Serverless data processing with Data Pipeline
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How to combine different AWS services to process big amounts of data without worrying about resources.
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Serverless data processing with Data Pipeline
- 1. Data Processing with Data Pipeline and Docker Radek Miazio AWS Barcelona User Group 09-05-2017
- 2. Who we are?
- 3. Offline process to generate solr indexes and metadata indexing What we do?
- 5. What do we need? Orchestration Resources management
- 6. What do we need? Orchestration Resources management SERVERLESS
- 7. What do we need?
- 8. Options
- 9. ECS Easy container deployment Internal tooling available Have to manage ECS cluster External orchestration needed Options
- 10. SWF Sophisticated task hierarchy Options Deploy worker processes
- 11. SWF / Step Functions Sophisticated task hierarchy Options Deploy worker processes
- 12. Data pipeline Manages resources Task hierarchy Built-in retries Built-in support for S3 Options No Docker support
- 13. Batch Automatic resources provisioning Task dependencies Built-in retries Docker support Options Available in Virginia only Nothing built in for S3
- 14. Options
- 15. Options ECS SWF/Step F. Data Pipeline Batch Effective ✔ n/a ✔ ✔ Orchestration ✖ ✔ ✔ ✔ Managed ✔ ✔ ✔ ✔ Compatible ✔ n/a ✔ ✔ Available ✔ ✔ ✔ ✖
- 16. Learnings
- 17. Complex setup • Massive JSON to define a Pipeline Learnings Use pipeline architect Abstract through a service API
- 18. Long initialization • For pipelines with ~50tasks, it can take >3 minutes • API Gateway timeout is 30 seconds Learnings Async Lambda execution
- 19. Logs delay • No realtime logs • No integration with CloudWatch logs • Logs uploaded to S3 with up to 20 minutes delay Learnings Send logs to Cloudwatch
- 20. How much to bake into the AMI • Need to create AMI to be used in the Data Pipeline Learnings What to do dependencies code data Build ☐ ☐ ☐ Run ☐ ☐ ☐
- 21. How much to bake into the AMI • Need to create AMI to be used in the Data Pipeline Learnings dependencies code data Build þ ☐ ☐ Run ☐ þ þ
- 22. No Docker support Learnings Wrap docker with shell task activity Mount S3 directories to container • No support for running containers in the Data Pipeline
- 23. Metrics • No metrics from pipeline itself • Can’t distinguish pipeline instances Learnings Send metrics manually from nodes
- 24. Limits • There are limits in Data Pipeline • Not everything can be raised • AWS resources are not infinite Learnings Raise limits if possible Provision instances in different AZs
- 25. ü Scalable ü Effective ü Automated ü Coordinated ü Managed ü Compatible What did we make?
- 26. What did we make? ü SERVERLESS
- 27. Voila!
- 28. Voila! Number of instances running inside a pipeline
- 29. Conclusion
- 30. Thank you