[ { "path": ".github/workflows/ci.yml", "content": "name: CI Pipeline\n\non:\n push:\n branches: [master]\n pull_request:\n\nenv:\n REGISTRY: ghcr.io\n IMAGE_NAME: ${{ github.repository }}\n\njobs:\n test-and-build-stats:\n runs-on: ubuntu-latest\n permissions:\n contents: read\n packages: write\n\n steps:\n - name: Checkout repository\n uses: actions/checkout@v4\n\n - name: Set up QEMU\n uses: docker/setup-qemu-action@v3\n\n - name: Set up Docker Buildx\n uses: docker/setup-buildx-action@v3\n\n - name: Log in to Container Registry\n uses: docker/login-action@v3\n with:\n registry: ${{ env.REGISTRY }}\n username: ${{ github.actor }}\n password: ${{ secrets.GITHUB_TOKEN }}\n\n - name: Extract metadata for stats image\n id: meta-stats\n uses: docker/metadata-action@v5\n with:\n images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}/stats\n tags: |\n type=ref,event=branch\n type=ref,event=pr\n type=sha\n type=raw,value=latest,enable={{is_default_branch}}\n\n - name: Build stats Docker image\n uses: docker/build-push-action@v5\n with:\n context: .\n file: ./stats.Dockerfile\n load: true\n platforms: linux/amd64\n tags: |\n ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}/stats:${{ github.sha }}\n\n - name: Run unit tests\n run: |\n docker run --rm \\\n -v ${{ github.workspace }}/tests:/app/tests \\\n ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}/stats:${{ github.sha }} \\\n python -m pytest -s tests/\n\n - name: Push stats Docker image\n if: |\n success() && (\n github.event_name == 'push' && github.ref == 'refs/heads/master' ||\n github.event_name == 'pull_request' && github.event.pull_request.head.repo.full_name == github.repository\n )\n uses: docker/build-push-action@v5\n with:\n context: .\n file: ./stats.Dockerfile\n push: true\n platforms: linux/amd64,linux/arm64\n tags: ${{ steps.meta-stats.outputs.tags }}\n labels: ${{ steps.meta-stats.outputs.labels }}\n\n build-site:\n runs-on: ubuntu-latest\n permissions:\n contents: read\n packages: write\n\n steps:\n - name: Checkout repository\n uses: actions/checkout@v4\n\n - name: Log in to Container Registry\n uses: docker/login-action@v3\n with:\n registry: ${{ env.REGISTRY }}\n username: ${{ github.actor }}\n password: ${{ secrets.GITHUB_TOKEN }}\n\n - name: Extract metadata for site image\n id: meta-site\n uses: docker/metadata-action@v5\n with:\n images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}/site\n tags: |\n type=ref,event=branch\n type=ref,event=pr\n type=sha\n type=raw,value=latest,enable={{is_default_branch}}\n\n - name: Build and push site Docker image\n uses: docker/build-push-action@v5\n with:\n context: .\n file: ./site.Dockerfile\n push: ${{ github.event.pull_request.head.repo.full_name == github.repository }}\n tags: ${{ steps.meta-site.outputs.tags }}\n labels: ${{ steps.meta-site.outputs.labels }}\n" }, { "path": ".gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# IPython Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# dotenv\n.env\n\n# virtualenv\nvenv/\nENV/\n\n# Spyder project settings\n.spyderproject\n\n# Rope project settings\n.ropeproject\n\n# Eclipse PyDev\n.project\n.pydevproject\n.settings/\n\n# Jekyll files to run github-pages locally\n_site\n.sass-cache\n.jekyll-metadata\nGemfile\nGemfile.lock\nassets\n_includes\n_sass\njs\nvendor/\n.bundle/\nthemes/\n\n# crawl statistics files\nstats/*.gz\nstats/crawls.txt\nstats/excerpt/\n\n# generated CSV data\ndata/\n\n# macOS Desktop Services Store\n.DS_Store\n" }, { "path": "LICENSE", "content": " 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. 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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": "README.md", "content": "Basic Statistics of Common Crawl Monthly Archives\n=================================================\n\nAnalyze the [Common Crawl](https://commoncrawl.org/) data to get metrics about the monthly crawl archives:\n* size of the monthly crawls, number of\n * fetched pages\n * unique URLs\n * unique documents (by content digest)\n * number of different hosts, domains, top-level domains\n* distribution of pages/URLs on hosts, domains, top-level domains\n* and ...\n * mime types\n * protocols / schemes (http vs. https)\n * content languages (since summer 2018)\n\nThis is a description how to generate the statistics from the Common Crawl URL index files.\n\nThe results are presented on https://commoncrawl.github.io/cc-crawl-statistics/.\n\n\nStep 1: Count Items\n-------------------\n\nThe items (URLs, hosts, domains, etc.) are counted using the Common Crawl index files\non AWS S3 `s3://commoncrawl/cc-index/collections/*/indexes/cdx-*.gz`.\n\n1. define a pattern of cdx files to process - usually from one monthly crawl (here: `CC-MAIN-2016-26`)\n - either smaller set of local files for testing\n ```\n INPUT=\"test/cdx/cdx-0000[0-3].gz\"\n ```\n - or one monthly crawl to be accessed via Hadoop on AWS S3:\n ```\n INPUT=\"s3a://commoncrawl/cc-index/collections/CC-MAIN-2016-26/indexes/cdx-*.gz\"\n ```\n\n2. run `crawlstats.py --job=count` to process the cdx files and count the items:\n ```\n python3 crawlstats.py --job=count --no-exact-counts \\\n --no-output --output-dir .../count/ $INPUT\n ```\n\nHelp on command-line parameters (including [mrjob](https://pypi.org/project/mrjob/) options) are shown by\n`python3 crawlstats.py --help`.\nThe option `--no-exact-counts` is recommended (and is the default) to save storage space and computation time\nwhen counting URLs and content digests.\n\n\nStep 2: Aggregate Counts\n------------------------\n\nRun `crawlstats.py --job=stats` on the output of step 1:\n```\npython3 crawlstats.py --job=stats --max-top-hosts-domains=500 \\\n --no-output --output-dir .../stats/ .../count/\n```\nThe max. number of most frequent thosts and domains contained in the output is set by the option\n`--max-top-hosts-domains=N`.\n\n\nStep 3: Download the Data\n-------------------------\n\nIn order to prepare the plots, the the output of step 2 must be downloaded to local disk.\nSimplest, the data is fetched from the Common Crawl Public Data Set bucket on AWS S3:\n```sh\nwhile read crawl; do\n aws s3 cp s3://commoncrawl/crawl-analysis/$crawl/stats/part-00000.gz ./stats/$crawl.gz\ndone <stats/count/$CRAWL/part-0000$i.bz2\n done\n ```\n\n\nStep 4: Plot the Data\n---------------------\n\nTo prepare the plots using the downloaded aggregated data:\n```\ngzip -dc stats/CC-MAIN-*.gz | python3 plot/crawl_size.py\n```\nThe full list of commands to prepare all plots is found in [plot.sh](plot.sh). Don't forget to install the Python\nmodules [required for plotting](requirements_plot.txt).\n\n\nStep 5: Local Site Preview\n--------------------------\n\nThe [crawl statistics site](https://commoncrawl.github.io/cc-crawl-statistics/) is hosted by [Github pages](https://pages.github.com/). The site is updated as soon as plots or description texts are updated, committed and pushed to the Github repository.\n\nTo preview local changes, it's possible to serve the site locally:\n1. build the Docker image with Ruby, Jekyll and the content to be served\n ```\n docker build -f site.Dockerfile -t cc-crawl-statistics-site:latest .\n ```\n2. run a Docker container to serve the site preview\n ```\n docker run --network=host --rm -ti cc-crawl-statistics-site:latest\n ```\n The site should be served on localhost, port 4000 (http://127.0.0.1:4000).\n If not, the correct location is shown in the output of the `docker run` command.\n\n If running this on a Mac, you may find that the loopback interface (127.0.0.1) within the container is not accessible, so you can change the line in the [Dockerfile](site.Dockerfile) to:\n\n ```\n CMD bundle exec jekyll serve --host 0.0.0.0\n ```\n\n ... and then the site will be served on http://0.0.0.0:4000 instead. (You will of course need to rebuild the Docker image after updating the Dockerfile.)\n\n\nRun via Container\n-----------------\n\nThe whole workflow can be run as a container (docker or podman) including downloading stats files from Common Crawl's S3 bucket and generating new plots.\n\n```bash\n# clone the repository (to have the latest crawl IDs)\ngit clone https://github.com/commoncrawl/cc-crawl-statistics.git\ncd cc-crawl-statistics\n\n# download stats and generate plots\n# SSH, AWS keys, and stats and plots directories must be mounted into the container\npodman run --rm -v ~/.aws:/root/.aws:ro -v $(pwd -P)/stats:/app/stats -v $(pwd -P)/plots:/app/plots ghcr.io/commoncrawl/cc-crawl-statistics/stats:latest \n\n# if needed you can manually build the container image\npodman build -f stats.Dockerfile -t ghcr.io/commoncrawl/cc-crawl-statistics/stats:latest\n\n# for development it is recommend to mount the whole repository into the container\npodman run -it -v ~/.aws:/root/.aws:ro -v $(pwd -P):/app ghcr.io/commoncrawl/cc-crawl-statistics/stats:latest /bin/bash\n\n```\n\n\nRelated Projects\n----------------\n\nThe [columnar index](https://commoncrawl.org/2018/03/index-to-warc-files-and-urls-in-columnar-format/)\nsimplifies counting and analytics a lot - easier to maintain, more transparent, reproducible and\nextensible than running two MapReduce jobs, see the the list of example\n- [SQL queries](https://github.com/commoncrawl/cc-index-table#query-the-table-in-amazon-athena) and\n- [Jupyter notebooks](https://github.com/commoncrawl/cc-notebooks)\n\n" }, { "path": "_config.yml", "content": "title: Statistics of Common Crawl Monthly Archives\ndescription: Number of pages, distribution of top-level domains, crawl overlaps, etc. - basic metrics about Common Crawl Monthly Crawl Archives\nrepository: commoncrawl/cc-crawl-statistics\nlatest_crawl: CC-MAIN-2026-17\n\nshow_navigation: True\nnavlist:\n - title: Home\n url: /\n - title: Size of crawls\n url: /plots/crawlsize\n - title: Top-level domains\n url: /plots/tlds\n - title: Registered domains\n url: /plots/domains\n - title: Crawler metrics\n url: /plots/crawlermetrics\n - title: Crawl overlaps\n url: /plots/crawloverlap\n - title: Media types\n url: /plots/mimetypes\n - title: Character sets\n url: /plots/charsets\n - title: Languages\n url: /plots/languages\n\ntheme: jekyll-theme-minimal\n" }, { "path": "_layouts/default.html", "content": "\n\n \n \n \n {{ site.title | default: site.github.repository_name }} by {{ site.github.owner_name }}\n\n \n\n \n\n" }, { "path": "_layouts/table.html", "content": "\n\n \n \n \n {{ site.title | default: site.github.repository_name }} by {{ site.github.owner_name }}\n\n \" + $(this).html() + \"\");\n });\n\t $(\"#search\").on(\"keyup\", function() {\n \t var value = $(this).val().toLowerCase();\n $(\".tablesearcher tbody tr\").filter(function() {\n $(this).toggle($(this).children('th').text().indexOf(value) > -1);\n });\n });\n });\n \n \n\n \n \n
\n
\n

{{ site.title | default: site.github.repository_name }}

\n

{{ site.description | default: site.github.project_tagline }}\n
Latest crawl: {{site.latest_crawl}}\n

\n\n {% if site.show_navigation %}\n \n {% endif %}\n\n {% if site.github.is_project_page %}\n

View the Project on GitHub {{ github_name }}

\n {% endif %}\n\n
\n
\n\n {{ content }}\n\n {% if page.table_searcher %}\n

\n {% endif %}\n\n {% for table in page.table_include %}\n {% include_relative {{ table }} %}\n
\n {% endfor %}\n\n
\n \n
\n \n\n \n\n" }, { "path": "crawlplot.py", "content": "\"\"\"\nBase plotting module for Common Crawl statistics visualization.\n\nThis module provides the CrawlPlot base class which handles:\n- Plot library selection (matplotlib, rpy2/ggplot2, or legacy ggplot)\n- Common plot styling to match ggplot2 aesthetics\n- Data input from stdin or files\n- Output directory management\n\nThe plot library is controlled by the PLOTLIB environment variable:\n- 'matplotlib' (recommended)\n- 'rpy2.ggplot2' (requires R and rpy2)\n- 'ggplot' (deprecated)\n\nThe output directory is controlled by PLOTDIR (defaults to 'plots/').\n\"\"\"\n\nimport json\nimport logging\nimport os\nimport os.path\nimport sys\nfrom typing import Literal\n\nimport fsspec\nimport numpy as np\n\n\n# Supported plot library backends\nPlotLibType = Literal[\"rpy2.ggplot2\", \"ggplot\", \"matplotlib\"]\n\n\nclass CrawlPlot:\n \"\"\"\n Base class for Common Crawl statistics plots.\n\n Provides common functionality for all plot types including:\n - Plot library initialization and configuration\n - Data reading from stdin or gzipped files\n - Line plot generation with consistent styling\n - Output directory management\n\n Subclasses should implement:\n - add(key, val): Process a single data record\n - plot(): Generate the specific visualization\n\n Attributes:\n PLOTLIB: The plotting library to use ('matplotlib', 'rpy2.ggplot2', or 'ggplot')\n PLOTDIR: Directory for saving plot output files\n DEFAULT_FIGSIZE: Default figure size in inches (7 = 2100px at 300 DPI)\n DEFAULT_DPI: Default resolution for saved figures\n \"\"\"\n\n GGPLOT2_THEME = None\n GGPLOT2_THEME_KWARGS = None\n\n # figure with square aspect ratio : 7 inches * 300 DPI = 2100 pixels\n DEFAULT_FIGSIZE = 7\n DEFAULT_DPI = 300\n\n title_fontsize = 15\n title_pad = 20\n title_fontweight = \"normal\"\n title_loc = \"left\"\n xlabel_fontsize = 12\n ylabel_fontsize = 12\n ticks_fontsize = 10\n ticks_color = \"#E6E6E6\"\n ticks_length = 8\n ticks_width = 1.0\n bar_width = 0.8\n legend_fontsize = 10\n legend_title_fontsize = 11\n line_width = 0.75\n marker_size = 4\n grid_major_linewidth = 1.0\n grid_minor_linewidth = 0.5\n grid_major_color = \"#E6E6E6\"\n grid_minor_color = \"#E6E6E6\"\n tight_layout_pad = 0.5\n savefig_facecolor = \"white\"\n savefig_bbox_inches = None\n\n # -------------------------------------------------------------------------\n # Matplotlib helper methods for reducing code duplication\n # -------------------------------------------------------------------------\n\n def create_figure(self, ratio=1.0):\n \"\"\"Create a matplotlib figure with consistent sizing.\n\n Args:\n ratio: Height ratio relative to width (default: 1.0 for square)\n\n Returns:\n Tuple of (fig, ax)\n \"\"\"\n import matplotlib.pyplot as plt\n return plt.subplots(figsize=(self.DEFAULT_FIGSIZE, self.DEFAULT_FIGSIZE * ratio))\n\n def set_title(self, ax, title):\n \"\"\"Apply consistent title styling to an axes.\n\n Args:\n ax: matplotlib Axes object\n title: Title text\n \"\"\"\n ax.set_title(\n title,\n fontsize=self.title_fontsize,\n fontweight=self.title_fontweight,\n pad=self.title_pad,\n loc=self.title_loc,\n )\n\n def apply_ggplot2_style(self, ax, show_grid=True, grid_axis='both'):\n \"\"\"Apply ggplot2-like minimal styling to an axes.\n\n Removes spines, adds grid lines, and sets axes below plot elements.\n\n Args:\n ax: matplotlib Axes object\n show_grid: Whether to show grid lines (default: True)\n grid_axis: Which axis to show grid on ('both', 'x', or 'y')\n \"\"\"\n # Remove all spines\n for spine in ['top', 'right', 'left', 'bottom']:\n ax.spines[spine].set_visible(False)\n\n # Add grid\n if show_grid:\n ax.grid(True, which='major', linewidth=self.grid_major_linewidth,\n color=self.grid_major_color, zorder=0, axis=grid_axis)\n\n ax.set_axisbelow(True)\n\n def set_tick_labels_black(self, ax):\n \"\"\"Set all tick labels to black color.\n\n Args:\n ax: matplotlib Axes object\n \"\"\"\n for label in ax.get_xticklabels() + ax.get_yticklabels():\n label.set_color('black')\n\n def apply_nice_ticks(self, ax, axis='y', use_scientific=True):\n \"\"\"Apply nice tick spacing using the nice_tick_step calculation.\n\n Sets minor and major ticks at 'nice' intervals (multiples of 1, 2, or 5).\n Optionally applies scientific notation for large values.\n\n Args:\n ax: matplotlib Axes object\n axis: Which axis to apply to ('x' or 'y')\n use_scientific: Whether to use scientific notation for large values\n \"\"\"\n from matplotlib.ticker import MultipleLocator, FormatStrFormatter\n\n if axis == 'y':\n vmin, vmax = ax.get_ylim()\n axis_obj = ax.yaxis\n else:\n vmin, vmax = ax.get_xlim()\n axis_obj = ax.xaxis\n\n minor = self.nice_tick_step(vmin, vmax, n=8)\n major = 2 * minor\n\n axis_obj.set_minor_locator(MultipleLocator(minor))\n axis_obj.set_major_locator(MultipleLocator(major))\n\n if use_scientific and vmax > 1e4:\n axis_obj.set_major_formatter(FormatStrFormatter('%.0e'))\n\n def save_figure(self, fig, img_path):\n \"\"\"Save figure with consistent settings and close it.\n\n Args:\n fig: matplotlib Figure object\n img_path: Output file path\n\n Returns:\n The figure object (for chaining)\n \"\"\"\n import matplotlib.pyplot as plt\n plt.tight_layout(pad=self.tight_layout_pad)\n plt.savefig(img_path, dpi=self.DEFAULT_DPI,\n bbox_inches=self.savefig_bbox_inches,\n facecolor=self.savefig_facecolor)\n plt.close()\n return fig\n\n def hide_tick_marks(self, ax, tick_color='#FFFFFF'):\n \"\"\"Hide tick marks by setting them to a background color.\n\n The tick labels remain visible but the tick marks themselves are hidden.\n\n Args:\n ax: matplotlib Axes object\n tick_color: Color to set ticks to (default: white)\n \"\"\"\n ax.tick_params(axis='both', which='both', colors=tick_color,\n length=self.ticks_length, width=self.ticks_width)\n\n def __init__(self):\n \"\"\"Initialize the plot with library selection and output directory setup.\"\"\"\n # Settings defined via environment variables\n self.PLOTLIB: PlotLibType = os.environ.get('PLOTLIB', 'matplotlib')\n self.PLOTDIR = os.environ.get('PLOTDIR', 'plots')\n\n if self.PLOTLIB == 'ggplot':\n # nothing to do here\n pass\n elif self.PLOTLIB == 'rpy2.ggplot2':\n from rpy2.robjects.lib import ggplot2\n from rpy2.robjects import pandas2ri\n pandas2ri.activate()\n # use minimal theme with white background set in plot constructor\n # https://ggplot2.tidyverse.org/reference/ggtheme.html\n self.GGPLOT2_THEME = ggplot2.theme_minimal(base_size=12, base_family=\"Helvetica\")\n\n self.GGPLOT2_THEME_KWARGS = {\n 'panel.background': ggplot2.element_rect(fill='white', color='white'),\n 'plot.background': ggplot2.element_rect(fill='white', color='white')\n }\n\n elif self.PLOTLIB == \"matplotlib\":\n import matplotlib.pyplot as plt\n\n # ggplot2-inspired color palette\n ggplot_colors = [\n \"#F8766D\", \"#00BE67\", \"#00A9FF\", \"#CD9600\", \"#7CAE00\",\n \"#00BFC4\", \"#C77CFF\", \"#FF61CC\",\n ]\n\n # Set up ggplot2-like minimal theme with larger fonts\n plt.style.use('default')\n plt.rcParams.update({\n 'font.family': 'sans-serif',\n 'font.sans-serif': ['Liberation Sans', 'Arial', 'DejaVu Sans'],\n 'font.size': 20, # Much larger base font size\n 'axes.linewidth': 1.5,\n 'axes.spines.left': True,\n 'axes.spines.bottom': True,\n 'axes.spines.top': False,\n 'axes.spines.right': False,\n 'axes.axisbelow': True,\n 'axes.grid': True,\n 'axes.grid.axis': 'both',\n 'grid.linewidth': 1.0,\n 'grid.color': '#E6E6E6', # Gray grid lines\n 'axes.facecolor': 'white', # White background\n 'figure.facecolor': 'white',\n 'xtick.bottom': True,\n 'xtick.top': False,\n 'ytick.left': True,\n 'ytick.right': False,\n 'xtick.direction': 'out',\n 'ytick.direction': 'out',\n 'axes.prop_cycle': plt.cycler(color=ggplot_colors),\n })\n\n else:\n raise ValueError(\"Invalid PLOTLIB defined\")\n\n # Make sure output directories exists\n os.makedirs(os.path.join(self.PLOTDIR, \"crawler\"), exist_ok=True)\n os.makedirs(os.path.join(self.PLOTDIR, \"crawloverlap\"), exist_ok=True)\n os.makedirs(os.path.join(self.PLOTDIR, \"crawlsize\"), exist_ok=True)\n os.makedirs(os.path.join(self.PLOTDIR, \"tld\"), exist_ok=True)\n\n\n def read_from_stdin_or_file(self):\n \"\"\"Read statistics data from a file argument or stdin.\n\n If a file path is provided as the first command line argument,\n reads from that file (supports gzip compression). Otherwise,\n reads from stdin.\n \"\"\"\n if len(sys.argv) > 1:\n # File provided as argument\n fp = sys.argv[1]\n compression = (\"gzip\" if fp.endswith(\".gz\") else None)\n\n with fsspec.open(fp, 'r', compression=compression) as f:\n self.read_data(f)\n else:\n # No argument, use stdin\n self.read_data(sys.stdin)\n\n def read_data(self, stream):\n \"\"\"Parse tab-separated JSON key-value pairs from a stream.\n\n Args:\n stream: Input stream containing lines of tab-separated JSON data.\n Each line should have format: JSON_KEYJSON_VALUE\n \"\"\"\n for line in stream:\n keyval = line.split('\\t')\n if len(keyval) == 2:\n key = json.loads(keyval[0])\n val = json.loads(keyval[1])\n self.add(key, val)\n else:\n logging.error(\"Not a key-value pair: {}\".find(line))\n\n def line_plot_with_ggplot(\n self,\n data,\n title,\n ylabel,\n img_path,\n x=\"date\",\n y=\"size\",\n c=\"type\",\n clabel=\"\",\n ratio=1.0,\n ):\n \"\"\"Generate a line plot using the legacy ggplot library (deprecated).\"\"\"\n from ggplot import ggplot, aes, ggtitle, ylab, xlab, scale_x_date, date_breaks, geom_line, geom_point\n\n date_label = \"%Y\\n%W\" # year + week number\n p = (\n ggplot(data, aes(x=x, y=y, color=c))\n + ggtitle(title)\n + ylab(ylabel)\n + xlab(\" \")\n + scale_x_date(breaks=date_breaks(\"3 months\"), labels=date_label)\n + geom_line()\n + geom_point()\n )\n p.save(img_path)\n return p\n\n def line_plot_with_rpy2_ggplot2(\n self,\n data,\n title,\n ylabel,\n img_path,\n x=\"date\",\n y=\"size\",\n c=\"type\",\n clabel=\"\",\n ratio=1.0,\n ):\n \"\"\"Generate a line plot using R's ggplot2 via rpy2.\"\"\"\n from rpy2.robjects.lib import ggplot2\n\n # Convert y axis to float because R uses 32-bit signed integers\n # and values >= 2 billion (2^31) will overflow\n data[y] = data[y].astype(float)\n if y != \"size\" and \"size\" in data.columns:\n data[\"size\"] = data[\"size\"].astype(float)\n p = (\n ggplot2.ggplot(data)\n + ggplot2.aes_string(x=x, y=y, color=c)\n + ggplot2.geom_line(linewidth=0.5)\n + ggplot2.geom_point()\n + self.GGPLOT2_THEME\n + ggplot2.theme(\n **{\n \"legend.position\": \"bottom\",\n \"aspect.ratio\": ratio,\n **self.GGPLOT2_THEME_KWARGS,\n }\n )\n + ggplot2.labs(title=title, x=\"\", y=ylabel, color=clabel)\n )\n\n p.save(img_path)\n\n return p\n\n @staticmethod\n def nice_tick_step(vmin, vmax, n=5):\n \"\"\"Calculate a 'nice' tick step for axis labels.\n\n Returns a tick step value that is a multiple of 1, 2, or 5 times\n a power of 10, which produces clean, readable axis labels.\n\n Args:\n vmin: Minimum value of the axis range\n vmax: Maximum value of the axis range\n n: Approximate number of tick intervals desired (default: 5)\n\n Returns:\n A 'nice' tick step value (1/2/5 * 10^k)\n \"\"\"\n span = abs(vmax - vmin)\n if span == 0:\n return 1.0\n raw = span / n\n exp = np.floor(np.log10(raw))\n frac = raw / (10**exp)\n nice_frac = 1 if frac <= 1 else 2 if frac <= 2 else 5 if frac <= 5 else 10\n return nice_frac * 10**exp\n \n @staticmethod\n def center_legend_title(fig, ax, leg_items, leg_title, x_axes=0.1):\n \"\"\"Center the legend title vertically with respect to legend items.\"\"\"\n fig.canvas.draw()\n r = fig.canvas.get_renderer()\n bb = leg_items.get_window_extent(r)\n y = fig.transFigure.inverted().transform((0, (bb.y0+bb.y1)/2))[1]\n x = fig.transFigure.inverted().transform(ax.transAxes.transform((x_axes, 0)))[0]\n leg_title.set_bbox_to_anchor((x, y), transform=fig.transFigure)\n\n def line_plot_with_matplotlib(\n self,\n data,\n title,\n ylabel,\n img_path,\n x=\"date\",\n y=\"size\",\n c=\"type\",\n clabel=\"\",\n ratio=1.0,\n ):\n \"\"\"Generate a line plot using matplotlib with ggplot2-like styling.\n\n Creates a multi-series line plot with markers, styled to match\n ggplot2's minimal theme aesthetic.\n\n Args:\n data: pandas DataFrame containing the plot data\n title: Plot title\n ylabel: Y-axis label\n img_path: Output file path for the saved image\n x: Column name for x-axis values (default: 'date')\n y: Column name for y-axis values (default: 'size')\n c: Column name for grouping/color (default: 'type')\n clabel: Legend title (default: '')\n ratio: Aspect ratio for the plot (default: 1.0)\n\n Returns:\n matplotlib Figure object\n \"\"\"\n from matplotlib.ticker import AutoMinorLocator\n from matplotlib.dates import YearLocator, DateFormatter\n\n # Convert y axis to float for consistency with large values\n data[y] = data[y].astype(float)\n if y != \"size\" and \"size\" in data.columns:\n data[\"size\"] = data[\"size\"].astype(float)\n\n fig, ax = self.create_figure()\n groups = data.groupby(c)\n\n # Use ggplot2 default colors for small group counts\n colors = [\"#F8766D\", \"#00BA38\", \"#619CFF\"] if len(groups) <= 3 else None\n\n for i, (group_key, group_df) in enumerate(groups):\n group_color = colors[i] if colors is not None else None\n ax.plot(\n group_df[x], group_df[y], \"o-\",\n color=group_color, label=group_key,\n linewidth=self.line_width, markersize=self.marker_size,\n )\n\n self.set_title(ax, title)\n ax.set_xlabel(\"\")\n ax.set_ylabel(ylabel, fontsize=self.ylabel_fontsize)\n\n # Apply nice y-axis ticks\n self.apply_nice_ticks(ax, axis='y')\n\n # Axes ratio\n axes_aspect_ratio = 1 / ax.get_data_ratio() * ratio\n if axes_aspect_ratio < 1:\n ax.set_aspect(axes_aspect_ratio)\n\n # Date formatting for x-axis\n ax.xaxis.set_major_formatter(DateFormatter(\"%Y\"))\n ax.xaxis.set_major_locator(YearLocator(base=5))\n ax.xaxis.set_minor_locator(AutoMinorLocator(2))\n\n ax.tick_params(axis=\"both\", labelsize=self.ticks_fontsize)\n\n # Grid with both major and minor lines\n ax.grid(True, which=\"major\", linewidth=self.grid_major_linewidth,\n color=self.grid_major_color, zorder=0)\n ax.grid(True, which=\"minor\", linewidth=self.grid_minor_linewidth,\n color=self.grid_minor_color, zorder=0)\n ax.set_axisbelow(True)\n\n # Apply ggplot2 style (remove spines)\n for spine in ['top', 'right', 'left', 'bottom']:\n ax.spines[spine].set_visible(False)\n\n # Hide tick marks but keep labels black\n self.hide_tick_marks(ax)\n self.set_tick_labels_black(ax)\n\n # Legend setup\n num_legend_items = len(groups)\n ncol = 5 if num_legend_items == 5 else 4\n\n if clabel:\n leg_items = ax.legend(\n loc=\"upper center\", ncol=ncol, bbox_to_anchor=(0.6, -0.1),\n frameon=False, fontsize=self.legend_fontsize,\n )\n ax.legend(\n [], [], title=clabel, loc=\"upper center\",\n bbox_to_anchor=(0.2, -0.075), frameon=False,\n title_fontsize=self.legend_title_fontsize,\n )\n ax.add_artist(leg_items)\n else:\n ax.legend(\n loc=\"upper center\", bbox_to_anchor=(0.5, -0.1),\n ncol=ncol, frameon=False, fontsize=self.legend_fontsize,\n )\n\n return self.save_figure(fig, img_path)\n\n def line_plot(\n self,\n data,\n title,\n ylabel,\n img_file,\n x=\"date\",\n y=\"size\",\n c=\"type\",\n clabel=\"\",\n ratio=1.0,\n ):\n \"\"\"Generate a line plot using the configured plotting library.\n\n This is the main entry point for creating line plots. It delegates\n to the appropriate backend based on the PLOTLIB setting.\n\n Args:\n data: pandas DataFrame containing the plot data\n title: Plot title\n ylabel: Y-axis label\n img_file: Output filename relative to PLOTDIR\n x: Column name for x-axis values (default: 'date')\n y: Column name for y-axis values (default: 'size')\n c: Column name for grouping/color (default: 'type')\n clabel: Legend title (default: '')\n ratio: Aspect ratio for the plot (default: 1.0)\n\n Returns:\n Plot object (type depends on backend)\n \"\"\"\n img_path = os.path.join(self.PLOTDIR, img_file)\n\n if self.PLOTLIB == \"ggplot\":\n return self.line_plot_with_ggplot(\n data=data,\n title=title,\n ylabel=ylabel,\n img_path=img_path,\n x=x,\n y=y,\n c=c,\n clabel=clabel,\n ratio=ratio,\n )\n\n elif self.PLOTLIB == \"rpy2.ggplot2\":\n return self.line_plot_with_rpy2_ggplot2(\n data=data,\n title=title,\n ylabel=ylabel,\n img_path=img_path,\n x=x,\n y=y,\n c=c,\n clabel=clabel,\n ratio=ratio,\n )\n\n elif self.PLOTLIB == \"matplotlib\":\n return self.line_plot_with_matplotlib(\n data=data,\n title=title,\n ylabel=ylabel,\n img_path=img_path,\n x=x,\n y=y,\n c=c,\n clabel=clabel,\n ratio=ratio,\n )\n" }, { "path": "crawlstats.py", "content": "import heapq\nimport json\nimport logging\nimport os\nimport re\n\nfrom collections import defaultdict, Counter\nfrom datetime import date\nfrom enum import Enum\nfrom urllib.parse import urlparse\n\nimport mrjob.util\nimport tldextract\nimport ujson\n\nfrom hyperloglog import HyperLogLog\nfrom isoweek import Week\nfrom mrjob.job import MRJob, MRStep\nfrom mrjob.protocol import JSONProtocol, RawValueProtocol\n\n\nHYPERLOGLOG_ERROR = .01\n\n# threshold when to add a HyperLogLog for SURT domains\nMIN_SURT_HLL_SIZE = 50000\n\nLOGGING_FORMAT = '%(asctime)s: [%(levelname)s]: %(message)s'\nLOGGING_LEVEL = logging.INFO\nLOG = logging.getLogger('CCStatsJob')\nmrjob.util.log_to_stream(format=LOGGING_FORMAT,\n level=LOGGING_LEVEL,\n name='CCStatsJob')\n\n\nclass MonthlyCrawl:\n \"\"\"Enumeration of monthly crawl archives\"\"\"\n\n by_name = {\n 'CC-MAIN-2008-2009': 88,\n 'CC-MAIN-2009-2010': 89,\n 'CC-MAIN-2012': 90,\n 'CC-MAIN-2013-20': 91,\n 'CC-MAIN-2013-48': 92,\n 'CC-MAIN-2014-10': 93,\n 'CC-MAIN-2014-15': 94,\n 'CC-MAIN-2014-23': 95,\n 'CC-MAIN-2014-35': 96,\n 'CC-MAIN-2014-41': 97,\n 'CC-MAIN-2014-42': 98,\n 'CC-MAIN-2014-49': 99,\n 'CC-MAIN-2014-52': 0,\n 'CC-MAIN-2015-06': 1,\n 'CC-MAIN-2015-11': 2,\n 'CC-MAIN-2015-14': 3,\n 'CC-MAIN-2015-18': 4,\n 'CC-MAIN-2015-22': 5,\n 'CC-MAIN-2015-27': 6,\n 'CC-MAIN-2015-32': 7,\n 'CC-MAIN-2015-35': 8,\n 'CC-MAIN-2015-40': 9,\n 'CC-MAIN-2015-48': 10,\n 'CC-MAIN-2016-07': 11,\n 'CC-MAIN-2016-18': 12,\n 'CC-MAIN-2016-22': 13,\n 'CC-MAIN-2016-26': 14,\n 'CC-MAIN-2016-30': 15,\n 'CC-MAIN-2016-36': 16,\n 'CC-MAIN-2016-40': 17,\n 'CC-MAIN-2016-44': 18,\n 'CC-MAIN-2016-50': 19,\n 'CC-MAIN-2017-04': 20,\n 'CC-MAIN-2017-09': 21,\n 'CC-MAIN-2017-13': 22,\n 'CC-MAIN-2017-17': 23,\n 'CC-MAIN-2017-22': 24,\n 'CC-MAIN-2017-26': 25,\n 'CC-MAIN-2017-30': 26,\n 'CC-MAIN-2017-34': 27,\n 'CC-MAIN-2017-39': 28,\n 'CC-MAIN-2017-43': 29,\n 'CC-MAIN-2017-47': 30,\n 'CC-MAIN-2017-51': 31,\n 'CC-MAIN-2018-05': 32,\n 'CC-MAIN-2018-09': 33,\n 'CC-MAIN-2018-13': 34,\n 'CC-MAIN-2018-17': 35,\n 'CC-MAIN-2018-22': 36,\n 'CC-MAIN-2018-26': 37,\n 'CC-MAIN-2018-30': 38,\n 'CC-MAIN-2018-34': 39,\n 'CC-MAIN-2018-39': 40,\n 'CC-MAIN-2018-43': 41,\n 'CC-MAIN-2018-47': 42,\n 'CC-MAIN-2018-51': 43,\n 'CC-MAIN-2019-04': 44,\n 'CC-MAIN-2019-09': 45,\n 'CC-MAIN-2019-13': 46,\n 'CC-MAIN-2019-18': 47,\n 'CC-MAIN-2019-22': 48,\n 'CC-MAIN-2019-26': 49,\n 'CC-MAIN-2019-30': 50,\n 'CC-MAIN-2019-35': 51,\n 'CC-MAIN-2019-39': 52,\n 'CC-MAIN-2019-43': 53,\n 'CC-MAIN-2019-47': 54,\n 'CC-MAIN-2019-51': 55,\n 'CC-MAIN-2020-05': 56,\n 'CC-MAIN-2020-10': 57,\n 'CC-MAIN-2020-16': 58,\n 'CC-MAIN-2020-24': 59,\n 'CC-MAIN-2020-29': 60,\n 'CC-MAIN-2020-34': 61,\n 'CC-MAIN-2020-40': 62,\n 'CC-MAIN-2020-45': 63,\n 'CC-MAIN-2020-50': 64,\n 'CC-MAIN-2021-04': 65,\n 'CC-MAIN-2021-10': 66,\n 'CC-MAIN-2021-17': 67,\n 'CC-MAIN-2021-21': 68,\n 'CC-MAIN-2021-25': 69,\n 'CC-MAIN-2021-31': 70,\n 'CC-MAIN-2021-39': 71,\n 'CC-MAIN-2021-43': 72,\n 'CC-MAIN-2021-49': 73,\n 'CC-MAIN-2022-05': 74,\n 'CC-MAIN-2022-21': 75,\n 'CC-MAIN-2022-27': 76,\n 'CC-MAIN-2022-33': 77,\n 'CC-MAIN-2022-40': 78,\n 'CC-MAIN-2022-49': 79,\n 'CC-MAIN-2023-06': 80,\n 'CC-MAIN-2023-14': 81,\n 'CC-MAIN-2023-23': 82,\n 'CC-MAIN-2023-40': 83,\n 'CC-MAIN-2023-50': 84,\n 'CC-MAIN-2024-10': 85,\n 'CC-MAIN-2024-18': 86,\n 'CC-MAIN-2024-22': 87,\n 'CC-MAIN-2024-26': 100,\n 'CC-MAIN-2024-30': 101,\n 'CC-MAIN-2024-33': 102,\n 'CC-MAIN-2024-38': 103,\n 'CC-MAIN-2024-42': 104,\n 'CC-MAIN-2024-46': 105,\n 'CC-MAIN-2024-51': 106,\n 'CC-MAIN-2025-05': 107,\n 'CC-MAIN-2025-08': 108,\n 'CC-MAIN-2025-13': 109,\n 'CC-MAIN-2025-18': 110,\n 'CC-MAIN-2025-21': 111,\n 'CC-MAIN-2025-26': 112,\n 'CC-MAIN-2025-30': 113,\n 'CC-MAIN-2025-33': 114,\n 'CC-MAIN-2025-38': 115,\n 'CC-MAIN-2025-43': 116,\n 'CC-MAIN-2025-47': 117,\n 'CC-MAIN-2025-51': 118,\n 'CC-MAIN-2026-04': 119,\n 'CC-MAIN-2026-08': 120,\n 'CC-MAIN-2026-12': 121,\n 'CC-MAIN-2026-17': 122,\n 'CC-MAIN-2026-21': 123,\n }\n\n by_id = dict(map(reversed, by_name.items()))\n\n @staticmethod\n def get_by_name(name):\n return MonthlyCrawl.by_name[name]\n\n @staticmethod\n def to_name(crawl):\n return MonthlyCrawl.by_id[crawl]\n\n @staticmethod\n def to_bit_mask(crawl):\n return (1 << crawl)\n\n @staticmethod\n def date_of(crawl):\n if crawl == 'CC-MAIN-2008-2009':\n return date(2009, 1, 12)\n if crawl == 'CC-MAIN-2009-2010':\n return date(2010, 9, 25)\n if crawl == 'CC-MAIN-2012':\n return date(2012, 11, 2)\n [_, _, year, week] = crawl.split('-')\n return Week(int(year), int(week)).monday()\n\n @staticmethod\n def year_of(crawl):\n return MonthlyCrawl.date_of(crawl).year\n\n @staticmethod\n def short_name(name):\n return name.replace('CC-MAIN-', '')\n\n @staticmethod\n def get_latest(n):\n return sorted(MonthlyCrawl.by_name.keys())[-n:]\n\n\nclass MonthlyCrawlSet:\n \"\"\"Dense representation of a list of monthly crawls.\n Represent in which crawls a given item (URL, but also\n domain, host, digest) occurs.\n \"\"\"\n\n def __init__(self, crawls=0):\n self.bits = crawls\n\n def add(self, crawl):\n self.bits |= MonthlyCrawl.to_bit_mask(crawl)\n\n def update(self, *others):\n for other in others:\n self.bits |= other.get_bits()\n\n def clear(self):\n self.bits = 0\n\n def discard(self, crawl):\n self.bits &= ~MonthlyCrawl.to_bit_mask(crawl)\n\n def __contains__(self, crawl):\n return (self.bits & MonthlyCrawl.to_bit_mask(crawl)) != 0\n\n def __len__(self):\n \"\"\"popcount of a 32 bit integer.\"\"\"\n i = self.bits\n i = i - ((i >> 1) & 0x55555555)\n i = (i & 0x33333333) + ((i >> 2) & 0x33333333)\n return (((i + (i >> 4) & 0xF0F0F0F) * 0x1010101) & 0xffffffff) >> 24\n\n def get_bits(self):\n return self.bits\n\n def get_crawls(self):\n i = self.bits\n r = 0\n while (i):\n if (i & 1):\n yield r\n r += 1\n i >>= 1\n\n def is_new(self, crawl):\n \"\"\"True if there are no older crawls in set (no lower id)\"\"\"\n if (self.bits == 0):\n return True\n i = self.bits\n i = (i ^ (i - 1)) >> 1 # set trailing 0s to 1s and zero rest\n r = 0\n while (i):\n if r == crawl:\n return True\n r += 1\n i >>= 1\n if (r < crawl):\n return False\n return True\n\n def is_newest(self, crawl):\n \"\"\"True if crawl is the newest crawl in set (highest id)\"\"\"\n # i = self.bits\n # j = MonthlyCrawl.to_bit_mask(crawl)\n # return (i & ~j) < j\n return self.bits.bit_length() == (crawl + 1)\n\n\nclass CST(Enum):\n \"\"\"Enum for crawl statistics types.\n Every line (key-value pair) has a marker which indicates the type\n of the count / frequency:\n - pages, URLs, hosts, etc.\n - size (number of unique items), histograms, etc.\n The type marker (the first element in the key tuple) determines\n the format of the line (key-value pair):\n <, >\n The format may vary for different steps (job, mapper, reducer).\n The count job (CCCountJob) uses the numeric types to reduce\n the data size, while CCCountJob outputs the type names for better\n readability.\n Types of countable items\n # <, >\n # For hosts, domains, etc. MultiCount is used to hold two counts -\n # the number of pages and URLs per item.\"\"\"\n url = 0\n \"\"\"(unique) URL\"\"\"\n digest = 1\n \"\"\"(unique) content digest (MD5)\"\"\"\n host = 2\n \"\"\"hostname (\"www.commoncrawl.org\")\"\"\"\n domain = 3\n \"\"\"pay-level domain or private domain (\"commoncrawl.org\")\"\"\"\n tld = 4\n \"\"\"public suffix (\"org\" or \"co.uk\")\n - not necessarily a TLD / \"top-level domain\" according to\n https://github.com/google/guava/wiki/InternetDomainNameExplained\n - here following https://github.com/john-kurkowski/tldextract\"\"\"\n surt_domain = 5\n \"\"\"surt_domain :- SURT domain (\"org,commoncrawl\")\n - Sort-friendly URI Reordering Transform, cf.\n http://crawler.archive.org/articles/user_manual/glossary.html#surt\"\"\"\n scheme = 6\n \"\"\"URI scheme (\"http\", \"https\")\n see https://en.wikipedia.org/wiki/Uniform_Resource_Identifier#Syntax\"\"\"\n mimetype = 7\n \"\"\"MIME type / media type / content type\n - as sent by the server as \"Content-Type\" in the HTTP header,\n weakly normalized, not verified\"\"\"\n mimetype_detected = 77\n \"\"\"MIME type detected based on content, URL and HTTP Content-Type\"\"\"\n page = 8\n \"\"\"number of successfully fetched pages (HTTP status 200),\n including URL-level and content-level duplicates\"\"\"\n fetch = 9\n \"\"\"number of fetches, including 404s, redirects, robots.txt, etc.\n - since CC-MAIN-2016-50\"\"\"\n http_status = 10\n \"\"\"detected charset\n - since CC-MAIN-2018-34\"\"\"\n charset = 11\n \"\"\"detected languages or combination of languages\n - since CC-MAIN-2018-34\n NOTE: since gld2 identifies 160 languages and up to 3 languages,\n the number of possible combinations is too high (4 millions) and\n only the more common ones are preserved\"\"\"\n languages = 12\n \"\"\"primary language of the document (first of the detected languages)\n - since CC-MAIN-2018-34\"\"\"\n primary_language = 13\n \"\"\"number of HTTP status codes (200, 404, etc.)\n - since CC-MAIN-2016-50\"\"\"\n crawl_status = 55\n \"\"\"crawl status (successful fetches, 404s, exceptions, etc.)\n - following Nutch CrawlDatum status codes\n - similar to HTTP status but less fine-grained\n - includes crawler-specific statuses (e.g., \"denied by robots.txt\")\"\"\"\n robotstxt_status = 56\n \"\"\"HTTP status of robots.txt responses\"\"\"\n size = 90\n \"\"\"size of a crawl (number of unique items):\n - pages,\n - URLs (one URL may be fetched multiple times),\n - content digests,\n - domains, hosts, top-level domains\n - mime types\n - etc.\n format:\n <, number_of_unique_items>\"\"\"\n size_estimate = 91\n \"\"\"estimates for unique URLs and content digests\n - estimates by HyperLogLog probabilistic counters\"\"\"\n size_estimate_for = 92\n \"\"\"estimates per large-sized item\n (domains, hosts, TLDs, SURT domains)\n - aimed to estimate domain coverage over time / multiple crawls\n - CC-MAIN-2016-44 adds HyperLogLogs for SURT domain (>=50,000 URLs)\n format:\n <, hll>\"\"\"\n size_robotstxt = 93\n \"\"\"number of robots.txt fetches\"\"\"\n new_items = 95\n \"\"\"new items (URLs, content digests) for a given crawl\n - first seen in this crawl, not observed in previous crawls\n - only with exact counts for all crawls\n - could be estimated by HyperLogLog set operations otherwise\"\"\"\n histogram = 96\n \"\"\"frequency of item counts per page or URL\n format:\n <, frequency>\"\"\"\n\n\nclass MultiCount(defaultdict):\n \"\"\"Dictionary with multiple counters for the same key\"\"\"\n\n def __init__(self, size):\n self.default_factory = lambda: [0]*size\n self.size = size\n\n def incr(self, key, *counts):\n for i in range(0, self.size):\n self[key][i] += counts[i]\n\n @staticmethod\n def compress(size, counts):\n compress_from = size-1\n last_val = counts[compress_from]\n while compress_from > 0 and last_val == counts[compress_from-1]:\n compress_from -= 1\n if compress_from == 0:\n return counts[0]\n else:\n return counts[0:compress_from+1]\n\n def get_compressed(self, key):\n return MultiCount.compress(self.size, self.get(key))\n\n @staticmethod\n def get_count(index, value):\n if isinstance(value, int):\n return value\n if len(value) <= index:\n return value[-1]\n return value[index]\n\n @staticmethod\n def sum_values(values, compress=True):\n counts = [0]\n size = 1\n for val in values:\n if isinstance(val, int):\n # compressed count, one unique count\n for i in range(0, size):\n counts[i] += val\n else:\n if len(val) >= size:\n # enlarge counts array\n base_count = counts[-1]\n for j in range(size, len(val)):\n counts.append(base_count)\n size = len(val)\n for i in range(0, len(val)):\n counts[i] += val[i]\n if len(val) < size:\n for j in range(i+1, size):\n # add compressed counts\n counts[j] += val[i]\n if compress:\n return MultiCount.compress(size, counts)\n else:\n return counts\n\n\nclass CrawlStatsJSONEncoder(json.JSONEncoder):\n\n def default(self, o):\n if isinstance(o, MonthlyCrawlSet):\n return o.get_bits()\n if isinstance(o, HyperLogLog):\n return CrawlStatsJSONEncoder.json_encode_hyperloglog(o)\n return json.JSONEncoder.default(self, o)\n\n @staticmethod\n def json_encode_hyperloglog(o):\n return {'__type__': 'HyperLogLog',\n 'card': o.card(),\n 'p': o.p, 'M': o.M, 'm': o.m, 'alpha': o.alpha}\n\n\nclass CrawlStatsJSONDecoder(json.JSONDecoder):\n\n def __init__(self, *args, **kargs):\n json.JSONDecoder.__init__(self, object_hook=self.dict_to_object,\n *args, **kargs)\n\n def dict_to_object(self, dic):\n if '__type__' not in dic:\n return dic\n if dic['__type__'] == 'HyperLogLog':\n try:\n return CrawlStatsJSONDecoder.json_decode_hyperloglog(dic)\n except Exception as e:\n LOG.error('Cannot decode object of type {0}'.format(\n dic['__type__']))\n raise e\n return dic\n\n @staticmethod\n def json_decode_hyperloglog(dic):\n hll = HyperLogLog(HYPERLOGLOG_ERROR)\n hll.p = dic['p']\n hll.m = dic['m']\n hll.alpha = dic['alpha']\n hll.M = dic['M']\n return hll\n\n\nclass HostDomainCount:\n \"\"\"Counts requiring URL parsing (host, domain, TLD, scheme).\n For each item both total pages and unique URLs are counted.\n \"\"\"\n\n IPpattern = re.compile(r'^\\d{1,3}.\\d{1,3}.\\d{1,3}.\\d{1,3}$')\n\n def __init__(self):\n self.hosts = MultiCount(2)\n self.schemes = MultiCount(2)\n\n def add(self, url, count):\n uri = urlparse(url)\n host = uri.hostname\n if host is not None:\n host = host.lower().strip('.')\n self.hosts.incr(host, count, 1)\n self.schemes.incr(uri.scheme, count, 1)\n\n def output(self, crawl):\n domains = MultiCount(3) # pages, URLs, hosts\n tlds = MultiCount(4) # pages, URLs, hosts, domains\n for scheme, counts in self.schemes.items():\n yield (CST.scheme.value, scheme, crawl), counts\n for host, counts in self.hosts.items():\n yield (CST.host.value, host, crawl), counts\n try:\n parsedhost = tldextract.extract(host)\n hosttld = parsedhost.suffix\n except TypeError as e:\n LOG.error('Failed to parse host {}: {}'.format(host, e))\n hosttld = None\n if hosttld is None:\n hostdomain = '(invalid)'\n elif hosttld == '':\n hostdomain = parsedhost.domain\n if self.IPpattern.match(host):\n hosttld = '(ip address)'\n else:\n hostdomain = '.'.join([parsedhost.domain, parsedhost.suffix])\n domains.incr((hostdomain, hosttld),\n counts[0], counts[1], 1)\n for dom, counts in domains.items():\n tlds.incr(dom[1], counts[0], counts[1], counts[2], 1)\n yield (CST.domain.value, dom[0], crawl), counts\n for tld, counts in tlds.items():\n yield (CST.tld.value, tld, crawl), counts\n\n\nclass SurtDomainCount:\n \"\"\"Counters for one single SURT prefix/domain.\"\"\"\n\n robots_txt_warc_pattern = re.compile(r'/robotstxt/')\n\n def __init__(self, surt_domain):\n self.surt_domain = surt_domain\n self.pages = 0\n self.url = defaultdict(int)\n self.digest = defaultdict(lambda: [0, 0])\n self.mime = defaultdict(lambda: [0, 0])\n self.mime_detected = defaultdict(lambda: [0, 0])\n self.charset = defaultdict(lambda: [0, 0])\n self.languages = defaultdict(lambda: [0, 0])\n self.http_status = defaultdict(int)\n self.robotstxt_status = defaultdict(lambda: [0, 0])\n self.robotstxt_url = defaultdict(int)\n\n def add(self, _path, metadata):\n status = -1\n if 'status' in metadata:\n status = int(metadata['status'])\n if self.robots_txt_warc_pattern.search(metadata['filename']):\n self.robotstxt_status[status][0] += 1\n if metadata['url'] not in self.robotstxt_url:\n self.robotstxt_status[status][1] += 1\n self.robotstxt_url[metadata['url']] += 1\n # do not count robots.txt responses as \"ordinary\" pages\n return\n self.http_status[status] += 1\n if status != 200:\n # skip content-related metrics for non-200 responses\n return\n self.pages += 1\n mime = 'unk'\n if 'mime' in metadata:\n mime = metadata['mime']\n self.mime[mime][0] += 1\n mime_detected = None\n if 'mime-detected' in metadata:\n mime_detected = metadata['mime-detected']\n self.mime_detected[mime_detected][0] += 1\n charset = None\n if 'charset' in metadata:\n charset = metadata['charset']\n self.charset[charset][0] += 1\n languages = None\n if 'languages' in metadata:\n languages = metadata['languages']\n self.languages[languages][0] += 1\n digest = None\n if 'digest' in metadata:\n digest = metadata['digest']\n self.digest[digest][0] += 1\n if metadata['url'] not in self.url:\n if digest:\n self.digest[digest][1] += 1\n self.mime[mime][1] += 1\n if mime_detected:\n self.mime_detected[mime_detected][1] += 1\n if languages:\n self.languages[languages][1] += 1\n if charset:\n self.charset[charset][1] += 1\n self.url[metadata['url']] += 1\n\n def unique_urls(self):\n return len(self.url)\n\n def output(self, crawl, exact_count=True, min_surt_hll_size=50000):\n counts = (self.pages, self.unique_urls())\n host_domain_count = HostDomainCount()\n surt_hll = None\n if self.unique_urls() >= min_surt_hll_size:\n surt_hll = HyperLogLog(HYPERLOGLOG_ERROR)\n for url, count in self.url.items():\n host_domain_count.add(url, count)\n if exact_count:\n yield (CST.url.value, self.surt_domain, url), (crawl, count)\n if surt_hll is not None:\n surt_hll.add(url)\n if exact_count:\n for digest, counts in self.digest.items():\n yield (CST.digest.value, digest), (crawl, counts)\n for mime, counts in self.mime.items():\n yield (CST.mimetype.value, mime, crawl), counts\n for mime, counts in self.mime_detected.items():\n yield (CST.mimetype_detected.value, mime, crawl), counts\n for charset, counts in self.charset.items():\n yield (CST.charset.value, charset, crawl), counts\n for languages, counts in self.languages.items():\n yield (CST.languages.value, languages, crawl), counts\n # yield primary language\n prim_l = languages.split(',')[0]\n yield (CST.primary_language.value, prim_l, crawl), counts\n for key, val in host_domain_count.output(crawl):\n yield key, val\n yield((CST.surt_domain.value, self.surt_domain, crawl),\n (self.pages, self.unique_urls(), len(host_domain_count.hosts)))\n if surt_hll is not None:\n yield((CST.size_estimate_for.value, CST.surt_domain.value,\n self.surt_domain, CST.url.value, crawl),\n (self.unique_urls(),\n CrawlStatsJSONEncoder.json_encode_hyperloglog(surt_hll)))\n for status, counts in self.http_status.items():\n yield (CST.http_status.value, status, crawl), counts\n for url, count in self.robotstxt_url.items():\n yield (CST.size_robotstxt.value, CST.url.value, crawl), 1\n yield (CST.size_robotstxt.value, CST.page.value, crawl), count\n for status, counts in self.robotstxt_status.items():\n yield (CST.robotstxt_status.value, status, crawl), counts\n\n\nclass UnhandledTypeError(Exception):\n def __init__(self, outputType):\n self.message = 'Unhandled type {}\\n'.format(outputType)\n\n\nclass InputError(Exception):\n def __init__(self, message):\n self.message = message\n\n\nclass CCStatsJob(MRJob):\n '''Job to get crawl statistics from Common Crawl index\n --job=count\n run count job (first step) to get counts\n from Common Crawl index files (cdx-*.gz)\n --job=stats\n run statistics job (second step) on output\n from count job'''\n\n OUTPUT_PROTOCOL = JSONProtocol\n\n JOBCONF = {\n 'mapreduce.task.timeout': '9600000',\n 'mapreduce.map.speculative': 'false',\n 'mapreduce.reduce.speculative': 'false',\n 'mapreduce.job.jvm.numtasks': '-1',\n }\n\n s3pattern = re.compile(r'^s3://([^/]+)/(.+)')\n gzpattern = re.compile(r'\\.gz$')\n crawlpattern = re.compile(r'(CC-MAIN-2\\d{3}-\\d{2})')\n\n def configure_args(self):\n \"\"\"Custom command line options for common crawl index statistics\"\"\"\n super(CCStatsJob, self).configure_args()\n self.add_passthru_arg(\n '--job', dest='job_to_run',\n default='', choices=['count', 'stats', ''],\n help='''Job(s) to run (\"count\", \"stats\", or empty to run both)''')\n self.add_passthru_arg(\n '--exact-counts', dest='exact_counts',\n action='store_true', default=None,\n help='''Exact counts for URLs and content digests,\n this increases the output size significantly''')\n self.add_passthru_arg(\n '--no-exact-counts', dest='exact_counts',\n action='store_false', default=None,\n help='''No exact counts for URLs and content digests\n to save storage space and computation time''')\n self.add_passthru_arg(\n '--max-top-hosts-domains', dest='max_hosts',\n type=int, default=200,\n help='''Max. number of most frequent hosts or domains shown\n in final statistics (cf. --min-urls-top-host-domain)''')\n self.add_passthru_arg(\n '--min-urls-top-host-domain', dest='min_domain_frequency',\n type=int, default=1,\n help='''Min. number of URLs required per host or domain shown\n in final statistics (cf. --max-top-hosts-domains).''')\n self.add_passthru_arg(\n '--min-lang-comb-freq', dest='min_lang_comb_freq',\n type=int, default=1,\n help='''Min. number of pages required for a combination of detected\n languages to be shown in final statistics.''')\n self.add_passthru_arg(\n '--crawl', dest='crawl', default=None,\n help='''ID/name of the crawl analyzed (if not given detected\n from input path)''')\n\n def input_protocol(self):\n if self.options.job_to_run != 'stats':\n LOG.debug('Reading text input from cdx files')\n return RawValueProtocol()\n LOG.debug('Reading JSON input from count job')\n return JSONProtocol()\n\n def hadoop_input_format(self):\n input_format = self.HADOOP_INPUT_FORMAT\n if self.options.job_to_run != 'stats':\n input_format = 'org.apache.hadoop.mapred.TextInputFormat'\n LOG.info(\"Setting input format for {} job: {}\".format(\n self.options.job_to_run, input_format))\n return input_format\n\n def count_mapper_init(self):\n \"\"\"Because cdx.gz files cannot be split and\n mapreduce.input.fileinputformat.split.minsize is set to a value larger\n than any cdx.gz file, the mapper is guaranteed to process the content\n of a single cdx file. Input lines of a cdx file are sorted by SURT URL\n which allows to aggregate URL counts for one SURT domain in memory.\n It may happen that one SURT domain spans over multiple cdx files.\n In this case (and without --exact-counts) the count of unique URLs\n and the URL histograms may be slightly off in case the same URL occurs\n also in a second cdx file. However, this problem is negligible because\n there are only 300 cdx files.\"\"\"\n self.counters = Counter()\n self.cdx_path = os.environ['mapreduce_map_input_file']\n LOG.info('Reading {0}'.format(self.cdx_path))\n self.crawl_name = None\n self.crawl = None\n if self.options.crawl is not None:\n self.crawl_name = self.options.crawl\n else:\n crawl_name_match = self.crawlpattern.search(self.cdx_path)\n if crawl_name_match is not None:\n self.crawl_name = crawl_name_match.group(1)\n else:\n raise InputError(\n \"Cannot determine ID of monthly crawl from input path {}\"\n .format(self.cdx_path))\n if self.crawl_name is None:\n raise InputError(\"Name of crawl not given\")\n self.crawl = MonthlyCrawl.get_by_name(self.crawl_name)\n self.fetches_total = 0\n self.pages_total = 0\n self.urls_total = 0\n self.urls_hll = HyperLogLog(HYPERLOGLOG_ERROR)\n self.digest_hll = HyperLogLog(HYPERLOGLOG_ERROR)\n self.url_histogram = Counter()\n self.count = None\n # first and last SURT may continue in previous/next cdx\n self.min_surt_hll_size = 1\n self.increment_counter('cdx-stats', 'cdx files processed', 1)\n\n def count_mapper(self, _, line):\n self.fetches_total += 1\n if (self.fetches_total % 1000) == 0:\n self.increment_counter('cdx-stats', 'cdx lines read', 1000)\n if (self.fetches_total % 100000) == 0:\n LOG.info('Read {0} cdx lines'.format(self.fetches_total))\n else:\n LOG.debug('Read {0} cdx lines'.format(self.fetches_total))\n parts = line.split(' ')\n [surt_domain, path] = parts[0].split(')', 1)\n if self.count is None:\n self.count = SurtDomainCount(surt_domain)\n if surt_domain != self.count.surt_domain:\n # output accumulated statistics for one SURT domain\n for pair in self.count.output(self.crawl,\n self.options.exact_counts,\n self.min_surt_hll_size):\n yield pair\n self.urls_total += self.count.unique_urls()\n for url, cnt in self.count.url.items():\n self.urls_hll.add(url)\n self.url_histogram[cnt] += 1\n for digest in self.count.digest:\n self.digest_hll.add(digest)\n self.pages_total += self.count.pages\n self.count = SurtDomainCount(surt_domain)\n self.min_surt_hll_size = MIN_SURT_HLL_SIZE\n json_string = ' '.join(parts[2:])\n try:\n metadata = ujson.loads(json_string)\n self.count.add(path, metadata)\n except ValueError as e:\n LOG.error('Failed to parse json: {0} - {1}'.format(\n e, json_string))\n\n def count_mapper_final(self):\n self.increment_counter('cdx-stats',\n 'cdx lines read', self.fetches_total % 1000)\n if self.count is None:\n return\n for pair in self.count.output(self.crawl, self.options.exact_counts, 1):\n yield pair\n self.urls_total += self.count.unique_urls()\n for url, cnt in self.count.url.items():\n self.urls_hll.add(url)\n self.url_histogram[cnt] += 1\n for digest in self.count.digest:\n self.digest_hll.add(digest)\n self.pages_total += self.count.pages\n if not self.options.exact_counts:\n for count, frequency in self.url_histogram.items():\n yield((CST.histogram.value, CST.url.value, self.crawl,\n CST.page.value, count), frequency)\n yield (CST.size.value, CST.page.value, self.crawl), self.pages_total\n yield (CST.size.value, CST.fetch.value, self.crawl), self.fetches_total\n if not self.options.exact_counts:\n yield (CST.size.value, CST.url.value, self.crawl), self.urls_total\n yield((CST.size_estimate.value, CST.url.value, self.crawl),\n CrawlStatsJSONEncoder.json_encode_hyperloglog(self.urls_hll))\n yield((CST.size_estimate.value, CST.digest.value, self.crawl),\n CrawlStatsJSONEncoder.json_encode_hyperloglog(self.digest_hll))\n self.increment_counter('cdx-stats', 'cdx files finished', 1)\n\n def reducer_init(self):\n self.counters = Counter()\n self.mostfrequent = defaultdict(list)\n\n def count_reducer(self, key, values):\n outputType = key[0]\n if outputType in (CST.size.value, CST.size_robotstxt.value):\n yield key, sum(values)\n elif outputType == CST.histogram.value:\n yield key, sum(values)\n elif outputType in (CST.url.value, CST.digest.value):\n # only with --exact-counts\n crawls = MonthlyCrawlSet()\n new_crawls = set()\n page_count = MultiCount(2)\n for val in values:\n if type(val) is list:\n if (outputType == CST.url.value):\n (crawl, pages) = val\n page_count.incr(crawl, pages, 1)\n else: # digest\n (crawl, (pages, urls)) = val\n page_count.incr(crawl, pages, urls)\n crawls.add(crawl)\n new_crawls.add(crawl)\n else:\n # crawl set bit mask\n crawls.update(val)\n yield key, crawls.get_bits()\n for new_crawl in new_crawls:\n if crawls.is_new(new_crawl):\n self.counters[(CST.new_items.value,\n outputType, new_crawl)] += 1\n # url/digest duplicate histograms\n for crawl, counts in page_count.items():\n items = (1+counts[0]-counts[1])\n self.counters[(CST.histogram.value, outputType,\n crawl, CST.page.value, items)] += 1\n # size in terms of unique URLs and unique content digests\n for crawl, counts in page_count.items():\n self.counters[(CST.size.value, outputType, crawl)] += 1\n elif outputType in (CST.mimetype.value,\n CST.mimetype_detected.value,\n CST.charset.value,\n CST.languages.value,\n CST.primary_language.value,\n CST.scheme.value,\n CST.tld.value,\n CST.domain.value,\n CST.surt_domain.value,\n CST.host.value,\n CST.http_status.value,\n CST.robotstxt_status.value):\n yield key, MultiCount.sum_values(values)\n elif outputType == CST.size_estimate.value:\n hll = HyperLogLog(HYPERLOGLOG_ERROR)\n for val in values:\n hll.update(\n CrawlStatsJSONDecoder.json_decode_hyperloglog(val))\n yield(key,\n CrawlStatsJSONEncoder.json_encode_hyperloglog(hll))\n elif outputType == CST.size_estimate_for.value:\n res = None\n hll = None\n cnt = 0\n for val in values:\n if res:\n if hll is None:\n cnt = res[0]\n hll = CrawlStatsJSONDecoder.json_decode_hyperloglog(res[1])\n cnt += val[0]\n hll.update(CrawlStatsJSONDecoder.json_decode_hyperloglog(val[1]))\n else:\n res = val\n if hll is not None and cnt >= MIN_SURT_HLL_SIZE:\n yield(key, (cnt, CrawlStatsJSONEncoder.json_encode_hyperloglog(hll)))\n elif res[0] >= MIN_SURT_HLL_SIZE:\n yield(key, res)\n else:\n raise UnhandledTypeError(outputType)\n\n def stats_mapper_init(self):\n self.counters = Counter()\n\n def stats_mapper(self, key, value):\n if key[0] in (CST.url.value, CST.digest.value,\n CST.size_estimate_for.value):\n return\n if ((self.options.min_domain_frequency > 1) and\n (key[0] in (CST.host.value, CST.domain.value,\n CST.surt_domain.value))):\n # quick skip of infrequent host and domains,\n # significantly limits amount of tuples processed in reducer\n page_count = MultiCount.get_count(0, value)\n url_count = MultiCount.get_count(1, value)\n self.counters[(CST.size.value, key[0], key[2])] += 1\n self.counters[(CST.histogram.value, key[0],\n key[2], CST.page.value, page_count)] += 1\n self.counters[(CST.histogram.value, key[0],\n key[2], CST.url.value, url_count)] += 1\n if key[0] in (CST.domain.value, CST.surt_domain.value):\n host_count = MultiCount.get_count(2, value)\n self.counters[(CST.histogram.value, key[0],\n key[2], CST.host.value, host_count)] += 1\n if url_count < self.options.min_domain_frequency:\n return\n if key[0] == CST.languages.value:\n # yield only frequent language combinations (if configured)\n page_count = MultiCount.get_count(0, value)\n if ((self.options.min_lang_comb_freq > 1) and\n (page_count < self.options.min_lang_comb_freq) and\n (',' in key[1])):\n return\n yield key, value\n\n def stats_mapper_final(self):\n for (counter, count) in self.counters.items():\n yield counter, count\n\n def stats_reducer(self, key, values):\n outputType = CST(key[0])\n item = key[1]\n crawl = MonthlyCrawl.to_name(key[2])\n if outputType in (CST.size, CST.new_items,\n CST.size_estimate, CST.size_robotstxt):\n verbose_key = (outputType.name, CST(item).name, crawl)\n if outputType in (CST.size, CST.size_robotstxt):\n val = sum(values)\n elif outputType == CST.new_items:\n val = MultiCount.sum_values(values)\n elif outputType == CST.size_estimate:\n # already \"reduced\" in count job\n for val in values:\n break\n yield verbose_key, val\n elif outputType == CST.histogram:\n yield((outputType.name, CST(item).name, crawl,\n CST(key[3]).name, key[4]), sum(values))\n elif outputType in (CST.mimetype, CST.mimetype_detected, CST.charset,\n CST.languages, CST.primary_language, CST.scheme,\n CST.surt_domain, CST.tld, CST.domain, CST.host,\n CST.http_status, CST.robotstxt_status):\n item = key[1]\n for counts in values:\n page_count = MultiCount.get_count(0, counts)\n url_count = MultiCount.get_count(1, counts)\n if outputType in (CST.domain, CST.surt_domain, CST.tld):\n host_count = MultiCount.get_count(2, counts)\n if (self.options.min_domain_frequency <= 1 or\n outputType not in (CST.host, CST.domain,\n CST.surt_domain)):\n self.counters[(CST.size.name, outputType.name, crawl)] += 1\n self.counters[(CST.histogram.name, outputType.name,\n crawl, CST.page.name, page_count)] += 1\n self.counters[(CST.histogram.name, outputType.name,\n crawl, CST.url.name, url_count)] += 1\n if outputType in (CST.domain, CST.surt_domain, CST.tld):\n self.counters[(CST.histogram.name, outputType.name,\n crawl, CST.host.name, host_count)] += 1\n if outputType == CST.tld:\n domain_count = MultiCount.get_count(3, counts)\n self.counters[(CST.histogram.name, outputType.name,\n crawl, CST.domain.name, domain_count)] += 1\n if outputType in (CST.domain, CST.host, CST.surt_domain):\n outKey = (outputType.name, crawl)\n outVal = (page_count, url_count, item)\n if outputType in (CST.domain, CST.surt_domain):\n outVal = (page_count, url_count, host_count, item)\n # take most common\n if len(self.mostfrequent[outKey]) < self.options.max_hosts:\n heapq.heappush(self.mostfrequent[outKey], outVal)\n else:\n heapq.heappushpop(self.mostfrequent[outKey], outVal)\n else:\n yield((outputType.name, item, crawl), counts)\n else:\n raise UnhandledTypeError(outputType)\n\n def reducer_final(self):\n for (counter, count) in self.counters.items():\n yield counter, count\n for key, mostfrequent in self.mostfrequent.items():\n (outputType, crawl) = key\n if outputType in (CST.domain.name, CST.surt_domain.name):\n for (pages, urls, hosts, item) in mostfrequent:\n yield((outputType, item, crawl),\n MultiCount.compress(3, [pages, urls, hosts]))\n else:\n for (pages, urls, item) in mostfrequent:\n yield((outputType, item, crawl),\n MultiCount.compress(2, [pages, urls]))\n\n def steps(self):\n reduces = 10\n cdxminsplitsize = 2**32 # do not split cdx map input files\n if self.options.exact_counts:\n # with exact counts need many reducers to aggregate the counts\n # in reasonable time and to get not too large partitions\n reduces = 200\n count_job = \\\n MRStep(mapper_init=self.count_mapper_init,\n mapper=self.count_mapper,\n mapper_final=self.count_mapper_final,\n reducer_init=self.reducer_init,\n reducer=self.count_reducer,\n reducer_final=self.reducer_final,\n jobconf={'mapreduce.job.reduces': reduces,\n 'mapreduce.input.fileinputformat.split.minsize':\n cdxminsplitsize,\n 'mapreduce.output.fileoutputformat.compress':\n \"true\",\n 'mapreduce.output.fileoutputformat.compress.codec':\n 'org.apache.hadoop.io.compress.BZip2Codec'})\n stats_job = \\\n MRStep(mapper_init=self.stats_mapper_init,\n mapper=self.stats_mapper,\n mapper_final=self.stats_mapper_final,\n reducer_init=self.reducer_init,\n reducer=self.stats_reducer,\n reducer_final=self.reducer_final,\n jobconf={'mapreduce.job.reduces': 1,\n 'mapreduce.output.fileoutputformat.compress':\n \"true\",\n 'mapreduce.output.fileoutputformat.compress.codec':\n 'org.apache.hadoop.io.compress.GzipCodec'})\n if self.options.job_to_run == 'count':\n return [count_job]\n if self.options.job_to_run == 'stats':\n return [stats_job]\n return [count_job, stats_job]\n\n\nif __name__ == '__main__':\n CCStatsJob.run()\n" }, { "path": "get_stats.sh", "content": "#!/bin/bash\n\nset -o pipefail\n\nif aws s3 ls s3://commoncrawl/crawl-analysis/ | sed -E 's@.* @@; s@/$@@' >./stats/crawls.txt; then\n ON_AWS=true;\n echo \"Running on AWS (AWS CLI configured for authenticated access)\"\nelse\n echo \"Downloading from https://data.commoncrawl.org/ using curl\"\n # list of crawls enumerated in crawlstats.py\n python3 -c 'from crawlstats import MonthlyCrawl; [print(c) for c in sorted(MonthlyCrawl.by_name.keys())]' >./stats/crawls.txt\n ON_AWS=false\nfi\n\nwhile read crawl; do\n echo $crawl\n if [ -e stats/$crawl.gz ]; then\n echo \" ... exists\"\n continue\n fi\n if $ON_AWS; then\n aws s3 cp s3://commoncrawl/crawl-analysis/$crawl/stats/part-00000.gz ./stats/$crawl.gz\n else\n curl --silent https://data.commoncrawl.org/crawl-analysis/$crawl/stats/part-00000.gz >./stats/$crawl.gz\n fi\ndone <./stats/crawls.txt\n" }, { "path": "get_stats_and_plot.sh", "content": "#!/bin/bash\nset -e\n\necho \"Starting ...\"\n\n./get_stats.sh\n\n# make sure plot directories exist\nmkdir -p plots/crawler\nmkdir -p plots/crawloverlap\nmkdir -p plots/crawlsize\nmkdir -p plots/throughput\nmkdir -p plots/tld\n\n./plot.sh\n\necho \"Done.\"" }, { "path": "index.md", "content": "Statistics of Common Crawl Monthly Archives\n===========================================\n\nStatistics of [Common Crawl](https://commoncrawl.org/)'s [web archives](https://commoncrawl.org/the-data/get-started/) released on a monthly base:\n\n* [size of the crawls](plots/crawlsize) - number of pages, unique URLs, hosts, domains, top-level domains (public suffixes), cumulative growth of crawled data over time\n* [top-level domains](plots/tlds) - distribution and comparison\n* [top-500 registered domains](plots/domains.md)\n* [crawler-related metrics](plots/crawlermetrics) - fetch status, etc.\n* [overlaps between monthly crawls](plots/crawloverlap)\n* distribution of\n - [media types (MIME)](plots/mimetypes)\n\t- [character encodings](plots/charsets.md)\n\t- [languages](plots/languages.md)\n\nAll metrics presented here are generated from [Common Crawl's URL index](https://index.commoncrawl.org/) data using the code of the [cc-crawl-statistics project](https://github.com/commoncrawl/cc-crawl-statistics). Inspired by Sebastian Spiegler's [Statistics of the Common Crawl Corpus 2012](https://commoncrawl.org/2013/08/a-look-inside-common-crawls-210tb-2012-web-corpus/).\n\nSee also our [Web Graph statistics](https://commoncrawl.github.io/cc-webgraph-statistics/).\n\n" }, { "path": "plot/charset.py", "content": "import sys\n\nfrom plot.table import TabularStats\nfrom crawlstats import CST, MonthlyCrawl\n\n\nclass CharsetStats(TabularStats):\n\n MIN_AVERAGE_COUNT = 500\n MAX_CHARSETS = 100\n\n def __init__(self):\n super().__init__()\n self.MAX_TYPE_VALUES = CharsetStats.MAX_CHARSETS\n\n def add(self, key, val):\n self.add_check_type(key, val, CST.charset)\n\n\nif __name__ == '__main__':\n plot_crawls = sys.argv[1:]\n plot_name = 'charsets'\n column_header = 'charset'\n if len(plot_crawls) == 0:\n plot_crawls = MonthlyCrawl.get_latest(3)\n print(plot_crawls)\n else:\n plot_name += '-' + '-'.join(plot_crawls)\n plot = CharsetStats()\n plot.read_from_stdin_or_file()\n plot.transform_data(CharsetStats.MAX_CHARSETS,\n CharsetStats.MIN_AVERAGE_COUNT,\n None)\n plot.save_data_percentage(plot_name, dir_name='plots', type_name='charset')\n plot.plot(plot_crawls, plot_name, column_header)\n" }, { "path": "plot/crawl_size.py", "content": "\"\"\"\nPlot crawl size metrics over time.\n\nThis module generates visualizations of crawl size statistics including:\n- Monthly crawl sizes (pages, URLs, content digests)\n- Cumulative sizes over time\n- New URLs per crawl\n- URL status by year (new, revisit, duplicate)\n- Domain/host/TLD counts\n\nThe plots show the growth and evolution of the Common Crawl archive.\n\"\"\"\n\nimport os\nimport re\nimport types\nfrom collections import defaultdict\n\nimport pandas\nfrom hyperloglog import HyperLogLog\n\nfrom crawlplot import CrawlPlot\nfrom crawlstats import CST, CrawlStatsJSONDecoder, HYPERLOGLOG_ERROR, MonthlyCrawl\n\n\nclass CrawlSizePlot(CrawlPlot):\n \"\"\"Generate plots showing crawl size metrics over time.\n\n Tracks various size metrics including page counts, unique URLs,\n unique content digests, and cumulative statistics across crawls.\n Uses HyperLogLog for efficient cardinality estimation.\n \"\"\"\n\n def __init__(self):\n super().__init__()\n\n self.size = defaultdict(dict)\n self.size_by_type = defaultdict(dict)\n self.type_index = defaultdict(dict)\n self.crawls = {}\n self.ncrawls = 0\n self.hll = defaultdict(dict)\n self.N = 0\n self.sum_counts = False\n\n def add(self, key, val):\n \"\"\"Process a size or size_estimate record from statistics data.\"\"\"\n cst = CST[key[0]]\n if cst not in (CST.size, CST.size_estimate):\n return\n item_type = key[1]\n crawl = key[2]\n count = 0\n if cst == CST.size_estimate:\n item_type = ' '.join([item_type, 'estim.'])\n hll = CrawlStatsJSONDecoder.json_decode_hyperloglog(val)\n count = len(hll)\n self.hll[item_type][crawl] = hll\n elif cst == CST.size:\n count = val\n self.add_by_type(crawl, item_type, count)\n\n def add_by_type(self, crawl, item_type, count):\n \"\"\"Add a count for a specific crawl and item type combination.\"\"\"\n if crawl not in self.crawls:\n self.crawls[crawl] = self.ncrawls\n self.size['crawl'][self.ncrawls] = crawl\n date = pandas.Timestamp(MonthlyCrawl.date_of(crawl))\n self.size['date'][self.ncrawls] = date\n self.ncrawls += 1\n else:\n date = self.size['date'][self.crawls[crawl]]\n if item_type in self.size and \\\n self.crawls[crawl] in self.size[item_type]:\n # add count to existing record?\n if self.sum_counts:\n count += self.size[item_type][self.crawls[crawl]]\n self.size[item_type][self.crawls[crawl]] = count\n _N = self.type_index[item_type][self.crawls[crawl]]\n self.size_by_type['size'][_N] = count\n return\n self.size[item_type][self.crawls[crawl]] = count\n self.size_by_type['crawl'][self.N] = crawl\n self.size_by_type['date'][self.N] = date\n self.size_by_type['type'][self.N] = item_type\n self.size_by_type['size'][self.N] = count\n self.type_index[item_type][self.crawls[crawl]] = self.N\n self.N += 1\n\n def cumulative_size(self):\n \"\"\"Calculate cumulative sizes across crawls using HyperLogLog unions.\"\"\"\n latest_n_crawls_cumul = [2, 3, 4, 6, 9, 12]\n total_pages = 0\n sorted_crawls = sorted(self.crawls)\n for crawl in sorted_crawls:\n total_pages += self.size['page'][self.crawls[crawl]]\n self.add_by_type(crawl, 'page cumul.', total_pages)\n urls_cumul = defaultdict(dict)\n for item_type in self.hll.keys():\n item_type_cumul = ' '.join([item_type, 'cumul.'])\n item_type_new = ' '.join([item_type, 'new'])\n cumul_hll = HyperLogLog(HYPERLOGLOG_ERROR)\n n = 0\n hlls = []\n for crawl in sorted(self.hll[item_type]):\n n += 1\n hll = self.hll[item_type][crawl]\n last_cumul_hll_len = len(cumul_hll)\n cumul_hll.update(hll)\n # cumulative size\n self.add_by_type(crawl, item_type_cumul, len(cumul_hll))\n # new unseen items this crawl (since the first analyzed crawl)\n unseen = (len(cumul_hll) - last_cumul_hll_len)\n if unseen > len(hll):\n # 1% error rate for cumulative HLLs is large in comparison\n # to crawl size, adjust to size of items in this crawl\n # (there can be no more new items than the size of the crawl)\n unseen = len(hll)\n self.add_by_type(crawl, item_type_new, unseen)\n hlls.append(hll)\n # cumulative size for last N crawls\n for n_crawls in latest_n_crawls_cumul:\n item_type_n_crawls = '{} cumul. last {} crawls'.format(\n item_type, n_crawls)\n if n_crawls <= len(hlls):\n cum_hll = HyperLogLog(HYPERLOGLOG_ERROR)\n for i in range(1, (n_crawls+1)):\n if i > len(hlls):\n break\n cum_hll.update(hlls[-i])\n size_last_n = len(cum_hll)\n if item_type == 'url estim.':\n urls_cumul[crawl][str(n_crawls)] = size_last_n\n else:\n size_last_n = 'nan'\n self.add_by_type(crawl, item_type_n_crawls, size_last_n)\n for n, crawl in enumerate(sorted_crawls):\n for n_crawls in latest_n_crawls_cumul:\n if n_crawls > (n+1):\n self.add_by_type(crawl,\n 'page cumul. last {} crawls'.format(n_crawls),\n 'nan')\n continue\n cumul_pages = 0\n for c in sorted_crawls[(1+n-n_crawls):(n+1)]:\n cumul_pages += self.size['page'][self.crawls[c]]\n self.add_by_type(crawl,\n 'page cumul. last {} crawls'.format(n_crawls),\n cumul_pages)\n urls_cumul[crawl][str(n_crawls)] = urls_cumul[crawl][str(n_crawls)]/cumul_pages\n for crawl in urls_cumul:\n for n_crawls in urls_cumul[crawl]:\n self.add_by_type(crawl,\n 'URLs/pages last {} crawls'.format(n_crawls),\n urls_cumul[crawl][n_crawls])\n\n def transform_data(self):\n \"\"\"Convert internal dictionaries to pandas DataFrames.\"\"\"\n self.size = pandas.DataFrame(self.size)\n self.size_by_type = pandas.DataFrame(self.size_by_type)\n\n def save_data(self):\n \"\"\"Save size data to CSV files.\"\"\"\n self.size.to_csv('data/crawlsize.csv')\n self.size_by_type.to_csv('data/crawlsizebytype.csv')\n\n def duplicate_ratio(self):\n \"\"\"Calculate and save URL and content duplicate ratios per crawl.\"\"\"\n data = self.size[['crawl', 'page', 'url', 'digest estim.']]\n data['1-(urls/pages)'] = 100 * (1.0 - (data['url'] / data['page']))\n data['1-(digests/pages)'] = \\\n 100 * (1.0 - (data['digest estim.'] / data['page']))\n floatf = '{0:.1f}%'.format\n print(data.to_string(formatters={'1-(urls/pages)': floatf,\n '1-(digests/pages)': floatf}),\n file=open('data/crawlduplicates.txt', 'w'))\n\n def plot(self):\n \"\"\"Generate all crawl size plots.\"\"\"\n # Size per crawl (pages, URL and content digest)\n row_types = ['page', 'url', 'digest estim.']\n self.size_plot(self.size_by_type, row_types, '',\n 'Crawl Size', 'Pages / Unique Items',\n 'crawlsize/monthly.png',\n data_export_csv='crawlsize/monthly.csv')\n # -- cumulative size\n row_types = ['page cumul.', 'url estim. cumul.',\n 'digest estim. cumul.']\n self.size_plot(self.size_by_type, row_types, r' cumul\\.$',\n 'Crawl Size Cumulative',\n 'Pages / Unique Items Cumulative',\n 'crawlsize/cumulative.png',\n data_export_csv='crawlsize/cumulative.csv')\n # -- new URLs per crawl\n row_types = ['url estim. new']\n self.size_plot(self.size_by_type, row_types, '',\n 'New URLs per Crawl (not observed in prior crawls)',\n 'New URLs', 'crawlsize/monthly_new.png',\n data_export_csv='crawlsize/monthly_new.csv')\n # -- cumulative URLs over last N crawls (this and preceding N-1 crawls)\n row_types = ['url', '1 crawl', # 'url' replaced by '1 crawl'\n 'url estim. cumul. last 2 crawls',\n 'url estim. cumul. last 3 crawls',\n 'url estim. cumul. last 4 crawls',\n 'url estim. cumul. last 6 crawls',\n 'url estim. cumul. last 9 crawls',\n 'url estim. cumul. last 12 crawls']\n data = self.size_by_type\n data = data[data['type'].isin(row_types)]\n data.replace(to_replace='url', value='1 crawl', inplace=True)\n self.size_plot(data, row_types, r'^url estim\\. cumul\\. last | crawls?$',\n 'URLs Cumulative Over Last N Crawls',\n 'Unique URLs cumulative',\n 'crawlsize/url_last_n_crawls.png',\n clabel='n crawls',\n data_export_csv='crawlsize/url_last_n_crawls.csv')\n # -- ratio unique URLs by total page captures over last N crawls (this and preceding N-1 crawls)\n row_types = ['URLs/pages last 2 crawls',\n 'URLs/pages last 3 crawls',\n 'URLs/pages last 4 crawls',\n 'URLs/pages last 6 crawls',\n 'URLs/pages last 9 crawls',\n 'URLs/pages last 12 crawls']\n data = self.size_by_type\n data = data[data['type'].isin(row_types)]\n data.replace(to_replace='url', value='1 crawl', inplace=True)\n self.size_plot(data, row_types, r'^URLs/pages last | crawls?$',\n 'Ratio Unique URLs / Total Pages Captured Over Last N Crawls',\n 'URLs/Pages',\n 'crawlsize/url_page_ratio_last_n_crawls.png',\n clabel='n crawls',\n data_export_csv='crawlsize/url_page_ratio_last_n_crawls.csv')\n # -- cumul. digests over last N crawls (this and preceding N-1 crawls)\n row_types = ['digest estim.', '1 crawl', # 'url' replaced by '1 crawl'\n 'digest estim. cumul. last 2 crawls',\n 'digest estim. cumul. last 3 crawls',\n 'digest estim. cumul. last 6 crawls',\n 'digest estim. cumul. last 12 crawls']\n data = self.size_by_type\n data = data[data['type'].isin(row_types)]\n data.replace(to_replace='digest estim.', value='1 crawl', inplace=True)\n self.size_plot(data, row_types,\n r'^digest estim\\. cumul\\. last | crawls?$',\n 'Content Digest Cumulative Over Last N Crawls',\n 'Unique content digests cumulative',\n 'crawlsize/digest_last_n_crawls.png',\n clabel='n crawls')\n # -- URLs, hosts, domains, tlds (normalized)\n data = self.size_by_type\n row_types = ['url', 'tld', 'domain', 'host']\n data = data[data['type'].isin(row_types)]\n self.export_csv(data, 'crawlsize/domain.csv')\n # --- domains only (not yet normalized)\n self.size_plot(data[data['type'].isin(['domain'])], '', '',\n 'Unique Domains per Crawl',\n '', 'crawlsize/registered-domains.png')\n # normalize scale (exponent) of counts so that they fit on one plot\n size_norm = data['size'] / 1000.0\n data['size'] = size_norm.where(data['type'] == 'tld',\n other=data['size'])\n data.replace(to_replace='tld', value='tld e+04', inplace=True)\n size_norm = size_norm / 10000.0\n data['size'] = size_norm.where(data['type'] == 'host',\n other=data['size'])\n data.replace(to_replace='host', value='host e+07', inplace=True)\n data['size'] = size_norm.where(data['type'] == 'domain',\n other=data['size'])\n data.replace(to_replace='domain', value='domain e+07', inplace=True)\n size_norm = size_norm / 100.0\n data['size'] = size_norm.where(data.type == 'url',\n other=data['size'])\n data.replace(to_replace='url', value='url e+09', inplace=True)\n self.size_plot(data, '', '',\n 'URLs / Hosts / Domains / TLDs per Crawl',\n 'Unique Items', 'crawlsize/domain.png')\n # -- URL status by year:\n # -- duplicates (pages - URLs), known URLs (URLs - new), new URLs\n data = self.size[['crawl', 'page', 'url', 'url estim. new']]\n data['year'] = data['crawl'].apply(lambda c: int(MonthlyCrawl.year_of(c)))\n by_year = data[['year', 'page', 'url', 'url estim. new']] \\\n .groupby('year').agg(sum).reset_index()\n by_year['revisit'] = by_year['url'] - by_year['url estim. new']\n by_year['duplicate'] = by_year['page'] - by_year['url']\n by_year['new'] = by_year['url estim. new']\n print('URL status by year:')\n print(by_year)\n by_year_by_type = by_year[['year', 'new', 'revisit', 'duplicate', 'page']].melt(\n id_vars=['year', 'page'],\n value_vars=['new', 'revisit', 'duplicate'],\n var_name='url_status', value_name='page_captures')\n by_year_by_type['ratio'] = by_year_by_type['page_captures'] / by_year_by_type['page']\n by_year_by_type['perc'] = by_year_by_type['ratio'].apply(lambda x: round((100.0*x), 1)).astype(str) + '%'\n by_year_by_type['year'] = pandas.Categorical(by_year_by_type['year'], ordered=True)\n by_year_by_type['url_status'] = pandas.Categorical(by_year_by_type['url_status'],\n ordered=True,\n categories=['duplicate',\n 'revisit', 'new'])\n by_year_by_type['page_captures'] = by_year_by_type['page_captures'].astype(float)\n\n # url_status_by_year\n img_path = os.path.join(self.PLOTDIR, 'crawlsize', 'url_status_by_year.png')\n\n if self.PLOTLIB == \"rpy2.ggplot2\":\n return self.plot_with_rpy2_ggplot2(by_year_by_type, img_path)\n elif self.PLOTLIB == \"matplotlib\":\n return self.plot_with_matplotlib(by_year_by_type, img_path)\n else:\n raise ValueError(\"Invalid PLOTLIB\")\n \n def plot_with_rpy2_ggplot2(self, by_year_by_type, img_path):\n \"\"\"Generate URL status by year stacked bar chart using rpy2/ggplot2.\"\"\"\n from rpy2.robjects.lib import ggplot2\n from rpy2 import robjects\n from rpy2.robjects import pandas2ri\n pandas2ri.activate()\n\n p = ggplot2.ggplot(by_year_by_type) \\\n + ggplot2.aes_string(x='year', y='page_captures', fill='url_status', label='perc') \\\n + ggplot2.geom_bar(stat='identity', position='stack') \\\n + ggplot2.geom_text(\n data=by_year_by_type[\n by_year_by_type['url_status'].isin(['new'])\n & ~by_year_by_type['year'].isin(by_year_by_type['year'].tolist()[0:3])],\n color='black', size=2,\n position=ggplot2.position_dodge(width=.5)) \\\n + self.GGPLOT2_THEME \\\n + ggplot2.scale_fill_manual(values=robjects.r('c(\"duplicate\"=\"#00BA38\", \"revisit\"=\"#619CFF\", \"new\"=\"#F8766D\")')) \\\n + ggplot2.theme(**{'legend.position': 'right',\n 'aspect.ratio': .7,\n **self.GGPLOT2_THEME_KWARGS},\n **{'axis.text.x':\n ggplot2.element_text(angle=45, size=10,\n vjust=1, hjust=1)}) \\\n + ggplot2.labs(title='Number of Page Captures', x='', y='', fill='URL status')\n p.save(img_path)\n\n return p\n\n\n def plot_with_matplotlib(self, by_year_by_type, img_path):\n \"\"\"Generate URL status by year stacked bar chart using matplotlib.\"\"\"\n import numpy as np\n\n aspect_ratio = 0.7\n bar_label_fontsize = 5\n title = 'Number of Page Captures'\n\n fig, ax = self.create_figure()\n\n # Prepare data for stacked bar chart\n years = by_year_by_type['year'].unique()\n url_statuses = ['new', 'revisit', 'duplicate']\n colors = {'duplicate': '#00BA38', 'revisit': '#619CFF', 'new': '#F8766D'}\n\n # Create stacked bars\n bottoms = np.zeros(len(years))\n bars = {}\n\n for status in url_statuses:\n status_data = by_year_by_type[by_year_by_type['url_status'] == status]\n values = []\n labels = []\n\n for year in years:\n year_data = status_data[status_data['year'] == year]\n if len(year_data) > 0:\n values.append(year_data['page_captures'].iloc[0])\n labels.append(year_data['perc'].iloc[0])\n else:\n values.append(0)\n labels.append('')\n\n bars[status] = ax.bar(range(len(years)), values, bottom=bottoms,\n color=colors[status], label=status, width=self.bar_width)\n\n # Add text labels only for 'new' status, excluding first 3 years\n if status == 'new':\n for i, (bar, label) in enumerate(zip(bars[status], labels)):\n if i >= 3 and label:\n height = bar.get_height()\n ax.text(bar.get_x() + bar.get_width() / 2.,\n bottoms[i] + height, label,\n ha='center', va='top', color='black',\n fontsize=bar_label_fontsize)\n\n bottoms += values\n\n self.set_title(ax, title)\n ax.set_xlabel('')\n ax.set_ylabel('')\n\n # Format x-axis\n ax.set_xticks(range(len(years)))\n ax.set_xticklabels(years, rotation=45, ha='right', va='top',\n fontsize=self.ticks_fontsize)\n ax.set_xlim(-0.5, len(years) - 0.5)\n\n # Axes ratio\n ax.set_aspect(1 / ax.get_data_ratio() * aspect_ratio)\n\n # Apply nice y-axis ticks\n self.apply_nice_ticks(ax, axis='y')\n\n # Grid styling\n ax.grid(True, which='minor', linewidth=self.grid_minor_linewidth,\n color=self.grid_minor_color, zorder=0, axis='both')\n ax.grid(True, which='major', linewidth=self.grid_major_linewidth,\n color=self.grid_major_color, zorder=0, axis='both')\n ax.set_axisbelow(True)\n\n # Apply ggplot2 style\n self.apply_ggplot2_style(ax, show_grid=False)\n\n # Set tick colors\n ax.tick_params(axis='y', which='both', colors='#FFFFFF',\n length=self.ticks_length, width=self.grid_major_linewidth,\n labelsize=self.ticks_fontsize)\n ax.tick_params(axis='x', which='both', colors='#E6E6E6',\n length=self.ticks_length, width=self.grid_major_linewidth,\n labelsize=self.ticks_fontsize)\n self.set_tick_labels_black(ax)\n\n # Position legend on right side with reversed order\n handles, labels = ax.get_legend_handles_labels()\n legend = ax.legend(handles[::-1], labels[::-1], loc='center left',\n bbox_to_anchor=(1.0, 0.5), frameon=False,\n fontsize=self.legend_fontsize, title='URL status',\n title_fontsize=self.legend_title_fontsize)\n legend._legend_box.align = 'left'\n\n return self.save_figure(fig, img_path)\n\n\n def export_csv(self, data, csv):\n \"\"\"Export pivot table data to CSV file.\"\"\"\n if csv is not None:\n data.reset_index().pivot(index='crawl',\n columns='type', values='size').to_csv(\n os.path.join(self.PLOTDIR, csv))\n\n def norm_data(self, data, row_filter, type_name_norm):\n \"\"\"Filter and normalize type names in the data for plotting.\"\"\"\n if len(row_filter) > 0:\n data = data[data['type'].isin(row_filter)]\n if type_name_norm != '':\n for value in row_filter:\n replacement = value\n if isinstance(type_name_norm, str):\n if re.search(type_name_norm, value):\n while re.search(type_name_norm, replacement):\n replacement = re.sub(type_name_norm,\n '', replacement)\n elif isinstance(type_name_norm, types.FunctionType):\n replacement = type_name_norm(value)\n if replacement != value:\n data.replace(to_replace=value, value=replacement,\n inplace=True)\n return data\n\n def size_plot(self, data, row_filter, type_name_norm,\n title, ylabel, img_file, clabel='', data_export_csv=None,\n x='date', y='size', c='type'):\n \"\"\"Generate a size plot with filtering and normalization.\n\n Args:\n data: DataFrame containing the size data\n row_filter: List of type values to include\n type_name_norm: Regex pattern or function to normalize type names\n title: Plot title\n ylabel: Y-axis label\n img_file: Output filename\n clabel: Legend title\n data_export_csv: Optional CSV export path\n x, y, c: Column names for x-axis, y-axis, and color grouping\n \"\"\"\n data = self.norm_data(data, row_filter, type_name_norm)\n self.export_csv(data, data_export_csv)\n return self.line_plot(data, title, ylabel, img_file,\n x=x, y=y, c=c, clabel=clabel, ratio=.9)\n\n\nif __name__ == '__main__':\n plot = CrawlSizePlot()\n plot.read_from_stdin_or_file()\n plot.cumulative_size()\n plot.transform_data()\n plot.save_data()\n plot.duplicate_ratio()\n plot.plot()\n" }, { "path": "plot/crawler_metrics.py", "content": "\"\"\"\nPlot crawler performance metrics.\n\nThis module generates visualizations of crawler metrics including:\n- Fetch status breakdown (success, redirect, denied, failed, skipped)\n- CrawlDb status counts\n- HTTP vs HTTPS URL distribution\n\nThese metrics help monitor crawler health and performance over time.\n\"\"\"\n\nimport logging\nimport os\nimport re\n\nimport pandas\n\nfrom crawlstats import CST, MultiCount\nfrom crawl_size import CrawlSizePlot\n\n\nLOGGING_LEVEL = logging.INFO\nlogging.basicConfig(level=LOGGING_LEVEL)\n\n\nclass CrawlerMetrics(CrawlSizePlot):\n \"\"\"Generate plots showing crawler performance metrics.\n\n Tracks fetch statuses, CrawlDb sizes, and URL protocol distribution\n across crawls.\n \"\"\"\n\n metrics_map = {\n 'fetcher:aggr:redirect': ('fetcher:temp_moved', 'fetcher:moved',\n 'fetcher:redirect_count_exceeded',\n 'fetcher:redirect_deduplicated',\n # new counter names (NUTCH-3132)\n # unchanged: 'fetcher:temp_moved', 'fetcher:moved',\n 'fetcher:redirect_count_exceeded_total',\n 'fetcher:redirect_deduplicated_total',\n 'fetcher:redirect_not_created_total'),\n 'fetcher:aggr:denied': ('fetcher:access_denied',\n 'fetcher:robots_denied',\n 'fetcher:robots_denied_maxcrawldelay',\n 'fetcher:robots_defer_visits_dropped',\n 'fetcher:filter_denied',\n # new counter names (NUTCH-3132)\n # unchanged: 'fetcher:access_denied',\n 'fetcher:robots_denied_total',\n 'fetcher:robots_denied_maxcrawldelay_total',\n 'fetcher:robots_defer_visits_dropped_total'),\n 'fetcher:aggr:failed': ('fetcher:gone', 'fetcher:notfound',\n 'fetcher:exception',\n # (no) new counter names (NUTCH-3132)\n ),\n 'fetcher:aggr:skipped': ('fetcher:hitByThrougputThreshold',\n 'fetcher:hitByTimeLimit',\n 'fetcher:AboveExceptionThresholdInQueue',\n 'fetcher:filtered',\n # new counter names (NUTCH-3132)\n 'fetcher:hit_by_throughput_threshold_total',\n 'fetcher:hit_by_timelimit_total',\n 'fetcher:above_exception_threshold_total',\n 'fetcher:hit_by_timeout_total',\n 'fetcher:filtered_total')\n }\n\n def __init__(self):\n super().__init__()\n self.sum_counts = True\n\n def add(self, key, val):\n \"\"\"Process crawl status, size, and scheme records.\"\"\"\n cst = CST[key[0]]\n item_type = key[1]\n crawl = key[2]\n if not (cst == CST.crawl_status or\n (cst == CST.size and item_type in ('page', 'url'))\n or cst == CST.scheme):\n return\n if cst == CST.scheme:\n item_type = 'scheme:' + item_type\n val = MultiCount.get_count(1, val)\n self.add_by_type(crawl, item_type, val)\n for metric in self.metrics_map:\n if item_type in self.metrics_map[metric]:\n logging.debug('Adding metric %s for <%s, %s> = %s', metric, crawl, item_type, val)\n self.add_by_type(crawl, metric, val)\n\n def save_data(self):\n \"\"\"Save crawler metrics data to CSV files.\"\"\"\n self.size.sort_values(['crawl'], inplace=True)\n self.size.to_csv('data/crawlmetrics.csv')\n self.size_by_type.to_csv('data/crawlmetricsbytype.csv')\n\n def add_percent(self):\n \"\"\"Calculate percentage values for fetch statuses and schemes.\"\"\"\n for crawl in self.crawls:\n if self.crawls[crawl] not in self.size['fetcher:total']:\n logging.debug('Crawl %s not found in fetch status data', crawl)\n continue\n total = self.size['fetcher:total'][self.crawls[crawl]]\n for item_type in self.type_index:\n if self.crawls[crawl] not in self.size[item_type]:\n continue\n count = self.size[item_type][self.crawls[crawl]]\n _N = self.type_index[item_type][self.crawls[crawl]]\n if (item_type.startswith('fetcher:') and\n item_type != 'fetcher:total'):\n self.size_by_type['percentage'][_N] = 100.0*count/total\n elif item_type.startswith('scheme:'):\n total = self.size['url'][self.crawls[crawl]]\n self.size_by_type['percentage'][_N] = 100.0*count/total\n\n @staticmethod\n def row2title(row):\n \"\"\"Convert metric row name to human-readable title.\"\"\"\n row = re.sub('(?<=^fetch)er(?::aggr)?|^generator:', '', row)\n row = re.sub('[:_]', ' ', row)\n if row == 'page':\n row = 'pages released'\n return row\n\n def plot(self):\n \"\"\"Generate all crawler metrics plots.\"\"\"\n row_types = ['generator:fetch_list',\n 'fetcher:success', 'fetcher:total',\n 'fetcher:aggr:redirect', 'fetcher:notmodified',\n 'fetcher:aggr:failed', 'fetcher:aggr:denied',\n 'fetcher:aggr:skipped', 'page']\n self.size_plot(self.size_by_type, row_types, CrawlerMetrics.row2title,\n 'Crawler Metrics', 'Pages',\n 'crawler/metrics.png')\n # -- stacked bar plot\n row_types = ['fetcher:success', 'fetcher:notmodified',\n 'fetcher:aggr:redirect', 'fetcher:aggr:failed',\n 'fetcher:aggr:denied', 'fetcher:aggr:skipped']\n ratio = 0.1 + self.ncrawls * .05\n self.plot_fetch_status(self.size_by_type, row_types,\n 'crawler/fetch_status_percentage.png',\n ratio=ratio)\n # -- status of pages in CrawlDb\n row_types = ['crawldb:status:db_fetched',\n 'crawldb:status:db_notmodified',\n 'crawldb:status:db_redir_perm',\n 'crawldb:status:db_redir_temp',\n 'crawldb:status:db_duplicate',\n 'crawldb:status:db_gone',\n 'crawldb:status:db_unfetched',\n 'crawldb:status:db_orphan']\n self.plot_crawldb_status(self.size_by_type, row_types,\n 'crawler/crawldb_status.png',\n ratio=ratio)\n # successfully fetched http:// vs https:// URLs\n self.size_plot(self.size_by_type, ['scheme:http', 'scheme:https'], lambda x: x.split(':')[1],\n 'HTTP vs HTTPS URLs', 'Successfully fetched URLs',\n 'crawler/url_protocols.png')\n self.size_plot(self.size_by_type, ['scheme:http', 'scheme:https'], lambda x: x.split(':')[1],\n 'Percentage of HTTP vs HTTPS URLs', 'Percentage of successfully fetched URLs',\n 'crawler/url_protocols_percentage.png', y='percentage')\n\n def plot_fetch_status_with_rpy2_ggplot2(self, data, img_path, ratio):\n \"\"\"Generate fetch status stacked bar chart using rpy2/ggplot2.\"\"\"\n from rpy2.robjects.lib import ggplot2\n\n p = ggplot2.ggplot(data) \\\n + ggplot2.aes_string(x='crawl', y='percentage', fill='type') \\\n + ggplot2.geom_bar(stat='identity', position='stack', width=.9) \\\n + ggplot2.coord_flip() \\\n + ggplot2.scale_fill_brewer(palette='RdYlGn', type='sequential',\n guide=ggplot2.guide_legend(reverse=True)) \\\n + self.GGPLOT2_THEME \\\n + ggplot2.theme(**{'legend.position': 'bottom',\n 'aspect.ratio': ratio,\n **self.GGPLOT2_THEME_KWARGS}) \\\n + ggplot2.labs(title='Percentage of Fetch Status',\n x='', y='', fill='')\n\n p.save(img_path, height = int(7 * ratio), width = 7)\n\n return p\n\n def plot_fetch_status_with_matplotlib(self, data, categories, img_path, ratio):\n \"\"\"Generate fetch status stacked bar chart using matplotlib.\"\"\"\n import numpy as np\n from matplotlib.ticker import MaxNLocator\n\n crawls = data['crawl'].unique()\n n_crawls = len(crawls)\n\n # Define colors from dark green (success) to dark red (denied)\n status_order = ['success', 'skipped', 'redirect', 'notmodified', 'failed', 'denied']\n status_colors = {\n 'success': '#1A9850', 'skipped': '#91CF60', 'redirect': '#D9EF8B',\n 'notmodified': '#FEE08B', 'failed': '#FC8D59', 'denied': '#D73027'\n }\n categories_ordered = [cat for cat in status_order if cat in categories]\n\n fig, ax = self.create_figure(ratio=ratio)\n\n # Prepare data for horizontal stacked bar chart\n bar_positions = np.arange(n_crawls)\n lefts = np.zeros(n_crawls)\n\n for category in categories_ordered:\n category_data = data[data['type'] == category]\n values = [\n category_data[category_data['crawl'] == crawl]['percentage'].iloc[0]\n if len(category_data[category_data['crawl'] == crawl]) > 0 else 0\n for crawl in crawls\n ]\n ax.barh(bar_positions, values, left=lefts, height=self.bar_width,\n color=status_colors[category], label=category)\n lefts += values\n\n self.set_title(ax, 'Percentage of Fetch Status')\n ax.set_xlabel('')\n ax.set_ylabel('')\n\n # Format y-axis (crawl names)\n ax.set_yticks(bar_positions)\n ax.set_yticklabels(crawls, fontsize=self.ticks_fontsize)\n ax.set_ylim(-0.5, n_crawls - 0.5)\n\n # Format x-axis (percentage)\n max_value = lefts.max()\n ax.set_xlim(0, max_value * 1.02)\n ax.xaxis.set_major_locator(MaxNLocator(nbins=5))\n\n # Apply ggplot2-like styling\n self.apply_ggplot2_style(ax, grid_axis='x')\n\n # Set tick colors\n ax.tick_params(axis='y', which='both', colors='#E6E6E6', length=20,\n width=1.5, labelsize=self.ticks_fontsize)\n ax.tick_params(axis='x', which='both', colors='#E6E6E6', length=4,\n width=1.5, labelsize=self.ticks_fontsize)\n self.set_tick_labels_black(ax)\n\n # Position legend at bottom\n handles, labels = ax.get_legend_handles_labels()\n ax.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, -0.05),\n ncol=min(3, len(categories)), frameon=False,\n fontsize=self.legend_fontsize, title='')\n\n return self.save_figure(fig, img_path)\n\n def plot_fetch_status(self, data, row_filter, img_file, ratio=1.0):\n \"\"\"Generate fetch status percentage stacked bar chart.\"\"\"\n if row_filter:\n data = data[data['type'].isin(row_filter)]\n data = data[['crawl', 'percentage', 'type']]\n categories = []\n for value in row_filter:\n if re.search('^fetcher:(?:aggr:)?', value):\n replacement = re.sub('^fetcher:(?:aggr:)?', '', value)\n categories.append(replacement)\n data.replace(to_replace=value, value=replacement, inplace=True)\n data['type'] = pandas.Categorical(data['type'], ordered=True,\n categories=categories.reverse())\n ratio = 0.1 + len(data['crawl'].unique()) * .03\n img_path = os.path.join(self.PLOTDIR, img_file)\n\n if self.PLOTLIB == \"rpy2.ggplot2\":\n return self.plot_fetch_status_with_rpy2_ggplot2(data=data, img_path=img_path, ratio=ratio)\n elif self.PLOTLIB == \"matplotlib\":\n return self.plot_fetch_status_with_matplotlib(data=data, categories=categories, img_path=img_path, ratio=ratio)\n else:\n raise ValueError(\"Invalid PLOTLIB\")\n\n def plot_crawldb_status_with_rpy2_ggplot2(self, data, img_path, ratio):\n \"\"\"Generate CrawlDb status stacked bar chart using rpy2/ggplot2.\"\"\"\n from rpy2.robjects.lib import ggplot2\n\n p = ggplot2.ggplot(data) \\\n + ggplot2.aes_string(x='crawl', y='size', fill='type') \\\n + ggplot2.geom_bar(stat='identity', position='stack', width=.9) \\\n + ggplot2.coord_flip() \\\n + ggplot2.scale_fill_brewer(palette='Pastel1', type='sequential',\n guide=ggplot2.guide_legend(reverse=False)) \\\n + self.GGPLOT2_THEME \\\n + ggplot2.theme(**{'legend.position': 'bottom',\n 'aspect.ratio': ratio,\n **self.GGPLOT2_THEME_KWARGS}) \\\n + ggplot2.labs(title='CrawlDb Size and Status Counts',\n x='', y='', fill='')\n\n p.save(img_path, height = int(7 * ratio), width = 7)\n return p\n\n def plot_crawldb_status_with_matplotlib(self, data, img_path, ratio):\n \"\"\"Generate CrawlDb status stacked bar chart using matplotlib.\"\"\"\n import numpy as np\n\n crawls = data['crawl'].unique()\n n_crawls = len(crawls)\n\n # Pastel1 palette colors\n pastel1_colors = ['#FDDAEC', '#E5D8BD', '#FFFFCC', '#FED9A6',\n '#DECBE4', '#CCEBC5', '#B3CDE3', '#FBB4AE', '#F2F2F2']\n categories_ordered = ['unfetched', 'redir_temp', 'redir_perm', 'orphan',\n 'notmodified', 'gone', 'fetched', 'duplicate']\n\n fig, ax = self.create_figure(ratio=ratio)\n\n bar_positions = np.arange(n_crawls)\n lefts = np.zeros(n_crawls)\n\n for i, category in enumerate(categories_ordered):\n category_data = data[data['type'] == category]\n values = [\n category_data[category_data['crawl'] == crawl]['size'].iloc[0]\n if len(category_data[category_data['crawl'] == crawl]) > 0 else 0\n for crawl in crawls\n ]\n color = pastel1_colors[i % len(pastel1_colors)]\n ax.barh(bar_positions, values, left=lefts, height=self.bar_width,\n color=color, label=category)\n lefts += values\n\n self.set_title(ax, 'CrawlDb Size and Status Counts')\n ax.set_xlabel('')\n ax.set_ylabel('')\n\n # Format y-axis (crawl names)\n ax.set_yticks(bar_positions)\n ax.set_yticklabels(crawls, fontsize=self.ticks_fontsize)\n ax.set_ylim(-0.5, n_crawls - 0.5)\n\n # Format x-axis (size counts)\n max_value = lefts.max()\n ax.set_xlim(0, max_value * 1.02)\n\n # Axes ratio\n ax.set_aspect(1 / ax.get_data_ratio() * ratio)\n\n # Apply nice x-axis ticks\n self.apply_nice_ticks(ax, axis='x')\n\n # Apply ggplot2-like styling with x-axis grid\n ax.grid(True, which='both', linewidth=self.grid_major_linewidth,\n color=self.grid_major_color, zorder=0, axis='x')\n ax.set_axisbelow(True)\n self.apply_ggplot2_style(ax, show_grid=False)\n\n # Set tick colors\n ax.tick_params(axis='both', which='both', colors=self.ticks_color,\n length=self.ticks_length, width=0.8,\n labelsize=self.ticks_fontsize)\n self.set_tick_labels_black(ax)\n\n # Position legend at bottom with reversed order\n handles, labels = ax.get_legend_handles_labels()\n ax.legend(handles[::-1], labels[::-1], loc='upper center',\n bbox_to_anchor=(0.5, -0.05), ncol=min(4, len(categories_ordered)),\n frameon=False, fontsize=self.legend_fontsize, title='')\n\n return self.save_figure(fig, img_path)\n\n def plot_crawldb_status(self, data, row_filter, img_file, ratio=1.0):\n \"\"\"Generate CrawlDb status stacked bar chart.\"\"\"\n if row_filter:\n data = data[data['type'].isin(row_filter)]\n categories = []\n for value in row_filter:\n if re.search('^crawldb:status:db_', value):\n replacement = re.sub('^crawldb:status:db_', '', value)\n categories.append(replacement)\n data.replace(to_replace=value, value=replacement, inplace=True)\n data['type'] = pandas.Categorical(data['type'], ordered=True,\n categories=categories.reverse())\n data['size'] = data['size'].astype(float)\n ratio = 0.1 + len(data['crawl'].unique()) * .03\n img_path = os.path.join(self.PLOTDIR, img_file)\n\n if self.PLOTLIB == \"rpy2.ggplot2\":\n return self.plot_crawldb_status_with_rpy2_ggplot2(\n data=data, img_path=img_path, ratio=ratio\n )\n\n elif self.PLOTLIB == \"matplotlib\":\n return self.plot_crawldb_status_with_matplotlib(\n data=data, img_path=img_path, ratio=ratio\n )\n\n else:\n raise ValueError(\"Invalid PLOTLIB\")\n\n\nif __name__ == '__main__':\n plot = CrawlerMetrics()\n plot.read_from_stdin_or_file()\n plot.add_percent()\n plot.transform_data()\n plot.save_data()\n plot.plot()\n" }, { "path": "plot/domain.py", "content": "import sys\n\nimport pandas\n\nfrom crawlstats import CST, MonthlyCrawl, MultiCount\nfrom plot.table import TabularStats\n\n\nclass DomainStats(TabularStats):\n\n # defined via crawlstats command-line option --max-top-hosts-domains\n MAX_TOP_DOMAINS = 500\n\n def __init__(self, crawl):\n super().__init__()\n self.crawl = crawl\n self.N = 0\n\n def add(self, key, val):\n cst = CST[key[0]]\n if cst not in (CST.size, CST.domain):\n return\n typeval = key[1]\n crawl = key[2]\n if crawl != self.crawl:\n return\n if cst == CST.size:\n self.size[typeval] = val\n return\n self.type_stats['domain'][self.N] = typeval \n self.type_stats['pages'][self.N] = MultiCount.get_count(0, val)\n self.type_stats['urls'][self.N] = MultiCount.get_count(1, val)\n self.type_stats['hosts'][self.N] = MultiCount.get_count(2, val)\n # self.type_stats['crawl'][self.N] = crawl\n self.N += 1\n\n def transform_data(self):\n data = pandas.DataFrame(self.type_stats)\n for cnt in ['pages', 'urls']:\n total = self.size[cnt[:-1]]\n data['%' + cnt] = 100.0 * data[cnt] / total\n data.sort_values(ascending=False, inplace=True, by='pages')\n print(data)\n self.type_stats = data\n\n def save_data(self, name, dir_name='data/'):\n self.type_stats.to_csv('{}/{}-top-{}.csv'.format(self.PLOTDIR, name, self.MAX_TOP_DOMAINS),\n float_format='%.6f', index=None)\n\n def plot(self, name):\n data = self.type_stats\n css_classes = ['tablesorter', 'tablesearcher']\n data = data.set_index('domain')\n data.columns.name = 'domain'\n data.index.name = None\n print(data.to_html('{}/{}-top-{}.html'.format(\n self.PLOTDIR, name, self.MAX_TOP_DOMAINS),\n float_format='%.6f',\n classes=css_classes, index='domain'))\n\nif __name__ == '__main__':\n plot_crawls = sys.argv[1:]\n if len(plot_crawls) == 0:\n plot_crawls = MonthlyCrawl.get_latest(1)\n print(plot_crawls)\n latest_crawl = plot_crawls[-1]\n plot_name = 'domains'\n plot = DomainStats(latest_crawl)\n plot.read_from_stdin_or_file()\n plot.transform_data()\n plot.save_data(plot_name, dir_name=plot.PLOTDIR)\n plot.plot(plot_name)\n" }, { "path": "plot/histogram.py", "content": "\"\"\"\nPlot histogram distributions for crawl statistics.\n\nThis module generates histogram visualizations showing distributions of:\n- Pages per URL (URL-level duplicates)\n- URLs per host/domain/TLD\n- Cumulative URL coverage by domain\n\nThese histograms help understand the distribution patterns in crawl data.\n\"\"\"\n\nimport os.path\nimport sys\nfrom collections import defaultdict\n\nimport pandas\n\nfrom crawlplot import CrawlPlot\nfrom crawlstats import CST\n\n\nclass CrawlHistogram(CrawlPlot):\n \"\"\"Generate histogram plots for crawl statistics.\n\n Produces histograms showing frequency distributions of various metrics\n like duplicate rates, coverage per domain, etc.\n \"\"\"\n\n PSEUDO_LOG_BINS = [0, 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000,\n 10000, 20000, 50000, 100000, 200000, 500000, 1000000,\n 2*10**6, 5*10**6, 10**7, 2*10**7, 5*10**7, 10**8,\n 2*10**8, 5*10**8, 10**9]\n\n def __init__(self):\n super().__init__()\n self.histogr = defaultdict(dict)\n self.N = 0\n\n def add(self, key, frequency):\n \"\"\"Process a histogram record from statistics data.\"\"\"\n cst = CST[key[0]]\n if cst != CST.histogram:\n return\n item_type = key[1]\n if item_type == 'surt_domain':\n return\n crawl = key[2]\n type_counted = key[3]\n count = key[4]\n self.histogr['crawl'][self.N] = crawl\n self.histogr['type'][self.N] = item_type\n self.histogr['type_counted'][self.N] = type_counted\n self.histogr['count'][self.N] = count\n self.histogr['frequency'][self.N] = frequency\n self.N += 1\n\n def transform_data(self):\n \"\"\"Convert internal dictionary to pandas DataFrame.\"\"\"\n self.histogr = pandas.DataFrame(self.histogr)\n\n def save_data(self):\n \"\"\"Save histogram data to CSV file.\"\"\"\n self.histogr.to_csv('data/crawlhistogr.csv')\n\n def plot_dupl_url(self):\n \"\"\"Plot histogram of pages per URL (URL-level duplicates).\"\"\"\n from rpy2.robjects.lib import ggplot2\n\n row_filter = ['url']\n data = self.histogr\n data = data[data['type'].isin(row_filter)]\n title = 'Pages per URL (URL-level duplicates)'\n p = ggplot2.ggplot(data) \\\n + ggplot2.aes_string(x='count', y='frequency') \\\n + ggplot2.geom_jitter() \\\n + ggplot2.facet_wrap('crawl', ncol=5) \\\n + ggplot2.labs(title=title, x='(duplicate) pages per URL',\n y='log(frequency)') \\\n + ggplot2.scale_y_log10()\n # + ggplot2.scale_x_log10() # could use log-log scale\n img_path = os.path.join(self.PLOTDIR, 'crawler/histogr_url_dupl.png')\n p.save(img_path)\n # data.to_csv(img_path + '.csv')\n return p\n\n def plot_host_domain_tld(self):\n \"\"\"Plot histogram of URLs per host/domain/TLD.\"\"\"\n from rpy2.robjects.lib import ggplot2\n\n data = self.histogr\n data = data[data['type'].isin(['host', 'domain', 'tld'])]\n data = data[data['type_counted'].isin(['url'])]\n img_path = os.path.join(self.PLOTDIR,\n 'crawler/histogr_host_domain_tld.png')\n # data.to_csv(img_path + '.csv')\n title = 'URLs per Host / Domain / TLD'\n p = ggplot2.ggplot(data) \\\n + ggplot2.aes_string(x='count', weight='frequency', color='type') \\\n + ggplot2.geom_freqpoly(bins=20) \\\n + ggplot2.facet_wrap('crawl', ncol=4) \\\n + ggplot2.labs(title='', x=title,\n y='Frequency') \\\n + ggplot2.scale_y_log10() \\\n + ggplot2.scale_x_log10()\n p.save(img_path)\n return p\n\n def plot_domain_cumul_with_rpy2_ggplot2(self, data, title, img_path):\n \"\"\"Generate cumulative domain coverage plot using rpy2/ggplot2.\"\"\"\n from rpy2.robjects.lib import ggplot2\n\n p = ggplot2.ggplot(data) \\\n + ggplot2.aes_string(x='cum_domains', y='cum_urls') \\\n + ggplot2.geom_line() + ggplot2.geom_point() \\\n + self.GGPLOT2_THEME \\\n + ggplot2.theme(**self.GGPLOT2_THEME_KWARGS) \\\n + ggplot2.labs(title=title, x='domains cumulative',\n y='URLs cumulative') \\\n + ggplot2.scale_y_log10() \\\n + ggplot2.scale_x_log10()\n p.save(img_path)\n \n return p\n \n def plot_domain_cumul(self, crawl):\n \"\"\"Plot cumulative URL coverage by domain for a specific crawl.\"\"\"\n data = self.histogr\n data = data[data['type'].isin(['domain'])]\n data = data[data['crawl'] == crawl]\n data = data[data['type_counted'].isin(['url'])]\n data['urls'] = data['count']*data['frequency']\n print(data)\n data = data[['urls', 'count', 'frequency']]\n data = data.sort_values(['count'], ascending=0)\n data['cum_domains'] = data['frequency'].cumsum()\n data['cum_urls'] = data['urls'].cumsum()\n data_perc = data.apply(lambda x: round(100.0*x/float(x.sum()), 1))\n data['%domains'] = data_perc['frequency']\n data['%urls'] = data_perc['urls']\n data['%cum_domains'] = data['cum_domains'].apply(\n lambda x: round(100.0*x/float(data['frequency'].sum()), 1))\n data['%cum_urls'] = data['cum_urls'].apply(\n lambda x: round(100.0*x/float(data['urls'].sum()), 1))\n\n img_path = os.path.join(self.PLOTDIR,\n 'crawler/histogr_domain_cumul.png')\n # data.to_csv(img_path + '.csv')\n title = 'Cumulative URLs for Top Domains'\n\n if self.PLOTLIB == \"rpy2.ggplot2\":\n return self.plot_domain_cumul_with_rpy2_ggplot2(data=data, title=title, img_path=img_path)\n \n elif self.PLOTLIB == \"matplotlib\":\n # this plot is currently not used\n raise NotImplementedError\n \n else:\n raise ValueError(\"Invalid PLOTLIB\")\n\n\n\nif __name__ == '__main__':\n latest_crawl = sys.argv[-1]\n plot = CrawlHistogram()\n plot.read_from_stdin_or_file()\n plot.transform_data()\n plot.save_data()\n plot.plot_dupl_url()\n plot.plot_host_domain_tld()\n plot.plot_domain_cumul(latest_crawl)\n" }, { "path": "plot/language.py", "content": "import string\nimport sys\n\nfrom plot.table import TabularStats\nfrom crawlstats import CST, MonthlyCrawl\n\n\nclass LanguageStats(TabularStats):\n\n MIN_AVERAGE_COUNT = 1\n MAX_LANGUAGES = 200\n\n def __init__(self):\n super().__init__()\n self.MAX_TYPE_VALUES = LanguageStats.MAX_LANGUAGES\n\n def add(self, key, val):\n self.add_check_type(key, val, CST.primary_language)\n\n\nif __name__ == '__main__':\n plot_crawls = sys.argv[1:]\n plot_name = 'languages'\n column_header = 'language'\n if len(plot_crawls) == 0:\n plot_crawls = MonthlyCrawl.get_latest(3)\n print(plot_crawls)\n else:\n plot_name += '-' + '-'.join(plot_crawls)\n plot = LanguageStats()\n plot.read_from_stdin_or_file()\n plot.transform_data(LanguageStats.MAX_LANGUAGES,\n LanguageStats.MIN_AVERAGE_COUNT,\n None)\n plot.save_data_percentage(plot_name, dir_name='plots', type_name='primary_language')\n plot.plot(plot_crawls, plot_name, column_header,\n ['iso639-3-language'])\n" }, { "path": "plot/mimetype.py", "content": "import re\nimport sys\n\nfrom plot.table import TabularStats\nfrom crawlstats import CST, MonthlyCrawl\n\n\nclass MimeTypeStats(TabularStats):\n\n MIN_AVERAGE_COUNT = 500\n MAX_MIME_TYPES = 100\n\n # see https://en.wikipedia.org/wiki/Media_type#Naming\n mime_pattern_str = \\\n r'(?:x-)?[a-z]+/[a-z0-9]+' \\\n r'(?:[.-](?:c\\+\\+[a-z]*|[a-z0-9]+))*(?:\\+[a-z0-9]+)?'\n mime_pattern = re.compile(r'^'+mime_pattern_str+r'$')\n mime_extract_pattern = re.compile(r'^\\s*(?:content\\s*=\\s*)?[\"\\']?\\s*(' +\n mime_pattern_str +\n r')(?:\\s*[;,].*)?\\s*[\"\\']?\\s*$')\n\n def __init__(self):\n super().__init__()\n self.MAX_TYPE_VALUES = MimeTypeStats.MAX_MIME_TYPES\n\n def norm_value(self, mimetype):\n if type(mimetype) is str:\n mimetype = mimetype.lower()\n m = MimeTypeStats.mime_extract_pattern.match(mimetype)\n if m:\n return m.group(1)\n return mimetype.strip('\"\\', \\t')\n return \"\"\n\n def add(self, key, val):\n self.add_check_type(key, val, CST.mimetype)\n\n\nif __name__ == '__main__':\n plot_crawls = sys.argv[1:]\n plot_name = 'mimetypes'\n column_header = 'mimetype'\n if len(plot_crawls) == 0:\n plot_crawls = MonthlyCrawl.get_latest(3)\n print(plot_crawls)\n else:\n plot_name += '-' + '-'.join(plot_crawls)\n plot = MimeTypeStats()\n plot.read_from_stdin_or_file()\n plot.transform_data(MimeTypeStats.MAX_MIME_TYPES,\n MimeTypeStats.MIN_AVERAGE_COUNT,\n MimeTypeStats.mime_pattern)\n plot.save_data_percentage(plot_name, dir_name='plots', type_name='mimetype')\n plot.plot(plot_crawls, plot_name, column_header, ['tablesearcher'])\n" }, { "path": "plot/mimetype_detected.py", "content": "import sys\n\nfrom plot.mimetype import MimeTypeStats\nfrom crawlstats import CST, MonthlyCrawl\n\n\nclass MimeTypeDetectedStats(MimeTypeStats):\n\n def __init__(self):\n super().__init__()\n self.MAX_TYPE_VALUES = MimeTypeStats.MAX_MIME_TYPES\n\n def norm_value(self, mimetype):\n return mimetype\n\n def add(self, key, val):\n self.add_check_type(key, val, CST.mimetype_detected)\n\n\nif __name__ == '__main__':\n plot_crawls = sys.argv[1:]\n plot_name = 'mimetypes_detected'\n column_header = 'mimetype_detected'\n if len(plot_crawls) == 0:\n plot_crawls = MonthlyCrawl.get_latest(3)\n print(plot_crawls)\n else:\n plot_name += '-' + '-'.join(plot_crawls)\n plot = MimeTypeDetectedStats()\n plot.read_from_stdin_or_file()\n plot.transform_data(MimeTypeStats.MAX_MIME_TYPES,\n MimeTypeStats.MIN_AVERAGE_COUNT,\n None)\n plot.save_data_percentage(plot_name, dir_name='plots', type_name='mimetype_detected')\n plot.plot(plot_crawls, plot_name, column_header, ['tablesearcher'])\n" }, { "path": "plot/overlap.py", "content": "\"\"\"\nPlot crawl overlap and similarity metrics.\n\nThis module generates visualizations showing the overlap between different\ncrawls based on URL or content digest similarities. Uses Jaccard similarity\nto measure the intersection over union of items between crawls.\n\"\"\"\n\nimport copy\nimport os.path\nfrom collections import defaultdict\n\nimport pandas\nimport pygraphviz\n\nfrom crawlplot import CrawlPlot\nfrom crawlstats import CST, CrawlStatsJSONDecoder, MonthlyCrawl\n\n\nclass CrawlOverlap(CrawlPlot):\n \"\"\"Generate overlap and similarity visualizations between crawls.\n\n Calculates and visualizes the Jaccard similarity between crawls\n based on unique URLs or content digests using HyperLogLog cardinality\n estimation.\n \"\"\"\n\n MAX_MATRIX_SIZE = 30\n\n def __init__(self):\n super().__init__()\n\n self.crawl_size = defaultdict(dict)\n self.overlap = defaultdict(dict)\n self.similarity = defaultdict(dict) # Jaccard index\n\n def add(self, key, val):\n \"\"\"Process a size_estimate record and store HyperLogLog for overlap calculation.\"\"\"\n cst = CST[key[0]]\n if cst != CST.size_estimate:\n return\n item_type = key[1]\n crawl = key[2]\n hll = CrawlStatsJSONDecoder.json_decode_hyperloglog(val)\n self.crawl_size[item_type][crawl] = hll\n\n def fill_overlap_matrix(self):\n \"\"\"Calculate pairwise overlap and Jaccard similarity between all crawls.\"\"\"\n for item_type in self.crawl_size:\n for crawl1 in self.crawl_size[item_type]:\n hll1 = self.crawl_size[item_type][crawl1]\n size1 = len(hll1)\n self.overlap[item_type][crawl1] = defaultdict(list)\n self.similarity[item_type][crawl1] = defaultdict(float)\n for crawl2 in self.crawl_size[item_type]:\n if crawl1 >= crawl2:\n continue\n hll2 = self.crawl_size[item_type][crawl2]\n size2 = len(hll2)\n union_hll = copy.deepcopy(hll1)\n union_hll.update(hll2)\n union = len(union_hll)\n intersection = size1 + size2 - union\n jaccard_sim = intersection / union\n self.overlap[item_type][crawl1][crawl2] \\\n = [intersection, union, size1, size2,\n (intersection/size2), jaccard_sim]\n self.similarity[item_type][crawl1][crawl2] = jaccard_sim\n\n def save_overlap_matrix(self):\n \"\"\"Save overlap and similarity matrices to CSV files.\"\"\"\n for item_type in self.overlap:\n data = pandas.DataFrame(self.similarity[item_type])\n data.to_csv('data/crawlsimilarity_' + item_type + '.csv')\n data = pandas.DataFrame(self.overlap[item_type])\n data.to_csv('data/crawloverlap_' + item_type + '.csv')\n\n def plot_similarity_graph(self, show_edges=False):\n \"\"\"Visualize similarity as a graph using GraphViz (experimental).\"\"\"\n g = pygraphviz.AGraph(directed=False, overlap='scale', splines=True)\n g.node_attr['shape'] = 'plaintext'\n g.node_attr['fontsize'] = '12'\n if show_edges:\n g.edge_attr['color'] = 'lightgrey'\n g.edge_attr['fontcolor'] = 'grey'\n g.edge_attr['fontsize'] = '8'\n else:\n g.edge_attr['style'] = 'invis'\n for crawl1 in sorted(self.similarity['url']):\n for crawl2 in sorted(self.similarity['url'][crawl1]):\n similarity = self.similarity['url'][crawl1][crawl2]\n distance = 1.0 - similarity\n g.add_edge(MonthlyCrawl.short_name(crawl1),\n MonthlyCrawl.short_name(crawl2),\n len=(distance),\n label='{0:.2f}'.format(distance))\n g.write(os.path.join(self.PLOTDIR, 'crawlsimilarity_url.dot'))\n g.draw(os.path.join(self.PLOTDIR, 'crawlsimilarity_url.svg'), prog='fdp')\n\n def plot_similarity_matrix_with_rpy2_ggplot2(self, data, midpoint, title, textsize, img_path):\n \"\"\"Generate similarity heatmap using rpy2/ggplot2.\"\"\"\n from rpy2.robjects.lib import ggplot2\n\n p = ggplot2.ggplot(data) \\\n + ggplot2.aes_string(x='crawl2', y='crawl1',\n fill='similarity', label='sim_rounded') \\\n + ggplot2.geom_tile(color=\"white\") \\\n + ggplot2.scale_fill_gradient2(low=\"red\", high=\"blue\", mid=\"white\",\n midpoint=midpoint, space=\"Lab\") \\\n + self.GGPLOT2_THEME \\\n + ggplot2.coord_fixed() \\\n + ggplot2.theme(**{'axis.text.x':\n ggplot2.element_text(angle=45,\n vjust=1, hjust=1),\n **self.GGPLOT2_THEME_KWARGS}) \\\n + ggplot2.labs(title=title, x='', y='') \\\n + ggplot2.geom_text(color='black', size=textsize)\n\n p.save(img_path)\n return p\n \n def plot_similarity_matrix_with_matplotlib(self, data, decimals, title, cell_textsize, img_path):\n \"\"\"Generate similarity heatmap using matplotlib.\n\n Creates a color-coded matrix showing Jaccard similarity between crawls,\n with color ranging from red (low) through white to blue (high).\n \"\"\"\n import matplotlib.pyplot as plt\n import numpy as np\n from matplotlib.colors import LinearSegmentedColormap, Normalize\n\n # Pivot data to create matrix\n pivot_data = data.pivot(index='crawl1', columns='crawl2', values='similarity')\n pivot_data_rounded = pivot_data.round(decimals)\n\n fig, ax = self.create_figure()\n\n # Create color map: red (low) -> white (mid) -> blue (high)\n vmin = pivot_data_rounded.min().min()\n vmax = pivot_data_rounded.max().max()\n\n if vmin < 0:\n colors = ['#ff0801', '#ff6b48', '#ffa388', '#ffd2c4', '#fff4ef',\n '#FFFFFF', '#eadaff', '#c6a5ff', '#a073ff', '#6e43ff',\n '#4020ff', '#1306ff']\n else:\n colors = ['#fff4ef', '#FFFFFF', '#eadaff', '#c6a5ff', '#a073ff',\n '#6e43ff', '#4020ff', '#1306ff']\n\n cmap = LinearSegmentedColormap.from_list('red_white_blue', colors, N=256)\n norm = Normalize(vmin=vmin, vmax=vmax)\n\n # Add grey grid lines behind everything\n ax.set_axisbelow(True)\n ax.grid(True, which='major', linewidth=0.8, color='#E6E6E6', zorder=-1)\n\n # Create heatmap with origin='lower' to match ggplot2 (bottom-up)\n im = ax.imshow(pivot_data_rounded.values, cmap=cmap, norm=norm,\n aspect='equal', origin='lower', zorder=1)\n\n # Add text annotations\n for i in range(len(pivot_data.index)):\n for j in range(len(pivot_data.columns)):\n similarity = pivot_data.iloc[i, j]\n if pandas.isna(similarity):\n continue\n\n # Draw white rectangle border around each cell\n rect = plt.Rectangle((j - 0.5, i - 0.5), 1, 1,\n fill=False, edgecolor='white',\n linewidth=0.5, zorder=1)\n ax.add_patch(rect)\n\n # Get the rounded text for this cell\n matching_rows = data[(data['crawl1'] == pivot_data.index[i]) &\n (data['crawl2'] == pivot_data.columns[j])]\n if len(matching_rows) > 0:\n text_val = matching_rows['sim_rounded'].iloc[0]\n ax.text(j, i, text_val, ha='center', va='center',\n color='black', fontsize=cell_textsize, zorder=2)\n\n # Set ticks and labels\n ax.set_xticks(np.arange(len(pivot_data.columns)))\n ax.set_yticks(np.arange(len(pivot_data.index)))\n ax.set_xticklabels(pivot_data.columns, fontsize=10)\n ax.set_yticklabels(pivot_data.index, fontsize=10)\n\n # Hide tick marks but keep labels black\n ax.tick_params(axis='both', which='both', colors='#FFFFFF', zorder=0)\n self.set_tick_labels_black(ax)\n\n # Rotate x-axis labels\n plt.setp(ax.get_xticklabels(), rotation=45, ha='right', va='top')\n\n self.set_title(ax, title)\n ax.set_xlabel('')\n ax.set_ylabel('')\n\n # Add colorbar\n cbar = plt.colorbar(im, ax=ax, aspect=5, pad=0.04, shrink=0.2)\n cbar.ax.set_title('similarity', fontsize=10, pad=10, loc=\"left\")\n cbar.ax.tick_params(labelsize=8)\n cbar.outline.set_visible(False)\n\n # Apply ggplot2-like styling\n self.apply_ggplot2_style(ax, show_grid=False)\n\n return self.save_figure(fig, img_path)\n\n\n def plot_similarity_matrix(self, item_type, image_file, title):\n \"\"\"Plot similarities of crawls as a heatmap matrix.\n\n Args:\n item_type: Type of items to compare ('url' or 'digest')\n image_file: Output filename relative to PLOTDIR\n title: Plot title\n \"\"\"\n data = defaultdict(dict)\n n = 1\n for crawl1 in self.similarity[item_type]:\n for crawl2 in self.similarity[item_type][crawl1]:\n similarity = self.similarity[item_type][crawl1][crawl2]\n data['crawl1'][n] = MonthlyCrawl.short_name(crawl1)\n data['crawl2'][n] = MonthlyCrawl.short_name(crawl2)\n data['similarity'][n] = similarity\n data['sim_rounded'][n] = similarity # to be rounded\n n += 1\n data = pandas.DataFrame(data)\n print(data)\n # select median of similarity values as midpoint of similarity scale\n midpoint = data['similarity'].median()\n decimals = 3\n textsize = 2\n minshown = .0005\n cell_textsize = 6\n\n if (data['similarity'].max()-data['similarity'].min()) > .2:\n decimals = 2\n textsize = 2.8\n minshown = .005\n cell_textsize = 8\n\n data['sim_rounded'] = data['sim_rounded'].apply(\n lambda x: ('{0:.'+str(decimals)+'f}').format(x).lstrip('0')\n if x >= minshown else '0')\n print('Median of similarities for', item_type, '=', midpoint)\n matrix_size = len(self.similarity[item_type])\n if matrix_size > self.MAX_MATRIX_SIZE:\n n = 0\n for crawl1 in sorted(self.similarity[item_type], reverse=True):\n short_name = MonthlyCrawl.short_name(crawl1)\n if n > self.MAX_MATRIX_SIZE:\n data = data[data['crawl1'] != short_name]\n data = data[data['crawl2'] != short_name]\n n += 1\n\n img_path = os.path.join(self.PLOTDIR, image_file)\n\n if self.PLOTLIB == \"rpy2.ggplot2\":\n return self.plot_similarity_matrix_with_rpy2_ggplot2(data=data, midpoint=midpoint, title=title, textsize=textsize, img_path=img_path)\n \n elif self.PLOTLIB == \"matplotlib\":\n return self.plot_similarity_matrix_with_matplotlib(data=data, decimals=decimals, title=title, cell_textsize=cell_textsize, img_path=img_path)\n \n else:\n raise ValueError(\"Invalid PLOTLIB\")\n\n\nif __name__ == '__main__':\n plot = CrawlOverlap()\n plot.read_from_stdin_or_file()\n plot.fill_overlap_matrix()\n plot.save_overlap_matrix()\n # plot.plot_similarity_graph()\n plot.plot_similarity_matrix(\n 'url', 'crawloverlap/crawlsimilarity_matrix_url.png',\n 'URL overlap between crawls (Jaccard similarity)')\n plot.plot_similarity_matrix(\n 'digest', 'crawloverlap/crawlsimilarity_matrix_digest.png',\n 'Content overlap between crawls (Jaccard similarity on digest)')\n" }, { "path": "plot/table.py", "content": "import heapq\n\nimport numpy\nimport pandas\n\nfrom collections import defaultdict, Counter\n\nfrom crawlplot import CrawlPlot\nfrom crawlstats import CST, MultiCount\n\n\nclass TabularStats(CrawlPlot):\n\n def __init__(self):\n super().__init__()\n\n self.crawls = set()\n self.types = defaultdict(dict)\n self.type_stats = defaultdict(dict)\n self.types_total = Counter()\n self.size = defaultdict(dict)\n self.N = 0\n\n def norm_value(self, typeval):\n return typeval\n\n def add_check_type(self, key, val, requ_type_cst):\n cst = CST[key[0]]\n if cst != requ_type_cst and cst != CST.size:\n return\n typeval = key[1]\n crawl = key[2]\n self.crawls.add(crawl)\n typeval = self.norm_value(typeval)\n if cst == CST.size:\n self.size[crawl][typeval] = int(val)\n return\n if crawl in self.types[typeval]:\n self.types[typeval][crawl] = \\\n MultiCount.sum_values([val, self.types[typeval][crawl]])\n else:\n self.types[typeval][crawl] = val\n npages = MultiCount.get_count(0, val)\n self.types_total[typeval] += npages\n if 'known_values' not in self.size[crawl]:\n self.size[crawl]['known_values'] = 0\n self.size[crawl]['known_values'] += npages\n\n def transform_data(self, top_n, min_avg_count, check_pattern=None):\n print(\"Number of different values after first normalization: {}\"\n .format(len(self.types)))\n typevals_for_deletion = set()\n typevals_mostfrequent = []\n for typeval in self.types:\n total_count = self.types_total[typeval]\n average_count = int(total_count / len(self.crawls))\n if average_count >= min_avg_count:\n if not check_pattern or check_pattern.match(typeval):\n print('{}\\t{}\\t{}'.format(typeval,\n average_count, total_count))\n fval = (total_count, typeval)\n if len(typevals_mostfrequent) < top_n:\n heapq.heappush(typevals_mostfrequent, fval)\n else:\n heapq.heappushpop(typevals_mostfrequent, fval)\n continue # ok, keep this type value\n else:\n print('Type value frequent but invalid: <{}> (avg. count = {})'\n .format(typeval, average_count))\n elif average_count >= (min_avg_count/10):\n if not check_pattern or check_pattern.match(typeval):\n print('Skipped type value because of low frequency: <{}> (avg. count = {}, min. = {})'\n .format(typeval, average_count, (min_avg_count/10)))\n typevals_for_deletion.add(typeval)\n # map low frequency or invalid type values to empty type\n keep_typevals = set()\n for (_, typeval) in typevals_mostfrequent:\n keep_typevals.add(typeval)\n for typeval in self.types:\n if (typeval not in keep_typevals and\n typeval not in typevals_for_deletion):\n print('Skipped type value because not in top {}: <{}> (avg. count = {})'\n .format(top_n, typeval,\n int(self.types_total[typeval]/len(self.crawls))))\n typevals_for_deletion.add(typeval)\n typevals_other = dict()\n for typeval in typevals_for_deletion:\n for crawl in self.types[typeval]:\n if crawl in typevals_other:\n val = typevals_other[crawl]\n else:\n val = 0\n typevals_other[crawl] = \\\n MultiCount.sum_values([val, self.types[typeval][crawl]])\n self.types.pop(typeval, None)\n self.types[''] = typevals_other\n print('Number of different type values after cleaning and'\n ' removal of low frequency types: {}'\n .format(len(self.types)))\n # unknown values\n for crawl in self.crawls:\n known_values = 0\n if 'known_values' in self.size[crawl]:\n known_values = self.size[crawl]['known_values']\n unknown = (self.size[crawl]['page'] - known_values)\n if unknown > 0:\n print(\"{} unknown values in {}\".format(unknown, crawl))\n self.types[''][crawl] = unknown\n for typeval in self.types:\n for crawl in self.types[typeval]:\n self.type_stats['type'][self.N] = typeval\n self.type_stats['crawl'][self.N] = crawl\n value = self.types[typeval][crawl]\n n_pages = MultiCount.get_count(0, value)\n self.type_stats['pages'][self.N] = n_pages\n n_urls = MultiCount.get_count(1, value)\n self.type_stats['urls'][self.N] = n_urls\n self.N += 1\n self.type_stats = pandas.DataFrame(self.type_stats)\n\n def save_data(self, base_name, dir_name='data/'):\n self.type_stats.to_csv(dir_name + base_name + '.csv')\n\n def save_data_percentage(self, base_name, dir_name='data/', type_name='type'):\n if dir_name[-1] != '/':\n dir_name += '/'\n data = self.type_stats\n data = data[['crawl', 'type', 'pages', 'urls']]\n sum_data = data.groupby(['crawl']).aggregate({'pages':'sum'}).add_suffix('_sum').reset_index()\n data = data.groupby(['crawl', 'type']).aggregate(numpy.sum).reset_index()\n data = pandas.merge(data, sum_data)\n data['%pages/crawl'] = 100.0 * data['pages'] / data['pages_sum']\n data.drop(['pages_sum'], inplace=True, axis=1)\n data = data.rename(columns={'type': type_name})\n data.to_csv(dir_name + base_name + '.csv', float_format='%.4f', index=None)\n\n def plot(self, crawls, name, column_header, xtra_css_classes=[]):\n # stats comparison for selected crawls\n field_percentage_formatter = '{0:,.4f}'.format\n data = self.type_stats\n data = data[data['crawl'].isin(crawls)]\n if data.size == 0:\n print(\"No data points in table for selected crawls ({})\"\n .format(crawls))\n return\n data[column_header] = data['type']\n data = data[['crawl', column_header, 'pages']]\n data = data.groupby(['crawl', column_header]).agg({'pages': 'sum'})\n data = data.groupby(level=0, as_index=False).apply(lambda x: 100.0*x/float(x.sum()))\n data = data.reset_index().pivot(index=column_header,\n columns='crawl', values='pages')\n print(\"\\n-----\\n\")\n formatters = {c: field_percentage_formatter for c in crawls}\n print(data.to_string(formatters=formatters))\n css_classes = ['tablesorter', 'tablepercentage']\n css_classes.extend(xtra_css_classes)\n data.to_html('{}/{}-top-{}.html'.format(\n self.PLOTDIR, name, self.MAX_TYPE_VALUES),\n formatters=formatters,\n classes=css_classes)\n\n" }, { "path": "plot/tld.py", "content": "import sys\n\nfrom collections import defaultdict\n\nimport pandas\n\nfrom crawlplot import CrawlPlot\nfrom crawlstats import CST, MonthlyCrawl, MultiCount\nfrom top_level_domain import TopLevelDomain\nfrom stats.tld_alexa_top_1m import alexa_top_1m_tlds\nfrom stats.tld_cisco_umbrella_top_1m import cisco_umbrella_top_1m_tlds\nfrom stats.tld_majestic_top_1m import majestic_top_1m_tlds\n\n# min. share of URLs for a TLD to be shown in metrics\nmin_urls_percentage = .05\n\n\nclass TldStats(CrawlPlot):\n\n def __init__(self):\n super().__init__()\n\n self.tlds = defaultdict(dict)\n self.tld_stats = defaultdict(dict)\n self.N = 0\n\n def add(self, key, val):\n cst = CST[key[0]]\n if cst != CST.tld:\n return\n tld = key[1]\n crawl = key[2]\n self.tlds[tld][crawl] = val\n\n def transform_data(self):\n crawl_has_host_domain_counts = {}\n for tld in self.tlds:\n tld_repr = tld\n tld_obj = None\n if tld in ('', '(ip address)'):\n continue\n else:\n try:\n tld_obj = TopLevelDomain(tld)\n tld_repr = tld_obj.tld\n except:\n print('error', tld)\n continue\n for crawl in self.tlds[tld]:\n self.tld_stats['suffix'][self.N] = tld_repr\n self.tld_stats['crawl'][self.N] = crawl\n date = pandas.Timestamp(MonthlyCrawl.date_of(crawl))\n self.tld_stats['date'][self.N] = date\n if tld_obj:\n self.tld_stats['type'][self.N] \\\n = TopLevelDomain.short_type(tld_obj.tld_type)\n self.tld_stats['subtype'][self.N] = tld_obj.sub_type\n self.tld_stats['tld'][self.N] = tld_obj.first_level\n else:\n self.tld_stats['type'][self.N] = ''\n self.tld_stats['subtype'][self.N] = ''\n self.tld_stats['tld'][self.N] = ''\n value = self.tlds[tld][crawl]\n n_pages = MultiCount.get_count(0, value)\n self.tld_stats['pages'][self.N] = n_pages\n n_urls = MultiCount.get_count(1, value)\n self.tld_stats['urls'][self.N] = n_urls\n n_hosts = MultiCount.get_count(2, value)\n self.tld_stats['hosts'][self.N] = n_hosts\n n_domains = MultiCount.get_count(3, value)\n self.tld_stats['domains'][self.N] = n_domains\n if n_urls != n_hosts:\n # multi counts including host counts are not (yet)\n # available for all crawls\n crawl_has_host_domain_counts[crawl] = True\n elif crawl not in crawl_has_host_domain_counts:\n crawl_has_host_domain_counts[crawl] = False\n self.N += 1\n for crawl in crawl_has_host_domain_counts:\n if not crawl_has_host_domain_counts[crawl]:\n print('No host and domain counts for', crawl)\n for n in self.tld_stats['crawl']:\n if self.tld_stats['crawl'][n] == crawl:\n del(self.tld_stats['hosts'][n])\n del(self.tld_stats['domains'][n])\n self.tld_stats = pandas.DataFrame(self.tld_stats)\n\n @staticmethod\n def field_percentage_formatter(precision=2, nan='-'):\n f = '{0:,.' + str(precision) + 'f}'\n return lambda x: nan if pandas.isna(x) else f.format(x)\n\n\n def save_data(self):\n self.tld_stats.to_csv('data/tlds.csv')\n\n def percent_agg(self, data, column, index, values, aggregate):\n data = data[[column, index, values]]\n data = data.groupby([column, index]).agg(aggregate)\n data = data.groupby(level=0, as_index=False).apply(lambda x: 100.0*x/float(x.sum()))\n # print(\"\\n-----\\n\")\n # print(data.to_string(formatters={'urls': TldStats.field_percentage_formatter()}))\n return data\n\n def pivot_percentage(self, data, column, index, values, aggregate):\n data = self.percent_agg(data, column, index, values, aggregate)\n return data.reset_index().pivot(index=index,\n columns=[column], values=values)\n\n def plot_groups(self):\n title = 'Groups of Top-Level Domains'\n ylabel = 'URLs %'\n clabel = ''\n img_file = 'tld/groups.png'\n data = self.pivot_percentage(self.tld_stats, 'crawl', 'type',\n 'urls', {'urls': 'sum'})\n data = data.transpose()\n print(\"\\n-----\\n\")\n types = set(self.tld_stats['type'].tolist())\n formatters = {c: TldStats.field_percentage_formatter() for c in types}\n print(data.to_string(formatters=formatters))\n data.to_html('{}/tld/groups-percentage.html'.format(self.PLOTDIR),\n formatters=formatters,\n classes=['tablesorter', 'tablepercentage'])\n data = self.percent_agg(self.tld_stats, 'date', 'type',\n 'urls', {'urls': 'sum'}).reset_index()\n return self.line_plot(data, title, ylabel, img_file,\n x='date', y='urls', c='type', clabel=clabel)\n\n def plot(self, crawls, latest_crawl):\n field_formatters = {c: '{:,.0f}'.format\n for c in ['pages', 'urls', 'hosts', 'domains']}\n for c in ['%urls', '%hosts', '%domains']:\n field_formatters[c] = TldStats.field_percentage_formatter()\n data = self.tld_stats\n data = data[data['crawl'].isin(crawls)]\n crawl_data = data\n top_tlds = []\n # stats per crawl\n for crawl in crawls:\n print(\"\\n-----\\n{}\\n\".format(crawl))\n for aggr_type in ('type', 'tld'):\n data = crawl_data\n data = data[data['crawl'].isin([crawl])]\n data = data[[aggr_type, 'pages', 'urls', 'hosts', 'domains']]\n data = data.set_index([aggr_type])\n data = data.groupby(level=0).sum().sort_values(\n by=['urls'], ascending=False)\n for count in ('urls', 'hosts', 'domains'):\n data['%'+count] = 100.0 * data[count] / data[count].sum()\n if aggr_type == 'tld':\n # skip less frequent TLDs\n data = data[data['%urls'] >= min_urls_percentage]\n for tld in data.index.values:\n top_tlds.append(tld)\n print(data.to_string(formatters=field_formatters))\n print()\n if crawl == latest_crawl:\n # latest crawl by convention\n type_name = aggr_type\n if aggr_type == 'type':\n type_name = 'group'\n path = '{}/tld/latest-crawl-{}s.html'.format(\n self.PLOTDIR, type_name)\n data.to_html(path,\n formatters=field_formatters,\n classes=['tablesorter', 'tablesearcher'])\n # stats comparison for selected crawls\n for aggr_type in ('type', 'tld'):\n data = crawl_data\n if aggr_type == 'tld':\n data = data[data['tld'].isin(top_tlds)]\n data = self.pivot_percentage(data, 'crawl', aggr_type,\n 'urls', {'urls': 'sum'})\n print(\"\\n----- {}\\n\".format(aggr_type))\n print(data.to_string(formatters={c: TldStats.field_percentage_formatter()\n for c in crawls}))\n if aggr_type == 'tld':\n # save as HTML table\n path = '{}/tld/selected-crawls-percentage.html'.format(\n self.PLOTDIR, len(crawls))\n data.to_html(path,\n float_format=TldStats.field_percentage_formatter(4),\n classes=['tablesorter', 'tablepercentage',\n 'tablesearcher'])\n\n def plot_comparison(self, crawl, name, topNlimit=None, method='spearman'):\n print()\n print('Comparison for', crawl, '-', name, '-', method)\n data = self.tld_stats\n data = data[data['crawl'].isin([crawl])]\n data = data[data['urls'] >= topNlimit]\n data = data.set_index(['tld'], drop=False)\n data = data.sum(level='tld')\n print(data)\n data['alexa'] = pandas.Series(alexa_top_1m_tlds)\n data['cisco'] = pandas.Series(cisco_umbrella_top_1m_tlds)\n data['majestic'] = pandas.Series(majestic_top_1m_tlds)\n fields = ('pages', 'urls', 'hosts', 'domains',\n 'alexa', 'cisco', 'majestic')\n formatters = {c: '{0:,.3f}'.format for c in fields}\n # relative frequency (percent)\n for count in fields:\n data[count] = 100.0 * data[count] / data[count].sum()\n # Spearman's rank correlation for all TLDs\n corr = data.corr(method=method, min_periods=1)\n print(corr.to_string(formatters=formatters))\n corr.to_html('{}/tld/{}-comparison-{}-all-tlds.html'\n .format(self.PLOTDIR, name, method),\n formatters=formatters,\n classes=['matrix'])\n if topNlimit is None:\n return\n # Spearman's rank correlation for TLDs covering\n # at least topNlimit % of urls\n data = data[data['urls'] >= topNlimit]\n print()\n print('Top', len(data), 'TLDs (>= ', topNlimit, '%)')\n print(data)\n data.to_html('{}/tld/{}-comparison.html'.format(self.PLOTDIR, name),\n formatters=formatters,\n classes=['tablesorter', 'tablepercentage'])\n print()\n corr = data.corr(method=method, min_periods=1)\n print(corr.to_string(formatters=formatters))\n corr.to_html('{}/tld/{}-comparison-{}-frequent-tlds.html'\n .format(self.PLOTDIR, name, method),\n formatters=formatters,\n classes=['matrix'])\n print()\n\n def plot_comparison_groups(self):\n # Alexa and Cisco types/groups:\n for (name, data) in [('Alexa', alexa_top_1m_tlds),\n ('Cisco', cisco_umbrella_top_1m_tlds),\n ('Majestic', majestic_top_1m_tlds)]:\n compare_types = defaultdict(int)\n for tld in data:\n compare_types[TopLevelDomain(tld).tld_type] += data[tld]\n print(name, 'TLD groups:')\n for tld in compare_types:\n c = compare_types[tld]\n print(' {:6d}\\t{:4.1f}\\t{}'.format(c, (100.0*c/1000000), tld))\n print()\n\n\nif __name__ == '__main__':\n plot_crawls = sys.argv[1:]\n latest_crawl = plot_crawls[-1]\n if len(plot_crawls) == 0:\n print(sys.argv[0], 'crawl-id...')\n print()\n print('Distribution of top-level domains for (selected) monthly crawls')\n print()\n print('Example:')\n print('', sys.argv[0], '[options]', 'CC-MAIN-2014-52', 'CC-MAIN-2016-50')\n print()\n print('Last argument is considered to be the latest crawl')\n print()\n print('Options:')\n print()\n sys.exit(1)\n plot = TldStats()\n plot.read_data(sys.stdin)\n plot.transform_data()\n plot.save_data()\n plot.plot_groups()\n plot.plot(plot_crawls, latest_crawl)\n if latest_crawl == 'CC-MAIN-2019-09':\n # plot comparison only for crawl of similar date as benchmark data\n plot.plot_comparison(latest_crawl, 'selected-crawl',\n min_urls_percentage)\n# plot.plot_comparison(latest_crawl, 'selected-crawl',\n# min_urls_percentage, 'pearson')\n plot.plot_comparison_groups()\n" }, { "path": "plot/tld_by_continent.py", "content": "\"\"\"\nPlot TLD distributions by continent.\n\nThis module generates visualizations showing how TLDs are distributed\nacross geographic continents and major TLD groups (com/net, org, edu, gov/mil).\nMaps country-code TLDs to their respective continents using ISO country codes.\n\"\"\"\n\nimport json\nimport os.path\nimport sys\nfrom collections import Counter, defaultdict\n\nimport fsspec\nimport matplotlib.pyplot as plt\nimport pandas\nfrom matplotlib.ticker import MaxNLocator\n\nfrom crawlplot import CrawlPlot\nfrom crawlstats import MonthlyCrawl, MultiCount\nfrom top_level_domain import TopLevelDomain\n\n\ntld_counts = defaultdict(lambda: Counter())\n\n# mapping of country-code TLDs to continents\ncontinent_cc_tlds = {\n 'Africa': {'ao', 'bf', 'bi', 'bj', 'bw', 'cd', 'cf', 'cg', 'ci', 'cm', 'cv',\n 'dj', 'dz', 'eg', 'eh', 'er', 'et', 'ga', 'gh', 'gm', 'gn', 'gq',\n 'gw', 'ke', 'km', 'lr', 'ls', 'ly', 'ma', 'mg', 'ml', 'mr', 'mu',\n 'mw', 'mz', 'na', 'ne', 'ng', 're', 'rw', 'sc', 'sd', 'sh', 'sl',\n 'sn', 'so', 'ss', 'st', 'sz', 'td', 'tg', 'tn', 'tz', 'ug', 'yt',\n 'za', 'zm', 'zw'},\n 'Antarctica': {'aq'},\n 'Asia': {'ae', 'af', 'am', 'az', 'bd', 'bh', 'bn', 'bt', 'cc', 'cn', 'cx',\n 'ge', 'hk', 'id', 'il', 'in', 'io', 'iq', 'ir', 'jo', 'jp', 'kg',\n 'kh', 'kp', 'kr', 'kw', 'kz', 'la', 'lb', 'lk', 'mm', 'mn', 'mo',\n 'mv', 'my', 'np', 'om', 'ph', 'pk', 'ps', 'qa', 'sa', 'sg', 'sy',\n 'th', 'tj', 'tm', 'tr', 'tw', 'uz', 'vn', 'ye',\n 'tp' # Timor-Leste: deleted in favor of .tl in 2015\n },\n 'Europe': {'ad', 'al', 'at', 'ba', 'be', 'bg', 'by', 'ch', 'cy', 'cz',\n 'de', 'dk', 'ee', 'es', 'fi', 'fo', 'fr', 'gg', 'gi', 'gr',\n 'hr', 'hu', 'ie', 'im', 'is', 'it', 'je', 'li', 'lt', 'lu', 'lv',\n 'mc', 'md', 'me', 'mk', 'mt', 'nl', 'no',\n 'pl', 'pt', 'ro', 'rs', 'ru', 'se', 'si', 'sj', 'sk', 'sm',\n 'ua', 'uk', 'va',\n 'xk', # https://en.wikipedia.org/wiki/.xk\n 'bv', # Bouvet Island (inactive, uninhabited Norwegian territory, South Atlantic Ocean)\n 'gb' # Great Britain (reserved)\n },\n 'North America': {'ag', 'ai', 'an', 'aw', 'bb', 'bm', 'bs', 'bz',\n 'ca', 'cr', 'cu', 'cw', 'dm', 'do', 'gd', 'gl', 'gp', 'gt',\n 'hn', 'ht', 'jm', 'kn', 'ky', 'lc', 'mq', 'ms', 'mx', 'ni',\n 'pa', 'pm', 'pr', 'sv', 'sx', 'tc', 'tt',\n 'us', 'vc', 'vg', 'vi',\n 'bl', # Saint Barthélemy (unused)\n 'bq', # Bonaire, Sint Eustatius and Saba (reserved)\n 'mf', # Saint Martin (unassigned)\n },\n 'Oceania': {'as', 'au', 'ck', 'fj', 'fm', 'gu', 'ki', 'mh', 'mp',\n 'nc', 'nf', 'nr', 'nu', 'nz', 'pf', 'pg', 'pn', 'pw',\n 'sb', 'tk', 'tl', 'to', 'tv', 'vu', 'wf', 'ws'\n },\n 'South America': {'ar', 'bo', 'br', 'cl', 'co', 'ec', 'fk', 'gf', 'gy',\n 'pe', 'py', 'sr', 'uy', 've'},\n}\n\n# Geographic TLDs mapped to continents\n# https://en.wikipedia.org/wiki/List_of_Internet_top-level_domains#Geographic_top-level_domains\ncontinent_geographic_tlds = {\n 'Africa': {'africa', 'capetown', 'durban', 'joburg'},\n 'Asia': {'abudhabi', 'arab', 'asia', 'doha', 'dubai', 'krd', 'kyoto',\n 'nagoya', 'okinawa', 'osaka', 'ryukyu', 'taipei', 'tokyo', 'yokohama',\n # https://en.wikipedia.org/wiki/List_of_Internet_top-level_domains#Internationalized_geographic_top-level_domains\n 'xn--1qqw23a', '佛山', # Foshan, China\n 'xn--xhq521b', '广东', # Guangdong, China\n 'xn--80adxhks', 'москва', # Moscow, Russia\n 'xn--p1acf', 'рус', # Russian language and culture - https://en.wikipedia.org/wiki/.%D1%80%D1%83%D1%81\n 'xn--mgbca7dzdo', 'ابوظبي', # Abu Dhabi\n 'xn--ngbrx', 'عرب', # Arab\n },\n 'Europe': {\n # France\n 'alsace', 'bzh', 'corsica', 'eus', 'paris',\n # Spain\n 'bcn', 'barcelona', 'cat', 'eus', 'gal', 'madrid',\n # Germany\n 'bayern', 'berlin', 'cologne', 'koeln', 'hamburg', 'nrw', 'ruhr', 'saarland',\n # other\n 'eu', 'amsterdam', 'bar', 'brussels', 'cymru', 'wales', 'frl', 'gent', 'helsinki', 'irish', 'ist', 'istanbul', 'london', 'moscow', 'scot', 'stockholm', 'swiss', 'tatar', 'tirol', 'vlaanderen', 'wien', 'zuerich', 'su',\n # https://en.wikipedia.org/wiki/.ax\n 'ax'\n },\n 'North America': {'boston', 'miami', 'nyc', 'quebec', 'vegas'},\n 'Oceania': {'kiwi', 'melbourne', 'sydney'},\n 'South America': {'lat', 'rio'}\n}\n\n# list of \"continents\" to be shown in the output\ncontinents = ['(other)', 'com,net', 'org', 'edu', 'gov,mil', 'North America', 'South America', 'Oceania', 'Africa', 'Asia', 'Europe']\n\n# lookup tables TLD -> continent\ntld_continent = {\n 'gov': 'gov,mil', 'mil': 'gov,mil',\n 'com': 'com,net', 'net': 'com,net',\n 'org': 'org', 'edu': 'edu'\n}\n\n# frequency counts of TLDs that cannot be mapped to a continent\ntld_unmapped = Counter()\n\n# fill the lookup table with TLD -> continent mappings\nfor continent in continent_cc_tlds:\n for tld in continent_cc_tlds[continent]:\n tld_continent[tld] = continent\n\nfor continent in continent_geographic_tlds:\n for tld in continent_geographic_tlds[continent]:\n tld_continent[tld] = continent\n\nfor icctld in TopLevelDomain.tld_ccs:\n if TopLevelDomain.tld_ccs[icctld] in tld_continent:\n tld_continent[icctld] = tld_continent[TopLevelDomain.tld_ccs[icctld]]\n\ndef tld2continent(tld):\n \"\"\"Map a TLD to its corresponding continent.\"\"\"\n continent = '(other)'\n tld = tld.lower()\n if tld in tld_continent and tld_continent[tld] != 'Antarctica':\n continent = tld_continent[tld]\n return continent\n\n\ndef get_data(f):\n \"\"\"Parse TLD statistics and aggregate by year and crawl.\n\n Returns two dictionaries: one aggregated by year, one by crawl name.\n \"\"\"\n d = defaultdict(lambda: defaultdict(list))\n dd = defaultdict(lambda: defaultdict(list))\n\n for line in f:\n keyval = line.split('\\t')\n if len(keyval) == 2:\n [_, suffix, crawl] = json.loads(keyval[0])\n year = MonthlyCrawl.year_of(crawl)\n val = json.loads(keyval[1])\n tld = suffix.split('.')[-1].lower()\n tld_cnt = tld2continent(tld)\n if tld_cnt == '(other)':\n tld_unmapped[tld] += MultiCount.get_count(0, val)\n if tld:\n # print(tld)\n tld_counts['(any)'][tld] += MultiCount.get_count(0, val)\n tld_counts[str(year)][tld] += MultiCount.get_count(0, val)\n d[str(year)][tld_cnt].append(val)\n dd[MonthlyCrawl.short_name(crawl)][tld_cnt].append(val)\n\n return d, dd\n\n\nclass TLDByContinentPlot(CrawlPlot):\n \"\"\"Generate TLD distribution by continent visualizations.\"\"\"\n\n def __init__(self):\n super().__init__()\n\n def plot(self):\n \"\"\"Generate TLD by continent/year plots and save data tables.\"\"\"\n # Read from file path or stdin\n if len(sys.argv) > 1 and os.path.exists(sys.argv[-1]):\n with fsspec.open(sys.argv[-1], compression=\"gzip\", mode=\"rt\") as f:\n d, dd = get_data(f)\n else:\n d, dd = get_data(sys.stdin)\n\n print(\"\\nyear\\t{}\".format(\"\\t\".join(continents)))\n continent_percentages = dict()\n for year in d:\n pages = dict()\n total = 0\n values = []\n for tld in continents:\n d[year][tld].append([0,0,0,0])\n val = MultiCount.sum_values(d[year][tld], False)\n total += val[0]\n values.append(val[0])\n # print(\"{}\\t{}\\t{}\\t{}\\t{}\\t{}\".format(year, tld, *val))\n percentages = [100*val/total for val in values]\n print(\"{}\\t{}\".format(year, \"\\t\".join(\n map(lambda x: '{:.2f}'.format(x), percentages))))\n continent_percentages[year] = percentages\n continent_percentages = pandas.DataFrame.from_dict(continent_percentages,\n orient='index',\n columns=continents)\n continent_percentages.index.name = 'year'\n print(continent_percentages)\n\n top_tlds = tld_counts['(any)'].most_common(16)\n #print(\"\\n\", top_tlds)\n\n top_tlds_by_year = defaultdict(list)\n print(\"\\nyear\\t{}\".format(\"\\t\".join([x[0] for x in top_tlds])))\n for year in tld_counts:\n total = sum(tld_counts[year].values())\n sys.stdout.write(year)\n for tld in top_tlds:\n perc = 100*tld_counts[year][tld[0]]/total\n sys.stdout.write('\\t{:.2f}'.format(perc))\n top_tlds_by_year[year].append(perc)\n sys.stdout.write('\\n')\n\n # table TLDs by year\n selected_tlds = pandas.DataFrame.from_dict(\n top_tlds_by_year,\n orient='index',\n columns=map(lambda tld: tld[0], top_tlds)\n )\n selected_tlds.index.name = 'year'\n selected_tlds.to_csv(\n os.path.join(self.PLOTDIR, 'tld', 'selected-tlds-by-year.csv'),\n index=True)\n css_classes = ['tablepercentage', 'tablesorter']\n selected_tlds.to_html(\n os.path.join(self.PLOTDIR, 'tld', 'selected-tlds-by-year.html'),\n float_format='%.2f',\n classes=css_classes,\n index_names=True)\n\n print(\"\\ncrawl\\t{}\".format(\"\\t\".join(continents)))\n for crawl in dd:\n pages = dict()\n total = 0\n values = []\n for tld in continents:\n dd[crawl][tld].append([0,0,0,0])\n val = MultiCount.sum_values(dd[crawl][tld], False)\n total += val[0]\n values.append(val[0])\n # print(\"{}\\t{}\\t{}\\t{}\\t{}\\t{}\".format(year, tld, *val))\n print(\"{}\\t{}\".format(crawl, \"\\t\".join(['{:.2f}'.format(100*val/total) for val in values])))\n\n # print unmapped TLDs to verify whether there are any TLDs\n # that need to be added to the mapping\n print(\"\\n\", len(tld_unmapped), \" unmapped TLDs: \", str(tld_unmapped), \"\\n\\n\")\n\n\n data = continent_percentages.melt(id_vars=[], var_name='continent',\n value_name='perc', ignore_index=False)\n data['continent'] = pandas.Categorical(data['continent'],\n ordered=True,\n categories=continents.reverse())\n \n if self.PLOTLIB == \"rpy2.ggplot2\":\n self.plot_with_rpy2_ggplot2(data=data)\n\n elif self.PLOTLIB == \"matplotlib\":\n self.plot_with_matplotlib(data=data)\n\n else:\n raise ValueError(\"Invalid PLOTLIB\")\n \n\n ### plot and table for print publication\n #plot = plot + ggplot2.labs(title='',\n # x='', y='', fill='TLD / Continent') \\\n # + ggplot2.theme()\n #plot.save(os.path.join(PLOTDIR, 'tld', 'tlds-by-year-and-continent.pdf'))\n #print(continent_percentages.to_latex(index=True, float_format='%.2f'))\n continent_percentages.to_csv(\n os.path.join(self.PLOTDIR, 'tld', 'tlds-by-year-and-continent.csv'),\n index=True)\n css_classes = ['tablepercentage', 'tablesorter']\n continent_percentages.to_html(\n os.path.join(self.PLOTDIR, 'tld', 'tlds-by-year-and-continent.html'),\n float_format='%.2f',\n classes=css_classes)\n\n def plot_with_rpy2_ggplot2(self, data):\n \"\"\"Generate TLD by continent stacked bar chart using rpy2/ggplot2.\"\"\"\n from rpy2.robjects.lib import ggplot2\n\n plot = ggplot2.ggplot(data.reset_index()) \\\n + ggplot2.aes_string(x='year', y='perc', fill='continent', label='perc') \\\n + ggplot2.geom_bar(stat='identity', position='stack') \\\n + self.GGPLOT2_THEME + ggplot2.scale_fill_hue() \\\n + ggplot2.labs(title='Percentage of Page Captures per TLD / Continent',\n x='', y='Percentage', fill='TLD / Continent') \\\n + ggplot2.theme(**{'legend.position': 'right',\n 'aspect.ratio': .7,\n **self.GGPLOT2_THEME_KWARGS,\n 'axis.text.x':\n ggplot2.element_text(angle=45,\n vjust=1, hjust=1)})\n plot.save(os.path.join(self.PLOTDIR, 'tld', 'tlds-by-year-and-continent.png'))\n\n return plot\n\n\n def plot_with_matplotlib(self, data):\n \"\"\"Generate TLD by continent stacked bar chart using matplotlib.\"\"\"\n aspect_ratio = 0.7\n title = 'Percentage of Page Captures per TLD / Continent'\n\n fig, ax = self.create_figure()\n\n # Colorblind-safe palette (Paul Tol's scheme)\n colors = ['#4477AA', '#EE6677', '#228833', '#CCBB44', '#AA3377',\n '#66CCEE', '#EE8866', '#44AA99', '#BBBBBB', '#99CC66', '#CC99BB']\n\n years = sorted(data.reset_index()['year'].unique())\n bottoms = [0] * len(years)\n sorted_continents = sorted(continents)[::-1]\n\n for i, continent in enumerate(sorted_continents):\n values = []\n for year in years:\n year_data = data.loc[year]\n continent_data = year_data[year_data['continent'] == continent]\n values.append(continent_data['perc'].values[0] if len(continent_data) > 0 else 0)\n\n ax.bar(range(len(years)), values, bottom=bottoms, label=continent,\n color=colors[i % len(colors)], width=self.bar_width)\n bottoms = [b + v for b, v in zip(bottoms, values)]\n\n # Axes ratio\n ax.set_aspect(1 / ax.get_data_ratio() * aspect_ratio)\n\n self.set_title(ax, title)\n ax.set_xlabel('')\n ax.set_ylabel('Percentage', fontsize=self.ylabel_fontsize)\n\n # Set x-axis ticks and labels\n ax.set_xticks(range(len(years)))\n ax.set_xticklabels(years, rotation=45, ha='right', fontsize=self.ticks_fontsize)\n ax.set_xlim(-0.5, len(years) - 0.5)\n\n # Set y-axis formatting\n ax.yaxis.set_major_locator(MaxNLocator(nbins=6))\n ax.set_ylim(0, 100)\n ax.tick_params(axis='y', labelsize=self.ticks_fontsize)\n\n # Apply ggplot2-like styling with y-axis grid\n ax.grid(True, which='major', linewidth=1.0, color='#E6E6E6', zorder=0, axis='y')\n ax.set_axisbelow(True)\n\n # Custom spine styling (thin borders at top/bottom)\n ax.spines['top'].set_visible(True)\n ax.spines['top'].set_linewidth(1.0)\n ax.spines['top'].set_color('#E6E6E6')\n ax.spines['right'].set_visible(False)\n ax.spines['left'].set_visible(False)\n ax.spines['bottom'].set_visible(True)\n ax.spines['bottom'].set_linewidth(1.0)\n ax.spines['bottom'].set_color('#E6E6E6')\n\n # Set tick colors\n ax.tick_params(axis='both', which='both', colors=self.ticks_color,\n length=self.ticks_length, width=1.0)\n self.set_tick_labels_black(ax)\n\n # Position legend on right side with reversed order\n handles, labels = ax.get_legend_handles_labels()\n legend = ax.legend(handles[::-1], labels[::-1], loc='center left',\n bbox_to_anchor=(1.0, 0.5), frameon=False,\n fontsize=self.legend_fontsize, title='TLD / Continent',\n title_fontsize=self.legend_title_fontsize)\n legend._legend_box.align = 'left'\n\n img_path = os.path.join(self.PLOTDIR, 'tld', 'tlds-by-year-and-continent.png')\n return self.save_figure(fig, img_path)\n\n\nif __name__ == '__main__':\n plot = TLDByContinentPlot()\n plot.plot()\n" }, { "path": "plot.sh", "content": "#!/bin/bash\n\nN_CRAWLS=$(python3 -c 'from crawlstats import MonthlyCrawl; print(len(MonthlyCrawl.by_name))')\nLATEST_CRAWL=$(python3 -c 'from crawlstats import MonthlyCrawl; print(sorted(MonthlyCrawl.by_name.keys())[-1])')\n\n# verify that all stats files are downloaded, cf. get_stats.sh\nN_CRAWLS_STATS_FILES=$(ls stats/CC-MAIN-*.gz | wc -l)\nif [[ $N_CRAWLS -ne $N_CRAWLS_STATS_FILES ]]; then\n echo \"Number of crawls registered in crawlstats.py ($N_CRAWLS) and\"\n echo \"the number of statistics files in stats/ ($N_CRAWLS_STATS_FILES) are not equal.\"\n echo \"Exiting!\"\n exit 1\nfi\n\necho \"Plotting crawl statistics for $N_CRAWLS crawls\"\necho \"Latest crawl is: $LATEST_CRAWL\"\necho\n\n\n# fail on any kind of error\nset -exo pipefail\n\n\n# register the latest crawl in the website configuration\nsed -i 's@^latest_crawl:.*@latest_crawl: '$LATEST_CRAWL'@' _config.yml\n\n\nfunction update_excerpt() {\n regex=\"$1\"\n excerpt=\"$2\"\n if [ -e \"$excerpt\" ]; then\n # short-cut for monthy update plots: only add data from latest crawl\n if ! zgrep -qF \"$LATEST_CRAWL\" $excerpt; then\n echo \"Updating excerpt $excerpt with latest crawl $LATEST_CRAWL\"\n zgrep -Eh \"$regex\" stats/$LATEST_CRAWL.gz | gzip >>$excerpt\n fi\n # sanity check: are all crawls excerpted?\n N_CRAWLS_EXCERPTED=$(zcat $excerpt | cut -f1 | jq -r '.[2]' | uniq | sort -u | wc -l)\n if [[ $N_CRAWLS_EXCERPTED -eq $N_CRAWLS ]]; then\n echo \"Excerpt $excerpt includes $N_CRAWLS crawls as expected.\"\n else\n echo \"Number of crawls excerpted in $excerpt ($N_CRAWLS_EXCERPTED) does not equal $N_CRAWLS\"\n echo \"Removing excerpt $excerpt\"\n rm $excerpt\n fi\n fi\n if ! [ -e \"$excerpt\" ]; then\n echo \"Rebuilding excerpt $excerpt\"\n zcat stats/CC-MAIN-*.gz | grep -Eh \"$regex\" | gzip >$excerpt\n fi\n}\n\n# filter data to speed-up reading while plotting\nmkdir -p stats/excerpt\nupdate_excerpt '^\\[\"size' stats/excerpt/size.json.gz\nupdate_excerpt '^\\[\"histogram\"' stats/excerpt/histogram.json.gz\nupdate_excerpt '^\\[\"tld\"' stats/excerpt/tld.json.gz\nupdate_excerpt '^\\[\"(size|domain)\"' stats/excerpt/domain.json.gz\nupdate_excerpt '^\\[\"(size\", *\"page|mimetype)\"' stats/excerpt/mimetype.json.gz\nupdate_excerpt '^\\[\"(size\", *\"page|mimetype_detected)\"' stats/excerpt/mimetype_detected.json.gz\nupdate_excerpt '^\\[\"(size\", *\"page|charset)\"' stats/excerpt/charset.json.gz\nupdate_excerpt '^\\[\"(size\", *\"page|primary_language|languages)\"' stats/excerpt/language.json.gz\nupdate_excerpt '^\\[\"scheme\"' stats/excerpt/url_protocol.json.gz\n\nmkdir -p data\n\nzcat stats/excerpt/size.json.gz \\\n | python3 plot/crawl_size.py\n\nzcat stats/excerpt/size.json.gz \\\n | python3 plot/overlap.py\n\n# zcat stats/excerpt/histogram.json.gz \\\n# | python3 plot/histogram.py \"$LATEST_CRAWL\"\n\n(cat stats/crawler/CC-MAIN-*.json;\n zcat stats/excerpt/size.json.gz | grep '^\\[\"size\"';\n zcat stats/excerpt/url_protocol.json.gz) \\\n\t| python3 plot/crawler_metrics.py\n\nzcat stats/excerpt/tld.json.gz \\\n | python3 plot/tld.py CC-MAIN-2008-2009 CC-MAIN-2012 CC-MAIN-2014-10 \\\n CC-MAIN-2016-30 CC-MAIN-2019-09 CC-MAIN-2022-49 $LATEST_CRAWL\nzcat stats/excerpt/tld.json.gz \\\n | python3 plot/tld_by_continent.py\n\nzcat stats/excerpt/mimetype.json.gz \\\n | python3 plot/mimetype.py\n\nzcat stats/excerpt/mimetype_detected.json.gz \\\n | python3 plot/mimetype_detected.py\n\nzcat stats/excerpt/charset.json.gz \\\n | python3 plot/charset.py\n\nzcat stats/excerpt/language.json.gz \\\n | python3 plot/language.py\n\nzcat stats/excerpt/domain.json.gz \\\n | python3 plot/domain.py\n\necho -e \"\\n\\nAll crawl statistics plotted\\n\"" }, { "path": "plots/README.md", "content": "Plots about Common Crawl Monthly Archives\n=========================================\n\n* [size of the crawls](crawlsize.md) - number of pages, unique URLs, hosts, domains, TLDs\n* [top-level domains](tlds.md) - distribution and comparison\n* [crawler-related metrics](crawlermetrics.md) - fetch status, etc.\n* [overlaps between monthly crawls](crawloverlap.md)\n* distribution of\n\t- [media types (MIME)](mimetypes.md)\n\t- [character sets](charsets.md)\n\t- [languages](languages.md)\n\t- [registered domains](domains.md)\n\t" }, { "path": "plots/charsets-top-100.html", "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 \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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
crawlCC-MAIN-2026-08CC-MAIN-2026-12CC-MAIN-2026-17
charset
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EUC-JP0.13110.13400.1266
EUC-KR0.07850.08190.0771
GB180300.01490.01650.0150
GB23120.23070.23880.2011
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IBM4240.00230.00210.0011
IBM5000.00110.00110.0008
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ISO-8859-70.00430.00430.0040
ISO-8859-80.00070.00080.0007
ISO-8859-90.01960.02010.0235
KOI8-R0.00670.00660.0076
KOI8-U0.00010.00010.0001
Shift_JIS0.17490.18130.1479
TIS-6200.00380.00380.0036
US-ASCII0.01890.02000.0203
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windows-12500.06810.06890.0666
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windows-12520.14330.15570.1526
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x-iso-8859-110.00010.00010.0000
x-windows-8740.00720.00670.0057
x-windows-9490.00000.00000.0000
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}, { "path": "plots/charsets.md", "content": "---\nlayout: table\ntable_include: charsets-top-100.html\ntable_sortlist: \"{sortList: [[1,1]]}\"\n---\n\nCharacter Encoding of HTML Pages\n================================\n\nThe [character set or encoding](https://en.wikipedia.org/wiki/Character_encoding) of HTML pages only is identified by [Tika](https://tika.apache.org/)'s [AutoDetectReader](https://tika.apache.org/1.25/api/org/apache/tika/detect/AutoDetectReader.html). The table shows the percentage how character sets have been used to encode HTML pages crawled by the latest monthly crawls. The underlying data including page counts is provided in [charsets.csv](./charsets.csv).\n" }, { "path": "plots/crawlermetrics.md", "content": "Crawler-Related Metrics\n=======================\n\nCrawler-related metrics are extracted from the crawler log files, cf. [../stats/crawler/](https://github.com/commoncrawl/cc-crawl-statistics/blob/master/stats/crawler/) and include\n- the size of the URL database (CrawlDb)\n- the fetch list size (number of URLs scheduled for fetching)\n- the response status of the fetch:\n - success\n - redirect\n - denied (forbidden by HTTP 403 or robots.txt)\n - failed (404, host not found, etc.)\n- usage of http/https URL protocols (schemes)\n\nThe first plot shows absolute number for the metrics.\n\n![Crawler metrics](./crawler/metrics.png)\n\nThe relative portion of the fetch status is shown in the second graphics.\n\n![Percentage of fetch status](./crawler/fetch_status_percentage.png)\n\nThe next figure shows the relative usage of http and https URL protocols (schemes). The increasing usage HTTPS on the web is reflected. But also crawler properties such as sampling, deduplication and URL canonicalization) may influence the actual amount of HTTPS URLs in a single monthly crawl.\n\n![Percentage of HTTP vs. HTTPS URLs](./crawler/url_protocols_percentage.png)\n\nThe crawls are backed by a CrawlDb which stores URLs, fetch time, status information, content checksum and various other metadata. HTTP response codes are mapped to coarse [CrawlDatum states](https://cwiki.apache.org/confluence/display/NUTCH/CrawlDatumStates) and so are other status signals, such as disallowed by robots.txt or the result of a deduplication job. By adding permanently new URLs, the CrawlDb is growing and requires a permanent cleanup which removes stale URLs. The figure below shows the development of the CrawlDb over time, including the amount of CrawlDatum states. Size and counts are recorded before the fetching of a monthly crawl.\n\n![CrawlDb size and status counts](./crawler/crawldb_status.png)\n\n" }, { "path": "plots/crawloverlap.md", "content": "Overlaps between Common Crawl Monthly Archives\n==============================================\n\nOverlaps between monthly crawl archives are calculated and plotted as [Jaccard similarity](https://en.wikipedia.org/wiki/Jaccard_index) of unique URLs or content digests. The cardinality of the monthly crawls and the union of two crawls are [Hyperloglog](https://en.wikipedia.org/wiki/HyperLogLog) estimates, cf. [plot/overlap.py](https://github.com/commoncrawl/cc-crawl-statistics/blob/master/plot/overlap.py) for details.\n\n![URL overlap (Jaccard similarity) between Common Crawl monthly crawls](./crawloverlap/crawlsimilarity_matrix_url.png)\n\n![Content overlap between Common Crawl monthly crawls (Jaccard similarity on unique content digests)](./crawloverlap/crawlsimilarity_matrix_digest.png)\n\nNote, that the content overlaps are small and in the same order of magnitude as the 1% error rate of the Hyperloglog cardinality estimates.\n" }, { "path": "plots/crawlsize/cumulative.csv", "content": "crawl,digest estim.,page,url 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}, { "path": "plots/crawlsize.md", "content": "Size of Common Crawl Monthly Archives\n=====================================\n\nThe number of released pages per month fluctuates varies over time due to changes to the number of available seeds, scheduling policy for page revists and crawler operating issues. Because of duplicates the numbers of unique URLs or unique content digests (here Hyperloglog estimates) are lower than the number of page captures.\n\n![Size of crawl archives (pages, URLs, unique content digest)](./crawlsize/monthly.png)\n\n([Number of page captures, unique URLs and content digests as CSV](./crawlsize/monthly.csv))\n\nThe size on various aggregation levels (host, domain, top-level domain / public suffix) is shown in the next plot. Note that the scale differs per level of aggregation, see the exponential notation behind the labels.\n\n![Coverage of unique URLs, host and domain names, top-level domains (public suffixes)](./crawlsize/domain.png)\n\n([Unique URLs, host and domain names, top-level domains (public suffixes) as CSV](./crawlsize/domain.csv))\n\n\n## Size and URL Status Per Year\n\n![Captures per year and URL status (new/unknown URL, revisit, duplicate)](./crawlsize/url_status_by_year.png)\n\nThe plot above shows the amount of page captures accumulated per year and its distribution by URL status – \"new\" URLs were visited the first time, \"revisits\" have been already archived a previous crawl, and \"duplicates\" designate captures of the same URL within a single, \"monthly\" crawl.\n\n\n## Cumulative Size\n\nEvery monthly crawl is a sample of the web and we try to make every monthly snapshot a representative and diverse sample by its own. We also try to make the sample diverse in time to cover more content over time while still providing fresh and frequent snapshots of popular pages. This and the following plots are based on Hyperloglog cardinality estimates with 1% error rate.\n\n![Cumulative size of monthly crawl archives since 2013](./crawlsize/cumulative.png)\n\nThe next plot shows the difference in the cumulative size of URLs to the preceding crawl. In other words, the amount of new URLs, not observed in any of the preceding crawls.\n\n![New URLs per Crawl, not observed in prior crawls](./crawlsize/monthly_new.png)\n\n([New URLs per crawl as CSV](./crawlsize/monthly_new.csv))\n\nHow many unique items (in terms of URLs or unique content by digest) are covered by the last n crawls? The coverage over certain time intervals went down early 2015 when continuous donations of verified seeds stopped. Since autumn 2016 we are able to extend the crawl by our own, and we try to increase the coverage for the last n crawls.\n\n![Number of unique URLs if the last n crawls are combined](./crawlsize/url_last_n_crawls.png)\n\n([Combined number of unique URLs over n last crawls as CSV](./crawlsize/url_last_n_crawls.csv))\n\n![Ratio of unique URLs by total pages captured over n last crawls](./crawlsize/url_page_ratio_last_n_crawls.png)\n\n![Number of unique content if the last n crawls are combined](./crawlsize/digest_last_n_crawls.png)\n" }, { "path": "plots/domains-top-500.csv", "content": 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" }, { "path": "plots/domains.md", "content": "---\nlayout: table\ntable_include: domains-top-500.html\ntable_sortlist: \"{sortList: [[1,1]]}\"\ntable_searcher: \"Filter for domain names\"\n---\n\nTop-500 Registered Domains of the Latest Main Crawl\n===================================================\n\nThe table below shows the top 500 registered domains (in terms of page captures) of the last main/monthly crawl\n({{ site.latest_crawl }}). The underlying data is also provided in CSV format, see [domains-top-500.csv](./domains-top-500.csv).\n\nNote that the ranking by page captures only partially corresponds to the importance of domains, as the\ncrawler respects the robots.txt and tries hard not to overload web servers. Highly ranked domains tend to be\nunderrepresented. If you're looking for a list of domain or host names ranked by page rank or harmonic centrality,\nconsider using one of the [webgraph datasets](https://github.com/commoncrawl/cc-webgraph#exploring-webgraph-data-sets)\ninstead.\n" }, { "path": "plots/languages-top-200.html", "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 \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 \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 \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 \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 \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 \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
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0183\nCC-MAIN-2021-25,tgk,123116,122658,0.0050\nCC-MAIN-2021-25,tgl,173295,172915,0.0071\nCC-MAIN-2021-25,tha,11657354,11623961,0.4743\nCC-MAIN-2021-25,tir,16052,15952,0.0007\nCC-MAIN-2021-25,ton,1096,1089,0.0000\nCC-MAIN-2021-25,tsn,8421,8207,0.0003\nCC-MAIN-2021-25,tso,1149,1148,0.0000\nCC-MAIN-2021-25,tuk,60197,60066,0.0024\nCC-MAIN-2021-25,tur,25576817,25455643,1.0407\nCC-MAIN-2021-25,uig,24288,24221,0.0010\nCC-MAIN-2021-25,ukr,10147902,10099183,0.4129\nCC-MAIN-2021-25,urd,655462,653501,0.0267\nCC-MAIN-2021-25,uzb,397403,395761,0.0162\nCC-MAIN-2021-25,ven,1071,1069,0.0000\nCC-MAIN-2021-25,vie,37417228,37263879,1.5225\nCC-MAIN-2021-25,vol,26111,26103,0.0011\nCC-MAIN-2021-25,war,74588,74320,0.0030\nCC-MAIN-2021-25,wol,3341,3335,0.0001\nCC-MAIN-2021-25,xho,40698,40640,0.0017\nCC-MAIN-2021-25,yid,36532,36425,0.0015\nCC-MAIN-2021-25,yor,23128,23091,0.0009\nCC-MAIN-2021-25,zha,1001,999,0.0000\nCC-MAIN-2021-25,zho,124579683,124114474,5.0691\nCC-MAIN-2021-25,zul,46595,46540,0.0019\nCC-MAIN-2021-31,,55152192,55152192,1.7425\nCC-MAIN-2021-31,aar,2338,2327,0.0001\nCC-MAIN-2021-31,abk,11703,11700,0.0004\nCC-MAIN-2021-31,afr,375588,374574,0.0119\nCC-MAIN-2021-31,aka,3062,3049,0.0001\nCC-MAIN-2021-31,amh,113846,112873,0.0036\nCC-MAIN-2021-31,ara,17865281,17778741,0.5644\nCC-MAIN-2021-31,asm,53779,53609,0.0017\nCC-MAIN-2021-31,aym,1932,1886,0.0001\nCC-MAIN-2021-31,aze,1232516,1228884,0.0389\nCC-MAIN-2021-31,bak,38187,38175,0.0012\nCC-MAIN-2021-31,bel,497646,493198,0.0157\nCC-MAIN-2021-31,ben,2640212,2634457,0.0834\nCC-MAIN-2021-31,bih,19383,19370,0.0006\nCC-MAIN-2021-31,bis,3328,3315,0.0001\nCC-MAIN-2021-31,bod,28084,27896,0.0009\nCC-MAIN-2021-31,bos,1277668,1274183,0.0404\nCC-MAIN-2021-31,bre,77833,77656,0.0025\nCC-MAIN-2021-31,bul,8418194,8376078,0.2660\nCC-MAIN-2021-31,cat,7201196,7166106,0.2275\nCC-MAIN-2021-31,ceb,47548,47510,0.0015\nCC-MAIN-2021-31,ces,35554783,35362940,1.1233\nCC-MAIN-2021-31,chr,1294,1291,0.0000\nCC-MAIN-2021-31,cos,76974,76745,0.0024\nCC-MAIN-2021-31,crs,1379,1375,0.0000\nCC-MAIN-2021-31,cym,316697,315528,0.0100\nCC-MAIN-2021-31,dan,13264653,13191056,0.4191\nCC-MAIN-2021-31,deu,186833537,185607191,5.9029\nCC-MAIN-2021-31,div,57259,57173,0.0018\nCC-MAIN-2021-31,dzo,2170,2170,0.0001\nCC-MAIN-2021-31,ell,17257444,17186945,0.5452\nCC-MAIN-2021-31,eng,1442029616,1433409824,45.5605\nCC-MAIN-2021-31,epo,211892,211433,0.0067\nCC-MAIN-2021-31,est,4212803,4188956,0.1331\nCC-MAIN-2021-31,eus,845252,819419,0.0267\nCC-MAIN-2021-31,fao,104088,103554,0.0033\nCC-MAIN-2021-31,fas,20794102,20595225,0.6570\nCC-MAIN-2021-31,fij,2379,2376,0.0001\nCC-MAIN-2021-31,fin,12457144,12389941,0.3936\nCC-MAIN-2021-31,fra,142712898,141884064,4.5090\nCC-MAIN-2021-31,fry,102329,102146,0.0032\nCC-MAIN-2021-31,gla,53779,53694,0.0017\nCC-MAIN-2021-31,gle,178887,178291,0.0057\nCC-MAIN-2021-31,glg,1089129,1085560,0.0344\nCC-MAIN-2021-31,glv,14751,14741,0.0005\nCC-MAIN-2021-31,got,1136,1135,0.0000\nCC-MAIN-2021-31,grn,21003,20941,0.0007\nCC-MAIN-2021-31,guj,352742,351773,0.0111\nCC-MAIN-2021-31,hat,47910,47823,0.0015\nCC-MAIN-2021-31,hau,75498,75312,0.0024\nCC-MAIN-2021-31,haw,17584,17548,0.0006\nCC-MAIN-2021-31,heb,7808734,7763728,0.2467\nCC-MAIN-2021-31,hin,4172426,4160011,0.1318\nCC-MAIN-2021-31,hmn,22252,18274,0.0007\nCC-MAIN-2021-31,hrv,5886172,5859501,0.1860\nCC-MAIN-2021-31,hun,16912426,16834432,0.5343\nCC-MAIN-2021-31,hye,872481,869469,0.0276\nCC-MAIN-2021-31,ibo,16545,16496,0.0005\nCC-MAIN-2021-31,iku,6157,6149,0.0002\nCC-MAIN-2021-31,ile,8429,8399,0.0003\nCC-MAIN-2021-31,ina,18575,18554,0.0006\nCC-MAIN-2021-31,ind,26903931,26815563,0.8500\nCC-MAIN-2021-31,ipk,5132,5120,0.0002\nCC-MAIN-2021-31,isl,1287077,1281402,0.0407\nCC-MAIN-2021-31,ita,77083323,76701902,2.4354\nCC-MAIN-2021-31,jav,71685,71560,0.0023\nCC-MAIN-2021-31,jpn,155954879,155432969,4.9273\nCC-MAIN-2021-31,kal,54599,54509,0.0017\nCC-MAIN-2021-31,kan,326927,326343,0.0103\nCC-MAIN-2021-31,kas,203,203,0.0000\nCC-MAIN-2021-31,kat,1037007,1033371,0.0328\nCC-MAIN-2021-31,kaz,681772,679821,0.0215\nCC-MAIN-2021-31,kha,5347,5239,0.0002\nCC-MAIN-2021-31,khm,281646,281149,0.0089\nCC-MAIN-2021-31,kin,58440,58228,0.0018\nCC-MAIN-2021-31,kir,153435,153188,0.0048\nCC-MAIN-2021-31,kor,18036214,17973907,0.5698\nCC-MAIN-2021-31,kur,159379,159005,0.0050\nCC-MAIN-2021-31,lao,116072,115570,0.0037\nCC-MAIN-2021-31,lat,3988538,3980647,0.1260\nCC-MAIN-2021-31,lav,2804613,2789589,0.0886\nCC-MAIN-2021-31,lif,18,18,0.0000\nCC-MAIN-2021-31,lin,6606,6592,0.0002\nCC-MAIN-2021-31,lit,5183948,5158606,0.1638\nCC-MAIN-2021-31,ltz,115753,115421,0.0037\nCC-MAIN-2021-31,lug,3661,3638,0.0001\nCC-MAIN-2021-31,mal,597752,595191,0.0189\nCC-MAIN-2021-31,mar,549317,547694,0.0174\nCC-MAIN-2021-31,mfe,2603,2578,0.0001\nCC-MAIN-2021-31,mkd,909667,906323,0.0287\nCC-MAIN-2021-31,mlg,89533,89193,0.0028\nCC-MAIN-2021-31,mlt,108309,107826,0.0034\nCC-MAIN-2021-31,mon,413846,412543,0.0131\nCC-MAIN-2021-31,mri,41925,41762,0.0013\nCC-MAIN-2021-31,msa,2682597,2675675,0.0848\nCC-MAIN-2021-31,mya,336444,335825,0.0106\nCC-MAIN-2021-31,nau,749,744,0.0000\nCC-MAIN-2021-31,nep,864248,861930,0.0273\nCC-MAIN-2021-31,nld,60694360,60318512,1.9176\nCC-MAIN-2021-31,nno,534678,531397,0.0169\nCC-MAIN-2021-31,nor,10918739,10847255,0.3450\nCC-MAIN-2021-31,nso,3228,3228,0.0001\nCC-MAIN-2021-31,nya,15011,14980,0.0005\nCC-MAIN-2021-31,oci,88565,88328,0.0028\nCC-MAIN-2021-31,ori,100034,99936,0.0032\nCC-MAIN-2021-31,orm,18921,18544,0.0006\nCC-MAIN-2021-31,pan,158173,157639,0.0050\nCC-MAIN-2021-31,pol,52132272,51844129,1.6471\nCC-MAIN-2021-31,por,69847083,69526983,2.2068\nCC-MAIN-2021-31,pus,92508,92047,0.0029\nCC-MAIN-2021-31,que,22598,22584,0.0007\nCC-MAIN-2021-31,roh,29741,29280,0.0009\nCC-MAIN-2021-31,ron,19121129,19043840,0.6041\nCC-MAIN-2021-31,run,1799,1772,0.0001\nCC-MAIN-2021-31,rus,230727698,229811731,7.2898\nCC-MAIN-2021-31,sag,479,479,0.0000\nCC-MAIN-2021-31,san,62499,62360,0.0020\nCC-MAIN-2021-31,sco,12708,12597,0.0004\nCC-MAIN-2021-31,sin,227135,226385,0.0072\nCC-MAIN-2021-31,slk,11579341,11522429,0.3658\nCC-MAIN-2021-31,slv,4759036,4731517,0.1504\nCC-MAIN-2021-31,smo,17917,17894,0.0006\nCC-MAIN-2021-31,sna,18000,17981,0.0006\nCC-MAIN-2021-31,snd,37378,37329,0.0012\nCC-MAIN-2021-31,som,152495,152318,0.0048\nCC-MAIN-2021-31,sot,15748,15732,0.0005\nCC-MAIN-2021-31,spa,139594921,138911086,4.4105\nCC-MAIN-2021-31,sqi,1176770,1173932,0.0372\nCC-MAIN-2021-31,srp,6488801,6445083,0.2050\nCC-MAIN-2021-31,ssw,1073,1060,0.0000\nCC-MAIN-2021-31,sun,33018,32990,0.0010\nCC-MAIN-2021-31,sux,83,83,0.0000\nCC-MAIN-2021-31,swa,218700,217010,0.0069\nCC-MAIN-2021-31,swe,23079384,22943036,0.7292\nCC-MAIN-2021-31,syr,11599,11586,0.0004\nCC-MAIN-2021-31,tam,1249314,1245512,0.0395\nCC-MAIN-2021-31,tat,207822,207680,0.0066\nCC-MAIN-2021-31,tel,532797,531552,0.0168\nCC-MAIN-2021-31,tgk,156330,156135,0.0049\nCC-MAIN-2021-31,tgl,228412,227856,0.0072\nCC-MAIN-2021-31,tha,13083761,13037034,0.4134\nCC-MAIN-2021-31,tir,31395,31236,0.0010\nCC-MAIN-2021-31,ton,1729,1721,0.0001\nCC-MAIN-2021-31,tsn,7880,7840,0.0002\nCC-MAIN-2021-31,tso,1730,1730,0.0001\nCC-MAIN-2021-31,tuk,66442,66294,0.0021\nCC-MAIN-2021-31,tur,32314997,32177054,1.0210\nCC-MAIN-2021-31,uig,35173,35130,0.0011\nCC-MAIN-2021-31,ukr,12714957,12661970,0.4017\nCC-MAIN-2021-31,urd,753982,751222,0.0238\nCC-MAIN-2021-31,uzb,478478,477059,0.0151\nCC-MAIN-2021-31,ven,809,808,0.0000\nCC-MAIN-2021-31,vie,29999571,29852213,0.9478\nCC-MAIN-2021-31,vol,31379,31359,0.0010\nCC-MAIN-2021-31,war,66153,65846,0.0021\nCC-MAIN-2021-31,wol,4627,4618,0.0001\nCC-MAIN-2021-31,xho,40787,40726,0.0013\nCC-MAIN-2021-31,yid,44706,44656,0.0014\nCC-MAIN-2021-31,yor,31646,31567,0.0010\nCC-MAIN-2021-31,zha,2165,2163,0.0001\nCC-MAIN-2021-31,zho,130563035,130195445,4.1251\nCC-MAIN-2021-31,zul,52136,52055,0.0016\nCC-MAIN-2021-39,,84000284,84000284,2.8379\nCC-MAIN-2021-39,aar,5272,5255,0.0002\nCC-MAIN-2021-39,abk,10366,10348,0.0004\nCC-MAIN-2021-39,afr,392282,391142,0.0133\nCC-MAIN-2021-39,aka,2436,2412,0.0001\nCC-MAIN-2021-39,amh,101499,100819,0.0034\nCC-MAIN-2021-39,ara,17703653,17622867,0.5981\nCC-MAIN-2021-39,asm,50564,50429,0.0017\nCC-MAIN-2021-39,aym,1898,1850,0.0001\nCC-MAIN-2021-39,aze,1317681,1310686,0.0445\nCC-MAIN-2021-39,bak,32321,32302,0.0011\nCC-MAIN-2021-39,bel,529748,528715,0.0179\nCC-MAIN-2021-39,ben,2635524,2630851,0.0890\nCC-MAIN-2021-39,bih,16497,16459,0.0006\nCC-MAIN-2021-39,bis,3909,3894,0.0001\nCC-MAIN-2021-39,bod,31104,31021,0.0011\nCC-MAIN-2021-39,bos,1265916,1262510,0.0428\nCC-MAIN-2021-39,bre,70080,70021,0.0024\nCC-MAIN-2021-39,bul,8033591,7995762,0.2714\nCC-MAIN-2021-39,cat,6985385,6951551,0.2360\nCC-MAIN-2021-39,ceb,67082,67055,0.0023\nCC-MAIN-2021-39,ces,30145576,29981425,1.0185\nCC-MAIN-2021-39,chr,775,775,0.0000\nCC-MAIN-2021-39,cos,76668,76458,0.0026\nCC-MAIN-2021-39,crs,2102,2093,0.0001\nCC-MAIN-2021-39,cym,303455,302214,0.0103\nCC-MAIN-2021-39,dan,11985154,11904758,0.4049\nCC-MAIN-2021-39,deu,165970137,164845645,5.6073\nCC-MAIN-2021-39,div,60243,60193,0.0020\nCC-MAIN-2021-39,dzo,3072,3070,0.0001\nCC-MAIN-2021-39,ell,16102905,16034280,0.5440\nCC-MAIN-2021-39,eng,1344556676,1334957345,45.4257\nCC-MAIN-2021-39,epo,265860,265435,0.0090\nCC-MAIN-2021-39,est,3952964,3925728,0.1336\nCC-MAIN-2021-39,eus,823984,801659,0.0278\nCC-MAIN-2021-39,fao,99739,99306,0.0034\nCC-MAIN-2021-39,fas,18858908,18703877,0.6371\nCC-MAIN-2021-39,fij,1812,1811,0.0001\nCC-MAIN-2021-39,fin,11555516,11487992,0.3904\nCC-MAIN-2021-39,fra,131526242,130731042,4.4436\nCC-MAIN-2021-39,fry,108942,108705,0.0037\nCC-MAIN-2021-39,gla,61172,60962,0.0021\nCC-MAIN-2021-39,gle,175478,174002,0.0059\nCC-MAIN-2021-39,glg,1122078,1118598,0.0379\nCC-MAIN-2021-39,glv,13693,13678,0.0005\nCC-MAIN-2021-39,got,807,807,0.0000\nCC-MAIN-2021-39,grn,20400,20366,0.0007\nCC-MAIN-2021-39,guj,350347,349426,0.0118\nCC-MAIN-2021-39,hat,45684,45525,0.0015\nCC-MAIN-2021-39,hau,58869,58604,0.0020\nCC-MAIN-2021-39,haw,20383,20337,0.0007\nCC-MAIN-2021-39,heb,7195050,7152013,0.2431\nCC-MAIN-2021-39,hin,3965681,3951650,0.1340\nCC-MAIN-2021-39,hmn,21760,18515,0.0007\nCC-M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MAIN-2022-21,sqi,1239081,1234372,0.0359\nCC-MAIN-2022-21,srp,6589980,6553114,0.1910\nCC-MAIN-2022-21,ssw,1096,1084,0.0000\nCC-MAIN-2022-21,sun,34465,34420,0.0010\nCC-MAIN-2022-21,sux,327,327,0.0000\nCC-MAIN-2022-21,swa,239268,238655,0.0069\nCC-MAIN-2022-21,swe,23106441,22932160,0.6698\nCC-MAIN-2022-21,syr,16254,16240,0.0005\nCC-MAIN-2022-21,tam,1404953,1401055,0.0407\nCC-MAIN-2022-21,tat,248976,248604,0.0072\nCC-MAIN-2022-21,tel,614819,604755,0.0178\nCC-MAIN-2022-21,tgk,155278,154990,0.0045\nCC-MAIN-2022-21,tgl,229776,228979,0.0067\nCC-MAIN-2022-21,tha,12363080,12294036,0.3584\nCC-MAIN-2022-21,tir,29678,29478,0.0009\nCC-MAIN-2022-21,ton,1539,1534,0.0000\nCC-MAIN-2022-21,tsn,4593,4567,0.0001\nCC-MAIN-2022-21,tso,1242,1237,0.0000\nCC-MAIN-2022-21,tuk,73803,73695,0.0021\nCC-MAIN-2022-21,tur,31130360,30971513,0.9024\nCC-MAIN-2022-21,uig,39887,39817,0.0012\nCC-MAIN-2022-21,ukr,13083660,13011811,0.3793\nCC-MAIN-2022-21,urd,900259,895497,0.0261\nCC-MAIN-2022-21,uzb,492508,490525,0.0143\nCC-MAIN-2022-21,ven,929,915,0.0000\nCC-MAIN-2022-21,vie,30408908,30261131,0.8815\nCC-MAIN-2022-21,vol,26975,26953,0.0008\nCC-MAIN-2022-21,war,86684,86251,0.0025\nCC-MAIN-2022-21,wol,4242,4229,0.0001\nCC-MAIN-2022-21,xho,48606,48572,0.0014\nCC-MAIN-2022-21,yid,46638,46600,0.0014\nCC-MAIN-2022-21,yor,36555,36497,0.0011\nCC-MAIN-2022-21,zha,1808,1803,0.0001\nCC-MAIN-2022-21,zho,154323429,153908101,4.4734\nCC-MAIN-2022-21,zul,42291,42203,0.0012\nCC-MAIN-2022-27,,90293544,90293544,2.9037\nCC-MAIN-2022-27,aar,4523,4494,0.0001\nCC-MAIN-2022-27,abk,16689,16660,0.0005\nCC-MAIN-2022-27,afr,370766,369902,0.0119\nCC-MAIN-2022-27,aka,2392,2364,0.0001\nCC-MAIN-2022-27,amh,94693,94186,0.0030\nCC-MAIN-2022-27,ara,18654406,18583091,0.5999\nCC-MAIN-2022-27,asm,60865,60724,0.0020\nCC-MAIN-2022-27,aym,1411,1400,0.0000\nCC-MAIN-2022-27,aze,1287850,1283604,0.0414\nCC-MAIN-2022-27,bak,40731,40698,0.0013\nCC-MAIN-2022-27,bel,469540,468383,0.0151\nCC-MAIN-2022-27,ben,2653829,2646252,0.0853\nCC-MAIN-2022-27,bih,18663,18653,0.0006\nCC-MAIN-2022-27,bis,4070,4019,0.0001\nCC-MAIN-2022-27,bod,34049,33952,0.0011\nCC-MAIN-2022-27,bos,1313836,1310292,0.0423\nCC-MAIN-2022-27,bre,79457,79361,0.0026\nCC-MAIN-2022-27,bul,8461024,8431909,0.2721\nCC-MAIN-2022-27,cat,7195465,7147750,0.2314\nCC-MAIN-2022-27,ceb,69199,69163,0.0022\nCC-MAIN-2022-27,ces,32075252,31880314,1.0315\nCC-MAIN-2022-27,chr,1427,1426,0.0000\nCC-MAIN-2022-27,cos,80730,80554,0.0026\nCC-MAIN-2022-27,crs,3171,3133,0.0001\nCC-MAIN-2022-27,cym,353980,352527,0.0114\nCC-MAIN-2022-27,dan,13385332,13299508,0.4305\nCC-MAIN-2022-27,deu,170479687,169425221,5.4824\nCC-MAIN-2022-27,div,52767,52734,0.0017\nCC-MAIN-2022-27,dzo,4825,4821,0.0002\nCC-MAIN-2022-27,ell,17381247,17320497,0.5590\nCC-MAIN-2022-27,eng,1447163236,1437323702,46.5384\nCC-MAIN-2022-27,epo,262302,261134,0.0084\nCC-MAIN-2022-27,est,4013648,3988381,0.1291\nCC-MAIN-2022-27,eus,1034372,1028027,0.0333\nCC-MAIN-2022-27,fao,146271,145814,0.0047\nCC-MAIN-2022-27,fas,18857919,18762718,0.6064\nCC-MAIN-2022-27,fij,3146,3141,0.0001\nCC-MAIN-2022-27,fin,12619710,12534144,0.4058\nCC-MAIN-2022-27,fra,136828705,136024269,4.4002\nCC-MAIN-2022-27,fry,99713,99426,0.0032\nCC-MAIN-2022-27,gla,59014,58945,0.0019\nCC-MAIN-2022-27,gle,198826,197380,0.0064\nCC-MAIN-2022-27,glg,1241707,1236437,0.0399\nCC-MAIN-2022-27,glv,14467,14448,0.0005\nCC-MAIN-2022-27,got,885,883,0.0000\nCC-MAIN-2022-27,grn,19325,19266,0.0006\nCC-MAIN-2022-27,guj,381582,372143,0.0123\nCC-MAIN-2022-27,hat,47595,47468,0.0015\nCC-MAIN-2022-27,hau,70951,70417,0.0023\nCC-MAIN-2022-27,haw,18870,18848,0.0006\nCC-MAIN-2022-27,heb,7450598,7415765,0.2396\nCC-MAIN-2022-27,hin,4235908,4218525,0.1362\nCC-MAIN-2022-27,hmn,17645,17615,0.0006\nCC-MAIN-2022-27,hrv,6281347,6256540,0.2020\nCC-MAIN-2022-27,hun,17765132,17686759,0.5713\nCC-MAIN-2022-27,hye,965131,961416,0.0310\nCC-MAIN-2022-27,ibo,19121,19086,0.0006\nCC-MAIN-2022-27,iku,6561,6550,0.0002\nCC-MAIN-2022-27,ile,8613,8597,0.0003\nCC-MAIN-2022-27,ina,19339,19280,0.0006\nCC-MAIN-2022-27,ind,21556146,21421227,0.6932\nCC-MAIN-2022-27,ipk,2177,2175,0.0001\nCC-MAIN-2022-27,isl,1239237,1232978,0.0399\nCC-MAIN-2022-27,ita,75832861,75444908,2.4387\nCC-MAIN-2022-27,jav,67389,67321,0.0022\nCC-MAIN-2022-27,jpn,149679948,149121175,4.8135\nCC-MAIN-2022-27,kal,28471,28383,0.0009\nCC-MAIN-2022-27,kan,363019,362339,0.0117\nCC-MAIN-2022-27,kas,115,115,0.0000\nCC-MAIN-2022-27,kat,1110618,1106334,0.0357\nCC-MAIN-2022-27,kaz,692518,689741,0.0223\nCC-MAIN-2022-27,kha,5099,5092,0.0002\nCC-MAIN-2022-27,khm,298974,297359,0.0096\nCC-MAIN-2022-27,kin,52616,52489,0.0017\nCC-MAIN-2022-27,kir,138111,137802,0.0044\nCC-MAIN-2022-27,kor,20256170,20194653,0.6514\nCC-MAIN-2022-27,kur,180331,179503,0.0058\nCC-MAIN-2022-27,lao,117022,116734,0.0038\nCC-MAIN-2022-27,lat,3309637,3276791,0.1064\nCC-MAIN-2022-27,lav,2545552,2531392,0.0819\nCC-MAIN-2022-27,lif,23,23,0.0000\nCC-MAIN-2022-27,lin,7183,7174,0.0002\nCC-MAIN-2022-27,lit,5069008,5043738,0.1630\nCC-MAIN-2022-27,ltz,114378,113921,0.0037\nCC-MAIN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7\nCC-MAIN-2023-14,sag,2981,2981,0.0001\nCC-MAIN-2023-14,san,100757,100471,0.0032\nCC-MAIN-2023-14,sco,19324,19277,0.0006\nCC-MAIN-2023-14,sin,252611,251835,0.0081\nCC-MAIN-2023-14,slk,12424095,12368965,0.3984\nCC-MAIN-2023-14,slv,5074186,5053157,0.1627\nCC-MAIN-2023-14,smo,19205,19183,0.0006\nCC-MAIN-2023-14,sna,20867,20824,0.0007\nCC-MAIN-2023-14,snd,54023,54001,0.0017\nCC-MAIN-2023-14,som,129920,129777,0.0042\nCC-MAIN-2023-14,sot,22800,22725,0.0007\nCC-MAIN-2023-14,spa,141834578,141103412,4.5480\nCC-MAIN-2023-14,sqi,1482532,1478560,0.0475\nCC-MAIN-2023-14,srp,6736756,6716034,0.2160\nCC-MAIN-2023-14,ssw,961,959,0.0000\nCC-MAIN-2023-14,sun,35354,35335,0.0011\nCC-MAIN-2023-14,sux,550,550,0.0000\nCC-MAIN-2023-14,swa,248090,247718,0.0080\nCC-MAIN-2023-14,swe,21834317,21707372,0.7001\nCC-MAIN-2023-14,syr,10103,10094,0.0003\nCC-MAIN-2023-14,tam,1409576,1406243,0.0452\nCC-MAIN-2023-14,tat,258069,257623,0.0083\nCC-MAIN-2023-14,tel,605698,604182,0.0194\nCC-MAIN-2023-14,tgk,158394,157943,0.0051\nCC-MAIN-2023-14,tgl,225117,224605,0.0072\nCC-MAIN-2023-14,tha,13728674,13678027,0.4402\nCC-MAIN-2023-14,tir,24779,24529,0.0008\nCC-MAIN-2023-14,ton,3056,3052,0.0001\nCC-MAIN-2023-14,tsn,2389,2380,0.0001\nCC-MAIN-2023-14,tso,1370,1368,0.0000\nCC-MAIN-2023-14,tuk,68876,68771,0.0022\nCC-MAIN-2023-14,tur,24626729,24536423,0.7897\nCC-MAIN-2023-14,uig,46166,46106,0.0015\nCC-MAIN-2023-14,ukr,11331460,11266429,0.3633\nCC-MAIN-2023-14,urd,906878,904760,0.0291\nCC-MAIN-2023-14,uzb,540037,537612,0.0173\nCC-MAIN-2023-14,ven,729,727,0.0000\nCC-MAIN-2023-14,vie,32916196,32629909,1.0555\nCC-MAIN-2023-14,vol,16632,16606,0.0005\nCC-MAIN-2023-14,war,79766,79511,0.0026\nCC-MAIN-2023-14,wol,3563,3559,0.0001\nCC-MAIN-2023-14,xho,33471,33420,0.0011\nCC-MAIN-2023-14,yid,55010,54983,0.0018\nCC-MAIN-2023-14,yor,35267,35229,0.0011\nCC-MAIN-2023-14,zha,1572,1571,0.0001\nCC-MAIN-2023-14,zho,144441597,143862479,4.6316\nCC-MAIN-2023-14,zul,50177,50088,0.0016\nCC-MAIN-2023-23,,90329526,90329526,2.8762\nCC-MAIN-2023-23,aar,4916,4818,0.0002\nCC-MAIN-2023-23,abk,15297,15282,0.0005\nCC-MAIN-2023-23,afr,221063,220244,0.0070\nCC-MAIN-2023-23,aka,1917,1910,0.0001\nCC-MAIN-2023-23,amh,117191,116752,0.0037\nCC-MAIN-2023-23,ara,20038578,19942411,0.6381\nCC-MAIN-2023-23,asm,78203,78067,0.0025\nCC-MAIN-2023-23,aym,2553,2518,0.0001\nCC-MAIN-2023-23,aze,1381167,1376987,0.0440\nCC-MAIN-2023-23,bak,83936,83912,0.0027\nCC-MAIN-2023-23,bel,474509,473133,0.0151\nCC-MAIN-2023-23,ben,3031440,3021716,0.0965\nCC-MAIN-2023-23,bih,21455,21447,0.0007\nCC-MAIN-2023-23,bis,3875,3756,0.0001\nCC-MAIN-2023-23,bod,42182,42076,0.0013\nCC-MAIN-2023-23,bos,1448619,1443138,0.0461\nCC-MAIN-2023-23,bre,88344,88253,0.0028\nCC-MAIN-2023-23,bul,9615101,9580953,0.3062\nCC-MAIN-2023-23,cat,7640411,7584774,0.2433\nCC-MAIN-2023-23,ceb,51428,51404,0.0016\nCC-MAIN-2023-23,ces,36059842,35836455,1.1482\nCC-MAIN-2023-23,chr,1672,1648,0.0001\nCC-MAIN-2023-23,cos,74433,74275,0.0024\nCC-MAIN-2023-23,crs,3048,3036,0.0001\nCC-MAIN-2023-23,cym,295521,293789,0.0094\nCC-MAIN-2023-23,dan,13464957,13374596,0.4287\nCC-MAIN-2023-23,deu,189436880,188146552,6.0319\nCC-MAIN-2023-23,div,73033,72928,0.0023\nCC-MAIN-2023-23,dzo,4453,4453,0.0001\nCC-MAIN-2023-23,ell,18647565,18579737,0.5938\nCC-MAIN-2023-23,eng,1420072670,1410144975,45.2170\nCC-MAIN-2023-23,epo,283617,282854,0.0090\nCC-MAIN-2023-23,est,4427564,4393808,0.1410\nCC-MAIN-2023-23,eus,1086293,1076865,0.0346\nCC-MAIN-2023-23,fao,128518,128171,0.0041\nCC-MAIN-2023-23,fas,22314494,22194494,0.7105\nCC-MAIN-2023-23,fij,2307,2304,0.0001\nCC-MAIN-2023-23,fin,12584981,12507703,0.4007\nCC-MAIN-2023-23,fra,149776489,148894676,4.7691\nCC-MAIN-2023-23,fry,90660,90562,0.0029\nCC-MAIN-2023-23,gla,48565,48487,0.0015\nCC-MAIN-2023-23,gle,205233,204487,0.0065\nCC-MAIN-2023-23,glg,1308475,1303146,0.0417\nCC-MAIN-2023-23,glv,12046,12037,0.0004\nCC-MAIN-2023-23,got,900,900,0.0000\nCC-MAIN-2023-23,grn,18913,18867,0.0006\nCC-MAIN-2023-23,guj,444806,443938,0.0142\nCC-MAIN-2023-23,hat,60726,60601,0.0019\nCC-MAIN-2023-23,hau,110528,110102,0.0035\nCC-MAIN-2023-23,haw,19814,19786,0.0006\nCC-MAIN-2023-23,heb,3486958,3470160,0.1110\nCC-MAIN-2023-23,hin,5560111,5544581,0.1770\nCC-MAIN-2023-23,hmn,22538,22300,0.0007\nCC-MAIN-2023-23,hrv,5832483,5806530,0.1857\nCC-MAIN-2023-23,hun,19348581,19265347,0.6161\nCC-MAIN-2023-23,hye,964351,961345,0.0307\nCC-MAIN-2023-23,ibo,19845,19824,0.0006\nCC-MAIN-2023-23,iku,6381,6368,0.0002\nCC-MAIN-2023-23,ile,9807,9767,0.0003\nCC-MAIN-2023-23,ina,15657,15621,0.0005\nCC-MAIN-2023-23,ind,24105965,23971941,0.7676\nCC-MAIN-2023-23,ipk,1883,1877,0.0001\nCC-MAIN-2023-23,isl,1294915,1288545,0.0412\nCC-MAIN-2023-23,ita,86442503,85988235,2.7524\nCC-MAIN-2023-23,jav,73434,73372,0.0023\nCC-MAIN-2023-23,jpn,158081970,157600680,5.0335\nCC-MAIN-2023-23,kal,27068,26828,0.0009\nCC-MAIN-2023-23,kan,414918,414291,0.0132\nCC-MAIN-2023-23,kas,216,215,0.0000\nCC-MAIN-2023-23,kat,1159038,1155614,0.0369\nCC-MAIN-2023-23,kaz,670798,668304,0.0214\nCC-MAIN-2023-23,kha,8854,8846,0.0003\nCC-MAIN-2023-23,khm,346617,345166,0.0110\nCC-MAIN-2023-23,kin,56897,56835,0.0018\nCC-MAIN-2023-23,kir,158970,158730,0.0051\nCC-MAIN-2023-23,kor,21229929,21151898,0.6760\nCC-MAIN-2023-23,kur,185601,184717,0.0059\nCC-MAIN-2023-23,lao,123291,122670,0.0039\nCC-MAIN-2023-23,lat,3701987,3695898,0.1179\nCC-MAIN-2023-23,lav,2712157,2696519,0.0864\nCC-MAIN-2023-23,lif,22,22,0.0000\nCC-MAIN-2023-23,lin,7801,7794,0.0002\nCC-MAIN-2023-23,lit,5284519,5253732,0.1683\nCC-MAIN-2023-23,ltz,134036,133582,0.0043\nCC-MAIN-2023-23,lug,5059,5046,0.0002\nCC-MAIN-2023-23,mal,717877,716643,0.0229\nCC-MAIN-2023-23,mar,743377,742111,0.0237\nCC-MAIN-2023-23,mfe,4057,4053,0.0001\nCC-MAIN-2023-23,mkd,1070136,1066629,0.0341\nCC-MAIN-2023-23,mlg,91712,91023,0.0029\nCC-MAIN-2023-23,mlt,135660,132898,0.0043\nCC-MAIN-2023-23,mon,504112,502606,0.0161\nCC-MAIN-2023-23,mri,34805,34622,0.0011\nCC-MAIN-2023-23,msa,2466720,2456215,0.0785\nCC-MAIN-2023-23,mya,365016,363720,0.0116\nCC-MAIN-2023-23,nau,1233,1223,0.0000\nCC-MAIN-2023-23,nep,1026347,1024472,0.0327\nCC-MAIN-2023-23,nld,67009562,66562830,2.1337\nCC-MAIN-2023-23,nno,487913,485975,0.0155\nCC-MAIN-2023-23,nor,10689018,10619029,0.3404\nCC-MAIN-2023-23,nso,2126,2119,0.0001\nCC-MAIN-2023-23,nya,18834,18791,0.0006\nCC-MAIN-2023-23,oci,103758,103456,0.0033\nCC-MAIN-2023-23,ori,163672,163404,0.0052\nCC-MAIN-2023-23,orm,15980,15963,0.0005\nCC-MAIN-2023-23,pan,193179,192787,0.0062\nCC-MAIN-2023-23,pol,52887114,52601731,1.6840\nCC-MAIN-2023-23,por,35798970,35625875,1.1399\nCC-MAIN-2023-23,pus,99214,99052,0.0032\nCC-MAIN-2023-23,que,21660,21653,0.0007\nCC-MAIN-2023-23,roh,42990,42791,0.0014\nCC-MAIN-2023-23,ron,15698365,15629173,0.4999\nCC-MAIN-2023-23,run,2171,2170,0.0001\nCC-MAIN-2023-23,rus,188113412,187275747,5.9898\nCC-MAIN-2023-23,sag,4286,4286,0.0001\nCC-MAIN-2023-23,san,99691,99509,0.0032\nCC-MAIN-2023-23,sco,21217,21176,0.0007\nCC-MAIN-2023-23,sin,251256,250419,0.0080\nCC-MAIN-2023-23,slk,12661916,12586359,0.4032\nCC-MAIN-2023-23,slv,5201895,5177393,0.1656\nCC-MAIN-2023-23,smo,15443,15422,0.0005\nCC-MAIN-2023-23,sna,18982,18955,0.0006\nCC-MAIN-2023-23,snd,44740,44713,0.0014\nCC-MAIN-2023-23,som,121949,121754,0.0039\nCC-MAIN-2023-23,sot,19436,19417,0.0006\nCC-MAIN-2023-23,spa,144508401,143744399,4.6013\nCC-MAIN-2023-23,sqi,1365974,1361394,0.0435\nCC-MAIN-2023-23,srp,6852343,6828310,0.2182\nCC-MAIN-2023-23,ssw,787,785,0.0000\nCC-MAIN-2023-23,sun,32985,32868,0.0011\nCC-MAIN-2023-23,sux,408,408,0.0000\nCC-MAIN-2023-23,swa,263339,262805,0.0084\nCC-MAIN-2023-23,swe,22657867,22503629,0.7215\nCC-MAIN-2023-23,syr,9891,9870,0.0003\nCC-MAIN-2023-23,tam,1445203,1440663,0.0460\nCC-MAIN-2023-23,tat,202232,201842,0.0064\nCC-MAIN-2023-23,tel,577344,575925,0.0184\nCC-MAIN-2023-23,tgk,168786,168400,0.0054\nCC-MAIN-2023-23,tgl,226027,225436,0.0072\nCC-MAIN-2023-23,tha,12753729,12689214,0.4061\nCC-MAIN-2023-23,tir,27629,27426,0.0009\nCC-MAIN-2023-23,ton,2377,2357,0.0001\nCC-MAIN-2023-23,tsn,2531,2523,0.0001\nCC-MAIN-2023-23,tso,1069,1069,0.0000\nCC-MAIN-2023-23,tuk,63198,63089,0.0020\nCC-MAIN-2023-23,tur,24077559,23973419,0.7667\nCC-MAIN-2023-23,uig,41527,41393,0.0013\nCC-MAIN-2023-23,ukr,11973681,11807131,0.3813\nCC-MAIN-2023-23,urd,858328,854618,0.0273\nCC-MAIN-2023-23,uzb,527582,525424,0.0168\nCC-MAIN-2023-23,ven,768,767,0.0000\nCC-MAIN-2023-23,vie,32268657,31958582,1.0275\nCC-MAIN-2023-23,vol,18113,18085,0.0006\nCC-MAIN-2023-23,war,59071,58899,0.0019\nCC-MAIN-2023-23,wol,3979,3961,0.0001\nCC-MAIN-2023-23,xho,58656,58567,0.0019\nCC-MAIN-2023-23,yid,51123,51091,0.0016\nCC-MAIN-2023-23,yor,31660,31642,0.0010\nCC-MAIN-2023-23,zha,1677,1673,0.0001\nCC-MAIN-2023-23,zho,135159547,134711695,4.3037\nCC-MAIN-2023-23,zul,64245,64175,0.0020\nCC-MAIN-2023-40,,93325787,93325787,2.7090\nCC-MAIN-2023-40,aar,4118,4062,0.0001\nCC-MAIN-2023-40,abk,17439,17408,0.0005\nCC-MAIN-2023-40,afr,188997,188243,0.0055\nCC-MAIN-2023-40,aka,2080,2072,0.0001\nCC-MAIN-2023-40,amh,129602,128248,0.0038\nCC-MAIN-2023-40,ara,20775066,20641210,0.6030\nCC-MAIN-2023-40,asm,82722,82678,0.0024\nCC-MAIN-2023-40,aym,4339,4025,0.0001\nCC-MAIN-2023-40,aze,1743493,1737932,0.0506\nCC-MAIN-2023-40,bak,89256,89185,0.0026\nCC-MAIN-2023-40,bel,528254,526436,0.0153\nCC-MAIN-2023-40,ben,3574928,3564529,0.1038\nCC-MAIN-2023-40,bih,22728,22645,0.0007\nCC-MAIN-2023-40,bis,4475,4458,0.0001\nCC-MAIN-2023-40,bod,51964,51849,0.0015\nCC-MAIN-2023-40,bos,1772162,1763655,0.0514\nCC-MAIN-2023-40,bre,67099,66953,0.0019\nCC-MAIN-2023-40,bul,11324157,11267870,0.3287\nCC-MAIN-2023-40,cat,8172439,8102055,0.2372\nCC-MAIN-2023-40,ceb,50295,50259,0.0015\nCC-MAIN-2023-40,ces,36556552,36292295,1.0611\nCC-MAIN-2023-40,chr,1335,1334,0.0000\nCC-MAIN-2023-40,cos,71487,71307,0.0021\nCC-MAIN-2023-40,crs,2758,2744,0.0001\nCC-MAIN-2023-40,cym,334735,332895,0.0097\nCC-MAIN-2023-40,dan,15415957,15282334,0.4475\nCC-MAIN-2023-40,deu,201035267,199402622,5.8355\nCC-MAIN-2023-40,div,84794,84671,0.0025\nCC-MAIN-2023-40,dzo,4478,4474,0.0001\nCC-MAIN-2023-40,ell,20190814,20093428,0.5861\nCC-MAIN-2023-40,eng,1599618192,1585126436,46.4328\nCC-MAIN-2023-40,epo,284100,283246,0.0082\nCC-MAIN-2023-40,est,5245711,5203692,0.1523\nCC-MAIN-2023-40,eus,1151260,1141107,0.0334\nCC-MAIN-2023-40,fao,175368,174463,0.0051\nCC-MAIN-2023-40,fas,23504433,23355216,0.6823\nCC-MAIN-2023-40,fij,3300,3219,0.0001\nCC-MAIN-2023-40,fin,14645794,14531103,0.4251\nCC-MAIN-2023-40,fra,159679989,158471884,4.6351\nCC-MAIN-2023-40,fry,81236,80937,0.0024\nCC-MAIN-2023-40,gla,55657,55564,0.0016\nCC-MAIN-2023-40,gle,232178,230164,0.0067\nCC-MAIN-2023-40,glg,1386534,1379648,0.0402\nCC-MAIN-2023-40,glv,11197,11174,0.0003\nCC-MAIN-2023-40,got,931,928,0.0000\nCC-MAIN-2023-40,grn,22224,22081,0.0006\nCC-MAIN-2023-40,guj,410338,408612,0.0119\nCC-MAIN-2023-40,hat,67250,67134,0.0020\nCC-MAIN-2023-40,hau,109065,108853,0.0032\nCC-MAIN-2023-40,haw,26550,26506,0.0008\nCC-MAIN-2023-40,heb,4217977,4191335,0.1224\nCC-MAIN-2023-40,hin,6045819,6023174,0.1755\nCC-MAIN-2023-40,hmn,28970,28126,0.0008\nCC-MAIN-2023-40,hrv,7746724,7705919,0.2249\nCC-MAIN-2023-40,hun,21522681,21408222,0.6247\nCC-MAIN-2023-40,hye,1106261,1100812,0.0321\nCC-MAIN-2023-40,ibo,25196,25087,0.0007\nCC-MAIN-2023-40,iku,7278,7223,0.0002\nCC-MAIN-2023-40,ile,11328,11233,0.0003\nCC-MAIN-2023-40,ina,19528,19442,0.0006\nCC-MAIN-2023-40,ind,30118440,29931211,0.8743\nCC-MAIN-2023-40,ipk,1894,1890,0.0001\nCC-MAIN-2023-40,isl,1586932,1576830,0.0461\nCC-MAIN-2023-40,ita,93851834,93264514,2.7243\nCC-MAIN-2023-40,jav,73509,73414,0.0021\nCC-MAIN-2023-40,jpn,163550952,162909495,4.7475\nCC-MAIN-2023-40,kal,28535,28370,0.0008\nCC-MAIN-2023-40,kan,477304,474921,0.0139\nCC-MAIN-2023-40,kas,41,41,0.0000\nCC-MAIN-2023-40,kat,1407941,1403163,0.0409\nCC-MAIN-2023-40,kaz,770725,767324,0.0224\nCC-MAIN-2023-40,kha,8849,8800,0.0003\nCC-MAIN-2023-40,khm,408610,405477,0.0119\nCC-MAIN-2023-40,kin,67244,67029,0.0020\nCC-MAIN-2023-40,kir,178026,177350,0.0052\nCC-MAIN-2023-40,kor,22413639,22326467,0.6506\nCC-MAIN-2023-40,kur,214816,213951,0.0062\nCC-MAIN-2023-40,lao,130594,130112,0.0038\nCC-MAIN-2023-40,lat,4032682,4023379,0.1171\nCC-MAIN-2023-40,lav,3503232,3478593,0.1017\nCC-MAIN-2023-40,lif,10,10,0.0000\nCC-MAIN-2023-40,lin,6601,6589,0.0002\nCC-MAIN-2023-40,lit,6991508,6940790,0.2029\nCC-MAIN-2023-40,ltz,130445,129837,0.0038\nCC-MAIN-2023-40,lug,5453,5443,0.0002\nCC-MAIN-2023-40,mal,761573,760190,0.0221\nCC-MAIN-2023-40,mar,803369,801515,0.0233\nCC-MAIN-2023-40,mfe,2852,2846,0.0001\nCC-MAIN-2023-40,mkd,1238508,1234446,0.0360\nCC-MAIN-2023-40,mlg,111292,110343,0.0032\nCC-MAIN-2023-40,mlt,166321,163031,0.0048\nCC-MAIN-2023-40,mon,591841,589862,0.0172\nCC-MAIN-2023-40,mri,46526,46396,0.0014\nCC-MAIN-2023-40,msa,2822681,2807995,0.0819\nCC-MAIN-2023-40,mya,390752,389528,0.0113\nCC-MAIN-2023-40,nau,1141,1133,0.0000\nCC-MAIN-2023-40,nep,1371532,1367947,0.0398\nCC-MAIN-2023-40,nld,74295966,73675675,2.1566\nCC-MAIN-2023-40,nno,532208,529456,0.0154\nCC-MAIN-2023-40,nor,12866650,12754474,0.3735\nCC-MAIN-2023-40,nso,1906,1891,0.0001\nCC-MAIN-2023-40,nya,24027,23992,0.0007\nCC-MAIN-2023-40,oci,91703,91489,0.0027\nCC-MAIN-2023-40,ori,168307,167835,0.0049\nCC-MAIN-2023-40,orm,18188,18181,0.0005\nCC-MAIN-2023-40,pan,210196,209413,0.0061\nCC-MAIN-2023-40,pol,59987966,59594505,1.7413\nCC-MAIN-2023-40,por,38478739,38248469,1.1169\nCC-MAIN-2023-40,pus,108768,108553,0.0032\nCC-MAIN-2023-40,que,22393,22383,0.0007\nCC-MAIN-2023-40,roh,47302,46989,0.0014\nCC-MAIN-2023-40,ron,19786769,19690824,0.5744\nCC-MAIN-2023-40,run,2459,2456,0.0001\nCC-MAIN-2023-40,rus,191917020,190848390,5.5709\nCC-MAIN-2023-40,sag,4797,4794,0.0001\nCC-MAIN-2023-40,san,112835,112307,0.0033\nCC-MAIN-2023-40,sco,20606,20537,0.0006\nCC-MAIN-2023-40,sin,277423,276248,0.0081\nCC-MAIN-2023-40,slk,15734686,15624606,0.4567\nCC-MAIN-2023-40,slv,6661448,6623472,0.1934\nCC-MAIN-2023-40,smo,22828,22772,0.0007\nCC-MAIN-2023-40,sna,21751,21728,0.0006\nCC-MAIN-2023-40,snd,60139,60074,0.0017\nCC-MAIN-2023-40,som,146711,146365,0.0043\nCC-MAIN-2023-40,sot,22245,22212,0.0006\nCC-MAIN-2023-40,spa,159155001,158029217,4.6199\nCC-MAIN-2023-40,sqi,1606526,1599288,0.0466\nCC-MAIN-2023-40,srp,8703162,8662640,0.2526\nCC-MAIN-2023-40,ssw,730,728,0.0000\nCC-MAIN-2023-40,sun,74081,71986,0.0022\nCC-MAIN-2023-40,sux,386,386,0.0000\nCC-MAIN-2023-40,swa,288723,288068,0.0084\nCC-MAIN-2023-40,swe,24158793,23951506,0.7013\nCC-MAIN-2023-40,syr,11268,11251,0.0003\nCC-MAIN-2023-40,tam,1488635,1482818,0.0432\nCC-MAIN-2023-40,tat,200771,200394,0.0058\nCC-MAIN-2023-40,tel,627316,625353,0.0182\nCC-MAIN-2023-40,tgk,183419,182967,0.0053\nCC-MAIN-2023-40,tgl,261715,260756,0.0076\nCC-MAIN-2023-40,tha,15537593,15435020,0.4510\nCC-MAIN-2023-40,tir,28608,28168,0.0008\nCC-MAIN-2023-40,ton,2594,2581,0.0001\nCC-MAIN-2023-40,tsn,2737,2723,0.0001\nCC-MAIN-2023-40,tso,1502,1498,0.0000\nCC-MAIN-2023-40,tuk,78205,78085,0.0023\nCC-MAIN-2023-40,tur,28670749,28518972,0.8322\nCC-MAIN-2023-40,uig,53065,52908,0.0015\nCC-MAIN-2023-40,ukr,11825186,11716205,0.3433\nCC-MAIN-2023-40,urd,944730,940519,0.0274\nCC-MAIN-2023-40,uzb,582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CC-MAIN-2024-42,bod,36426,36232,0.0015\nCC-MAIN-2024-42,bos,1306203,1303276,0.0523\nCC-MAIN-2024-42,bre,84216,84167,0.0034\nCC-MAIN-2024-42,bul,7466418,7443423,0.2992\nCC-MAIN-2024-42,cat,5202242,5166138,0.2085\nCC-MAIN-2024-42,ceb,49577,49509,0.0020\nCC-MAIN-2024-42,ces,26523613,26331032,1.0630\nCC-MAIN-2024-42,chr,1137,1137,0.0000\nCC-MAIN-2024-42,cos,50203,50074,0.0020\nCC-MAIN-2024-42,crs,2858,2841,0.0001\nCC-MAIN-2024-42,cym,292253,291219,0.0117\nCC-MAIN-2024-42,dan,11578641,11490004,0.4640\nCC-MAIN-2024-42,deu,132338944,131500938,5.3038\nCC-MAIN-2024-42,div,57520,57472,0.0023\nCC-MAIN-2024-42,dzo,3534,3534,0.0001\nCC-MAIN-2024-42,ell,14293044,14246131,0.5728\nCC-MAIN-2024-42,eng,1083513299,1077124596,43.4241\nCC-MAIN-2024-42,epo,208797,208418,0.0084\nCC-MAIN-2024-42,est,3307831,3288496,0.1326\nCC-MAIN-2024-42,eus,999461,993610,0.0401\nCC-MAIN-2024-42,fao,120324,119958,0.0048\nCC-MAIN-2024-42,fas,17594396,17545147,0.7051\nCC-MAIN-2024-42,fij,2498,2498,0.0001\nCC-MAIN-2024-42,fin,8593516,8541494,0.3444\nCC-MAIN-2024-42,fra,109687926,109128005,4.3960\nCC-MAIN-2024-42,fry,71537,71367,0.0029\nCC-MAIN-2024-42,gla,34176,34100,0.0014\nCC-MAIN-2024-42,gle,175974,174578,0.0071\nCC-MAIN-2024-42,glg,909830,903845,0.0365\nCC-MAIN-2024-42,glv,8708,8692,0.0003\nCC-MAIN-2024-42,got,447,447,0.0000\nCC-MAIN-2024-42,grn,16269,16222,0.0007\nCC-MAIN-2024-42,guj,282755,282072,0.0113\nCC-MAIN-2024-42,hat,47165,47059,0.0019\nCC-MAIN-2024-42,hau,68262,67896,0.0027\nCC-MAIN-2024-42,haw,17704,17652,0.0007\nCC-MAIN-2024-42,heb,6169483,6145150,0.2473\nCC-MAIN-2024-42,hin,5211550,5198234,0.2089\nCC-MAIN-2024-42,hmn,14399,14351,0.0006\nCC-MAIN-2024-42,hrv,5362886,5344823,0.2149\nCC-MAIN-2024-42,hun,14096256,14032587,0.5649\nCC-MAIN-2024-42,hye,849092,845053,0.0340\nCC-MAIN-2024-42,ibo,16825,16778,0.0007\nCC-MAIN-2024-42,iku,5706,5703,0.0002\nCC-MAIN-2024-42,ile,9051,9018,0.0004\nCC-MAIN-2024-42,ina,13058,13045,0.0005\nCC-MAIN-2024-42,ind,28033891,27837647,1.1235\nCC-MAIN-2024-42,ipk,1736,1734,0.0001\nCC-MAIN-2024-42,isl,992003,987916,0.0398\nCC-MAIN-2024-42,ita,63082635,62785741,2.5282\nCC-MAIN-2024-42,jav,55212,55142,0.0022\nCC-MAIN-2024-42,jpn,125805829,125404049,5.0419\nCC-MAIN-2024-42,kal,22981,22822,0.0009\nCC-MAIN-2024-42,kan,355203,354744,0.0142\nCC-MAIN-2024-42,kas,15,15,0.0000\nCC-MAIN-2024-42,kat,1053050,1042575,0.0422\nCC-MAIN-2024-42,kaz,703746,701334,0.0282\nCC-MAIN-2024-42,kha,9020,9009,0.0004\nCC-MAIN-2024-42,khm,264291,263214,0.0106\nCC-MAIN-2024-42,kin,56377,56231,0.0023\nCC-MAIN-2024-42,kir,244370,243637,0.0098\nCC-MAIN-2024-42,kor,18435212,18366866,0.7388\nCC-MAIN-2024-42,kur,184030,183746,0.0074\nCC-MAIN-2024-42,lao,93468,92865,0.0037\nCC-MAIN-2024-42,lat,2512878,2508096,0.1007\nCC-MAIN-2024-42,lav,2350056,2334490,0.0942\nCC-MAIN-2024-42,lif,42,42,0.0000\nCC-MAIN-2024-42,lin,7514,7497,0.0003\nCC-MAIN-2024-42,lit,4162556,4141732,0.1668\nCC-MAIN-2024-42,ltz,99637,99363,0.0040\nCC-MAIN-2024-42,lug,5425,5381,0.0002\nCC-MAIN-2024-42,mal,670259,668501,0.0269\nCC-MAIN-2024-42,mar,643695,641895,0.0258\nCC-MAIN-2024-42,mfe,3742,3735,0.0001\nCC-MAIN-2024-42,mkd,955225,952511,0.0383\nCC-MAIN-2024-42,mlg,90814,90491,0.0036\nCC-MAIN-2024-42,mlt,122428,120509,0.0049\nCC-MAIN-2024-42,mon,401925,400924,0.0161\nCC-MAIN-2024-42,mri,29912,29758,0.0012\nCC-MAIN-2024-42,msa,1963309,1957982,0.0787\nCC-MAIN-2024-42,mya,348788,347874,0.0140\nCC-MAIN-2024-42,nau,1092,1089,0.0000\nCC-MAIN-2024-42,nep,1229976,1227407,0.0493\nCC-MAIN-2024-42,nld,45275645,45000682,1.8145\nCC-MAIN-2024-42,nno,323831,322475,0.0130\nCC-MAIN-2024-42,nor,7636107,7593408,0.3060\nCC-MAIN-2024-42,nso,1346,1345,0.0001\nCC-MAIN-2024-42,nya,14940,14904,0.0006\nCC-MAIN-2024-42,oci,85086,84852,0.0034\nCC-MAIN-2024-42,ori,141619,141222,0.0057\nCC-MAIN-2024-42,orm,12658,12640,0.0005\nCC-MAIN-2024-42,pan,196613,195754,0.0079\nCC-MAIN-2024-42,pol,45076434,44867835,1.8065\nCC-MAIN-2024-42,por,57754152,57512240,2.3146\nCC-MAIN-2024-42,pus,86340,86196,0.0035\nCC-MAIN-2024-42,que,13575,13525,0.0005\nCC-MAIN-2024-42,roh,25137,24968,0.0010\nCC-MAIN-2024-42,ron,14916026,14861884,0.5978\nCC-MAIN-2024-42,run,1670,1664,0.0001\nCC-MAIN-2024-42,rus,150820146,150248225,6.0444\nCC-MAIN-2024-42,sag,10405,10405,0.0004\nCC-MAIN-2024-42,san,83674,83424,0.0034\nCC-MAIN-2024-42,sco,17887,17842,0.0007\nCC-MAIN-2024-42,sin,224196,223888,0.0090\nCC-MAIN-2024-42,slk,9244448,9200711,0.3705\nCC-MAIN-2024-42,slv,3886713,3861262,0.1558\nCC-MAIN-2024-42,smo,16623,16542,0.0007\nCC-MAIN-2024-42,sna,15401,15349,0.0006\nCC-MAIN-2024-42,snd,42464,42342,0.0017\nCC-MAIN-2024-42,som,124950,123993,0.0050\nCC-MAIN-2024-42,sot,15071,15024,0.0006\nCC-MAIN-2024-42,spa,113249321,112711686,4.5387\nCC-MAIN-2024-42,sqi,1239080,1236250,0.0497\nCC-MAIN-2024-42,srp,5391996,5371839,0.2161\nCC-MAIN-2024-42,ssw,799,797,0.0000\nCC-MAIN-2024-42,sun,26131,26061,0.0010\nCC-MAIN-2024-42,sux,314,313,0.0000\nCC-MAIN-2024-42,swa,240410,239627,0.0096\nCC-MAIN-2024-42,swe,16590009,16496719,0.6649\nCC-MAIN-2024-42,syr,7868,7861,0.0003\nCC-MAIN-2024-42,tam,1179123,1176780,0.0473\nCC-MAIN-2024-42,tat,215926,215677,0.0087\nCC-MAIN-2024-42,tel,548086,547201,0.0220\nCC-MAIN-2024-42,tgk,160623,160335,0.0064\nCC-MAIN-2024-42,tgl,220739,219855,0.0088\nCC-MAIN-2024-42,tha,10158510,10111907,0.4071\nCC-MAIN-2024-42,tir,17881,17747,0.0007\nCC-MAIN-2024-42,ton,1991,1980,0.0001\nCC-MAIN-2024-42,tsn,2808,2802,0.0001\nCC-MAIN-2024-42,tso,1474,1470,0.0001\nCC-MAIN-2024-42,tuk,52562,52462,0.0021\nCC-MAIN-2024-42,tur,30995273,30885982,1.2422\nCC-MAIN-2024-42,uig,25965,25918,0.0010\nCC-MAIN-2024-42,ukr,15063301,14701575,0.6037\nCC-MAIN-2024-42,urd,763665,761940,0.0306\nCC-MAIN-2024-42,uzb,730957,728668,0.0293\nCC-MAIN-2024-42,ven,673,671,0.0000\nCC-MAIN-2024-42,vie,26084993,25900759,1.0454\nCC-MAIN-2024-42,vol,12814,12792,0.0005\nCC-MAIN-2024-42,war,57329,57220,0.0023\nCC-MAIN-2024-42,wol,3500,3496,0.0001\nCC-MAIN-2024-42,xho,57372,57216,0.0023\nCC-MAIN-2024-42,yid,42276,42191,0.0017\nCC-MAIN-2024-42,yor,24547,24265,0.0010\nCC-MAIN-2024-42,zha,2587,2585,0.0001\nCC-MAIN-2024-42,zho,120090598,119635961,4.8129\nCC-MAIN-2024-42,zul,63272,63157,0.0025\nCC-MAIN-2024-46,,81132463,81132463,3.0185\nCC-MAIN-2024-46,aar,2942,2923,0.0001\nCC-MAIN-2024-46,abk,13268,13186,0.0005\nCC-MAIN-2024-46,afr,262061,261583,0.0097\nCC-MAIN-2024-46,aka,2289,2288,0.0001\nCC-MAIN-2024-46,amh,74149,73887,0.0028\nCC-MAIN-2024-46,ara,18344689,18274530,0.6825\nCC-MAIN-2024-46,asm,69704,69626,0.0026\nCC-MAIN-2024-46,aym,1774,1773,0.0001\nCC-MAIN-2024-46,aze,1697797,1691472,0.0632\nCC-MAIN-2024-46,bak,75028,74969,0.0028\nCC-MAIN-2024-46,bel,428267,427097,0.0159\nCC-MAIN-2024-46,ben,3109039,3102840,0.1157\nCC-MAIN-2024-46,bih,13998,13978,0.0005\nCC-MAIN-2024-46,bis,3253,3250,0.0001\nCC-MAIN-2024-46,bod,37260,36959,0.0014\nCC-MAIN-2024-46,bos,1390386,1386802,0.0517\nCC-MAIN-2024-46,bre,58576,58481,0.0022\nCC-MAIN-2024-46,bul,7830069,7807909,0.2913\nCC-MAIN-2024-46,cat,5370705,5337676,0.1998\nCC-MAIN-2024-46,ceb,50077,50024,0.0019\nCC-MAIN-2024-46,ces,27367515,27188104,1.0182\nCC-MAIN-2024-46,chr,825,825,0.0000\nCC-MAIN-2024-46,cos,55103,55011,0.0021\nCC-MAIN-2024-46,crs,2853,2847,0.0001\nCC-MAIN-2024-46,cym,324599,323125,0.0121\nCC-MAIN-2024-46,dan,12637983,12552768,0.4702\nCC-MAIN-2024-46,deu,143428305,142603353,5.3362\nCC-MAIN-2024-46,div,60689,60663,0.0023\nCC-MAIN-2024-46,dzo,4442,4441,0.0002\nCC-MAIN-2024-46,ell,15563778,15515421,0.5791\nCC-MAIN-2024-46,eng,1155554705,1149249615,42.9924\nCC-MAIN-2024-46,epo,234033,233680,0.0087\nCC-MAIN-2024-46,est,3580363,3559960,0.1332\nCC-MAIN-2024-46,eus,966440,961498,0.0360\nCC-MAIN-2024-46,fao,120661,120213,0.0045\nCC-MAIN-2024-46,fas,19854104,19802178,0.7387\nCC-MAIN-2024-46,fij,2847,2841,0.0001\nCC-MAIN-2024-46,fin,9297001,9247224,0.3459\nCC-MAIN-2024-46,fra,117030857,116431650,4.3541\nCC-MAIN-2024-46,fry,55038,54868,0.0020\nCC-MAIN-2024-46,gla,34522,34452,0.0013\nCC-MAIN-2024-46,gle,174162,173328,0.0065\nCC-MAIN-2024-46,glg,874520,868608,0.0325\nCC-MAIN-2024-46,glv,9951,9929,0.0004\nCC-MAIN-2024-46,got,490,490,0.0000\nCC-MAIN-2024-46,grn,21424,21364,0.0008\nCC-MAIN-2024-46,guj,330527,329902,0.0123\nCC-MAIN-2024-46,hat,51031,50898,0.0019\nCC-MAIN-2024-46,hau,71117,70718,0.0026\nCC-MAIN-2024-46,haw,19963,19923,0.0007\nCC-MAIN-2024-46,heb,6767347,6743566,0.2518\nCC-MAIN-2024-46,hin,5552268,5537403,0.2066\nCC-MAIN-2024-46,hmn,16458,16415,0.0006\nCC-MAIN-2024-46,hrv,6043330,6022904,0.2248\nCC-MAIN-2024-46,hun,15266264,15201367,0.5680\nCC-MAIN-2024-46,hye,914074,909665,0.0340\nCC-MAIN-2024-46,ibo,16866,16817,0.0006\nCC-MAIN-2024-46,iku,5635,5621,0.0002\nCC-MAIN-2024-46,ile,10011,9980,0.0004\nCC-MAIN-2024-46,ina,15111,15078,0.0006\nCC-MAIN-2024-46,ind,30066768,29860494,1.1186\nCC-MAIN-2024-46,ipk,1574,1571,0.0001\nCC-MAIN-2024-46,isl,1075904,1071717,0.0400\nCC-MAIN-2024-46,ita,67067190,66779634,2.4952\nCC-MAIN-2024-46,jav,57667,57601,0.0021\nCC-MAIN-2024-46,jpn,138324031,137935609,5.1463\nCC-MAIN-2024-46,kal,23987,23595,0.0009\nCC-MAIN-2024-46,kan,380991,379764,0.0142\nCC-MAIN-2024-46,kas,22,22,0.0000\nCC-MAIN-2024-46,kat,1107571,1104663,0.0412\nCC-MAIN-2024-46,kaz,731749,726454,0.0272\nCC-MAIN-2024-46,kha,11443,11428,0.0004\nCC-MAIN-2024-46,khm,281981,281338,0.0105\nCC-MAIN-2024-46,kin,56554,56454,0.0021\nCC-MAIN-2024-46,kir,243869,243312,0.0091\nCC-MAIN-2024-46,kor,19426897,19357553,0.7228\nCC-MAIN-2024-46,kur,191223,190658,0.0071\nCC-MAIN-2024-46,lao,93249,92818,0.0035\nCC-MAIN-2024-46,lat,3414580,3407776,0.1270\nCC-MAIN-2024-46,lav,2425212,2407632,0.0902\nCC-MAIN-2024-46,lif,57,57,0.0000\nCC-MAIN-2024-46,lin,7679,7665,0.0003\nCC-MAIN-2024-46,lit,4297823,4275951,0.1599\nCC-MAIN-2024-46,ltz,105435,105179,0.0039\nCC-MAIN-2024-46,lug,4666,4659,0.0002\nCC-MAIN-2024-46,mal,724320,722009,0.0269\nCC-MAIN-2024-46,mar,737173,735227,0.0274\nCC-MAIN-2024-46,mfe,3844,3833,0.0001\nCC-MAIN-2024-46,mkd,1029521,1027149,0.0383\nCC-MAIN-2024-46,mlg,78089,77934,0.0029\nCC-MAIN-2024-46,mlt,117888,116766,0.0044\nCC-MAIN-2024-46,mon,408709,407451,0.0152\nCC-MAIN-2024-46,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25-08,mfe,3009,2997,0.0001\nCC-MAIN-2025-08,mkd,1017554,1014585,0.0380\nCC-MAIN-2025-08,mlg,92935,92547,0.0035\nCC-MAIN-2025-08,mlt,86171,84765,0.0032\nCC-MAIN-2025-08,mon,434114,433104,0.0162\nCC-MAIN-2025-08,mri,34919,34800,0.0013\nCC-MAIN-2025-08,msa,2102918,2095466,0.0785\nCC-MAIN-2025-08,mya,366841,365965,0.0137\nCC-MAIN-2025-08,nau,1097,1094,0.0000\nCC-MAIN-2025-08,nep,1480299,1476124,0.0552\nCC-MAIN-2025-08,nld,48076024,47778492,1.7941\nCC-MAIN-2025-08,nno,446863,445064,0.0167\nCC-MAIN-2025-08,nor,8150934,8098969,0.3042\nCC-MAIN-2025-08,nso,2614,2611,0.0001\nCC-MAIN-2025-08,nya,13670,13634,0.0005\nCC-MAIN-2025-08,oci,87892,87656,0.0033\nCC-MAIN-2025-08,ori,161575,161249,0.0060\nCC-MAIN-2025-08,orm,13290,13255,0.0005\nCC-MAIN-2025-08,pan,186572,186096,0.0070\nCC-MAIN-2025-08,pol,46815742,46613120,1.7470\nCC-MAIN-2025-08,por,62368181,62104996,2.3274\nCC-MAIN-2025-08,pus,82633,82503,0.0031\nCC-MAIN-2025-08,que,14235,14229,0.0005\nCC-MAIN-2025-08,roh,26550,26434,0.0010\nCC-MAIN-2025-08,ron,16892255,16828189,0.6304\nCC-MAIN-2025-08,run,1689,1687,0.0001\nCC-MAIN-2025-08,rus,163413027,162694966,6.0982\nCC-MAIN-2025-08,sag,3219,3217,0.0001\nCC-MAIN-2025-08,san,87077,86887,0.0032\nCC-MAIN-2025-08,sco,15202,15143,0.0006\nCC-MAIN-2025-08,sin,252631,252115,0.0094\nCC-MAIN-2025-08,slk,9561089,9517274,0.3568\nCC-MAIN-2025-08,slv,4043928,4022032,0.1509\nCC-MAIN-2025-08,smo,16997,16946,0.0006\nCC-MAIN-2025-08,sna,16948,16838,0.0006\nCC-MAIN-2025-08,snd,41752,41670,0.0016\nCC-MAIN-2025-08,som,138936,138563,0.0052\nCC-MAIN-2025-08,sot,14964,14844,0.0006\nCC-MAIN-2025-08,spa,121822815,121216333,4.5461\nCC-MAIN-2025-08,sqi,1305076,1299344,0.0487\nCC-MAIN-2025-08,srp,5768040,5748186,0.2152\nCC-MAIN-2025-08,ssw,881,876,0.0000\nCC-MAIN-2025-08,sun,26631,26581,0.0010\nCC-MAIN-2025-08,sux,289,285,0.0000\nCC-MAIN-2025-08,swa,222870,222378,0.0083\nCC-MAIN-2025-08,swe,17189696,17087247,0.6415\nCC-MAIN-2025-08,syr,9602,9576,0.0004\nCC-MAIN-2025-08,tam,1223551,1220848,0.0457\nCC-MAIN-2025-08,tat,217345,217138,0.0081\nCC-MAIN-2025-08,tel,530267,529025,0.0198\nCC-MAIN-2025-08,tgk,174198,173839,0.0065\nCC-MAIN-2025-08,tgl,222563,221638,0.0083\nCC-MAIN-2025-08,tha,13497351,13438886,0.5037\nCC-MAIN-2025-08,tir,19312,19159,0.0007\nCC-MAIN-2025-08,ton,2281,2273,0.0001\nCC-MAIN-2025-08,tsn,2945,2939,0.0001\nCC-MAIN-2025-08,tso,1774,1770,0.0001\nCC-MAIN-2025-08,tuk,53314,53215,0.0020\nCC-MAIN-2025-08,tur,30578827,30442115,1.1411\nCC-MAIN-2025-08,uig,32224,32171,0.0012\nCC-MAIN-2025-08,ukr,16280144,15940551,0.6075\nCC-MAIN-2025-08,urd,811181,807013,0.0303\nCC-MAIN-2025-08,uzb,605894,603669,0.0226\nCC-MAIN-2025-08,ven,894,893,0.0000\nCC-MAIN-2025-08,vie,28273761,28091125,1.0551\nCC-MAIN-2025-08,vol,12431,12419,0.0005\nCC-MAIN-2025-08,war,61339,61268,0.0023\nCC-MAIN-2025-08,wol,3208,3204,0.0001\nCC-MAIN-2025-08,xho,20538,20465,0.0008\nCC-MAIN-2025-08,yid,44392,44300,0.0017\nCC-MAIN-2025-08,yor,21006,20956,0.0008\nCC-MAIN-2025-08,zha,4175,4169,0.0002\nCC-MAIN-2025-08,zho,136510537,136177689,5.0942\nCC-MAIN-2025-08,zul,34611,34494,0.0013\nCC-MAIN-2025-13,,82300022,82300022,3.0027\nCC-MAIN-2025-13,aar,1948,1939,0.0001\nCC-MAIN-2025-13,abk,14155,14095,0.0005\nCC-MAIN-2025-13,afr,261293,260675,0.0095\nCC-MAIN-2025-13,aka,3627,3599,0.0001\nCC-MAIN-2025-13,amh,91459,91082,0.0033\nCC-MAIN-2025-13,ara,18739027,18651235,0.6837\nCC-MAIN-2025-13,asm,64051,63847,0.0023\nCC-MAIN-2025-13,aym,2253,2225,0.0001\nCC-MAIN-2025-13,aze,1677722,1671883,0.0612\nCC-MAIN-2025-13,bak,86601,86597,0.0032\nCC-MAIN-2025-13,bel,451710,450683,0.0165\nCC-MAIN-2025-13,ben,3033293,3024843,0.1107\nCC-MAIN-2025-13,bih,12613,12589,0.0005\nCC-MAIN-2025-13,bis,2151,2140,0.0001\nCC-MAIN-2025-13,bod,40120,40079,0.0015\nCC-MAIN-2025-13,bos,1473804,1463441,0.0538\nCC-MAIN-2025-13,bre,63653,63589,0.0023\nCC-MAIN-2025-13,bul,8296521,8264464,0.3027\nCC-MAIN-2025-13,cat,4841957,4811793,0.1767\nCC-MAIN-2025-13,ceb,49888,49828,0.0018\nCC-MAIN-2025-13,ces,28451138,28227478,1.0380\nCC-MAIN-2025-13,chr,806,805,0.0000\nCC-MAIN-2025-13,cos,56699,56575,0.0021\nCC-MAIN-2025-13,crs,2289,2279,0.0001\nCC-MAIN-2025-13,cym,327827,326478,0.0120\nCC-MAIN-2025-13,dan,12689895,12595398,0.4630\nCC-MAIN-2025-13,deu,140204402,139358953,5.1154\nCC-MAIN-2025-13,div,64431,64377,0.0024\nCC-MAIN-2025-13,dzo,4930,4926,0.0002\nCC-MAIN-2025-13,ell,16175423,16119062,0.5902\nCC-MAIN-2025-13,eng,1204156851,1196822790,43.9338\nCC-MAIN-2025-13,epo,231867,231196,0.0085\nCC-MAIN-2025-13,est,3585717,3562472,0.1308\nCC-MAIN-2025-13,eus,899444,895008,0.0328\nCC-MAIN-2025-13,fao,120230,119653,0.0044\nCC-MAIN-2025-13,fas,20144139,20064242,0.7350\nCC-MAIN-2025-13,fij,2678,2676,0.0001\nCC-MAIN-2025-13,fin,9909858,9844110,0.3616\nCC-MAIN-2025-13,fra,115851868,115153732,4.2269\nCC-MAIN-2025-13,fry,60306,60218,0.0022\nCC-MAIN-2025-13,gla,35342,35288,0.0013\nCC-MAIN-2025-13,gle,201525,200683,0.0074\nCC-MAIN-2025-13,glg,835627,830529,0.0305\nCC-MAIN-2025-13,glv,25967,25933,0.0009\nCC-MAIN-2025-13,got,446,446,0.0000\nCC-MAIN-2025-13,grn,18029,17991,0.0007\nCC-MAIN-2025-13,guj,342820,341785,0.0125\nCC-MAIN-2025-13,hat,51210,51090,0.0019\nCC-MAIN-2025-13,hau,86698,86434,0.0032\nCC-MAIN-2025-13,haw,15594,15550,0.0006\nCC-MAIN-2025-13,heb,7047083,7016592,0.2571\nCC-MAIN-2025-13,hin,5555621,5538797,0.2027\nCC-MAIN-2025-13,hmn,17127,16949,0.0006\nCC-MAIN-2025-13,hrv,6289022,6263051,0.2295\nCC-MAIN-2025-13,hun,13678095,13624335,0.4990\nCC-MAIN-2025-13,hye,899571,894876,0.0328\nCC-MAIN-2025-13,ibo,17611,17571,0.0006\nCC-MAIN-2025-13,iku,5973,5932,0.0002\nCC-MAIN-2025-13,ile,9202,9183,0.0003\nCC-MAIN-2025-13,ina,13321,13246,0.0005\nCC-MAIN-2025-13,ind,32914493,32695963,1.2009\nCC-MAIN-2025-13,ipk,1749,1747,0.0001\nCC-MAIN-2025-13,isl,1064775,1060814,0.0388\nCC-MAIN-2025-13,ita,66277391,65948986,2.4181\nCC-MAIN-2025-13,jav,62364,62283,0.0023\nCC-MAIN-2025-13,jpn,133271896,132828948,4.8624\nCC-MAIN-2025-13,kal,21598,21450,0.0008\nCC-MAIN-2025-13,kan,378900,378159,0.0138\nCC-MAIN-2025-13,kas,10,10,0.0000\nCC-MAIN-2025-13,kat,1215618,1211500,0.0444\nCC-MAIN-2025-13,kaz,870518,867499,0.0318\nCC-MAIN-2025-13,kha,8963,8948,0.0003\nCC-MAIN-2025-13,khm,290330,289035,0.0106\nCC-MAIN-2025-13,kin,62839,62690,0.0023\nCC-MAIN-2025-13,kir,304398,303877,0.0111\nCC-MAIN-2025-13,kor,22002797,21925284,0.8028\nCC-MAIN-2025-13,kur,188065,187674,0.0069\nCC-MAIN-2025-13,lao,106372,105793,0.0039\nCC-MAIN-2025-13,lat,2906860,2902210,0.1061\nCC-MAIN-2025-13,lav,2340300,2322976,0.0854\nCC-MAIN-2025-13,lif,84,84,0.0000\nCC-MAIN-2025-13,lin,6943,6935,0.0003\nCC-MAIN-2025-13,lit,4548331,4522396,0.1659\nCC-MAIN-2025-13,ltz,112657,112274,0.0041\nCC-MAIN-2025-13,lug,4085,4049,0.0001\nCC-MAIN-2025-13,mal,693416,692062,0.0253\nCC-MAIN-2025-13,mar,794274,791915,0.0290\nCC-MAIN-2025-13,mfe,3266,3258,0.0001\nCC-MAIN-2025-13,mkd,1061290,1057350,0.0387\nCC-MAIN-2025-13,mlg,111908,111008,0.0041\nCC-MAIN-2025-13,mlt,87948,85765,0.0032\nCC-MAIN-2025-13,mon,418968,417680,0.0153\nCC-MAIN-2025-13,mri,34497,34223,0.0013\nCC-MAIN-2025-13,msa,2122021,2114418,0.0774\nCC-MAIN-2025-13,mya,406971,405605,0.0148\nCC-MAIN-2025-13,nau,1055,1055,0.0000\nCC-MAIN-2025-13,nep,1516502,1512451,0.0553\nCC-MAIN-2025-13,nld,49285101,48973935,1.7982\nCC-MAIN-2025-13,nno,369745,368218,0.0135\nCC-MAIN-2025-13,nor,8548787,8500796,0.3119\nCC-MAIN-2025-13,nso,1887,1885,0.0001\nCC-MAIN-2025-13,nya,13617,13535,0.0005\nCC-MAIN-2025-13,oci,102166,101995,0.0037\nCC-MAIN-2025-13,ori,167708,167418,0.0061\nCC-MAIN-2025-13,orm,20394,20376,0.0007\nCC-MAIN-2025-13,pan,185091,184429,0.0068\nCC-MAIN-2025-13,pol,46382644,46163942,1.6923\nCC-MAIN-2025-13,por,64900965,64620240,2.3679\nCC-MAIN-2025-13,pus,113252,112989,0.0041\nCC-MAIN-2025-13,que,14633,14625,0.0005\nCC-MAIN-2025-13,roh,28099,27961,0.0010\nCC-MAIN-2025-13,ron,17875496,17797740,0.6522\nCC-MAIN-2025-13,run,1918,1918,0.0001\nCC-MAIN-2025-13,rus,162515317,161712840,5.9294\nCC-MAIN-2025-13,sag,20473,20473,0.0007\nCC-MAIN-2025-13,san,87238,87027,0.0032\nCC-MAIN-2025-13,sco,16470,16370,0.0006\nCC-MAIN-2025-13,sin,253454,252927,0.0092\nCC-MAIN-2025-13,slk,10096106,10050368,0.3684\nCC-MAIN-2025-13,slv,4253209,4229357,0.1552\nCC-MAIN-2025-13,smo,17508,17406,0.0006\nCC-MAIN-2025-13,sna,18814,18756,0.0007\nCC-MAIN-2025-13,snd,42762,42547,0.0016\nCC-MAIN-2025-13,som,132910,132634,0.0048\nCC-MAIN-2025-13,sot,14495,14398,0.0005\nCC-MAIN-2025-13,spa,121764776,121151506,4.4426\nCC-MAIN-2025-13,sqi,1349756,1346104,0.0492\nCC-MAIN-2025-13,srp,5910940,5890328,0.2157\nCC-MAIN-2025-13,ssw,825,821,0.0000\nCC-MAIN-2025-13,sun,32604,32490,0.0012\nCC-MAIN-2025-13,sux,266,266,0.0000\nCC-MAIN-2025-13,swa,260579,260144,0.0095\nCC-MAIN-2025-13,swe,18146052,18034925,0.6621\nCC-MAIN-2025-13,syr,9605,9598,0.0004\nCC-MAIN-2025-13,tam,1197822,1195183,0.0437\nCC-MAIN-2025-13,tat,224677,224406,0.0082\nCC-MAIN-2025-13,tel,533488,531650,0.0195\nCC-MAIN-2025-13,tgk,188257,187837,0.0069\nCC-MAIN-2025-13,tgl,238900,237833,0.0087\nCC-MAIN-2025-13,tha,12423595,12363526,0.4533\nCC-MAIN-2025-13,tir,18829,18742,0.0007\nCC-MAIN-2025-13,ton,2541,2535,0.0001\nCC-MAIN-2025-13,tsn,3854,3849,0.0001\nCC-MAIN-2025-13,tso,1686,1672,0.0001\nCC-MAIN-2025-13,tuk,61581,61447,0.0022\nCC-MAIN-2025-13,tur,31258207,31117563,1.1405\nCC-MAIN-2025-13,uig,34173,34062,0.0012\nCC-MAIN-2025-13,ukr,17102386,16685530,0.6240\nCC-MAIN-2025-13,urd,822344,818268,0.0300\nCC-MAIN-2025-13,uzb,625942,623245,0.0228\nCC-MAIN-2025-13,ven,698,697,0.0000\nCC-MAIN-2025-13,vie,28774150,28556755,1.0498\nCC-MAIN-2025-13,vol,14334,14312,0.0005\nCC-MAIN-2025-13,war,57235,57138,0.0021\nCC-MAIN-2025-13,wol,3754,3748,0.0001\nCC-MAIN-2025-13,xho,19732,19611,0.0007\nCC-MAIN-2025-13,yid,39811,39752,0.0015\nCC-MAIN-2025-13,yor,22000,21965,0.0008\nCC-MAIN-2025-13,zha,2588,2582,0.0001\nCC-MAIN-2025-13,zho,148376315,148049423,5.4135\nCC-MAIN-2025-13,zul,30624,30418,0.0011\nCC-MAIN-2025-18,,81917923,81917923,2.9817\nCC-MAIN-2025-18,aar,2433,2414,0.0001\nCC-MAIN-2025-18,abk,13549,13489,0.0005\nCC-MAIN-2025-18,afr,274445,273633,0.0100\nCC-MAIN-2025-18,aka,2965,2964,0.0001\nCC-MAIN-2025-18,amh,85872,85420,0.0031\nCC-MAIN-2025-18,ara,18466909,18370024,0.6722\nCC-MAIN-2025-18,asm,67462,67379,0.0025\nCC-MAIN-2025-18,aym,3524,3475,0.0001\nCC-MAIN-2025-18,aze,1652960,1647697,0.0602\nCC-MAIN-2025-18,bak,51515,51506,0.0019\nCC-MAIN-2025-18,bel,406446,405564,0.0148\nCC-MAIN-2025-18,ben,2922132,2914599,0.1064\nCC-MAIN-2025-18,bih,16244,16197,0.0006\nCC-MAIN-2025-18,bis,2303,2294,0.0001\nCC-MAIN-2025-18,bod,36909,36876,0.0013\nCC-MAIN-2025-18,bos,1535568,1524587,0.0559\nCC-MAIN-2025-18,bre,74704,74581,0.0027\nCC-MAIN-2025-18,bul,8142245,8109405,0.2964\nCC-MAIN-2025-18,cat,5200013,5168685,0.1893\nCC-MAIN-2025-18,ceb,50895,50808,0.0019\nCC-MAIN-2025-18,ces,28790470,28583698,1.0479\nCC-MAIN-2025-18,chr,783,783,0.0000\nCC-MAIN-2025-18,cos,52972,52820,0.0019\nCC-MAIN-2025-18,crs,2282,2273,0.0001\nCC-MAIN-2025-18,cym,312490,310694,0.0114\nCC-MAIN-2025-18,dan,13165631,13069307,0.4792\nCC-MAIN-2025-18,deu,153000641,152010369,5.5691\nCC-MAIN-2025-18,div,69694,69648,0.0025\nCC-MAIN-2025-18,dzo,5266,5263,0.0002\nCC-MAIN-2025-18,ell,15524989,15470617,0.5651\nCC-MAIN-2025-18,eng,1207452488,1199710507,43.9499\nCC-MAIN-2025-18,epo,201609,201014,0.0073\nCC-MAIN-2025-18,est,3637280,3619046,0.1324\nCC-MAIN-2025-18,eus,920989,916423,0.0335\nCC-MAIN-2025-18,fao,123213,122644,0.0045\nCC-MAIN-2025-18,fas,19470020,19396877,0.7087\nCC-MAIN-2025-18,fij,2520,2518,0.0001\nCC-MAIN-2025-18,fin,10025118,9960036,0.3649\nCC-MAIN-2025-18,fra,118879662,118142069,4.3271\nCC-MAIN-2025-18,fry,70895,70654,0.0026\nCC-MAIN-2025-18,gla,34315,34239,0.0012\nCC-MAIN-2025-18,gle,199710,198814,0.0073\nCC-MAIN-2025-18,glg,919115,914568,0.0335\nCC-MAIN-2025-18,glv,9898,9885,0.0004\nCC-MAIN-2025-18,got,529,529,0.0000\nCC-MAIN-2025-18,grn,17689,17637,0.0006\nCC-MAIN-2025-18,guj,305686,304965,0.0111\nCC-MAIN-2025-18,hat,52578,52424,0.0019\nCC-MAIN-2025-18,hau,79775,79464,0.0029\nCC-MAIN-2025-18,haw,17870,17833,0.0007\nCC-MAIN-2025-18,heb,7291661,7254261,0.2654\nCC-MAIN-2025-18,hin,5506347,5488715,0.2004\nCC-MAIN-2025-18,hmn,15333,15155,0.0006\nCC-MAIN-2025-18,hrv,6426815,6400866,0.2339\nCC-MAIN-2025-18,hun,13960932,13902278,0.5082\nCC-MAIN-2025-18,hye,900168,896269,0.0328\nCC-MAIN-2025-18,ibo,19280,19147,0.0007\nCC-MAIN-2025-18,iku,7181,7142,0.0003\nCC-MAIN-2025-18,ile,16310,16217,0.0006\nCC-MAIN-2025-18,ina,13907,13658,0.0005\nCC-MAIN-2025-18,ind,32306367,32098399,1.1759\nCC-MAIN-2025-18,ipk,1877,1872,0.0001\nCC-MAIN-2025-18,isl,1074507,1070229,0.0391\nCC-MAIN-2025-18,ita,66099736,65788639,2.4060\nCC-MAIN-2025-18,jav,59114,58996,0.0022\nCC-MAIN-2025-18,jpn,135037254,134614082,4.9152\nCC-MAIN-2025-18,kal,22484,22347,0.0008\nCC-MAIN-2025-18,kan,356941,355454,0.0130\nCC-MAIN-2025-18,kas,9,9,0.0000\nCC-MAIN-2025-18,kat,1196062,1192516,0.0435\nCC-MAIN-2025-18,kaz,870689,867110,0.0317\nCC-MAIN-2025-18,kha,8809,8700,0.0003\nCC-MAIN-2025-18,khm,280780,279308,0.0102\nCC-MAIN-2025-18,kin,55482,55382,0.0020\nCC-MAIN-2025-18,kir,264190,261280,0.0096\nCC-MAIN-2025-18,kor,21607515,21527166,0.7865\nCC-MAIN-2025-18,kur,187244,186749,0.0068\nCC-MAIN-2025-18,lao,96085,95372,0.0035\nCC-MAIN-2025-18,lat,2815790,2811275,0.1025\nCC-MAIN-2025-18,lav,2462211,2439876,0.0896\nCC-MAIN-2025-18,lif,164,164,0.0000\nCC-MAIN-2025-18,lin,6333,6316,0.0002\nCC-MAIN-2025-18,lit,4597028,4568438,0.1673\nCC-MAIN-2025-18,ltz,104611,104261,0.0038\nCC-MAIN-2025-18,lug,4162,4155,0.0002\nCC-MAIN-2025-18,mal,641389,639430,0.0233\nCC-MAIN-2025-18,mar,749498,747553,0.0273\nCC-MAIN-2025-18,mfe,3373,3362,0.0001\nCC-MAIN-2025-18,mkd,1047943,1043881,0.0381\nCC-MAIN-2025-18,mlg,136042,134858,0.0050\nCC-MAIN-2025-18,mlt,84984,83423,0.0031\nCC-MAIN-2025-18,mon,422013,420184,0.0154\nCC-MAIN-2025-18,mri,39704,39485,0.0014\nCC-MAIN-2025-18,msa,2108445,2100736,0.0767\nCC-MAIN-2025-18,mya,361955,360613,0.0132\nCC-MAIN-2025-18,nau,1036,1031,0.0000\nCC-MAIN-2025-18,nep,1435057,1432074,0.0522\nCC-MAIN-2025-18,nld,49680450,49351675,1.8083\nCC-MAIN-2025-18,nno,394013,392043,0.0143\nCC-MAIN-2025-18,nor,8613103,8561554,0.3135\nCC-MAIN-2025-18,nso,2074,2073,0.0001\nCC-MAIN-2025-18,nya,12349,12256,0.0004\nCC-MAIN-2025-18,oci,89462,89310,0.0033\nCC-MAIN-2025-18,ori,158016,157876,0.0058\nCC-MAIN-2025-18,orm,12024,11985,0.0004\nCC-MAIN-2025-18,pan,187939,187656,0.0068\nCC-MAIN-2025-18,pol,51495096,51234036,1.8744\nCC-MAIN-2025-18,por,64203206,63917214,2.3369\nCC-MAIN-2025-18,pus,108224,108001,0.0039\nCC-MAIN-2025-18,que,14858,14824,0.0005\nCC-MAIN-2025-18,roh,29290,29118,0.0011\nCC-MAIN-2025-18,ron,17511190,17436416,0.6374\nCC-MAIN-2025-18,run,2030,2029,0.0001\nCC-MAIN-2025-18,rus,158285564,157542152,5.7614\nCC-MAIN-2025-18,sag,2251,2250,0.0001\nCC-MAIN-2025-18,san,88706,88199,0.0032\nCC-MAIN-2025-18,sco,17058,16941,0.0006\nCC-MAIN-2025-18,sin,235317,234620,0.0086\nCC-MAIN-2025-18,slk,10182503,10130850,0.3706\nCC-MAIN-2025-18,slv,4408722,4384489,0.1605\nCC-MAIN-2025-18,smo,18117,17979,0.0007\nCC-MAIN-2025-18,sna,18159,18005,0.0007\nCC-MAIN-2025-18,snd,42086,41997,0.0015\nCC-MAIN-2025-18,som,123917,123562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}, { "path": "plots/languages.md", "content": "---\nlayout: table\ntable_include: languages-top-200.html\ntable_sortlist: \"{sortList: [[1,1]]}\"\n---\n\nDistribution of Languages\n=========================\n\nThe language of a document is identified by [Compact Language Detector 2 (CLD2)](https://github.com/CLD2Owners/cld2). It is able to identify 160 different languages and up to 3 languages per document. The table lists the percentage covered by the primary language of a document (returned first by CLD2). So far, only HTML pages are passed to the language detector. The underlying data including page counts is provided in [languages.csv](./languages.csv).\n\n" }, { "path": "plots/mimetypes-top-100.html", "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 \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 \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 \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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
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}, { "path": "plots/mimetypes.md", "content": "---\nlayout: table\ntable_include:\n - mimetypes-top-100.html\n - mimetypes_detected-top-100.html\ntable_sortlist: \"{sortList: [[3,1]]}\"\ntable_searcher: \"Filter for MIME types\"\n---\n\nMIME Types\n==========\n\nThe crawled content is dominated by HTML pages and contains only a small percentage of other document formats. The tables show the percentage of the top 100 media or MIME types of the latest monthly crawls.\n\nWhile the first table is based the `Content-Type` HTTP header, the second uses the MIME type detected by [Apache Tika](https://tika.apache.org/) based on the actual content. The underlying data including page counts is provided in [mimetypes.csv](./mimetypes.csv) resp. [mimetypes_detected.csv](./mimetypes_detected.csv).\n" }, { "path": "plots/mimetypes_detected-top-100.html", "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 \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 \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 \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
crawlCC-MAIN-2026-08CC-MAIN-2026-12CC-MAIN-2026-17
mimetype_detected
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}, { "path": "plots/tld/by-year-and-continent.md", "content": "---\nlayout: table\ntable_include:\n- tlds-by-year-and-continent.html\n- selected-tlds-by-year.html\ntable_sortlist: \"{sortList: [[0,1]]}\"\n---\n\nTop-Level Domains By Year and Continent\n=======================================\n\nMany top-level domains can be mapped to geographic regions, for example, the country-code TLDs or regional one (`.asia`). For other domains, such as `.com` or `.net` this is not possible. \n\nThe aggregation by year and the percentage allows to better understand the long-term trends of top-level domain and geographical coverage. For the latter, TLDs are mapped to continents.\ndistribution over continents\n\n![Percentage of top-level domains by year and continent](tlds-by-year-and-continent.png)\n\n\nThe underlying data of the plot is provided in the table below or as CSV ([tlds-by-year-and-continent.csv](./tlds-by-year-and-continent.csv)).\n\nThe second table shows the percentage of the 16 most common top-level domains. The data is also provided as CSV: [selected-tlds-by-year.csv](./selected-tlds-by-year.csv)." }, { "path": "plots/tld/comparison.md", "content": "---\nlayout: table\ntable_include:\n - selected-crawl-comparison-spearman-frequent-tlds.html\n - selected-crawl-comparison.html\ntable_sortlist: \"{sortList: [[4,1]]}\"\n---\n\nEstimation of Representativeness of a Recent Crawl\n==================================================\n\nThe representativeness of the November 2019 crawl (CC-MAIN-2019-47) is estimated by a comparison with the frequency of top-level domains in\n\n- the list of [top-1-million sites](http://s3.amazonaws.com/alexa-static/top-1m.csv.zip) published by [Alexa](https://support.alexa.com/hc/en-us/sections/200063274-Top-Sites), based on unique visitors and page views\n- the [Cisco Umbrella Popularity list](http://s3-us-west-1.amazonaws.com/umbrella-static/index.html) which reflects DNS usage\n- the [Majestic Million](http://downloads.majestic.com/majestic_million.csv), \"[ordered by the number of referring subnets](https://blog.majestic.com/development/majestic-million-csv-daily/)\"\n\nAll three lists have been fetched at the same time the crawl was performed. For the one million domains/sites in the lists the TLDs have been extracted, and for all TLDs the relative frequency has been calculated and compared to the relative frequency of pages, URLs, hosts and domains in the crawl. Comparisons for older crawls are available via the git version history of the project.\n\nThe first table shows [Spearman's rank correlation coefficient](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient) (*ρ*) for the 76 TLDs which cover at least 0.05% of the URLs. The method is similar to [Sebastian Spiegler's analysis of the 2012 crawl archives](http://commoncrawl.org/2013/08/a-look-inside-common-crawls-210tb-2012-web-corpus/). He reported *ρ* = 0.84 based on [W3Techs TLD usage statistics](https://w3techs.com/technologies/overview/top_level_domain/all) for comparison which were/are derived from the top Alexa sites.\n\nAs the three lists used for comparison have a different notion of popularity their correlation results differ. There are also small differences between pages/URLs and hosts/domains. It is an open question whether differences in the relative frequency by TLD are caused by Common Crawl's crawling strategy or a different average size of sites under various TLDs.\n\n\n\nThe second table shows the relative frequency per TLD for the lists and the recent crawl. The data in this tables was used to calculate the correlation matrix.\n" }, { "path": "plots/tld/groups-percentage.html", "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 \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 \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 \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 \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 \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 \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 \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 \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
typeARPAIDN ccTLDIDN gTLDccTLDgTLDgrTLDsTLD
crawl
CC-MAIN-2008-2009NaNNaNNaN7.4990.300.551.66
CC-MAIN-2009-20100.00NaNNaN15.3782.300.371.96
CC-MAIN-20120.000.00NaN30.3068.300.420.99
CC-MAIN-2013-200.000.00NaN11.8983.690.064.36
CC-MAIN-2013-480.000.00NaN7.9187.580.054.45
CC-MAIN-2014-100.000.00NaN7.4387.670.044.85
CC-MAIN-2014-150.000.00NaN7.8986.910.055.15
CC-MAIN-2014-230.000.000.007.8787.370.044.71
CC-MAIN-2014-350.000.000.007.8187.210.054.93
CC-MAIN-2014-410.000.000.007.7787.220.054.96
CC-MAIN-2014-420.000.000.008.3186.440.055.20
CC-MAIN-2014-490.000.000.008.6186.000.055.34
CC-MAIN-2014-52NaN0.000.007.9287.100.044.95
CC-MAIN-2015-06NaN0.000.008.0286.860.045.09
CC-MAIN-2015-11NaN0.000.007.9686.650.045.35
CC-MAIN-2015-14NaN0.000.008.1286.490.045.35
CC-MAIN-2015-18NaN0.000.007.8786.990.035.11
CC-MAIN-2015-22NaN0.000.007.9386.850.035.18
CC-MAIN-2015-27NaN0.000.008.0286.810.045.13
CC-MAIN-2015-32NaN0.000.008.0086.820.045.14
CC-MAIN-2015-35NaN0.000.008.0286.810.045.14
CC-MAIN-2015-40NaN0.000.008.1586.610.045.19
CC-MAIN-2015-48NaN0.000.008.0986.700.045.17
CC-MAIN-2016-07NaN0.000.008.0286.720.045.22
CC-MAIN-2016-180.000.000.007.9386.720.055.30
CC-MAIN-2016-220.000.000.0012.8882.260.074.79
CC-MAIN-2016-260.000.01NaN13.0682.320.084.54
CC-MAIN-2016-30NaN0.000.0012.2183.080.064.65
CC-MAIN-2016-360.000.000.0012.8982.440.064.60
CC-MAIN-2016-400.000.010.0013.6181.870.064.46
CC-MAIN-2016-440.000.050.0030.4866.850.192.43
CC-MAIN-2016-500.000.040.0030.5266.950.222.27
CC-MAIN-2017-040.000.060.0031.8265.840.262.03
CC-MAIN-2017-09NaN0.040.0030.8466.910.231.98
CC-MAIN-2017-13NaN0.040.0031.0067.050.231.69
CC-MAIN-2017-170.000.030.0029.6168.220.241.89
CC-MAIN-2017-22NaN0.050.0030.2167.960.221.56
CC-MAIN-2017-26NaN0.040.0029.4968.950.221.30
CC-MAIN-2017-300.000.050.0029.5268.430.221.78
CC-MAIN-2017-34NaN0.040.0028.9169.700.201.15
CC-MAIN-2017-39NaN0.070.0032.9365.260.231.50
CC-MAIN-2017-430.000.050.0030.0168.400.221.32
CC-MAIN-2017-470.000.070.0033.7164.490.251.47
CC-MAIN-2017-510.000.060.0031.4466.940.251.31
CC-MAIN-2018-050.000.090.0034.9563.220.321.42
CC-MAIN-2018-090.000.090.0033.3964.870.311.34
CC-MAIN-2018-130.000.080.0033.9864.380.271.28
CC-MAIN-2018-170.000.110.0036.2262.010.311.35
CC-MAIN-2018-220.000.120.0036.1862.180.321.20
CC-MAIN-2018-260.000.100.0035.3762.900.301.33
CC-MAIN-2018-300.000.110.0034.3664.200.301.04
CC-MAIN-2018-340.000.130.0038.5759.900.351.06
CC-MAIN-2018-390.000.120.0038.6759.760.331.11
CC-MAIN-2018-430.000.140.0038.9359.570.321.04
CC-MAIN-2018-470.000.160.0139.5259.010.330.97
CC-MAIN-2018-510.000.140.0139.6358.870.311.03
CC-MAIN-2019-040.000.140.0039.6858.920.320.94
CC-MAIN-2019-090.000.150.0140.3858.030.361.06
CC-MAIN-2019-130.000.130.0140.4158.020.321.11
CC-MAIN-2019-180.000.130.0139.9458.290.341.29
CC-MAIN-2019-220.000.110.0139.9958.280.311.31
CC-MAIN-2019-260.000.120.0139.6558.450.301.49
CC-MAIN-2019-300.000.100.0139.3358.720.281.56
CC-MAIN-2019-350.000.120.0139.5158.530.281.55
CC-MAIN-2019-390.000.100.0139.4458.350.261.84
CC-MAIN-2019-43NaN0.120.0139.6158.090.261.91
CC-MAIN-2019-47NaN0.110.0139.6358.290.261.70
CC-MAIN-2019-51NaN0.120.0039.3658.510.251.75
CC-MAIN-2020-05NaN0.130.0139.9658.010.241.65
CC-MAIN-2020-10NaN0.120.0140.3457.670.251.62
CC-MAIN-2020-16NaN0.140.0141.1856.950.241.48
CC-MAIN-2020-24NaN0.120.0140.6357.480.241.52
CC-MAIN-2020-29NaN0.140.0140.9057.200.241.51
CC-MAIN-2020-340.000.110.0140.5557.130.221.98
CC-MAIN-2020-400.000.190.0142.2155.810.271.51
CC-MAIN-2020-450.000.150.0141.6755.940.251.98
CC-MAIN-2020-50NaN0.160.0141.6655.960.261.96
CC-MAIN-2021-04NaN0.170.0141.6156.250.261.71
CC-MAIN-2021-10NaN0.160.0141.9755.740.271.86
CC-MAIN-2021-17NaN0.170.0142.1855.640.271.74
CC-MAIN-2021-21NaN0.160.0141.7555.930.261.89
CC-MAIN-2021-25NaN0.150.0140.8056.750.252.04
CC-MAIN-2021-31NaN0.160.0141.2056.700.271.66
CC-MAIN-2021-390.000.140.0141.0656.430.252.11
CC-MAIN-2021-430.000.150.0141.2656.360.251.98
CC-MAIN-2021-49NaN0.120.0141.0656.100.242.47
CC-MAIN-2022-05NaN0.130.0141.4056.070.252.15
CC-MAIN-2022-21NaN0.130.0140.7356.840.252.03
CC-MAIN-2022-27NaN0.130.0140.8056.630.252.19
CC-MAIN-2022-33NaN0.120.0141.0956.020.242.53
CC-MAIN-2022-40NaN0.150.0137.0160.380.272.18
CC-MAIN-2022-49NaN0.140.0137.1560.240.262.20
CC-MAIN-2023-060.000.140.0137.1160.350.262.12
CC-MAIN-2023-140.000.150.0137.4760.090.252.03
CC-MAIN-2023-230.000.150.0138.1659.370.252.05
CC-MAIN-2023-400.000.150.0138.5359.120.261.94
CC-MAIN-2023-500.000.170.0141.9455.710.251.92
CC-MAIN-2024-100.000.140.0142.1155.710.241.79
CC-MAIN-2024-180.000.140.0141.6356.130.241.84
CC-MAIN-2024-220.000.140.0142.4355.340.231.84
CC-MAIN-2024-260.000.150.0141.7756.000.231.84
CC-MAIN-2024-300.000.150.0142.3255.480.241.79
CC-MAIN-2024-330.000.140.0142.3755.340.251.90
CC-MAIN-2024-380.000.160.0142.9255.000.241.67
CC-MAIN-2024-42NaN0.170.0143.2254.570.241.80
CC-MAIN-2024-46NaN0.160.0142.5755.320.241.70
CC-MAIN-2024-510.000.140.0142.9554.860.251.79
CC-MAIN-2025-050.000.140.0143.0254.970.251.61
CC-MAIN-2025-080.000.140.0142.8354.960.251.81
CC-MAIN-2025-130.000.140.0142.3755.380.241.86
CC-MAIN-2025-180.000.130.0142.7255.080.241.81
CC-MAIN-2025-210.000.140.0142.7855.130.251.69
CC-MAIN-2025-260.000.150.0143.0554.810.261.74
CC-MAIN-2025-300.000.140.0142.6055.350.241.66
CC-MAIN-2025-330.000.150.0144.2253.790.251.58
CC-MAIN-2025-380.000.170.0144.2853.700.261.59
CC-MAIN-2025-430.000.140.0142.7955.420.231.41
CC-MAIN-2025-470.000.160.0144.5753.440.241.57
CC-MAIN-2025-510.000.160.0144.9453.070.241.58
CC-MAIN-2026-040.000.150.0144.4953.580.241.53
CC-MAIN-2026-080.000.150.0144.6153.470.241.52
CC-MAIN-2026-120.000.160.0145.1652.900.241.53
CC-MAIN-2026-170.000.180.0144.6753.430.241.47
" }, { "path": "plots/tld/groups.md", "content": "---\nlayout: table\ntable_include: groups-percentage.html\ntable_sortlist: \"{sortList: [[0,1]]}\"\n---\n\nGroups of Top-Level Domains\n===========================\n\nThe [IANA Root Zone Database](http://www.iana.org/domains/root/db) classifies top-level domains into the following groups:\n\n- [generic](https://en.wikipedia.org/wiki/Generic_top-level_domain) (\"gTLD\"), not bound to a specific country. The core is formed by `.com`, `.info`, `.net` and `.org` TLDs, but was later extended to a long list of generic terms (`.creditcard`, `.bike`), or brands (`.apple`, `.volkswagen`), geographical or cultural entities (`.paris`, `.kiwi`)\n- [sponsored](https://en.wikipedia.org/wiki/Generic_top-level_domain#Sponsored_gTLD) TLDs (\"sTLD\") are restricted to defined groups of users, registration of domains isn't open to anybody. That's obvious for `.gov`, `.mil`, `.edu` but also applies to `.museum` and others\n- generic-restricted (\"grTLD\"): `.biz`, `.name` and `.pro`\n- one single \"infrastructure\" TLD: `.arpa`\n- [country-code top-level domains](https://en.wikipedia.org/wiki/Country_code_top-level_domain): `.uk`, `.fr`, `.jp`, etc.\n\nThe generic and country-code TLDs include [internationalized top-level domains](https://en.wikipedia.org/wiki/Internationalized_domain_name) written in non-Latin alphabets or containing non-ASCII characters. These are listed here as own groups:\n\n- [internationalized country-code TLD](https://en.wikipedia.org/wiki/Internationalized_country_code_top-level_domain) (\"IDN ccTLD\"): `.рф` - Russia\n- internationalized generic TLD (\"IDN gTLD\"): `.セール` - Japanese for 'sale', `.vermögensberatung` - German 'financial consulting'\n\n\n![Percentage of top-level domains groups](groups.png)\n\nSignificant changes to the ratio between generic and country-code top-level domains happened in [May 2016](http://commoncrawl.org/2016/06/may-2016-crawl-archive-now-available/) (400 million seed donation by Moz.com) and [October 2016](http://commoncrawl.org/2016/11/october-2016-crawl-archive-now-available/) where we started to extend the crawl based on host/domain ranks and also increased the crawl size from 1.5--2 billion to about 3 billion pages.\n\nWhile country-code TLDs have been underrepresented in crawls before 2016, the 30% share of country-code TLDs now comes closer to reality. In the present [Alexa top-1-million site list](http://s3.amazonaws.com/alexa-static/top-1m.csv.zip) (for details cf. [comparison of TLDs](./comparison.md)) 61% of the TLDs are generic and 38% country-code TLDs.\n\n" }, { "path": "plots/tld/latest-crawl-groups.html", "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
pagesurlshostsdomains%urls%hosts%domains
type
gTLD1,171,334,8551,164,494,36824,710,88023,512,59753.4357.1957.44
ccTLD978,982,191973,606,53718,109,68517,050,02944.6741.9141.65
sTLD32,358,18432,108,658187,582180,6501.470.430.44
grTLD5,193,2285,169,879126,260120,9780.240.290.30
IDN ccTLD3,840,6793,829,45570,93369,0590.180.160.17
IDN gTLD172,377171,9484,0623,9810.010.010.01
ARPA11110.000.000.00
" }, { "path": "plots/tld/latest-crawl-tlds.html", "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 \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 \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 \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 \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 \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 \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
pagesurlshostsdomains%urls%hosts%domains
tld
com918,035,054912,637,64419,928,57518,965,47441.8846.1246.33
org120,478,054119,738,9311,831,0921,712,0485.494.244.18
ru106,756,027106,349,9191,188,1371,121,9544.882.752.74
de95,650,54194,950,4272,496,8882,307,5934.365.785.64
net65,050,60464,753,6821,220,2481,148,7662.972.822.81
jp50,331,10450,090,914731,504691,5712.301.691.69
uk46,106,32045,806,1541,062,3041,010,4982.102.462.47
fr42,237,00042,002,602917,847880,1841.932.122.15
pl41,491,47141,309,416634,884603,4011.901.471.47
it38,060,55237,875,266662,750627,5681.741.531.53
br37,254,47437,094,432562,266525,1911.701.301.28
nl32,137,62931,937,772820,393769,9871.471.901.88
cz24,497,31224,243,284377,628353,4431.110.870.86
cn23,650,35423,599,9611,026,423969,0281.082.382.37
es19,880,76719,756,848309,111295,4920.910.720.72
ua19,359,64619,197,917123,964119,0240.880.290.29
au19,300,78019,191,339431,138414,2150.881.001.01
ca18,388,02818,282,578399,341382,0480.840.920.93
edu18,167,19918,038,391106,032103,6070.830.250.25
eu15,928,46815,829,300311,177292,7470.730.720.72
in14,703,15014,650,031278,968264,1090.670.650.65
se14,641,75414,549,917309,180294,0040.670.720.72
ch14,139,66914,023,492401,642371,0510.640.930.91
vn13,013,79312,962,675129,026123,0170.590.300.30
info12,790,04012,741,500220,350207,8250.580.510.51
ro12,775,62812,724,876147,761139,0690.580.340.34
be11,861,57811,785,902293,514274,8810.540.680.67
dk11,544,78611,471,765275,476263,1960.530.640.64
gr11,427,10011,386,614145,816136,3830.520.340.33
hu11,124,20011,076,250193,742181,6600.510.450.44
at10,516,66410,440,830258,354242,5690.480.600.59
id9,633,1489,605,620136,284131,1290.440.320.32
co9,499,7179,398,703189,183183,3790.430.440.45
tw9,396,9979,360,501124,182112,0770.430.290.27
tr9,177,1399,143,395128,514116,7680.420.300.29
kr9,155,7439,128,208195,165163,5550.420.450.40
sk8,689,3208,627,849127,792119,0070.400.300.29
gov8,689,5548,624,66127,02825,4950.400.060.06
fi7,844,9087,794,541144,227137,5730.360.330.34
ar7,591,8797,567,442125,889113,6990.350.290.28
no7,252,9627,205,272141,135133,7200.330.330.33
io7,107,0207,038,495212,249208,1090.320.490.51
pt6,500,5686,462,77694,76088,0540.300.220.22
mx6,367,3736,341,409100,17394,7400.290.230.23
cl5,111,9705,088,77681,02677,2240.230.190.19
il4,828,8014,804,20279,69976,9170.220.180.19
by4,666,9874,645,39643,32941,2660.210.100.10
za4,509,8054,489,262113,557108,4130.210.260.26
tv4,470,7694,451,51735,65533,4630.200.080.08
shop4,447,5724,435,50374,99873,5360.200.170.18
nz4,238,8924,213,404106,498102,2140.190.250.25
rs4,128,2264,112,39444,17440,9780.190.100.10
lt3,893,4503,876,18662,25958,6160.180.140.14
me3,880,5143,856,06884,78582,5910.180.200.20
cc3,674,6163,658,75875,29472,0810.170.170.18
us3,651,2993,628,82293,42289,0060.170.220.22
hr3,501,0793,487,00741,72938,6280.160.100.09
ee3,473,9323,457,25348,65843,8160.160.110.11
app3,203,3413,181,514113,218111,7190.150.260.27
ie3,115,2393,097,57954,71852,5730.140.130.13
online3,087,6163,071,52979,20077,7130.140.180.19
si3,084,1303,070,99942,99139,8200.140.100.10
ai3,097,5453,064,14980,87278,3100.140.190.19
kz3,053,6503,043,28729,24527,8710.140.070.07
bg3,001,6502,988,36726,48225,2330.140.060.06
biz2,883,4802,871,39274,01070,3390.130.170.17
my2,684,8042,673,68647,15544,7500.120.110.11
cat2,616,1792,603,02225,07923,5850.120.060.06
hk2,565,2712,547,02335,54331,3230.120.080.08
xyz2,514,4662,500,638134,418132,3440.110.310.32
th2,491,2642,482,86235,24231,5240.110.080.08
lv2,262,9242,246,86329,82727,2630.100.070.07
ir2,219,5422,214,19021,17020,4700.100.050.05
pro2,020,7372,011,06247,43946,1380.090.110.11
top2,016,7732,006,69188,93287,4530.090.210.21
su1,906,4161,899,09320,52819,4600.090.050.05
pe1,900,7931,888,59524,70223,5040.090.060.06
news1,891,9681,885,5497,6146,9110.090.020.02
pk1,727,1581,721,95123,81822,6780.080.060.06
ae1,582,4781,573,64827,09225,9000.070.060.06
sg1,564,0831,551,08029,81628,3770.070.070.07
club1,390,6281,384,28831,65530,8340.060.070.08
store1,378,8951,371,31733,08132,4750.060.080.08
ng1,345,3571,341,78716,20615,4910.060.040.04
ph1,276,9121,251,14016,40415,4760.060.040.04
ba1,200,3991,196,79911,50110,6200.050.030.03
az1,193,3711,189,9409,1138,0300.050.020.02
md1,194,5371,189,71510,82210,0610.050.030.02
uy1,161,8191,156,51213,55012,4930.050.030.03
is1,116,1641,107,68215,52814,7230.050.040.04
dev1,110,7301,101,29251,05450,3310.050.120.12
ma1,096,6751,092,41314,15113,3320.050.030.03
" }, { "path": "plots/tld/latestcrawl.md", "content": "---\nlayout: table\ntable_include:\n - latest-crawl-groups.html\n - latest-crawl-tlds.html\ntable_sortlist: \"{sortList: [[5,1]]}\"\ntable_searcher: \"Filter for top-level domains\"\n---\n\nTop-Level Domains of the Latest Monthly Crawl\n=============================================\n\nDetailed numbers and percentage of top-level domains (groups) in the latest monthly crawl ({{ site.latest_crawl }}). Note that [Internationalized country code TLDs (IDN ccTLDs)](https://en.wikipedia.org/wiki/List_of_Internet_top-level_domains#Internationalized_country_code_top-level_domains) are mapped to their ASCII equivalents before counting TLDs. E.g., the counts of `.ru` contain also the occurrences of [.рф](https://en.wikipedia.org/wiki/.%D1%80%D1%84).\n\n\n" }, { "path": "plots/tld/percentage.md", "content": "---\nlayout: table\ntable_include: selected-crawls-percentage.html\ntable_sortlist: \"{sortList: [[7,1]]}\"\ntable_searcher: \"Filter for top-level domains\"\n---\n\nTop-Level Domains of Selected Crawls\n====================================\n\nJust the distribution (percentage / relative frequency) of URLs on top-level domains for few selected crawls, include the latest monthly crawl... Only TLDs which reach at least 0.05% in one of the selected crawls are shown.\n" }, { "path": "plots/tld/selected-crawl-comparison-spearman-all-tlds.html", "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
pagesurlshostsdomainsalexaciscomajestic
pages1.0000.9990.9750.9740.9240.8240.886
urls0.9991.0000.9760.9750.9250.8240.887
hosts0.9750.9761.0001.0000.9070.8060.881
domains0.9740.9751.0001.0000.9050.8050.878
alexa0.9240.9250.9070.9051.0000.8730.894
cisco0.8240.8240.8060.8050.8731.0000.839
majestic0.8860.8870.8810.8780.8940.8391.000
" }, { "path": "plots/tld/selected-crawl-comparison-spearman-frequent-tlds.html", "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
pagesurlshostsdomainsalexaciscomajestic
pages1.0001.0000.8690.8620.8120.7340.812
urls1.0001.0000.8700.8640.8120.7340.813
hosts0.8690.8701.0000.9990.8200.7170.868
domains0.8620.8640.9991.0000.8190.7210.873
alexa0.8120.8120.8200.8191.0000.7650.831
cisco0.7340.7340.7170.7210.7651.0000.825
majestic0.8120.8130.8680.8730.8310.8251.000
" }, { "path": "plots/tld/selected-crawl-comparison.html", "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 \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 \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 \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 \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 \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
pagesurlshostsdomainsalexaciscomajestic
tld
hu0.4370.4370.3970.3880.3680.0900.226
bg0.1270.1270.0590.0580.1080.0260.043
se0.6290.6280.6860.6900.4060.1260.356
ca0.8180.8190.8180.8200.6820.3610.641
uk2.3002.2952.6302.6231.6841.1302.567
jp1.8211.8261.5461.5341.0740.3601.525
it1.6211.6221.5841.5821.1300.4280.985
at0.4000.4000.5870.5820.3100.0870.275
be0.4830.4830.6010.5850.3310.1470.283
cn1.3821.3913.2173.2330.2650.6322.942
id0.2020.2020.1800.1820.1980.1400.111
il0.2670.2670.1720.1710.1570.1210.114
in0.3530.3540.3940.3941.4070.6320.603
ir0.2400.2400.2350.2311.2930.0780.323
kr0.3430.3450.3860.3510.2580.1000.144
me0.2650.2660.2240.2310.3590.7870.206
nz0.2180.2180.2660.2660.1370.0750.148
rs0.1530.1530.0770.0730.1010.0170.045
ru4.5134.5182.9872.9914.4981.4404.311
th0.0930.0930.0790.0740.1410.0530.053
vn0.3810.3810.2430.2440.1620.2690.316
za0.1920.1930.2960.2940.3860.0540.256
au0.9280.9281.1221.1281.1100.1920.848
br1.2521.2531.1921.1582.4170.4880.892
fr1.6811.6811.6401.6430.9680.4790.956
pl1.4791.4791.3601.3461.5510.3391.154
us0.2240.2240.2520.2530.3350.3960.660
fi0.3380.3380.3170.3160.2080.0540.150
no0.3630.3620.2990.2980.3640.1060.163
ar0.2050.2050.2160.2060.4810.0640.148
ro0.4580.4580.3100.3060.5890.0650.282
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az0.0580.0580.0170.0160.1630.0060.022
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crawlCC-MAIN-2008-2009CC-MAIN-2012CC-MAIN-2014-10CC-MAIN-2016-30CC-MAIN-2019-09CC-MAIN-2022-49CC-MAIN-2026-17
tld
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za0.01260.16130.07370.14550.20970.03540.2119
" }, { "path": "plots/tld/selected-tlds-by-year.csv", "content": "year,com,org,ru,net,de,uk,jp,edu,fr,it,pl,nl,br,cz,au,es\n(any),51.89498041088741,6.374690312897634,4.043836128047363,3.7540135387074742,3.3057420391065255,2.0636941568332174,1.6835267130601943,1.4252882516137377,1.378812124865615,1.3646056974912153,1.2560181267062749,1.121195143198995,0.9993935660133176,0.8268963567184797,0.8218748742850955,0.7555833791162748\n2009,71.19780889022911,8.098929313625348,0.05351614532270588,9.192244767771701,0.05229943066335908,4.035882904228814,0.05172600187665833,1.2758001274752773,0.019721612003983577,0.04384950419892673,0.18616156990118463,0.007326984931601306,0.3371385283395139,0.006517566005039137,0.601235299647618,0.03384002941450515\n2010,68.93182873699625,7.140400107811788,0.46442692696666116,6.0261543153559165,1.5071477519179124,3.1231446830343264,0.5019212492802659,1.3083111025801328,0.4374130392617395,0.5685715050740612,0.48197880870021537,0.4423738714382255,0.7884784029067625,0.1696544885787117,1.0174189743866877,0.2536865504094791\n2012,55.856399418493176,6.022725691210052,1.7092228719397122,5.430585937678828,4.752871358541379,3.454940323911269,1.1427540929401447,0.6065172816762283,1.2974944702218174,1.3003447285636904,1.794793492276718,1.4343236655889144,0.7561427194830521,0.7643201919764638,0.9834737749202592,0.6679580672316351\n2013,73.07832933177185,9.397807679592292,0.0576101583509711,3.269726670817926,1.122655213970551,1.9973525831718704,0.16378503435221878,2.906697821403832,0.3795106738238928,0.33725209585561516,0.02455189987001929,0.24826465816108723,0.2165408066061675,0.09510312100280675,0.633941239564323,0.22165410747988268\n2014,73.25154993537471,9.748709303575934,0.11357902937548757,3.2006759407993606,0.8102633151462019,1.7445533155041957,0.13037620976505668,3.36436596270053,0.3023998644590416,0.2627330934011718,0.1575522142061087,0.16476339518765715,0.2801926420741301,0.05591932198087427,0.508761576408202,0.180034989378696\n2015,73.30584506468368,10.25005088968117,0.10884730008636287,3.163612596472046,0.755234139038026,1.6705744755475465,0.1416879605482132,3.344217600154009,0.2810126137710399,0.24869594328930453,0.13983018448402285,0.1661677306088631,0.25895815983586756,0.047732897166349104,0.5070904400084749,0.17552997164679915\n2016,64.54523740307623,8.962913504852864,2.5554470749563514,3.6593123663986775,1.9722821636683794,1.7895415990305006,0.8124400136775266,2.4817418083983913,0.6309310471298197,0.6481674607899223,0.48744016390253647,0.5113775166677983,0.43462600911804555,0.520424381598946,0.6046418501995843,0.3567211033635578\n2017,56.54142420097272,5.643604702140739,5.298772605425257,4.217553207459812,2.8272361487220534,2.030203752445904,2.094932027320141,1.0213536337719042,1.1462942617265288,1.0752783830848343,0.9875203959783877,0.7282896047329713,0.8073278394888976,0.8525616264541258,0.70379930272525,0.7080291752576782\n2018,50.06329875319154,5.277805347037557,6.059784566481229,4.411463764554048,3.493396558772616,2.2178148687676367,2.164062012953355,0.7471671647649256,1.4179426241400284,1.278994949295683,1.3704808710201752,1.0185913442100039,0.9893937030239857,0.9309126422286781,0.781953100203275,0.802891385809908\n2019,46.20532576745491,5.676807344971467,5.085952469840225,3.9819656860964496,3.8838680030819392,2.3803074166762186,1.9926785479648448,0.888637799619372,1.6682126002159616,1.6259115040790517,1.5062165054968,1.381020765676773,1.187565937889545,0.9904209503277482,0.9193488692014473,0.9006401307095173\n2020,44.965464764988575,5.680418883349085,4.851169812730146,3.7145583596844514,4.030039960587272,2.295895268261546,1.8924009010047067,1.0761603584806487,1.7504655275200778,1.7043980826477478,1.5247171265991937,1.4581695573792826,1.3056568434903515,1.0175013685624041,0.9797505695472748,0.9101720911446329\n2021,44.32560044163435,5.712679745200201,4.785891026180248,3.5893809528625042,4.147310238160972,2.3843450021653423,1.885449413654711,1.281048084913162,1.790613167633786,1.7719417632847865,1.5852346915602094,1.517795518795522,1.398618093474787,1.0486188707374928,1.0501915728082938,0.9687100013794343\n2022,45.37548169622278,6.158432878698863,4.136582366324254,3.558225755498412,4.20563086555731,1.750006179943287,1.7826016159829337,1.488170085944872,1.8476059411985666,1.8766664931169015,1.656898832651494,1.6432687465511873,1.0531541785479854,1.0284847667932469,0.798074391363609,0.9768547566332577\n2023,46.52580333169464,6.059793038227193,4.274594205438922,3.4923339674259486,4.295582610988845,0.7721516959075141,1.6617919730015456,1.3069957401439511,1.9138263335101504,2.060756103048291,1.6413990191796046,1.7580674048160572,0.4870726818081391,1.0794611766053126,0.41106841526393656,1.018274387928326\n2024,43.64514945495107,5.509630912040156,4.400192617327216,3.3161531547511496,4.028478270272748,2.0924533910547045,2.0801574283126305,1.1379001775504274,1.7504729335015614,1.9166658226647484,1.74936524608057,1.5397250866024828,1.4133306498645226,1.0307223655323625,1.0498701103812977,0.9845486508528206\n2025,43.16689356863301,5.164674228080854,4.284308762325859,3.2709505785983404,4.1637023690852555,2.175032223492076,2.2662357981506456,1.0180548378551648,1.7219803080962657,1.738790226931858,1.6972942763569192,1.4786093202261839,1.5635812915029117,1.015281826976945,1.1162430662993146,0.9009949248586931\n2026,41.87555481631105,5.510759830749804,4.599670566972649,3.049980817631032,4.368193211724322,2.039847060234517,2.47274095181078,0.8569106252161244,1.8887017950570846,1.7983108059302308,1.8780644166455187,1.4491169071701397,1.714666149334643,1.105213042595473,0.8949063165011313,0.9262104112494509\n" }, { "path": "plots/tld/selected-tlds-by-year.html", "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 \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 \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
comorgrunetdeukjpedufritplnlbrczaues
year
(any)51.896.374.043.753.312.061.681.431.381.361.261.121.000.830.820.76
200971.208.100.059.190.054.040.051.280.020.040.190.010.340.010.600.03
201068.937.140.466.031.513.120.501.310.440.570.480.440.790.171.020.25
201255.866.021.715.434.753.451.140.611.301.301.791.430.760.760.980.67
201373.089.400.063.271.122.000.162.910.380.340.020.250.220.100.630.22
201473.259.750.113.200.811.740.133.360.300.260.160.160.280.060.510.18
201573.3110.250.113.160.761.670.143.340.280.250.140.170.260.050.510.18
201664.558.962.563.661.971.790.812.480.630.650.490.510.430.520.600.36
201756.545.645.304.222.832.032.091.021.151.080.990.730.810.850.700.71
201850.065.286.064.413.492.222.160.751.421.281.371.020.990.930.780.80
201946.215.685.093.983.882.381.990.891.671.631.511.381.190.990.920.90
202044.975.684.853.714.032.301.891.081.751.701.521.461.311.020.980.91
202144.335.714.793.594.152.381.891.281.791.771.591.521.401.051.050.97
202245.386.164.143.564.211.751.781.491.851.881.661.641.051.030.800.98
202346.536.064.273.494.300.771.661.311.912.061.641.760.491.080.411.02
202443.655.514.403.324.032.092.081.141.751.921.751.541.411.031.050.98
202543.175.164.283.274.162.182.271.021.721.741.701.481.561.021.120.90
202641.885.514.603.054.372.042.470.861.891.801.881.451.711.110.890.93
" }, { "path": "plots/tld/tlds-by-year-and-continent.csv", "content": "year,(other),\"com,net\",org,edu,\"gov,mil\",North America,South America,Oceania,Africa,Asia,Europe\n2009,2.372710231294118,80.38104003392658,8.098021232327786,1.2756570801426825,0.3793266584367303,0.11401450241006646,0.4869093014580774,0.6385207762024301,0.025662649035679254,1.4786198795909986,4.749517655174848\n2010,1.4855696236647693,74.95629887048294,7.140239674834392,1.3082817069941184,0.5120370711261071,0.7706887698166217,0.9790683746172806,1.4112024309658973,0.1281535582272385,1.7830943051527208,9.525365614117918\n2012,1.805603499529646,61.286829663077846,6.022710391113422,0.6065157408833226,0.2674993425437423,1.313681628396945,1.1912177609532053,1.7423530678298849,0.22608598259612694,2.774087757007317,22.763415166068544\n2013,0.40492028804343894,76.34802196410801,9.397803489739635,2.906696525502064,1.4128169971802005,1.0172778318175024,0.422744478041168,1.0805405428554966,0.18532918473122176,0.8537253370084816,5.970123360972776\n2014,0.41733410009547656,76.45221478702612,9.748707889557197,3.364365474710134,1.6853772456589369,0.9603473219225983,0.5335950655704679,0.8683960090486706,0.18209174352684082,0.8291928743976551,4.958377488485904\n2015,0.3746039648183713,76.46945214130866,10.250050149794749,3.3442173587560675,1.6839290167887637,0.8405363480989,0.51476511310795,0.8530288444537862,0.17400684980450837,0.8053354007332064,4.690074812335029\n2016,0.7364446130600686,68.20454902568343,8.962913407109552,2.481741781334238,1.2664009984490998,0.9542985272580663,0.8108407328828081,1.0907254528906893,0.23798517351067525,2.482341437572674,12.771758850248698\n2017,1.3803362668953434,60.758977390531555,5.643604700478004,1.0213536334709903,0.42362111261162405,1.1501900986422446,1.3815340022573843,1.3300550265072695,0.2969733599295535,5.127185253115497,21.486169155560535\n2018,2.4722427093505788,54.47476249849602,5.277805345172555,0.7471671645009016,0.2809316181870313,1.403865320751203,1.7183262532181955,1.5125740042582414,0.40486852829194125,5.872907599592247,25.834548958181088\n2019,2.799127291037323,50.187220637958696,5.6767993348464945,0.8886365457279859,0.3075981672638809,1.3562237983356609,2.04299088912311,1.6477873839691597,0.4757171673848548,6.62478803338319,27.993110750969645\n2020,2.7569743182402955,48.67997702017013,5.680413503464852,1.0761593392568443,0.3511239201356604,1.3601336273656373,2.180024157926504,1.6606431478052264,0.4766815781708249,7.164844271609742,28.61302511585429\n2021,2.807726498809468,47.914979148097146,5.712679477372442,1.281048024853741,0.4114774978120561,1.4514077514517278,2.343827844096823,1.7198842195845663,0.500569399436175,6.682231757594182,29.174168380891672\n2022,2.8780538409820116,48.93370393081929,6.1584324355843085,1.488169978867335,0.47988094726490643,1.5060979491331428,2.0843334295330225,1.4029550288832249,0.4837686122978544,6.370098503155943,28.214505343478958\n2023,2.9878111904742277,50.01810770292156,6.059789452591052,1.306994966782364,0.47024376514043276,1.567826346305796,1.6625261765574342,0.9366992918690097,0.4255690144713344,6.35774534328999,28.206686749596795\n2024,3.0454213699438024,46.961250802687076,5.509624833896743,1.137898922235765,0.43758927914542306,1.5944360037056953,2.627106931726031,1.6589448982493866,0.5954904899067033,7.677015083900745,28.75522138460263\n2025,3.1710095569417116,46.437826174591486,5.164672229219115,1.0180544438417627,0.43464626049835847,1.638992711199414,2.816431662118706,1.743696094446849,0.6276925586809861,8.45079220305241,28.496186105409205\n2026,3.042738116164256,44.92553232030033,5.510759424284224,0.8569105620116508,0.43252031132823504,1.593273577276783,2.966933213245479,1.4690889036041748,0.6041385687836416,8.779568331734819,29.818536671266408\n" }, { "path": "plots/tld/tlds-by-year-and-continent.html", "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 \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
(other)com,netorgedugov,milNorth AmericaSouth AmericaOceaniaAfricaAsiaEurope
year
20092.3780.388.101.280.380.110.490.640.031.484.75
20101.4974.967.141.310.510.770.981.410.131.789.53
20121.8161.296.020.610.271.311.191.740.232.7722.76
20130.4076.359.402.911.411.020.421.080.190.855.97
20140.4276.459.753.361.690.960.530.870.180.834.96
20150.3776.4710.253.341.680.840.510.850.170.814.69
20160.7468.208.962.481.270.950.811.090.242.4812.77
20171.3860.765.641.020.421.151.381.330.305.1321.49
20182.4754.475.280.750.281.401.721.510.405.8725.83
20192.8050.195.680.890.311.362.041.650.486.6227.99
20202.7648.685.681.080.351.362.181.660.487.1628.61
20212.8147.915.711.280.411.452.341.720.506.6829.17
20222.8848.936.161.490.481.512.081.400.486.3728.21
20232.9950.026.061.310.471.571.660.940.436.3628.21
20243.0546.965.511.140.441.592.631.660.607.6828.76
20253.1746.445.161.020.431.642.821.740.638.4528.50
20263.0444.935.510.860.431.592.971.470.608.7829.82
" }, { "path": "plots/tlds.md", "content": "Top-Level Domains\n=================\n\n[Top-level domains](https://en.wikipedia.org/wiki/Top-level_domain) (abbrev. \"TLD\"/\"TLDs\") are a significant indicator for the representativeness of the data, whether the data set or particular crawl is biased towards certain countries, regions or languages.\n\nMetrics about top-level domains are show on the following pages:\n\n* [Groups of TLDs](./tld/groups.md): generic vs. country-code TLDs (e.g., `.com` vs. `.jp`)\n* [TLDs of latest monthly crawl](./tld/latestcrawl.md)\n* [Estimation of representativeness](./tld/comparison.md): a comparison of a recent crawl with an external data source\n* [TLDs of selected crawls](./tld/percentage.md): lists percentage of TLDs for selected crawls from 2008--2025\n* [TLDs by year and continent](./tld/by-year-and-continent.md): TLDs which are bound to geographical regions are mapped to the corresponding continents.\n\nNote, that top-level domain is defined here as the left-most element of a host name (`com` in `www.example.com`). [Country-code second-level domains](https://en.wikipedia.org/wiki/Second-level_domain#Country-code_second-level_domains) (\"ccSLD\") and [public suffixes](https://en.wikipedia.org/wiki/Public_Suffix_List) are not covered by this metrics.\n\n" }, { "path": "requirements.txt", "content": "hyperloglog==0.0.14\nisoweek==1.3.3\nmrjob==0.7.4\ntldextract==5.1.2\nujson==5.12.0\n\n# tests\npytest\njsonpickle\nsetuptools\n" }, { "path": "requirements_plot.txt", "content": "ggplot==0.11.5\nidna==3.7\n#pandas==2.1.4+dfsg\npandas==2.1.4\npygraphviz==1.13\nrpy2==3.5.15\n\nmatplotlib==3.10.7\nfsspec[s3]" }, { "path": "run_stats_hadoop.sh", "content": "#!/bin/bash\n\nCRAWL=\"$1\"\n\nif [ -z \"$CRAWL\" ]; then\n echo \"Usage: $0 \"\n echo \" Argument indicating year and week of crawl (e.g., 2016-40) to be processed required\"\n exit 1\nfi\n\n\nOUTPUT_COUNT=ccstats/$CRAWL/count/\nOUTPUT_STATS=ccstats/$CRAWL/stats/\n\nhadoop fs -mkdir -p ccstats/$CRAWL/\n\n# check that output paths do not exist (jobs will fail otherwise)\nif hadoop fs -ls $OUTPUT_COUNT; then\n echo \"Output path $OUTPUT_COUNT already exists: delete path before running the count job\"\n exit 1\nfi\n\nif hadoop fs -ls $OUTPUT_STATS; then\n echo \"Output path $OUTPUT_STATS already exists: delete path before running the stats job\"\n exit 1\nfi\n\n\nset -e\nset -x\nset -o pipefail\n\n\nINPUT=\"s3a://commoncrawl/cc-index/collections/CC-MAIN-$CRAWL/indexes/cdx-*.gz\"\n\nHADOOP_USER=${HADOOP_USER:-$USER}\n\n\npython3 crawlstats.py --job=count \\\n --no-exact-counts \\\n -r hadoop \\\n --jobconf \"mapreduce.map.memory.mb=720\" \\\n --jobconf \"mapreduce.map.java.opts=-Xmx512m\" \\\n --jobconf \"mapreduce.reduce.memory.mb=640\" \\\n --jobconf \"mapreduce.reduce.java.opts=-Xmx512m\" \\\n --jobconf \"mapreduce.output.fileoutputformat.compress=true\" \\\n --output-dir hdfs:///user/$HADOOP_USER/$OUTPUT_COUNT \\\n --no-output \\\n --cleanup NONE \\\n \"$INPUT\" \\\n 2>&1 | tee cc-stats.$CRAWL.count.log\n\n#for i in `seq 0 9`; do\n# hadoop distcp ccstats/$CRAWL/count/part-0000$i.bz2 s3a://commoncrawl/crawl-analysis/CC-MAIN-$CRAWL/count/part-0000$i.bz2\n#done\nhadoop fs -rm ccstats/$CRAWL/count/_SUCCESS\nhadoop distcp -direct ccstats/$CRAWL/count s3a://commoncrawl/crawl-analysis/CC-MAIN-$CRAWL/count\n\n\npython3 crawlstats.py --job=stats \\\n --max-top-hosts-domains=500 \\\n --min-urls-top-host-domain=100 \\\n --min-lang-comb-freq=50 \\\n -r hadoop \\\n --jobconf \"mapreduce.map.memory.mb=1200\" \\\n --jobconf \"mapreduce.map.java.opts=-Xmx1024m\" \\\n --jobconf \"mapreduce.reduce.memory.mb=1200\" \\\n --jobconf \"mapreduce.reduce.java.opts=-Xmx1024m\" \\\n --jobconf \"mapreduce.output.fileoutputformat.compress=true\" \\\n --no-output \\\n --cleanup NONE \\\n --output-dir hdfs:///user/$HADOOP_USER/$OUTPUT_STATS \\\n hdfs:///user/$HADOOP_USER/$OUTPUT_COUNT \\\n 2>&1 | tee cc-stats.$CRAWL.stats.log\n\nhadoop fs -mkdir -p s3a://commoncrawl/crawl-analysis/CC-MAIN-$CRAWL/stats/\nhadoop fs -cp -d -f ccstats/$CRAWL/stats/part-00000.gz s3a://commoncrawl/crawl-analysis/CC-MAIN-$CRAWL/stats/part-00000.gz\n" }, { "path": "setup.py", "content": "from setuptools import setup\n\n\nsetup(\n setup_requires=['pytest-runner'],\n tests_require=['pytest'],\n)" }, { "path": "site.Dockerfile", "content": "# See\n# https://docs.github.com/en/pages/setting-up-a-github-pages-site-with-jekyll\n# https://github.com/BillRaymond/my-jekyll-docker-website\n\n# As of 2024-12-15, Github pages require Ruby 3.3.4 and Jekyll 3.10.0\n# see https://pages.github.com/versions/\nFROM ruby:3.3-alpine\n\n# Add Jekyll build dependencies to Alpine\nRUN apk update\nRUN apk add --no-cache build-base gcc cmake git\n\n# Update the Ruby bundler and install Jekyll,\n# pin dependency versions to avoid conflicts\nRUN gem install bundler -v 2.5.23\nRUN gem install ffi -v 1.17.0\nRUN gem install jekyll -v 3.10.0\n\n\nRUN mkdir /cc-crawl-statistics\nWORKDIR /cc-crawl-statistics\n\nRUN echo -e \"source 'https://rubygems.org'\\ngem 'github-pages', group: :jekyll_plugins\" >Gemfile\nRUN bundle install\nRUN bundle exec jekyll clean\n\nCOPY index.md _config.yml ./\nCOPY _layouts/ ./_layouts/\nCOPY assets/ ./assets/\nCOPY plots/ ./plots/\n\nCMD bundle exec jekyll serve\n#CMD bundle exec jekyll serve --host 0.0.0.0\n" }, { "path": "stats/crawler/CC-MAIN-2016-18.json", "content": "[\"crawl_status\", \"generator:crawldb_size\", \"CC-MAIN-2016-18\"]\t7706034375\n[\"crawl_status\", \"generator:fetch_list\", \"CC-MAIN-2016-18\"]\t4884080239\n[\"crawl_status\", \"generator:skipped_domains_overflow\", \"CC-MAIN-2016-18\"]\t1225\n[\"crawl_status\", \"fetcher:total\", \"CC-MAIN-2016-18\"]\t4823256591\n[\"crawl_status\", \"fetcher:success\", \"CC-MAIN-2016-18\"]\t1338807996\n[\"crawl_status\", \"fetcher:notmodified\", \"CC-MAIN-2016-18\"]\t327260\n[\"crawl_status\", \"fetcher:temp_moved\", \"CC-MAIN-2016-18\"]\t338160914\n[\"crawl_status\", \"fetcher:moved\", \"CC-MAIN-2016-18\"]\t1090448325\n[\"crawl_status\", \"fetcher:notfound\", \"CC-MAIN-2016-18\"]\t426625500\n[\"crawl_status\", \"fetcher:access_denied\", \"CC-MAIN-2016-18\"]\t1426295\n[\"crawl_status\", \"fetcher:gone\", \"CC-MAIN-2016-18\"]\t19915502\n[\"crawl_status\", \"fetcher:exception\", \"CC-MAIN-2016-18\"]\t82830014\n[\"crawl_status\", \"fetcher:robots_denied\", \"CC-MAIN-2016-18\"]\t521424978\n[\"crawl_status\", \"fetcher:robots_denied_maxcrawldelay\", \"CC-MAIN-2016-18\"]\t7854886\n[\"crawl_status\", \"fetcher:filter_denied\", \"CC-MAIN-2016-18\"]\t0\n[\"crawl_status\", \"fetcher:hitByThrougputThreshold\", \"CC-MAIN-2016-18\"]\t305983410\n[\"crawl_status\", \"fetcher:hitByTimeLimit\", \"CC-MAIN-2016-18\"]\t284383017\n[\"crawl_status\", \"fetcher:AboveExceptionThresholdInQueue\", \"CC-MAIN-2016-18\"]\t405068494\n[\"crawl_status\", \"fetcher:bytes_downloaded\", \"CC-MAIN-2016-18\"]\t102758408401405\n" }, { "path": "stats/crawler/CC-MAIN-2016-22.json", "content": "[\"crawl_status\", \"generator:crawldb_size\", \"CC-MAIN-2016-22\"]\t8087074988\n[\"crawl_status\", \"generator:fetch_list\", \"CC-MAIN-2016-22\"]\t5261630738\n[\"crawl_status\", \"generator:skipped_domains_overflow\", \"CC-MAIN-2016-22\"]\t1227\n[\"crawl_status\", \"fetcher:total\", \"CC-MAIN-2016-22\"]\t5227623812\n[\"crawl_status\", \"fetcher:success\", \"CC-MAIN-2016-22\"]\t1468074295\n[\"crawl_status\", \"fetcher:notmodified\", \"CC-MAIN-2016-22\"]\t256139\n[\"crawl_status\", \"fetcher:temp_moved\", \"CC-MAIN-2016-22\"]\t334686475\n[\"crawl_status\", \"fetcher:moved\", \"CC-MAIN-2016-22\"]\t1117011316\n[\"crawl_status\", \"fetcher:notfound\", \"CC-MAIN-2016-22\"]\t453120454\n[\"crawl_status\", \"fetcher:access_denied\", \"CC-MAIN-2016-22\"]\t1219720\n[\"crawl_status\", \"fetcher:gone\", \"CC-MAIN-2016-22\"]\t23171460\n[\"crawl_status\", \"fetcher:exception\", \"CC-MAIN-2016-22\"]\t159901466\n[\"crawl_status\", \"fetcher:robots_denied\", \"CC-MAIN-2016-22\"]\t671241422\n[\"crawl_status\", \"fetcher:robots_denied_maxcrawldelay\", \"CC-MAIN-2016-22\"]\t7796696\n[\"crawl_status\", \"fetcher:filter_denied\", \"CC-MAIN-2016-22\"]\t492\n[\"crawl_status\", \"fetcher:hitByThrougputThreshold\", \"CC-MAIN-2016-22\"]\t299420928\n[\"crawl_status\", \"fetcher:hitByTimeLimit\", \"CC-MAIN-2016-22\"]\t255346003\n[\"crawl_status\", \"fetcher:AboveExceptionThresholdInQueue\", \"CC-MAIN-2016-22\"]\t436376946\n[\"crawl_status\", \"fetcher:bytes_downloaded\", \"CC-MAIN-2016-22\"]\t109013317353257\n" }, { "path": "stats/crawler/CC-MAIN-2016-26.json", "content": "[\"crawl_status\", \"generator:crawldb_size\", \"CC-MAIN-2016-26\"]\t7180806080\n[\"crawl_status\", \"generator:fetch_list\", \"CC-MAIN-2016-26\"]\t3578181474\n[\"crawl_status\", \"generator:selected:db_fetched\", \"CC-MAIN-2016-26\"]\t1299552667\n[\"crawl_status\", \"generator:selected:db_gone\", \"CC-MAIN-2016-26\"]\t7849358\n[\"crawl_status\", \"generator:selected:db_notmodified\", \"CC-MAIN-2016-26\"]\t4542\n[\"crawl_status\", \"generator:selected:db_redir_perm\", \"CC-MAIN-2016-26\"]\t1026907236\n[\"crawl_status\", \"generator:selected:db_redir_temp\", \"CC-MAIN-2016-26\"]\t295675118\n[\"crawl_status\", \"generator:selected:db_unfetched\", \"CC-MAIN-2016-26\"]\t948192553\n[\"crawl_status\", \"generator:skipped_domains_overflow\", \"CC-MAIN-2016-26\"]\t820\n[\"crawl_status\", \"fetcher:total\", \"CC-MAIN-2016-26\"]\t3555622846\n[\"crawl_status\", \"fetcher:success\", \"CC-MAIN-2016-26\"]\t1239037032\n[\"crawl_status\", \"fetcher:notmodified\", \"CC-MAIN-2016-26\"]\t283288\n[\"crawl_status\", \"fetcher:temp_moved\", \"CC-MAIN-2016-26\"]\t281679547\n[\"crawl_status\", \"fetcher:moved\", \"CC-MAIN-2016-26\"]\t995729397\n[\"crawl_status\", \"fetcher:notfound\", \"CC-MAIN-2016-26\"]\t48853116\n[\"crawl_status\", \"fetcher:access_denied\", \"CC-MAIN-2016-26\"]\t171324\n[\"crawl_status\", \"fetcher:gone\", \"CC-MAIN-2016-26\"]\t2442289\n[\"crawl_status\", \"fetcher:exception\", \"CC-MAIN-2016-26\"]\t219056540\n[\"crawl_status\", \"fetcher:robots_denied\", \"CC-MAIN-2016-26\"]\t116429750\n[\"crawl_status\", \"fetcher:robots_denied_maxcrawldelay\", \"CC-MAIN-2016-26\"]\t271438\n[\"crawl_status\", \"fetcher:filter_denied\", \"CC-MAIN-2016-26\"]\t226\n[\"crawl_status\", \"fetcher:hitByThrougputThreshold\", \"CC-MAIN-2016-26\"]\t360488201\n[\"crawl_status\", \"fetcher:hitByTimeLimit\", \"CC-MAIN-2016-26\"]\t167326318\n[\"crawl_status\", \"fetcher:AboveExceptionThresholdInQueue\", \"CC-MAIN-2016-26\"]\t123854380\n[\"crawl_status\", \"fetcher:bytes_downloaded\", \"CC-MAIN-2016-26\"]\t88623547035512\n[\"crawl_status\", \"crawldb:status:db_unfetched\", \"CC-MAIN-2016-26\"]\t2959816783\n[\"crawl_status\", \"crawldb:status:db_fetched\", \"CC-MAIN-2016-26\"]\t1458288238\n[\"crawl_status\", \"crawldb:status:db_gone\", \"CC-MAIN-2016-26\"]\t1208261302\n[\"crawl_status\", \"crawldb:status:db_redir_temp\", \"CC-MAIN-2016-26\"]\t341362333\n[\"crawl_status\", \"crawldb:status:db_redir_perm\", \"CC-MAIN-2016-26\"]\t1212535320\n[\"crawl_status\", \"crawldb:status:db_notmodified\", \"CC-MAIN-2016-26\"]\t542104\n" }, { "path": "stats/crawler/CC-MAIN-2016-30.json", "content": "[\"crawl_status\", \"generator:crawldb_size\", \"CC-MAIN-2016-30\"]\t7180518032\n[\"crawl_status\", \"generator:fetch_list\", \"CC-MAIN-2016-30\"]\t2973093696\n[\"crawl_status\", \"generator:selected:db_duplicate\", \"CC-MAIN-2016-30\"]\t1245810\n[\"crawl_status\", \"generator:selected:db_fetched\", \"CC-MAIN-2016-30\"]\t1329353152\n[\"crawl_status\", \"generator:selected:db_gone\", \"CC-MAIN-2016-30\"]\t8699898\n[\"crawl_status\", \"generator:selected:db_notmodified\", \"CC-MAIN-2016-30\"]\t4372\n[\"crawl_status\", \"generator:selected:db_redir_perm\", \"CC-MAIN-2016-30\"]\t682024659\n[\"crawl_status\", \"generator:selected:db_redir_temp\", \"CC-MAIN-2016-30\"]\t174730909\n[\"crawl_status\", \"generator:selected:db_unfetched\", \"CC-MAIN-2016-30\"]\t777034896\n[\"crawl_status\", \"generator:skipped_domains_overflow\", \"CC-MAIN-2016-30\"]\t703\n[\"crawl_status\", \"fetcher:total\", \"CC-MAIN-2016-30\"]\t4197629689\n[\"crawl_status\", \"fetcher:success\", \"CC-MAIN-2016-30\"]\t1810244403\n[\"crawl_status\", \"fetcher:notmodified\", \"CC-MAIN-2016-30\"]\t59201\n[\"crawl_status\", \"fetcher:temp_moved\", \"CC-MAIN-2016-30\"]\t290453958\n[\"crawl_status\", \"fetcher:moved\", \"CC-MAIN-2016-30\"]\t981585540\n[\"crawl_status\", \"fetcher:notfound\", \"CC-MAIN-2016-30\"]\t206900007\n[\"crawl_status\", \"fetcher:access_denied\", \"CC-MAIN-2016-30\"]\t324964\n[\"crawl_status\", \"fetcher:gone\", \"CC-MAIN-2016-30\"]\t9945025\n[\"crawl_status\", \"fetcher:exception\", \"CC-MAIN-2016-30\"]\t228070081\n[\"crawl_status\", \"fetcher:robots_denied\", \"CC-MAIN-2016-30\"]\t141536480\n[\"crawl_status\", \"fetcher:robots_denied_maxcrawldelay\", \"CC-MAIN-2016-30\"]\t425474\n[\"crawl_status\", \"fetcher:filter_denied\", \"CC-MAIN-2016-30\"]\t0\n[\"crawl_status\", \"fetcher:hitByThrougputThreshold\", \"CC-MAIN-2016-30\"]\t203614009\n[\"crawl_status\", \"fetcher:hitByTimeLimit\", \"CC-MAIN-2016-30\"]\t205019936\n[\"crawl_status\", \"fetcher:AboveExceptionThresholdInQueue\", \"CC-MAIN-2016-30\"]\t119450611\n[\"crawl_status\", \"fetcher:bytes_downloaded\", \"CC-MAIN-2016-30\"]\t152939938979003\n[\"crawl_status\", \"crawldb:status:db_unfetched\", \"CC-MAIN-2016-30\"]\t2700852810\n[\"crawl_status\", \"crawldb:status:db_fetched\", \"CC-MAIN-2016-30\"]\t1511329903\n[\"crawl_status\", \"crawldb:status:db_gone\", \"CC-MAIN-2016-30\"]\t1301660933\n[\"crawl_status\", \"crawldb:status:db_redir_temp\", \"CC-MAIN-2016-30\"]\t363167187\n[\"crawl_status\", \"crawldb:status:db_redir_perm\", \"CC-MAIN-2016-30\"]\t1302685912\n[\"crawl_status\", \"crawldb:status:db_notmodified\", \"CC-MAIN-2016-30\"]\t821287\n" }, { "path": "stats/crawler/CC-MAIN-2016-36.json", "content": "[\"crawl_status\", \"generator:crawldb_size\", \"CC-MAIN-2016-36\"]\t7218846495\n[\"crawl_status\", \"generator:fetch_list\", \"CC-MAIN-2016-36\"]\t2837438550\n[\"crawl_status\", \"generator:selected:db_duplicate\", \"CC-MAIN-2016-36\"]\t4016986\n[\"crawl_status\", \"generator:selected:db_fetched\", \"CC-MAIN-2016-36\"]\t1310692190\n[\"crawl_status\", \"generator:selected:db_gone\", \"CC-MAIN-2016-36\"]\t11216926\n[\"crawl_status\", \"generator:selected:db_notmodified\", \"CC-MAIN-2016-36\"]\t4322\n[\"crawl_status\", \"generator:selected:db_redir_perm\", \"CC-MAIN-2016-36\"]\t723192092\n[\"crawl_status\", \"generator:selected:db_redir_temp\", \"CC-MAIN-2016-36\"]\t157229196\n[\"crawl_status\", \"generator:selected:db_unfetched\", \"CC-MAIN-2016-36\"]\t631086838\n[\"crawl_status\", \"generator:skipped_domains_overflow\", \"CC-MAIN-2016-36\"]\t659\n[\"crawl_status\", \"fetcher:total\", \"CC-MAIN-2016-36\"]\t3299721802\n[\"crawl_status\", \"fetcher:success\", \"CC-MAIN-2016-36\"]\t1669540210\n[\"crawl_status\", \"fetcher:notmodified\", \"CC-MAIN-2016-36\"]\t297150\n[\"crawl_status\", \"fetcher:temp_moved\", \"CC-MAIN-2016-36\"]\t141377763\n[\"crawl_status\", \"fetcher:moved\", \"CC-MAIN-2016-36\"]\t358938772\n[\"crawl_status\", \"fetcher:notfound\", \"CC-MAIN-2016-36\"]\t199755423\n[\"crawl_status\", \"fetcher:access_denied\", \"CC-MAIN-2016-36\"]\t403716\n[\"crawl_status\", \"fetcher:gone\", \"CC-MAIN-2016-36\"]\t10264655\n[\"crawl_status\", \"fetcher:exception\", \"CC-MAIN-2016-36\"]\t156733905\n[\"crawl_status\", \"fetcher:robots_denied\", \"CC-MAIN-2016-36\"]\t118198417\n[\"crawl_status\", \"fetcher:robots_denied_maxcrawldelay\", \"CC-MAIN-2016-36\"]\t354645\n[\"crawl_status\", \"fetcher:filter_denied\", \"CC-MAIN-2016-36\"]\t434913\n[\"crawl_status\", \"fetcher:hitByThrougputThreshold\", \"CC-MAIN-2016-36\"]\t376252865\n[\"crawl_status\", \"fetcher:hitByTimeLimit\", \"CC-MAIN-2016-36\"]\t138243661\n[\"crawl_status\", \"fetcher:AboveExceptionThresholdInQueue\", \"CC-MAIN-2016-36\"]\t128925707\n[\"crawl_status\", \"fetcher:bytes_downloaded\", \"CC-MAIN-2016-36\"]\t137795391799291\n[\"crawl_status\", \"crawldb:status:db_unfetched\", \"CC-MAIN-2016-36\"]\t2417090235\n[\"crawl_status\", \"crawldb:status:db_fetched\", \"CC-MAIN-2016-36\"]\t1513758138\n[\"crawl_status\", \"crawldb:status:db_gone\", \"CC-MAIN-2016-36\"]\t1515748969\n[\"crawl_status\", \"crawldb:status:db_redir_temp\", \"CC-MAIN-2016-36\"]\t196286452\n[\"crawl_status\", \"crawldb:status:db_redir_perm\", \"CC-MAIN-2016-36\"]\t946721948\n[\"crawl_status\", \"crawldb:status:db_notmodified\", \"CC-MAIN-2016-36\"]\t814877\n[\"crawl_status\", \"crawldb:status:db_duplicate\", \"CC-MAIN-2016-36\"]\t628425876\n" }, { "path": "stats/crawler/CC-MAIN-2016-40.json", "content": "[\"crawl_status\", \"generator:crawldb_size\", \"CC-MAIN-2016-40\"]\t6936860504\n[\"crawl_status\", \"generator:fetch_list\", \"CC-MAIN-2016-40\"]\t2438332392\n[\"crawl_status\", \"generator:skipped_domains_overflow\", \"CC-MAIN-2016-40\"]\t477\n[\"crawl_status\", \"fetcher:total\", \"CC-MAIN-2016-40\"]\t2706529909\n[\"crawl_status\", \"fetcher:success\", \"CC-MAIN-2016-40\"]\t1751866875\n[\"crawl_status\", \"fetcher:notmodified\", \"CC-MAIN-2016-40\"]\t258041\n[\"crawl_status\", \"fetcher:temp_moved\", \"CC-MAIN-2016-40\"]\t78975219\n[\"crawl_status\", \"fetcher:moved\", \"CC-MAIN-2016-40\"]\t199660245\n[\"crawl_status\", \"fetcher:notfound\", \"CC-MAIN-2016-40\"]\t68408887\n[\"crawl_status\", \"fetcher:access_denied\", \"CC-MAIN-2016-40\"]\t653219\n[\"crawl_status\", \"fetcher:gone\", \"CC-MAIN-2016-40\"]\t3887018\n[\"crawl_status\", \"fetcher:exception\", \"CC-MAIN-2016-40\"]\t111251283\n[\"crawl_status\", \"fetcher:robots_denied\", \"CC-MAIN-2016-40\"]\t66420383\n[\"crawl_status\", \"fetcher:robots_denied_maxcrawldelay\", \"CC-MAIN-2016-40\"]\t434523\n[\"crawl_status\", \"fetcher:filter_denied\", \"CC-MAIN-2016-40\"]\t112653\n[\"crawl_status\", \"fetcher:hitByThrougputThreshold\", 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\"CC-MAIN-2026-17\"]\t2895\n[\"crawl_status\", \"fetcher:hit_by_timeout_total\", \"CC-MAIN-2026-17\"]\t578\n[\"crawl_status\", \"fetcher:bytes_downloaded_total\", \"CC-MAIN-2026-17\"]\t416956151754369\n[\"crawl_status\", \"crawldb:size\", \"CC-MAIN-2026-17\"]\t24642277769\n[\"crawl_status\", \"crawldb:status:db_unfetched\", \"CC-MAIN-2026-17\"]\t3297370708\n[\"crawl_status\", \"crawldb:status:db_fetched\", \"CC-MAIN-2026-17\"]\t14792539087\n[\"crawl_status\", \"crawldb:status:db_gone\", \"CC-MAIN-2026-17\"]\t1796708613\n[\"crawl_status\", \"crawldb:status:db_redir_temp\", \"CC-MAIN-2026-17\"]\t205870956\n[\"crawl_status\", \"crawldb:status:db_redir_perm\", \"CC-MAIN-2026-17\"]\t402958862\n[\"crawl_status\", \"crawldb:status:db_notmodified\", \"CC-MAIN-2026-17\"]\t978689541\n[\"crawl_status\", \"crawldb:status:db_duplicate\", \"CC-MAIN-2026-17\"]\t1173128233\n[\"crawl_status\", \"crawldb:status:db_orphan\", \"CC-MAIN-2026-17\"]\t1995011769\n" }, { "path": "stats/crawler/README.md", "content": "Crawler-Related Metrics\n=======================\n\nJSON files in this folder contain metrics\n- written by the crawler ([Apache Nutch](http://nutch.apache.org/) in a [modified version](https://github.com/commoncrawl/nutch/) adapted for Common Crawl) as Hadoop job counters,\n- extracted from crawler log files, and\n- saved in the JSON-based, tab-separated key-value output format used by [mrjob](https://pythonhosted.org/mrjob/)" }, { "path": "stats/tld_alexa_top_1m.py", "content": "# derived from\n# http://s3.amazonaws.com/alexa-static/top-1m.csv.zip\n# fetched 2019-02-06, see also\n# https://support.alexa.com/hc/en-us/sections/200063274-Top-Sites\n\nalexa_top_1m_tlds_about = {\n 'date': '2019-02-06',\n 'source': 'http://s3.amazonaws.com/alexa-static/top-1m.csv.zip'\n}\n\nalexa_top_1m_tlds = {\n # zcat top-1m.csv.zip \\\n # | perl -ne 'chomp; s/.+\\.//; $h{$_}++;\n # END {\n # for (sort { $h{$b} <=> $h{$a} } keys %h) {\n # print \"\\047\", $_, \"\\047:\", $h{$_}, \", \";\n # }\n # }' \\\n # | fold --width=70 -s | perl -lpe 's/^/ /; s/:/: /g; s/\\s+$//'\n 'com': 477407, 'org': 48692, 'ru': 44928, 'net': 40595, 'de': 31039,\n 'br': 24140, 'uk': 16820, 'pl': 15490, 'in': 14051, 'ir': 12913,\n 'it': 11283, 'au': 11085, 'jp': 10723, 'fr': 9673, 'info': 8625,\n 'cz': 7787, 'es': 6967, 'co': 6967, 'ca': 6813, 'gr': 6548, 'mx': 6325,\n 'io': 6249, 'ua': 5918, 'ro': 5885, 'nl': 5677, 'eu': 5001, 'ar': 4800,\n 'ch': 4616, 'se': 4053, 'za': 3855, 'sk': 3791, 'hu': 3671, 'no': 3640,\n 'me': 3587, 'us': 3343, 'be': 3303, 'tr': 3254, 'tv': 3242, 'tw': 3228,\n 'dk': 3165, 'cl': 3111, 'at': 3097, 'edu': 3055, 'cn': 2644, 'kr': 2573,\n 'xyz': 2209, 'biz': 2191, 'pt': 2127, 'fi': 2074, 'id': 1982, 'club': 1876,\n 'ie': 1770, 'online': 1686, 'pro': 1666, 'az': 1630, 'vn': 1615,\n 'il': 1570, 'by': 1406, 'th': 1404, 'cc': 1382, 'nz': 1372, 'kz': 1287,\n 'pe': 1240, 'lt': 1153, 'su': 1116, 'hr': 1102, 'bg': 1081,\n 'xn--p1ai': 1076, 'pk': 1075, 'gov': 1065, 'my': 1053, 'site': 1033,\n 'ng': 1010, 'rs': 1008, 'sg': 978, 'hk': 967, 'si': 955, 'sa': 821,\n 'ae': 812, 'top': 793, 'pw': 747, 'uz': 730, 'ph': 668, 'lv': 576,\n 'live': 569, 'to': 566, 'tk': 537, 'cat': 512, 'mobi': 491, 've': 488,\n 'lk': 483, 'ee': 475, 'ws': 461, 'bd': 454, 'ma': 438, 'do': 421, 'is': 406,\n 'fm': 377, 'ge': 364, 'news': 364, 'ba': 362, 'space': 356, 'ec': 353,\n 'life': 353, 'tn': 352, 'uy': 347, 'xxx': 335, 'ml': 322, 'eg': 321,\n 'ai': 315, 'nu': 313, 'am': 311, 'blog': 310, 'ga': 310, 'guru': 308,\n 'ke': 306, 'fun': 296, 'cr': 292, 'shop': 290, 'mk': 285, 'cf': 284,\n 'website': 280, 'app': 276, 'asia': 272, 'lu': 272, 'tech': 271,\n 'cloud': 258, 'md': 257, 'today': 253, 'ly': 246, 'gg': 243, 'icu': 232,\n 'media': 230, 'store': 230, 'dz': 229, 'one': 226, 'qa': 212, 'host': 210,\n 'link': 204, 'py': 202, 'la': 198, 'al': 198, 'win': 193, 'tz': 189,\n 'world': 186, 'work': 186, 'name': 184, 'travel': 172, 'af': 172,\n 'vip': 172, 'rocks': 163, 'cu': 161, 'bo': 159, 'kg': 156, 'bz': 153,\n 'im': 152, 'ug': 150, 'gt': 150, 'aero': 148, 'pa': 148, 'kw': 146,\n 'cy': 142, 'video': 141, 'network': 140, 'coop': 135, 'mm': 134, 'ao': 131,\n 'tj': 131, 'sv': 130, 'global': 130, 'om': 129, 'eus': 128,\n 'download': 127, 'digital': 126, 'tokyo': 126, 'design': 117, 'jobs': 116,\n 'gq': 112, 'vc': 109, 'mn': 108, 'np': 107, 'stream': 106, 'ag': 105,\n 'wiki': 105, 'jo': 105, 'sy': 102, 'click': 100, 'int': 99, 'center': 99,\n 'zone': 98, 'cm': 92, 'academy': 91, 'plus': 90, 'agency': 89, 'sh': 87,\n 'ni': 87, 'mz': 86, 'li': 83, 'moe': 83, 'gh': 82, 'lb': 81, 'nyc': 79,\n 'company': 79, 'ninja': 78, 'ci': 76, 'tools': 76, 'ac': 75, 'games': 74,\n 'city': 74, 'bid': 73, 'sd': 73, 'education': 71, 'studio': 71, 'ovh': 71,\n 'zw': 70, 'st': 69, 'porn': 69, 'ps': 69, 'tm': 69, 'rw': 68, 'mg': 68,\n 'et': 68, 'hn': 66, 'press': 66, 'group': 64, 're': 63, 'iq': 62,\n 'expert': 62, 'red': 62, 'ooo': 62, 'london': 61, 'bh': 61,\n 'solutions': 61, 'school': 61, 'bank': 60, 'services': 60, 'sn': 60,\n 'mu': 60, 'bet': 59, 'guide': 59, 'tips': 58, 'market': 57, 'cool': 57,\n 'trade': 56, 'cd': 56, 'cx': 55, 'kh': 55, 'ink': 54, 'mil': 54,\n 'community': 53, 'ms': 53, 'pub': 53, 'help': 52, 'art': 51, 'bw': 51,\n 'cash': 51, 'mt': 50, 'pics': 50, 'gratis': 50, 'chat': 49, 'email': 49,\n 'sc': 49, 'sexy': 48, 'social': 48, 'land': 47, 'tt': 47, 'zm': 47,\n 'team': 46, 'pg': 46, 'berlin': 46, 'systems': 45, 'events': 45, 'ltd': 45,\n 'watch': 44, 'works': 42, 'review': 42, 'so': 42, 'support': 41,\n 'church': 41, 'bn': 40, 'men': 39, 'ad': 39, 'na': 38, 'gs': 37, 'run': 36,\n 'pr': 36, 'gal': 35, 'bt': 35, 'swiss': 35, 'paris': 35, 'gdn': 35,\n 'kim': 34, 'love': 34, 'exchange': 34, 'marketing': 34, 'bike': 34,\n 'tf': 33, 'mw': 33, 'bio': 33, 'software': 33, 'tube': 32, 'training': 32,\n 'istanbul': 31, 'wtf': 31, 'money': 31, 'fo': 31, 'sex': 30, 'moscow': 30,\n 'bf': 30, 'mo': 30, 'pf': 30, 'farm': 29, 'lol': 29, 'fit': 29, 'pm': 29,\n 'energy': 29, 'coffee': 28, 'party': 28, 'codes': 28, 'ht': 28,\n 'audio': 28, 'capital': 28, 'as': 28, 'report': 28, 'deals': 28,\n 'technology': 27, 'jm': 27, 'scot': 27, 'nc': 27, 'blue': 26, 'gl': 26,\n 'sx': 26, 'africa': 26, 'care': 25, 'careers': 25, 'xn--80asehdb': 24,\n 'uno': 24, 'tl': 24, 'tc': 24, 'photography': 23, 'movie': 23, 'wales': 23,\n 'cafe': 23, 'bar': 23, 'direct': 22, 'nrw': 22, 'house': 22, 'science': 22,\n 'film': 21, 'date': 21, 'institute': 21, 'buzz': 20, 'xn--j1amh': 20,\n 'express': 20, 'bi': 20, 'bm': 20, 'xn--p1acf': 20, 'brussels': 19,\n 'gold': 19, 'vision': 19, 'fyi': 19, 'dev': 19, 'taipei': 19, 'sale': 19,\n 'show': 19, 'dating': 18, 'gallery': 18, 'business': 18, 'style': 18,\n 'university': 18, 'yt': 18, 'gy': 18, 'fj': 18, 'game': 18, 'je': 18,\n 'pink': 18, 'webcam': 18, 'mv': 17, 'tel': 17, 'fund': 17, 'vg': 17,\n 'build': 17, 'bj': 17, 'directory': 17, 'photos': 17, 'lat': 17,\n 'clinic': 16, 'museum': 16, 'wang': 16, 'vet': 16, 'gi': 16, 'menu': 16,\n 'sm': 16, 'pet': 16, 'rip': 16, 'dj': 16, 'legal': 16, 'cg': 15,\n 'partners': 15, 'lc': 15, 'earth': 15, 'desi': 14, 'gd': 14, 'cam': 14,\n 'engineering': 14, 'bzh': 14, 'cv': 14, 'graphics': 14, 'hosting': 14,\n 'canon': 14, 'ye': 14, 'ky': 14, 'photo': 13, 'place': 13, 'pizza': 13,\n 'ist': 13, 'va': 13, 'international': 13, 'best': 13, 'reviews': 13,\n 'onl': 13, 'mr': 13, 'law': 13, 'health': 12, 'rest': 12, 'how': 12,\n 'xn--90ais': 12, 'tours': 12, 'beer': 12, 'bb': 12, 'kp': 12, 'ngo': 12,\n 'restaurant': 11, 'finance': 11, 'sz': 11, 'delivery': 11,\n 'xn--d1acj3b': 11, 'dog': 11, 'bs': 11, 'foundation': 11,\n 'management': 11, 'black': 11, 'kiwi': 11, 'leclerc': 11, 'sb': 11,\n 'eco': 10, 'sl': 10, 'tg': 10, 'ventures': 10, 'dance': 10, 'parts': 10,\n 'abbott': 10, 'fitness': 10, 'radio': 10, 'wien': 10, 'boutique': 10,\n 'vu': 10, 'loan': 10, 'google': 10, 'town': 10, 'college': 10, 'bayern': 9,\n 'toys': 9, 'shoes': 9, 'tax': 9, 'ls': 9, 'band': 9, 'immo': 9, 'coach': 9,\n 'ne': 9, 'gm': 9, 'box': 9, 'hamburg': 9, 'mba': 9, 'promo': 9, 'koeln': 9,\n 'football': 9, 'green': 9, 'pictures': 9, 'cards': 9, 'vin': 9, 'mc': 9,\n 'cw': 8, 'dental': 8, 'yoga': 8, 'schule': 8, 'amsterdam': 8,\n 'bnpparibas': 8, 'wedding': 8, 'camp': 8, 'sap': 8, 'ax': 8, 'casa': 8,\n 'ski': 8, 'xn--80adxhks': 8, 'domains': 8, 'camera': 8, 'supply': 8,\n 'garden': 8, 'krd': 7, 'vegas': 7, 'bot': 7, 'sr': 7, 'page': 7, 'army': 7,\n 'jetzt': 7, 'barcelona': 7, 'cab': 7, 'quebec': 7, 'horse': 7, 'aws': 7,\n 'clothing': 7, 'casino': 7, 'bradesco': 7, 'taxi': 6, 'cymru': 6,\n 'exposed': 6, 'weber': 6, 'post': 6, 'tirol': 6, 'soy': 6, 'wine': 6,\n 'basketball': 6, 'builders': 6, 'fish': 6, 'recipes': 6, 'audi': 6,\n 'td': 6, 'diet': 6, 'realtor': 6, 'haus': 6, 'ads': 6, 'rentals': 6,\n 'pharmacy': 6, 'mp': 6, 'xn--80aswg': 5, 'cricket': 5, 'miami': 5,\n 'vote': 5, 'prod': 5, 'productions': 5, 'kitchen': 5, 'racing': 5, 'dm': 5,\n 'lighting': 5, 'xn--c1avg': 5, 'computer': 5, 'wf': 5, 'solar': 5,\n 'credit': 5, 'bible': 5, 'consulting': 5, 'gent': 5, 'equipment': 5,\n 'family': 5, 'jewelry': 5, 'auction': 5, 'forsale': 5, 'poker': 5,\n 'rio': 5, 'barclays': 5, 'shiksha': 4, 'gift': 4, 'office': 4, 'nagoya': 4,\n 'shopping': 4, 'discount': 4, 'lawyer': 4, 'flights': 4, 'aw': 4,\n 'sydney': 4, 'srl': 4, 'golf': 4, 'rent': 4, 'surf': 4, 'gn': 4, 'hm': 4,\n 'observer': 4, 'corsica': 4, 'singles': 4, 'estate': 4, 'fashion': 4,\n 'sport': 4, 'vi': 4, 'fail': 4, 'trading': 4, 'nico': 4, 'limited': 4,\n 'ruhr': 4, 'adult': 4, 'nf': 4, 'tattoo': 4, 'sbi': 4, 'apartments': 4,\n 'insure': 4, 'cern': 3, 'neustar': 3, 'salon': 3, 'gp': 3, 'tatar': 3,\n 'lr': 3, 'med': 3, 'frl': 3, 'repair': 3, 'moda': 3, 'theater': 3,\n 'here': 3, 'xn--90ae': 3, 'fans': 3, 'yokohama': 3, 'forex': 3, 'pn': 3,\n 'sener': 3, 'gifts': 3, 'ipiranga': 3, 'gf': 3, 'holdings': 3, 'auto': 3,\n 'xn--3e0b707e': 3, 'cooking': 3, 'contractors': 3, 'futbol': 3,\n 'edeka': 3, 'cheap': 3, 'flowers': 3, 'study': 3, 'gmbh': 3, 'fishing': 3,\n 'healthcare': 3, 'hockey': 3, 'stockholm': 3, 'kn': 3, 'hot': 3, 'cars': 3,\n 'car': 3, 'archi': 3, 'telefonica': 3, 'vlaanderen': 3, 'industries': 3,\n 'claims': 2, 'vodka': 2, 'mom': 2, 'abudhabi': 2, 'faith': 2, 'career': 2,\n 'xin': 2, 'attorney': 2, 'bv': 2, 'ong': 2, 'sncf': 2, 'ricoh': 2,\n 'teva': 2, 'pioneer': 2, 'yandex': 2, 'lease': 2, 'investments': 2,\n 'security': 2, 'gop': 2, 'immobilien': 2, 'okinawa': 2, 'pictet': 2,\n 'goog': 2, 'tienda': 2, 'fan': 2, 'abc': 2, 'coupons': 2, 'sandvik': 2,\n 'monash': 2, 'ki': 2, 'inc': 2, 'country': 2, 'alsace': 2,\n 'barclaycard': 2, 'sony': 2, 'melbourne': 2, 'dhl': 2, 'brother': 2,\n 'glass': 2, 'ceb': 2, 'dentist': 2, 'aq': 2, 'mortgage': 2, 'sharp': 2,\n 'associates': 2, 'kpmg': 2, 'gw': 2, 'luxury': 2, 'afl': 2, 'mq': 2,\n 'sky': 2, 'total': 2, 'kaufen': 1, 'rwe': 1, 'woodside': 1, 'crs': 1,\n 'voyage': 1, 'tickets': 1, 'kpn': 1, 'xn--wgbl6a': 1, 'epson': 1,\n 'financial': 1, 'honda': 1, 'llc': 1, 'markets': 1, 'xn--90a3ac': 1,\n 'reisen': 1, 'reit': 1, 'courses': 1, 'ren': 1, 'drive': 1, 'nr': 1,\n 'xn--h2brj9c': 1, 'xn--tckwe': 1, 'gives': 1, 'sarl': 1, 'seat': 1,\n 'kred': 1, 'java': 1, 'fox': 1, 'komatsu': 1, 'abb': 1, 'gripe': 1,\n 'osaka': 1, 'property': 1, 'insurance': 1, 'chrome': 1, 'hiphop': 1,\n 'crown': 1, 'actor': 1, 'zip': 1, 'rmit': 1, 'ceo': 1, 'limo': 1, 'luxe': 1,\n 'properties': 1, 'mango': 1, 'rodeo': 1, 'versicherung': 1, 'orange': 1,\n 'jcb': 1, 'xn--fiqs8s': 1, 'goo': 1, 'ryukyu': 1, 'xn--pgbs0dh': 1,\n 'off': 1, 'ck': 1, 'scb': 1, 'enterprises': 1, 'star': 1, 'capetown': 1,\n 'dvag': 1, 'ismaili': 1, 'spot': 1, 'uol': 1, 'vanguard': 1, 'axa': 1,\n 'accountant': 1, 'soccer': 1, 'cologne': 1, 'bargains': 1, 'juegos': 1,\n 'lamborghini': 1, 'cruises': 1, 'vacations': 1, 'stada': 1, 'skype': 1,\n 'hospital': 1, 'xn--54b7fta0cc': 1, 'xn--q9jyb4c': 1, 'weir': 1,\n 'guitars': 1, 'tires': 1, 'vivo': 1, 'madrid': 1, 'globo': 1, 'trv': 1,\n 'loans': 1, 'holiday': 1, 'rugby': 1, 'storage': 1, 'joburg': 1, 'ses': 1,\n 'creditcard': 1, 'degree': 1, 'surgery': 1, 'saxo': 1, 'softbank': 1,\n 'accountants': 1, 'cbn': 1, 'lundbeck': 1, 'physio': 1, 'organic': 1,\n 'you': 1, 'doctor': 1, 'kyoto': 1, 'realty': 1, 'cba': 1, 'praxi': 1,\n 'broker': 1, 'tennis': 1, 'qvc': 1, 'gmail': 1, 'viajes': 1,\n}\n" }, { "path": "stats/tld_cisco_umbrella_top_1m.py", "content": "# derived from\n# http://s3-us-west-1.amazonaws.com/umbrella-static/top-1m.csv.zip\n# fetched 2019-02-06, see also\n# http://s3-us-west-1.amazonaws.com/umbrella-static/index.html\n#\n# \"The popularity list contains our most queried domains based on\n# passive DNS usage across our Umbrella global network of more than\n# 100 Billion requests per day with 65 million unique active users, in\n# more than 165 countries. Unlike Alexa, the metric is not based on\n# only browser based 'http' requests from users but rather takes into\n# account the number of unique client IPs invoking this domain\n# relative to the sum of all requests to all domains. In other words,\n# our popularity ranking reflects the domain’s relative internet\n# activity agnostic to the invocation protocols and applications where\n# as ’site ranking’ models (such as Alexa) focus on the web activity\n# over port 80 mainly from browsers.\n#\n# As for Alexa, the site’s rank is based on combined measure of unique\n# visitors (Alexa users who visit the site per day) and page views\n# (total URL requests from Alexa users for a site). Umbrella\n# popularity lists are generated on a daily basis reflecting the\n# actual world-wide usage of domains by Umbrella global network users\n# and includes root domains, subdomains in addition to TLDs (Alexa\n# list has only this). In addition, Umbrella popularity algorithm also\n# applies data normalization methodologies to smoothen potential\n# biases that may occur in the data due to sampling of the DNS usage\n# data.\"\n#\n#\n# NOTE: The list contains also domain names from intranets - 'localhost',\n# 'belkin', 'dlinkrouter', 'dmz', etc. These are filtered out by\n# allowing only IANA-registered domain name suffixes.\n#\n\ncisco_umbrella_top_1m_tlds_about = {\n 'date': '2019-02-06',\n 'source': 'http://s3-us-west-1.amazonaws.com/umbrella-static/top-1m.csv.zip'\n}\n\ncisco_umbrella_top_1m_tlds = {\n # zcat top-1m.csv.zip \\\n # | perl -ne 'chomp; s/^\\d+,//; s/.+\\.//; $h{$_}++;\n # END {\n # for (sort { $h{$b} <=> $h{$a} } keys %h) {\n # print \"\\047\", $_, \"\\047:\", $h{$_}, \", \";\n # }\n # }' \\\n # | fold --width=70 -s | perl -lpe 's/^/ /; s/:/: /g; s/\\s+$//'\n 'com': 579421, 'net': 155319, 'org': 41951, 'ru': 14293, 'uk': 11216,\n 'edu': 10324, 'biz': 9884, 'io': 9316, 'de': 9179, 'me': 7807,\n 'info': 7483, 'cn': 6274, 'in': 6267, 'cc': 5401, 'co': 5352, 'gov': 5068,\n 'br': 4846, 'fr': 4752, 'nl': 4499, 'it': 4251, 'us': 3929, 'tv': 3813,\n 'eu': 3689, 'ca': 3585, 'jp': 3570, 'pl': 3361, 'vn': 2666, 'ua': 2572,\n 'es': 2249, 'au': 1901, 'mx': 1504, 'ch': 1462, 'be': 1455, 'id': 1386,\n 'se': 1253, 'dk': 1236, 'il': 1198, 'pt': 1180, 'link': 1153, 'cz': 1133,\n 'xyz': 1078, 'email': 1065, 'no': 1054, 'tr': 1044, 'kr': 996, 'club': 962,\n 'hu': 898, 'tw': 890, 'at': 866, 'mobi': 861, 'gr': 801, 'ir': 778,\n 'nz': 740, 'to': 735, 'pro': 707, 'cloud': 656, 'ro': 645, 'ar': 633,\n 'my': 631, 'hk': 612, 'online': 600, 'cl': 561, 'top': 540, 'za': 536,\n 'fi': 534, 'th': 526, 'mil': 489, 'sg': 488, 'sk': 486, 'pw': 463,\n 'name': 447, 'ie': 438, 'ng': 431, 'by': 419, 'ms': 402, 'st': 376,\n 'ec': 369, 'fm': 364, 'site': 362, 'ai': 361, 'ws': 357, 'is': 333,\n 'ph': 330, 'live': 326, 'pk': 325, 'im': 318, 'pe': 317, 'icu': 282,\n 've': 269, 'ae': 269, 'su': 264, 'media': 256, 'bg': 254, 'ly': 250,\n 'lt': 248, 'kz': 228, 'xxx': 227, 'gg': 223, 'do': 218, 'space': 216,\n 'hr': 211, 'services': 207, 'app': 202, 'cr': 198, 'tech': 196,\n 'systems': 182, 'gl': 175, 'ga': 172, 'rs': 166, 'host': 163, 'news': 162,\n 'video': 154, 'box': 154, 'network': 148, 'bz': 142, 'md': 142, 'sh': 140,\n 'ee': 138, 'lv': 138, 'tc': 137, 'eg': 131, 'tk': 131, 'la': 128,\n 'asia': 128, 'solutions': 127, 'life': 127, 'si': 126, 'lk': 125,\n 'nu': 124, 'one': 123, 'fun': 122, 'int': 122, 'stream': 122, 'world': 121,\n 'zone': 118, 'bid': 118, 'uy': 113, 'gt': 113, 'rocks': 113, 'bd': 112,\n 'lu': 111, 'am': 110, 'ag': 109, 'cat': 108, 'today': 108, 'sa': 105,\n 'store': 102, 'click': 102, 'ninja': 101, 'digital': 100, 'website': 94,\n 'aero': 94, 'win': 92, 'li': 85, 're': 85, 'ba': 84, 'ke': 82, 'work': 78,\n 'pa': 77, 'ovh': 76, 'games': 74, 'support': 73, 'hn': 70, 'dz': 69,\n 'vip': 68, 'sc': 67, 'al': 66, 'arpa': 65, 'sex': 63, 'tools': 63,\n 'guru': 60, 'ml': 60, 'az': 59, 'global': 59, 'py': 58, 'management': 58,\n 'download': 57, 'travel': 57, 'ma': 56, 'coop': 56, 'cm': 55, 'cu': 54,\n 'aws': 54, 'works': 54, 'vc': 53, 'sv': 53, 'cy': 53, 'cool': 51,\n 'jobs': 51, 'chat': 51, 'so': 50, 'kh': 50, 'plus': 50, 'np': 50, 'cf': 49,\n 'kg': 47, 'technology': 47, 'uz': 47, 'gs': 47, 'qa': 46, 'date': 46,\n 'bo': 46, 'lb': 46, 'pub': 45, 'ge': 45, 'ac': 43, 'ni': 42, 'blog': 41,\n 'bet': 41, 'tf': 41, 'ad': 40, 'sx': 39, 'af': 39, 'cx': 37, 'money': 37,\n 'ps': 36, 'gh': 33, 'tn': 33, 'goog': 32, 'xn--p1ai': 32, 'agency': 32,\n 'zw': 32, 'as': 31, 'tt': 31, 'mn': 30, 'mk': 30, 'pm': 30, 'press': 29,\n 'shop': 29, 'events': 29, 'run': 29, 'mt': 29, 'wiki': 28, 'trade': 28,\n 'porn': 28, 'pr': 27, 'tl': 27, 'tube': 26, 'om': 26, 'studio': 26,\n 'sn': 24, 'ci': 24, 'mm': 23, 'gdn': 23, 'ug': 23, 'team': 23, 'moe': 23,\n 'sexy': 23, 'software': 22, 'pics': 21, 'tips': 21, 'vg': 21, 'review': 20,\n 'iq': 20, 'tz': 20, 'sap': 20, 'mg': 20, 'science': 20, 'land': 20,\n 'na': 20, 'party': 19, 'gq': 19, 'google': 19, 'help': 19, 'lol': 19,\n 'wtf': 19, 'center': 19, 'mu': 18, 'company': 18, 'cd': 17, 'gy': 17,\n 'ink': 17, 'jm': 17, 'sncf': 17, 'fyi': 17, 'engineering': 17, 'group': 17,\n 'va': 17, 'market': 17, 'eus': 17, 'tm': 16, 'cash': 16, 'cricket': 16,\n 'care': 16, 'guide': 15, 'film': 15, 'mv': 15, 'bm': 15, 'buzz': 15,\n 'pg': 15, 'ne': 15, 'ooo': 15, 'direct': 15, 'bn': 14, 'expert': 14,\n 'lat': 14, 'game': 14, 'watch': 14, 'scot': 14, 'photos': 13, 'ao': 13,\n 'love': 13, 'bh': 13, 'uno': 13, 'design': 13, 'ky': 13, 'report': 13,\n 'menu': 13, 'ltd': 12, 'bank': 12, 'moscow': 12, 'movie': 12, 'bs': 12,\n 'bw': 12, 'mz': 12, 'nyc': 12, 'ht': 11, 'tj': 11, 'sm': 11, 'leclerc': 11,\n 'jo': 11, 'yandex': 11, 'dj': 11, 'show': 11, 'gd': 11, 'london': 11,\n 'nc': 10, 'rw': 10, 'training': 10, 'et': 10, 'social': 10, 'express': 10,\n 'gift': 10, 'church': 10, 'ye': 10, 'health': 10, 'dm': 10, 'bike': 10,\n 'fj': 10, 'school': 10, 'city': 10, 'hosting': 10, 'photo': 9, 'llc': 9,\n 'rip': 9, 'community': 9, 'recipes': 9, 'men': 9, 'domains': 9,\n 'supply': 9, 'education': 9, 'dev': 8, 'sky': 8, 'pn': 8, 'delivery': 8,\n 'law': 8, 'audio': 8, 'gratis': 8, 'kw': 8, 'cab': 8, 'gm': 8, 'pet': 8,\n 'rest': 8, 'build': 8, 'tel': 8, 'loan': 7, 'energy': 7, 'farm': 7, 'lc': 7,\n 'museum': 7, 'cv': 7, 'red': 7, 'vision': 7, 'ist': 7, 'ki': 7, 'fit': 7,\n 'je': 7, 'onl': 7, 'art': 7, 'pictures': 7, 'wf': 7, 'webcam': 7, 'pink': 7,\n 'vu': 7, 'zm': 7, 'supplies': 6, 'codes': 6, 'faith': 6, 'marketing': 6,\n 'pharmacy': 6, 'academy': 6, 'wang': 6, 'gold': 6, 'blue': 6, 'fail': 6,\n 'football': 6, 'business': 6, 'gi': 6, 'sl': 6, 'bio': 6, 'exchange': 6,\n 'post': 6, 'fo': 6, 'style': 6, 'army': 6, 'gp': 6, 'cafe': 6, 'paris': 6,\n 'hm': 6, 'gal': 6, 'cisco': 5, 'uol': 5, 'here': 5, 'nf': 5, 'house': 5,\n 'dog': 5, 'taxi': 5, 'bi': 5, 'sb': 5, 'schwarz': 5, 'sr': 5, 'careers': 5,\n 'kim': 5, 'bt': 5, 'canon': 5, 'pf': 5, 'sy': 5, 'yt': 5, 'ads': 4,\n 'tokyo': 4, 'international': 4, 'realtor': 4, 'earth': 4, 'legal': 4,\n 'nr': 4, 'dhl': 4, 'garden': 4, 'meet': 4, 'consulting': 4, 'abbott': 4,\n 'weir': 4, 'toys': 4, 'ski': 4, 'bb': 4, 'deals': 4, 'bradesco': 4,\n 'fitness': 4, 'bot': 4, 'golf': 4, 'cw': 4, 'fish': 4, 'kpmg': 4, 'bj': 4,\n 'best': 4, 'cam': 4, 'boutique': 4, 'diet': 3, 'sd': 3, 'cheap': 3, 'td': 3,\n 'mo': 3, 'ck': 3, 'tours': 3, 'bnpparibas': 3, 'tax': 3, 'bzh': 3,\n 'directory': 3, 'adult': 3, 'fashion': 3, 'new': 3, 'istanbul': 3,\n 'neustar': 3, 'band': 3, 'bar': 3, 'vacations': 3, 'bf': 3, 'tg': 3,\n 'inc': 3, 'limited': 3, 'koeln': 3, 'barclays': 3, 'wales': 3, 'ren': 3,\n 'camera': 3, 'kiwi': 3, 'vet': 3, 'solar': 3, 'discount': 3, 'partners': 3,\n 'haus': 3, 'lease': 3, 'glass': 3, 'forsale': 3, 'radio': 3, 'builders': 3,\n 'hsbc': 3, 'racing': 3, 'shopping': 3, 'ax': 3, 'vi': 3, 'coffee': 2,\n 'nrw': 2, 'bingo': 2, 'cars': 2, 'saxo': 2, 'crs': 2, 'beer': 2,\n 'brother': 2, 'pizza': 2, 'chrome': 2, 'dance': 2, 'fans': 2, 'town': 2,\n 'flights': 2, 'car': 2, 'storage': 2, 'nra': 2, 'sony': 2, 'mc': 2,\n 'nico': 2, 'coach': 2, 'mp': 2, 'degree': 2, 'camp': 2, 'trust': 2,\n 'graphics': 2, 'miami': 2, 'sale': 2, 'reviews': 2, 'mw': 2, 'rugby': 2,\n 'ls': 2, 'finance': 2, 'cards': 2, 'how': 2, 'moda': 2, 'madrid': 2,\n 'quebec': 2, 'office': 2, 'xn--9dbq2a': 2, 'dating': 2, 'gallery': 2,\n 'kn': 2, 'basketball': 2, 'tattoo': 2, 'cg': 2, 'lgbt': 2, 'foundation': 2,\n 'africa': 2, 'barclaycard': 2, 'baby': 1, 'bing': 1, 'hitachi': 1,\n 'vin': 1, 'goo': 1, 'hockey': 1, 'insure': 1, 'actor': 1, 'saarland': 1,\n 'java': 1, 'gmail': 1, 'youtube': 1, 'ltda': 1, 'brussels': 1, 'navy': 1,\n 'xn--54b7fta0cc': 1, 'institute': 1, 'green': 1, 'prod': 1, 'diamonds': 1,\n 'ice': 1, 'cern': 1, 'itau': 1, 'seven': 1, 'associates': 1,\n 'williamhill': 1, 'auction': 1, 'abc': 1, 'xn--j1amh': 1, 'holiday': 1,\n 'xn--c1avg': 1, 'study': 1, 'tatar': 1, 'ss': 1, 'xn--d1acj3b': 1,\n 'vote': 1, 'docs': 1, 'amex': 1, 'page': 1, 'itv': 1, 'healthcare': 1,\n 'honda': 1, 'cymru': 1, 'place': 1, 'play': 1, 'dental': 1, 'yahoo': 1,\n 'audible': 1, 'fage': 1, 'college': 1, 'eco': 1, 'aw': 1, 'gifts': 1,\n 'mr': 1, 'jaguar': 1, 'berlin': 1, 'photography': 1, 'secure': 1,\n 'xn--80asehdb': 1, 'crown': 1, 'gn': 1, 'landrover': 1, 'horse': 1,\n 'ventures': 1, 'kp': 1, 'vanguard': 1, 'casa': 1, 'luxury': 1,\n}\n" }, { "path": "stats/tld_majestic_top_1m.py", "content": "# derived from\n# http://downloads.majestic.com/majestic_million.csv\n# fetched 2019-02-06\n#\n# see also\n#\n# https://majestic.com/reports/majestic-million\n#\n# The Majestic Million is a list of the top 1 million website in the\n# world, ordered by the number of referring subnets. A subnet is a bit\n# complex – but to a layman it is basically anything within an IP\n# range, ignoring the last three digits of the IP number.\n#\n# https://majestic.com/support/faq#Where\n#\n# Where is the data coming from?\n# Our data comes from the World Wide Web itself. The Majestic-12:\n# Distributed Search Engine (http://www.majestic12.co.uk/) does not\n# meta-search or otherwise query other search engines: we are the\n# search engine! Over a long period of time we have developed\n# software capable of crawling and indexing large amounts of web\n# data. This index is a big stepping stone towards relevant\n# full-text search. The purpose of the index is to allow relevancy\n# research as well as to help fund continued activites in\n# development of a competitive community-driven general-purpose\n# web-scale search engine.\n#\n\nmajestic_top_1m_tlds_about = {\n 'date': '2019-02-06',\n 'source': 'http://downloads.majestic.com/majestic_million.csv'\n}\n\nmajestic_top_1m_tlds = {\n # cut -d, -f4 majestic_million.csv | tail -n+2 \\\n # | perl -ne 'chomp; $h{$_}++;\n # END {\n # for (sort { $h{$b} <=> $h{$a} } keys %h) {\n # print \"\\047\", $_, \"\\047:\", $h{$_}, \", \";\n # }\n # }' \\\n # | fold --width=70 -s | perl -lpe 's/^/ /; s/:/: /g; s/\\s+$//'\n 'com': 495190, 'org': 72365, 'net': 44704, 'ru': 43014, 'cn': 29361,\n 'uk': 25616, 'de': 23928, 'jp': 15220, 'pl': 11514, 'nl': 11167,\n 'info': 10601, 'it': 9829, 'fr': 9542, 'br': 8905, 'au': 8457,\n 'club': 8136, 'tw': 7140, 'us': 6584, 'ca': 6393, 'in': 6014, 'co': 5533,\n 'es': 5240, 'eu': 4791, 'ua': 4010, 'edu': 3975, 'cz': 3689, 'se': 3549,\n 'ch': 3285, 'ir': 3220, 'vn': 3156, 'biz': 3050, 'be': 2827, 'ro': 2817,\n 'at': 2743, 'za': 2550, 'nu': 2428, 'dk': 2396, 'hu': 2252, 'tr': 2154,\n 'gr': 2110, 'top': 2105, 'io': 2064, 'me': 2057, 'tv': 2017, 'xyz': 1881,\n 'gov': 1721, 'xn--p1ai': 1715, 'site': 1668, 'mx': 1636, 'no': 1631,\n 'cc': 1559, 'fi': 1497, 'nz': 1481, 'ar': 1478, 'cl': 1463, 'kr': 1435,\n 'mobi': 1406, 'ie': 1322, 'pt': 1285, 'cf': 1258, 'by': 1157, 'il': 1133,\n 'id': 1109, 'my': 1085, 'sk': 1073, 'online': 1057, 'kz': 1014, 'tk': 1012,\n 'su': 904, 'hk': 877, 'store': 849, 'lt': 836, 'sg': 827, 'pro': 720,\n 'hr': 650, 'pw': 646, 'space': 602, 'pk': 568, 'ng': 566, 'th': 530,\n 'shop': 528, 'lv': 525, 'live': 514, 'gq': 505, 'ee': 504, 'doctor': 502,\n 'icu': 487, 'si': 487, 'cat': 480, 'world': 478, 'ph': 461, 'rs': 445,\n 'ae': 444, 'name': 443, 'bg': 432, 'to': 429, 'pe': 420, 'life': 403,\n 'ga': 401, 'ws': 367, 'is': 362, 'xn--fiqs8s': 355, 'ml': 353, 'fm': 326,\n 'ke': 317, 'fun': 296, 'sa': 279, 'win': 270, 'ma': 261, 'website': 260,\n 'video': 255, 'ge': 252, 'md': 251, 'bid': 247, 'stream': 232, 'uz': 232,\n 'ovh': 231, 'asia': 228, 'lk': 220, 'az': 220, 'am': 218, 'bd': 216,\n 'reisen': 207, 'ec': 206, 'tech': 206, 'news': 205, 've': 198, 'mk': 193,\n 'ba': 186, 'ooo': 185, 'np': 175, 'uy': 174, 'lu': 172, 'today': 169,\n 'vip': 167, 'science': 166, 'travel': 162, 'work': 160, 'ly': 150,\n 'men': 147, 'ai': 147, 'network': 144, 'im': 139, 'la': 138, 'review': 136,\n 'tn': 136, 'aero': 131, 'ltd': 129, 'bz': 127, 'ink': 127, 'do': 126,\n 'int': 123, 'coop': 123, 'st': 119, 'trade': 118, 'al': 117, 'guru': 116,\n 'press': 114, 'services': 114, 'eg': 113, 'mn': 112, 'host': 108,\n 'kg': 105, 'media': 104, 'cy': 98, 'li': 95, 'py': 93, 'webcam': 92,\n 'party': 92, 'zw': 90, 'link': 89, 'wiki': 89, 'one': 86, 'cx': 86,\n 'mil': 85, 'company': 81, 'eus': 80, 'mo': 80, 'global': 77, 'tz': 75,\n 'cu': 75, 'gt': 74, 'tel': 73, 'blog': 72, 'gdn': 72, 'cr': 71, 'ms': 71,\n 'mt': 70, 'sh': 69, 'gs': 68, 'rocks': 67, 'gg': 67, 'vc': 66, 'jo': 66,\n 'lb': 65, 'zone': 65, 'bo': 65, 'ag': 63, 'cricket': 62, 'tips': 62,\n 'so': 62, 'ps': 60, 'team': 60, 'ug': 59, 'center': 57, 'qa': 57,\n 'accountant': 55, 'ninja': 55, 'pub': 55, 'agency': 55, 'dz': 54, 'tc': 53,\n 'vu': 53, 're': 52, 'tj': 51, 'city': 51, 'xxx': 50, 'blue': 47,\n 'moscow': 47, 'ac': 46, 'kh': 45, 'xn--p1acf': 45, 'london': 45,\n 'design': 45, 'gh': 45, 'tt': 44, 'group': 44, 'mu': 44, 'sc': 44,\n 'app': 43, 'cm': 42, 'studio': 41, 'click': 40, 'af': 40, 'nyc': 40,\n 'tokyo': 39, 'pet': 39, 'movie': 38, 'tools': 38, 'directory': 38,\n 'art': 38, 'promo': 37, 'as': 37, 'date': 36, 'social': 36, 'digital': 35,\n 'pink': 35, 'sn': 35, 'wang': 35, 'cloud': 35, 'wales': 35, 'vg': 35,\n 'pa': 34, 'scot': 33, 'college': 33, 'bio': 33, 'sv': 33, 'na': 33,\n 'download': 32, 'bt': 32, 'rent': 32, 'ni': 32, 'band': 31, 'mz': 31,\n 'kw': 31, 'toys': 31, 'museum': 30, 'cd': 30, 'jobs': 30, 'rw': 30,\n 'bw': 30, 'va': 29, 'education': 29, 'hn': 29, 'solutions': 29, 'gd': 29,\n 'actor': 29, 'mv': 29, 'systems': 28, 'om': 28, 'auction': 28,\n 'schule': 27, 'expert': 27, 'community': 27, 'guide': 27, 'gal': 27,\n 'love': 26, 'mg': 26, 'games': 26, 'ci': 26, 'tl': 26, 'mba': 26, 'gl': 26,\n 'plus': 26, 'nf': 25, 'works': 25, 'et': 25, 'bet': 25, 'mc': 25,\n 'paris': 25, 'loan': 24, 'zm': 24, 'yu': 24, 'berlin': 24, 'market': 24,\n 'fund': 24, 'sy': 24, 'ht': 23, 'help': 23, 'nrw': 23, 'bh': 22,\n 'events': 22, 'xn--80adxhks': 22, 'red': 22, 'gy': 21, 'support': 21,\n 'cash': 21, 'iq': 21, 'dj': 20, 'gm': 20, 'technology': 20, 'fj': 20,\n 'ao': 20, 'academy': 20, 'fail': 19, 'school': 19, 'ad': 19, 'faith': 19,\n 'swiss': 19, 'foundation': 19, 'run': 19, 'moe': 18, 'fo': 18, 'nc': 18,\n 'pn': 18, 'bzh': 18, 'pm': 18, 'racing': 17, 'xn--90ais': 17,\n 'reviews': 17, 'exchange': 17, 'cv': 17, 'land': 17, 'gi': 16, 'ren': 16,\n 'jm': 16, 'google': 16, 'cars': 16, 'sm': 16, 'email': 16, 'tf': 16,\n 'ky': 16, 'mw': 15, 'sx': 15, 'coffee': 15, 'software': 15, 'cafe': 15,\n 'sd': 15, 'bb': 15, 'xn--j1amh': 15, 'pg': 15, 'pr': 15, 'gallery': 14,\n 'care': 14, 'poker': 14, 'bf': 14, 'xin': 14, 'amsterdam': 14, 'codes': 13,\n 'earth': 13, 'uno': 13, 'sr': 13, 'gp': 13, 'church': 13, 'pics': 13,\n 'brussels': 13, 'mm': 12, 'bm': 12, 'capital': 12, 'cool': 12,\n 'institute': 12, 'ls': 12, 'photo': 12, 'international': 12, 'tm': 12,\n 'je': 11, 'chat': 11, 'bike': 11, 'africa': 11, 'istanbul': 11,\n 'photos': 11, 'show': 11, 'money': 11, 'onl': 11, 'pf': 11, 'casino': 11,\n 'consulting': 11, 'camp': 11, 'report': 11, 'mr': 11, 'lc': 10,\n 'finance': 10, 'photography': 10, 'clinic': 10, 'tours': 10,\n 'graphics': 10, 'yt': 10, 'bi': 10, 'town': 10, 'legal': 10, 'bank': 9,\n 'marketing': 9, 'ngo': 9, 'farm': 9, 'bn': 9, 'kim': 9, 'dm': 9, 'ax': 9,\n 'abbott': 9, 'xn--80asehdb': 9, 'bayern': 9, 'tg': 9, 'energy': 9,\n 'porn': 8, 'sale': 8, 'lol': 8, 'bs': 8, 'nr': 8, 'pictures': 8, 'cymru': 8,\n 'fyi': 8, 'business': 8, 'ye': 8, 'deals': 8, 'audio': 8, 'rest': 7,\n 'ki': 7, 'game': 7, 'buzz': 7, 'sexy': 7, 'hm': 7, 'fit': 7, 'sex': 7,\n 'film': 7, 'domains': 7, 'ski': 7, 'coach': 7, 'best': 7, 'barcelona': 7,\n 'taipei': 7, 'style': 7, 'dev': 7, 'sz': 7, 'sl': 7, 'fitness': 7,\n 'house': 7, 'law': 7, 'place': 7, 'fish': 6, 'eco': 6, 'express': 6,\n 'basketball': 6, 'canon': 6, 'parts': 6, 'ne': 6, 'realtor': 6, 'green': 6,\n 'vi': 6, 'direct': 6, 'cg': 6, 'ist': 6, 'hosting': 6, 'recipes': 6,\n 'engineering': 6, 'wtf': 6, 'partners': 6, 'training': 6, 'engineer': 6,\n 'vision': 6, 'bj': 6, 'gratis': 6, 'mp': 6, 'kiwi': 6, 'gop': 5,\n 'properties': 5, 'delivery': 5, 'tirol': 5, 'watch': 5, 'cern': 5,\n 'careers': 5, 'sb': 5, 'estate': 5, 'joburg': 5, 'football': 5, 'koeln': 5,\n 'desi': 5, 'bar': 5, 'wien': 5, 'vote': 5, 'hamburg': 5, 'wf': 5,\n 'camera': 5, 'neustar': 5, 'fashion': 5, 'weber': 5, 'mom': 5, 'taxi': 4,\n 'corsica': 4, 'university': 4, 'surf': 4, 'kp': 4, 'shoes': 4, 'wine': 4,\n 'dating': 4, 'kn': 4, 'lgbt': 4, 'health': 4, 'bnpparibas': 4, 'vegas': 4,\n 'beer': 4, 'rip': 4, 'repair': 4, 'productions': 4, 'vet': 4, 'gold': 4,\n 'family': 4, 'gift': 4, 'aq': 4, 'saarland': 4, 'srl': 4, 'dance': 4,\n 'dental': 4, 'supply': 4, 'how': 4, 'solar': 3, 'casa': 3, 'build': 3,\n 'tp': 3, 'pizza': 3, 'construction': 3, 'cab': 3, 'flowers': 3,\n 'credit': 3, 'jetzt': 3, 'xn--6qq986b3xl': 3, 'futbol': 3, 'garden': 3,\n 'boutique': 3, 'cheap': 3, 'pharmacy': 3, 'frl': 3, 'gifts': 3, 'yoga': 3,\n 'quebec': 3, 'auto': 3, 'builders': 3, 'xn--d1acj3b': 3, 'cards': 3,\n 'lat': 3, 'lighting': 3, 'sandvik': 3, 'villas': 3, 'krd': 3, 'ck': 3,\n 'tube': 3, 'sport': 3, 'xn--3ds443g': 3, 'sydney': 3, 'clothing': 3,\n 'lawyer': 3, 'archi': 3, 'brother': 2, 'army': 2, 'barclays': 2,\n 'discount': 2, 'aws': 2, 'hockey': 2, 'alsace': 2, 'an': 2, 'mortgage': 2,\n 'rio': 2, 'kpmg': 2, 'km': 2, 'restaurant': 2, 'dog': 2, 'menu': 2,\n 'cam': 2, 'catering': 2, 'moda': 2, 'active': 2, 'yokohama': 2,\n 'vlaanderen': 2, 'haus': 2, 'apple': 2, 'komatsu': 2, 'xn--6frz82g': 2,\n 'shiksha': 2, 'black': 2, 'kred': 2, 'bot': 2, 'tattoo': 2, 'gf': 2,\n 'golf': 2, 'miami': 2, 'hiv': 2, 'vin': 2, 'guitars': 2, 'physio': 2,\n 'post': 2, 'healthcare': 2, 'xn--80aswg': 2, 'tax': 2, 'kitchen': 2,\n 'tatar': 2, 'broker': 2, 'equipment': 2, 'nico': 2, 'xn--e1a4c': 2,\n 'nagoya': 2, 'forsale': 2, 'contractors': 2, 'kyoto': 2, 'diamonds': 2,\n 'immobilien': 2, 'page': 2, '\" \"': 2, 'industries': 2, 'radio': 2,\n 'horse': 2, 'okinawa': 2, 'holiday': 2, 'luxury': 2, 'loans': 2,\n 'citic': 1, 'enterprises': 1, 'vanguard': 1, 'kaufen': 1, 'sbi': 1,\n 'surgery': 1, 'yandex': 1, 'fast': 1, 'sony': 1, 'tickets': 1,\n 'dentist': 1, 'associates': 1, 'schmidt': 1, 'kpn': 1, 'bingo': 1,\n 'new': 1, 'glass': 1, 'security': 1, 'property': 1, 'financial': 1,\n 'gu': 1, 'holdings': 1, 'diet': 1, 'gmail': 1, 'gw': 1, 'forum': 1,\n 'bible': 1, 'investments': 1, 'fans': 1, 'gent': 1, 'abc': 1,\n 'barclaycard': 1, 'fage': 1, 'here': 1, 'theater': 1, 'realestate': 1,\n 'attorney': 1, 'xn--3e0b707e': 1, 'markets': 1, 'mq': 1, 'goo': 1,\n 'reise': 1, 'box': 1, 'ismaili': 1, 'fan': 1, 'rentals': 1, 'irish': 1,\n 'gn': 1, 'florist': 1, 'td': 1, 'hitachi': 1, 'goog': 1, 'exposed': 1,\n 'flickr': 1, 'rehab': 1, 'monash': 1, 'voyage': 1, 'computer': 1, 'you': 1,\n 'uol': 1, 'cs': 1, 'pioneer': 1, 'limo': 1, 'bradesco': 1, 'madrid': 1,\n 'flights': 1, 'bbc': 1, 'dhl': 1, 'juegos': 1, 'free': 1, 'leclerc': 1,\n 'jll': 1, 'lidl': 1, 'cuisinella': 1, 'edeka': 1, 'cologne': 1, 'cw': 1,\n 'hot': 1, 'honda': 1, 'immo': 1, 'ipiranga': 1, 'youtube': 1, 'jewelry': 1,\n 'saxo': 1, 'sncf': 1, 'baidu': 1, 'charity': 1, 'softbank': 1, 'jcb': 1,\n 'audi': 1, 'bv': 1, 'rmit': 1, 'shopping': 1, 'java': 1, 'rwe': 1, 'ses': 1,\n 'fk': 1, 'ruhr': 1, 'xn--kpry57d': 1, 'gmbh': 1, 'weir': 1, 'pictet': 1,\n 'furniture': 1, 'melbourne': 1, 'rugby': 1, 'wedding': 1, 'ltda': 1,\n 'supplies': 1, 'trust': 1, 'vodka': 1, 'yahoo': 1, 'management': 1,\n 'microsoft': 1, 'big': 1, 'sener': 1, 'theatre': 1, 'mobile': 1,\n 'office': 1, 'soccer': 1, 'car': 1, 'qpon': 1, 'buy': 1, 'map': 1,\n 'lixil': 1, 'chrome': 1, 'hiphop': 1, 'study': 1, 'sky': 1, 'lr': 1,\n}\n" }, { "path": "stats.Dockerfile", "content": "# Replicating pjox/cc-crawl-statistics\nFROM python:3.12\n\n# Install system dependencies\nRUN apt-get update && apt-get install -y \\\n git \\\n python3-rpy2 \\\n r-cran-ggplot2 \\\n graphviz-dev \\\n r-base jq \\\n awscli\n\n# Install Liberation Sans (Helvetica alternative) and other fonts\nRUN apt-get update && apt-get install -y \\\n fontconfig \\\n fonts-liberation \\\n fonts-dejavu \\\n && fc-cache -fv \\\n && rm -rf /var/lib/apt/lists/*\n\n# Set working directory\nWORKDIR /app\n\n# Copy dependency config files (first for cache)\nCOPY requirements.txt .\nCOPY requirements_plot.txt .\n\n# Install Python dependencies\nRUN pip3 install -r requirements.txt\nRUN pip3 install -r requirements_plot.txt\n\n# Copy the remaining repository files\nCOPY stats/crawler ./stats/crawler\nCOPY plots/ ./plots/\nCOPY plot/ ./plot/\nCOPY tests/ ./tests/\n\nCOPY *.sh ./\nCOPY *.py ./\nCOPY _config.yml ./\n\n# Set environment variables\nENV PYTHONPATH=/app\n\n# Supported plotlibs: matplotlib, rpy2.ggplot2, ggplot (partially deprecated)\nENV PLOTLIB=matplotlib\n\n# ggplot2 is already installed via r-cran-ggplot2 system package above\n\n# Default command\nCMD [\"./get_stats_and_plot.sh\"]" }, { "path": "tests/test_crawlstat.py", "content": "import json\nimport sys\n\nimport ujson\nimport jsonpickle\n\nfrom crawlstats import MonthlyCrawl, MonthlyCrawlSet\nfrom crawlstats import CrawlStatsJSONDecoder, CrawlStatsJSONEncoder\nfrom crawlstats import CST\nfrom crawlstats import MultiCount\nfrom hyperloglog import HyperLogLog\n\ncrawl1 = MonthlyCrawl.get_by_name('CC-MAIN-2014-52')\ncrawl2 = MonthlyCrawl.get_by_name('CC-MAIN-2015-06')\ncrawl3 = MonthlyCrawl.get_by_name('CC-MAIN-2016-26')\n\n\ndef test_monthly_crawl():\n assert(crawl1 != crawl2)\n assert(crawl1 < crawl2)\n assert('{}'.format(crawl1) == json.dumps(crawl1))\n assert('CC-MAIN-2014-52' == MonthlyCrawl.to_name(crawl1))\n\n\ndef test_monthly_crawl_set():\n crawls = MonthlyCrawlSet()\n assert('0' == json.dumps(crawls, cls=CrawlStatsJSONEncoder))\n\n # add crawl 1 - CC-MAIN-2014-52\n crawls.add(crawl1)\n assert(crawl1 in crawls)\n assert('1' == json.dumps(crawls, cls=CrawlStatsJSONEncoder))\n assert(crawls.is_newest(crawl1))\n\n # add crawl 2 - CC-MAIN-2015-06\n crawls.add(crawl2)\n assert(crawl2 in crawls)\n assert(crawls.is_newest(crawl2))\n assert(not crawls.is_newest(crawl1))\n\n # create second crawl set and add crawl 3 - CC-MAIN-2016-26\n crawls2 = MonthlyCrawlSet()\n crawls2.add(crawl3)\n assert(crawl3 in crawls2)\n\n # merge the two crawl sets\n crawls.update(crawls2)\n assert(crawl3 in crawls)\n assert(3 == len(crawls))\n\n # check iterator over crawl set\n for crawl in crawls.get_crawls():\n assert(crawl in crawls)\n assert((crawl == crawl1) or (crawl == crawl2) or (crawl == crawl3))\n crawl = MonthlyCrawl.to_name(crawl)\n assert((crawl == 'CC-MAIN-2014-52') or\n (crawl == 'CC-MAIN-2015-06') or\n (crawl == 'CC-MAIN-2016-26'))\n\n # check is_newest in merged crawl set\n assert(not crawls.is_newest(crawl2))\n assert(crawls.is_newest(crawl3))\n\n # check deletions from crawl set\n crawls.discard(crawl2)\n assert(crawl2 not in crawls)\n assert(crawl1 in crawls)\n\n # check is_new\n assert(not crawls.is_new(crawl2))\n assert(not crawls.is_new(crawl3))\n crawls.discard(crawl1)\n assert(crawls.is_new(crawl3))\n # although crawl2 is not in crawls there is no older crawl\n assert(crawls.is_new(crawl2))\n\n\ndef test_crawlstatstype():\n cst = CST.url\n assert(cst.value == CST.url.value)\n\n\ndef test_json_hyperloglog():\n hll1 = HyperLogLog(.01)\n for i in range(0, 50):\n hll1.add(i)\n jsons = json.dumps(hll1, cls=CrawlStatsJSONEncoder)\n hll2 = json.loads(jsons, cls=CrawlStatsJSONDecoder)\n assert(hll1.card() == hll2.card())\n # test jsonpickle serialization\n jsonp = jsonpickle.encode(hll2)\n hll3 = jsonpickle.decode(jsonp)\n assert(hll1.card() == hll3.card())\n\n\ndef test_multicount():\n cnt = MultiCount(2)\n cnt.incr('a', 1, 1)\n assert([1, 1] == cnt.get('a'))\n assert(1 == cnt.get_compressed('a'))\n cnt.incr('a', 2, 1)\n assert([3, 2] == cnt.get_compressed('a'))\n assert([3, 2] == MultiCount.sum_values([[2, 1], 1]))\n assert([6, 4, 3] == MultiCount.sum_values([[3, 2, 1], [2, 1], 1]))\n cnt.incr('b', *[2, 1])\n" }, { "path": "top_level_domain.py", "content": "import fileinput\nimport idna\nimport re\n\n\nclass TopLevelDomain:\n \"\"\"Classify top-level domains (TLDs) to provide the following information:\n- type: generic, country-code, ...\n- \"\"\"\n\n tld_ccs = {}\n tld_types = {}\n short_types = {'generic': 'gTLD',\n 'generic-restricted': 'grTLD',\n 'infrastructure': 'ARPA',\n 'country-code': 'ccTLD',\n 'sponsored': 'sTLD',\n 'test': 'tTLD',\n 'internationalized generic': 'IDN gTLD',\n 'internationalized country-code TLD': 'IDN ccTLD',\n 'internationalized test TLD': 'IDN tTLD'\n }\n\n def __init__(self, tld):\n self.tld = tld = tld.lower()\n self.first_level = self.tld\n self.tld_type = None\n self.sub_type = None\n if tld in TopLevelDomain.tld_ccs:\n self.first_level = TopLevelDomain.tld_ccs[tld]\n elif tld.find('.'):\n self.first_level = re.sub(r'^.+\\.', '', tld)\n if tld in TopLevelDomain.tld_types:\n self.tld_type = TopLevelDomain.tld_types[tld]\n elif tld in TopLevelDomain.tld_ccs:\n self.tld_type = 'country-code'\n self.sub_type = 'internationalized'\n self.first_level = TopLevelDomain.tld_ccs[tld]\n elif self.first_level in TopLevelDomain.tld_types:\n self.tld_type = TopLevelDomain.tld_types[self.first_level]\n self.sub_type = 'second-level'\n\n def __str__(self):\n _str = self.tld + '('\n if self.tld_type:\n _str += self.tld_type\n if self.sub_type:\n _str += ', ' + self.sub_type\n if self.first_level and self.first_level != self.tld:\n _str += ' of ' + self.first_level\n idn = idna.encode(self.tld).decode('utf-8')\n if idn != self.tld:\n _str += ', idn: ' + idn\n _str += ')'\n return _str\n\n @staticmethod\n def _read_data():\n state = ''\n state_pattern = re.compile('^__([A-Z_]+)__$')\n for line in TopLevelDomain.__DATA__.splitlines():\n # print(line)\n if len(line) == 0 or '#' == line[0]:\n continue\n m = state_pattern.match(line)\n if m:\n state = m.group(1)\n continue\n if state == 'IANA':\n (tld, tld_type, _sponsoring_organization) = line.split('\\t')\n tld = tld.strip('\\u200e\\u200f')\n tld = tld.lstrip('.')\n idn = idna.encode(tld).decode('utf-8')\n if idn != tld:\n if tld_type == 'country-code':\n tld_type = 'internationalized country-code TLD'\n else:\n tld_type = 'internationalized ' + tld_type\n TopLevelDomain.tld_types[idn] = tld_type\n TopLevelDomain.tld_types[tld] = tld_type\n elif state == 'ICCTLD':\n (dns, idn, _country, _lang, _script,\n _translit, _comment, cctld, _dnssec) = line.split('\\t')\n dns = dns.lstrip('.')\n cctld = cctld.lstrip('.')\n idn = idn.lstrip('.')\n if '[' in idn:\n # strip Wikipedia footnotes and links\n idn = re.sub(r'\\s*\\[.+?\\]\\s*$', '', idn)\n for tld in (dns, idn):\n TopLevelDomain.tld_types[tld] \\\n = 'internationalized country-code TLD'\n TopLevelDomain.tld_ccs[tld] = cctld\n elif state == 'INTERNATIONAL_BRAND_TLD':\n (dns, idn, _entity, _script, _translit,\n _comments, _dnssec) = line.split('\\t')\n dns = dns.lstrip('.')\n idn = idn.lstrip('.')\n TopLevelDomain.tld_types[dns] = 'internationalized generic TLD'\n TopLevelDomain.tld_types[idn] = 'internationalized generic TLD'\n elif state == 'INTERNATIONAL_GEOGRAPHIC_TLD':\n # geographic TLDs (not internationalized) are classified in IANA list as generic\n (dns, idn, _entity, _script, _translit,\n _comments, _other_tld, _dnssec) = line.split('\\t')\n dns = dns.lstrip('.')\n idn = idn.lstrip('.')\n TopLevelDomain.tld_types[dns] = 'internationalized generic TLD'\n TopLevelDomain.tld_types[idn] = 'internationalized generic TLD'\n elif state == 'INTERNATIONAL_TEST_TLD':\n (dns, idn, _translit, _lang, _script, _test) = line.split('\\t')\n dns = dns.lstrip('.')\n idn = idn.lstrip('.')\n TopLevelDomain.tld_types[dns] = 'internationalized test TLD'\n TopLevelDomain.tld_types[idn] = 'internationalized test TLD'\n\n @staticmethod\n def short_type(name):\n if name in TopLevelDomain.short_types:\n return TopLevelDomain.short_types[name]\n return name\n\n __DATA__ = '''\\\n__IANA__\n# https://www.iana.org/domains/root/db\n# (update 2025-09-26)\n# Domain\tType\tTLD Manager\n.aaa\tgeneric\tAmerican Automobile Association, Inc.\n.aarp\tgeneric\tAARP\n.abarth\tgeneric\tNot assigned\n.abb\tgeneric\tABB Ltd\n.abbott\tgeneric\tAbbott Laboratories, Inc.\n.abbvie\tgeneric\tAbbVie Inc.\n.abc\tgeneric\tDisney Enterprises, Inc.\n.able\tgeneric\tAble Inc.\n.abogado\tgeneric\tRegistry Services, LLC\n.abudhabi\tgeneric\tAbu Dhabi Systems and Information Centre\n.ac\tcountry-code\tInternet Computer Bureau Limited\n.academy\tgeneric\tBinky Moon, LLC\n.accenture\tgeneric\tAccenture plc\n.accountant\tgeneric\tdot Accountant Limited\n.accountants\tgeneric\tBinky Moon, LLC\n.aco\tgeneric\tACO Severin Ahlmann GmbH & Co. KG\n.active\tgeneric\tNot assigned\n.actor\tgeneric\tDog Beach, LLC\n.ad\tcountry-code\tAndorra Telecom\n.adac\tgeneric\tNot assigned\n.ads\tgeneric\tCharleston Road Registry Inc.\n.adult\tgeneric\tICM Registry AD LLC\n.ae\tcountry-code\tTelecommunications and Digital Government Regulatory Authority (TDRA)\n.aeg\tgeneric\tAktiebolaget Electrolux\n.aero\tsponsored\tSociete Internationale de Telecommunications Aeronautique (SITA INC USA)\n.aetna\tgeneric\tAetna Life Insurance Company\n.af\tcountry-code\tMinistry of Communications and IT\n.afamilycompany\tgeneric\tNot assigned\n.afl\tgeneric\tAustralian Football League\n.africa\tgeneric\tZA Central Registry NPC trading as Registry.Africa\n.ag\tcountry-code\tUHSA School of Medicine\n.agakhan\tgeneric\tFondation Aga Khan (Aga Khan Foundation)\n.agency\tgeneric\tBinky Moon, LLC\n.ai\tcountry-code\tGovernment of Anguilla\n.aig\tgeneric\tAmerican International Group, Inc.\n.aigo\tgeneric\tNot assigned\n.airbus\tgeneric\tAirbus S.A.S.\n.airforce\tgeneric\tDog Beach, LLC\n.airtel\tgeneric\tBharti Airtel Limited\n.akdn\tgeneric\tFondation Aga Khan (Aga Khan Foundation)\n.al\tcountry-code\tElectronic and Postal Communications Authority - AKEP\n.alfaromeo\tgeneric\tNot assigned\n.alibaba\tgeneric\tAlibaba Group Holding Limited\n.alipay\tgeneric\tAlibaba Group Holding Limited\n.allfinanz\tgeneric\tAllfinanz Deutsche Vermögensberatung Aktiengesellschaft\n.allstate\tgeneric\tAllstate Fire and Casualty Insurance Company\n.ally\tgeneric\tAlly Financial Inc.\n.alsace\tgeneric\tREGION GRAND EST\n.alstom\tgeneric\tALSTOM\n.am\tcountry-code\t\"Internet Society\" Non-governmental Organization\n.amazon\tgeneric\tAmazon Registry Services, Inc.\n.americanexpress\tgeneric\tAmerican Express Travel Related Services Company, Inc.\n.americanfamily\tgeneric\tAmFam, Inc.\n.amex\tgeneric\tAmerican Express Travel Related Services Company, Inc.\n.amfam\tgeneric\tAmFam, Inc.\n.amica\tgeneric\tAmica Mutual Insurance Company\n.amsterdam\tgeneric\tGemeente Amsterdam\n.an\tcountry-code\tNot assigned\n.analytics\tgeneric\tCampus IP LLC\n.android\tgeneric\tCharleston Road Registry Inc.\n.anquan\tgeneric\tQIHOO 360 TECHNOLOGY CO. LTD.\n.anz\tgeneric\tAustralia and New Zealand Banking Group Limited\n.ao\tcountry-code\tMinistry of Telecommunications and Information Technologies (MTTI)\n.aol\tgeneric\tYahoo Inc.\n.apartments\tgeneric\tBinky Moon, LLC\n.app\tgeneric\tCharleston Road Registry Inc.\n.apple\tgeneric\tApple Inc.\n.aq\tcountry-code\tAntarctica Network Information Centre Limited\n.aquarelle\tgeneric\tAquarelle.com\n.ar\tcountry-code\tPresidencia de la Nación , Secretaría Legal y Técnica\n.arab\tgeneric\tLeague of Arab States\n.aramco\tgeneric\tAramco Services Company\n.archi\tgeneric\tIdentity Digital Limited\n.army\tgeneric\tDog Beach, LLC\n.arpa\tinfrastructure\tInternet Architecture Board (IAB)\n.art\tgeneric\tUK Creative Ideas Limited\n.arte\tgeneric\tAssociation Relative à la Télévision Européenne G.E.I.E.\n.as\tcountry-code\tAS Domain Registry\n.asda\tgeneric\tAsda Stores Limited\n.asia\tsponsored\tDotAsia Organisation Ltd.\n.associates\tgeneric\tBinky Moon, LLC\n.at\tcountry-code\tnic.at GmbH\n.athleta\tgeneric\tThe Gap, Inc.\n.attorney\tgeneric\tDog Beach, LLC\n.au\tcountry-code\t.au Domain Administration (auDA)\n.auction\tgeneric\tDog Beach, LLC\n.audi\tgeneric\tAUDI Aktiengesellschaft\n.audible\tgeneric\tAmazon Registry Services, Inc.\n.audio\tgeneric\tXYZ.COM LLC\n.auspost\tgeneric\tAustralian Postal Corporation\n.author\tgeneric\tAmazon Registry Services, Inc.\n.auto\tgeneric\tXYZ.COM LLC\n.autos\tgeneric\tXYZ.COM LLC\n.avianca\tgeneric\tNot assigned\n.aw\tcountry-code\tSETAR\n.aws\tgeneric\tAWS Registry LLC\n.ax\tcountry-code\tÅlands landskapsregering\n.axa\tgeneric\tAXA Group Operations SAS\n.az\tcountry-code\tIntraNS\n.azure\tgeneric\tMicrosoft Corporation\n.ba\tcountry-code\tUniverstiy Telinformatic Centre (UTIC)\n.baby\tgeneric\tXYZ.COM LLC\n.baidu\tgeneric\tBaidu, Inc.\n.banamex\tgeneric\tCitigroup Inc.\n.bananarepublic\tgeneric\tNot assigned\n.band\tgeneric\tDog Beach, LLC\n.bank\tgeneric\tfTLD Registry Services, LLC\n.bar\tgeneric\tPunto 2012 Sociedad Anonima Promotora de Inversion de Capital Variable\n.barcelona\tgeneric\tMunicipi de Barcelona\n.barclaycard\tgeneric\tBarclays Bank PLC\n.barclays\tgeneric\tBarclays Bank PLC\n.barefoot\tgeneric\tGallo Vineyards, Inc.\n.bargains\tgeneric\tBinky Moon, LLC\n.baseball\tgeneric\tMLB Advanced Media DH, LLC\n.basketball\tgeneric\tFédération Internationale de Basketball (FIBA)\n.bauhaus\tgeneric\tWerkhaus GmbH\n.bayern\tgeneric\tBayern Connect GmbH\n.bb\tcountry-code\tMinistry of Innovation, Science and Smart Technology\n.bbc\tgeneric\tBritish Broadcasting Corporation\n.bbt\tgeneric\tBB&T Corporation\n.bbva\tgeneric\tBANCO BILBAO VIZCAYA ARGENTARIA, S.A.\n.bcg\tgeneric\tThe Boston Consulting Group, Inc.\n.bcn\tgeneric\tMunicipi de Barcelona\n.bd\tcountry-code\tPosts and Telecommunications Division\n.be\tcountry-code\tDNS Belgium vzw/asbl\n.beats\tgeneric\tBeats Electronics, LLC\n.beauty\tgeneric\tXYZ.COM LLC\n.beer\tgeneric\tRegistry Services, LLC\n.bentley\tgeneric\tNot assigned\n.berlin\tgeneric\tdotBERLIN GmbH & Co. KG\n.best\tgeneric\tBestTLD Pty Ltd\n.bestbuy\tgeneric\tBBY Solutions, Inc.\n.bet\tgeneric\tIdentity Digital Limited\n.bf\tcountry-code\tAutorité de Régulation des Communications Electroniques et des Postes (ARCEP)\n.bg\tcountry-code\tRegister.BG\n.bh\tcountry-code\tTelecommunications Regulatory Authority (TRA)\n.bharti\tgeneric\tBharti Enterprises (Holding) Private Limited\n.bi\tcountry-code\tCentre National de l'Informatique\n.bible\tgeneric\tAmerican Bible Society\n.bid\tgeneric\tdot Bid Limited\n.bike\tgeneric\tBinky Moon, LLC\n.bing\tgeneric\tMicrosoft Corporation\n.bingo\tgeneric\tBinky Moon, LLC\n.bio\tgeneric\tIdentity Digital Limited\n.biz\tgeneric-restricted\tRegistry Services, LLC\n.bj\tcountry-code\tAutorité de Régulation des Communications Electroniques et de la Poste du Bénin (ARCEP BENIN)\n.bl\tcountry-code\tNot assigned\n.black\tgeneric\tIdentity Digital Limited\n.blackfriday\tgeneric\tRegistry Services, LLC\n.blanco\tgeneric\tNot assigned\n.blockbuster\tgeneric\tDish DBS Corporation\n.blog\tgeneric\tKnock Knock WHOIS There, LLC\n.bloomberg\tgeneric\tBloomberg IP Holdings LLC\n.blue\tgeneric\tIdentity Digital Limited\n.bm\tcountry-code\tRegistry General Department, Ministry of Home Affairs\n.bms\tgeneric\tBristol-Myers Squibb Company\n.bmw\tgeneric\tBayerische Motoren Werke Aktiengesellschaft\n.bn\tcountry-code\tAuthority for Info-communications Technology Industry of Brunei Darussalam (AITI)\n.bnl\tgeneric\tNot assigned\n.bnpparibas\tgeneric\tBNP Paribas\n.bo\tcountry-code\tAgencia para el Desarrollo de la Información de la Sociedad en Bolivia\n.boats\tgeneric\tXYZ.COM LLC\n.boehringer\tgeneric\tBoehringer Ingelheim International GmbH\n.bofa\tgeneric\tBank of America Corporation\n.bom\tgeneric\tNúcleo de Informação e Coordenação do Ponto BR - NIC.br\n.bond\tgeneric\tShortdot SA\n.boo\tgeneric\tCharleston Road Registry Inc.\n.book\tgeneric\tAmazon Registry Services, Inc.\n.booking\tgeneric\tBooking.com B.V.\n.boots\tgeneric\tNot assigned\n.bosch\tgeneric\tRobert Bosch GMBH\n.bostik\tgeneric\tBostik SA\n.boston\tgeneric\tRegistry Services, LLC\n.bot\tgeneric\tAmazon Registry Services, Inc.\n.boutique\tgeneric\tBinky Moon, LLC\n.box\tgeneric\tIntercap Registry Inc.\n.bq\tcountry-code\tNot assigned\n.br\tcountry-code\tComite Gestor da Internet no Brasil\n.bradesco\tgeneric\tBanco Bradesco S.A.\n.bridgestone\tgeneric\tBridgestone Corporation\n.broadway\tgeneric\tCelebrate Broadway, Inc.\n.broker\tgeneric\tDog Beach, LLC\n.brother\tgeneric\tBrother Industries, Ltd.\n.brussels\tgeneric\tDNS.be vzw\n.bs\tcountry-code\tUniversity of The Bahamas\n.bt\tcountry-code\tMinistry of Information and Communications\n.budapest\tgeneric\tNot assigned\n.bugatti\tgeneric\tNot assigned\n.build\tgeneric\tPlan Bee LLC\n.builders\tgeneric\tBinky Moon, LLC\n.business\tgeneric\tBinky Moon, LLC\n.buy\tgeneric\tAmazon Registry Services, INC\n.buzz\tgeneric\tDOTSTRATEGY CO.\n.bv\tcountry-code\tNorid A/S\n.bw\tcountry-code\tBotswana Communications Regulatory Authority (BOCRA)\n.by\tcountry-code\tBelarusian Cloud Technologies LLC\n.bz\tcountry-code\tUniversity of Belize\n.bzh\tgeneric\tAssociation www.bzh\n.ca\tcountry-code\tCanadian Internet Registration Authority (CIRA) Autorité Canadienne pour les enregistrements Internet (ACEI)\n.cab\tgeneric\tBinky Moon, LLC\n.cafe\tgeneric\tBinky Moon, LLC\n.cal\tgeneric\tCharleston Road Registry Inc.\n.call\tgeneric\tAmazon Registry Services, Inc.\n.calvinklein\tgeneric\tPVH gTLD Holdings LLC\n.cam\tgeneric\tCAM Connecting SARL\n.camera\tgeneric\tBinky Moon, LLC\n.camp\tgeneric\tBinky Moon, LLC\n.cancerresearch\tgeneric\tNot assigned\n.canon\tgeneric\tCanon Inc.\n.capetown\tgeneric\tZA Central Registry NPC trading as ZA Central Registry\n.capital\tgeneric\tBinky Moon, LLC\n.capitalone\tgeneric\tCapital One Financial Corporation\n.car\tgeneric\tXYZ.COM LLC\n.caravan\tgeneric\tCaravan International, Inc.\n.cards\tgeneric\tBinky Moon, LLC\n.care\tgeneric\tBinky Moon, LLC\n.career\tgeneric\tdotCareer LLC\n.careers\tgeneric\tBinky Moon, LLC\n.cars\tgeneric\tXYZ.COM LLC\n.cartier\tgeneric\tNot assigned\n.casa\tgeneric\tRegistry Services, LLC\n.case\tgeneric\tDigity, LLC\n.caseih\tgeneric\tNot assigned\n.cash\tgeneric\tBinky Moon, LLC\n.casino\tgeneric\tBinky Moon, LLC\n.cat\tsponsored\tFundacio puntCAT\n.catering\tgeneric\tBinky Moon, LLC\n.catholic\tgeneric\tPontificium Consilium de Comunicationibus Socialibus (PCCS) (Pontifical Council for Social Communication)\n.cba\tgeneric\tCOMMONWEALTH BANK OF AUSTRALIA\n.cbn\tgeneric\tThe Christian Broadcasting Network, Inc.\n.cbre\tgeneric\tCBRE, Inc.\n.cbs\tgeneric\tNot assigned\n.cc\tcountry-code\teNIC Cocos (Keeling) Islands Pty. Ltd. d/b/a Island Internet Services\n.cd\tcountry-code\tOffice Congolais des Postes et Télécommunications - OCPT\n.ceb\tgeneric\tNot assigned\n.center\tgeneric\tBinky Moon, LLC\n.ceo\tgeneric\tXYZ.COM LLC\n.cern\tgeneric\tEuropean Organization for Nuclear Research (\"CERN\")\n.cf\tcountry-code\tSociete Centrafricaine de Telecommunications (SOCATEL)\n.cfa\tgeneric\tCFA Institute\n.cfd\tgeneric\tShortdot SA\n.cg\tcountry-code\tInterpoint Switzerland\n.ch\tcountry-code\tSWITCH The Swiss Education & Research Network\n.chanel\tgeneric\tChanel International B.V.\n.channel\tgeneric\tCharleston Road Registry Inc.\n.charity\tgeneric\tPublic Interest Registry (PIR)\n.chase\tgeneric\tJPMorgan Chase Bank, National Association\n.chat\tgeneric\tBinky Moon, LLC\n.cheap\tgeneric\tBinky Moon, LLC\n.chintai\tgeneric\tCHINTAI Corporation\n.chloe\tgeneric\tNot assigned\n.christmas\tgeneric\tXYZ.COM LLC\n.chrome\tgeneric\tCharleston Road Registry Inc.\n.chrysler\tgeneric\tNot assigned\n.church\tgeneric\tBinky Moon, LLC\n.ci\tcountry-code\tAutorité de Régulation des Télécommunications/TIC de Côte d’lvoire (ARTCI)\n.cipriani\tgeneric\tHotel Cipriani Srl\n.circle\tgeneric\tAmazon Registry Services, Inc.\n.cisco\tgeneric\tCisco Technology, Inc.\n.citadel\tgeneric\tCitadel Domain LLC\n.citi\tgeneric\tCitigroup Inc.\n.citic\tgeneric\tCITIC Group Corporation\n.city\tgeneric\tBinky Moon, LLC\n.cityeats\tgeneric\tNot assigned\n.ck\tcountry-code\tTelecom Cook Islands Ltd.\n.cl\tcountry-code\tNIC Chile (University of Chile)\n.claims\tgeneric\tBinky Moon, LLC\n.cleaning\tgeneric\tBinky Moon, LLC\n.click\tgeneric\tInternet Naming Co.\n.clinic\tgeneric\tBinky Moon, LLC\n.clinique\tgeneric\tThe Estée Lauder Companies Inc.\n.clothing\tgeneric\tBinky Moon, LLC\n.cloud\tgeneric\tARUBA PEC S.p.A.\n.club\tgeneric\tRegistry Services, LLC\n.clubmed\tgeneric\tClub Méditerranée S.A.\n.cm\tcountry-code\tAgence Nationale des Technologies de l'Information et de la Communication (ANTIC)\n.cn\tcountry-code\tChina Internet Network Information Center (CNNIC)\n.co\tcountry-code\tMinistry of Information and Communications Technologies (MinTIC)\n.coach\tgeneric\tBinky Moon, LLC\n.codes\tgeneric\tBinky Moon, LLC\n.coffee\tgeneric\tBinky Moon, LLC\n.college\tgeneric\tXYZ.COM LLC\n.cologne\tgeneric\tdotKoeln GmbH\n.com\tgeneric\tVeriSign Global Registry Services\n.comcast\tgeneric\tNot assigned\n.commbank\tgeneric\tCOMMONWEALTH BANK OF AUSTRALIA\n.community\tgeneric\tBinky Moon, LLC\n.company\tgeneric\tBinky Moon, LLC\n.compare\tgeneric\tRegistry Services, LLC\n.computer\tgeneric\tBinky Moon, LLC\n.comsec\tgeneric\tVeriSign, Inc.\n.condos\tgeneric\tBinky Moon, LLC\n.construction\tgeneric\tBinky Moon, LLC\n.consulting\tgeneric\tDog Beach, LLC\n.contact\tgeneric\tDog Beach, LLC\n.contractors\tgeneric\tBinky Moon, LLC\n.cooking\tgeneric\tRegistry Services, LLC\n.cookingchannel\tgeneric\tNot assigned\n.cool\tgeneric\tBinky Moon, LLC\n.coop\tsponsored\tDotCooperation LLC\n.corsica\tgeneric\tCollectivité de Corse\n.country\tgeneric\tInternet Naming Co.\n.coupon\tgeneric\tAmazon Registry Services, Inc.\n.coupons\tgeneric\tBinky Moon, LLC\n.courses\tgeneric\tRegistry Services, LLC\n.cpa\tgeneric\tAmerican Institute of Certified Public Accountants\n.cr\tcountry-code\tNational Academy of Sciences (Academia Nacional de Ciencias)\n.credit\tgeneric\tBinky Moon, LLC\n.creditcard\tgeneric\tBinky Moon, LLC\n.creditunion\tgeneric\tDotCooperation, LLC\n.cricket\tgeneric\tdot Cricket Limited\n.crown\tgeneric\tCrown Equipment Corporation\n.crs\tgeneric\tFederated Co-operatives Limited\n.cruise\tgeneric\tViking River Cruises (Bermuda) Ltd.\n.cruises\tgeneric\tBinky Moon, LLC\n.csc\tgeneric\tNot assigned\n.cu\tcountry-code\tCENIAInternet\n.cuisinella\tgeneric\tSCHMIDT GROUPE S.A.S.\n.cv\tcountry-code\tAgência Reguladora Multissectorial da Economia (ARME)\n.cw\tcountry-code\tUniversity of Curacao\n.cx\tcountry-code\tChristmas Island Domain Administration Limited\n.cy\tcountry-code\tUniversity of Cyprus\n.cymru\tgeneric\tNominet UK\n.cyou\tgeneric\tShortdot SA\n.cz\tcountry-code\tCZ.NIC, z.s.p.o\n.dabur\tgeneric\tNot assigned\n.dad\tgeneric\tCharleston Road Registry Inc.\n.dance\tgeneric\tDog Beach, LLC\n.data\tgeneric\tDish DBS Corporation\n.date\tgeneric\tdot Date Limited\n.dating\tgeneric\tBinky Moon, LLC\n.datsun\tgeneric\tNISSAN MOTOR CO., LTD.\n.day\tgeneric\tCharleston Road Registry Inc.\n.dclk\tgeneric\tCharleston Road Registry Inc.\n.dds\tgeneric\tRegistry Services, LLC\n.de\tcountry-code\tDENIC eG\n.deal\tgeneric\tAmazon Registry Services, Inc.\n.dealer\tgeneric\tIntercap Registry Inc.\n.deals\tgeneric\tBinky Moon, LLC\n.degree\tgeneric\tDog Beach, LLC\n.delivery\tgeneric\tBinky Moon, LLC\n.dell\tgeneric\tDell Inc.\n.deloitte\tgeneric\tDeloitte Touche Tohmatsu\n.delta\tgeneric\tDelta Air Lines, Inc.\n.democrat\tgeneric\tDog Beach, LLC\n.dental\tgeneric\tBinky Moon, LLC\n.dentist\tgeneric\tDog Beach, LLC\n.desi\tgeneric\tEmergency Back-End Registry Operator Program - ICANN\n.design\tgeneric\tRegistry Services, LLC\n.dev\tgeneric\tCharleston Road Registry Inc.\n.dhl\tgeneric\tDeutsche Post AG\n.diamonds\tgeneric\tBinky Moon, LLC\n.diet\tgeneric\tXYZ.COM LLC\n.digital\tgeneric\tBinky Moon, LLC\n.direct\tgeneric\tBinky Moon, LLC\n.directory\tgeneric\tBinky Moon, LLC\n.discount\tgeneric\tBinky Moon, LLC\n.discover\tgeneric\tDiscover Financial Services\n.dish\tgeneric\tDish DBS Corporation\n.diy\tgeneric\tInternet Naming Co.\n.dj\tcountry-code\tDjibouti Telecom S.A\n.dk\tcountry-code\tDansk Internet Forum\n.dm\tcountry-code\tDotDM Corporation\n.dnp\tgeneric\tDai Nippon Printing Co., Ltd.\n.do\tcountry-code\tPontificia Universidad Catolica Madre y Maestra, Recinto Santo Tomas de Aquino\n.docs\tgeneric\tCharleston Road Registry Inc.\n.doctor\tgeneric\tBinky Moon, LLC\n.dodge\tgeneric\tNot assigned\n.dog\tgeneric\tBinky Moon, LLC\n.doha\tgeneric\tNot assigned\n.domains\tgeneric\tBinky Moon, LLC\n.doosan\tgeneric\tNot assigned\n.dot\tgeneric\tDish DBS Corporation\n.download\tgeneric\tdot Support Limited\n.drive\tgeneric\tCharleston Road Registry Inc.\n.dtv\tgeneric\tDish DBS Corporation\n.dubai\tgeneric\tDubai Smart Government Department\n.duck\tgeneric\tNot assigned\n.dunlop\tgeneric\tThe Goodyear Tire & Rubber Company\n.duns\tgeneric\tNot assigned\n.dupont\tgeneric\tDuPont Specialty Products USA, LLC\n.durban\tgeneric\tZA Central Registry NPC trading as ZA Central Registry\n.dvag\tgeneric\tDeutsche Vermögensberatung Aktiengesellschaft DVAG\n.dvr\tgeneric\tDISH Technologies L.L.C.\n.dz\tcountry-code\tCERIST\n.earth\tgeneric\tInterlink Systems Innovation Institute K.K.\n.eat\tgeneric\tCharleston Road Registry Inc.\n.ec\tcountry-code\tECUADORDOMAIN S.A.\n.eco\tgeneric\tBig Room Inc.\n.edeka\tgeneric\tEDEKA Verband kaufmännischer Genossenschaften e.V.\n.edu\tsponsored\tEDUCAUSE\n.education\tgeneric\tBinky Moon, LLC\n.ee\tcountry-code\tEesti Interneti Sihtasutus (EIS)\n.eg\tcountry-code\tEgyptian Universities Network (EUN), Supreme Council of Universities\n.eh\tcountry-code\tNot assigned\n.email\tgeneric\tBinky Moon, LLC\n.emerck\tgeneric\tMerck KGaA\n.emerson\tgeneric\tNot assigned\n.energy\tgeneric\tBinky Moon, LLC\n.engineer\tgeneric\tDog Beach, LLC\n.engineering\tgeneric\tBinky Moon, LLC\n.enterprises\tgeneric\tBinky Moon, LLC\n.epost\tgeneric\tNot assigned\n.epson\tgeneric\tSeiko Epson Corporation\n.equipment\tgeneric\tBinky Moon, LLC\n.er\tcountry-code\tEritrea Telecommunication Services Corporation (EriTel)\n.ericsson\tgeneric\tTelefonaktiebolaget L M Ericsson\n.erni\tgeneric\tERNI Group Holding AG\n.es\tcountry-code\tRed.es\n.esq\tgeneric\tCharleston Road Registry Inc.\n.estate\tgeneric\tBinky Moon, LLC\n.esurance\tgeneric\tNot assigned\n.et\tcountry-code\tEthio telecom\n.etisalat\tgeneric\tNot assigned\n.eu\tcountry-code\tEURid vzw/asbl\n.eurovision\tgeneric\tEuropean Broadcasting Union (EBU)\n.eus\tgeneric\tPuntueus Fundazioa\n.events\tgeneric\tBinky Moon, LLC\n.everbank\tgeneric\tNot assigned\n.exchange\tgeneric\tBinky Moon, LLC\n.expert\tgeneric\tBinky Moon, LLC\n.exposed\tgeneric\tBinky Moon, LLC\n.express\tgeneric\tBinky Moon, LLC\n.extraspace\tgeneric\tExtra Space Storage LLC\n.fage\tgeneric\tFage International S.A.\n.fail\tgeneric\tBinky Moon, LLC\n.fairwinds\tgeneric\tFairWinds Partners, LLC\n.faith\tgeneric\tdot Faith Limited\n.family\tgeneric\tDog Beach, LLC\n.fan\tgeneric\tDog Beach, LLC\n.fans\tgeneric\tZDNS International Limited\n.farm\tgeneric\tBinky Moon, LLC\n.farmers\tgeneric\tFarmers Insurance Exchange\n.fashion\tgeneric\tRegistry Services, LLC\n.fast\tgeneric\tAmazon Registry Services, Inc.\n.fedex\tgeneric\tFederal Express Corporation\n.feedback\tgeneric\tTop Level Spectrum, Inc.\n.ferrari\tgeneric\tFiat Chrysler Automobiles N.V.\n.ferrero\tgeneric\tFerrero Trading Lux S.A.\n.fi\tcountry-code\tFinnish Transport and Communications Agency Traficom\n.fiat\tgeneric\tNot assigned\n.fidelity\tgeneric\tFidelity Brokerage Services LLC\n.fido\tgeneric\tRogers Communications Canada Inc.\n.film\tgeneric\tMotion Picture Domain Registry Pty Ltd\n.final\tgeneric\tNúcleo de Informação e Coordenação do Ponto BR - NIC.br\n.finance\tgeneric\tBinky Moon, LLC\n.financial\tgeneric\tBinky Moon, LLC\n.fire\tgeneric\tAmazon Registry Services, Inc.\n.firestone\tgeneric\tBridgestone Licensing Services, Inc.\n.firmdale\tgeneric\tFirmdale Holdings Limited\n.fish\tgeneric\tBinky Moon, LLC\n.fishing\tgeneric\tRegistry Services, LLC\n.fit\tgeneric\tRegistry Services, LLC\n.fitness\tgeneric\tBinky Moon, LLC\n.fj\tcountry-code\tThe University of the South Pacific IT Services\n.fk\tcountry-code\tFalkland Islands Government\n.flickr\tgeneric\tFlickr, Inc.\n.flights\tgeneric\tBinky Moon, LLC\n.flir\tgeneric\tFLIR Systems, Inc.\n.florist\tgeneric\tBinky Moon, LLC\n.flowers\tgeneric\tXYZ.COM LLC\n.flsmidth\tgeneric\tNot assigned\n.fly\tgeneric\tCharleston Road Registry Inc.\n.fm\tcountry-code\tFSM Telecommunications Corporation\n.fo\tcountry-code\tFO Council\n.foo\tgeneric\tCharleston Road Registry Inc.\n.food\tgeneric\tInternet Naming Co.\n.foodnetwork\tgeneric\tNot assigned\n.football\tgeneric\tBinky Moon, LLC\n.ford\tgeneric\tFord Motor Company\n.forex\tgeneric\tDog Beach, LLC\n.forsale\tgeneric\tDog Beach, LLC\n.forum\tgeneric\tFegistry, LLC\n.foundation\tgeneric\tPublic Interest Registry (PIR)\n.fox\tgeneric\tFOX Registry, LLC\n.fr\tcountry-code\tAssociation Française pour le Nommage Internet en Coopération (A.F.N.I.C.)\n.free\tgeneric\tAmazon Registry Services, Inc.\n.fresenius\tgeneric\tFresenius Immobilien-Verwaltungs-GmbH\n.frl\tgeneric\tFRLregistry B.V.\n.frogans\tgeneric\tOP3FT\n.frontdoor\tgeneric\tNot assigned\n.frontier\tgeneric\tFrontier Communications Corporation\n.ftr\tgeneric\tFrontier Communications Corporation\n.fujitsu\tgeneric\tFujitsu Limited\n.fujixerox\tgeneric\tNot assigned\n.fun\tgeneric\tRadix Technologies Inc.\n.fund\tgeneric\tBinky Moon, LLC\n.furniture\tgeneric\tBinky Moon, LLC\n.futbol\tgeneric\tDog Beach, LLC\n.fyi\tgeneric\tBinky Moon, LLC\n.ga\tcountry-code\tAgence Nationale des Infrastructures Numériques et des Fréquences (ANINF)\n.gal\tgeneric\tAsociación puntoGAL\n.gallery\tgeneric\tBinky Moon, LLC\n.gallo\tgeneric\tGallo Vineyards, Inc.\n.gallup\tgeneric\tGallup, Inc.\n.game\tgeneric\tXYZ.COM LLC\n.games\tgeneric\tDog Beach, LLC\n.gap\tgeneric\tThe Gap, Inc.\n.garden\tgeneric\tRegistry Services, LLC\n.gay\tgeneric\tRegistry Services, LLC\n.gb\tcountry-code\tReserved Domain - IANA\n.gbiz\tgeneric\tCharleston Road Registry Inc.\n.gd\tcountry-code\tThe National Telecommunications Regulatory Commission (NTRC)\n.gdn\tgeneric\tJoint Stock Company \"Navigation-information systems\"\n.ge\tcountry-code\tCaucasus Online LLC\n.gea\tgeneric\tGEA Group Aktiengesellschaft\n.gent\tgeneric\tCombell nv\n.genting\tgeneric\tResorts World Inc. Pte. Ltd.\n.george\tgeneric\tWal-Mart Stores, Inc.\n.gf\tcountry-code\tCANAL+ TELECOM\n.gg\tcountry-code\tIsland Networks Ltd.\n.ggee\tgeneric\tGMO Internet, Inc.\n.gh\tcountry-code\tNetwork Computer Systems Limited\n.gi\tcountry-code\tSapphire Networks\n.gift\tgeneric\tUniregistry, Corp.\n.gifts\tgeneric\tBinky Moon, LLC\n.gives\tgeneric\tPublic Interest Registry (PIR)\n.giving\tgeneric\tPublic Interest Registry (PIR)\n.gl\tcountry-code\tTELE Greenland A/S\n.glade\tgeneric\tNot assigned\n.glass\tgeneric\tBinky Moon, LLC\n.gle\tgeneric\tCharleston Road Registry Inc.\n.global\tgeneric\tIdentity Digital Limited\n.globo\tgeneric\tGlobo Comunicação e Participações S.A\n.gm\tcountry-code\tGM-NIC\n.gmail\tgeneric\tCharleston Road Registry Inc.\n.gmbh\tgeneric\tBinky Moon, LLC\n.gmo\tgeneric\tGMO Internet, Inc.\n.gmx\tgeneric\t1&1 Mail & Media GmbH\n.gn\tcountry-code\tCentre National des Sciences Halieutiques de Boussoura\n.godaddy\tgeneric\tGo Daddy East, LLC\n.gold\tgeneric\tBinky Moon, LLC\n.goldpoint\tgeneric\tYODOBASHI CAMERA CO.,LTD.\n.golf\tgeneric\tBinky Moon, LLC\n.goo\tgeneric\tNTT Resonant Inc.\n.goodhands\tgeneric\tNot assigned\n.goodyear\tgeneric\tThe Goodyear Tire & Rubber Company\n.goog\tgeneric\tCharleston Road Registry Inc.\n.google\tgeneric\tCharleston Road Registry Inc.\n.gop\tgeneric\tRepublican State Leadership Committee, Inc.\n.got\tgeneric\tAmazon Registry Services, Inc.\n.gov\tsponsored\tCybersecurity and Infrastructure Security Agency\n.gp\tcountry-code\tNetworking Technologies Group\n.gq\tcountry-code\tGETESA\n.gr\tcountry-code\tICS-FORTH GR\n.grainger\tgeneric\tGrainger Registry Services, LLC\n.graphics\tgeneric\tBinky Moon, LLC\n.gratis\tgeneric\tBinky Moon, LLC\n.green\tgeneric\tIdentity Digital Limited\n.gripe\tgeneric\tBinky Moon, LLC\n.grocery\tgeneric\tWal-Mart Stores, Inc.\n.group\tgeneric\tBinky Moon, LLC\n.gs\tcountry-code\tGovernment of South Georgia and South Sandwich Islands (GSGSSI)\n.gt\tcountry-code\tUniversidad del Valle de Guatemala\n.gu\tcountry-code\tUniversity of Guam\n.guardian\tgeneric\tNot assigned\n.gucci\tgeneric\tGuccio Gucci S.p.a.\n.guge\tgeneric\tCharleston Road Registry Inc.\n.guide\tgeneric\tBinky Moon, LLC\n.guitars\tgeneric\tXYZ.COM LLC\n.guru\tgeneric\tBinky Moon, LLC\n.gw\tcountry-code\tAutoridade Reguladora Nacional - Tecnologias de Informação e Comunicação da Guiné-Bissau\n.gy\tcountry-code\tUniversity of Guyana\n.hair\tgeneric\tXYZ.COM LLC\n.hamburg\tgeneric\tHamburg Top-Level-Domain GmbH\n.hangout\tgeneric\tCharleston Road Registry Inc.\n.haus\tgeneric\tDog Beach, LLC\n.hbo\tgeneric\tHBO Registry Services, Inc.\n.hdfc\tgeneric\tHOUSING DEVELOPMENT FINANCE CORPORATION LIMITED\n.hdfcbank\tgeneric\tHDFC Bank Limited\n.health\tgeneric\tRegistry Services, LLC\n.healthcare\tgeneric\tBinky Moon, LLC\n.help\tgeneric\tInnovation Service Ltd\n.helsinki\tgeneric\tCity of Helsinki\n.here\tgeneric\tCharleston Road Registry Inc.\n.hermes\tgeneric\tHermes International\n.hgtv\tgeneric\tNot assigned\n.hiphop\tgeneric\tDot Hip Hop, LLC\n.hisamitsu\tgeneric\tHisamitsu Pharmaceutical Co.,Inc.\n.hitachi\tgeneric\tHitachi, Ltd.\n.hiv\tgeneric\tInternet Naming Co.\n.hk\tcountry-code\tHong Kong Internet Registration Corporation Ltd.\n.hkt\tgeneric\tPCCW-HKT DataCom Services Limited\n.hm\tcountry-code\tHM Domain Registry\n.hn\tcountry-code\tRed de Desarrollo Sostenible Honduras\n.hockey\tgeneric\tBinky Moon, LLC\n.holdings\tgeneric\tBinky Moon, LLC\n.holiday\tgeneric\tBinky Moon, LLC\n.homedepot\tgeneric\tHome Depot Product Authority, LLC\n.homegoods\tgeneric\tThe TJX Companies, Inc.\n.homes\tgeneric\tXYZ.COM LLC\n.homesense\tgeneric\tThe TJX Companies, Inc.\n.honda\tgeneric\tHonda Motor Co., Ltd.\n.honeywell\tgeneric\tNot assigned\n.horse\tgeneric\tRegistry Services, LLC\n.hospital\tgeneric\tBinky Moon, LLC\n.host\tgeneric\tRadix Technologies Inc.\n.hosting\tgeneric\tXYZ.COM LLC\n.hot\tgeneric\tAmazon Registry Services, Inc.\n.hoteles\tgeneric\tNot assigned\n.hotels\tgeneric\tBooking.com B.V.\n.hotmail\tgeneric\tMicrosoft Corporation\n.house\tgeneric\tBinky Moon, LLC\n.how\tgeneric\tCharleston Road Registry Inc.\n.hr\tcountry-code\tCARNet - Croatian Academic and Research Network\n.hsbc\tgeneric\tHSBC Global Services (UK) Limited\n.ht\tcountry-code\tConsortium FDS/RDDH\n.htc\tgeneric\tNot assigned\n.hu\tcountry-code\tCouncil of Hungarian Internet Providers (CHIP)\n.hughes\tgeneric\tHughes Satellite Systems Corporation\n.hyatt\tgeneric\tHyatt GTLD, L.L.C.\n.hyundai\tgeneric\tHyundai Motor Company\n.ibm\tgeneric\tInternational Business Machines Corporation\n.icbc\tgeneric\tIndustrial and Commercial Bank of China Limited\n.ice\tgeneric\tIntercontinentalExchange, Inc.\n.icu\tgeneric\tShortdot SA\n.id\tcountry-code\tPerkumpulan Pengelola Nama Domain Internet Indonesia (PANDI)\n.ie\tcountry-code\tUniversity College Dublin, Computing Services, Computer Centre\n.ieee\tgeneric\tIEEE Global LLC\n.ifm\tgeneric\tifm electronic gmbh\n.iinet\tgeneric\tNot assigned\n.ikano\tgeneric\tIkano S.A.\n.il\tcountry-code\tThe Israel Internet Association (RA)\n.im\tcountry-code\tIsle of Man Government\n.imamat\tgeneric\tFondation Aga Khan (Aga Khan Foundation)\n.imdb\tgeneric\tAmazon Registry Services, Inc.\n.immo\tgeneric\tBinky Moon, LLC\n.immobilien\tgeneric\tDog Beach, LLC\n.in\tcountry-code\tNational Internet Exchange of India\n.inc\tgeneric\tIntercap Registry Inc.\n.industries\tgeneric\tBinky Moon, LLC\n.infiniti\tgeneric\tNISSAN MOTOR CO., LTD.\n.info\tgeneric\tIdentity Digital Limited\n.ing\tgeneric\tCharleston Road Registry Inc.\n.ink\tgeneric\tRegistry Services, LLC\n.institute\tgeneric\tBinky Moon, LLC\n.insurance\tgeneric\tfTLD Registry Services LLC\n.insure\tgeneric\tBinky Moon, LLC\n.int\tsponsored\tInternet Assigned Numbers Authority\n.intel\tgeneric\tNot assigned\n.international\tgeneric\tBinky Moon, LLC\n.intuit\tgeneric\tIntuit Administrative Services, Inc.\n.investments\tgeneric\tBinky Moon, LLC\n.io\tcountry-code\tInternet Computer Bureau Limited\n.ipiranga\tgeneric\tIpiranga Produtos de Petroleo S.A.\n.iq\tcountry-code\tCommunications and Media Commission (CMC)\n.ir\tcountry-code\tInstitute for Research in Fundamental Sciences\n.irish\tgeneric\tBinky Moon, LLC\n.is\tcountry-code\tISNIC - Internet á Íslandi hf.\n.iselect\tgeneric\tNot assigned\n.ismaili\tgeneric\tFondation Aga Khan (Aga Khan Foundation)\n.ist\tgeneric\tIstanbul Metropolitan Municipality\n.istanbul\tgeneric\tIstanbul Metropolitan Municipality\n.it\tcountry-code\tIIT - CNR\n.itau\tgeneric\tItau Unibanco Holding S.A.\n.itv\tgeneric\tITV Services Limited\n.iveco\tgeneric\tNot assigned\n.iwc\tgeneric\tNot assigned\n.jaguar\tgeneric\tJaguar Land Rover Ltd\n.java\tgeneric\tOracle Corporation\n.jcb\tgeneric\tJCB Co., Ltd.\n.jcp\tgeneric\tNot assigned\n.je\tcountry-code\tIsland Networks (Jersey) Ltd.\n.jeep\tgeneric\tFCA US LLC.\n.jetzt\tgeneric\tBinky Moon, LLC\n.jewelry\tgeneric\tBinky Moon, LLC\n.jio\tgeneric\tReliance Industries Limited\n.jlc\tgeneric\tNot assigned\n.jll\tgeneric\tJones Lang LaSalle Incorporated\n.jm\tcountry-code\tUniversity of West Indies\n.jmp\tgeneric\tMatrix IP LLC\n.jnj\tgeneric\tJohnson & Johnson Services, Inc.\n.jo\tcountry-code\tMinistry of Digital Economy and Entrepreneurship (MoDEE)\n.jobs\tsponsored\tEmploy Media LLC\n.joburg\tgeneric\tZA Central Registry NPC trading as ZA Central Registry\n.jot\tgeneric\tAmazon Registry Services, Inc.\n.joy\tgeneric\tAmazon Registry Services, Inc.\n.jp\tcountry-code\tJapan Registry Services Co., Ltd.\n.jpmorgan\tgeneric\tJPMorgan Chase Bank, National Association\n.jprs\tgeneric\tJapan Registry Services Co., Ltd.\n.juegos\tgeneric\tDog Beach, LLC\n.juniper\tgeneric\tJUNIPER NETWORKS, INC.\n.kaufen\tgeneric\tDog Beach, LLC\n.kddi\tgeneric\tKDDI CORPORATION\n.ke\tcountry-code\tKenya Network Information Center (KeNIC)\n.kerryhotels\tgeneric\tKerry Trading Co. Limited\n.kerrylogistics\tgeneric\tNot assigned\n.kerryproperties\tgeneric\tKerry Trading Co. Limited\n.kfh\tgeneric\tKuwait Finance House\n.kg\tcountry-code\tAsiaInfo Telecommunication Enterprise\n.kh\tcountry-code\tTelecommunication Regulator of Cambodia (TRC)\n.ki\tcountry-code\tMinistry of Information, Communications and Transport (MICT)\n.kia\tgeneric\tKIA MOTORS CORPORATION\n.kids\tgeneric\tDotKids Foundation Limited\n.kim\tgeneric\tIdentity Digital Limited\n.kinder\tgeneric\tNot assigned\n.kindle\tgeneric\tAmazon Registry Services, Inc.\n.kitchen\tgeneric\tBinky Moon, LLC\n.kiwi\tgeneric\tDOT KIWI LIMITED\n.km\tcountry-code\tComores Telecom\n.kn\tcountry-code\tMinistry of Finance, Sustainable Development Information & Technology\n.koeln\tgeneric\tdotKoeln GmbH\n.komatsu\tgeneric\tKomatsu Ltd.\n.kosher\tgeneric\tKosher Marketing Assets LLC\n.kp\tcountry-code\tStar Joint Venture Company\n.kpmg\tgeneric\tKPMG International Cooperative (KPMG International Genossenschaft)\n.kpn\tgeneric\tKoninklijke KPN N.V.\n.kr\tcountry-code\tKorea Internet & Security Agency (KISA)\n.krd\tgeneric\tKRG Department of Information Technology\n.kred\tgeneric\tKredTLD Pty Ltd\n.kuokgroup\tgeneric\tKerry Trading Co. Limited\n.kw\tcountry-code\tCommunications and Information Technology Regulatory Authority\n.ky\tcountry-code\tUtility Regulation and Competition Office (OfReg)\n.kyoto\tgeneric\tAcademic Institution: Kyoto Jyoho Gakuen\n.kz\tcountry-code\tAssociation of IT Companies of Kazakhstan\n.la\tcountry-code\tLao National Internet Center (LANIC), Ministry of Technology and Communications\n.lacaixa\tgeneric\tFundación Bancaria Caixa d'Estalvis i Pensions de Barcelona, \"la Caixa\"\n.ladbrokes\tgeneric\tNot assigned\n.lamborghini\tgeneric\tAutomobili Lamborghini S.p.A.\n.lamer\tgeneric\tThe Estée Lauder Companies Inc.\n.lancaster\tgeneric\tNot assigned\n.lancia\tgeneric\tNot assigned\n.lancome\tgeneric\tNot assigned\n.land\tgeneric\tBinky Moon, LLC\n.landrover\tgeneric\tJaguar Land Rover Ltd\n.lanxess\tgeneric\tLANXESS Corporation\n.lasalle\tgeneric\tJones Lang LaSalle Incorporated\n.lat\tgeneric\tXYZ.COM LLC\n.latino\tgeneric\tDish DBS Corporation\n.latrobe\tgeneric\tLa Trobe University\n.law\tgeneric\tRegistry Services, LLC\n.lawyer\tgeneric\tDog Beach, LLC\n.lb\tcountry-code\tInternet Society Lebanon\n.lc\tcountry-code\tUniversity of Puerto Rico\n.lds\tgeneric\tIRI Domain Management, LLC\n.lease\tgeneric\tBinky Moon, LLC\n.leclerc\tgeneric\tA.C.D. LEC Association des Centres Distributeurs Edouard Leclerc\n.lefrak\tgeneric\tLeFrak Organization, Inc.\n.legal\tgeneric\tBinky Moon, LLC\n.lego\tgeneric\tLEGO Juris A/S\n.lexus\tgeneric\tTOYOTA MOTOR CORPORATION\n.lgbt\tgeneric\tIdentity Digital Limited\n.li\tcountry-code\tSWITCH The Swiss Education & Research Network\n.liaison\tgeneric\tNot assigned\n.lidl\tgeneric\tSchwarz Domains und Services GmbH & Co. KG\n.life\tgeneric\tBinky Moon, LLC\n.lifeinsurance\tgeneric\tAmerican Council of Life Insurers\n.lifestyle\tgeneric\tInternet Naming Co.\n.lighting\tgeneric\tBinky Moon, LLC\n.like\tgeneric\tAmazon Registry Services, Inc.\n.lilly\tgeneric\tEli Lilly and Company\n.limited\tgeneric\tBinky Moon, LLC\n.limo\tgeneric\tBinky Moon, LLC\n.lincoln\tgeneric\tFord Motor Company\n.linde\tgeneric\tNot assigned\n.link\tgeneric\tNova Registry Ltd.\n.lipsy\tgeneric\tNot assigned\n.live\tgeneric\tDog Beach, LLC\n.living\tgeneric\tInternet Naming Co.\n.lixil\tgeneric\tNot assigned\n.lk\tcountry-code\tCouncil for Information Technology LK Domain Registrar\n.llc\tgeneric\tIdentity Digital Limited\n.llp\tgeneric\tIntercap Registry Inc.\n.loan\tgeneric\tdot Loan Limited\n.loans\tgeneric\tBinky Moon, LLC\n.locker\tgeneric\tOrange Domains LLC\n.locus\tgeneric\tLocus Analytics LLC\n.loft\tgeneric\tNot assigned\n.lol\tgeneric\tXYZ.COM LLC\n.london\tgeneric\tDot London Domains Limited\n.lotte\tgeneric\tLotte Holdings Co., Ltd.\n.lotto\tgeneric\tIdentity Digital Limited\n.love\tgeneric\tWaterford Limited\n.lpl\tgeneric\tLPL Holdings, Inc.\n.lplfinancial\tgeneric\tLPL Holdings, Inc.\n.lr\tcountry-code\tData Technology Solutions, Inc.\n.ls\tcountry-code\tLesotho Network Information Centre Proprietary (LSNIC)\n.lt\tcountry-code\tKaunas University of Technology\n.ltd\tgeneric\tBinky Moon, LLC\n.ltda\tgeneric\tInterNetX Corp.\n.lu\tcountry-code\tRESTENA\n.lundbeck\tgeneric\tH. Lundbeck A/S\n.lupin\tgeneric\tNot assigned\n.luxe\tgeneric\tRegistry Services, LLC\n.luxury\tgeneric\tLuxury Partners LLC\n.lv\tcountry-code\tUniversity of Latvia, Institute of Mathematics and Computer Science, Department of Network Solutions (DNS)\n.ly\tcountry-code\tGeneral Post and Telecommunication Company\n.ma\tcountry-code\tAgence Nationale de Réglementation des Télécommunications (ANRT)\n.macys\tgeneric\tNot assigned\n.madrid\tgeneric\tComunidad de Madrid\n.maif\tgeneric\tMutuelle Assurance Instituteur France (MAIF)\n.maison\tgeneric\tBinky Moon, LLC\n.makeup\tgeneric\tXYZ.COM LLC\n.man\tgeneric\tMAN Truck & Bus SE\n.management\tgeneric\tBinky Moon, LLC\n.mango\tgeneric\tPUNTO FA S.L.\n.map\tgeneric\tCharleston Road Registry Inc.\n.market\tgeneric\tDog Beach, LLC\n.marketing\tgeneric\tBinky Moon, LLC\n.markets\tgeneric\tDog Beach, LLC\n.marriott\tgeneric\tMarriott Worldwide Corporation\n.marshalls\tgeneric\tThe TJX Companies, Inc.\n.maserati\tgeneric\tNot assigned\n.mattel\tgeneric\tMattel IT Services, Inc.\n.mba\tgeneric\tBinky Moon, LLC\n.mc\tcountry-code\tDirection des Plateformes et des Ressources Numériques\n.mcd\tgeneric\tNot assigned\n.mcdonalds\tgeneric\tNot assigned\n.mckinsey\tgeneric\tMcKinsey Holdings, Inc.\n.md\tcountry-code\tIP Serviciul Tehnologia Informatiei si Securitate Cibernetica\n.me\tcountry-code\tGovernment of Montenegro\n.med\tgeneric\tMedistry LLC\n.media\tgeneric\tBinky Moon, LLC\n.meet\tgeneric\tCharleston Road Registry Inc.\n.melbourne\tgeneric\tThe Crown in right of the State of Victoria, represented by its Department of State Development, Business and Innovation\n.meme\tgeneric\tCharleston Road Registry Inc.\n.memorial\tgeneric\tDog Beach, LLC\n.men\tgeneric\tExclusive Registry Limited\n.menu\tgeneric\tDot Menu Registry LLC\n.meo\tgeneric\tNot assigned\n.merckmsd\tgeneric\tMSD Registry Holdings, Inc.\n.metlife\tgeneric\tNot assigned\n.mf\tcountry-code\tNot assigned\n.mg\tcountry-code\tNIC-MG (Network Information Center Madagascar)\n.mh\tcountry-code\tOffice of the Cabinet\n.miami\tgeneric\tRegistry Services, LLC\n.microsoft\tgeneric\tMicrosoft Corporation\n.mil\tsponsored\tDoD Network Information Center\n.mini\tgeneric\tBayerische Motoren Werke Aktiengesellschaft\n.mint\tgeneric\tIntuit Administrative Services, Inc.\n.mit\tgeneric\tMassachusetts Institute of Technology\n.mitsubishi\tgeneric\tMitsubishi Corporation\n.mk\tcountry-code\tMacedonian Academic Research Network Skopje\n.ml\tcountry-code\tAgence des Technologies de l’Information et de la Communication\n.mlb\tgeneric\tMLB Advanced Media DH, LLC\n.mls\tgeneric\tThe Canadian Real Estate Association\n.mm\tcountry-code\tMinistry of Transport and Communications\n.mma\tgeneric\tMMA IARD\n.mn\tcountry-code\tDatacom Co., Ltd.\n.mo\tcountry-code\tMacao Post and Telecommunications Bureau (CTT)\n.mobi\tgeneric\tIdentity Digital Limited\n.mobile\tgeneric\tDish DBS Corporation\n.mobily\tgeneric\tNot assigned\n.moda\tgeneric\tDog Beach, LLC\n.moe\tgeneric\tInterlink Systems Innovation Institute K.K.\n.moi\tgeneric\tAmazon Registry Services, Inc.\n.mom\tgeneric\tXYZ.COM LLC\n.monash\tgeneric\tMonash University\n.money\tgeneric\tBinky Moon, LLC\n.monster\tgeneric\tXYZ.COM LLC\n.montblanc\tgeneric\tNot assigned\n.mopar\tgeneric\tNot assigned\n.mormon\tgeneric\tIRI Domain Management, LLC (\"Applicant\")\n.mortgage\tgeneric\tDog Beach, LLC\n.moscow\tgeneric\tFoundation for Assistance for Internet Technologies and Infrastructure Development (FAITID)\n.moto\tgeneric\tMotorola Trademark Holdings, LLC\n.motorcycles\tgeneric\tXYZ.COM LLC\n.mov\tgeneric\tCharleston Road Registry Inc.\n.movie\tgeneric\tBinky Moon, LLC\n.movistar\tgeneric\tNot assigned\n.mp\tcountry-code\tSaipan Datacom, Inc.\n.mq\tcountry-code\tCANAL+ TELECOM\n.mr\tcountry-code\tUniversité de Nouakchott Al Aasriya\n.ms\tcountry-code\tMNI Networks Ltd.\n.msd\tgeneric\tMSD Registry Holdings, Inc.\n.mt\tcountry-code\tNIC (Malta)\n.mtn\tgeneric\tMTN Dubai Limited\n.mtpc\tgeneric\tNot assigned\n.mtr\tgeneric\tMTR Corporation Limited\n.mu\tcountry-code\tInternet Direct Ltd\n.museum\tsponsored\tMuseum Domain Management Association\n.music\tgeneric\tDotMusic Limited\n.mutual\tgeneric\tNot assigned\n.mutuelle\tgeneric\tNot assigned\n.mv\tcountry-code\tDhivehi Raajjeyge Gulhun PLC\n.mw\tcountry-code\tMalawi Sustainable Development Network Programme (Malawi SDNP)\n.mx\tcountry-code\tNIC-Mexico ITESM - Campus Monterrey\n.my\tcountry-code\tMYNIC Berhad\n.mz\tcountry-code\tCentro de Informatica de Universidade Eduardo Mondlane\n.na\tcountry-code\tNamibian Network Information Center\n.nab\tgeneric\tNational Australia Bank Limited\n.nadex\tgeneric\tNot assigned\n.nagoya\tgeneric\tGMO Registry, Inc.\n.name\tgeneric-restricted\tVeriSign Information Services, Inc.\n.nationwide\tgeneric\tNot assigned\n.natura\tgeneric\tNot assigned\n.navy\tgeneric\tDog Beach, LLC\n.nba\tgeneric\tNBA REGISTRY, LLC\n.nc\tcountry-code\tOffice des Postes et Telecommunications\n.ne\tcountry-code\tSONITEL\n.nec\tgeneric\tNEC Corporation\n.net\tgeneric\tVeriSign Global Registry Services\n.netbank\tgeneric\tCOMMONWEALTH BANK OF AUSTRALIA\n.netflix\tgeneric\tNetflix, Inc.\n.network\tgeneric\tBinky Moon, LLC\n.neustar\tgeneric\tNeuStar, Inc.\n.new\tgeneric\tCharleston Road Registry Inc.\n.newholland\tgeneric\tNot assigned\n.news\tgeneric\tDog Beach, LLC\n.next\tgeneric\tNext plc\n.nextdirect\tgeneric\tNext plc\n.nexus\tgeneric\tCharleston Road Registry Inc.\n.nf\tcountry-code\tNorfolk Island Data Services\n.nfl\tgeneric\tNFL Reg Ops LLC\n.ng\tcountry-code\tNigeria Internet Registration Association\n.ngo\tgeneric\tPublic Interest Registry\n.nhk\tgeneric\tJapan Broadcasting Corporation (NHK)\n.ni\tcountry-code\tUniversidad Nacional del Ingernieria. Division de Tecnologias de la Informacion.\n.nico\tgeneric\tDWANGO Co., Ltd.\n.nike\tgeneric\tNIKE, Inc.\n.nikon\tgeneric\tNIKON CORPORATION\n.ninja\tgeneric\tDog Beach, LLC\n.nissan\tgeneric\tNISSAN MOTOR CO., LTD.\n.nissay\tgeneric\tNippon Life Insurance Company\n.nl\tcountry-code\tSIDN (Stichting Internet Domeinregistratie Nederland)\n.no\tcountry-code\tNorid A/S\n.nokia\tgeneric\tNokia Corporation\n.northwesternmutual\tgeneric\tNot assigned\n.norton\tgeneric\tGen Digital Inc.\n.now\tgeneric\tAmazon Registry Services, Inc.\n.nowruz\tgeneric\tEmergency Back-End Registry Operator Program - ICANN\n.nowtv\tgeneric\tStarbucks (HK) Limited\n.np\tcountry-code\tMercantile Communications Pvt. Ltd.\n.nr\tcountry-code\tCENPAC NET\n.nra\tgeneric\tNational Rifle Association of America\n.nrw\tgeneric\tMinds + Machines GmbH\n.ntt\tgeneric\tNIPPON TELEGRAPH AND TELEPHONE CORPORATION\n.nu\tcountry-code\tThe IUSN Foundation\n.nyc\tgeneric\tThe City of New York by and through the New York City Department of Information Technology & Telecommunications\n.nz\tcountry-code\tInternetNZ\n.obi\tgeneric\tOBI Group Holding SE & Co. KGaA\n.observer\tgeneric\tFegistry, LLC\n.off\tgeneric\tNot assigned\n.office\tgeneric\tMicrosoft Corporation\n.okinawa\tgeneric\tBRregistry, Inc.\n.olayan\tgeneric\tCompetrol (Luxembourg) Sarl\n.olayangroup\tgeneric\tCompetrol (Luxembourg) Sarl\n.oldnavy\tgeneric\tNot assigned\n.ollo\tgeneric\tDish DBS Corporation\n.om\tcountry-code\tTelecommunications Regulatory Authority (TRA)\n.omega\tgeneric\tThe Swatch Group Ltd\n.one\tgeneric\tOne.com A/S\n.ong\tgeneric\tPublic Interest Registry\n.onl\tgeneric\tiRegistry GmbH\n.online\tgeneric\tRadix Technologies Inc. SEZC\n.onyourside\tgeneric\tNot assigned\n.ooo\tgeneric\tINFIBEAM AVENUES LIMITED\n.open\tgeneric\tAmerican Express Travel Related Services Company, Inc.\n.oracle\tgeneric\tOracle Corporation\n.orange\tgeneric\tOrange Brand Services Limited\n.org\tgeneric\tPublic Interest Registry (PIR)\n.organic\tgeneric\tIdentity Digital Limited\n.orientexpress\tgeneric\tNot assigned\n.origins\tgeneric\tThe Estée Lauder Companies Inc.\n.osaka\tgeneric\tOsaka Registry Co., Ltd.\n.otsuka\tgeneric\tOtsuka Holdings Co., Ltd.\n.ott\tgeneric\tDish DBS Corporation\n.ovh\tgeneric\tOVH SAS\n.pa\tcountry-code\tUniversidad Tecnologica de Panama\n.page\tgeneric\tCharleston Road Registry Inc.\n.pamperedchef\tgeneric\tNot assigned\n.panasonic\tgeneric\tPanasonic Corporation\n.panerai\tgeneric\tNot assigned\n.paris\tgeneric\tCity of Paris\n.pars\tgeneric\tEmergency Back-End Registry Operator Program - ICANN\n.partners\tgeneric\tBinky Moon, LLC\n.parts\tgeneric\tBinky Moon, LLC\n.party\tgeneric\tBlue Sky Registry Limited\n.passagens\tgeneric\tNot assigned\n.pay\tgeneric\tAmazon Registry Services, Inc.\n.pccw\tgeneric\tPCCW Enterprises Limited\n.pe\tcountry-code\tRed Cientifica Peruana\n.pet\tgeneric\tIdentity Digital Limited\n.pf\tcountry-code\tGouvernement de la Polynésie française\n.pfizer\tgeneric\tPfizer Inc.\n.pg\tcountry-code\tPNG DNS Administration, Vice Chancellors Office, The Papua New Guinea University of Technology\n.ph\tcountry-code\tPH Domain Foundation\n.pharmacy\tgeneric\tNational Association of Boards of Pharmacy\n.phd\tgeneric\tCharleston Road Registry Inc.\n.philips\tgeneric\tKoninklijke Philips N.V.\n.phone\tgeneric\tDish DBS Corporation\n.photo\tgeneric\tRegistry Services, LLC\n.photography\tgeneric\tBinky Moon, LLC\n.photos\tgeneric\tBinky Moon, LLC\n.physio\tgeneric\tPhysBiz Pty Ltd\n.piaget\tgeneric\tNot assigned\n.pics\tgeneric\tXYZ.COM LLC\n.pictet\tgeneric\tBanque Pictet & Cie SA\n.pictures\tgeneric\tBinky Moon, LLC\n.pid\tgeneric\tTop Level Spectrum, Inc.\n.pin\tgeneric\tAmazon Registry Services, Inc.\n.ping\tgeneric\tPing Registry Provider, Inc.\n.pink\tgeneric\tIdentity Digital Limited\n.pioneer\tgeneric\tPioneer Corporation\n.pizza\tgeneric\tBinky Moon, LLC\n.pk\tcountry-code\tPKNIC\n.pl\tcountry-code\tResearch and Academic Computer Network\n.place\tgeneric\tBinky Moon, LLC\n.play\tgeneric\tCharleston Road Registry Inc.\n.playstation\tgeneric\tSony Computer Entertainment Inc.\n.plumbing\tgeneric\tBinky Moon, LLC\n.plus\tgeneric\tBinky Moon, LLC\n.pm\tcountry-code\tAssociation Française pour le Nommage Internet en Coopération (A.F.N.I.C.)\n.pn\tcountry-code\tPitcairn Island Administration\n.pnc\tgeneric\tPNC Domain Co., LLC\n.pohl\tgeneric\tDeutsche Vermögensberatung Aktiengesellschaft DVAG\n.poker\tgeneric\tIdentity Digital Limited\n.politie\tgeneric\tPolitie Nederland\n.porn\tgeneric\tICM Registry PN LLC\n.post\tsponsored\tUniversal Postal Union\n.pr\tcountry-code\tGauss Research Laboratory Inc.\n.pramerica\tgeneric\tNot assigned\n.praxi\tgeneric\tPraxi S.p.A.\n.press\tgeneric\tRadix Technologies Inc.\n.prime\tgeneric\tAmazon Registry Services, Inc.\n.pro\tgeneric-restricted\tIdentity Digital Limited\n.prod\tgeneric\tCharleston Road Registry Inc.\n.productions\tgeneric\tBinky Moon, LLC\n.prof\tgeneric\tCharleston Road Registry Inc.\n.progressive\tgeneric\tProgressive Casualty Insurance Company\n.promo\tgeneric\tIdentity Digital Limited\n.properties\tgeneric\tBinky Moon, LLC\n.property\tgeneric\tDigital Property Infrastructure Limited\n.protection\tgeneric\tXYZ.COM LLC\n.pru\tgeneric\tPrudential Financial, Inc.\n.prudential\tgeneric\tPrudential Financial, Inc.\n.ps\tcountry-code\tMinistry of Telecommunications & Information Technology, Government Computer Center\n.pt\tcountry-code\tAssociação DNS.PT\n.pub\tgeneric\tDog Beach, LLC\n.pw\tcountry-code\tMicronesia Investment and Development Corporation\n.pwc\tgeneric\tPricewaterhouseCoopers LLP\n.py\tcountry-code\tNIC-PY\n.qa\tcountry-code\tCommunications Regulatory Authority\n.qpon\tgeneric\tDOTQPON LLC.\n.quebec\tgeneric\tPointQuébec Inc\n.quest\tgeneric\tXYZ.COM LLC\n.qvc\tgeneric\tNot assigned\n.racing\tgeneric\tPremier Registry Limited\n.radio\tgeneric\tEuropean Broadcasting Union (EBU)\n.raid\tgeneric\tNot assigned\n.re\tcountry-code\tAssociation Française pour le Nommage Internet en Coopération (A.F.N.I.C.)\n.read\tgeneric\tAmazon Registry Services, Inc.\n.realestate\tgeneric\tdotRealEstate LLC\n.realtor\tgeneric\tReal Estate Domains LLC\n.realty\tgeneric\tInternet Naming Co.\n.recipes\tgeneric\tBinky Moon, LLC\n.red\tgeneric\tIdentity Digital Limited\n.redstone\tgeneric\tNot assigned\n.redumbrella\tgeneric\tTravelers TLD, LLC\n.rehab\tgeneric\tDog Beach, LLC\n.reise\tgeneric\tBinky Moon, LLC\n.reisen\tgeneric\tBinky Moon, LLC\n.reit\tgeneric\tNational Association of Real Estate Investment Trusts, Inc.\n.reliance\tgeneric\tReliance Industries Limited\n.ren\tgeneric\tZDNS International Limited\n.rent\tgeneric\tXYZ.COM LLC\n.rentals\tgeneric\tBinky Moon, LLC\n.repair\tgeneric\tBinky Moon, LLC\n.report\tgeneric\tBinky Moon, LLC\n.republican\tgeneric\tDog Beach, LLC\n.rest\tgeneric\tPunto 2012 Sociedad Anonima Promotora de Inversion de Capital Variable\n.restaurant\tgeneric\tBinky Moon, LLC\n.review\tgeneric\tdot Review Limited\n.reviews\tgeneric\tDog Beach, LLC\n.rexroth\tgeneric\tRobert Bosch GMBH\n.rich\tgeneric\tiRegistry GmbH\n.richardli\tgeneric\tPacific Century Asset Management (HK) Limited\n.ricoh\tgeneric\tRicoh Company, Ltd.\n.rightathome\tgeneric\tNot assigned\n.ril\tgeneric\tReliance Industries Limited\n.rio\tgeneric\tEmpresa Municipal de Informática SA - IPLANRIO\n.rip\tgeneric\tDog Beach, LLC\n.rmit\tgeneric\tNot assigned\n.ro\tcountry-code\tNational Institute for R&D in Informatics\n.rocher\tgeneric\tNot assigned\n.rocks\tgeneric\tDog Beach, LLC\n.rodeo\tgeneric\tRegistry Services, LLC\n.rogers\tgeneric\tRogers Communications Canada Inc.\n.room\tgeneric\tAmazon Registry Services, Inc.\n.rs\tcountry-code\tSerbian National Internet Domain Registry (RNIDS)\n.rsvp\tgeneric\tCharleston Road Registry Inc.\n.ru\tcountry-code\tCoordination Center for TLD RU\n.rugby\tgeneric\tWorld Rugby Strategic Developments Limited\n.ruhr\tgeneric\tdotSaarland GmbH\n.run\tgeneric\tBinky Moon, LLC\n.rw\tcountry-code\tRwanda Internet Community and Technology Alliance (RICTA) Ltd\n.rwe\tgeneric\tRWE AG\n.ryukyu\tgeneric\tBRregistry, Inc.\n.sa\tcountry-code\tCommunications, Space and Technology Commission\n.saarland\tgeneric\tdotSaarland GmbH\n.safe\tgeneric\tAmazon Registry Services, Inc.\n.safety\tgeneric\tSafety Registry Services, LLC.\n.sakura\tgeneric\tSAKURA internet Inc.\n.sale\tgeneric\tDog Beach, LLC\n.salon\tgeneric\tBinky Moon, LLC\n.samsclub\tgeneric\tWal-Mart Stores, Inc.\n.samsung\tgeneric\tSAMSUNG SDS CO., LTD\n.sandvik\tgeneric\tSandvik AB\n.sandvikcoromant\tgeneric\tSandvik AB\n.sanofi\tgeneric\tSanofi\n.sap\tgeneric\tSAP AG\n.sapo\tgeneric\tNot assigned\n.sarl\tgeneric\tBinky Moon, LLC\n.sas\tgeneric\tResearch IP LLC\n.save\tgeneric\tAmazon Registry Services, Inc.\n.saxo\tgeneric\tSaxo Bank A/S\n.sb\tcountry-code\tSolomon Telekom Company Limited\n.sbi\tgeneric\tSTATE BANK OF INDIA\n.sbs\tgeneric\tShortdot SA\n.sc\tcountry-code\tVCS Pty Ltd\n.sca\tgeneric\tNot assigned\n.scb\tgeneric\tThe Siam Commercial Bank Public Company Limited (\"SCB\")\n.schaeffler\tgeneric\tSchaeffler Technologies AG & Co. KG\n.schmidt\tgeneric\tSCHMIDT GROUPE S.A.S.\n.scholarships\tgeneric\tScholarships.com, LLC\n.school\tgeneric\tBinky Moon, LLC\n.schule\tgeneric\tBinky Moon, LLC\n.schwarz\tgeneric\tSchwarz Domains und Services GmbH & Co. KG\n.science\tgeneric\tdot Science Limited\n.scjohnson\tgeneric\tNot assigned\n.scor\tgeneric\tNot assigned\n.scot\tgeneric\tDot Scot Registry Limited\n.sd\tcountry-code\tSudan Internet Society\n.se\tcountry-code\tThe Internet Infrastructure Foundation\n.search\tgeneric\tCharleston Road Registry Inc.\n.seat\tgeneric\tSEAT, S.A. (Sociedad Unipersonal)\n.secure\tgeneric\tAmazon Registry Services, Inc.\n.security\tgeneric\tXYZ.COM LLC\n.seek\tgeneric\tSeek Limited\n.select\tgeneric\tRegistry Services, LLC\n.sener\tgeneric\tSener Ingeniería y Sistemas, S.A.\n.services\tgeneric\tBinky Moon, LLC\n.ses\tgeneric\tNot assigned\n.seven\tgeneric\tSeven West Media Ltd\n.sew\tgeneric\tSEW-EURODRIVE GmbH & Co KG\n.sex\tgeneric\tICM Registry SX LLC\n.sexy\tgeneric\tInternet Naming Co.\n.sfr\tgeneric\tSociete Francaise du Radiotelephone - SFR\n.sg\tcountry-code\tSingapore Network Information Centre (SGNIC) Pte Ltd\n.sh\tcountry-code\tGovernment of St. Helena\n.shangrila\tgeneric\tShangri-La International Hotel Management Limited\n.sharp\tgeneric\tSharp Corporation\n.shaw\tgeneric\tNot assigned\n.shell\tgeneric\tShell Information Technology International Inc\n.shia\tgeneric\tEmergency Back-End Registry Operator Program - ICANN\n.shiksha\tgeneric\tIdentity Digital Limited\n.shoes\tgeneric\tBinky Moon, LLC\n.shop\tgeneric\tGMO Registry, Inc.\n.shopping\tgeneric\tBinky Moon, LLC\n.shouji\tgeneric\tQIHOO 360 TECHNOLOGY CO. LTD.\n.show\tgeneric\tBinky Moon, LLC\n.showtime\tgeneric\tNot assigned\n.shriram\tgeneric\tNot assigned\n.si\tcountry-code\tAcademic and Research Network of Slovenia (ARNES)\n.silk\tgeneric\tAmazon Registry Services, Inc.\n.sina\tgeneric\tSina Corporation\n.singles\tgeneric\tBinky Moon, LLC\n.site\tgeneric\tRadix Technologies Inc. SEZC\n.sj\tcountry-code\tNorid A/S\n.sk\tcountry-code\tSK-NIC, a.s.\n.ski\tgeneric\tIdentity Digital Limited\n.skin\tgeneric\tXYZ.COM LLC\n.sky\tgeneric\tSky UK Limited\n.skype\tgeneric\tMicrosoft Corporation\n.sl\tcountry-code\tSierratel\n.sling\tgeneric\tDISH Technologies L.L.C.\n.sm\tcountry-code\tTelecom Italia San Marino S.p.A.\n.smart\tgeneric\tSmart Communications, Inc. (SMART)\n.smile\tgeneric\tAmazon Registry Services, Inc.\n.sn\tcountry-code\tUniversite Cheikh Anta Diop\n.sncf\tgeneric\tSociété Nationale SNCF\n.so\tcountry-code\tMinistry of Post and Telecommunications\n.soccer\tgeneric\tBinky Moon, LLC\n.social\tgeneric\tDog Beach, LLC\n.softbank\tgeneric\tSoftBank Group Corp.\n.software\tgeneric\tDog Beach, LLC\n.sohu\tgeneric\tSohu.com Limited\n.solar\tgeneric\tBinky Moon, LLC\n.solutions\tgeneric\tBinky Moon, LLC\n.song\tgeneric\tAmazon Registry Services, Inc.\n.sony\tgeneric\tSony Corporation\n.soy\tgeneric\tCharleston Road Registry Inc.\n.spa\tgeneric\tAsia Spa and Wellness Promotion Council Limited\n.space\tgeneric\tRadix Technologies Inc.\n.spiegel\tgeneric\tNot assigned\n.sport\tgeneric\tSportAccord\n.spot\tgeneric\tAmazon Registry Services, Inc.\n.spreadbetting\tgeneric\tNot assigned\n.sr\tcountry-code\tTelesur\n.srl\tgeneric\tInterNetX Corp.\n.srt\tgeneric\tNot assigned\n.ss\tcountry-code\tNational Communication Authority (NCA)\n.st\tcountry-code\tTecnisys\n.stada\tgeneric\tSTADA Arzneimittel AG\n.staples\tgeneric\tStaples, Inc.\n.star\tgeneric\tStar India Private Limited\n.starhub\tgeneric\tNot assigned\n.statebank\tgeneric\tSTATE BANK OF INDIA\n.statefarm\tgeneric\tState Farm Mutual Automobile Insurance Company\n.statoil\tgeneric\tNot assigned\n.stc\tgeneric\tSaudi Telecom Company\n.stcgroup\tgeneric\tSaudi Telecom Company\n.stockholm\tgeneric\tStockholms kommun\n.storage\tgeneric\tXYZ.COM LLC\n.store\tgeneric\tRadix Technologies Inc.\n.stream\tgeneric\tdot Stream Limited\n.studio\tgeneric\tDog Beach, LLC\n.study\tgeneric\tRegistry Services, LLC\n.style\tgeneric\tBinky Moon, LLC\n.su\tcountry-code\tRussian Institute for Development of Public Networks (ROSNIIROS)\n.sucks\tgeneric\tVox Populi Registry Ltd.\n.supplies\tgeneric\tBinky Moon, LLC\n.supply\tgeneric\tBinky Moon, LLC\n.support\tgeneric\tBinky Moon, LLC\n.surf\tgeneric\tRegistry Services, LLC\n.surgery\tgeneric\tBinky Moon, LLC\n.suzuki\tgeneric\tSUZUKI MOTOR CORPORATION\n.sv\tcountry-code\tSVNet\n.swatch\tgeneric\tThe Swatch Group Ltd\n.swiftcover\tgeneric\tNot assigned\n.swiss\tgeneric\tSwiss Confederation\n.sx\tcountry-code\tSX Registry SA B.V.\n.sy\tcountry-code\tNational Agency for Network Services (NANS)\n.sydney\tgeneric\tState of New South Wales, Department of Premier and Cabinet\n.symantec\tgeneric\tNot assigned\n.systems\tgeneric\tBinky Moon, LLC\n.sz\tcountry-code\tUniversity of Swaziland Department of Computer Science\n.tab\tgeneric\tTabcorp Holdings Limited\n.taipei\tgeneric\tTaipei City Government\n.talk\tgeneric\tAmazon Registry Services, Inc.\n.taobao\tgeneric\tAlibaba Group Holding Limited\n.target\tgeneric\tTarget Domain Holdings, LLC\n.tatamotors\tgeneric\tTata Motors Ltd\n.tatar\tgeneric\tLimited Liability Company \"Coordination Center of Regional Domain of Tatarstan Republic\"\n.tattoo\tgeneric\tRegistry Services, LLC\n.tax\tgeneric\tBinky Moon, LLC\n.taxi\tgeneric\tBinky Moon, LLC\n.tc\tcountry-code\tMelrex TC\n.tci\tgeneric\tEmergency Back-End Registry Operator Program - ICANN\n.td\tcountry-code\tl'Agence de Développement des Technologies de l'Information et de la Communication (ADETIC)\n.tdk\tgeneric\tTDK Corporation\n.team\tgeneric\tBinky Moon, LLC\n.tech\tgeneric\tRadix Technologies Inc.\n.technology\tgeneric\tBinky Moon, LLC\n.tel\tsponsored\tTelnames Ltd.\n.telecity\tgeneric\tNot assigned\n.telefonica\tgeneric\tNot assigned\n.temasek\tgeneric\tTemasek Holdings (Private) Limited\n.tennis\tgeneric\tBinky Moon, LLC\n.teva\tgeneric\tTeva Pharmaceutical Industries Limited\n.tf\tcountry-code\tAssociation Française pour le Nommage Internet en Coopération (A.F.N.I.C.)\n.tg\tcountry-code\tAutorité de Régulation des Communications Electroniques et des Postes (ARCEP)\n.th\tcountry-code\tThai Network Information Center Foundation\n.thd\tgeneric\tHome Depot Product Authority, LLC\n.theater\tgeneric\tBinky Moon, LLC\n.theatre\tgeneric\tXYZ.COM LLC\n.tiaa\tgeneric\tTeachers Insurance and Annuity Association of America\n.tickets\tgeneric\tXYZ.COM LLC\n.tienda\tgeneric\tBinky Moon, LLC\n.tiffany\tgeneric\tNot assigned\n.tips\tgeneric\tBinky Moon, LLC\n.tires\tgeneric\tBinky Moon, LLC\n.tirol\tgeneric\tpunkt Tirol GmbH\n.tj\tcountry-code\tInformation Technology Center\n.tjmaxx\tgeneric\tThe TJX Companies, Inc.\n.tjx\tgeneric\tThe TJX Companies, Inc.\n.tk\tcountry-code\tTelecommunication Tokelau Corporation (Teletok)\n.tkmaxx\tgeneric\tThe TJX Companies, Inc.\n.tl\tcountry-code\tAutoridade Nacional de Comunicações\n.tm\tcountry-code\tTM Domain Registry Ltd\n.tmall\tgeneric\tAlibaba Group Holding Limited\n.tn\tcountry-code\tAgence Tunisienne d'Internet\n.to\tcountry-code\tGovernment of the Kingdom of Tonga, H.R.H. Crown Prince Tupouto'a, c/o Consulate of Tonga\n.today\tgeneric\tBinky Moon, LLC\n.tokyo\tgeneric\tGMO Registry, Inc.\n.tools\tgeneric\tBinky Moon, LLC\n.top\tgeneric\t.TOP Registry\n.toray\tgeneric\tToray Industries, Inc.\n.toshiba\tgeneric\tTOSHIBA Corporation\n.total\tgeneric\tTotalEnergies SE\n.tours\tgeneric\tBinky Moon, LLC\n.town\tgeneric\tBinky Moon, LLC\n.toyota\tgeneric\tTOYOTA MOTOR CORPORATION\n.toys\tgeneric\tBinky Moon, LLC\n.tp\tcountry-code\tNot assigned\n.tr\tcountry-code\tBilgi Teknolojileri ve İletişim Kurumu (BTK)\n.trade\tgeneric\tElite Registry Limited\n.trading\tgeneric\tDog Beach, LLC\n.training\tgeneric\tBinky Moon, LLC\n.travel\tsponsored\tDog Beach, LLC\n.travelchannel\tgeneric\tNot assigned\n.travelers\tgeneric\tTravelers TLD, LLC\n.travelersinsurance\tgeneric\tTravelers TLD, LLC\n.trust\tgeneric\tInternet Naming Co.\n.trv\tgeneric\tTravelers TLD, LLC\n.tt\tcountry-code\tUniversity of the West Indies, Faculty of Engineering\n.tube\tgeneric\tLatin American Telecom LLC\n.tui\tgeneric\tTUI AG\n.tunes\tgeneric\tAmazon Registry Services, Inc.\n.tushu\tgeneric\tAmazon Registry Services, Inc.\n.tv\tcountry-code\tMinistry of Transport, Energy, Communications and Innovations\n.tvs\tgeneric\tT V SUNDRAM IYENGAR & SONS PRIVATE LIMITED\n.tw\tcountry-code\tTaiwan Network Information Center (TWNIC)\n.tz\tcountry-code\tTanzania Communications Regulatory Authority\n.ua\tcountry-code\tHostmaster Ltd.\n.ubank\tgeneric\tNational Australia Bank Limited\n.ubs\tgeneric\tUBS AG\n.uconnect\tgeneric\tNot assigned\n.ug\tcountry-code\tUganda Online Ltd.\n.uk\tcountry-code\tNominet UK\n.um\tcountry-code\tNot assigned\n.unicom\tgeneric\tChina United Network Communications Corporation Limited\n.university\tgeneric\tBinky Moon, LLC\n.uno\tgeneric\tRadix Technologies Inc.\n.uol\tgeneric\tUBN INTERNET LTDA.\n.ups\tgeneric\tUPS Market Driver, Inc.\n.us\tcountry-code\tRegistry Services, LLC\n.uy\tcountry-code\tSeCIU - Universidad de la Republica\n.uz\tcountry-code\tSingle Integrator for Creation and Support of State Information Systems UZINFOCOM\n.va\tcountry-code\tHoly See - Vatican City State\n.vacations\tgeneric\tBinky Moon, LLC\n.vana\tgeneric\tD3 Registry LLC\n.vanguard\tgeneric\tThe Vanguard Group, Inc.\n.vc\tcountry-code\tMinistry of Telecommunications, Science, Technology and Industry\n.ve\tcountry-code\tComisión Nacional de Telecomunicaciones (CONATEL)\n.vegas\tgeneric\tDot Vegas, Inc.\n.ventures\tgeneric\tBinky Moon, LLC\n.verisign\tgeneric\tVeriSign, Inc.\n.versicherung\tgeneric\ttldbox GmbH\n.vet\tgeneric\tDog Beach, LLC\n.vg\tcountry-code\tTelecommunications Regulatory Commission of the Virgin Islands\n.vi\tcountry-code\tVirgin Islands Public Telecommunications System, Inc.\n.viajes\tgeneric\tBinky Moon, LLC\n.video\tgeneric\tDog Beach, LLC\n.vig\tgeneric\tVIENNA INSURANCE GROUP AG Wiener Versicherung Gruppe\n.viking\tgeneric\tViking River Cruises (Bermuda) Ltd.\n.villas\tgeneric\tBinky Moon, LLC\n.vin\tgeneric\tBinky Moon, LLC\n.vip\tgeneric\tRegistry Services, LLC\n.virgin\tgeneric\tVirgin Enterprises Limited\n.visa\tgeneric\tVisa Worldwide Pte. Limited\n.vision\tgeneric\tBinky Moon, LLC\n.vista\tgeneric\tNot assigned\n.vistaprint\tgeneric\tNot assigned\n.viva\tgeneric\tSaudi Telecom Company\n.vivo\tgeneric\tTelefonica Brasil S.A.\n.vlaanderen\tgeneric\tDNS.be vzw\n.vn\tcountry-code\tViet Nam Internet Network Information Center (VNNIC)\n.vodka\tgeneric\tRegistry Services, LLC\n.volkswagen\tgeneric\tNot assigned\n.volvo\tgeneric\tVolvo Holding Sverige Aktiebolag\n.vote\tgeneric\tMonolith Registry LLC\n.voting\tgeneric\tValuetainment Corp.\n.voto\tgeneric\tMonolith Registry LLC\n.voyage\tgeneric\tBinky Moon, LLC\n.vu\tcountry-code\tTelecommunications Radiocommunications and Broadcasting Regulator (TRBR)\n.vuelos\tgeneric\tNot assigned\n.wales\tgeneric\tNominet UK\n.walmart\tgeneric\tWal-Mart Stores, Inc.\n.walter\tgeneric\tSandvik AB\n.wang\tgeneric\tZodiac Wang Limited\n.wanggou\tgeneric\tAmazon Registry Services, Inc.\n.warman\tgeneric\tNot assigned\n.watch\tgeneric\tBinky Moon, LLC\n.watches\tgeneric\tIdentity Digital Limited\n.weather\tgeneric\tInternational Business Machines Corporation\n.weatherchannel\tgeneric\tInternational Business Machines Corporation\n.webcam\tgeneric\tdot Webcam Limited\n.weber\tgeneric\tSaint-Gobain Weber SA\n.website\tgeneric\tRadix Technologies Inc.\n.wed\tgeneric\tEmergency Back-End Registry Operator Program - ICANN\n.wedding\tgeneric\tRegistry Services, LLC\n.weibo\tgeneric\tSina Corporation\n.weir\tgeneric\tWeir Group IP Limited\n.wf\tcountry-code\tAssociation Française pour le Nommage Internet en Coopération (A.F.N.I.C.)\n.whoswho\tgeneric\tWho's Who Registry\n.wien\tgeneric\tpunkt.wien GmbH\n.wiki\tgeneric\tRegistry Services, LLC\n.williamhill\tgeneric\tWilliam Hill Organization Limited\n.win\tgeneric\tFirst Registry Limited\n.windows\tgeneric\tMicrosoft Corporation\n.wine\tgeneric\tBinky Moon, LLC\n.winners\tgeneric\tThe TJX Companies, Inc.\n.wme\tgeneric\tWilliam Morris Endeavor Entertainment, LLC\n.wolterskluwer\tgeneric\tWolters Kluwer N.V.\n.woodside\tgeneric\tWoodside Petroleum Limited\n.work\tgeneric\tRegistry Services, LLC\n.works\tgeneric\tBinky Moon, LLC\n.world\tgeneric\tBinky Moon, LLC\n.wow\tgeneric\tAmazon Registry Services, Inc.\n.ws\tcountry-code\tGovernment of Samoa Ministry of Foreign Affairs & Trade\n.wtc\tgeneric\tWorld Trade Centers Association, Inc.\n.wtf\tgeneric\tBinky Moon, LLC\n.xbox\tgeneric\tMicrosoft Corporation\n.xerox\tgeneric\tXerox DNHC LLC\n.xfinity\tgeneric\tNot assigned\n.xihuan\tgeneric\tQIHOO 360 TECHNOLOGY CO. LTD.\n.xin\tgeneric\tElegant Leader Limited\n.测试\ttest\tNot assigned\n.कॉम\tgeneric\tVeriSign Sarl\n.परीक्षा\ttest\tNot assigned\n.セール\tgeneric\tAmazon Registry Services, Inc.\n.佛山\tgeneric\tGuangzhou YU Wei Information Technology Co., Ltd.\n.ಭಾರತ\tcountry-code\tNational Internet eXchange of India\n.慈善\tgeneric\tExcellent First Limited\n.集团\tgeneric\tEagle Horizon Limited\n.在线\tgeneric\tBeijing Tld Registry Technology Limited\n.한국\tcountry-code\tKISA (Korea Internet & Security Agency)\n.ଭାରତ\tcountry-code\tNational Internet eXchange of India\n.大众汽车\tgeneric\tNot assigned\n.点看\tgeneric\tVeriSign Sarl\n.คอม\tgeneric\tVeriSign Sarl\n.ভাৰত\tcountry-code\tNational Internet eXchange of India\n.ভারত\tcountry-code\tNational Internet Exchange of India\n.八卦\tgeneric\tZodiac Gemini Ltd\n‏.ישראל‎\tcountry-code\tThe Israel Internet Association (RA)\n‏.موقع‎\tgeneric\tHelium TLDs Ltd\n.বাংলা\tcountry-code\tPosts and Telecommunications Division\n.公益\tgeneric\tChina Organizational Name Administration Center\n.公司\tgeneric\tChina Internet Network Information Center (CNNIC)\n.香格里拉\tgeneric\tShangri-La International Hotel Management Limited\n.网站\tgeneric\tGlobal Website TLD Asia Limited\n.移动\tgeneric\tIdentity Digital Limited\n.我爱你\tgeneric\tTycoon Treasure Limited\n.москва\tgeneric\tFoundation for Assistance for Internet Technologies and Infrastructure Development (FAITID)\n.испытание\ttest\tNot assigned\n.қаз\tcountry-code\tAssociation of IT Companies of Kazakhstan\n.католик\tgeneric\tPontificium Consilium de Comunicationibus Socialibus (PCCS) (Pontifical Council for Social Communication)\n.онлайн\tgeneric\tCORE Association\n.сайт\tgeneric\tCORE Association\n.联通\tgeneric\tChina United Network Communications Corporation Limited\n.срб\tcountry-code\tSerbian National Internet Domain Registry (RNIDS)\n.бг\tcountry-code\tImena.BG AD\n.бел\tcountry-code\tBelarusian Cloud Technologies LLC\n‏.קום‎\tgeneric\tVeriSign Sarl\n.时尚\tgeneric\tRISE VICTORY LIMITED\n.微博\tgeneric\tSina Corporation\n.테스트\ttest\tNot assigned\n.淡马锡\tgeneric\tTemasek Holdings (Private) Limited\n.ファッション\tgeneric\tAmazon Registry Services, Inc.\n.орг\tgeneric\tPublic Interest Registry\n.नेट\tgeneric\tVeriSign Sarl\n.ストア\tgeneric\tAmazon Registry Services, Inc.\n.アマゾン\tgeneric\tAmazon Registry Services, Inc.\n.삼성\tgeneric\tSAMSUNG SDS CO., LTD\n.சிங்கப்பூர்\tcountry-code\tSingapore Network Information Centre (SGNIC) Pte Ltd\n.商标\tgeneric\tInternet DotTrademark Organisation Limited\n.商店\tgeneric\tBinky Moon, LLC\n.商城\tgeneric\tZodiac Aquarius Limited\n.дети\tgeneric\tThe Foundation for Network Initiatives “The Smart Internet”\n.мкд\tcountry-code\tMacedonian Academic Research Network Skopje\n‏.טעסט‎\ttest\tNot assigned\n.ею\tcountry-code\tEURid vzw\n.ポイント\tgeneric\tAmazon Registry Services, Inc.\n.新闻\tgeneric\tGuangzhou YU Wei Information and Technology Co.,Ltd\n.工行\tgeneric\tNot assigned\n.家電\tgeneric\tAmazon Registry Services, Inc.\n‏.كوم‎\tgeneric\tVeriSign Sarl\n.中文网\tgeneric\tTLD REGISTRY LIMITED\n.中信\tgeneric\tCITIC Group Corporation\n.中国\tcountry-code\tChina Internet Network Information Center (CNNIC)\n.中國\tcountry-code\tChina Internet Network Information Center (CNNIC)\n.娱乐\tgeneric\tBinky Moon, LLC\n.谷歌\tgeneric\tCharleston Road Registry Inc.\n.భారత్\tcountry-code\tNational Internet Exchange of India\n.ලංකා\tcountry-code\tLK Domain Registry\n.電訊盈科\tgeneric\tPCCW Enterprises Limited\n.购物\tgeneric\tNawang Heli(Xiamen) Network Service Co., LTD.\n.測試\ttest\tNot assigned\n.クラウド\tgeneric\tAmazon Registry Services, Inc.\n.ભારત\tcountry-code\tNational Internet Exchange of India\n.通販\tgeneric\tAmazon Registry Services, Inc.\n.भारतम्\tcountry-code\tNational Internet eXchange of India\n.भारत\tcountry-code\tNational Internet Exchange of India\n.भारोत\tcountry-code\tNational Internet eXchange of India\n‏.آزمایشی‎\ttest\tNot assigned\n.பரிட்சை\ttest\tNot assigned\n.网店\tgeneric\tZodiac Taurus Ltd.\n.संगठन\tgeneric\tPublic Interest Registry\n.餐厅\tgeneric\tInternet DotTrademark Organisation Limited\n.网络\tgeneric\tChina Internet Network Information Center (CNNIC)\n.ком\tgeneric\tVeriSign Sarl\n.укр\tcountry-code\tUkrainian Network Information Centre (UANIC), Inc.\n.香港\tcountry-code\tHong Kong Internet Registration Corporation Ltd.\n.亚马逊\tgeneric\tAmazon Registry Services, Inc.\n.诺基亚\tgeneric\tNot assigned\n.食品\tgeneric\tAmazon Registry Services, Inc.\n.δοκιμή\ttest\tNot assigned\n.飞利浦\tgeneric\tKoninklijke Philips N.V.\n‏.إختبار‎\ttest\tNot assigned\n.台湾\tcountry-code\tTaiwan Network Information Center (TWNIC)\n.台灣\tcountry-code\tTaiwan Network Information Center (TWNIC)\n.手表\tgeneric\tNot assigned\n.手机\tgeneric\tBeijing RITT-Net Technology Development Co., Ltd\n.мон\tcountry-code\tDatacom Co.,Ltd\n‏.الجزائر‎\tcountry-code\tCERIST\n‏.عمان‎\tcountry-code\tTelecommunications Regulatory Authority (TRA)\n‏.ارامكو‎\tgeneric\tAramco Services Company\n‏.ایران‎\tcountry-code\tInstitute for Research in Fundamental Sciences (IPM)\n‏.العليان‎\tgeneric\tCompetrol (Luxembourg) Sarl\n‏.اتصالات‎\tgeneric\tNot assigned\n‏.امارات‎\tcountry-code\tTelecommunications and Digital Government Regulatory Authority (TDRA)\n‏.بازار‎\tgeneric\tCORE Association\n‏.موريتانيا‎\tcountry-code\tUniversité de Nouakchott Al Aasriya\n‏.پاکستان‎\tcountry-code\tNational Telecommunication Corporation\n‏.الاردن‎\tcountry-code\tMinistry of Digital Economy and Entrepreneurship (MoDEE)\n‏.موبايلي‎\tgeneric\tNot assigned\n‏.بارت‎\tcountry-code\tNational Internet eXchange of India\n‏.بھارت‎\tcountry-code\tNational Internet Exchange of India\n‏.المغرب‎\tcountry-code\tAgence Nationale de Réglementation des Télécommunications (ANRT)\n‏.ابوظبي‎\tgeneric\tAbu Dhabi Systems and Information Centre\n‏.البحرين‎\tcountry-code\tTelecommunications Regulatory Authority (TRA)\n‏.السعودية‎\tcountry-code\tCommunications, Space and Technology Commission\n‏.ڀارت‎\tcountry-code\tNational Internet eXchange of India\n‏.كاثوليك‎\tgeneric\tPontificium Consilium de Comunicationibus Socialibus (PCCS) (Pontifical Council for Social Communication)\n‏.سودان‎\tcountry-code\tSudan Internet Society\n‏.همراه‎\tgeneric\tEmergency Back-End Registry Operator Program - ICANN\n‏.عراق‎\tcountry-code\tCommunications and Media Commission (CMC)\n‏.مليسيا‎\tcountry-code\tMYNIC Berhad\n.澳門\tcountry-code\tMacao Post and Telecommunications Bureau (CTT)\n.닷컴\tgeneric\tVeriSign Sarl\n.政府\tgeneric\tNet-Chinese Co., Ltd.\n‏.شبكة‎\tgeneric\tInternational Domain Registry Pty. Ltd.\n‏.بيتك‎\tgeneric\tKuwait Finance House\n‏.عرب‎\tgeneric\tLeague of Arab States\n.გე\tcountry-code\tInformation Technologies Development Center (ITDC)\n.机构\tgeneric\tPublic Interest Registry\n.组织机构\tgeneric\tPublic Interest Registry\n.健康\tgeneric\tStable Tone Limited\n.ไทย\tcountry-code\tThai Network Information Center Foundation\n‏.سورية‎\tcountry-code\tNational Agency for Network Services (NANS)\n.招聘\tgeneric\tJiang Yu Liang Cai Technology Company Limited\n.рус\tgeneric\tRusnames Limited\n.рф\tcountry-code\tCoordination Center for TLD RU\n.珠宝\tgeneric\tNot assigned\n‏.تونس‎\tcountry-code\tAgence Tunisienne d'Internet\n.大拿\tgeneric\tVeriSign Sarl\n.ລາວ\tcountry-code\tLao National Internet Center (LANIC), Ministry of Technology and Communications\n.みんな\tgeneric\tCharleston Road Registry Inc.\n.グーグル\tgeneric\tCharleston Road Registry Inc.\n.ευ\tcountry-code\tEURid vzw\n.ελ\tcountry-code\tICS-FORTH GR\n.世界\tgeneric\tStable Tone Limited\n.書籍\tgeneric\tAmazon Registry Services, Inc.\n.ഭാരതം\tcountry-code\tNational Internet eXchange of India\n.ਭਾਰਤ\tcountry-code\tNational Internet Exchange of India\n.网址\tgeneric\tKNET Co., Ltd\n.닷넷\tgeneric\tVeriSign Sarl\n.コム\tgeneric\tVeriSign Sarl\n.天主教\tgeneric\tPontificium Consilium de Comunicationibus Socialibus (PCCS) (Pontifical Council for Social Communication)\n.游戏\tgeneric\tBinky Moon, LLC\n.vermögensberater\tgeneric\tDeutsche Vermögensberatung Aktiengesellschaft DVAG\n.vermögensberatung\tgeneric\tDeutsche Vermögensberatung Aktiengesellschaft DVAG\n.企业\tgeneric\tBinky Moon, LLC\n.信息\tgeneric\tBeijing Tele-info Technology Co., Ltd.\n.嘉里大酒店\tgeneric\tKerry Trading Co. Limited\n.嘉里\tgeneric\tKerry Trading Co. Limited\n‏.مصر‎\tcountry-code\tNational Telecommunication Regulatory Authority - NTRA\n‏.قطر‎\tcountry-code\tCommunications Regulatory Authority\n.广东\tgeneric\tGuangzhou YU Wei Information Technology Co., Ltd.\n.இலங்கை\tcountry-code\tLK Domain Registry\n.இந்தியா\tcountry-code\tNational Internet Exchange of India\n.հայ\tcountry-code\t\"Internet Society\" Non-governmental Organization\n.新加坡\tcountry-code\tSingapore Network Information Centre (SGNIC) Pte Ltd\n‏.فلسطين‎\tcountry-code\tMinistry of Telecom & Information Technology (MTIT)\n.テスト\ttest\tNot assigned\n.政务\tgeneric\tChina Organizational Name Administration Center\n.xperia\tgeneric\tNot assigned\n.xxx\tsponsored\tICM Registry LLC\n.xyz\tgeneric\tXYZ.COM LLC\n.yachts\tgeneric\tXYZ.COM LLC\n.yahoo\tgeneric\tYahoo Inc.\n.yamaxun\tgeneric\tAmazon Registry Services, Inc.\n.yandex\tgeneric\tYANDEX LLC\n.ye\tcountry-code\tTeleYemen\n.yodobashi\tgeneric\tYODOBASHI CAMERA CO.,LTD.\n.yoga\tgeneric\tRegistry Services, LLC\n.yokohama\tgeneric\tGMO Registry, Inc.\n.you\tgeneric\tAmazon Registry Services, Inc.\n.youtube\tgeneric\tCharleston Road Registry Inc.\n.yt\tcountry-code\tAssociation Française pour le Nommage Internet en Coopération (A.F.N.I.C.)\n.yun\tgeneric\tQIHOO 360 TECHNOLOGY CO. LTD.\n.za\tcountry-code\tZA Domain Name Authority\n.zappos\tgeneric\tAmazon Registry Services, Inc.\n.zara\tgeneric\tIndustria de Diseño Textil, S.A. (INDITEX, S.A.)\n.zero\tgeneric\tAmazon Registry Services, Inc.\n.zip\tgeneric\tCharleston Road Registry Inc.\n.zippo\tgeneric\tNot assigned\n.zm\tcountry-code\tZambia Information and Communications Technology Authority (ZICTA)\n.zone\tgeneric\tBinky Moon, LLC\n.zuerich\tgeneric\tKanton Zürich (Canton of Zurich)\n.zw\tcountry-code\tPostal and Telecommunications Regulatory Authority of Zimbabwe (POTRAZ)\n\n__ICCTLD__\n# https://en.wikipedia.org/wiki/Country_code_top-level_domain#Internationalized_ccTLDs\n# https://en.wikipedia.org/wiki/Internationalized_country_code_top-level_domain#List\n# https://www.icann.org/resources/pages/string-evaluation-completion-2014-02-19-en\n# (update 2025-09-26)\n# DNS name\tIDN ccTLD\tCountry/Region\tLanguage\tScript\tTransliteration\tComments\tOther ccTLD\tDNSSEC\nxn--lgbbat1ad8j\t.الجزائر\tAlgeria\tArabic\tArabic (Arabic)\tal-Jazā'ir\t\t.dz\tNo\nxn--y9a3aq\t.հայ\tArmenia\tArmenian\tArmenian\thay\t\t.am\tYes\nxn--mgbcpq6gpa1a\t.البحرين\tBahrain\tArabic\tArabic\tal-Baḥrain\tNot in use\t.bh\tYes\nxn--54b7fta0cc\t.বাংলা\tBangladesh\tBengali\tBengali\tBangla\t\t.bd\tNo\nxn--90ais\t.бел\tBelarus\tBelarusian\tCyrillic\tbel\t\t.by\tYes\nxn--90ae\t.бг[16]\tBulgaria\tBulgarian\tCyrillic\tbg\t\t.bg\tYes\nxn--fiqs8s\t.中国\tChina\tChinese\tChinese (Simplified)\tZhōngguó\t\t.cn\tYes\nxn--fiqz9s\t.中國\tChina\tChinese\tChinese (Traditional)\tZhōngguó\t\t.cn\tYes\nxn--wgbh1c\t.مصر\tEgypt\tArabic\tArabic (Arabic)\tMiṣr / Maṣr[17]\t\t.eg\tYes\nxn--e1a4c\t.ею\tEuropean Union\tBulgarian\tCyrillic\teyu\t\t.eu\tYes\nxn--qxa6a\t.ευ\tEuropean Union\tGreek\tGreek\tey\tIn use since 2022\t.eu\tYes\nxn--node\t.გე\tGeorgia\tGeorgian\tGeorgian (Mkhedruli)\tGE\t\t.ge\tNo\nxn--qxam\t.ελ[16]\tGreece\tGreek\tGreek\tel\tIn use since July 2018\t.gr\tYes\nxn--j6w193g\t.香港\tHong Kong\tChinese\tChinese (Simplified and Traditional)\tHoeng1 gong2 / Xiānggǎng\t\t.hk\tYes\nxn--h2brj9c\t.भारत\tIndia\tHindi\tDevanagari\tBhārat\tBecame available 27 August 2014[18]\t.in\tYes\nxn--mgbbh1a71e\t.بھارت\tIndia\tUrdu\tArabic (Urdu)\tBhārat\tBecame available 2017\t.in\tYes\nxn--fpcrj9c3d\t.భారత్\tIndia\tTelugu\tTelugu\tBhārat\tBecame available 2017\t.in\tYes\nxn--gecrj9c\t.ભારત\tIndia\tGujarati\tGujarati\tBhārat\tBecame available 2017\t.in\tYes\nxn--s9brj9c\t.ਭਾਰਤ\tIndia\tPunjabi\tGurmukhī\tBhārat\tBecame available 2017\t.in\tYes\nxn--xkc2dl3a5ee0h\t.இந்தியா\tIndia\tTamil\tTamil\tIntiyā\tBecame available 2015\t.in\tYes\nxn--45brj9c\t.ভারত\tIndia\tBengali\tBengali\tBharôt\tBecame available 2017\t.in\tYes\nxn--2scrj9c\t.ಭಾರತ\tIndia\tKannada\tKannada\tBhārata\tBecame available 2020\t.in\tYes\nxn--rvc1e0am3e\t.ഭാരതം\tIndia\tMalayalam\tMalayalam\tBhāratam\tBecame available 2020\t.in\tYes\nxn--45br5cyl\t.ভাৰত\tIndia\tAssamese\tBengali\tBharatam\tBecame available 2022\t.in\tYes\nxn--3hcrj9c\t.ଭାରତ\tIndia\tOriya\tOriya\tBhārat\tBecame available 2021\t.in\tYes\nxn--mgbbh1a\t.بارت\tIndia\tKashmiri\tArabic (Kashmiri)\tBārat\tBecame available 2022\t.in\tYes\nxn--h2breg3eve\t.भारतम्\tIndia\tSanskrit\tDevanagari\tBhāratam\tBecame available 2022\t.in\tYes\nxn--h2brj9c8c\t.भारोत\tIndia\tSantali\tDevanagari\tBharot\tBecame available 2022\t.in\tYes\nxn--mgbgu82a\t.ڀارت\tIndia\tSindhi\tArabic (Sindhi)\tBhārat\tBecame available 2022\t.in\tYes\nxn--mgba3a4f16a\t.ایران\tIran\tPersian\tArabic (Persian)\tĪrān\t\t.ir\tNo\nxn--mgbtx2b\t.عراق\tIraq\tArabic\tArabic (Arabic)\tʿIrāq\tNot in use\t.iq\tNo\nxn--4dbrk0ce\t.ישראל\tIsrael\tHebrew\tHebrew\tIsrael\tBecame available 2022\t.il\tYes\nxn--mgbayh7gpa\t.الاردن\tJordan\tArabic\tArabic (Arabic)\tal-Urdun\t\t.jo\tNo\nxn--80ao21a\t.қаз\tKazakhstan\tKazakh\tCyrillic (Kazakh)\tqaz\t\t.kz\tNo\nxn--q7ce6a\t.ລາວ\tLaos\tLao\tLao\tLao\tBecame available 2020\t.la\tYes\nxn--mgbb7fyab\t.ليبيا\tLibya\tArabic\tArabic (Arabic)\t\tNot delegated[15]\t.ly\tNo\nxn--mix082f\t.澳门\tMacao\tChinese\tChinese (Simplified)\tOu3 mun4 / Àomén\tNot in use\t.mo\tNo\nxn--mix891f\t.澳門\tMacao\tChinese\tChinese (Traditional)\tOu3 mun4 / Àomén\tBecame available 2020\t.mo\tNo\nxn--mgbx4cd0ab\t.مليسيا\tMalaysia\tMalay\tArabic (Jawi)\tMalaysīyā\t\t.my\tYes\nxn--mgbah1a3hjkrd\t.موريتانيا\tMauritania\tArabic\tArabic (Arabic)\tMūrītāniyā\t\t.mr\tYes\nxn--l1acc\t.мон\tMongolia\tMongolian\tCyrillic (Mongolian)\tmon\t\t.mn\tYes\nxn--mgbc0a9azcg\t.المغرب\tMorocco\tArabic\tArabic (Arabic)\tal-Maġrib\t\t.ma\tNo\nxn--d1alf\t.мкд\tNorth Macedonia\tMacedonian\tCyrillic (Macedonian)\tmkd\t\t.mk\tNo\nxn--mgb9awbf\t.عمان\tOman\tArabic\tArabic (Arabic)\tʿUmān\t\t.om\tNo\nxn--mgbai9azgqp6j\t.پاکستان\tPakistan\tUrdu\tArabic (Urdu)\tPākistān\t\t.pk\tYes\nxn--ygbi2ammx\t.فلسطين\tPalestinian Authority\tArabic\tArabic (Arabic)\tFilasṭīn\t\t.ps\tNo\nxn--wgbl6a\t.قطر\tQatar\tArabic\tArabic (Arabic)\tQaṭar\t\t.qa\tNo\nxn--p1ai\t.рф\tRussia\tRussian\tCyrillic (Russian)\trf\t\t.ru\tYes\nxn--mgberp4a5d4ar\t.السعودية\tSaudi Arabia\tArabic\tArabic (Arabic)\tas-Suʿūdīya\t\t.sa\tYes\nxn--90a3ac\t.срб\tSerbia\tSerbian\tCyrillic (Serbian)\tsrb\t\t.rs\tYes\nxn--yfro4i67o\t.新加坡\tSingapore\tChinese\tChinese (Simplified and Traditional)\tXīnjiāpō\t\t.sg\tYes\nxn--clchc0ea0b2g2a9gcd\t.சிங்கப்பூர்\tSingapore\tTamil\tTamil\tCinkappūr\t\t.sg\tYes\nxn--3e0b707e\t.한국\tSouth Korea\tKorean\tHangul\tHanguk\t\t.kr\tYes\nxn--fzc2c9e2c\t.ලංකා\tSri Lanka\tSinhala\tSinhala\tLanka\t\t.lk\tNo\nxn--xkc2al3hye2a\t.இலங்கை\tSri Lanka\tTamil\tTamil\tIlaṅkai\t\t.lk\tNo\nxn--mgbpl2fh\t.سودان\tSudan\tArabic\tArabic (Arabic)\tSūdān\t\t.sd\tNo\nxn--ogbpf8fl\t.سورية\tSyria\tArabic\tArabic (Arabic)\tSūriyya\t\t.sy\tNo\nxn--kprw13d\t.台湾\tTaiwan\tChinese\tChinese (Simplified)\tTáiwān\t\t.tw\tYes\nxn--kpry57d\t.台灣\tTaiwan\tChinese\tChinese (Traditional)\tTáiwān\t\t.tw\tYes\nxn--o3cw4h\t.ไทย\tThailand\tThai\tThai\tThai\t\t.th\tYes\nxn--pgbs0dh\t.تونس\tTunisia\tArabic\tArabic (Arabic)\tTūnis\t\t.tn\tYes\nxn--j1amh\t.укр\tUkraine\tUkrainian\tCyrillic (Ukrainian)\tukr\t\t.ua\tNo\nxn--mgbaam7a8h\t.امارات\tUnited Arab Emirates\tArabic\tArabic (Arabic)\tImārāt\t\t.ae\tNo\nxn--mgb2ddes\t.اليمن\tYemen\tArabic\tArabic (Arabic)\tal-Yaman\tNot delegated\t.ye\tNo\n__INTERNATIONALIZED_GEOGRAPHIC_TLD__\n# https://en.wikipedia.org/wiki/List_of_Internet_top-level_domains#Internationalized_geographic_top-level_domains\n# (update 2025-09-26)\n# DNS name\tDisplay name\tEntity\tLanguage\tScript\tTransliteration\tNotes\tOther TLD\tIDN\tDNSSEC\nxn--1qqw23a[24]\t.佛山\tFoshan, China\tChinese\tChinese (Simplified)\tfat6 saan1\t[85]\t\tYes\tYes\nxn--xhq521b[24]\t.广东\tGuangdong, China\tChinese\tChinese (Simplified)\tgwong2 dung1\t[14]\t\t\t\nxn--80adxhks[24]\t.москва [ru]\tMoscow, Russia\tRussian\tCyrillic (Russian)\tmoskva\t[86]\t.moscow\tRussian[15]\tYes\nxn--p1acf[24]\t.рус\tRussian language, post-Soviet states\tRussian\tCyrillic (Russian)\trus\t[14]\t.su\t\t\nxn--mgbca7dzdo[24]\t.ابوظبي\tAbu Dhabi\tArabic\tArabic\tAbū Ẓabī\t[14]\t.abudhabi\t\t\nxn--ngbrx[24]\t.عرب\tArab\tArabic\tArabic\t‘Arab\t[14]\t\t\t\n__INTERNATIONALIZED_BRAND_TLD__\n# Internationalized brand top-level domains\n# https://en.wikipedia.org/wiki/List_of_Internet_top-level_domains#Internationalized_brand_top-level_domains\n# (update 2025-09-26)\n# DNS name\tIDN TLD\tEntity\tScript\tTransliteration\tComments\tDNSSEC\nxn--jlq480n2rg\t.亚马逊\tAmazon\tChinese (Simplified)\tYàmǎxùn\t[161]\tYes\nxn--cckwcxetd\t.アマゾン\tAmazon\tKatakana\tamazon\t[162]\tYes\nxn--mgba3a3ejt\t.ارامكو\tAramco Services Company\tArabic\t\t[14]\t\nxn--mgbaakc7dvf\t.اتصالات\tEmirates Telecommunications Corporation (trading as Etisalat)\tArabic\t\t[14]\t\nxn--8y0a063a\t.联通\tChina United Network Communications Corporation Limited\tChinese (Simplified)\tLiántōng\t[14]\t\nxn--6frz82g\t.移动\tChina Mobile Communications Corporation\tChinese (Simplified)\tYídòng\t\t\nxn--fiq64b\t.中信\tCITIC Group\tChinese\tzhōngxìn\t[163]\tYes\nxn--5su34j936bgsg\t.香格里拉\tShangri‐La International Hotel Management Limited\tChinese\tXiānggélǐlā\t[14]\t\nxn--b4w605ferd\t.淡马锡\tTemasek Holdings (Private) Limited\tChinese (Simplified)\tDànmǎxī\t[14]\t\nxn--3oq18vl8pn36a\t.大众汽车\tVolkswagen (China) Investment Co., Ltd.\tChinese (Simplified)\tDàzhòngqìchē\t[14]\t\nxn--vermgensberater-ctb\t.vermögensberater\tDeutsche Vermögensberatung Aktiengesellschaft\tLatin\t\t[164]\tYes\nxn--vermgensberatung-pwb\t.vermögensberatung\tDeutsche Vermögensberatung Aktiengesellschaft\tLatin\t\t[165]\tYes\nxn--qcka1pmc\t.グーグル\tGoogle\tKatakana\tgūguru\t[166]\tYes\nxn--flw351e\t.谷歌\tGoogle\tChinese\tgǔgē\t[167]\tYes\nxn--estv75g\t.工行\tIndustrial and Commercial Bank of China Limited\tChinese\tGōngháng\t[14]\t\nxn--w4rs40l\t.嘉里\tKerry Trading Co. Limited\tChinese\tJiālǐ\t[14]\t\nxn--w4r85el8fhu5dnra\t.嘉里大酒店\tKerry Trading Co. Limited\tChinese\tJiālǐdàjiǔdiàn\t[14]\t\nxn--kcrx77d1x4a\t.飞利浦\tKoninklijke Philips N.V.\tChinese (Simplified)\tFēilìpǔ\t[14]\t\nxn--jlq61u9w7b\t.诺基亚\tNokia Corporation\tChinese (Simplified)\tNuòjīyà\t[14][168]\t\nxn--fzys8d69uvgm\t.電訊盈科\tPCCW Enterprises Limited\tChinese (Traditional)\tdin6 soen3 jing4 fo1\t[14]\t\nxn--cg4bki\t.삼성\tSamsung\tHangul\tsamseong\t[169]\tYes\n__INTERNATIONALIZED_TEST_TLD__\n# https://en.wikipedia.org/wiki/List_of_Internet_top-level_domains#Test_TLDs\nxn--kgbechtv\tإختبار.\tik͡htibār\tArabic\tArabic\thttp://مثال.إختبار\nxn--hgbk6aj7f53bba\tآزمایشی.\tậzmạy̰sẖy\tPersian\tPerso-Arabic\thttp://مثال.آزمایشی\nxn--0zwm56d\t.测试\tcèshì\tChinese Simplified\tChinese\thttp://例子.测试\nxn--g6w251d\t.測試\tcèshì\tChinese Traditional\tChinese\thttp://例子.測試\nxn--80akhbyknj4f\t.испытание\tispytánije\tRussian\tCyrillic\thttp://пример.испытание\nxn--11b5bs3a9aj6g\t.परीक्षा\tparīkṣā\tHindi\tDevanagari\thttp://उदाहरण.परीक्षा\nxn--jxalpdlp\t.δοκιμή\tdokimé\tGreek\tGreek\thttp://παράδειγμα.δοκιμή\nxn--9t4b11yi5a\t.테스트\tteseuteu\tKorean\tHangul\thttp://실례.테스트\nxn--deba0ad\tטעסט.\ttest\tYiddish\tHebrew\thttp://בײַשפּיל.טעסט\nxn--zckzah\t.テスト\ttesuto\tJapanese\tKatakana[229]\thttp://例え.テスト\nxn--hlcj6aya9esc7a\t.பரிட்சை\tpariṭcai\tTamil\tTamil\thttp://உதாரணம்.பரிட்சை\n'''\n\n\nTopLevelDomain._read_data()\n\n\nif __name__ == '__main__':\n for line in fileinput.input():\n line = line.replace('\\n', '')\n (cc, freq) = line.split('\\t')\n tld = TopLevelDomain(cc)\n # print(tld)\n sub_type = \"\"\n if tld.sub_type is not None:\n sub_type = tld.sub_type\n print('\\t'.join([cc, tld.tld_type, tld.first_level, sub_type, freq]))\n" } ]