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Change Data Streaming Patterns in Distributed Systems @ BerlinBuzzwords 2021

Change Data Streaming Patterns in Distributed Systems @ BerlinBuzzwords 2021


Microservices are one of the big trends in software engineering of the last few years; organising business functionality in several self-contained, loosely coupled services helps teams to work efficiently, make the most suitable technical decisions, and react quickly to new business requirements.

In this session we'll discuss and showcase how open-source change data capture (CDC) with Debezium can help developers with typical challenges they often face when working on microservices. Come and join us to learn how to:

* Employ the outbox pattern for reliable, eventually consistent data exchange between microservices, without incurring unsafe dual writes or tight coupling

* Gradually extract microservices from existing monolithic applications, using CDC and the strangler fig pattern

* Coordinate long-running business transactions across multiple services using CDC-based saga orchestration, ensuring such activity gets consistently applied or aborted by all participating services

Event Link: https://2021.berlinbuzzwords.de/session/change-data-streaming-patterns-distributed-systems

Recording Link:

Hans-Peter Grahsl

June 16, 2021

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  1. Change Data Streaming Patterns in Distributed Systems Gunnar Morling Software

    Engineer, Red Hat @gunnarmorling Hans-Peter Grahsl Technical Trainer, Netconomy @hpgrahsl
  2. #CDCPatterns @gunnarmorling @hpgrahsl … implemented using Change Data Capture Today’s

  3. #CDCPatterns @gunnarmorling @hpgrahsl • Open source software engineer at Red

    Hat ◦ Debezium ◦ Quarkus • Spec Lead for Bean Validation 2.0 • Java Champion • @gunnarmorling Gunnar Morling
  4. #CDCPatterns @gunnarmorling @hpgrahsl • Technical Trainer at NETCONOMY • Independent

    Engineer & Consultant • Confluent Community Catalyst • MongoDB Champion • @hpgrahsl Hans-Peter Grahsl
  5. #CDCPatterns @gunnarmorling @hpgrahsl • Taps into TX log to capture

    INSERT/UPDATE/DELETE events • Propagated to consumers via Apache Kafka and Kafka Connect Debezium — Log-based Change Data Capture
  6. #CDCPatterns @gunnarmorling @hpgrahsl Detour: Data Change Events • Old and

    new row state • Metadata on table, TX id, etc. • Operation type, timestamp
  7. #CDCPatterns @gunnarmorling @hpgrahsl • Old and new row state •

    Metadata on table, TX id, etc. • Operation type, timestamp Detour: Data Change Events
  8. #CDCPatterns @gunnarmorling @hpgrahsl • Old and new row state •

    Metadata on table, TX id, etc. • Operation type, timestamp Detour: Data Change Events
  9. Outbox Pattern

  10. #CDCPatterns @gunnarmorling @hpgrahsl • Services need to update their database,

    • send messages to other services, • and that consistently! The Problem: Microservices Data Exchange
  11. #CDCPatterns @gunnarmorling @hpgrahsl “Dual writes” are prone to inconsistencies! Outbox

  12. #CDCPatterns @gunnarmorling @hpgrahsl Outbox Pattern

  13. #CDCPatterns @gunnarmorling @hpgrahsl Outbox Pattern

  14. #CDCPatterns @gunnarmorling @hpgrahsl Outbox Pattern

  15. #CDCPatterns @gunnarmorling @hpgrahsl Outbox Pattern

  16. Strangler Fig Pattern

  17. #CDCPatterns @gunnarmorling @hpgrahsl • Gradually evolve from old into new

    • Support temporary coexistence • Avoid big bang cut-over The Problem: Migrating Systems
  18. #CDCPatterns @gunnarmorling @hpgrahsl Strangler Fig Pattern

  19. #CDCPatterns @gunnarmorling @hpgrahsl Strangler Fig Pattern

  20. #CDCPatterns @gunnarmorling @hpgrahsl Strangler Fig Pattern

  21. #CDCPatterns @gunnarmorling @hpgrahsl Strangler Fig Pattern

  22. #CDCPatterns @gunnarmorling @hpgrahsl Strangler Fig Pattern

  23. #CDCPatterns @gunnarmorling @hpgrahsl Strangler Fig Pattern

  24. #CDCPatterns @gunnarmorling @hpgrahsl Strangler Fig Pattern

  25. #CDCPatterns @gunnarmorling @hpgrahsl • Incremental migration → “baby steps” •

    Pause or stop migration without losing spent efforts • Migration steps ideally reversible Rationale: ⚠ minimize risk ⚠ Benefits
  26. #CDCPatterns @gunnarmorling @hpgrahsl CDC Close-Up

  27. #CDCPatterns @gunnarmorling @hpgrahsl Enhanced CDC Processing

  28. #CDCPatterns @gunnarmorling @hpgrahsl Enhanced CDC Processing

  29. #CDCPatterns @gunnarmorling @hpgrahsl Enhanced CDC Processing

  30. Saga Pattern

  31. #CDCPatterns @gunnarmorling @hpgrahsl • Multiple services need to act collaboratively

    to achieve a consistent outcome • Without 2-phase commit protocols • Ensure correctness in case of failures The Problem: Long-running Business Transactions
  32. #CDCPatterns @gunnarmorling @hpgrahsl Saga Pattern

  33. #CDCPatterns @gunnarmorling @hpgrahsl Saga Pattern

  34. #CDCPatterns @gunnarmorling @hpgrahsl Saga Pattern

  35. Demo

  36. Wrap-Up

  37. #CDCPatterns @gunnarmorling @hpgrahsl • CDC: a powerful tool in the

    box for event-driven architectures • Debezium: open-source CDC for a variety of databases • Call to Action: Would you like built-in Saga support? Takeaways
  38. #CDCPatterns @gunnarmorling @hpgrahsl • Outbox implementation https://debezium.io/blog/2019/02/19/reliable-microservices-data -exchange-with-the-outbox-pattern/ • Strangler

    fig pattern https://martinfowler.com/bliki/StranglerFigApplication.html • Saga implementation https://www.infoq.com/articles/saga-orchestration-outbox/ • Demo repo https://github.com/debezium/debezium-examples Resources
  39. #CDCPatterns @gunnarmorling @hpgrahsl Q & A [email protected] @gunnarmorling 📧 [email protected]

    @hpgrahsl 📧 Thank You!
  40. #CDCPatterns @gunnarmorling @hpgrahsl Unsplash https://unsplash.com/license © Pablo García Saldaña https://unsplash.com/photos/lPQIndZz8Mo

    © David Clode https://unsplash.com/photos/T49WTav4LgU © Aaron Burden https://unsplash.com/photos/GFpxQ2ZyNc0 © Nathan Dumlao https://unsplash.com/photos/wQDysNUCKfw © mari lezhava https://unsplash.com/photos/q65bNe9fW-w © Michał Parzuchowski https://unsplash.com/photos/Bt0PM7cNJFQ © Charles Forerunner https://unsplash.com/photos/3fPXt37X6UQ Flickr Attribution 2.0 Generic https://creativecommons.org/licenses/by/2.0/ © Thomas Kamann https://flic.kr/p/coa2c CC0 1.0 Universal Public Domain Dedication https://creativecommons.org/publicdomain/zero/1.0/ © Wall Boat https://flic.kr/p/Y6zkmX Attribution-ShareAlike 2.0 Generic https://creativecommons.org/licenses/by-sa/2.0/ © Andrew Hart https://flic.kr/p/dmjkSk Image Credits In Order of Appearance