Event-Driven Architecture Patterns
Expert guidance for designing, implementing, and operating event-driven systems with proven patterns for event sourcing, CQRS, message brokers, saga coordination, and eventual consistency management.
When to Use This Skill
- Designing systems with asynchronous, decoupled communication
- Implementing event sourcing and CQRS patterns
- Building systems requiring eventual consistency and high scalability
- Managing distributed transactions across microservices
- Processing real-time event streams and data pipelines
- Implementing publish-subscribe or message queue architectures
- Designing reactive systems with complex event flows
Core Principles
1. Events as First-Class Citizens
Events represent immutable facts that have occurred in the system. Use past tense naming (OrderCreated, PaymentProcessed) and include all necessary context.
2. Eventual Consistency
Systems achieve consistency over time rather than immediately. Trade strong consistency for higher availability and scalability.
3. Loose Coupling
Services communicate through events without direct dependencies, enabling independent evolution and deployment.
4. Asynchronous Communication
Operations don't block waiting for responses, improving system responsiveness and resilience.
5. Event-Driven Thinking
Design around what happened (events) rather than what to do (commands).
Quick Reference
| Topic | Load reference |
|---|---|
| Event structure, types, and characteristics | skills/event-driven-architecture/references/event-fundamentals.md |
| Event sourcing pattern and implementation | skills/event-driven-architecture/references/event-sourcing.md |
| CQRS pattern with read/write separation | skills/event-driven-architecture/references/cqrs.md |
| Message brokers (RabbitMQ, Kafka, SQS/SNS) | skills/event-driven-architecture/references/message-brokers.md |
| Saga pattern for distributed transactions | skills/event-driven-architecture/references/saga-pattern.md |
| Choreography vs orchestration patterns | skills/event-driven-architecture/references/choreography-orchestration.md |
| Eventual consistency and conflict resolution | skills/event-driven-architecture/references/eventual-consistency.md |
| Best practices, anti-patterns, testing | skills/event-driven-architecture/references/best-practices.md |
Workflow
1. Design Phase
- Identify Events: What business facts need to be captured?
- Define Boundaries: Which events are domain vs integration events?
- Choose Patterns: Event sourcing? CQRS? Sagas? Choreography or orchestration?
- Select Technology: Kafka for high throughput? RabbitMQ for routing? AWS managed services?
2. Implementation Phase
- Event Schema: Define versioned event structures with correlation IDs
- Event Store: Implement append-only storage with optimistic concurrency
- Projections: Create read models from events for query optimization
- Handlers: Ensure idempotent, at-least-once delivery handling
- Sagas: Implement compensating transactions for failures
3. Operation Phase
- Monitoring: Track event lag, processing time, failure rates
- Replay: Build capability to replay events for debugging/recovery
- Versioning: Support multiple event schema versions simultaneously
- Scaling: Partition by aggregate ID, scale consumers horizontally
- Testing: Test handlers in isolation with contract testing
Common Mistakes
Event Design Errors
- ❌ Using commands instead of events (CreateOrder vs OrderCreated)
- ❌ Mutable events or missing versioning
- ❌ Events without correlation/causation IDs
- ✓ Immutable, past-tense, self-contained events
Consistency Issues
- ❌ Assuming immediate consistency across services
- ❌ Not handling duplicate event delivery
- ❌ Missing idempotency in handlers
- ✓ Design for eventual consistency, idempotent handlers
Architecture Mistakes
- ❌ Synchronous event chains (waiting for responses)
- ❌ Events coupled to specific service implementations
- ❌ No compensation strategy for sagas
- ✓ Async fire-and-forget, domain-focused events, compensating transactions
Operational Gaps
- ❌ No event replay capability
- ❌ Missing monitoring for event lag
- ❌ No schema registry or version management
- ✓ Replay-ready, monitored, schema-managed events
Pattern Selection Guide
Use Event Sourcing When:
- Need complete audit trail of all changes
- Temporal queries required ("state at time T")
- Multiple projections from same events
- Event replay for debugging/recovery
Use CQRS When:
- High read:write ratio (10:1+)
- Complex query requirements
- Need to scale reads independently
- Different databases for read/write optimal
Use Sagas When:
- Distributed transactions across services
- Need atomicity without 2PC
- Complex multi-step workflows
- Compensation logic required
Choose Choreography When:
- Simple workflows (2-4 steps)
- High service autonomy desired
- Event-driven culture established
- No complex dependencies
Choose Orchestration When:
- Complex workflows (5+ steps)
- Sequential dependencies
- Need centralized visibility
- Business logic in workflow
Resources
- Books: "Designing Event-Driven Systems" (Stopford), "Versioning in an Event Sourced System" (Young)
- Sites: eventuate.io, event-driven.io, Martin Fowler's event sourcing articles
- Tools: Kafka, EventStoreDB, RabbitMQ, Axon Framework, MassTransit
- Patterns: Event Sourcing, CQRS, Saga, Outbox, CDC, Event Streaming