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Building Better Data Pipelines using Apache Airflow
Apache Airflow is a platform for authoring, scheduling, and monitoring workflows or directed acyclic graphs (DAGs). It allows users to programmatically author DAGs in Python without needing to bundle many XML files. The UI provides a tree view to see DAG runs over time and Gantt charts to see performance trends. Airflow is useful for ETL pipelines, machine learning workflows, and general job scheduling. It handles task dependencies and failures, monitors performance, and enforces service level agreements. Behind the scenes, the scheduler distributes tasks from the metadata database to Celery workers via RabbitMQ.
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- 1. Building (Better) DataPipelines using Apache Airflow Sid Anand (@r39132) QCon.AI 2018 1
- 2. About Me 2 Work [ed| s] @ Maintainer of Spare time Co-Chair for
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- 4. 4 Apache Airflow :What is it? In a : Airflow is a platform to programmatically author, schedule and monitor workflows (a.k.a. DAGs or Directed Acyclic Graphs)
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- 6. 6 Apache Airflow :UI Walk-through
- 7. Airflow - AuthoringDAGs 7 Airflow: Visualizing a DAG
- 8. 8 Airflow: Author DAGsin Python! No need to bundle many XML files! Airflow - Authoring DAGs
- 9. 9 Airflow: The TreeView offers a view of DAG Runs over time! Airflow - Authoring DAGs
- 10. Airflow - PerformanceInsights 10 Airflow: Gantt charts reveal the slowest tasks for a run!
- 11. 11 Airflow: …And wecan easily see performance trends over time Airflow - Performance Insights
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- 13. 13 Apache Airflow :Why use it? When would you use a Workflow Scheduler like Airflow? • ETL Pipelines • Machine Learning Pipelines • Predictive Data Pipelines • Fraud Detection, Scoring/Ranking, Classification, Recommender System, etc… • General Job Scheduling (e.g. Cron) • DB Back-ups, Scheduled code/config deployment
- 14. 14 What should aWorkflow Scheduler do well? • Schedule a graph of dependencies • where Workflow = A DAG of Tasks • Handle task failures • Report / Alert on failures • Monitor performance of tasks over time • Enforce SLAs • E.g. Alerting if time or correctness SLAs are not met • Easily scale for growing load Apache Airflow : Why use it?
- 15. 15 What Does ApacheAirflow Add? • Configuration-as-code • Usability - Stunning UI / UX • Centralized configuration • Resource Pooling • Extensibility Apache Airflow : Why use it?
- 16. Use-Case : Message Scoring BatchPipeline Architecture 16
- 17. Use-Case : MessageScoring 17 enterprise A enterprise B enterprise C S3 S3 uploads every 15 minutes
- 18. Use-Case : MessageScoring 18 enterprise A enterprise B enterprise C S3 Airflow kicks of a Spark message scoring job every hour
- 19. Use-Case : MessageScoring 19 enterprise A enterprise B enterprise C S3 Spark job writes scored messages and stats to another S3 bucket S3
- 20. Use-Case : MessageScoring 20 enterprise A enterprise B enterprise C S3 This triggers SNS/SQS messages events S3 SNS SQS
- 21. Use-Case : MessageScoring 21 enterprise A enterprise B enterprise C S3 An Autoscale Group (ASG) of Importers spins up when it detects SQS messages S3 SNS SQS Importers ASG
- 22. 22 enterprise A enterprise B enterpriseC S3 The importers rapidly ingest scored messages and aggregate statistics into the DB S3 SNS SQS Importers ASG DB Use-Case : Message Scoring
- 23. 23 enterprise A enterprise B enterpriseC S3 Users receive alerts of untrusted emails & can review them in the web app S3 SNS SQS Importers ASG DB Use-Case : Message Scoring
- 24. 24 enterprise A enterprise B enterpriseC S3 S3 SNS SQS Importers ASG DB Airflow manages the entire process Use-Case : Message Scoring
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- 27. 27 Apache Airflow :Incubating Timeline • Airflow was created @ Airbnb in 2015 by Maxime Beauchemin • Max launched it @ Hadoop Summit in Summer 2015 • On 3/31/2016, Airflow —> Apache Incubator Today • 2400+ Forks • 7600+ GitHub Stars • 430+ Contributors • 150+ companies officially using it! • 14 Committers/Maintainers <— We’re growing here
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- 30. 30 Airflow is aplatform to programmatically author, schedule and monitor workflows (a.k.a. DAGs) It ships with a • DAG Scheduler • Web application (UI) • Powerful CLI • Celery Workers! Apache Airflow : Behind the Scenes
- 31. 31 Apache Airflow :Behind the Scenes Webserver Scheduler WorkerWorkerWorker Meta DB 1. A user schedules / manages DAGs using the Airflow UI! 2. Airflow’s webserver stores scheduling metadata in the metadata DB 3. The scheduler picks up new schedules and distributes work over Celery / RabbitMQ 4. Airflow workers pick up Airflow tasks over Celery Celery / RabbitMQ
- 32. 32 Webserver Scheduler WorkerWorkerWorker Meta DB 1. Auser schedules / manages DAGs using the Airflow UI! 2. Airflow’s webserver stores scheduling metadata in the metadata DB 3. The scheduler picks up new schedules and distributes work over Celery / RabbitMQ 4. Airflow workers pick up Airflow tasks over Celery Celery / RabbitMQ Apache Airflow : Behind the Scenes
- 33. 1. A userschedules / manages DAGs using the Airflow UI! 2. Airflow’s webserver stores scheduling metadata in the metadata DB 3. The scheduler picks up new schedules and distributes work over Celery / RabbitMQ 4. Airflow workers pick up Airflow tasks over Celery 33 Webserver Scheduler WorkerWorkerWorker Meta DB Celery / RabbitMQ Apache Airflow : Behind the Scenes
- 34. 34 Webserver Scheduler WorkerWorkerWorker Meta DB 1. Auser schedules / manages DAGs using the Airflow UI! 2. Airflow’s webserver stores scheduling metadata in the metadata DB 3. The scheduler picks up new schedules and distributes work over Celery / RabbitMQ 4. Airflow workers pick up Airflow tasks from RabbitMQ Celery / RabbitMQ Apache Airflow : Behind the Scenes
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