Event Sourcing for the Cloud Developer
summary
Argues that event sourcing is a storage choice separate from messaging and shows concrete patterns: writers are single stream per writer state machines that emit business events, processors persist their master-log positions for catch‑up and idempotency, and public events flow through Kafka/Kinesis but are not a primary database. Demonstrates optimistic concurrency via sequence numbers, anti‑corruption layers to publish a stable public schema, and rebuilds from snapshots and checkpoints; warns against treating the ingress event bus as the committed store and recommends rolling/retiring event streams for long‑tail and GDPR needs.
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chapters (26)
- 0:00Introduction
- 0:44Simple scalable cloud applications
- 1:20Whats the enemy
- 2:19Problem of combinatorial complexity
- 3:35In the cloud too
- 3:56How to avoid that
- 4:27DDD
- 4:55Oneway integration
- 6:56Patterns
- 7:25What is Event Sourcing
- 9:00What is Ordered Storage
- 10:50The Turing Machine
- 11:15The State Machine
- 15:37Microservice Definition
- 16:08Versioning
- 16:34Application Decisions
- 19:50Message Delivery
- 23:25Database Guarantee
- 24:44Venn Processors
- 28:44Track Your Position
- 30:20Vector Clocks
- 30:50Rebuild
- 31:05Application Boundaries
- 36:40Common Mistake
- 41:06Reproduce
- 42:53Public Schema
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