This talk highlights why we chose flink as a microservice for stateful asynchronous event processing and challenges we faced in production, how we solved those and recommendations for productionization of the applications using Apache flink.

Key takeaways:
- Architecture pattern of using Flink/similar platform as a microservice for statuful async event processing
- Flink fault tolerance concepts in-depth understanding
- Production issues/challenges faced and insights on how to solve (& also prevent) them

Basic understanding of stream processing will be an advantage.

Outline

• Brief summary of what is flink and important terminologies
• Flink as a microservice for asynchronous stateful event stream processing
  • Challenges in doing it in a conventional way
• Prerequisite concepts
  • Fault tolerance and checkpointing
  • Scalable partitioned state
  • State Backend - Rocksdb
  • Asynchronous checkpointing details
• Production Experiences
  • Flink taskmanager failover time tuning
  • Failure detection mechanism
  • Tuning Akka Deathwatch
  • How state leaks happen and how to prevent and monitor them
  • How to clear old state (result of state leak) of running system, without taking downtime
  • How state size and checkpointing can cause processing delays and how to tune it
• Recommendations & Summary

Speaker bio

Software architect at Helpshift. Have worked on streaming processing,various backend architectures and end-end data pipelines before. Have a good understanding of systems side of software as well. More details can be found on https://www.linkedin.com/in/jagadish-bihani-1335a04a/

Slides

http://slides.com/jagadishbihani/apache-flink-production-report/fullscreen