Repository: airflow-plugins/Example-Airflow-DAGs Branch: master Commit: 3cf6b9bb59aa Files: 25 Total size: 93.3 KB Directory structure: gitextract_p8z34sgi/ ├── .gitignore ├── LICENSE ├── README.md ├── _config.yml ├── etl/ │ ├── facebook_ads_to_redshift.py │ ├── github_to_redshift.py │ ├── google_analytics_to_redshift.py │ ├── hubspot_to_redshift.py │ ├── imap_to_redshift.py │ ├── marketo_to_redshift.py │ ├── mongo_to_redshift/ │ │ ├── collections/ │ │ │ ├── __init__.py │ │ │ ├── _collection_processing.py │ │ │ └── example_mongo_collection.json │ │ └── mongo_to_redshift.py │ ├── salesforce_to_redshift.py │ └── sftp_to_mongo.py ├── poc/ │ ├── dbt_example.py │ ├── dummy_sensor_example.py │ ├── dynamic_dag_example.py │ ├── imap_example.py │ ├── mailgun_validation_example.py │ ├── selenium_example.py │ └── singer_example.py └── system/ ├── dynamic_connection_creation.py └── rate_limit_reset.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ .DS_Store ================================================ FILE: LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: README.md ================================================ # Example DAGs This repository contains example DAGs that can be used "out-of-the-box" using operators found in the Airflow Plugins organization. These DAGs have a range of use cases and vary from moving data (see [ETL](https://github.com/airflow-plugins/example_dags/tree/master/etl)) to background system automation that can give your Airflow "super-powers". ## Getting Started The example DAGs found here can be split into three main categories: ### ETL These DAGs focus on pulling data from various systems and putting them into Amazon Redshift, with S3 as a staging store. These represent the simplest implementation of an "ETL" workflow and can either be used "out-of-the-box" or extended to add additional custom logic. ### PoC (Proof of Concept) These DAGs demonstrate simple implementations of custom operators and Airflow setups. They are typically not "copy-and-paste" DAGs but rather walk through how something would work. ### System These DAGs are used on the system administration level and can be thought of as "meta-DAGs" that maintain various states and configurations within Airflow itself. In some cases, these DAGs are used in concert with other custom operators, such as the `rate_limit_reset` DAG. ## Contributions Contributions of your own DAGs are very welcome. Please see some of the example DAGs for a sense of general formatting guidelines. ## License Apache 2.0 ================================================ FILE: _config.yml ================================================ theme: jekyll-theme-cayman ================================================ FILE: etl/facebook_ads_to_redshift.py ================================================ """ Facebook Ads to Redshift This file contains one ongoing daily DAG. This DAG makes use of two custom operators: - FacebookAdsInsightsToS3Operator https://github.com/airflow-plugins/facebook_ads_plugin/blob/master/operators/facebook_ads_to_s3_operator.py#L10 - S3ToRedshiftOperator https://github.com/airflow-plugins/redshift_plugin/blob/master/operators/s3_to_redshift_operator.py#L13 This DAG creates four breakdown reports: - age_gender - device_platform - region_country - no_breakdown The standard fields included in each report are as follows: - account_id - ad_id - adset_id - ad_name - adset_name - campaign_id - date_start - date_stop - campaign_name - clicks - cpc - cpm - cpp - ctr - impressions - objective - reach - social_clicks - social_impressions - social_spend - spend - total_unique_actions In addition these standard fields, custom fields can also be specified. """ from datetime import datetime, timedelta from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from airflow.operators import FacebookAdsInsightsToS3Operator, S3ToRedshiftOperator time_string = '{{ ts_nodash }}' FACEBOOK_CONN_ID = '' ACCOUNT_ID = '' S3_BUCKET = '' S3_CONN_ID = '' REDSHIFT_CONN_ID = '' REDSHIFT_SCHEMA = '' default_args = { 'start_date': datetime(2016, 1, 1, 0, 0), 'email': [], 'email_on_failure': True, 'email_on_retry': False, 'depends_on_past': True, 'retries': 5, 'retry_delay': timedelta(minutes=5) } dag = DAG('facebook_ads_to_redshift', schedule_interval='@daily', default_args=default_args, catchup=True) COPY_PARAMS = ["COMPUPDATE OFF", "STATUPDATE OFF", "JSON 'auto'", "TIMEFORMAT 'auto'" "TRUNCATECOLUMNS", "region as 'us-east-1'"] execution_date = '{{ execution_date }}' next_execution_date = '{{ next_execution_date }}' breakdowns = [ { 'name': 'age_gender', 'fields': [ {'name': 'age', 'type': 'varchar(64)'}, {'name': 'gender', 'type': 'varchar(64)'} ] }, { 'name': 'device_platform', 'fields': [ {'name': 'device_platform', 'type': 'varchar(64)'} ] }, { 'name': 'region_country', 'fields': [ {'name': 'region', 'type': 'varchar(128)'}, {'name': 'country', 'type': 'varchar(128)'} ] }, { 'name': 'no_breakdown', 'fields': [] } ] fields = [ {'name': 'account_id', 'type': 'varchar(64)'}, {'name': 'ad_id', 'type': 'varchar(64)'}, {'name': 'adset_id', 'type': 'varchar(64)'}, {'name': 'campaign_id', 'type': 'varchar(64)'}, {'name': 'date_start', 'type': 'date'}, {'name': 'date_stop', 'type': 'date'}, {'name': 'ad_name', 'type': 'varchar(255)'}, {'name': 'adset_name', 'type': 'varchar(255)'}, {'name': 'campaign_name', 'type': 'varchar(255)'}, {'name': 'clicks', 'type': 'int(11)'}, {'name': 'cpc', 'type': 'decimal(20,6)'}, {'name': 'cpm', 'type': 'decimal(20,6)'}, {'name': 'cpp', 'type': 'decimal(20,6)'}, {'name': 'ctr', 'type': 'decimal(20,6)'}, {'name': 'impressions', 'type': 'int(11)'}, {'name': 'objective', 'type': 'varchar(255)'}, {'name': 'reach', 'type': 'int(11)'}, {'name': 'social_clicks', 'type': 'int(11)'}, {'name': 'social_impressions', 'type': 'int(11)'}, {'name': 'social_spend', 'type': 'decimal(20,6)'}, {'name': 'spend', 'type': 'decimal(20,6)'}, {'name': 'total_unique_actions', 'type': 'int(11)'} ] field_names = [field['name'] for field in fields] # Add any custom fields after building insight api field_names fields.extend([{'name': 'example', 'type': 'text'}]) start = DummyOperator( task_id='start', dag=dag ) for breakdown in breakdowns: breakdown_fields = [field['name'] for field in breakdown['fields']] S3_KEY = 'facebook_insights/{}_{}'.format(breakdown['name'], time_string) facebook_ads = FacebookAdsInsightsToS3Operator( task_id='facebook_ads_{}_to_s3'.format(breakdown['name']), facebook_conn_id=FACEBOOK_CONN_ID, s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=S3_KEY, account_ids=ACCOUNT_ID, insight_fields=field_names, breakdowns=breakdown_fields, since=execution_date, until=next_execution_date, time_increment=1, level='ad', limit=200, dag=dag ) # Append breakdown fields (primary keys) after # primary keys which are in every workflow output_table_fields = list(fields) output_table_fields = output_table_fields[:4] + breakdown['fields'] + output_table_fields[4:] primary_key = ['ad_id', 'adset_id', 'campaign_id', 'account_id', 'date_start'] primary_key.extend(breakdown_fields) s3_to_redshift = S3ToRedshiftOperator( task_id='s3_{}_to_redshift'.format(breakdown['name']), s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=S3_KEY, redshift_conn_id=REDSHIFT_CONN_ID, redshift_schema=REDSHIFT_SCHEMA, copy_params=COPY_PARAMS, table=breakdown['name'], origin_schema=output_table_fields, schema_location='local', primary_key=primary_key, load_type='upsert', dag=dag ) start >> facebook_ads >> s3_to_redshift ================================================ FILE: etl/github_to_redshift.py ================================================ """ Github to Redshift This file contains one ongoing hourly DAG. This DAG makes use of two custom operators: - GithubToS3Operator https://github.com/airflow-plugins/github_plugin/blob/master/operators/github_to_s3_operator.py#L9 - S3ToRedshiftOperator https://github.com/airflow-plugins/redshift_plugin/blob/master/operators/s3_to_redshift_operator.py#L13 This DAG accesses the following objects: - commits - issue_comments - issues - repositories - members - pull_requests """ from datetime import datetime from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from airflow.operators import GithubToS3Operator, S3ToRedshiftOperator S3_CONN_ID = '' S3_BUCKET = '' REDSHIFT_CONN_ID = '' REDSHIFT_SCHEMA = '' ORIGIN_SCHEMA = '' SCHEMA_LOCATION = '' LOAD_TYPE = '' # For each org being accessed, add additional objects # with 'name' and 'github_conn_id' to this list. orgs = [{'name': '', 'github_conn_id': ''}] default_args = {'owner': 'airflow', 'start_date': datetime(2018, 2, 13), 'email': [''], 'email_on_failure': True, 'email_on_retry': False } dag = DAG('github_to_redshift', default_args=default_args, schedule_interval='@hourly', catchup=False ) endpoints = [{"name": "commits", "payload": {}, "load_type": "rebuild"}, {"name": "issue_comments", "payload": {"state": "all"}, "load_type": "rebuild"}, {"name": "issues", "payload": {"state": "all"}, "load_type": "rebuild"}, {"name": "repositories", "payload": {}, "load_type": "rebuild"}, {"name": "members", "payload": {}, "load_type": "rebuild"}, {"name": "pull_requests", "payload": {"state": "all"}, "load_type": "rebuild"}] COPY_PARAMS = ["COMPUPDATE OFF", "STATUPDATE OFF", "JSON 'auto'", "TIMEFORMAT 'auto'", "TRUNCATECOLUMNS", "region as 'us-east-1'"] with dag: kick_off_dag = DummyOperator(task_id='kick_off_dag') for endpoint in endpoints: for org in orgs: S3_KEY = 'github/{0}/{1}.json'.format(org['name'], endpoint['name']) TI_PREFIX = 'github_{0}_from_{1}'.format(endpoint['name'], org['name']) GITHUB_TASK_ID = '{0}_to_s3'.format(TI_PREFIX) REDSHIFT_TASK_ID = '{0}_to_redshift'.format(TI_PREFIX) github = GithubToS3Operator(task_id=GITHUB_TASK_ID, github_conn_id=org['github_conn_id'], github_org=org['name'], github_repo='all', github_object=endpoint['name'], payload=endpoint['payload'], s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=S3_KEY) redshift = S3ToRedshiftOperator(task_id=REDSHIFT_TASK_ID, s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=S3_KEY, origin_schema=ORIGIN_SCHEMA, SCHEMA_LOCATION=SCHEMA_LOCATION, load_type=LOAD_TYPE, copy_params=COPY_PARAMS, redshift_schema=REDSHIFT_SCHEMA, table='{0}_{1}'.format(org['name'], endpoint['name']), redshift_conn_id=REDSHIFT_CONN_ID, primary_key='id') kick_off_dag >> github >> redshift ================================================ FILE: etl/google_analytics_to_redshift.py ================================================ """ Google Analytics to Redshift This file contains one ongoing hourly DAG. This DAG makes use of two custom operators: - GoogleAnalyticsToS3Operator https://github.com/airflow-plugins/google_analytics_plugin/blob/master/operators/google_analytics_reporting_to_s3_operator.py#L11 - S3ToRedshiftOperator https://github.com/airflow-plugins/redshift_plugin/blob/master/operators/s3_to_redshift_operator.py#L13 This DAG generates a report using v4 of the Google Analytics Core Reporting API. The dimensions and metrics are as follows. Note that while these can be modified, a maximum of 10 metrics and 7 dimensions can be requested at once. METRICS - pageView - bounces - users - newUsers - goal1starts - goal1completions DIMENSIONS - dateHourMinute - keyword - referralPath - campaign - sourceMedium Not all metrics and dimensions are compatible with each other. When forming the request, please refer to the official Google Analytics API Reference docs: https://developers.google.com/analytics/devguides/reporting/core/dimsmets """ from datetime import datetime, timedelta from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from GoogleAnalyticsPlugin.schemas.google_analytics_schemas import google_analytics_reporting_schema from airflow.operators import (GoogleAnalyticsReportingToS3Operator, S3ToRedshiftOperator) S3_CONN_ID = '' S3_BUCKET = '' GOOGLE_ANALYTICS_CONN_ID = '' REDSHIFT_CONN_ID = '' REDSHIFT_SCHEMA = '' # Google Analytics has a "lookback window" that defaults to 30 days. # During this period, metrics are in flux as users return to the property # and complete various actions and conversion goals. # https://support.google.com/analytics/answer/1665189?hl=en # The period set as the LOOKBACK_WINDOW will be dropped and replaced during # each run of this workflow. LOOKBACK_WINDOW = 30 # NOTE: While GA supports relative input dates, it is not advisable to use # these in case older workflows need to be re-run. # https://developers.google.com/analytics/devguides/reporting/core/v4/basics SINCE = "{{{{ macros.ds_add(ds, -{0}) }}}}".format(str(LOOKBACK_WINDOW)) UNTIL = "{{ ds }}" view_ids = [] # https://developers.google.com/analytics/devguides/reporting/core/v3/reference#sampling SAMPLING_LEVEL = None # https://developers.google.com/analytics/devguides/reporting/core/v3/reference#includeEmptyRows INCLUDE_EMPTY_ROWS = False PAGE_SIZE = 1000 # NOTE: Not all metrics and dimensions are available together. It is # advisable to test with the GA explorer before deploying. # https://developers.google.com/analytics/devguides/reporting/core/dimsmets METRICS = [{'expression': 'ga:pageViews'}, {'expression': 'ga:bounces'}, {'expression': 'ga:users'}, {'expression': 'ga:newUsers'}, {'expression': 'ga:goal1starts'}, {'expression': 'ga:goal1completions'}] DIMENSIONS = [{'name': 'ga:dateHourMinute'}, {'name': 'ga:keyword'}, {'name': 'ga:referralPath'}, {'name': 'ga:campaign'}, {'name': 'ga:sourceMedium'}] # The specified TIMEFORMAT is based on the ga:dateHourMinute dimension. # If using ga:date or ga:dateHour, this format will need to adjust accordingly. COPY_PARAMS = ["COMPUPDATE OFF", "STATUPDATE OFF", "JSON 'auto'", "TIMEFORMAT 'YYYYMMDDHHMI'" "TRUNCATECOLUMNS", "region as 'us-east-1'"] # Primary and Incremental Keys are set to same value as no other reliable # primary_key can found. This will result in all records with matching values of # dateHourMinute to be deleted and new records inserted for the period of time # covered by the lookback window. Timestamps matching records greater than # the lookback window from the current data will not be pulled again and # therefore not replaced. PRIMARY_KEY = 'datehourminute' INCREMENTAL_KEY = 'datehourminute' default_args = {'start_date': datetime(2018, 2, 22), 'retries': 2, 'retry_delay': timedelta(minutes=5), 'email': [], 'email_on_failure': True} dag = DAG('{}_to_redshift_hourly'.format(GOOGLE_ANALYTICS_CONN_ID), schedule_interval='@hourly', default_args=default_args, catchup=False) with dag: d = DummyOperator(task_id='kick_off_dag') for view_id in view_ids: S3_KEY = 'google_analytics/{0}/{1}_{2}_{3}.json'.format(REDSHIFT_SCHEMA, GOOGLE_ANALYTICS_CONN_ID, view_id, "{{ ts_nodash }}") g = GoogleAnalyticsReportingToS3Operator(task_id='get_google_analytics_data', google_analytics_conn_id=GOOGLE_ANALYTICS_CONN_ID, view_id=view_id, since=SINCE, until=UNTIL, sampling_level=SAMPLING_LEVEL, dimensions=DIMENSIONS, metrics=METRICS, page_size=PAGE_SIZE, include_empty_rows=INCLUDE_EMPTY_ROWS, s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=S3_KEY ) redshift = S3ToRedshiftOperator(task_id='sink_to_redshift', redshift_conn_id=REDSHIFT_CONN_ID, redshift_schema=REDSHIFT_SCHEMA, table='report_{}'.format(view_id), s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=S3_KEY, origin_schema=google_analytics_reporting_schema, schema_location='local', copy_params=COPY_PARAMS, load_type='upsert', primary_key=PRIMARY_KEY, incremental_key=INCREMENTAL_KEY ) d >> g >> redshift ================================================ FILE: etl/hubspot_to_redshift.py ================================================ """ Hubspot to Redshift This files contains three dags: - A monthly backfill from Jan 1, 2010. - A daily backfill from Jan 1, 2018. - An ongoing hourly workflow. Each DAG makes use of two custom operators: - HubspotToS3Operator https://github.com/airflow-plugins/hubspot_plugin/blob/master/operators/hubspot_to_s3_operator.py#L16 - S3ToRedshiftOperator https://github.com/airflow-plugins/redshift_plugin/blob/master/operators/s3_to_redshift_operator.py#L13 This dag pulls the following endpoints and inserts data to the following table/subtable based on the followings schedules: NOTE: Only endpoints with the appropriate scope will be included in this dag. The associated scope to the varius endpoints can be found in the "scope" field within the endpoints array below. The scope available to a given token can be found by passing the associated token to: https://api.hubapi.com/oauth/v1/access-tokens/{OAUTH_TOKEN} NOTE: The contacts table and associated subtables are built based on an incrementing contact id that is stored as an Airflow Variable with the naming convention "INCREMENTAL_KEY__{DAG_ID}_{TASK_ID}_vidOffset" at the end of each run and then pulled on the next to be used as an offset. As such, while accessing the Contacts endpoint, "max_active_runs" should be set to 1 to avoid pulling the same incremental key offset and therefore pulling the same data twice. - Campaigns - Rebuild - Companies - Rebuild - Contacts - Append - Built based on incremental contact id - Form Submissions - Append - Identity Profiles - Append - List Memberships - Append - Merge Audits - Append - Deals - rebuild - Associations_AssociatedVids - Append - Associations_AssociatedCompanyVids - Append - Associations_AssociatedDealIds - Append - Deal Pipelines - Rebuild - Engagments - Rebuild - Associations - Rebuild - Attachments - Rebuild - Events - Append - Built based on incremental date - Forms - Rebuild - Field Groups - Rebuild - Keywords - Rebuild - Lists - Rebuild - Filters - Rebuild - Owners - Rebuild - Remote List - Rebuild - Social - Rebuild - Timeline - Append - Built based on incremental date - Workflow - Rebuild - Persona Tag Ids - Rebuild - Contact List Ids Steps - Rebuild """ from datetime import datetime, timedelta from os import path from airflow import DAG from airflow.hooks.http_hook import HttpHook from airflow.operators.dummy_operator import DummyOperator from airflow.operators import HubspotToS3Operator, S3ToRedshiftOperator from HubspotPlugin.schemas import hubspot_schema S3_CONN_ID = '' S3_BUCKET = '' HUBSPOT_CONN_ID = '' REDSHIFT_SCHEMA = '' REDSHIFT_CONN_ID = '' endpoints = [{"name": "campaigns", "scope": "content", "hubspot_args": {}, "load_type": "rebuild", "primary_key": "id", "subtables": []}, {"name": "companies", "scope": "contacts", "hubspot_args": {}, "load_type": "rebuild", "primary_key": "company_id", "subtables": []}, {"name": "contacts", "scope": "contacts", "hubspot_args": {}, "load_type": "append", "primary_key": "vid", "subtables": ["formsubmissions", "identityprofiles", "listmemberships", "mergeaudits"]}, {"name": "deals", "scope": "contacts", "hubspot_args": {}, "load_type": "rebuild", "primary_key": "deal_id", "subtables": ["associations_associatedvids", "associations_associatedcompanyvids", "associations_associateddealids"]}, {"name": "deal_pipelines", "scope": "contacts", "hubspot_args": {}, "load_type": "rebuild", "subtables": ['stages']}, {"name": "engagements", "scope": "contacts", "hubspot_args": {}, "load_type": "rebuild", "subtables": ["associations", "attachments"]}, {"name": "events", "scope": "content", "hubspot_args": { "startTimestamp": "{{ execution_date }}", "endTimestamp": "{{ next_execution_date }}"}, "load_type": "append", "primary_key": "id", "subtables": []}, {"name": "forms", "scope": "forms", "hubspot_args": {}, "load_type": "rebuild", "subtables": ["fieldgroups"]}, {"name": "keywords", "scope": "reports", "hubspot_args": {}, "load_type": "rebuild", "subtables": []}, {"name": "lists", "scope": "contacts", "hubspot_args": {}, "load_type": "rebuild", "primary_key": "internal_list_id", "subtables": ["filters"]}, {"name": "owners", "scope": "contacts", "hubspot_args": {}, "load_type": "rebuild", "primary_key": "owner_id", "subtables": ["remote_list"]}, {"name": "social", "scope": "social", "hubspot_args": {}, "load_type": "rebuild", "primary_key": "channel_guid", "subtables": []}, {"name": "timeline", "scope": "timeline", "hubspot_args": { "startTimestamp": "{{ execution_date }}", "endTimestamp": "{{ next_execution_date }}"}, "load_type": "append", "subtables": ["changes"]}, {"name": "workflows", "scope": "automation", "hubspot_args": {}, "load_type": "rebuild", "primary_key": "id", "subtables": ["persona_tag_ids", "contact_list_ids_steps"]}] hourly_id = '{}_to_redshift_hourly'.format(HUBSPOT_CONN_ID) daily_id = '{}_to_redshift_daily_backfill'.format(HUBSPOT_CONN_ID) monthly_id = '{}_to_redshift_monthly_backfill'.format(HUBSPOT_CONN_ID) COPY_PARAMS = ["COMPUPDATE OFF", "STATUPDATE OFF", "JSON 'auto'", "TIMEFORMAT 'epochmillisecs'" "TRUNCATECOLUMNS", "region as 'us-east-1'"] def create_dag(dag_id, schedule, hubspot_conn_id, redshift_conn_id, redshift_schema, default_args, catchup=False, max_active_runs=3): try: h = HttpHook(method='GET', http_conn_id=hubspot_conn_id) pw = h.get_connection(conn_id=hubspot_conn_id).password response = h.run('oauth/v1/access-tokens/{0}'.format(pw)) scopes = response.json()['scopes'] dag = DAG(dag_id, default_args=default_args, schedule_interval=schedule, catchup=catchup, max_active_runs=max_active_runs ) with dag: kick_off_dag = DummyOperator(task_id='kick_off_dag') kick_off_dag tables_to_build = [] for endpoint in endpoints: if endpoint['scope'] in scopes: if 'backfill' in dag_id and 'startTimestamp': if endpoint['hubspot_args'].keys(): tables_to_build.append(endpoint) else: tables_to_build.append(endpoint) for table in tables_to_build: HUBSPOT_ARGS = table.get('hubspot_args', {}) TABLE_NAME = table.get('name', '') LOAD_TYPE = table.get('load_type', '') PRIMARY_KEY = None INCREMENTAL_KEY = None if 'primary_key' in table.keys(): PRIMARY_KEY = table['primary_key'] if 'incremental_key' in table.keys(): INCREMENTAL_KEY = table['incremental_key'] S3_KEY = ('hubspot/{0}/{1}_{2}.json'.format( redshift_schema, TABLE_NAME, "{{ ts_nodash }}")) split_key = path.splitext(S3_KEY) LOAD_KEY = '{0}_core'.format(split_key[0]) h = HubspotToS3Operator(task_id='hubspot_{0}_data_to_s3' .format(TABLE_NAME), hubspot_conn_id=hubspot_conn_id, hubspot_object=TABLE_NAME, hubspot_args=HUBSPOT_ARGS, s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=S3_KEY) kick_off_dag >> h if table['name'] == 'timeline': pass else: r = S3ToRedshiftOperator(task_id='hubspot_{0}_to_redshift' .format(TABLE_NAME), s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=LOAD_KEY, origin_schema=getattr(hubspot_schema, TABLE_NAME), origin_datatype='json', copy_params=COPY_PARAMS, load_type=LOAD_TYPE, primary_key=PRIMARY_KEY, incremental_key=INCREMENTAL_KEY, schema_location='local', redshift_schema=redshift_schema, table=TABLE_NAME, redshift_conn_id=redshift_conn_id) h >> r if table['subtables']: for subtable in table['subtables']: SUBTABLE_LOAD_KEY = '{0}_{1}'.format(split_key[0], subtable) SUBTABLE_NAME = '{0}_{1}'.format(TABLE_NAME, subtable) if SUBTABLE_NAME == 'timeline': SUBTABLE_NAME = TABLE_NAME s = S3ToRedshiftOperator(task_id='hubspot_{0}_{1}_to_redshift' .format(TABLE_NAME, subtable), s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=SUBTABLE_LOAD_KEY, origin_schema=getattr(hubspot_schema, '{0}_{1}'.format(TABLE_NAME,subtable)), origin_datatype='json', load_type=LOAD_TYPE, schema_location='local', copy_params=COPY_PARAMS, redshift_schema=redshift_schema, table=SUBTABLE_NAME, redshift_conn_id=REDSHIFT_CONN_ID) h >> s return dag except: pass globals()[hourly_id] = create_dag(hourly_id, '@hourly', HUBSPOT_CONN_ID, REDSHIFT_CONN_ID, REDSHIFT_SCHEMA, {'start_date': datetime(2018, 2, 13), 'retries': 2, 'retry_delay': timedelta(minutes=5), 'email': [], 'email_on_failure': True}, catchup=False, max_active_runs=1) globals()[daily_id] = create_dag(daily_id, '@daily', HUBSPOT_CONN_ID, REDSHIFT_CONN_ID, REDSHIFT_SCHEMA, {'start_date': datetime(2018, 1, 1), 'end_date': datetime(2018, 2, 13), 'retries': 2, 'retry_delay': timedelta(minutes=5), 'email': [], 'email_on_failure': True}, catchup=True) globals()[monthly_id] = create_dag(monthly_id, '@monthly', HUBSPOT_CONN_ID, REDSHIFT_CONN_ID, REDSHIFT_SCHEMA, {'start_date': datetime(2010, 1, 1), 'end_date': datetime(2018, 1, 1), 'retries': 2, 'retry_delay': timedelta(minutes=5), 'email': [], 'email_on_failure': True}, catchup=True) ================================================ FILE: etl/imap_to_redshift.py ================================================ """ An example DAG designed to: 1) Access an IMAP Server 2) Search the inbox for an email with a specific subject 3) Pull in the csv attachments of the email and store them in S3 4) Load these files into Redshift. This files contains one dag: - An ongoing daily workflow. Each DAG makes use of two custom operators: - IMAPToS3Operator https://github.com/airflow-plugins/imap_plugin/blob/master/operators/imap_to_s3_operator.py#L13 - S3ToRedshiftOperator https://github.com/airflow-plugins/redshift_plugin/blob/master/operators/s3_to_redshift_operator.py#L13 """ from datetime import datetime, timedelta from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from airflow.operators import ImapToS3Operator from airflow.operators import S3ToRedshiftOperator IMAP_CONN_ID = '' IMAP_EMAIL = '' S3_CONN_ID = '' S3_BUCKET = '' REDSHIFT_SCHEMA = '' REDSHIFT_CONN_ID = '' TIME = '{{ ts_nodash }}' email_workflows = [ { 'id': 'transaction', 'name': 'Transaction', 'fields': [ {'name': "event_code", 'type': "varchar(256)"}, {'name': "customer_id", 'type': "varchar(256)"}, {'name': "date", 'type': "timestamp"}, {'name': "code", 'type': "varchar(256)"}, {'name': "name", 'type': "varchar(256)"}, {'name': "class_code", 'type': "varchar(256)"}, {'name': "price", 'type': "varchar(256)"}, {'name': "order_qty", 'type': "varchar(256)"}, ] }, { 'id': 'customer', 'name': 'Customers', 'fields': [ {'name': "customer_id", 'type': "varchar(256)"}, {'name': "full_name", 'type': "varchar(256)"}, {'name': "mail_addr1", 'type': "varchar(256)"}, {'name': "mail_addr2", 'type': "varchar(256)"}, {'name': "mail_city", 'type': "varchar(256)"}, {'name': "mail_state", 'type': "varchar(256)"}, {'name': "mail_zip", 'type': "varchar(256)"}, {'name': "mail_country", 'type': "varchar(256)"}, {'name': "bill_addr1", 'type': "varchar(256)"}, {'name': "bill_addr2", 'type': "varchar(256)"}, {'name': "bill_state", 'type': "varchar(256)"}, {'name': "bill_city", 'type': "varchar(256)"}, {'name': "bill_zip", 'type': "varchar(256)"}, {'name': "bill_country", 'type': "varchar(256)"}, {'name': "bill_name", 'type': "varchar(256)"}, {'name': "phone", 'type': "varchar(256)"}, {'name': "email", 'type': "varchar(256)"} ] }, { 'id': 'event', 'name': 'Events', 'fields': [ {'name': "code", 'type': "varchar(256)"}, {'name': "name", 'type': "varchar(256)"}, {'name': "facility_code", 'type': "varchar(256)"}, {'name': "facility_name", 'type': "varchar(256)"}, {'name': "group_code", 'type': "varchar(256)"}, {'name': "group_name", 'type': "varchar(256)"}, {'name': "type_code", 'type': "varchar(256)"}, {'name': "type", 'type': "varchar(256)"}, {'name': "date", 'type': "timestamp"}, {'name': "keywords", 'type': "varchar"}, {'name': "tags", 'type': "varchar"} ] } ] default_args = { 'start_date': datetime(2017, 2, 14, 0, 0), 'email': [], 'email_on_failure': True, 'email_on_retry': False, 'retries': 2, 'retry_delay': timedelta(minutes=5) } dag = DAG( 'caa_imap_to_redshift', schedule_interval='@daily', default_args=default_args, catchup=False ) with dag: kick_off_dag = DummyOperator(task_id='kick_off_dag') for workflow in email_workflows: type = workflow.get('id', None) name = workflow.get('name', None) fields = workflow.get('fields', None) S3_KEY = '{type}_{time}.csv'.format(type=workflow['id'], time=TIME) imap_to_s3 = ImapToS3Operator( task_id='{}_to_s3'.format(type), imap_conn_id=IMAP_CONN_ID, imap_email=IMAP_EMAIL, imap_subject=name, s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=S3_KEY, ) s3_to_redshift = S3ToRedshiftOperator( task_id='{}_to_redshift'.format(type), s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=S3_KEY, redshift_conn_id=REDSHIFT_CONN_ID, redshift_schema=REDSHIFT_SCHEMA, table=type, origin_schema=fields, schema_location='local', ) kick_off_dag >> imap_to_s3 >> s3_to_redshift ================================================ FILE: etl/marketo_to_redshift.py ================================================ """ Marketo to Redshift This files contains three dags: - A monthly backfill from Jan 1, 2013. - A daily backfill from Jan 1, 2018. - An ongoing hourly workflow. Each DAG makes use of three custom operators: - RateLimitOperator https://github.com/airflow-plugins/rate_limit_plugin/blob/master/operators/rate_limit_operator.py - MarketoToS3Operator https://github.com/airflow-plugins/marketo_plugin/blob/master/operators/marketo_to_s3_operator.py#L19 - S3ToRedshiftOperator https://github.com/airflow-plugins/redshift_plugin/blob/master/operators/s3_to_redshift_operator.py#L13 This ongoing DAG pulls the following Marketo objects: - Activities - Campaigns - Leads - Lead Lists - Programs When backfilling, only the leads object is pulled. By default, it begins pulling since Jan 1, 2013. """ from datetime import datetime, timedelta from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from airflow.operators import (MarketoToS3Operator, S3ToRedshiftOperator, RateLimitOperator) from MarketoPlugin.schemas._schema import schema MARKETO_CONN_ID = '' MARKETO_SCHEMA = '' REDSHIFT_SCHEMA = '' REDSHIFT_CONN_ID = '' S3_CONN_ID = '' S3_BUCKET = '' RATE_LIMIT_THRESHOLD = 0.8 RATE_LIMIT_THRESHOLD_TYPE = 'percentage' hourly_id = '{}_to_redshift_hourly'.format(MARKETO_CONN_ID) daily_id = '{}_to_redshift_daily_backfill'.format(MARKETO_CONN_ID) monthly_id = '{}_to_redshift_monthly_backfill'.format(MARKETO_CONN_ID) def create_dag(dag_id, schedule, marketo_conn_id, redshift_conn_id, redshift_schema, s3_conn_id, s3_bucket, default_args, catchup=False): dag = DAG(dag_id, schedule_interval=schedule, default_args=default_args, catchup=catchup) if 'backfill' in dag_id: endpoints = ['leads'] else: endpoints = ['activities', 'campaigns', 'leads', 'programs', 'lead_lists'] COPY_PARAMS = ["COMPUPDATE OFF", "STATUPDATE OFF", "JSON 'auto'", "TIMEFORMAT 'auto'" "TRUNCATECOLUMNS", "region as 'us-east-1'"] with dag: d = DummyOperator(task_id='kick_off_dag') l = RateLimitOperator(task_id='check_rate_limit', service='marketo', service_conn_id=marketo_conn_id, threshold=RATE_LIMIT_THRESHOLD, threshold_type=RATE_LIMIT_THRESHOLD_TYPE) d >> l for endpoint in endpoints: MARKETO_SCHEMA = schema[endpoint] TABLE_NAME = 'mkto_{0}'.format(endpoint) S3_KEY = 'marketo/{0}/{1}_{2}.json'.format(redshift_schema, endpoint, "{{ ts_nodash }}") MARKETO_TASK_ID = 'get_{0}_marketo_data'.format(endpoint) REDSHIFT_TASK_ID = 'marketo_{0}_to_redshift'.format(endpoint) START_AT = "{{ execution_date.isoformat() }}" END_AT = "{{ next_execution_date.isoformat() }}" m = MarketoToS3Operator(task_id=MARKETO_TASK_ID, marketo_conn_id=marketo_conn_id, endpoint=endpoint, s3_conn_id=s3_conn_id, s3_bucket=s3_bucket, s3_key=S3_KEY, output_format='json', start_at=START_AT, end_at=END_AT) l >> m if endpoint != 'leads': r = S3ToRedshiftOperator(task_id=REDSHIFT_TASK_ID, s3_conn_id=s3_conn_id, s3_bucket=s3_bucket, s3_key=S3_KEY, load_type='rebuild', load_format='JSON', schema_location='local', origin_schema=MARKETO_SCHEMA, redshift_schema=redshift_schema, table=TABLE_NAME, copy_params=COPY_PARAMS, redshift_conn_id=redshift_conn_id) m >> r else: rl = S3ToRedshiftOperator(task_id=REDSHIFT_TASK_ID, s3_conn_id=s3_conn_id, s3_bucket=s3_bucket, s3_key=S3_KEY, load_type='upsert', load_format='JSON', schema_location='local', origin_schema=MARKETO_SCHEMA, redshift_schema=redshift_schema, table=TABLE_NAME, primary_key='id', copy_params=COPY_PARAMS, incremental_key='updated_at', redshift_conn_id=redshift_conn_id) m >> rl return dag globals()[hourly_id] = create_dag(hourly_id, '@hourly', MARKETO_CONN_ID, REDSHIFT_CONN_ID, REDSHIFT_SCHEMA, S3_CONN_ID, S3_BUCKET, {'start_date': datetime(2018, 2, 13), 'retries': 2, 'retry_delay': timedelta(minutes=5), 'email': [], 'email_on_failure': True}, catchup=True) globals()[daily_id] = create_dag(daily_id, '@daily', MARKETO_CONN_ID, REDSHIFT_CONN_ID, REDSHIFT_SCHEMA, S3_CONN_ID, S3_BUCKET, {'start_date': datetime(2018, 1, 1), 'end_date': datetime(2018, 2, 13), 'retries': 2, 'retry_delay': timedelta(minutes=5), 'email': [], 'email_on_failure': True}, catchup=True) globals()[monthly_id] = create_dag(monthly_id, '@monthly', MARKETO_CONN_ID, REDSHIFT_CONN_ID, REDSHIFT_SCHEMA, S3_CONN_ID, S3_BUCKET, {'start_date': datetime(2013, 1, 1), 'end_date': datetime(2018, 1, 1), 'retries': 2, 'retry_delay': timedelta(minutes=5), 'email': [], 'email_on_failure': True}, catchup=True) ================================================ FILE: etl/mongo_to_redshift/collections/__init__.py ================================================ ================================================ FILE: etl/mongo_to_redshift/collections/_collection_processing.py ================================================ """ This file contains a single method that accepts a mongo formatted schema (see "example_monog_collection.json") and outputs a mongo field projection and Redshift formatted schema mapping. This method will recursively crawl through the inputted json file, flattening out nested dictionaries and ignoring arrays. To flatten arrays, it is recommended that that gets broken out as a separate table. """ def _prepareData(data, subtable=''): schemaMapper = [{"mongo": "string", "redshift": "varchar(512)"}, {"mongo": "text", "redshift": "varchar(6000)"}, {"mongo": "integer", "redshift": "integer"}, {"mongo": "bigint", "redshift": "bigint"}, {"mongo": "boolean", "redshift": "boolean"}, {"mongo": "datetime", "redshift": "datetime"}, {"mongo": "float", "redshift": "float"}, {"mongo": "double", "redshift": "float(53)"}] def projection(d, parent_key='', sep='_', subtable=''): projection_items = [] for k, v in d.items(): new_key = parent_key + sep + k if parent_key else k alt_key = parent_key + '.' + k if parent_key else k if isinstance(v, dict): projection_items.extend(projection(v, new_key.replace(sep, '.'), sep=sep).items()) elif isinstance(v, str): projection_items.append((new_key.replace('.', '_'), '${}'.format(alt_key))) return dict(projection_items) def schema(d, parent_key='', sep='_'): schema_items = [] for k, v in d.items(): new_key = parent_key + sep + k if parent_key else k if isinstance(v, dict): schema_items.extend(schema(v, new_key, sep=sep).items()) elif isinstance(v, str): schema_items.append((new_key, v)) return dict(schema_items) def _convertSchema(schema): output_array = [] for k, v in schema.items(): base_dict = dict() base_dict['name'] = k base_dict['type'] = v for mapping in schemaMapper: if mapping['mongo'] == v.lower(): base_dict['type'] = mapping['redshift'] output_array.append(base_dict) return output_array return projection(data, subtable), _convertSchema(schema(data)) ================================================ FILE: etl/mongo_to_redshift/collections/example_mongo_collection.json ================================================ """ Example MongoDB Collection Schema Below is an example schema mapping for a mongo collection. Available datatypes include: - string - text - integer - bigint - boolean - datetime - float - double The Redshift mappings these translate to can be found in the '_collection_processing' file. """ { "_id": "String", "title": "String", "archived": "Boolean", "description": "String", "dateLastActivity": "DateTime", "companyId": "String", "userId": "String", "createdAt": "DateTime", "relatedUsers": [ "String" ], "address": { "ID": "Integer", "Address1": "String", "Address2": "String", "City": "String", "State": "String", "ZipCode": "String", "PrimaryPhone": "String", "SecondaryPhone": "String", "Fax": "String" } } ================================================ FILE: etl/mongo_to_redshift/mongo_to_redshift.py ================================================ """ MongoDB to Redshift This file contains one ongoing daily DAG. This DAG makes use of two custom operators: - MongoToS3Operator https://github.com/airflow-plugins/mongo_plugin/blob/master/operators/mongo_to_s3_operator.py#L9 - S3ToRedshiftOperator https://github.com/airflow-plugins/redshift_plugin/blob/master/operators/s3_to_redshift_operator.py#L13 This DAG also uses a Mongo collection processing script that accepts a json formatted Mongo schema mapping and outputs both a Mongo query projection and a compatible Redshift schema mapping. This script can be found in "etl/mongo_to_redshift/collections/_collection_processing.py". This DAG also contains a flattening script that removes invalid characters from the Mongo keys as well as scrubbing out the "_$date" suffix that PyMongo appends to datetime fields. """ from datetime import datetime, timedelta import json import re import os from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from airflow.hooks import S3Hook from airflow.operators import (MongoToS3Operator, S3ToRedshiftOperator, PythonOperator) from mongo_to_redshift.collections._collection_processing import _prepareData S3_CONN = '' S3_BUCKET = '' REDSHIFT_SCHEMA = '' REDSHIFT_CONN_ID = '' MONGO_CONN_ID = '' MONGO_DATABASE = '' default_args = {'start_date': datetime(2018, 2, 22), 'retries': 2, 'retry_delay': timedelta(minutes=5), 'email': [], 'email_on_failure': True, 'email_on_retry': False} dag = DAG('mongo_to_redshift_daily', default_args=default_args, schedule_interval='@daily', catchup=True) cwd = os.getcwd() def process_collection(collection): file_path = 'dags/etl/mongo_to_redshift/collections/{0}.json'.format(collection) new_path = os.path.join(cwd, file_path) order = json.load(open(new_path)) projection, schema = _prepareData(order) collections = [{'name': 'example_mongo_collection', 'collection': 'example_mongo_collection', 'mongo_query': [{"$match": {"dateLastActivity": { "$lt": '{{ next_execution_date }}', "$gte": '{{ execution_date }}' }}}, {"$project": process_collection('example_mongo_collection')[0]}], 'schema': process_collection('example_mongo_collection')[1], 'primary_key': ["id"], 'incremental_key': 'dateLastActivity', 'load_type': 'upsert'}] def flatten_py(**kwargs): s3_key = kwargs['templates_dict']['s3_key'] flattened_key = kwargs['templates_dict']['flattened_key'] s3_conn = kwargs['templates_dict']['s3_conn'] s3_bucket = kwargs['templates_dict']['s3_bucket'] s3 = S3Hook(s3_conn) output = (s3.get_key(s3_key, bucket_name=s3_bucket) .get_contents_as_string(encoding='utf-8')) output = output.split('\n') output = '\n'.join([json.dumps({re.sub(r'[?|$|.|!]', r'', k.lower().replace('_$date', '')): v for k, v in i.items()}) for i in output]) s3.load_string(output, flattened_key, bucket_name=s3_bucket, replace=True) with dag: kick_off_dag = DummyOperator(task_id='kick_off_dag') for collection in collections: if 'collection' in collection.keys(): collection_name = collection['collection'] else: collection_name = collection['name'] S3_KEY = 'mongo/raw/{0}_{1}.json'.format(collection['name'], '{{ ts_nodash}}') FLATTENED_KEY = 'mongo/flattened/{0}_{1}_flattened.json'.format(collection['name'], '{{ ts_nodash}}') mongo = MongoToS3Operator(task_id='{0}_to_s3'.format(collection['name']), mongo_conn_id=MONGO_CONN_ID, mongo_collection=collection_name, mongo_database=MONGO_DATABASE, mongo_query=collection['mongo_query'], s3_conn_id=S3_CONN, s3_bucket=S3_BUCKET, s3_key=S3_KEY, replace=True ) flatten_object = PythonOperator(task_id='flatten_{0}'.format(collection['name']), python_callable=flatten_py, templates_dict={'s3_key': S3_KEY, 's3_conn': S3_CONN, 's3_bucket': S3_BUCKET, 'collection_name': collection['name'], 'flattened_key': FLATTENED_KEY, 'origin_schema': collection['schema']}, provide_context=True) redshift = S3ToRedshiftOperator(task_id='{0}_to_redshift'.format(collection['name']), s3_conn_id=S3_CONN, s3_bucket=S3_BUCKET, s3_key=FLATTENED_KEY, redshift_conn_id=REDSHIFT_CONN_ID, redshift_schema=REDSHIFT_SCHEMA, origin_schema=collection['schema'], redshift_table=collection['name'], primary_key=collection.get('primary_key', None), incremental_key=collection.get('incremental_key', None), load_type=collection['load_type']) kick_off_dag >> mongo >> flatten_object >> redshift ================================================ FILE: etl/salesforce_to_redshift.py ================================================ """ Salesforce to Redshift This files contains an ongoing hourly workflow. Each DAG makes use of three custom operators: - SalesforceToS3Operator https://github.com/airflow-plugins/salesforce_plugin/blob/master/operators/salesforce_to_s3_operator.py#L60 - S3ToRedshiftOperator https://github.com/airflow-plugins/redshift_plugin/blob/master/operators/s3_to_redshift_operator.py#L13 This ongoing DAG pulls the following Salesforce objects: - Account - Campaign - CampaignMember - Contact - Lead - Opportunity - OpportunityContactRole - OpportunityHistory - Task - User The output from Salesforce will be formatted as newline delimited JSON (ndjson) and will include """ from datetime import datetime, timedelta from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from airflow.operators.salesforce_plugin import SalesforceToS3Operator from airflow.operators import S3ToRedshiftOperator SF_CONN_ID = '' S3_CONN_ID = '' S3_BUCKET = '' REDSHIFT_CONN_ID = '' REDSHIFT_SCHEMA_NAME = '' ORIGIN_SCHEMA = '' SCHEMA_LOCATION = '' default_args = { 'owner': 'airflow', 'depends_on_past': False, 'start_date': datetime(2017, 8, 29), 'email': [], 'email_on_failure': False, 'email_on_retry': False, 'retries': 1, 'retry_delay': timedelta(minutes=5) } dag = DAG('salesforce_to_redshift', default_args=default_args, schedule_interval='@hourly', catchup=False) tables = [{'name': 'Account', 'load_type': 'Upsert'}, {'name': 'Campaign', 'load_type': 'Upsert'}, {'name': 'CampaignMember', 'load_type': 'Upsert'}, {'name': 'Contact', 'load_type': 'Upsert'}, {'name': 'Lead', 'load_type': 'Upsert'}, {'name': 'Opportunity', 'load_type': 'Upsert'}, {'name': 'OpportunityContactRole', 'load_type': 'Upsert'}, {'name': 'OpportunityHistory', 'load_type': 'Upsert'}, {'name': 'Task', 'load_type': 'Upsert'}, {'name': 'User', 'load_type': 'Upsert'}] COPY_PARAMS = ["JSON 'auto'", "TRUNCATECOLUMNS", "region as 'us-east-1'"] kick_off_dag = DummyOperator(task_id='kick_off_dag', dag=dag) for table in tables: S3_KEY = 'salesforce/{}/{}.json'.format('{{ execution_date }}', table['name'].lower()) salesforce_to_s3 = SalesforceToS3Operator(task_id='{0}_to_S3'.format(table['name']), sf_conn_id=SF_CONN_ID, sf_obj=table, fmt='ndjson', s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, record_time_added=True, coerce_to_timestamp=True, dag=dag) s3_to_redshift = S3ToRedshiftOperator(task_id='{0}_to_Redshift'.format(table['name']), redshift_conn_id=REDSHIFT_CONN_ID, redshift_schema=REDSHIFT_SCHEMA_NAME, table=table, s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=S3_KEY, origin_schema=ORIGIN_SCHEMA, copy_params=COPY_PARAMS, schema_location=SCHEMA_LOCATION, load_type=table['load_type'], dag=dag) kick_off_dag >> salesforce_to_s3 >> s3_to_redshift ================================================ FILE: etl/sftp_to_mongo.py ================================================ """ SFTP to Mongo This files contains an ongoing daily workflow that: 1) Looks for a csv in SFTP 2) If that file exists, retrieves it. 3) Converts that csv to json. 4) Inserts these records into Mongo. Throughout this process, S3 is used as a intermediary storage layer. This DAG makes use of two custom operators: - S3ToMongoOperator https://github.com/airflow-plugins/mongo_plugin/blob/master/operators/s3_to_mongo_operator.py#L11 - SFTPToS3Operator https://github.com/airflow-plugins/sftp_plugin/blob/master/operators.py/sftp_to_s3_operator.py#L7 """ from datetime import datetime, timedelta from flatten_json import unflatten_list import pandas as pd import logging import json from airflow import DAG from airflow.hooks import S3Hook, SSHHook from airflow.operators.python_operator import ShortCircuitOperator, PythonOperator from mongo_plugin.operators.s3_to_mongo_operator import S3ToMongoOperator from sftp_plugin.operators.sftp_to_s3_operator import SFTPToS3Operator SSH_CONN_ID = '' MONGO_CONN_ID = '' MONGO_DB = '' MONGO_COLLECTION = '' FILENAME = '' FILEPATH = '' SFTP_PATH = '/{0}/{1}'.format(FILEPATH, FILENAME) S3_CONN_ID = '' S3_BUCKET = '' S3_KEY = '' today = "{{ ds }}" S3_KEY_TRANSFORMED = '{0}_{1}.json'.format(S3_KEY, today) default_args = { 'start_date': datetime(2018, 4, 12, 0, 0, 0), 'email': [], 'email_on_failure': True, 'email_on_retry': False, 'retries': 5, 'retry_delay': timedelta(minutes=5) } dag = DAG( 'sftp_to_mongo', schedule_interval='@daily', default_args=default_args, catchup=True ) def check_for_file_py(**kwargs): path = kwargs.get('path', None) sftp_conn_id = kwargs.get('sftp_conn_id', None) filename = kwargs.get('templates_dict').get('filename', None) ssh_hook = SSHHook(ssh_conn_id=sftp_conn_id) sftp_client = ssh_hook.get_conn().open_sftp() ftp_files = sftp_client.listdir(path) logging.info('Filename: ' + str(filename)) if filename in ftp_files: return True else: return False def transform_py(**kwargs): s3 = kwargs.get('s3_conn_id', None) s3_key = kwargs.get('templates_dict').get('s3_key', None) transformed_key = kwargs.get('templates_dict').get('transformed_key', None) s3_bucket = kwargs.get('s3_bucket', None) hook = S3Hook(s3) (hook.get_key(s3_key, bucket_name=s3_bucket) .get_contents_to_filename('temp.csv')) df = pd.read_csv('temp.csv') records = json.loads(df.to_json(orient='records')) del df records = [unflatten_list(record) for record in records] records = '\n'.join([json.dumps(record) for record in records]) hook.load_string(string_data=records, key=transformed_key, bucket_name=s3_bucket, replace=True) with dag: files = ShortCircuitOperator(task_id='check_for_file', python_callable=check_for_file_py, templates_dict={'filename': FILENAME}, op_kwargs={"path": FILEPATH, "sftp_conn_id": SSH_CONN_ID}, provide_context=True) sftp = SFTPToS3Operator( task_id='retrieve_file', sftp_conn_id=SSH_CONN_ID, sftp_path=SFTP_PATH, s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=S3_KEY ) transform = PythonOperator(task_id='transform_to_json', python_callable=transform_py, templates_dict={'s3_key': S3_KEY, 'transformed_key': S3_KEY_TRANSFORMED}, op_kwargs={"s3_conn_id": S3_CONN_ID, "s3_bucket": S3_BUCKET}, provide_context=True) mongo = S3ToMongoOperator( task_id='sink_to_mongo', s3_conn_id=S3_CONN_ID, s3_bucket=S3_BUCKET, s3_key=S3_KEY_TRANSFORMED, mongo_conn_id=MONGO_CONN_ID, mongo_collection=MONGO_COLLECTION, mongo_db=MONGO_COLLECTION, mongo_method='replace', mongo_replacement_filter='', upsert=True ) files >> sftp >> transform, mongo ================================================ FILE: poc/dbt_example.py ================================================ # Originally from @adamhaney -- https://gist.github.com/adamhaney/916a21b0adcabef4038c38e3ac96a36f from datetime import datetime, timedelta import networkx as nx from airflow import DAG from airflow.operators import BashOperator, SubDagOperator start_date = datetime(year=2017, month=6, day=13, hour=19, minute=0) schedule_interval = '0 * * * 1-5' default_args = { 'owner': 'The Owner', 'email': ['theoperator@email.com'], 'retry_interval': timedelta(minutes=15), 'sla': timedelta(minutes=60), 'depends_on_downstream': True, 'email_on_failure': True, 'email_on_retry': True, 'provide_context': True, } dag = DAG('dbt', start_date=start_date, schedule_interval=schedule_interval, default_args=default_args, max_active_runs=1) dbt_clone = BashOperator( task_id='dbt_clone', bash_command='cd ~/project && git fetch --all && git reset --hard origin/master', dag=dag ) dbt_deps = BashOperator( task_id='dbt_deps', bash_command='cd ~/project && dbt deps --profile=warehouse --target=prod', dag=dag ) dbt_deps.set_upstream(dbt_clone) dbt_clean = BashOperator( task_id='dbt_clean', bash_command='cd ~/project && dbt clean --profile=warehouse --target=prod', dag=dag ) dbt_clean.set_upstream(dbt_deps) dbt_archive = BashOperator( task_id='dbt_archive', bash_command='cd ~/project && dbt archive --profile=warehouse --target=prod', dag=dag ) dbt_archive.set_upstream(dbt_clean) dbt_seed = BashOperator( task_id='dbt_seed', bash_command='cd ~/gospel && dbt seed --profile=warehouse --target=prod', dag=dag ) dbt_seed.set_upstream(dbt_archive) dbt_compile = BashOperator( task_id='dbt_compile', bash_command='''cd ~/project && dbt compile --profile=warehouse --target=prod && find target/build | xargs -I {} bash -c "echo '-------------------- '{}' --------------------' && cat {} || exit 0"''', dag=dag ) dbt_compile.set_upstream(dbt_seed) copy_gpickle_file = BashOperator( task_id='copy_gpickle_file', bash_command='''cp ~/project/target/graph.gpickle ~/project/graph.gpickle''', dag=dag ) copy_gpickle_file.set_upstream(dbt_compile) def dbt_dag(start_date, schedule_interval, default_args): temp_dag = DAG('gospel_.dbt_sub_dag', start_date=start_date, schedule_interval=schedule_interval, default_args=default_args) G = nx.read_gpickle('/home/airflowuser/project/graph.gpickle') def make_dbt_task(model_name): simple_model_name = model_name.split('.')[-1] dbt_task = BashOperator( task_id=model_name, bash_command='cd ~/gospel && dbt run --profile=warehouse --target=prod --non-destructive --models {simple_model_name}'.format(simple_model_name=simple_model_name), dag=temp_dag ) return dbt_task dbt_tasks = {} for node_name in set(G.nodes()): dbt_task = make_dbt_task(node_name) dbt_tasks[node_name] = dbt_task for edge in G.edges(): dbt_tasks[edge[0]].set_downstream(dbt_tasks[edge[1]]) return temp_dag dbt_sub_dag = SubDagOperator( subdag=dbt_dag(dag.start_date, dag.schedule_interval, default_args=default_args), task_id='dbt_sub_dag', dag=dag, trigger_rule='all_done' ) dbt_sub_dag.set_upstream(copy_gpickle_file) dbt_test = BashOperator( task_id='dbt_test', bash_command='cd ~/project && dbt test --profile=warehouse --target=prod', dag=dag ) dbt_test.set_upstream(dbt_sub_dag) ================================================ FILE: poc/dummy_sensor_example.py ================================================ """ This example dag uses the custom "DummySensorOperator" found here: https://github.com/airflow-plugins/dummy_sensor_plugin/blob/master/operators/dummy_sensor_operator.py """ from datetime import datetime from airflow import DAG from airflow.operators.dummy_operator import DummyOperator # Custom Plugin from airflow.operators import DummySensorOperator args = { 'owner': 'airflow', 'start_date': datetime(2017, 4, 20, 0, 0), 'provide_context': True } dag = DAG( 'replicate_skipped_bug', schedule_interval="@once", default_args=args ) start = DummyOperator( task_id='start_pipeline', dag=dag ) with dag: for i in range(0, 75): d_sensor = DummySensorOperator( task_id='dummy_sensor_{}'.format(i), timeout=1, poke_interval=2, flag=False, soft_fail=True, dag=dag ) d_operator = DummyOperator(task_id='dummy_operator_{}'.format(i)) start >> d_sensor >> d_operator ================================================ FILE: poc/dynamic_dag_example.py ================================================ """ This example uses the existing Dummy Operator and Variable model to demonstrate dynamic creation of DAGs based on a Variable setting. As shown below, a list of customer objects is retrieved and used to create unique dags based on the imput. """ from datetime import datetime, timedelta from airflow.models import DAG from airflow.models import Variable from airflow.operators.dummy_operator import DummyOperator # Create JSON Variable if it doesn't exist CUSTOMERS = [ { 'customer_name': 'Faux Customer', 'customer_id': 'faux_customer', 'email': ['admin@fauxcustomer.com', 'admin@astronomer.io'], 'schedule_interval': None, 'enabled': True }, { 'customer_name': 'Bogus Customer', 'customer_id': 'bogus_customer', 'email': ['admin@boguscustomer.com', 'admin@astronomer.io'], 'schedule_interval': '@once', 'enabled': True } ] # Get JSON Variable CUSTOMERS = Variable.get("customer_list", default_var=CUSTOMERS, deserialize_json=True) def create_dag(customer): """ Accepts a customer parameters dict and overrides default args to create a DAG object Returns: DAG() Object """ default_args = { 'owner': 'airflow', 'depends_on_past': False, 'email': 'xyz@xyz.com', 'retries': 1, 'retry_delay': timedelta(minutes=5), 'start_date': datetime(2017, 1, 1, 0, 0), 'end_date': None } """ This allows DAG parameters to be passed in from the Variable if a customer needs something specific overridden in their DAG. Consider how email being passed in from the customer object overrides email in the resulting replaced_args object. """ replaced_args = {k: default_args[k] if customer.get( k, None) is None else customer[k] for k in default_args} dag_id = '{base_name}_{id}'.format( base_name='load_clickstream_data', id=customer['customer_id']) return DAG(dag_id=dag_id, default_args=replaced_args, schedule_interval=customer['schedule_interval']) # Loop customers array of containing customer objects for cust in CUSTOMERS: if cust['enabled']: dag = create_dag(cust) globals()[dag.dag_id] = dag extract = DummyOperator( task_id='extract_data', dag=dag ) transform = DummyOperator( task_id='transform_data', dag=dag ) load = DummyOperator( task_id='load_data', dag=dag ) extract >> transform >> load else: # TODO Create but programmatically pause pass ================================================ FILE: poc/imap_example.py ================================================ """ An example DAG using the IMAP plugin to: 1) Access an IMAP Server. 2) Search the inbox for an email with a specific subject 3) Extract the body of the specified email 4) Parse the body according to a Regular Expression 5) Return the parsed result as an XCOM This files contains one dag: - An ongoing daily workflow. Each DAG makes use of one custom hook: - ImapHook https://github.com/airflow-plugins/imap_plugin/blob/master/hooks/imap_hook.py#L9 """ from datetime import datetime, timedelta import re import logging from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from airflow.operators.python_operator import PythonOperator from imap_plugin.hooks.imap_hook import ImapHook IMAP_CONN_ID = '' SUBJECT = '' SEARCH_STRING = r'' default_args = { 'start_date': datetime(2018, 2, 10, 0, 0), 'email': [], 'email_on_failure': True, 'email_on_retry': False, 'retries': 2, 'retry_delay': timedelta(minutes=5), 'catchup': False } dag = DAG( 'imap_inbox_search', schedule_interval='@daily', default_args=default_args, catchup=False ) def imap_py(**kwargs): imap_conn_id = kwargs.get('templates_dict', None).get('imap_conn_id', None) imap_subject = kwargs.get('templates_dict', None).get('imap_subject', None) reg_ex = kwargs.get('templates_dict', None).get('reg_ex', None) search_criteria = '(HEADER Subject "{}")'.format(imap_subject) logging.info('Retrieving emails...') message = ImapHook(imap_conn_id=imap_conn_id).read_body(search_criteria) logging.info('Successfully retrieved email...') logging.info('Parsing Email...') result = re.search(reg_ex, message).group(0) logging.info('Result: {}'.format(result)) return result with dag: kick_off_dag = DummyOperator(task_id='kick_off_dag') imap = PythonOperator( task_id='imap_search', python_callable=imap_py, templates_dict={"imap_conn_id": IMAP_CONN_ID, "imap_subject": SUBJECT, "reg_ex": SEARCH_STRING }, provide_context=True ) kick_off_dag >> imap ================================================ FILE: poc/mailgun_validation_example.py ================================================ """ An example DAG using the Mailgun plugin to validate an existing list of emails. This files contains one dag: - An ongoing daily workflow. Each DAG makes use of two custom operators: - EmailListChangedSensor https://github.com/airflow-plugins/mailgun_plugin/blob/master/operators/sensor.py#L4 - EmailValidationOperator https://github.com/airflow-plugins/mailgun_plugin/blob/master/operators/validate.py#L14 """ from datetime import datetime, timedelta from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from airflow.operators import EmailListChangedSensor from airflow.operators import EmailValidationOperator default_args = { 'owner': 'airflow', 'depends_on_past': False, 'start_date': datetime(2018, 4, 6), 'email': 'taylor@astronomer.io', 'email_on_failure': False, 'email_on_retry': False, 'retries': 0, 'retry_delay': timedelta(minutes=0), } dag = DAG( 'mailgun_dag', default_args=default_args, schedule_interval='@daily', ) start = DummyOperator( task_id='start', dag=dag, ) emails_changed = EmailListChangedSensor( task_id='email_list_delta_sensor', poke_interval=0, timeout=0, soft_fail=True, dag=dag, ) emails_changed.set_upstream(start) email_validation_operator = EmailValidationOperator( task_id='validate_emails', mailgun_conn_id='mailgun_api', aws_conn_id='aws_s3', s3_bucket_name='my_bucket', s3_key_source='my_contacts_list.json', dag=dag, ) email_validation_operator.set_upstream(emails_changed) ================================================ FILE: poc/selenium_example.py ================================================ """ Headless Site Navigation and File Download (Using Selenium) to S3 This example demonstrates using Selenium (via Firefox/GeckoDriver) to: 1) Log into a website w/ credentials stored in connection labeled 'selenium_conn_id' 2) Download a file (initiated on login) 3) Transform the CSV into JSON formatting 4) Append the current data to each record 5) Load the corresponding file into S3 To use this DAG, you will need to have the following installed: [XVFB](https://www.x.org/archive/X11R7.6/doc/man/man1/Xvfb.1.xhtml) [GeckoDriver](https://github.com/mozilla/geckodriver/releases/download) selenium==3.11.0 xvfbwrapper==0.2.9 """ from datetime import datetime, timedelta import os import boa import csv import json import time import logging from selenium import webdriver from selenium.webdriver.common.keys import Keys from selenium.webdriver.firefox.options import Options from xvfbwrapper import Xvfb from airflow import DAG from airflow.models import Connection from airflow.utils.db import provide_session from airflow.operators.dummy_operator import DummyOperator from airflow.operators.python_operator import PythonOperator from airflow.hooks import S3Hook S3_CONN_ID = '' S3_BUCKET = '' S3_KEY = '' date = '{{ ds }}' default_args = { 'start_date': datetime(2018, 2, 10, 0, 0), 'email': [], 'email_on_failure': True, 'email_on_retry': False, 'retries': 2, 'retry_delay': timedelta(minutes=5), 'catchup': False } dag = DAG( 'selenium_extraction_to_s3', schedule_interval='@daily', default_args=default_args, catchup=False ) def imap_py(**kwargs): selenium_conn_id = kwargs.get('templates_dict', None).get('selenium_conn_id', None) filename = kwargs.get('templates_dict', None).get('filename', None) s3_conn_id = kwargs.get('templates_dict', None).get('s3_conn_id', None) s3_bucket = kwargs.get('templates_dict', None).get('s3_bucket', None) s3_key = kwargs.get('templates_dict', None).get('s3_key', None) date = kwargs.get('templates_dict', None).get('date', None) @provide_session def get_conn(conn_id, session=None): conn = ( session.query(Connection) .filter(Connection.conn_id == conn_id) .first() ) return conn url = get_conn(selenium_conn_id).host email = get_conn(selenium_conn_id).user pwd = get_conn(selenium_conn_id).password vdisplay = Xvfb() vdisplay.start() caps = webdriver.DesiredCapabilities.FIREFOX caps["marionette"] = True profile = webdriver.FirefoxProfile() profile.set_preference("browser.download.manager.showWhenStarting", False) profile.set_preference('browser.helperApps.neverAsk.saveToDisk', "text/csv") logging.info('Profile set...') options = Options() options.set_headless(headless=True) logging.info('Options set...') logging.info('Initializing Driver...') driver = webdriver.Firefox(firefox_profile=profile, firefox_options=options, capabilities=caps) logging.info('Driver Intialized...') driver.get(url) logging.info('Authenticating...') elem = driver.find_element_by_id("email") elem.send_keys(email) elem = driver.find_element_by_id("password") elem.send_keys(pwd) elem.send_keys(Keys.RETURN) logging.info('Successfully authenticated.') sleep_time = 15 logging.info('Downloading File....Sleeping for {} Seconds.'.format(str(sleep_time))) time.sleep(sleep_time) driver.close() vdisplay.stop() dest_s3 = S3Hook(s3_conn_id=s3_conn_id) os.chdir('/root/Downloads') csvfile = open(filename, 'r') output_json = 'file.json' with open(output_json, 'w') as jsonfile: reader = csv.DictReader(csvfile) for row in reader: row = dict((boa.constrict(k), v) for k, v in row.items()) row['run_date'] = date json.dump(row, jsonfile) jsonfile.write('\n') dest_s3.load_file( filename=output_json, key=s3_key, bucket_name=s3_bucket, replace=True ) dest_s3.connection.close() with dag: kick_off_dag = DummyOperator(task_id='kick_off_dag') selenium = PythonOperator( task_id='selenium_retrieval_to_s3', python_callable=imap_py, templates_dict={"s3_conn_id": S3_CONN_ID, "s3_bucket": S3_BUCKET, "s3_key": S3_KEY, "date": date}, provide_context=True ) kick_off_dag >> selenium ================================================ FILE: poc/singer_example.py ================================================ """ Singer This example shows how to use Singer within Airflow using a custom operator: - SingerOperator https://github.com/airflow-plugins/singer_plugin/blob/master/operators/singer_operator.py#L5 A complete list of Taps and Targets can be found in the Singer.io Github org: https://github.com/singer-io """ from datetime import datetime from airflow import DAG from airflow.operators import SingerOperator default_args = {'start_date': datetime(2018, 2, 22), 'retries': 0, 'email': [], 'email_on_failure': True, 'email_on_retry': False} dag = DAG('__singer__fixerio_to_csv', schedule_interval='@hourly', default_args=default_args) with dag: singer = SingerOperator(task_id='singer', tap='fixerio', target='csv') singer ================================================ FILE: system/dynamic_connection_creation.py ================================================ """ Dynamic Connection Creation from a Variable This file contains one ongoing DAG that executes every 15 minutes. This DAG makes use of one custom operator: - CreateConnectionsFromVariable https://github.com/airflow-plugins/variable_connection_plugin/blob/master/operator/variable_connection_operator.py#L36 If using encrypted tokens in the Variable (recommended), it is necessary to create a separate "Fernet Key Connection" with the relevant Fernet Key kept in the password field. This Conn ID can then be specified in the operator below. """ from datetime import datetime from airflow import DAG from airflow.operators import CreateConnectionsFromVariable FERNET_KEY_CONN_ID = None CONFIG_VARIABLE_KEY = '' args = { 'owner': 'airflow', 'start_date': datetime(2018, 2, 22, 0, 0), 'provide_context': True, 'email': [], 'email_on_failure': True } dag = DAG( '__VARIABLE_CONNECTION_CREATION__', schedule_interval="*/15 * * * *", default_args=args, catchup=False ) create_airflow_connections = CreateConnectionsFromVariable( task_id='create_airflow_connections', fernet_key_conn_id=FERNET_KEY_CONN_ID, config_variable_key=CONFIG_VARIABLE_KEY, dag=dag) create_airflow_connections ================================================ FILE: system/rate_limit_reset.py ================================================ """ Rate Limit Reset This files contains one dag that executes every 12 hours by default. This DAG should be used in tandem with the RateLimitOperator: https://github.com/airflow-plugins/rate_limit_plugin/blob/master/operators/rate_limit_operator.py Every run, this DAG will look for variables with the specified key as it's name. By default, this key is set to '__SYSTEM__RATE_LIMIT_EXCEEDED__'. Once found, this DAG will clear the task that caused the variable to be set (see above Operator) as well as downstream tasks. It will then set the relevant DAG to "running" and clear the variable. """ from datetime import datetime, timedelta from sqlalchemy import and_ import json from airflow import DAG from airflow.models import Variable, TaskInstance, DagRun from airflow.utils.db import provide_session from airflow.operators.python_operator import PythonOperator default_args = {'start_date': datetime(2018, 2, 9), 'retries': 2, 'retry_delay': timedelta(minutes=2), 'email': [], 'email_on_failure': True} dag = DAG('__CHECK_FOR_RATE_LIMIT_VARIABLES', default_args=default_args, schedule_interval='30 */12 * * *', catchup=False ) @provide_session def check_py(session=None, **kwargs): key = '__SYSTEM__RATE_LIMIT_EXCEEDED__' obj = (session .query(Variable) .filter(Variable.key.ilike('{}%'.format(key))) .all()) if obj is None: raise KeyError('Variable {} does not exist'.format(key)) else: for _ in obj: _ = json.loads(_.val) # Clear the rate limit operator task in the specified Dag Run. (session .query(TaskInstance) .filter(and_(TaskInstance.task_id == _['task_id'], TaskInstance.dag_id == _['dag_id'], TaskInstance.execution_date == datetime.strptime(_['ts'], "%Y-%m-%dT%H:%M:%S"))) .delete()) # Clear downstream tasks in the specified Dag Run. for task in _['downstream_tasks']: (session .query(TaskInstance) .filter(and_(TaskInstance.task_id == task, TaskInstance.dag_id == _['dag_id'], TaskInstance.execution_date == datetime.strptime(_['ts'], "%Y-%m-%dT%H:%M:%S"))) .delete()) # Set the Dag Run state to "running" dag_run = (session .query(DagRun) .filter(and_(DagRun.dag_id == _['dag_id'], DagRun.execution_date == datetime.strptime(_['ts'], "%Y-%m-%dT%H:%M:%S"))) .first()) dag_run.set_state('running') # Clear the rate limit exceeded variable. variable_identifier = '_'.join([_['dag_id'], _['task_id'], _['ts']]) variable_name = ''.join([key, variable_identifier]) (session .query(Variable) .filter(Variable.key == variable_name) .delete()) with dag: run_check = PythonOperator(task_id='run_check', python_callable=check_py, provide_context=True) run_check