Change Data Streaming Patterns in Distributed Systems @ TheDevConf (TDC) Connections 2021

Abstract

Microservices are one of the biggest trends in software engineering: organising functionality in self-contained, loosely coupled services lets teams make the most suitable technical decisions and react quickly to new requirements.

In this session we'll discuss how change data capture (CDC) with Debezium can help developers with challenges they face when working on microservices. Join us to learn how to:

* Employ the outbox pattern for reliable data exchange between microservices
* Gradually extract microservices from existing monolithic applications with the strangler fig pattern
* Coordinate long-running business transactions across multiple services using CDC-based saga orchestration

NOTE: There was no recording of this session AFAIK

Transcript

1. Gunnar Morling
   Software Engineer, Red Hat
   @gunnarmorling
   Hans-Peter Grahsl
   Technical Trainer, Netconomy
   @hpgrahsl
   Change Data Streaming Patterns
   in Distributed Systems
   

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2. #CDCPatterns @gunnarmorling @hpgrahsl
   … implemented using Change Data Capture
   Today’s Objectives
   

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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
   

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4. #CDCPatterns @gunnarmorling @hpgrahsl
   ● Technical Trainer at NETCONOMY
   ● Independent Engineer & Consultant
   ● Confluent Community Catalyst
   ● MongoDB Champion
   ● @hpgrahsl
   Hans-Peter Grahsl
   

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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
   

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6. #CDCPatterns @gunnarmorling @hpgrahsl
   ● Message Key: table PK
   ● Message Value:
   ■ Old and new row state
   ■ Metadata on table, TX id, etc.
   ■ Operation type, timestamp
   ● Serialized as JSON, Avro, etc.
   Detour: Data Change Events
   

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7. #CDCPatterns @gunnarmorling @hpgrahsl
   ● Message Key: table PK
   ● Message Value:
   ■ Old and new row state
   ■ Metadata on table, TX id, etc.
   ■ Operation type, timestamp
   ● Serialized as JSON, Avro, etc.
   Detour: Data Change Events
   

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8. #CDCPatterns @gunnarmorling @hpgrahsl
   ● Message Key: table PK
   ● Message Value:
   ■ Old and new row state
   ■ Metadata on table, TX id, etc.
   ■ Operation type, timestamp
   ● Serialized as JSON, Avro, etc.
   Detour: Data Change Events
   

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9. Outbox Pattern
   

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10. #CDCPatterns @gunnarmorling @hpgrahsl
    ● Services need to update their database,
    ● send messages to other services,
    ● and that consistently!
    The Problem: Microservices Data Exchange
    

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11. #CDCPatterns @gunnarmorling @hpgrahsl
    “Dual writes” are prone to inconsistencies!
    Outbox Pattern
    

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12. #CDCPatterns @gunnarmorling @hpgrahsl
    Outbox Pattern
    

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13. #CDCPatterns @gunnarmorling @hpgrahsl
    Outbox Pattern
    

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14. #CDCPatterns @gunnarmorling @hpgrahsl
    Outbox Pattern
    

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15. #CDCPatterns @gunnarmorling @hpgrahsl
    Outbox Pattern
    

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16. Strangler Fig Pattern
    

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17. #CDCPatterns @gunnarmorling @hpgrahsl
    ● Gradually evolve from old into new
    ● Support temporary coexistence
    ● Avoid big bang cut-over
    The Problem: Migrating Systems
    

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18. #CDCPatterns @gunnarmorling @hpgrahsl
    Strangler Fig Pattern
    

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19. #CDCPatterns @gunnarmorling @hpgrahsl
    Strangler Fig Pattern
    

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20. #CDCPatterns @gunnarmorling @hpgrahsl
    Strangler Fig Pattern
    

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21. #CDCPatterns @gunnarmorling @hpgrahsl
    Strangler Fig Pattern
    

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22. #CDCPatterns @gunnarmorling @hpgrahsl
    Strangler Fig Pattern
    

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23. #CDCPatterns @gunnarmorling @hpgrahsl
    Strangler Fig Pattern
    

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24. #CDCPatterns @gunnarmorling @hpgrahsl
    Strangler Fig Pattern
    

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25. #CDCPatterns @gunnarmorling @hpgrahsl
    ● Incremental migration → “baby steps”
    ● Pause or stop migration
    without losing spent efforts
    ● Migration steps ideally reversible
    Rationale: ⚠ minimize risk ⚠
    Benefits
    

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26. #CDCPatterns @gunnarmorling @hpgrahsl
    CDC Close-Up
    

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27. #CDCPatterns @gunnarmorling @hpgrahsl
    Enhanced CDC Processing
    

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28. #CDCPatterns @gunnarmorling @hpgrahsl
    Enhanced CDC Processing
    

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29. #CDCPatterns @gunnarmorling @hpgrahsl
    Enhanced CDC Processing
    

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30. Saga Pattern
    

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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
    

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32. #CDCPatterns @gunnarmorling @hpgrahsl
    Saga Pattern
    

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33. #CDCPatterns @gunnarmorling @hpgrahsl
    Saga Pattern
    

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34. #CDCPatterns @gunnarmorling @hpgrahsl
    Saga Pattern
    

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35. Demo
    

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36. Wrap-Up
    

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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
    

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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
    

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39. #CDCPatterns @gunnarmorling @hpgrahsl
    Q & A
    gunnar@hibernate.org
    @gunnarmorling
    📧 grahslhp@gmail.com
    @hpgrahsl
    📧
    Thank You!
    

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40. #CDCPatterns @gunnarmorling @hpgrahsl
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