What problem does it solve?
Event-driven architecture principles prevent distributed systems from failing silently when events are delivered asynchronously, duplicated, or evolve over time.
Core Features & Use Cases
- Event Sourcing discipline: model state changes as an append-only immutable event log and rebuild state via replayable projections, using snapshots to control replay cost.
- CQRS separation: split command-side writes from query-side reads to enable denormalized, query-optimized projections while treating consistency lag as a contract.
- Safe saga workflows: choose choreography vs orchestration and implement idempotent compensation steps for long-running distributed transactions.
- Reliable consumer processing: design partitioning for ordering guarantees, implement idempotent handlers to survive at-least-once delivery, and use the outbox pattern to avoid dual-write failures.
- Operational resilience: configure dead letter queues (DLQs), emit structured alerts, and plan replay SLAs for poison messages.
- Event schema evolution: enforce backward compatibility rules and require versioned event types with dual-publish periods for breaking changes.
Quick Start
When designing an event-driven system, ask your AI to review your proposed topics, partition key strategy, consumer group design, idempotency approach, outbox/DLQ plan, and schema evolution strategy for correctness and operational safety.
