[],\"configuration\":{},\"createdTime\":1603723995084}}\n{\"add\":{\"path\":\"foo\",\"partitionValues\":{},\"size\":1,\"modificationTime\":1603723995077,\"dataChange\":true}}\n" }, { "path": "connectors/golden-tables/src/main/resources/golden/versions-not-contiguous/_delta_log/00000000000000000002.json", "content": "{\"commitInfo\":{\"timestamp\":1603723996094,\"operation\":\"Manual Update\",\"operationParameters\":{},\"readVersion\":1,\"isBlindAppend\":true}}\n{\"add\":{\"path\":\"foo\",\"partitionValues\":{},\"size\":1,\"modificationTime\":1603723996088,\"dataChange\":true}}\n" }, { "path": "connectors/golden-tables/src/main/scala/io/delta/golden/GoldenTableUtils.scala", "content": "/*\n * Copyright (2026) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage io.delta.golden\n\nimport java.io.File\n\nobject GoldenTableUtils {\n\n lazy val classLoader = GoldenTableUtils.getClass.getClassLoader()\n lazy val goldenResourceURL = classLoader.getResource(\"golden\")\n\n def goldenTablePath(name: String): String = {\n classLoader.getResource(s\"golden/$name\").getPath\n }\n\n def goldenTableFile(name: String): File = {\n new File(classLoader.getResource(s\"golden/$name\").getFile)\n }\n\n def allTableNames(): Seq[String] = {\n val root = new File(goldenResourceURL.getFile)\n\n def loop(dir: File): Seq[File] = {\n val children = Option(dir.listFiles()).getOrElse(Array.empty[File])\n val subdirs = children.filter(_.isDirectory)\n val here = if (new File(dir, \"_delta_log\").isDirectory) Seq(dir) else Seq.empty[File]\n here ++ subdirs.flatMap(loop)\n }\n\n val rootPath = root.toPath\n // Find all directories containing `_delta_log` under the `golden` resource and return their\n // relative paths (to the golden root), sorted.\n loop(root).map(f => rootPath.relativize(f.toPath).toString).sorted\n }\n}\n" }, { "path": "connectors/golden-tables/src/test/scala/io/delta/golden/GoldenTables.scala", "content": "/*\n * Copyright (2020-present) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage io.delta.golden\n\nimport java.io.File\nimport java.math.{BigInteger, BigDecimal => JBigDecimal}\nimport java.sql.Timestamp\nimport java.time.ZoneOffset.UTC\nimport java.time.LocalDateTime\nimport java.util.{Locale, Random, TimeZone}\n\nimport scala.collection.mutable.ArrayBuffer\nimport scala.concurrent.duration._\nimport scala.language.implicitConversions\n\nimport io.delta.tables.DeltaTable\nimport org.apache.commons.io.FileUtils\nimport org.apache.hadoop.fs.Path\n\nimport org.apache.spark.SparkConf\nimport org.apache.spark.network.util.JavaUtils\nimport org.apache.spark.sql.{QueryTest, Row}\nimport org.apache.spark.sql.delta.{DeltaLog, OptimisticTransaction}\nimport org.apache.spark.sql.delta.DeltaOperations.ManualUpdate\nimport org.apache.spark.sql.delta.actions.{Metadata, _}\nimport org.apache.spark.sql.delta.sources.DeltaSQLConf\nimport org.apache.spark.sql.delta.util.{FileNames, JsonUtils}\nimport org.apache.spark.sql.internal.SQLConf\nimport org.apache.spark.sql.test.SharedSparkSession\nimport org.apache.spark.sql.types._\n\n/**\n * This is a special class to generate golden tables for other projects. Run the following commands\n * to re-generate all golden tables:\n * ```\n * GENERATE_GOLDEN_TABLES=1 build/sbt 'goldenTables/test'\n * ```\n *\n * To generate a single table (that is specified below) run:\n * ```\n * GENERATE_GOLDEN_TABLES=1 build/sbt 'goldenTables/testOnly *GoldenTables -- -z \"tbl_name\"'\n * ```\n *\n * After generating golden tables, be sure to package or test project standalone, otherwise the\n * test resources won't be available when running tests with IntelliJ.\n */\nclass GoldenTables extends QueryTest with SharedSparkSession {\n import testImplicits._\n\n override def sparkConf: SparkConf = super.sparkConf\n .set(\"spark.sql.extensions\", \"io.delta.sql.DeltaSparkSessionExtension\")\n .set(\"spark.sql.catalog.spark_catalog\", \"org.apache.spark.sql.delta.catalog.DeltaCatalog\")\n // disable _SUCCESS files\n .set(\"spark.hadoop.mapreduce.fileoutputcommitter.marksuccessfuljobs\", \"false\")\n\n // Timezone is fixed to America/Los_Angeles for timezone-sensitive tests\n TimeZone.setDefault(TimeZone.getTimeZone(\"America/Los_Angeles\"))\n // Add Locale setting\n Locale.setDefault(Locale.US)\n\n private val shouldGenerateGoldenTables = sys.env.contains(\"GENERATE_GOLDEN_TABLES\")\n\n private lazy val goldenTablePath = {\n val dir = new File(\"src/main/resources/golden\").getCanonicalFile\n require(dir.exists(),\n s\"Cannot find $dir. Please run `GENERATE_GOLDEN_TABLES=1 build/sbt 'goldenTables/test'`.\")\n dir\n }\n\n private def copyDir(src: String, dest: String): Unit = {\n FileUtils.copyDirectory(createGoldenTableFile(src), createGoldenTableFile(dest))\n }\n\n private def createGoldenTableFile(name: String): File = new File(goldenTablePath, name)\n\n private def createHiveGoldenTableFile(name: String): File =\n new File(createGoldenTableFile(\"hive\"), name)\n\n private def generateGoldenTable(name: String,\n createTableFile: String => File = createGoldenTableFile) (generator: String => Unit): Unit = {\n if (shouldGenerateGoldenTables) {\n test(name) {\n val tablePath = createTableFile(name)\n JavaUtils.deleteRecursively(tablePath)\n generator(tablePath.getCanonicalPath)\n }\n }\n }\n\n /**\n * Helper class for to ensure initial commits contain a Metadata action.\n */\n private implicit class OptimisticTxnTestHelper(txn: OptimisticTransaction) {\n def commitManually(actions: Action*): Long = {\n if (txn.readVersion == -1 && !actions.exists(_.isInstanceOf[Metadata])) {\n val schema = new StructType()\n .add(\"intCol\", IntegerType)\n .json\n txn.commit(Metadata(schemaString = schema) +: actions, ManualUpdate)\n } else {\n txn.commit(actions, ManualUpdate)\n }\n }\n }\n\n ///////////////////////////////////////////////////////////////////////////\n // io.delta.standalone.internal.DeltaLogSuite\n ///////////////////////////////////////////////////////////////////////////\n\n /** TEST: DeltaLogSuite > checkpoint */\n generateGoldenTable(\"checkpoint\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n (1 to 15).foreach { i =>\n val txn = log.startTransaction()\n val file = AddFile(i.toString, Map.empty, 1, 1, true) :: Nil\n val delete: Seq[Action] = if (i > 1) {\n RemoveFile((i - 1).toString, Some(System.currentTimeMillis()), true) :: Nil\n } else {\n Nil\n }\n txn.commitManually(delete ++ file: _*)\n }\n }\n\n /** TEST: DeltaLogSuite > snapshot */\n private def writeData(data: Seq[(Int, String)], mode: String, tablePath: String): Unit = {\n data.toDS\n .toDF(\"col1\", \"col2\")\n .write\n .mode(mode)\n .format(\"delta\")\n .save(tablePath)\n }\n\n generateGoldenTable(\"snapshot-data0\") { tablePath =>\n writeData((0 until 10).map(x => (x, s\"data-0-$x\")), \"append\", tablePath)\n }\n\n generateGoldenTable(\"snapshot-data1\") { tablePath =>\n copyDir(\"snapshot-data0\", \"snapshot-data1\")\n writeData((0 until 10).map(x => (x, s\"data-1-$x\")), \"append\", tablePath)\n }\n\n generateGoldenTable(\"snapshot-data2\") { tablePath =>\n copyDir(\"snapshot-data1\", \"snapshot-data2\")\n writeData((0 until 10).map(x => (x, s\"data-2-$x\")), \"overwrite\", tablePath)\n }\n\n generateGoldenTable(\"snapshot-data3\") { tablePath =>\n copyDir(\"snapshot-data2\", \"snapshot-data3\")\n writeData((0 until 20).map(x => (x, s\"data-3-$x\")), \"append\", tablePath)\n }\n\n generateGoldenTable(\"snapshot-data2-deleted\") { tablePath =>\n copyDir(\"snapshot-data3\", \"snapshot-data2-deleted\")\n DeltaTable.forPath(spark, tablePath).delete(\"col2 like 'data-2-%'\")\n }\n\n generateGoldenTable(\"snapshot-repartitioned\") { tablePath =>\n copyDir(\"snapshot-data2-deleted\", \"snapshot-repartitioned\")\n spark.read\n .format(\"delta\")\n .load(tablePath)\n .repartition(2)\n .write\n .option(\"dataChange\", \"false\")\n .format(\"delta\")\n .mode(\"overwrite\")\n .save(tablePath)\n }\n\n generateGoldenTable(\"snapshot-vacuumed\") { tablePath =>\n copyDir(\"snapshot-repartitioned\", \"snapshot-vacuumed\")\n withSQLConf(DeltaSQLConf.DELTA_VACUUM_RETENTION_CHECK_ENABLED.key -> \"false\") {\n DeltaTable.forPath(spark, tablePath).vacuum(0.0)\n }\n }\n\n /** TEST: DeltaLogSuite > SC-8078: update deleted directory */\n generateGoldenTable(\"update-deleted-directory\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n val txn = log.startTransaction()\n val files = (1 to 10).map(f => AddFile(f.toString, Map.empty, 1, 1, true))\n txn.commitManually(files: _*)\n log.checkpoint()\n }\n\n /** TEST: DeltaLogSuite > handle corrupted '_last_checkpoint' file */\n generateGoldenTable(\"corrupted-last-checkpoint\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n val checkpointInterval = log.checkpointInterval(log.unsafeVolatileSnapshot.metadata)\n for (f <- 0 to checkpointInterval) {\n val txn = log.startTransaction()\n txn.commitManually(AddFile(f.toString, Map.empty, 1, 1, true))\n }\n }\n\n generateGoldenTable(\"corrupted-last-checkpoint-kernel\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n val checkpointInterval = log.checkpointInterval(log.unsafeVolatileSnapshot.metadata)\n for (f <- 0 to checkpointInterval) {\n spark.range(10).write.format(\"delta\").mode(\"append\").save(tablePath)\n }\n spark.range(100).write.format(\"delta\").mode(\"overwrite\").save(tablePath)\n\n // Create an empty \"_last_checkpoint\" (corrupted)\n val fs = log.LAST_CHECKPOINT.getFileSystem(log.newDeltaHadoopConf())\n fs.create(log.LAST_CHECKPOINT, true /* overwrite */).close()\n }\n\n /** TEST: DeltaLogSuite > paths should be canonicalized */\n {\n def helper(scheme: String, path: String, tableSuffix: String): Unit = {\n generateGoldenTable(s\"canonicalized-paths-$tableSuffix\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n new File(log.logPath.toUri).mkdirs()\n\n val add = AddFile(path, Map.empty, 100L, 10L, dataChange = true)\n val rm = RemoveFile(s\"$scheme$path\", Some(200L))\n\n log.startTransaction().commitManually(add)\n log.startTransaction().commitManually(rm)\n }\n }\n\n // normal characters\n helper(\"file:\", \"/some/unqualified/absolute/path\", \"normal-a\")\n helper(\"file://\", \"/some/unqualified/absolute/path\", \"normal-b\")\n\n // special characters\n helper(\"file:\", new Path(\"/some/unqualified/with space/p@#h\").toUri.toString, \"special-a\")\n helper(\"file://\", new Path(\"/some/unqualified/with space/p@#h\").toUri.toString, \"special-b\")\n }\n\n /** TEST: DeltaLogSuite > delete and re-add the same file in different transactions */\n generateGoldenTable(s\"delete-re-add-same-file-different-transactions\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n assert(new File(log.logPath.toUri).mkdirs())\n\n val add1 = AddFile(\"foo\", Map.empty, 1L, 1600000000000L, dataChange = true)\n log.startTransaction().commitManually(add1)\n\n val rm = add1.remove\n log.startTransaction().commit(rm :: Nil, ManualUpdate)\n\n val add2 = AddFile(\"foo\", Map.empty, 1L, 1700000000000L, dataChange = true)\n log.startTransaction().commit(add2 :: Nil, ManualUpdate)\n\n // Add a new transaction to replay logs using the previous snapshot. If it contained\n // AddFile(\"foo\") and RemoveFile(\"foo\"), \"foo\" would get removed and fail this test.\n val otherAdd = AddFile(\"bar\", Map.empty, 1L, System.currentTimeMillis(), dataChange = true)\n log.startTransaction().commit(otherAdd :: Nil, ManualUpdate)\n }\n\n /** TEST: DeltaLogSuite > error - versions not contiguous */\n generateGoldenTable(\"versions-not-contiguous\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n assert(new File(log.logPath.toUri).mkdirs())\n\n val add1 = AddFile(\"foo\", Map.empty, 1L, System.currentTimeMillis(), dataChange = true)\n log.startTransaction().commitManually(add1)\n\n val add2 = AddFile(\"foo\", Map.empty, 1L, System.currentTimeMillis(), dataChange = true)\n log.startTransaction().commit(add2 :: Nil, ManualUpdate)\n\n val add3 = AddFile(\"foo\", Map.empty, 1L, System.currentTimeMillis(), dataChange = true)\n log.startTransaction().commit(add3 :: Nil, ManualUpdate)\n\n new File(new Path(log.logPath, \"00000000000000000001.json\").toUri).delete()\n }\n\n /** TEST: DeltaLogSuite > state reconstruction without Protocol/Metadata should fail */\n Seq(\"protocol\", \"metadata\").foreach { action =>\n generateGoldenTable(s\"deltalog-state-reconstruction-without-$action\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n assert(new File(log.logPath.toUri).mkdirs())\n\n val selectedAction = if (action == \"metadata\") {\n Protocol()\n } else {\n val schema = new StructType()\n .add(\"intCol\", IntegerType)\n .json\n Metadata(schemaString = schema)\n }\n\n val file = AddFile(\"abc\", Map.empty, 1, 1, true)\n log.store.write(\n FileNames.unsafeDeltaFile(log.logPath, 0L),\n Iterator(selectedAction, file).map(a => JsonUtils.toJson(a.wrap)))\n }\n }\n\n /**\n * TEST: DeltaLogSuite > state reconstruction from checkpoint with missing Protocol/Metadata\n * should fail\n */\n Seq(\"protocol\", \"metadata\").foreach { action =>\n generateGoldenTable(s\"deltalog-state-reconstruction-from-checkpoint-missing-$action\") {\n tablePath =>\n val log = DeltaLog.forTable(spark, tablePath)\n val checkpointInterval = log.checkpointInterval(log.unsafeVolatileSnapshot.metadata)\n // Create a checkpoint regularly\n for (f <- 0 to checkpointInterval) {\n val txn = log.startTransaction()\n if (f == 0) {\n txn.commitManually(AddFile(f.toString, Map.empty, 1, 1, true))\n } else {\n txn.commit(Seq(AddFile(f.toString, Map.empty, 1, 1, true)), ManualUpdate)\n }\n }\n\n // Create an incomplete checkpoint without the action and overwrite the\n // original checkpoint\n val checkpointPath = FileNames.checkpointFileSingular(log.logPath, log.snapshot.version)\n withTempDir { tmpCheckpoint =>\n val takeAction = if (action == \"metadata\") {\n \"protocol\"\n } else {\n \"metadata\"\n }\n val corruptedCheckpointData = spark.read.parquet(checkpointPath.toString)\n .where(s\"add is not null or $takeAction is not null\")\n .as[SingleAction].collect()\n\n // Keep the add files and also filter by the additional condition\n corruptedCheckpointData.toSeq.toDS().coalesce(1).write\n .mode(\"overwrite\").parquet(tmpCheckpoint.toString)\n val writtenCheckpoint =\n tmpCheckpoint.listFiles().toSeq.filter(_.getName.startsWith(\"part\")).head\n val checkpointFile = new File(checkpointPath.toUri)\n new File(log.logPath.toUri).listFiles().toSeq.foreach { file =>\n if (file.getName.startsWith(\".0\")) {\n // we need to delete checksum files, otherwise trying to replace our incomplete\n // checkpoint file fails due to the LocalFileSystem's checksum checks.\n require(file.delete(), \"Failed to delete checksum file\")\n }\n }\n require(checkpointFile.delete(), \"Failed to delete old checkpoint\")\n require(writtenCheckpoint.renameTo(checkpointFile),\n \"Failed to rename corrupt checkpoint\")\n }\n }\n }\n\n /** TEST: DeltaLogSuite > table protocol version greater than client reader protocol version */\n generateGoldenTable(\"deltalog-invalid-protocol-version\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n assert(new File(log.logPath.toUri).mkdirs())\n\n val file = AddFile(\"abc\", Map.empty, 1, 1, true)\n val metadata = Metadata(\n schemaString = new StructType().add(\"id\", IntegerType).json\n )\n log.store.write(FileNames.unsafeDeltaFile(log.logPath, 0L),\n\n // Protocol reader version explicitly set too high\n // Also include a Metadata\n Iterator(Protocol(99), metadata, file).map(a => JsonUtils.toJson(a.wrap)))\n }\n\n /** TEST: DeltaLogSuite > get commit info */\n generateGoldenTable(\"deltalog-commit-info\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n assert(new File(log.logPath.toUri).mkdirs())\n\n val commitInfoFile = CommitInfo(\n version = Some(0L),\n inCommitTimestamp = None,\n timestamp = new Timestamp(1540415658000L),\n userId = Some(\"user_0\"),\n userName = Some(\"username_0\"),\n operation = \"WRITE\",\n operationParameters = Map(\"test\" -> \"\\\"test\\\"\"),\n job = Some(JobInfo(\n \"job_id_0\", \"job_name_0\", \"job_run_id_0\", \"run_id_0\", \"job_owner_0\", \"trigger_type_0\")),\n notebook = Some(NotebookInfo(\"notebook_id_0\")),\n clusterId = Some(\"cluster_id_0\"),\n readVersion = Some(-1L),\n isolationLevel = Some(\"default\"),\n isBlindAppend = Some(true),\n operationMetrics = Some(Map(\"test\" -> \"test\")),\n userMetadata = Some(\"foo\"),\n tags = Some(Map(\"test\" -> \"test\")),\n engineInfo = Some(\"OSS\"),\n txnId = Some(\"txn_id_0\")\n )\n\n val addFile = AddFile(\"abc\", Map.empty, 1, 1, true)\n log.store.write(\n FileNames.unsafeDeltaFile(log.logPath, 0L),\n Iterator(Metadata(), Protocol(), commitInfoFile, addFile).map(a => JsonUtils.toJson(a.wrap)))\n }\n\n /** TEST: DeltaLogSuite > getChanges - no data loss */\n generateGoldenTable(\"deltalog-getChanges\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n\n val schema = new StructType()\n .add(\"part\", IntegerType)\n .add(\"id\", IntegerType)\n val metadata = Metadata(schemaString = schema.json)\n\n val add1 = AddFile(\"fake/path/1\", Map.empty, 1, 1, dataChange = true)\n val txn1 = log.startTransaction()\n txn1.commitManually(metadata :: add1 :: Nil: _*)\n\n val addCDC2 = AddCDCFile(\"fake/path/2\", Map(\"partition_foo\" -> \"partition_bar\"), 1,\n Map(\"tag_foo\" -> \"tag_bar\"))\n val remove2 = RemoveFile(\"fake/path/1\", Some(100), dataChange = true)\n val txn2 = log.startTransaction()\n txn2.commitManually(addCDC2 :: remove2 :: Nil: _*)\n\n val setTransaction3 = SetTransaction(\"fakeAppId\", 3L, Some(200))\n val txn3 = log.startTransaction()\n txn3.commitManually(Protocol(1, 2) :: setTransaction3 :: Nil: _*)\n }\n\n ///////////////////////////////////////////////////////////////////////////\n // io.delta.standalone.internal.ReadOnlyLogStoreSuite\n ///////////////////////////////////////////////////////////////////////////\n\n /** TEST: ReadOnlyLogStoreSuite > read */\n generateGoldenTable(\"log-store-read\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n assert(new File(log.logPath.toUri).mkdirs())\n\n val deltas = Seq(0, 1).map(i => new File(tablePath, i.toString)).map(_.getCanonicalPath)\n log.store.write(deltas.head, Iterator(\"zero\", \"none\"))\n log.store.write(deltas(1), Iterator(\"one\"))\n }\n\n /** TEST: ReadOnlyLogStoreSuite > listFrom */\n generateGoldenTable(\"log-store-listFrom\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n assert(new File(log.logPath.toUri).mkdirs())\n\n val deltas = Seq(0, 1, 2, 3, 4)\n .map(i => new File(tablePath, i.toString))\n .map(_.getCanonicalPath)\n\n log.store.write(deltas(1), Iterator(\"zero\"))\n log.store.write(deltas(2), Iterator(\"one\"))\n log.store.write(deltas(3), Iterator(\"two\"))\n }\n\n ///////////////////////////////////////////////////////////////////////////\n // io.delta.standalone.internal.DeltaTimeTravelSuite\n ///////////////////////////////////////////////////////////////////////////\n\n private implicit def durationToLong(duration: FiniteDuration): Long = {\n duration.toMillis\n }\n\n /** Generate commits with the given timestamp in millis. */\n private def generateCommits(location: String, commits: Long*): Unit = {\n val deltaLog = DeltaLog.forTable(spark, location)\n var startVersion = deltaLog.snapshot.version + 1\n commits.foreach { ts =>\n val rangeStart = startVersion * 10\n val rangeEnd = rangeStart + 10\n spark.range(rangeStart, rangeEnd).write.format(\"delta\").mode(\"append\").save(location)\n val file = new File(FileNames.unsafeDeltaFile(deltaLog.logPath, startVersion).toUri)\n file.setLastModified(ts)\n startVersion += 1\n }\n }\n\n val start = 1540415658000L\n\n generateGoldenTable(\"time-travel-start\") { tablePath =>\n generateCommits(tablePath, start)\n }\n\n generateGoldenTable(\"time-travel-start-start20\") { tablePath =>\n copyDir(\"time-travel-start\", \"time-travel-start-start20\")\n generateCommits(tablePath, start + 20.minutes)\n }\n\n generateGoldenTable(\"time-travel-start-start20-start40\") { tablePath =>\n copyDir(\"time-travel-start-start20\", \"time-travel-start-start20-start40\")\n generateCommits(tablePath, start + 40.minutes)\n }\n\n /**\n * TEST: DeltaTimeTravelSuite > time travel with schema changes - should instantiate old schema\n */\n generateGoldenTable(\"time-travel-schema-changes-a\") { tablePath =>\n spark.range(10).write.format(\"delta\").mode(\"append\").save(tablePath)\n }\n\n generateGoldenTable(\"time-travel-schema-changes-b\") { tablePath =>\n copyDir(\"time-travel-schema-changes-a\", \"time-travel-schema-changes-b\")\n spark.range(10, 20).withColumn(\"part\", 'id)\n .write.format(\"delta\").mode(\"append\").option(\"mergeSchema\", true).save(tablePath)\n }\n\n /**\n * TEST: DeltaTimeTravelSuite > time travel with partition changes - should instantiate old schema\n */\n generateGoldenTable(\"time-travel-partition-changes-a\") { tablePath =>\n spark.range(10).withColumn(\"part5\", 'id % 5).write.format(\"delta\")\n .partitionBy(\"part5\").mode(\"append\").save(tablePath)\n }\n\n generateGoldenTable(\"time-travel-partition-changes-b\") { tablePath =>\n copyDir(\"time-travel-partition-changes-a\", \"time-travel-partition-changes-b\")\n spark.range(10, 20).withColumn(\"part2\", 'id % 2)\n .write\n .format(\"delta\")\n .partitionBy(\"part2\")\n .mode(\"overwrite\")\n .option(\"overwriteSchema\", true)\n .save(tablePath)\n }\n\n ///////////////////////////////////////////////////////////////////////////\n // io.delta.standalone.internal.DeltaDataReaderSuite\n ///////////////////////////////////////////////////////////////////////////\n\n private def writeDataWithSchema(tblLoc: String, data: Seq[Row], schema: StructType): Unit = {\n val df = spark.createDataFrame(spark.sparkContext.parallelize(data), schema)\n df.write.format(\"delta\").mode(\"append\").save(tblLoc)\n }\n\n /** TEST: DeltaDataReaderSuite > read - primitives */\n generateGoldenTable(\"data-reader-primitives\") { tablePath =>\n def createRow(i: Int): Row = {\n Row(i, i.longValue, i.toByte, i.shortValue, i % 2 == 0, i.floatValue, i.doubleValue,\n i.toString, Array[Byte](i.toByte, i.toByte), new JBigDecimal(i))\n }\n\n def createRowWithNullValues(): Row = {\n Row(null, null, null, null, null, null, null, null, null, null)\n }\n\n val schema = new StructType()\n .add(\"as_int\", IntegerType)\n .add(\"as_long\", LongType)\n .add(\"as_byte\", ByteType)\n .add(\"as_short\", ShortType)\n .add(\"as_boolean\", BooleanType)\n .add(\"as_float\", FloatType)\n .add(\"as_double\", DoubleType)\n .add(\"as_string\", StringType)\n .add(\"as_binary\", BinaryType)\n .add(\"as_big_decimal\", DecimalType(1, 0))\n\n val data = createRowWithNullValues() +: (0 until 10).map(createRow)\n writeDataWithSchema(tablePath, data, schema)\n }\n\n /** TEST: DeltaDataReaderSuite > data reader can read partition values */\n generateGoldenTable(\"data-reader-partition-values\") { tablePath =>\n def createRow(i: Int): Row = {\n Row(i, i.longValue, i.toByte, i.shortValue, i % 2 == 0, i.floatValue, i.doubleValue,\n i.toString, \"null\", java.sql.Date.valueOf(\"2021-09-08\"),\n java.sql.Timestamp.valueOf(\"2021-09-08 11:11:11\"), new JBigDecimal(i),\n Array(Row(i), Row(i), Row(i)),\n Row(i.toString, i.toString, Row(i, i.toLong)),\n i.toString)\n }\n\n def createRowWithNullPartitionValues(): Row = {\n Row(\n // partition values\n null, null, null, null, null, null, null, null, null, null, null, null,\n // data values\n Array(Row(2), Row(2), Row(2)),\n Row(\"2\", \"2\", Row(2, 2L)),\n \"2\")\n }\n\n val schema = new StructType()\n // partition fields\n .add(\"as_int\", IntegerType)\n .add(\"as_long\", LongType)\n .add(\"as_byte\", ByteType)\n .add(\"as_short\", ShortType)\n .add(\"as_boolean\", BooleanType)\n .add(\"as_float\", FloatType)\n .add(\"as_double\", DoubleType)\n .add(\"as_string\", StringType)\n .add(\"as_string_lit_null\", StringType)\n .add(\"as_date\", DateType)\n .add(\"as_timestamp\", TimestampType)\n .add(\"as_big_decimal\", DecimalType(1, 0))\n // data fields\n .add(\"as_list_of_records\", ArrayType(new StructType().add(\"val\", IntegerType)))\n .add(\"as_nested_struct\", new StructType()\n .add(\"aa\", StringType)\n .add(\"ab\", StringType)\n .add(\"ac\", new StructType()\n .add(\"aca\", IntegerType)\n .add(\"acb\", LongType)\n )\n )\n .add(\"value\", StringType)\n\n val data = (0 until 2).map(createRow) :+ createRowWithNullPartitionValues()\n\n val df = spark.createDataFrame(spark.sparkContext.parallelize(data), schema)\n df.write\n .format(\"delta\")\n .partitionBy(\"as_int\", \"as_long\", \"as_byte\", \"as_short\", \"as_boolean\", \"as_float\",\n \"as_double\", \"as_string\", \"as_string_lit_null\", \"as_date\", \"as_timestamp\", \"as_big_decimal\")\n .save(tablePath)\n }\n\n Seq(\"name\", \"id\").foreach { columnMappingMode =>\n generateGoldenTable(s\"table-with-columnmapping-mode-$columnMappingMode\") { tablePath =>\n withSQLConf(\n (\"spark.databricks.delta.properties.defaults.columnMapping.mode\", columnMappingMode)) {\n generateCMIcebegCompatTableHelper(tablePath)\n }\n }\n }\n\n generateGoldenTable(\"table-with-icebegCompatV2Enabled\") { tablePath =>\n withSQLConf(\n (\"spark.databricks.delta.properties.defaults.columnMapping.mode\", \"id\"),\n (\"spark.databricks.delta.properties.defaults.enableIcebergCompatV2\", \"true\")) {\n generateCMIcebegCompatTableHelper(tablePath)\n }\n }\n\n def generateCMIcebegCompatTableHelper(tablePath: String): Unit = {\n val timeZone = java.util.TimeZone.getTimeZone(\"UTC\")\n java.util.TimeZone.setDefault(timeZone)\n import java.sql._\n\n val decimalType = DecimalType(10, 2)\n\n val allDataTypes = Seq(\n ByteType,\n ShortType,\n IntegerType,\n LongType,\n FloatType,\n DoubleType,\n decimalType,\n BooleanType,\n StringType,\n BinaryType,\n DateType,\n TimestampType\n )\n\n var fields = allDataTypes.map(dt => {\n val name = if (dt.isInstanceOf[DecimalType]) {\n \"decimal\"\n } else {\n dt.toString\n }\n StructField(name, dt)\n })\n\n fields = fields :+ StructField(\"nested_struct\", new StructType()\n .add(\"aa\", StringType)\n .add(\"ac\", new StructType()\n .add(\"aca\", IntegerType)\n )\n )\n\n fields = fields :+ StructField(\"array_of_prims\", ArrayType(IntegerType))\n fields = fields :+ StructField(\"array_of_arrays\", ArrayType(ArrayType(IntegerType)))\n fields = fields :+ StructField(\"array_of_map_of_arrays\",\n ArrayType(MapType(IntegerType, ArrayType(IntegerType))))\n fields = fields :+ StructField(\n \"array_of_structs\",\n ArrayType(new StructType().add(\"ab\", IntegerType)))\n fields = fields :+ StructField(\n \"struct_of_arrays_maps_of_structs\",\n new StructType()\n .add(\"aa\", ArrayType(IntegerType))\n .add(\"ab\", MapType(ArrayType(IntegerType), new StructType().add(\"aca\", IntegerType)))\n )\n\n fields = fields :+ StructField(\n \"map_of_prims\",\n MapType(IntegerType, LongType)\n )\n fields = fields :+ StructField(\n \"map_of_rows\",\n MapType(IntegerType, new StructType().add(\"ab\", LongType))\n )\n fields = fields :+ StructField(\n \"map_of_arrays\",\n MapType(LongType, ArrayType(IntegerType))\n )\n\n fields = fields :+ StructField(\n \"map_of_maps\",\n MapType(LongType, MapType(IntegerType, IntegerType))\n )\n\n val schema = StructType(fields)\n\n def createRow(i: Int): Row = {\n Row(\n i.toByte, // byte\n i.toShort, // short\n i, // integer\n i.toLong, // long\n i.toFloat, // float\n i.toDouble, // double\n new java.math.BigDecimal(i), // decimal\n i % 2 == 0, // boolean\n i.toString, // string\n i.toString.getBytes, // binary\n Date.valueOf(\"2021-11-18\"), // date\n new Timestamp(i.toLong), // timestamp\n Row(i.toString, Row(i)), // nested_struct\n scala.Array(i, i + 1), // array_of_prims\n scala.Array(scala.Array(i, i + 1), scala.Array(i + 2, i + 3)), // array_of_arrays\n scala.Array(\n Map(i -> scala.Array(2, 3), i + 1 -> scala.Array(4, 5))), // array_of_map_of_arrays\n scala.Array(Row(i), Row(i)), // array_of_structs\n Row( // struct_of_arrays_maps_of_structs\n scala.Array(i, i + 1),\n Map(scala.Array(i, i + 1) -> Row(i + 2))\n ),\n Map(i -> (i + 1).toLong, (i + 2) -> (i + 3).toLong), // map_of_prims\n Map(i + 1 -> Row((i * 20).toLong)), // map_of_rows\n {\n val val1 = scala.Array(i, null, i + 1)\n val val2 = scala.Array[Integer]()\n Map(\n i.longValue() -> val1,\n (i + 1).longValue() -> val2\n ) // map_of_arrays\n },\n Map( // map_of_maps\n i.toLong -> Map(i -> i),\n (i + 1).toLong -> Map(i + 2 -> i)\n )\n )\n }\n\n def createNullRow(): Row = {\n Row(Seq.fill(schema.length)(null): _*)\n }\n\n val rows = Seq.range(0, 5).map(i => createRow(i)) ++ Seq(createNullRow())\n\n val df = spark.createDataFrame(spark.sparkContext.parallelize(rows), schema)\n df.repartition(2)\n .write\n .format(\"delta\")\n .save(tablePath)\n }\n\n /** TEST: DeltaDataReaderSuite > read - date types */\n Seq(\"UTC\", \"Iceland\", \"PST\", \"America/Los_Angeles\", \"Etc/GMT+9\", \"Asia/Beirut\",\n \"JST\").foreach { timeZoneId =>\n generateGoldenTable(s\"data-reader-date-types-$timeZoneId\") { tablePath =>\n val timeZone = TimeZone.getTimeZone(timeZoneId)\n TimeZone.setDefault(timeZone)\n\n val timestamp = Timestamp.valueOf(\"2020-01-01 08:09:10\")\n val date = java.sql.Date.valueOf(\"2020-01-01\")\n\n val data = Row(timestamp, date) :: Nil\n val schema = new StructType()\n .add(\"timestamp\", TimestampType)\n .add(\"date\", DateType)\n\n writeDataWithSchema(tablePath, data, schema)\n }\n }\n\n /** TEST: DeltaDataReaderSuite > read - array of primitives */\n generateGoldenTable(\"data-reader-array-primitives\") { tablePath =>\n def createRow(i: Int): Row = {\n Row(Array(i), Array(i.longValue), Array(i.toByte), Array(i.shortValue),\n Array(i % 2 == 0), Array(i.floatValue), Array(i.doubleValue), Array(i.toString),\n Array(Array(i.toByte, i.toByte)),\n Array(new JBigDecimal(i))\n )\n }\n\n val schema = new StructType()\n .add(\"as_array_int\", ArrayType(IntegerType))\n .add(\"as_array_long\", ArrayType(LongType))\n .add(\"as_array_byte\", ArrayType(ByteType))\n .add(\"as_array_short\", ArrayType(ShortType))\n .add(\"as_array_boolean\", ArrayType(BooleanType))\n .add(\"as_array_float\", ArrayType(FloatType))\n .add(\"as_array_double\", ArrayType(DoubleType))\n .add(\"as_array_string\", ArrayType(StringType))\n .add(\"as_array_binary\", ArrayType(BinaryType))\n .add(\"as_array_big_decimal\", ArrayType(DecimalType(1, 0)))\n\n val data = (0 until 10).map(createRow)\n writeDataWithSchema(tablePath, data, schema)\n }\n\n /** TEST: DeltaDataReaderSuite > read - array of complex objects */\n generateGoldenTable(\"data-reader-array-complex-objects\") { tablePath =>\n def createRow(i: Int): Row = {\n Row(\n i,\n Array(Array(Array(i, i, i), Array(i, i, i)), Array(Array(i, i, i), Array(i, i, i))),\n Array(\n Array(Array(Array(i, i, i), Array(i, i, i)), Array(Array(i, i, i), Array(i, i, i))),\n Array(Array(Array(i, i, i), Array(i, i, i)), Array(Array(i, i, i), Array(i, i, i)))\n ),\n Array(\n Map[String, Long](i.toString -> i.toLong),\n Map[String, Long](i.toString -> i.toLong)\n ),\n Array(Row(i), Row(i), Row(i))\n )\n }\n\n val schema = new StructType()\n .add(\"i\", IntegerType)\n .add(\"3d_int_list\", ArrayType(ArrayType(ArrayType(IntegerType))))\n .add(\"4d_int_list\", ArrayType(ArrayType(ArrayType(ArrayType(IntegerType)))))\n .add(\"list_of_maps\", ArrayType(MapType(StringType, LongType)))\n .add(\"list_of_records\", ArrayType(new StructType().add(\"val\", IntegerType)))\n\n val data = (0 until 10).map(createRow)\n writeDataWithSchema(tablePath, data, schema)\n }\n\n /** TEST: DeltaDataReaderSuite > read - map */\n generateGoldenTable(\"data-reader-map\") { tablePath =>\n def createRow(i: Int): Row = {\n Row(\n i,\n Map(i -> i),\n Map(i.toLong -> i.toByte),\n Map(i.toShort -> (i % 2 == 0)),\n Map(i.toFloat -> i.toDouble),\n Map(i.toString -> new JBigDecimal(i)),\n Map(i -> Array(Row(i), Row(i), Row(i)))\n )\n }\n\n val schema = new StructType()\n .add(\"i\", IntegerType)\n .add(\"a\", MapType(IntegerType, IntegerType))\n .add(\"b\", MapType(LongType, ByteType))\n .add(\"c\", MapType(ShortType, BooleanType))\n .add(\"d\", MapType(FloatType, DoubleType))\n .add(\"e\", MapType(StringType, DecimalType(1, 0)))\n .add(\"f\", MapType(IntegerType, ArrayType(new StructType().add(\"val\", IntegerType))))\n\n val data = (0 until 10).map(createRow)\n writeDataWithSchema(tablePath, data, schema)\n }\n\n /** TEST: DeltaDataReaderSuite > read - nested struct */\n generateGoldenTable(\"data-reader-nested-struct\") { tablePath =>\n def createRow(i: Int): Row = Row(Row(i.toString, i.toString, Row(i, i.toLong)), i)\n\n val schema = new StructType()\n .add(\"a\", new StructType()\n .add(\"aa\", StringType)\n .add(\"ab\", StringType)\n .add(\"ac\", new StructType()\n .add(\"aca\", IntegerType)\n .add(\"acb\", LongType)\n )\n )\n .add(\"b\", IntegerType)\n\n val data = (0 until 10).map(createRow)\n writeDataWithSchema(tablePath, data, schema)\n }\n\n /** TEST: DeltaDataReaderSuite > read - nullable field, invalid schema column key */\n generateGoldenTable(\"data-reader-nullable-field-invalid-schema-key\") { tablePath =>\n val data = Row(Seq(null, null, null)) :: Nil\n val schema = new StructType()\n .add(\"array_can_contain_null\", ArrayType(StringType, containsNull = true))\n writeDataWithSchema(tablePath, data, schema)\n }\n\n /** TEST: DeltaDataReaderSuite > test escaped char sequences in path */\n generateGoldenTable(\"data-reader-escaped-chars\") { tablePath =>\n val data = Seq(\"foo1\" -> \"bar+%21\", \"foo2\" -> \"bar+%22\", \"foo3\" -> \"bar+%23\")\n\n data.foreach { row =>\n Seq(row).toDF().write.format(\"delta\").mode(\"append\").partitionBy(\"_2\").save(tablePath)\n }\n }\n\n /** TEST: DeltaDataReaderSuite > #124: decimal decode bug */\n generateGoldenTable(\"124-decimal-decode-bug\") { tablePath =>\n val data = Seq(Row(new JBigDecimal(1000000)))\n val schema = new StructType().add(\"large_decimal\", DecimalType(10, 0))\n writeDataWithSchema(tablePath, data, schema)\n }\n\n /** TEST: DeltaDataReaderSuite > #125: iterator bug */\n generateGoldenTable(\"125-iterator-bug\") { tablePath =>\n val datas = Seq(\n Seq(),\n Seq(1),\n Seq(2), Seq(),\n Seq(3), Seq(), Seq(),\n Seq(4), Seq(), Seq(), Seq(),\n Seq(5)\n )\n datas.foreach { data =>\n data.toDF(\"col1\").write.format(\"delta\").mode(\"append\").save(tablePath)\n }\n }\n\n generateGoldenTable(\"deltatbl-not-allow-write\", createHiveGoldenTableFile) { tablePath =>\n val data = (0 until 10).map(x => (x, s\"foo${x % 2}\"))\n data.toDF(\"a\", \"b\").write.format(\"delta\").save(tablePath)\n }\n\n generateGoldenTable(\"deltatbl-schema-match\", createHiveGoldenTableFile) { tablePath =>\n val data = (0 until 10).map(x => (x, s\"foo${x % 2}\", s\"test${x % 3}\"))\n data.toDF(\"a\", \"b\", \"c\").write.format(\"delta\").partitionBy(\"b\").save(tablePath)\n }\n\n generateGoldenTable(\"deltatbl-non-partitioned\", createHiveGoldenTableFile) { tablePath =>\n val data = (0 until 10).map(x => (x, s\"foo${x % 2}\"))\n data.toDF(\"c1\", \"c2\").write.format(\"delta\").save(tablePath)\n }\n\n generateGoldenTable(\"deltatbl-partitioned\", createHiveGoldenTableFile) { tablePath =>\n val data = (0 until 10).map(x => (x, s\"foo${x % 2}\"))\n data.toDF(\"c1\", \"c2\").write.format(\"delta\").partitionBy(\"c2\").save(tablePath)\n }\n\n generateGoldenTable(\"deltatbl-partition-prune\", createHiveGoldenTableFile) { tablePath =>\n val data = Seq(\n (\"hz\", \"20180520\", \"Jim\", 3),\n (\"hz\", \"20180718\", \"Jone\", 7),\n (\"bj\", \"20180520\", \"Trump\", 1),\n (\"sh\", \"20180512\", \"Jay\", 4),\n (\"sz\", \"20181212\", \"Linda\", 8)\n )\n data.toDF(\"city\", \"date\", \"name\", \"cnt\")\n .write.format(\"delta\").partitionBy(\"date\", \"city\").save(tablePath)\n }\n\n generateGoldenTable(\"deltatbl-touch-files-needed-for-partitioned\", createHiveGoldenTableFile) {\n tablePath =>\n val data = (0 until 10).map(x => (x, s\"foo${x % 2}\"))\n data.toDF(\"c1\", \"c2\").write.format(\"delta\").partitionBy(\"c2\").save(tablePath)\n }\n\n generateGoldenTable(\"deltatbl-special-chars-in-partition-column\", createHiveGoldenTableFile) {\n tablePath =>\n val data = (0 until 10).map(x => (x, s\"+ =%${x % 2}\"))\n data.toDF(\"c1\", \"c2\").write.format(\"delta\").partitionBy(\"c2\").save(tablePath)\n }\n\n generateGoldenTable(\"deltatbl-map-types-correctly\", createHiveGoldenTableFile) { tablePath =>\n val data = Seq(\n TestClass(\n 97.toByte,\n Array(98.toByte, 99.toByte),\n true,\n 4,\n 5L,\n \"foo\",\n 6.0f,\n 7.0,\n 8.toShort,\n new java.sql.Date(60000000L),\n new java.sql.Timestamp(60000000L),\n new java.math.BigDecimal(12345.6789),\n Array(\"foo\", \"bar\"),\n Map(\"foo\" -> 123L),\n TestStruct(\"foo\", 456L)\n )\n )\n data.toDF.write.format(\"delta\").save(tablePath)\n }\n\n generateGoldenTable(\"deltatbl-column-names-case-insensitive\", createHiveGoldenTableFile) {\n tablePath =>\n val data = (0 until 10).map(x => (x, s\"foo${x % 2}\"))\n data.toDF(\"FooBar\", \"BarFoo\").write.format(\"delta\").partitionBy(\"BarFoo\").save(tablePath)\n }\n\n generateGoldenTable(\"deltatbl-deleted-path\", createHiveGoldenTableFile) {\n tablePath =>\n val data = (0 until 10).map(x => (x, s\"foo${x % 2}\"))\n data.toDF(\"c1\", \"c2\").write.format(\"delta\").save(tablePath)\n }\n\n generateGoldenTable(\"deltatbl-incorrect-format-config\", createHiveGoldenTableFile) { tablePath =>\n val data = (0 until 10).map(x => (x, s\"foo${x % 2}\"))\n data.toDF(\"a\", \"b\").write.format(\"delta\").save(tablePath)\n }\n\n generateGoldenTable(\"dv-partitioned-with-checkpoint\") { tablePath =>\n withSQLConf((\"spark.databricks.delta.properties.defaults.enableDeletionVectors\", \"true\")) {\n val data = (0 until 50).map(x => (x%10, x, s\"foo${x % 5}\"))\n data.toDF(\"part\", \"col1\", \"col2\").write\n .format(\"delta\")\n .partitionBy(\"part\")\n .save(tablePath)\n (0 until 15).foreach { n =>\n spark.sql(s\"DELETE FROM delta.`$tablePath` WHERE col1 = ${n*2}\")\n }\n }\n }\n\n generateGoldenTable(\"dv-with-columnmapping\") { tablePath =>\n withSQLConf(\n (\"spark.databricks.delta.properties.defaults.columnMapping.mode\", \"name\"),\n (\"spark.databricks.delta.properties.defaults.enableDeletionVectors\", \"true\")) {\n val data = (0 until 50).map(x => (x%10, x, s\"foo${x % 5}\"))\n data.toDF(\"part\", \"col1\", \"col2\").write\n .format(\"delta\")\n .partitionBy(\"part\")\n .save(tablePath)\n (0 until 15).foreach { n =>\n spark.sql(s\"DELETE FROM delta.`$tablePath` WHERE col1 = ${n*2}\")\n }\n }\n }\n\n def writeBasicTimestampTable(path: String, timeZone: TimeZone): Unit = {\n TimeZone.setDefault(timeZone)\n // Create a partition value of both {year}-{month}-{day} {hour}:{minute}:{second} format and\n // {year}-{month}-{day} {hour}:{minute}:{second}.{microsecond}\n val data = Row(0, Timestamp.valueOf(\"2020-01-01 08:09:10.001\"), Timestamp.valueOf(\"2020-02-01 08:09:10\")) ::\n Row(1, Timestamp.valueOf(\"2021-10-01 08:09:20\"), Timestamp.valueOf(\"1999-01-01 09:00:00\")) ::\n Row(2, Timestamp.valueOf(\"2021-10-01 08:09:20\"), Timestamp.valueOf(\"2000-01-01 09:00:00\")) ::\n Row(3, Timestamp.valueOf(\"1969-01-01 00:00:00\"), Timestamp.valueOf(\"1969-01-01 00:00:00\")) ::\n Row(4, null, null) :: Nil\n\n val schema = new StructType()\n .add(\"id\", IntegerType)\n .add(\"part\", TimestampType)\n .add(\"time\", TimestampType)\n\n spark.createDataFrame(spark.sparkContext.parallelize(data), schema)\n .write\n .format(\"delta\")\n .partitionBy(\"part\")\n .save(path)\n }\n\n for (parquetTimestampType <- SQLConf.ParquetOutputTimestampType.values) {\n generateGoldenTable(s\"kernel-timestamp-${parquetTimestampType.toString}\") { tablePath =>\n withSQLConf((\"spark.sql.parquet.outputTimestampType\", parquetTimestampType.toString)) {\n writeBasicTimestampTable(tablePath, TimeZone.getTimeZone(\"UTC\"))\n }\n }\n }\n\n generateGoldenTable(\"kernel-timestamp-PST\") { tablePath =>\n writeBasicTimestampTable(tablePath, TimeZone.getTimeZone(\"PST\"))\n }\n\n generateGoldenTable(\"parquet-all-types-legacy-format\") { tablePath =>\n withSQLConf((\"spark.sql.parquet.writeLegacyFormat\", \"true\")) {\n generateAllTypesTable(tablePath)\n }\n }\n\n generateGoldenTable(\"parquet-all-types\") { tablePath =>\n // generating using the standard parquet format\n generateAllTypesTable(tablePath)\n }\n\n def generateAllTypesTable(tablePath: String): Unit = {\n val timeZone = java.util.TimeZone.getTimeZone(\"UTC\")\n java.util.TimeZone.setDefault(timeZone)\n import java.sql._\n\n val decimalType = DecimalType(10, 2)\n\n val allDataTypes = Seq(\n ByteType,\n ShortType,\n IntegerType,\n LongType,\n FloatType,\n DoubleType,\n decimalType,\n BooleanType,\n StringType,\n BinaryType,\n DateType,\n TimestampType,\n TimestampNTZType\n )\n\n var fields = allDataTypes.map(dt => {\n val name = if (dt.isInstanceOf[DecimalType]) {\n \"decimal\"\n } else {\n dt.toString\n }\n StructField(name, dt)\n })\n\n fields = fields :+ StructField(\"nested_struct\", new StructType()\n .add(\"aa\", StringType)\n .add(\"ac\", new StructType()\n .add(\"aca\", IntegerType)\n )\n )\n\n fields = fields :+ StructField(\"array_of_prims\", ArrayType(IntegerType))\n fields = fields :+ StructField(\"array_of_arrays\", ArrayType(ArrayType(IntegerType)))\n fields = fields :+ StructField(\n \"array_of_structs\",\n ArrayType(new StructType().add(\"ab\", LongType)))\n\n fields = fields :+ StructField(\n \"map_of_prims\",\n MapType(IntegerType, LongType)\n )\n fields = fields :+ StructField(\n \"map_of_rows\",\n MapType(IntegerType, new StructType().add(\"ab\", LongType))\n )\n fields = fields :+ StructField(\n \"map_of_arrays\",\n MapType(LongType, ArrayType(IntegerType))\n )\n\n val schema = StructType(fields)\n\n def createRow(i: Int): Row = {\n Row(\n if (i % 72 != 0) i.byteValue() else null,\n if (i % 56 != 0) i.shortValue() else null,\n if (i % 23 != 0) i else null,\n if (i % 25 != 0) (i + 1).longValue() else null,\n if (i % 28 != 0) (i * 0.234).floatValue() else null,\n if (i % 54 != 0) (i * 234234.23).doubleValue() else null,\n if (i % 67 != 0) new java.math.BigDecimal(i * 123.52) else null,\n if (i % 87 != 0) i % 2 == 0 else null,\n if (i % 57 != 0) (i).toString else null,\n if (i % 59 != 0) (i).toString.getBytes else null,\n if (i % 61 != 0) new java.sql.Date(i * 20000000L) else null,\n if (i % 62 != 0) new Timestamp(i * 23423523L) else null,\n if (i % 69 != 0) LocalDateTime.ofEpochSecond(i * 234234L, 200012, UTC) else null,\n // nested_struct\n if (i % 63 != 0) {\n if (i % 19 == 0) {\n // write a struct with all fields null\n Row(null, null)\n } else {\n Row(i.toString, if (i % 23 != 0) Row(i) else null)\n }\n } else null,\n // array_of_prims\n if (i % 25 != 0) {\n if (i % 29 == 0) {\n scala.Array()\n } else {\n scala.Array(i, null, i + 1)\n }\n } else null,\n // array_of_arrays\n if (i % 8 != 0) {\n val singleElemArray = scala.Array(i)\n val doubleElemArray = scala.Array(i + 10, i + 20)\n val arrayWithNulls = scala.Array(null, i + 200)\n val singleElemNullArray = scala.Array(null)\n val emptyArray = scala.Array()\n (i % 7) match {\n case 0 => scala.Array(singleElemArray, singleElemArray, arrayWithNulls)\n case 1 => scala.Array(singleElemArray, doubleElemArray, emptyArray)\n case 2 => scala.Array(arrayWithNulls)\n case 3 => scala.Array(singleElemNullArray)\n case 4 => scala.Array(null)\n case 5 => scala.Array(emptyArray)\n case 6 => scala.Array()\n }\n } else null,\n // array_of_structs\n if (i % 10 != 0) {\n scala.Array(Row(i.longValue()), null)\n } else null,\n // map_of_prims\n if (i % 28 != 0) {\n if (i % 30 == 0) {\n Map()\n } else {\n Map(\n i -> (if (i % 29 != 0) (i + 2).longValue() else null),\n (if (i % 27 != 0) i + 2 else i + 3) -> (i + 9).longValue()\n )\n }\n } else null,\n // map_of_rows\n if (i % 25 != 0) {\n Map(i + 1 -> (if (i % 10 == 0) Row((i * 20).longValue()) else null))\n } else null,\n // map_of_arrays\n if (i % 30 != 0) {\n if (i % 24 == 0) {\n Map()\n } else {\n val val1 = if (i % 4 == 0) scala.Array(i, null, i + 1) else scala.Array()\n val val2 = if (i % 7 == 0) scala.Array[Integer]() else scala.Array[Integer](null)\n Map(\n i.longValue() -> val1,\n (i + 1).longValue() -> val2\n )\n }\n } else null\n )\n }\n\n val rows = Seq.range(0, 200).map(i => createRow(i))\n\n val df = spark.createDataFrame(spark.sparkContext.parallelize(rows), schema)\n df.repartition(1)\n .write\n .format(\"delta\")\n .mode(\"append\")\n .save(tablePath)\n }\n\n def writeBasicDecimalTable(tablePath: String): Unit = {\n val data = Seq(\n Seq(\"234\", \"1\", \"2\", \"3\"),\n Seq(\"2342222.23454\", \"111.11\", \"22222.22222\", \"3333333333.3333333333\"),\n Seq(\"0.00004\", \"0.001\", \"0.000002\", \"0.00000000003\"),\n Seq(\"-2342342.23423\", \"-999.99\", \"-99999.99999\", \"-9999999999.9999999999\")\n ).map(_.map(new JBigDecimal(_))).map(Row(_: _*))\n\n val schema = new StructType()\n .add(\"part\", new DecimalType(12, 5)) // serialized to a string\n .add(\"col1\", new DecimalType(5, 2)) // INT32: 1 <= precision <= 9\n .add(\"col2\", new DecimalType(10, 5)) // INT64: 10 <= precision <= 18\n .add(\"col3\", new DecimalType(20, 10)) // FIXED_LEN_BYTE_ARRAY\n\n spark.createDataFrame(spark.sparkContext.parallelize(data), schema)\n .repartition(1)\n .write\n .format(\"delta\")\n .partitionBy(\"part\")\n .save(tablePath)\n }\n\n generateGoldenTable(\"basic-decimal-table\") { tablePath =>\n writeBasicDecimalTable(tablePath)\n }\n\n generateGoldenTable(\"basic-decimal-table-legacy\") { tablePath =>\n withSQLConf((\"spark.sql.parquet.writeLegacyFormat\", \"true\")) {\n writeBasicDecimalTable(tablePath)\n }\n }\n\n generateGoldenTable(\"decimal-various-scale-precision\") { tablePath =>\n val fields = ArrayBuffer[StructField]()\n Seq(0, 4, 7, 12, 15, 18, 25, 35, 38).foreach { precision =>\n Seq.range(start = 0, end = precision, step = 6).foreach { scale =>\n fields.append(\n StructField(s\"decimal_${precision}_${scale}\", DecimalType(precision, scale)))\n }\n }\n\n val schema = StructType(fields)\n\n val random = new Random(27 /* seed */)\n def generateRandomBigDecimal(precision: Int, scale: Int): JBigDecimal = {\n // Generate a random BigInteger with the specified precision\n val unscaledValue = new BigInteger(precision, random)\n\n // Create a BigDecimal with the unscaled value and the specified scale\n new JBigDecimal(unscaledValue, scale)\n }\n\n val rows = ArrayBuffer[Row]()\n Seq.range(start = 0, end = 3).foreach { i =>\n val rowValues = ArrayBuffer[BigDecimal]()\n Seq(0, 4, 7, 12, 15, 18, 25, 35, 38).foreach { precision =>\n Seq.range(start = 0, end = precision, step = 3).foreach { scale =>\n i match {\n case 0 =>\n rowValues.append(null)\n case 1 =>\n // Generate a positive random BigDecimal with the specified precision and scale\n rowValues.append(generateRandomBigDecimal(precision, scale))\n case 2 =>\n // Generate a negative random BigDecimal with the specified precision and scale\n rowValues.append(generateRandomBigDecimal(precision, scale).negate())\n }\n }\n }\n rows.append(Row(rowValues: _*))\n }\n\n spark.createDataFrame(spark.sparkContext.parallelize(rows), schema)\n .repartition(1)\n .write\n .format(\"delta\")\n .save(tablePath)\n }\n\n for (parquetFormat <- Seq(\"v1\", \"v2\")) {\n // PARQUET_1_0 doesn't support dictionary encoding for FIXED_LEN_BYTE_ARRAY (only PARQUET_2_0)\n generateGoldenTable(s\"parquet-decimal-dictionaries-$parquetFormat\") { tablePath =>\n\n def withHadoopConf(key: String, value: String)(f: => Unit): Unit = {\n try {\n spark.sparkContext.hadoopConfiguration.set(key, value)\n f\n } finally {\n spark.sparkContext.hadoopConfiguration.unset(key)\n }\n }\n\n withHadoopConf(\"parquet.writer.version\", parquetFormat) {\n val data = (0 until 1000000).map { i =>\n Row(i, JBigDecimal.valueOf(i % 5), JBigDecimal.valueOf(i % 6), JBigDecimal.valueOf(i % 2))\n }\n\n val schema = new StructType()\n .add(\"id\", IntegerType)\n .add(\"col1\", new DecimalType(9, 0)) // INT32: 1 <= precision <= 9\n .add(\"col2\", new DecimalType(12, 0)) // INT64: 10 <= precision <= 18\n .add(\"col3\", new DecimalType(25, 0)) // FIXED_LEN_BYTE_ARRAY\n\n spark.createDataFrame(spark.sparkContext.parallelize(data), schema)\n .repartition(1)\n .write\n .format(\"delta\")\n .save(tablePath)\n }\n }\n }\n\n generateGoldenTable(\"parquet-decimal-type\") { tablePath =>\n\n def expand(n: JBigDecimal): JBigDecimal = {\n n.scaleByPowerOfTen(5).add(n)\n }\n\n val data = (0 until 99998).map { i =>\n if (i % 85 == 0) {\n val n = JBigDecimal.valueOf(i)\n Row(i, n.movePointLeft(1), n, n)\n } else {\n val negation = if (i % 33 == 0) {\n -1\n } else {\n 1\n }\n val n = JBigDecimal.valueOf(i*negation)\n\n Row(\n i,\n n.movePointLeft(1),\n expand(n).movePointLeft(5),\n expand(expand(expand(n))).movePointLeft(5)\n )\n }\n }\n\n val schema = new StructType()\n .add(\"id\", IntegerType)\n .add(\"col1\", new DecimalType(5, 1)) // INT32: 1 <= precision <= 9\n .add(\"col2\", new DecimalType(10, 5)) // INT64: 10 <= precision <= 18\n .add(\"col3\", new DecimalType(20, 5)) // FIXED_LEN_BYTE_ARRAY\n\n spark.createDataFrame(spark.sparkContext.parallelize(data), schema)\n .repartition(1)\n .write\n .format(\"delta\")\n .save(tablePath)\n }\n\n /* START: TIMESTAMP_NTZ golden tables */\n def generateTimestampNtzTable(tablePath: String): Unit = {\n spark.sql(\n s\"\"\"\n | CREATE TABLE delta.`$tablePath`(id INTEGER, tsNtz TIMESTAMP_NTZ, tsNtzPartition TIMESTAMP_NTZ)\n | USING DELTA\n | PARTITIONED BY(tsNtzPartition)\n \"\"\".stripMargin)\n\n spark.sql(\n s\"\"\"\n | INSERT INTO delta.`$tablePath` VALUES\n | (0, '2021-11-18 02:30:00.123456','2021-11-18 02:30:00.123456'),\n | (1, '2013-07-05 17:01:00.123456','2021-11-18 02:30:00.123456'),\n | (2, NULL,'2021-11-18 02:30:00.123456'),\n | (3, '2021-11-18 02:30:00.123456','2013-07-05 17:01:00.123456'),\n | (4, '2013-07-05 17:01:00.123456','2013-07-05 17:01:00.123456'),\n | (5, NULL,'2013-07-05 17:01:00.123456'),\n | (6, '2021-11-18 02:30:00.123456', NULL),\n | (7, '2013-07-05 17:01:00.123456', NULL),\n | (8, NULL, NULL)\n |\"\"\".stripMargin)\n }\n\n generateGoldenTable(\"data-reader-timestamp_ntz\") { tablePath =>\n generateTimestampNtzTable(tablePath)\n }\n\n Seq(\"id\", \"name\").foreach {\n columnMappingMode => {\n generateGoldenTable(s\"data-reader-timestamp_ntz-$columnMappingMode-mode\") { tablePath =>\n withSQLConf(\n (\"spark.databricks.delta.properties.defaults.columnMapping.mode\", columnMappingMode)) {\n generateTimestampNtzTable(tablePath)\n }\n }\n }\n }\n /* END: TIMESTAMP_NTZ golden tables */\n\n generateGoldenTable(\"basic-with-inserts-deletes-checkpoint\") { tablePath =>\n // scalastyle:off line.size.limit\n spark.range(0, 10).repartition(1).write.format(\"delta\").mode(\"append\").save(tablePath)\n spark.range(10, 20).repartition(1).write.format(\"delta\").mode(\"append\").save(tablePath)\n spark.range(20, 30).repartition(1).write.format(\"delta\").mode(\"append\").save(tablePath)\n spark.range(30, 40).repartition(1).write.format(\"delta\").mode(\"append\").save(tablePath)\n spark.range(40, 50).repartition(1).write.format(\"delta\").mode(\"append\").save(tablePath)\n sql(s\"DELETE FROM delta.`$tablePath` WHERE id >= 5 AND id <= 9\")\n sql(s\"DELETE FROM delta.`$tablePath` WHERE id >= 15 AND id <= 19\")\n sql(s\"DELETE FROM delta.`$tablePath` WHERE id >= 25 AND id <= 29\")\n sql(s\"DELETE FROM delta.`$tablePath` WHERE id >= 35 AND id <= 39\")\n sql(s\"DELETE FROM delta.`$tablePath` WHERE id >= 45 AND id <= 49\")\n spark.range(50, 60).repartition(1).write.format(\"delta\").mode(\"append\").save(tablePath)\n spark.range(60, 70).repartition(1).write.format(\"delta\").mode(\"append\").save(tablePath)\n spark.range(70, 80).repartition(1).write.format(\"delta\").mode(\"append\").save(tablePath)\n sql(s\"DELETE FROM delta.`$tablePath` WHERE id >= 66\")\n // scalastyle:on line.size.limit\n }\n\n generateGoldenTable(\"multi-part-checkpoint\") { tablePath =>\n withSQLConf(\n (\"spark.databricks.delta.checkpoint.partSize\", \"5\"),\n (\"spark.databricks.delta.properties.defaults.checkpointInterval\", \"1\")\n ) {\n spark.range(1).repartition(1).write.format(\"delta\").save(tablePath)\n spark.range(30).repartition(9).write.format(\"delta\").mode(\"append\").save(tablePath)\n }\n }\n\n Seq(\"parquet\", \"json\").foreach { ckptFormat =>\n val tbl = \"tbl\"\n generateGoldenTable(s\"v2-checkpoint-$ckptFormat\") { tablePath =>\n withTable(tbl) {\n withSQLConf(\n (DeltaSQLConf.DELTA_CHECKPOINT_PART_SIZE.key, \"2\"),\n (\"spark.databricks.delta.properties.defaults.checkpointInterval\", \"2\"),\n (DeltaSQLConf.CHECKPOINT_V2_TOP_LEVEL_FILE_FORMAT.key, ckptFormat)) {\n spark.conf.set(DeltaSQLConf.CHECKPOINT_V2_TOP_LEVEL_FILE_FORMAT.key, ckptFormat)\n sql(s\"CREATE TABLE $tbl (id LONG) USING delta LOCATION '$tablePath'\")\n sql(s\"ALTER TABLE $tbl SET TBLPROPERTIES('delta.checkpointPolicy' = 'v2')\")\n spark.range(10).repartition(4)\n .write.format(\"delta\").mode(\"append\").saveAsTable(tbl)\n }\n }\n }\n }\n\n generateGoldenTable(\"no-delta-log-folder\") { tablePath =>\n spark.range(20).write.format(\"parquet\").save(tablePath)\n }\n\n generateGoldenTable(\"log-replay-latest-metadata-protocol\") { tablePath =>\n spark.range(20).toDF(\"col1\")\n .write.format(\"delta\").save(tablePath)\n // update the table schema\n spark.range(20).toDF(\"col1\").withColumn(\"col2\", 'col1 % 2)\n .write.format(\"delta\").mode(\"append\").option(\"mergeSchema\", \"true\").save(tablePath)\n // update the protocol version\n DeltaTable.forPath(spark, tablePath).upgradeTableProtocol(3, 7)\n }\n\n generateGoldenTable(\"only-checkpoint-files\") { tablePath =>\n withSQLConf((\"spark.databricks.delta.properties.defaults.checkpointInterval\", \"1\")) {\n spark.range(10).repartition(10).write.format(\"delta\").save(tablePath)\n spark.sql(s\"DELETE FROM delta.`$tablePath` WHERE id < 5\")\n spark.range(20).write.format(\"delta\").mode(\"append\").save(tablePath)\n }\n }\n\n generateGoldenTable(\"log-replay-special-characters-a\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n new File(log.logPath.toUri).mkdirs()\n\n val add = AddFile(new Path(\"special p@#h\").toUri.toString, Map.empty, 100L,\n 10L, dataChange = true)\n val remove = add.remove\n\n log.startTransaction().commitManually(add)\n log.startTransaction().commitManually(remove)\n }\n\n generateGoldenTable(\"log-replay-special-characters-b\") { tablePath =>\n val log = DeltaLog.forTable(spark, new Path(tablePath))\n new File(log.logPath.toUri).mkdirs()\n\n val add = AddFile(new Path(\"special p@#h\").toUri.toString, Map.empty, 100L,\n 10L, dataChange = true)\n\n log.startTransaction().commitManually(add)\n }\n\n generateGoldenTable(\"log-replay-dv-key-cases\") { tablePath =>\n withSQLConf((\"spark.databricks.delta.properties.defaults.enableDeletionVectors\", \"true\")) {\n spark.range(50).repartition(1).write.format(\"delta\").save(tablePath)\n (0 until 3).foreach { n =>\n spark.sql(s\"DELETE FROM delta.`$tablePath` WHERE id = ${n*7}\")\n }\n }\n }\n\n generateGoldenTable(\"basic-with-vacuum-protocol-check-feature\") { tablePath =>\n val data = (0 until 100).map(x => (x, s\"val=$x\"))\n data.toDF(\"id\", \"str\").write.format(\"delta\").save(tablePath)\n sql(s\"\"\"\n |ALTER TABLE delta.`$tablePath`\n |SET TBLPROPERTIES('delta.feature.vacuumProtocolCheck' = 'supported')\n |\"\"\".stripMargin)\n }\n\n generateGoldenTable(\"basic-with-inserts-updates\") { tablePath =>\n val data = (0 until 100).map(x => (x, s\"val=$x\"))\n data.toDF(\"id\", \"str\").write.format(\"delta\").save(tablePath)\n sql(s\"UPDATE delta.`$tablePath` SET str = 'N/A' WHERE id < 50\")\n }\n\n generateGoldenTable(\"basic-with-inserts-merge\") { tablePath =>\n val data = (0 until 100).map(x => (x, s\"val=$x\"))\n data.toDF(\"id\", \"str\").write.format(\"delta\").save(tablePath)\n spark.range(50, 150).createTempView(\"source\")\n sql(\n s\"\"\"\n |MERGE INTO delta.`$tablePath` t\n |USING source\n |ON source.id = t.id\n |WHEN MATCHED\n | THEN UPDATE SET str = 'N/A'\n |WHEN NOT MATCHED\n | THEN INSERT (id, str) VALUES (source.id, 'EXT')\n |WHEN NOT MATCHED BY SOURCE AND t.id < 10\n | THEN DELETE\n |\"\"\".stripMargin)\n }\n\n generateGoldenTable(\"basic-with-inserts-overwrite-restore\") { tablePath =>\n spark.range(100).write.format(\"delta\").save(tablePath)\n spark.range(100, 200).write.format(\"delta\").mode(\"append\").save(tablePath)\n spark.range(500, 1000).write.format(\"delta\").mode(\"overwrite\").save(tablePath)\n sql(s\"RESTORE TABLE delta.`$tablePath` TO VERSION AS OF 1\")\n }\n\n /* ----- Data skipping tables for Kernel ------ */\n\n def writeBasicStatsAllTypesTable(tablePath: String): Unit = {\n val schema = new StructType()\n .add(\"as_int\", IntegerType)\n .add(\"as_long\", LongType)\n .add(\"as_byte\", ByteType)\n .add(\"as_short\", ShortType)\n .add(\"as_float\", FloatType)\n .add(\"as_double\", DoubleType)\n .add(\"as_string\", StringType)\n .add(\"as_date\", DateType)\n .add(\"as_timestamp\", TimestampType)\n .add(\"as_big_decimal\", DecimalType(1, 0))\n\n writeDataWithSchema(\n tablePath,\n Row(0, 0.longValue, 0.byteValue, 0.shortValue, 0.floatValue, 0.doubleValue, \"0\",\n java.sql.Date.valueOf(\"2000-01-01\"), Timestamp.valueOf(\"2000-01-01 00:00:00\"),\n new JBigDecimal(0)) :: Nil,\n schema\n )\n }\n generateGoldenTable(\"data-skipping-basic-stats-all-types\") { tablePath =>\n writeBasicStatsAllTypesTable(tablePath)\n }\n Seq(\"name\", \"id\").foreach { columnMappingMode =>\n generateGoldenTable(s\"data-skipping-basic-stats-all-types-columnmapping-$columnMappingMode\") {\n tablePath =>\n withSQLConf(\n (\"spark.databricks.delta.properties.defaults.columnMapping.mode\", columnMappingMode)) {\n writeBasicStatsAllTypesTable(tablePath)\n }\n }\n }\n generateGoldenTable(\"data-skipping-basic-stats-all-types-checkpoint\") { tablePath =>\n withSQLConf(\n (\"spark.databricks.delta.properties.defaults.checkpointInterval\", \"1\")\n ) {\n writeBasicStatsAllTypesTable(tablePath)\n }\n }\n\n generateGoldenTable(\"data-skipping-change-stats-collected-across-versions\") { tablePath =>\n val schema = new StructType()\n .add(\"col1\", IntegerType)\n .add(\"col2\", IntegerType)\n // write stats for all columns\n writeDataWithSchema(\n tablePath,\n Row(0, 0) :: Nil,\n schema\n )\n // write stats for just 1 column\n sql(\n s\"\"\"\n |ALTER TABLE delta.`$tablePath`\n |SET TBLPROPERTIES('delta.dataSkippingNumIndexedCols' = 1)\n |\"\"\".stripMargin)\n writeDataWithSchema(\n tablePath,\n Row(0, 0) :: Nil,\n schema)\n // write stats for no columns\n sql(\n s\"\"\"\n |ALTER TABLE delta.`$tablePath`\n |SET TBLPROPERTIES('delta.dataSkippingNumIndexedCols' = 0)\n |\"\"\".stripMargin)\n writeDataWithSchema(\n tablePath,\n Row(0, 0) :: Nil,\n schema)\n }\n\n generateGoldenTable(\"data-skipping-partition-and-data-column\") { tablePath =>\n val schema = new StructType()\n .add(\"part\", IntegerType)\n .add(\"id\", IntegerType)\n writeDataWithSchema(\n tablePath,\n Row(1, 0) :: Nil,\n schema\n )\n writeDataWithSchema(\n tablePath,\n Row(1, 1) :: Nil,\n schema)\n writeDataWithSchema(\n tablePath,\n Row(0, 1) :: Nil,\n schema)\n writeDataWithSchema(\n tablePath,\n Row(0, 0) :: Nil,\n schema)\n }\n\n generateGoldenTable(\"commit-info-containing-arbitrary-operationParams-types\") { tablePath =>\n spark.sql(\n f\"\"\"\n |CREATE TABLE delta.`$tablePath`\n |USING DELTA\n |PARTITIONED BY (month)\n |TBLPROPERTIES (delta.enableChangeDataFeed = true)\n |AS\n |SELECT 1 AS id, 1 AS month\"\"\".stripMargin)\n\n // Add some data\n spark.sql(\"INSERT INTO delta.`%s` VALUES (2, 2)\".format(tablePath))\n\n // Run optimize that generates a commitInfo with arbitrary value types\n // operationParameters\n spark.sql(\"OPTIMIZE delta.`%s` ZORDER BY id\".format(tablePath))\n }\n}\n\ncase class TestStruct(f1: String, f2: Long)\n\n/** A special test class that covers all Spark types we support in the Hive connector. */\ncase class TestClass(\n c1: Byte,\n c2: Array[Byte],\n c3: Boolean,\n c4: Int,\n c5: Long,\n c6: String,\n c7: Float,\n c8: Double,\n c9: Short,\n c10: java.sql.Date,\n c11: java.sql.Timestamp,\n c12: BigDecimal,\n c13: Array[String],\n c14: Map[String, Long],\n c15: TestStruct\n)\n\ncase class OneItem[T](t: T)\n" }, { "path": "connectors/licenses/LICENSE-apache-spark.txt", "content": "\n Apache License\n Version 2.0, January 2004\n 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However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n\n END OF TERMS AND CONDITIONS\n\n APPENDIX: How to apply the Apache License to your work.\n\n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"[]\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright [yyyy] [name of copyright owner]\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n" }, { "path": "connectors/licenses/LICENSE-parquet4s.txt", "content": "MIT License\n\nCopyright (c) 2018 Marcin Jakubowski\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE." }, { "path": "contribs/src/main/scala/io/delta/storage/IBMCOSLogStore.scala", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage io.delta.storage\n\nimport com.google.common.base.Throwables\nimport java.io.IOException\nimport java.nio.charset.StandardCharsets.UTF_8\nimport java.nio.file.FileAlreadyExistsException\n\nimport org.apache.hadoop.conf.Configuration\nimport org.apache.hadoop.fs.Path\n\nimport org.apache.spark.SparkConf\nimport org.apache.spark.annotation.Unstable\n\n/**\n * :: Unstable ::\n *\n * LogStore implementation for IBM Cloud Object Storage.\n *\n * We assume the following from COS's [[FileSystem]] implementations:\n * - Write on COS is all-or-nothing, whether overwrite or not.\n * - Write is atomic.\n * Note: Write is atomic when using the Stocator v1.1.1+ - Storage Connector for Apache Spark\n * (https://github.com/CODAIT/stocator) by setting the configuration `fs.cos.atomic.write` to true\n * (for more info see the documentation for Stocator)\n * - List-after-write is consistent.\n *\n * @note This class is not meant for direct access but for configuration based on storage system.\n * See https://docs.delta.io/latest/delta-storage.html for details.\n */\n@Unstable\nclass IBMCOSLogStore(sparkConf: SparkConf, initHadoopConf: Configuration)\n extends org.apache.spark.sql.delta.storage.HadoopFileSystemLogStore(sparkConf, initHadoopConf) {\n val preconditionFailedExceptionMessage =\n \"At least one of the preconditions you specified did not hold\"\n\n assert(initHadoopConf.getBoolean(\"fs.cos.atomic.write\", false) == true,\n \"'fs.cos.atomic.write' must be set to true to use IBMCOSLogStore \" +\n \"in order to enable atomic write\")\n\n override def write(path: Path, actions: Iterator[String], overwrite: Boolean = false): Unit = {\n write(path, actions, overwrite, getHadoopConfiguration)\n }\n\n override def write(\n path: Path,\n actions: Iterator[String],\n overwrite: Boolean,\n hadoopConf: Configuration): Unit = {\n val fs = path.getFileSystem(hadoopConf)\n\n val exists = fs.exists(path)\n if (exists && overwrite == false) {\n throw new FileAlreadyExistsException(path.toString)\n } else {\n // write is atomic when overwrite == false\n val stream = fs.create(path, overwrite)\n try {\n actions.map(_ + \"\\n\").map(_.getBytes(UTF_8)).foreach(stream.write)\n stream.close()\n } catch {\n case e: IOException if isPreconditionFailure(e) =>\n if (fs.exists(path)) {\n throw new FileAlreadyExistsException(path.toString)\n } else {\n throw new IllegalStateException(s\"Failed due to concurrent write\", e)\n }\n }\n }\n }\n\n private def isPreconditionFailure(x: Throwable): Boolean = {\n Throwables.getCausalChain(x)\n .stream()\n .filter(p => p != null)\n .filter(p => p.getMessage != null)\n .filter(p => p.getMessage.contains(preconditionFailedExceptionMessage))\n .findFirst\n .isPresent;\n }\n\n override def invalidateCache(): Unit = {}\n\n override def isPartialWriteVisible(path: Path): Boolean = false\n\n override def isPartialWriteVisible(path: Path, hadoopConf: Configuration): Boolean = false\n}\n" }, { "path": "contribs/src/main/scala/io/delta/storage/OracleCloudLogStore.scala", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage io.delta.storage\n\nimport org.apache.hadoop.conf.Configuration\nimport org.apache.hadoop.fs.Path\n\nimport org.apache.spark.SparkConf\nimport org.apache.spark.annotation.Unstable\n\n/**\n * :: Unstable ::\n *\n * LogStore implementation for OCI (Oracle Cloud Infrastructure).\n *\n * We assume the following from OCI (Oracle Cloud Infrastructure)'s BmcFilesystem implementations:\n * - Rename without overwrite is atomic.\n * - List-after-write is consistent.\n *\n * Regarding file creation, this implementation:\n * - Uses atomic rename when overwrite is false; if the destination file exists or the rename\n * fails, throws an exception.\n * - Uses create-with-overwrite when overwrite is true. This does not make the file atomically\n * visible and therefore the caller must handle partial files.\n *\n * @note This class is not meant for direct access but for configuration based on storage system.\n * See https://docs.delta.io/latest/delta-storage.html for details.\n */\n@Unstable\nclass OracleCloudLogStore(sparkConf: SparkConf, initHadoopConf: Configuration)\n extends org.apache.spark.sql.delta.storage.HadoopFileSystemLogStore(sparkConf, initHadoopConf) {\n\n override def write(path: Path, actions: Iterator[String], overwrite: Boolean = false): Unit = {\n write(path, actions, overwrite, getHadoopConfiguration)\n }\n\n override def write(\n path: Path,\n actions: Iterator[String],\n overwrite: Boolean,\n hadoopConf: Configuration): Unit = {\n writeWithRename(path, actions, overwrite, hadoopConf)\n }\n\n override def invalidateCache(): Unit = {}\n\n override def isPartialWriteVisible(path: Path): Boolean = true\n\n override def isPartialWriteVisible(path: Path, hadoopConf: Configuration): Boolean = true\n}\n" }, { "path": "contribs/src/test/scala/io/delta/storage/IBMCOSLogStoreSuite.scala", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage io.delta.storage\n\nimport org.apache.spark.sql.delta.{FakeFileSystem, LogStoreSuiteBase}\n\nclass IBMCOSLogStoreSuite extends LogStoreSuiteBase {\n\n protected override def sparkConf = {\n super.sparkConf.set(logStoreClassConfKey, logStoreClassName)\n .set(\"spark.hadoop.fs.cos.atomic.write\", \"true\")\n }\n\n override val logStoreClassName: String = classOf[IBMCOSLogStore].getName\n\n testHadoopConf(\n expectedErrMsg = \".*No FileSystem for scheme.*fake.*\",\n \"fs.fake.impl\" -> classOf[FakeFileSystem].getName,\n \"fs.fake.impl.disable.cache\" -> \"true\")\n\n protected def shouldUseRenameToWriteCheckpoint: Boolean = false\n}\n" }, { "path": "contribs/src/test/scala/io/delta/storage/OracleCloudLogStoreSuite.scala", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\npackage io.delta.storage\n\nimport org.apache.spark.sql.delta.{FakeFileSystem, LogStoreSuiteBase}\n\nclass OracleCloudLogStoreSuite extends LogStoreSuiteBase {\n\n override val logStoreClassName: String = classOf[OracleCloudLogStore].getName\n\n testHadoopConf(\n expectedErrMsg = \"No FileSystem for scheme \\\"fake\\\"\",\n \"fs.fake.impl\" -> classOf[FakeFileSystem].getName,\n \"fs.fake.impl.disable.cache\" -> \"true\")\n\n protected def shouldUseRenameToWriteCheckpoint: Boolean = true\n}\n" }, { "path": "dev/check-delta-connect-codegen-python.py", "content": "#!/usr/bin/env python3\n\n#\n# Copyright (2024) The Delta Lake Project Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n# Utility for checking whether generated the Delta Connect Python protobuf codes are in sync.\n# usage: ./dev/check-delta-connect-codegen-python.py\n\nimport os\nimport sys\nimport filecmp\nimport tempfile\nimport subprocess\n\n# Location of your Delta git development area\nDELTA_HOME = os.environ.get(\"DELTA_HOME\", os.path.abspath(os.path.join(__file__, os.pardir, os.pardir)))\n\n\ndef fail(msg):\n print(msg)\n sys.exit(-1)\n\n\ndef run_cmd(cmd):\n print(f\"RUN: {cmd}\")\n if isinstance(cmd, list):\n return subprocess.check_output(cmd).decode(\"utf-8\")\n else:\n return subprocess.check_output(cmd.split(\" \")).decode(\"utf-8\")\n\n\ndef check_connect_protos():\n generated_python_proto_codes_path = os.path.join(DELTA_HOME, \"python\", \"delta\", \"connect\", \"proto\")\n print(f\"Start checking the generated codes in {generated_python_proto_codes_path}\")\n generate_python_proto_codes_file = os.path.join(DELTA_HOME, \"dev\", \"delta-connect-gen-protos.sh\")\n with tempfile.TemporaryDirectory() as tmp:\n run_cmd([generate_python_proto_codes_file, tmp])\n result = filecmp.dircmp(\n generated_python_proto_codes_path,\n tmp,\n ignore=[\"__init__.py\", \"__pycache__\"],\n )\n success = True\n\n if len(result.left_only) > 0:\n print(f\"Unexpected files: {result.left_only}\")\n success = False\n\n if len(result.right_only) > 0:\n print(f\"Missing files: {result.right_only}\")\n success = False\n\n if len(result.funny_files) > 0:\n print(f\"Incomparable files: {result.funny_files}\")\n success = False\n\n if len(result.diff_files) > 0:\n print(f\"Different files: {result.diff_files}\")\n success = False\n\n if success:\n print(f\"Finish checking the generated codes in {generated_python_proto_codes_path}: SUCCESS\")\n else:\n fail(\n f\"Generated files for {generated_python_proto_codes_path} are out of sync! \" +\n f\"Please run {generate_python_proto_codes_file}.\"\n )\n\n\ncheck_connect_protos()\n" }, { "path": "dev/checkstyle-suppressions.xml", "content": "\n\n\n\n\n\n\n\n" }, { "path": "dev/connectors-checkstyle.xml", "content": "\n\n\n\n\n\n\n \n\n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "dev/copyrightHeader", "content": "\\/\\*\n \\* Copyright \\(\\d\\d\\d\\d\\) The Delta Lake Project Authors\\.\n \\*\n \\* Licensed under the Apache License, Version 2\\.0 \\(the \"License\"\\);\n \\* you may not use this file except in compliance with the License\\.\n \\* You may obtain a copy of the License at\n \\*\n \\* http:\\/\\/www\\.apache\\.org\\/licenses\\/LICENSE-2\\.0\n \\*\n \\* Unless required by applicable law or agreed to in writing, software\n \\* distributed under the License is distributed on an \"AS IS\" BASIS,\n \\* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied\\.\n \\* See the License for the specific language governing permissions and\n \\* limitations under the License\\.\n \\*\\/" }, { "path": "dev/delta-connect-gen-protos.sh", "content": "#!/usr/bin/env bash\n\n#\n# Copyright (2024) The Delta Lake Project Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\nexport PATH=\"$PATH:~/buf/bin\"\n\nset -ex\n\nif [[ $# -gt 1 ]]; then\n echo \"Illegal number of parameters.\"\n echo \"Usage: $0 [path]\"\n exit -1\nfi\n\nDELTA_HOME=\"$(cd \"`dirname $0`\"/..; pwd)\"\ncd \"$DELTA_HOME\"\n\n\nOUTPUT_PATH=${DELTA_HOME}/python/delta/connect/proto/\nif [[ $# -eq 1 ]]; then\n rm -Rf $1\n mkdir -p $1\n OUTPUT_PATH=$1\nfi\n\npushd ${DELTA_HOME}/spark-connect/common/src/main\n\nLICENSE=$(cat <<'EOF'\n#\n# Copyright (2024) The Delta Lake Project Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\nEOF)\necho \"$LICENSE\" > /tmp/tmp_licence\n\n\n# Delete the old generated protobuf files.\nrm -Rf gen\n\n# Now, regenerate the new files\nbuf generate --debug -vvv\n\n# We need to edit the generated python files to account for the actual package location and not\n# the location generated by proto.\nfor f in `find gen/proto/python/delta/connect -name \"*.py*\"`; do\n # First fix the imports.\n if [[ $f == *_pb2.py || $f == *_pb2_grpc.py ]]; then\n sed \\\n -e 's/import spark.connect./import pyspark.sql.connect.proto./g' \\\n -e \"s/DESCRIPTOR, 'spark.connect/DESCRIPTOR, 'pyspark.sql.connect.proto/g\" \\\n -e 's/from spark.connect import/from pyspark.sql.connect.proto import/g' \\\n -e \"s/DESCRIPTOR, 'delta.connect/DESCRIPTOR, 'delta.connect.proto/g\" \\\n -e 's/from delta.connect import/from delta.connect.proto import/g' \\\n $f > $f.tmp\n mv $f.tmp $f\n elif [[ $f == *.pyi ]]; then\n sed \\\n -e 's/import spark.connect./import pyspark.sql.connect.proto./g' \\\n -e 's/spark.connect./pyspark.sql.connect.proto./g' \\\n -e 's/import delta.connect./import delta.connect.proto./g' \\\n -e 's/delta.connect./delta.connect.proto./g' \\\n $f > $f.tmp\n mv $f.tmp $f\n fi\n\n\n # Prepend the Apache licence header to the files.\n cp $f $f.bak\n cat /tmp/tmp_licence $f.bak > $f\n LC=$(wc -l < $f)\n echo $LC\n if [[ $f == *_grpc.py && $LC -eq 20 ]]; then\n rm $f\n fi\n rm $f.bak\ndone\n\nblack --config $DELTA_HOME/dev/pyproject.toml gen/proto/python/delta/connect\n\n# Last step copy the result files to the destination module.\nfor f in `find gen/proto/python/delta/connect -name \"*.py*\"`; do\n cp $f $OUTPUT_PATH\ndone\n\n# Clean up everything.\nrm -Rf gen\n" }, { "path": "dev/kernel-checkstyle.xml", "content": "\n\n\n\n\n\n\n \n\n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n\n \n \n \n \n \n \n\n" }, { "path": "dev/lint-python", "content": "#!/usr/bin/env bash\n#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# define test binaries + versions\nPYDOCSTYLE_BUILD=\"pydocstyle\"\nMINIMUM_PYDOCSTYLE=\"3.0.0\"\n\nFLAKE8_BUILD=\"flake8\"\nMINIMUM_FLAKE8=\"3.5.0\"\n\nPYCODESTYLE_BUILD=\"pycodestyle\"\nMINIMUM_PYCODESTYLE=\"2.4.0\"\n\n\nfunction compile_python_test {\n local COMPILE_STATUS=\n local COMPILE_REPORT=\n\n if [[ ! \"$1\" ]]; then\n echo \"No python files found! Something is very wrong -- exiting.\"\n exit 1;\n fi\n\n # compileall: https://docs.python.org/2/library/compileall.html\n echo \"starting python compilation test...\"\n COMPILE_REPORT=$( (python3 -B -mcompileall -q -l $1) 2>&1)\n COMPILE_STATUS=$?\n\n if [ $COMPILE_STATUS -ne 0 ]; then\n echo \"Python compilation failed with the following errors:\"\n echo \"$COMPILE_REPORT\"\n echo \"$COMPILE_STATUS\"\n exit \"$COMPILE_STATUS\"\n else\n echo \"python compilation succeeded.\"\n echo\n fi\n}\n\nfunction pycodestyle_test {\n local PYCODESTYLE_STATUS=\n local PYCODESTYLE_REPORT=\n local RUN_LOCAL_PYCODESTYLE=\n local VERSION=\n local EXPECTED_PYCODESTYLE=\n local PYCODESTYLE_SCRIPT_PATH=\"$DELTA_ROOT_DIR/dev/pycodestyle-$MINIMUM_PYCODESTYLE.py\"\n local PYCODESTYLE_SCRIPT_REMOTE_PATH=\"https://raw.githubusercontent.com/PyCQA/pycodestyle/$MINIMUM_PYCODESTYLE/pycodestyle.py\"\n\n if [[ ! \"$1\" ]]; then\n echo \"No python files found! Something is very wrong -- exiting.\"\n exit 1;\n fi\n\n # check for locally installed pycodestyle & version\n RUN_LOCAL_PYCODESTYLE=\"False\"\n if hash \"$PYCODESTYLE_BUILD\" 2> /dev/null; then\n VERSION=$( $PYCODESTYLE_BUILD --version 2> /dev/null)\n EXPECTED_PYCODESTYLE=$( (python3 -c 'from distutils.version import LooseVersion;\n print(LooseVersion(\"\"\"'${VERSION[0]}'\"\"\") >= LooseVersion(\"\"\"'$MINIMUM_PYCODESTYLE'\"\"\"))')\\\n 2> /dev/null)\n\n if [ \"$EXPECTED_PYCODESTYLE\" == \"True\" ]; then\n RUN_LOCAL_PYCODESTYLE=\"True\"\n fi\n fi\n\n # download the right version or run locally\n if [ $RUN_LOCAL_PYCODESTYLE == \"False\" ]; then\n # Get pycodestyle at runtime so that we don't rely on it being installed on the build server.\n # See: https://github.com/apache/spark/pull/1744#issuecomment-50982162\n # Updated to the latest official version of pep8. pep8 is formally renamed to pycodestyle.\n echo \"downloading pycodestyle from $PYCODESTYLE_SCRIPT_REMOTE_PATH...\"\n if [ ! -e \"$PYCODESTYLE_SCRIPT_PATH\" ]; then\n curl --silent -o \"$PYCODESTYLE_SCRIPT_PATH\" \"$PYCODESTYLE_SCRIPT_REMOTE_PATH\"\n local curl_status=\"$?\"\n\n if [ \"$curl_status\" -ne 0 ]; then\n echo \"Failed to download pycodestyle.py from $PYCODESTYLE_SCRIPT_REMOTE_PATH\"\n exit \"$curl_status\"\n fi\n fi\n\n echo \"starting pycodestyle test...\"\n PYCODESTYLE_REPORT=$( (python3 \"$PYCODESTYLE_SCRIPT_PATH\" --config=dev/tox.ini $1) 2>&1)\n PYCODESTYLE_STATUS=$?\n else\n # we have the right version installed, so run locally\n echo \"starting pycodestyle test...\"\n PYCODESTYLE_REPORT=$( ($PYCODESTYLE_BUILD --config=dev/tox.ini $1) 2>&1)\n PYCODESTYLE_STATUS=$?\n fi\n\n if [ $PYCODESTYLE_STATUS -ne 0 ]; then\n echo \"pycodestyle checks failed:\"\n echo \"$PYCODESTYLE_REPORT\"\n exit \"$PYCODESTYLE_STATUS\"\n else\n echo \"pycodestyle checks passed.\"\n echo\n fi\n}\n\nfunction flake8_test {\n local FLAKE8_VERSION=\n local VERSION=\n local EXPECTED_FLAKE8=\n local FLAKE8_REPORT=\n local FLAKE8_STATUS=\n\n if ! hash \"$FLAKE8_BUILD\" 2> /dev/null; then\n echo \"The flake8 command was not found.\"\n echo \"flake8 checks failed.\"\n exit 1\n fi\n\n FLAKE8_VERSION=\"$($FLAKE8_BUILD --version 2> /dev/null)\"\n VERSION=($FLAKE8_VERSION)\n EXPECTED_FLAKE8=$( (python3 -c 'from distutils.version import LooseVersion;\n print(LooseVersion(\"\"\"'${VERSION[0]}'\"\"\") >= LooseVersion(\"\"\"'$MINIMUM_FLAKE8'\"\"\"))') \\\n 2> /dev/null)\n\n if [[ \"$EXPECTED_FLAKE8\" == \"False\" ]]; then\n echo \"\\\nThe minimum flake8 version needs to be $MINIMUM_FLAKE8. Your current version is $FLAKE8_VERSION\n\nflake8 checks failed.\"\n exit 1\n fi\n\n echo \"starting $FLAKE8_BUILD test...\"\n FLAKE8_REPORT=$( ($FLAKE8_BUILD $1 --count --select=E901,E999,F821,F822,F823 \\\n --max-line-length=100 --show-source --statistics) 2>&1)\n FLAKE8_STATUS=$?\n\n if [ \"$FLAKE8_STATUS\" -ne 0 ]; then\n echo \"flake8 checks failed:\"\n echo \"$FLAKE8_REPORT\"\n echo \"$FLAKE8_STATUS\"\n exit \"$FLAKE8_STATUS\"\n else\n echo \"flake8 checks passed.\"\n echo\n fi\n}\n\nfunction pydocstyle_test {\n local PYDOCSTYLE_REPORT=\n local PYDOCSTYLE_STATUS=\n local PYDOCSTYLE_VERSION=\n local EXPECTED_PYDOCSTYLE=\n\n # Exclude auto-generated configuration file.\n local DOC_PATHS_TO_CHECK=\"$( cd \"${DELTA_ROOT_DIR}\" && find . -name \"*.py\" | grep -vF 'functions.py' )\"\n\n # Check python document style, skip check if pydocstyle is not installed.\n if ! hash \"$PYDOCSTYLE_BUILD\" 2> /dev/null; then\n echo \"The pydocstyle command was not found. Skipping pydocstyle checks for now.\"\n echo\n return\n fi\n\n PYDOCSTYLE_VERSION=\"$($PYDOCSTYLEBUILD --version 2> /dev/null)\"\n EXPECTED_PYDOCSTYLE=$(python3 -c 'from distutils.version import LooseVersion; \\\n print(LooseVersion(\"\"\"'$PYDOCSTYLE_VERSION'\"\"\") >= LooseVersion(\"\"\"'$MINIMUM_PYDOCSTYLE'\"\"\"))' \\\n 2> /dev/null)\n\n if [[ \"$EXPECTED_PYDOCSTYLE\" == \"False\" ]]; then\n echo \"\\\nThe minimum version of pydocstyle needs to be $MINIMUM_PYDOCSTYLE.\nYour current version is $PYDOCSTYLE_VERSION.\nSkipping pydocstyle checks for now.\"\n echo\n return\n fi\n\n echo \"starting $PYDOCSTYLE_BUILD test...\"\n PYDOCSTYLE_REPORT=$( ($PYDOCSTYLE_BUILD --config=dev/tox.ini $DOC_PATHS_TO_CHECK) 2>&1)\n PYDOCSTYLE_STATUS=$?\n\n if [ \"$PYDOCSTYLE_STATUS\" -ne 0 ]; then\n echo \"pydocstyle checks failed:\"\n echo \"$PYDOCSTYLE_REPORT\"\n exit \"$PYDOCSTYLE_STATUS\"\n else\n echo \"pydocstyle checks passed.\"\n echo\n fi\n}\n\nSCRIPT_DIR=\"$( cd \"$( dirname \"$0\" )\" && pwd )\"\nDELTA_ROOT_DIR=\"$(dirname \"${SCRIPT_DIR}\")\"\n\npushd \"$DELTA_ROOT_DIR\" &> /dev/null\n\nPYTHON_SOURCE=\"$(find \"${DELTA_ROOT_DIR}/python\" -name \"*.py\")\"\n\ncompile_python_test \"$PYTHON_SOURCE\"\npycodestyle_test \"$PYTHON_SOURCE\"\n#flake8_test \"$PYTHON_SOURCE\"\npydocstyle_test\n\necho\necho \"all lint-python tests passed!\"\n\npopd &> /dev/null\n" }, { "path": "dev/pyproject.toml", "content": "#\n# Copyright (2024) The Delta Lake Project Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n[tool.black]\n# When changing the version, we have to update\n# GitHub workflow version\nrequired-version = \"23.12.1\"\nline-length = 100\ntarget-version = ['py38']\ninclude = '\\.pyi?$'\n" }, { "path": "dev/requirements.txt", "content": "# Linter\nmypy==1.8.0\nflake8==3.9.0\n\n# Code Formatter\nblack==23.12.1\n\n# Spark Connect (required)\ngrpcio>=1.67.0\ngrpcio-status>=1.67.0\ngoogleapis-common-protos>=1.65.0\n\n# Spark and Delta Connect python proto generation plugin (optional)\nmypy-protobuf==3.3.0\n" }, { "path": "dev/spark_structured_logging_style.py", "content": "#!/usr/bin/env python3\n\n#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n#\n# Copyright (2021) The Delta Lake Project Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nimport os\nimport sys\nimport re\nimport glob\n\n\ndef main():\n log_pattern = r\"log(?:Info|Warning|Error)\\(.*?\\)\\n\"\n inner_log_pattern = r'\".*?\"\\.format\\(.*\\)|s?\".*?(?:\\$|\\\"\\+(?!s?\")).*|[^\"]+\\+\\s*\".*?\"'\n compiled_inner_log_pattern = re.compile(inner_log_pattern)\n\n # Regex patterns for file paths to exclude from the Structured Logging style check\n excluded_file_patterns = [\n \"[Tt]est\",\n \"sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen\"\n \"/CodeGenerator.scala\",\n \"streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala\",\n \"sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver\"\n \"/SparkSQLCLIService.scala\",\n \"core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala\",\n ]\n\n nonmigrated_files = {}\n\n target_directories = [\"../spark\", \"../iceberg\", \"../hudi\"]\n scala_files = []\n for directory in target_directories:\n scala_files.extend(glob.glob(os.path.join(directory, \"**\", \"*.scala\"), recursive=True))\n\n for file in scala_files:\n skip_file = False\n for exclude_pattern in excluded_file_patterns:\n if re.search(exclude_pattern, file):\n skip_file = True\n break\n\n if not skip_file and not os.path.isdir(file):\n with open(file, \"r\") as f:\n content = f.read()\n\n log_statements = re.finditer(log_pattern, content, re.DOTALL)\n\n if log_statements:\n nonmigrated_files[file] = []\n for log_statement in log_statements:\n log_statement_str = log_statement.group(0).strip()\n # trim first ( and last )\n first_paren_index = log_statement_str.find(\"(\")\n inner_log_statement = re.sub(\n r\"\\s+\", \"\", log_statement_str[first_paren_index + 1 : -1]\n )\n\n if compiled_inner_log_pattern.fullmatch(inner_log_statement):\n start_pos = log_statement.start()\n preceding_content = content[:start_pos]\n line_number = preceding_content.count(\"\\n\") + 1\n start_char = start_pos - preceding_content.rfind(\"\\n\") - 1\n nonmigrated_files[file].append((line_number, start_char))\n\n if all(len(issues) == 0 for issues in nonmigrated_files.values()):\n print(\"Structured logging style check passed.\", file=sys.stderr)\n sys.exit(0)\n else:\n for file_path, issues in nonmigrated_files.items():\n for line_number, start_char in issues:\n print(f\"[error] {file_path}:{line_number}:{start_char}\", file=sys.stderr)\n print(\n \"[error]\\tPlease use the Structured Logging Framework for logging messages \"\n 'with variables. For example: log\"...${{MDC(TASK_ID, taskId)}}...\"'\n \"\\n\\tRefer to the guidelines in the file `shims/LoggingShims.scala`.\",\n file=sys.stderr,\n )\n\n sys.exit(-1)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "dev/tox.ini", "content": "#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n[pycodestyle]\nignore=E226,E231,E241,E305,E402,E722,E731,E741,W503,W504,W604\nmax-line-length=100\nexclude=cloudpickle.py,heapq3.py,shared.py,python/docs/conf.py,work/*/*.py,python/.eggs/*,dist/*,*python/delta/connect*\n[pydocstyle]\nignore=D100,D101,D102,D103,D104,D105,D106,D107,D200,D201,D202,D203,D204,D205,D206,D207,D208,D209,D210,D211,D212,D213,D214,D215,D300,D301,D302,D400,D401,D402,D403,D404,D405,D406,D407,D408,D409,D410,D411,D412,D413,D414,D415,D417\n" }, { "path": "docs/.gitignore", "content": "# build output\ndist/\npublic/api\n\n# generated types\n.astro/\n\n# dependencies\nnode_modules/\n\n# logs\nnpm-debug.log*\nyarn-debug.log*\nyarn-error.log*\npnpm-debug.log*\n\n\n# environment variables\n.env\n.env.production\n\n# macOS-specific files\n.DS_Store\n# Local Netlify folder\n.netlify\n" }, { "path": "docs/.nvmrc", "content": "v22.18.0" }, { "path": "docs/.prettierignore", "content": "coverage\npublic\ndist\n.astro\npnpm-lock.yaml\n.DS_Store\nsrc/content/**/*.mdx" }, { "path": "docs/.prettierrc.json", "content": "{\n \"plugins\": [\"prettier-plugin-astro\"],\n \"overrides\": [\n {\n \"files\": \"*.astro\",\n \"options\": {\n \"parser\": \"astro\"\n }\n },\n {\n \"files\": [\"*.md\", \"*.mdx\"],\n \"options\": {\n \"parser\": \"mdx\",\n \"proseWrap\": \"never\",\n \"embeddedLanguageFormatting\": \"auto\",\n \"htmlWhitespaceSensitivity\": \"css\",\n \"bracketSameLine\": false,\n \"singleAttributePerLine\": true,\n \"tabWidth\": 2\n }\n }\n ]\n}\n" }, { "path": "docs/README.md", "content": "# Docs Generation Scripts\n\nThis directory contains scripts to generate docs for https://docs.delta.io, including the API Docs for Scala, Java, and Python APIs.\n\n## Setup Environment\n\n### Install Node environment\n\nInstall node v22.14.0 using [nvm](https://github.com/nvm-sh/nvm):\n\n```\nnvm install\n```\n\nThen, install [pnpm](https://pnpm.io/):\n\n```\nnpm install --global corepack@latest\ncorepack enable pnpm\n```\n\nFinally, install dependencies:\n\n```\npnpm i\n```\n\n### Install Conda environment\n\nFollow [Conda Download](https://www.anaconda.com/download/) to install Anaconda.\n\nThen, follow [Create Environment From Environment file](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-from-file) to create a Conda environment from `/delta/docs/environment.yml` and activate the newly created `delta_docs` environment.\n\n```\n# Note the `--file` argument should be a fully qualified path. Using `~` in file\n# path doesn't work. Example valid path: `/Users/macuser/delta/docs/environment.yml`\n\nconda env create --name delta_docs --file=/docs/environment.yml`\n```\n\n### JDK Setup\n\nAPI doc generation needs JDK 1.8. Make sure to setup `JAVA_HOME` that points to JDK 1.8.\n\n### Set the Delta Lake version\n\nSet the version of Delta Lake release these docs are being generated for.\n\n```\nexport _DELTA_LAKE_RELEASE_VERSION_=3.3.0\n```\n\n## Usage\n\nRun the command from the `delta` repo root directory:\n\n```\npython3 docs/generate_docs.py --livehtml --api-docs\n```\n\nAbove command will print a URL to preview the docs.\n\n### Skip generating API docs\n\nAbove command generates API docs which take time. If you are just interested in the docs\nthat go on https://docs.delta.io, use the following command.\n\n```\npython3 docs/generate_docs.py --livehtml\n```\n\n### Building for production\n\nTo build the docs for production, run the following command:\n\n```python\npython3 docs/generate_docs.py --api-docs\n```\n\nThe resulting files will be found in `docs/dist`.\n\n### Additional docs site commands\n\nThe docs site is built on [Astro](https://astro.build/). Using pnpm, you can run a variety of commands:\n\n| Command | Description |\n| --------------------- | ----------------------------------------------- |\n| `pnpm run lint` | Run ESLint on the docs site code |\n| `pnpm run format` | Format docs site code using Prettier |\n| `pnpm run dev` | Start Astro in development mode |\n| `pnpm run build` | Build the Astro site for production |\n| `pnpm run preview` | Preview the built Astro site |\n| `pnpm run astro` | Run Astro CLI |\n" }, { "path": "docs/apis/api-docs.css", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/*\n * This file contains code from the Apache Spark project (original license above).\n * It contains modifications, which are licensed as follows:\n */\n\n/*\n * Licensed to the Apache Software Foundation (ASF) under one or more\n * contributor license agreements. See the NOTICE file distributed with\n * this work for additional information regarding copyright ownership.\n * The ASF licenses this file to You under the Apache License, Version 2.0\n * (the \"License\"); you may not use this file except in compliance with\n * the License. You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/* Dynamically injected style for the API docs */\n\n.unstable {\n background-color: #EE4B2B;\n}\n\n.developer {\n background-color: #0047AB;\n}\n\n.evolving {\n background-color: #44751E;\n}\n\n.badge {\n font-family: Arial, san-serif;\n float: right;\n}\n" }, { "path": "docs/apis/api-docs.js", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/*\n * This file contains code from the Apache Spark project (original license above).\n * It contains modifications, which are licensed as follows:\n */\n\n/*\n * Licensed to the Apache Software Foundation (ASF) under one or more\n * contributor license agreements. See the NOTICE file distributed with\n * this work for additional information regarding copyright ownership.\n * The ASF licenses this file to You under the Apache License, Version 2.0\n * (the \"License\"); you may not use this file except in compliance with\n * the License. You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/* Dynamically injected post-processing code for the API docs */\n\n$(document).ready(function() {\n var annotations = $(\"dt:contains('Annotations')\").next(\"dd\").children(\"span.name\");\n addBadges(annotations, \"Unstable\", \":: Unstable ::\", 'Unstable API');\n addBadges(annotations, \"Developer\", \":: DeveloperApi ::\", 'Developer API');\n addBadges(annotations, \"Evolving\", \":: Evolving ::\", 'Evolving API');\n});\n\nfunction addBadges(allAnnotations, name, tag, html) {\n var annotations = allAnnotations.filter(\":contains('\" + name + \"')\")\n var tags = $(\".cmt:contains(\" + tag + \")\")\n\n // Remove identifier tags from comments\n tags.each(function(index) {\n var oldHTML = $(this).html();\n var newHTML = oldHTML.replace(tag, \"\");\n $(this).html(newHTML);\n });\n\n // Add badges to all containers\n tags\n // Scala 2.11 docs require these\n .prevAll(\"h4.signature\")\n .add(annotations.closest(\"div.fullcommenttop\"))\n .add(annotations.closest(\"div.fullcomment\").prevAll(\"h4.signature\"))\n // Scala 2.12 docs require this\n .add(tags.prevAll(\"span.symbol\"))\n .add(annotations.closest(\"div.fullcomment\").prevAll(\"span.symbol\"))\n .prepend(html);\n}\n" }, { "path": "docs/apis/api-javadocs.css", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/*\n * This file contains code from the Apache Spark project (original license above).\n * It contains modifications, which are licensed as follows:\n */\n\n/*\n * Licensed to the Apache Software Foundation (ASF) under one or more\n * contributor license agreements. See the NOTICE file distributed with\n * this work for additional information regarding copyright ownership.\n * The ASF licenses this file to You under the Apache License, Version 2.0\n * (the \"License\"); you may not use this file except in compliance with\n * the License. You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/* Dynamically injected style for the API docs */\n\n.badge {\n font-family: Arial, san-serif;\n float: right;\n margin: 4px;\n /* The following declarations are taken from the ScalaDoc template.css */\n display: inline-block;\n padding: 2px 4px;\n font-size: 11.844px;\n font-weight: bold;\n line-height: 14px;\n color: #ffffff;\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);\n white-space: nowrap;\n vertical-align: baseline;\n background-color: #999999;\n padding-right: 9px;\n padding-left: 9px;\n -webkit-border-radius: 9px;\n -moz-border-radius: 9px;\n border-radius: 9px;\n}\n\n.unstable {\n background-color: #EE4B2B;\n}\n\n.developer {\n background-color: #0047AB;\n}\n\n.evolving {\n background-color: #44751E;\n}\n\n" }, { "path": "docs/apis/api-javadocs.js", "content": "/*\n * Copyright (2021) The Delta Lake Project Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/*\n * This file contains code from the Apache Spark project (original license above).\n * It contains modifications, which are licensed as follows:\n */\n\n/*\n * Licensed to the Apache Software Foundation (ASF) under one or more\n * contributor license agreements. See the NOTICE file distributed with\n * this work for additional information regarding copyright ownership.\n * The ASF licenses this file to You under the Apache License, Version 2.0\n * (the \"License\"); you may not use this file except in compliance with\n * the License. You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n/* Dynamically injected post-processing code for the API docs */\n\n$(document).ready(function() {\n addBadges(\":: Unstable ::\", 'Unstable API');\n addBadges(\":: DeveloperApi ::\", 'Developer API');\n addBadges(\":: Evolving ::\", 'Evolving API');\n});\n\nfunction addBadges(tag, html) {\n var tags = $(\".block:contains(\" + tag + \")\")\n\n // Remove identifier tags\n tags.each(function(index) {\n var oldHTML = $(this).html();\n var newHTML = oldHTML.replace(tag, \"\");\n $(this).html(newHTML);\n });\n\n // Add html badge tags\n tags.each(function(index) {\n if ($(this).parent().is('td.colLast')) {\n $(this).parent().prepend(html);\n } else if ($(this).parent('li.blockList')\n .parent('ul.blockList')\n .parent('div.description')\n .parent().is('div.contentContainer')) {\n var contentContainer = $(this).parent('li.blockList')\n .parent('ul.blockList')\n .parent('div.description')\n .parent('div.contentContainer')\n var header = contentContainer.prev('div.header');\n if (header.length > 0) {\n header.prepend(html);\n } else {\n contentContainer.prepend(html);\n }\n } else if ($(this).parent().is('li.blockList')) {\n $(this).parent().prepend(html);\n } else {\n $(this).prepend(html);\n }\n });\n}\n" }, { "path": "docs/apis/generate_api_docs.py", "content": "# !/usr/bin/env python3\n#\n# Copyright (2021) The Delta Lake Project Authors.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nimport os\nimport sys\nimport subprocess\nimport argparse\n\n\ndef main():\n # Parse arguments\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-v\", \"--verbose\", default=False, action='store_true')\n args = parser.parse_args()\n global verbose\n verbose = args.verbose\n\n # Set up the directories\n docs_root_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\n repo_root_dir = os.path.dirname(docs_root_dir)\n\n # --- dirs where docs are generated\n spark_scaladoc_gen_dir = repo_root_dir + \"/spark/target/scala-2.13/unidoc\"\n spark_javadoc_gen_dir = repo_root_dir + \"/spark/target/javaunidoc\"\n spark_pythondoc_dir = repo_root_dir + \"/docs/apis/python\"\n spark_pythondoc_gen_dir = spark_pythondoc_dir + \"/_build/html\"\n\n kernel_javadoc_gen_dir = repo_root_dir + \"/kernelGroup/target/javaunidoc\"\n\n # --- final dirs where the docs will be copied to\n all_docs_final_dir = docs_root_dir + \"/apis/_site/api\"\n all_javadocs_final_dir = all_docs_final_dir + \"/java\"\n all_scaladocs_final_dir = all_docs_final_dir + \"/scala\"\n all_pythondocs_final_dir = all_docs_final_dir + \"/python\"\n\n spark_javadoc_final_dir = all_javadocs_final_dir + \"/spark\"\n spark_scaladoc_final_dir = all_scaladocs_final_dir + \"/spark\"\n spark_pythondoc_final_dir = all_pythondocs_final_dir + \"/spark\"\n\n kernel_javadoc_final_dir = all_javadocs_final_dir + \"/kernel\"\n\n\n # Generate Java and Scala docs\n print(\"## Generating Scala and Java docs ...\")\n with WorkingDirectory(repo_root_dir):\n run_cmd([\"build/sbt\", \";clean;unidoc\"], stream_output=verbose)\n\n # Update Scala docs\n print(\"## Patching Scala docs ...\")\n patch_scala_docs(spark_scaladoc_gen_dir, docs_root_dir)\n\n # Update Java docs\n print(\"## Patching Java docs ...\")\n jquery_path = spark_scaladoc_gen_dir + \"/lib/jquery.min.js\" # grab the JQuery library from Scaladocs\n all_javadoc_gen_dirs = [\n spark_javadoc_gen_dir,\n kernel_javadoc_gen_dir,\n ]\n for javadoc_gen_dir in all_javadoc_gen_dirs:\n patch_java_docs(javadoc_gen_dir, docs_root_dir, jquery_path)\n\n # Generate Python docs\n print('## Generating Python docs ...')\n with WorkingDirectory(spark_pythondoc_dir):\n run_cmd([\"make\", \"html\"], stream_output=verbose)\n\n # Copy to final location\n log(\"## Copying to API doc directory %s\" % all_docs_final_dir)\n src_dst_dirs = [\n (spark_javadoc_gen_dir, spark_javadoc_final_dir),\n (spark_scaladoc_gen_dir, spark_scaladoc_final_dir),\n (spark_pythondoc_gen_dir, spark_pythondoc_final_dir),\n (kernel_javadoc_gen_dir, kernel_javadoc_final_dir),\n ]\n\n run_cmd([\"rm\", \"-rf\", all_docs_final_dir])\n run_cmd([\"mkdir\", \"-p\", all_docs_final_dir])\n for (src_dir, dst_dir) in src_dst_dirs:\n run_cmd([\"mkdir\", \"-p\", dst_dir])\n run_cmd([\"cp\", \"-r\", src_dir.rstrip(\"/\") + \"/\", dst_dir])\n\n print(\"## API docs generated in \" + all_docs_final_dir)\n\n\ndef patch_scala_docs(scaladoc_dir, docs_root_dir):\n with WorkingDirectory(scaladoc_dir):\n # Patch the js and css files\n append(docs_root_dir + \"/apis/api-docs.js\", \"./lib/template.js\") # append new js functions\n append(docs_root_dir + \"/apis/api-docs.css\", \"./lib/template.css\") # append new styles\n\n\ndef patch_java_docs(javadoc_dir, docs_root_dir, jquery_path):\n print(\"### Patching JavaDoc in %s ...\" % javadoc_dir)\n with WorkingDirectory(javadoc_dir):\n # Find html files to patch\n (_, stdout, _) = run_cmd([\"find\", \".\", \"-name\", \"*.html\", \"-mindepth\", \"2\"])\n log(\"HTML files found:\\n\" + stdout)\n javadoc_files = [line for line in stdout.split('\\n') if line.strip() != '']\n\n js_script_start = '