Introduction to Apache Airflow - Data Day Seattle 2016
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Apache Airflow is a platform for authoring, scheduling, and monitoring workflows or directed acyclic graphs (DAGs) of tasks. It includes a DAG scheduler, web UI, and CLI. Airflow allows users to author DAGs in Python without needing to bundle many XML files. The UI provides tree and Gantt chart views to monitor DAG runs over time. Airflow was accepted into the Apache Incubator in 2016 and has over 300 users from 40+ companies. Agari uses Airflow to orchestrate message scoring pipelines across AWS services like S3, Spark, SQS, and databases to enforce SLAs on correctness and timeliness. Areas for further improvement include security, APIs, execution scaling, and on
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Introduction to Apache Airflow - Data Day Seattle 2016
- 1. Introducing Apache Airflow (Incubating) Sid Anand (@r39132) Data Day Seattle 2016 1
- 2. About Me 2 Work [ed | s] @ Maintainer on Reports to Co-Chair for
- 3. Apache Airflow 3 What is it?
- 4. 4 Apache Airflow : What is it? Airflow is a platform to programmatically author, schedule and monitor workflows (a.k.a. DAGs)
- 5. 5 Apache Airflow : What is it? Airflow is a platform to programmatically author, schedule and monitor workflows (a.k.a. DAGs) It ships with • DAG Scheduler • Web application (UI) • Powerful CLI
- 6. 6 Apache Airflow : What is it?
- 7. Airflow - Authoring DAGs 7 Airflow: Visualizing a DAG
- 8. 8 Airflow: Author DAGs in Python! No need to bundle many XML files! Airflow - Authoring DAGs
- 9. 9 Airflow: The Tree View offers a view of DAG Runs over time! Airflow - Authoring DAGs
- 10. Airflow - Performance Insights 10 Airflow: Gantt charts reveal the slowest tasks for a run!
- 11. 11 Airflow: …And we can easily see performance trends over time Airflow - Performance Insights
- 12. 12 Apache Airflow : What is 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
- 13. 13 Apache Airflow : What is it? What should a Workflow 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 • Scale
- 14. 14 Apache Airflow : What is it? What Does Apache Airflow Add? • Configuration-as-code • Usability - Stunning UI / UX • Centralized configuration • Resource Pooling • Extensibility
- 16. 16 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 • 166+ Contributors • 300+ Users • 40+ companies officially using it! • 9 Committers/Maintainers <— We’re growing here
- 18. Agari : What We Do 18
- 19. 19 Agari : What We Do
- 20. 20 Agari : What We Do
- 21. 21 Agari : What We Do
- 22. 22 Agari : What We Do
- 23. 23 Enterprise Customers email metadata apply trust models email md + trust score Agari’s Current Product Agari : What We Do
- 24. 24 email metadata apply trust models email md + trust score Agari’s Future ProductEnterprise Customers Agari : What We Do
- 25. Apache Airflow @ Agari How Do We Use It? 25
- 26. Classes of Orchestration 26 apply trust models (message scoring) build trust models cron++ (general job scheduler) New Product (Enterprise Protect) Operational Automation BI / ETL N / A
- 27. Classes of Orchestration 27 apply trust models (message scoring) build trust models cron++ (general job scheduler) New Product (Enterprise Protect) Operational Automation BI / ETL N / A This Talk
- 28. Use-Case : Message Scoring Batch Pipeline Architecture 28
- 29. Use-Case : Message Scoring 29 enterprise A enterprise B enterprise C S3 S3 uploads every 15 minutes
- 30. Use-Case : Message Scoring 30 enterprise A enterprise B enterprise C S3 Airflow kicks of a Spark message scoring job every hour
- 31. Use-Case : Message Scoring 31 enterprise A enterprise B enterprise C S3 Spark job writes scored messages and stats to another S3 bucket S3
- 32. Use-Case : Message Scoring 32 enterprise A enterprise B enterprise C S3 This triggers SNS/SQS messages events S3 SNS SQS
- 33. Use-Case : Message Scoring 33 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
- 34. 34 enterprise A enterprise B enterprise C S3 The importers rapidly ingest scored messages and aggregate statistics into the DB S3 SNS SQS Importers ASG DB Use-Case : Message Scoring
- 35. 35 enterprise A enterprise B enterprise C S3 Users receive alerts of untrusted emails & can review them in the web app S3 SNS SQS Importers ASG DB Use-Case : Message Scoring
- 36. 36 enterprise A enterprise B enterprise C S3 S3 SNS SQS Importers ASG DB Airflow manages the entire process Use-Case : Message Scoring
- 37. Use-Case : Message Scoring Airflow DAG 37
- 38. 38 Airflow DAG
- 39. 39 5 minute wait for S3 eventual consistency Airflow DAG
- 40. 40 1 hour a day, we also build new models Airflow DAG
- 42. 42 dummy needed for branch operator Airflow DAG
- 45. 45 • Run Spark • Verify a record is written to the DB • Wait for the SQS queue to empty Airflow DAG
- 46. 46 • Compute discrepancies • Send email report • Update monitoring graphs • Raise SLA (correctness) alerts Airflow DAG
- 48. 48 Desirable Qualities of a Resilient Data Pipeline OperabilityCorrectness Timeliness Scalable/Available • Data Integrity (no loss, etc…) • Expected data distributions • All output within time-bound SLAs (e.g. 1 hour) • Fine-grained Monitoring & Alerting of Correctness & Timeliness SLAs • Quick Recoverability • ASGs, SQS, SNS, S3
- 49. 49 Desirable Qualities of a Resilient Data Pipeline OperabilityCorrectness Timeliness • Data Integrity (no loss, etc…) • Expected data distributions • All output within time-bound SLAs (e.g. 1 hour) • Fine-grained Monitoring & Alerting of Correctness & Timeliness SLAs • Quick Recoverability SLA SLA • ASGs, SQS, SNS, S3 Scalable/Available
- 50. 50 Correctness : Email Reporting orgs
- 51. 51 Correctness : Email Reporting For each org, we check for duplicate or missing data as a count & percentage orgs
- 52. 52 Correctness : Email Reporting These are the 3 stages of the pipeline. We can detect where a discrepancy is coming from - often related to a code push! orgs
- 54. 54 Airflow: …And easy to integrate with Ops tools! Correctness & Timeliness : Alerting
- 55. 55 Airflow: …And easy to integrate with Ops tools! Correctness & Timeliness : Alerting Timeliness SLA miss
- 56. 56 Airflow: …And easy to integrate with Ops tools! Correctness & Timeliness : Alerting Timeliness SLA miss dag = DAG(DAG_NAME, schedule_interval='@hourly', default_args=default_args, sla_miss_callback=sla_alert_func)
- 57. 57 Airflow: …And easy to integrate with Ops tools! Correctness & Timeliness : Alerting Timeliness SLA miss Correctness SLA miss
- 58. 58 Airflow: …And easy to integrate with Ops tools! Correctness & Timeliness : Alerting Timeliness & Correctness SLA misses sent to PagerDuty/VictorOps
- 59. Use-Case : Model Building v2 For Both Batch & Near Realtime Scoring Pipelines 59
- 60. 60 Airflow DAG
- 61. 61 Model Building DAG Launch an EMR cluster
- 62. 62 Run model building as EMR steps Model Building DAG
- 63. 63 Validate models Send email notification if tests fail Model Building DAG
- 65. Apache Airflow Next Steps 65 Areas for Improvement
- 66. 66 Apache Airflow Next Steps Improvement Areas • Security • API (though we do have a CLI) • Deployment / Versioning • Execution Scale Out • On-demand Execution
- 67. Acknowledgments 67 • Vidur Apparao • Stephen Cattaneo • Jon Chase • Andrew Flury • William Forrester • Chris Haag • Mike Jones • Scot Kennedy • Thede Loder • Paul Lorence • Kevin Mandich • Gabriel Ortiz • Jacob Rideout • Josh Yang • Julian Mehnle None of this work would be possible without the contributions of the strong team below