Event-driven architecture: patterns and best practices
Event-driven architecture (EDA) has become essential for building scalable, responsive systems. According to Gartner, by 2025, 60% of new digital business solutions will incorporate event-driven capabilities. EDA enables loose coupling, real-time processing, and better scalability than traditional request-response patterns.
Why event-driven architecture
According to Confluent's State of Data Streaming, organizations using event streaming report 47% faster time to market and 35% reduction in data infrastructure costs.
Core EDA concepts
Event Producer
Service that detects something happened and publishes an event
Event Channel
Message broker or streaming platform that routes events
Event Consumer
Service that subscribes to and processes events
Event Store
Persistent log of events for replay and audit
Event Schema
Contract defining event structure and semantics
Event Router
Logic determining which consumers receive events
Events vs Commands: Events describe something that happened ("OrderPlaced"). Commands request something to happen ("PlaceOrder"). Events are past tense, immutable facts. This distinction is fundamental to good EDA design.
Event-driven patterns
Event Notification
Simple notification that something happened. Minimal payload, consumer fetches details.
Event-Carried State Transfer
Event contains all data consumer needs. Enables autonomy but larger payloads.
Event Sourcing
Store state as sequence of events. Rebuild state by replaying events.
CQRS
Separate read and write models. Events connect command side to query side.
Saga/Choreography
Distributed transactions via events. Services react to events autonomously.
Request-response vs event-driven
Request-Response vs Event-Driven
| Feature | Request-Response | Event-Driven | Hybrid |
|---|---|---|---|
| Loose Coupling | ✗ | ✓ | ✓ |
| Scalability | ✗ | ✓ | ✓ |
| Real-Time Processing | ✗ | ✓ | ✓ |
| Simplicity | ✓ | ✗ | ✗ |
| Debugging Ease | ✓ | ✗ | ✗ |
| Immediate Response | ✓ | ✗ | ✓ |
Event streaming platforms
Event Platform Adoption (%)
Event Platform Comparison
| Feature | Kafka | RabbitMQ | Pulsar |
|---|---|---|---|
| High Throughput | ✓ | ✗ | ✓ |
| Message Ordering | ✓ | ✗ | ✓ |
| Replay Capability | ✓ | ✗ | ✓ |
| Managed Service | ✓ | ✓ | ✓ |
| Easy to Operate | ✗ | ✓ | ✗ |
| Exactly-Once | ✓ | ✗ | ✓ |
Event schema design
Define Events
Identify domain events from business processes
Schema Format
Choose Avro, Protobuf, or JSON Schema
Schema Registry
Central registry for schema versions
Versioning Strategy
Plan for backward/forward compatibility
Documentation
Document events in event catalog
Governance
Review process for schema changes
Schema Evolution: Plan for schema changes from day one. Breaking changes in events can cascade through your entire system. Use schema registries and enforce compatibility checks.
Handling failures
Idempotency
Design consumers to handle duplicate events safely. Use idempotency keys.
Dead Letter Queues
Route failed events to DLQ for investigation. Don't lose events.
Retry with Backoff
Automatic retry with exponential backoff for transient failures.
Circuit Breakers
Stop processing when downstream services are failing.
Compensating Events
Publish compensating events to undo effects of failed transactions.
Observability for EDA
Key Observability Concerns in EDA
Event sourcing deep dive
Capture Event
Store every state change as immutable event
Event Store
Append-only log of all events
Rebuild State
Replay events to reconstruct current state
Snapshots
Periodic snapshots to speed up replay
Projections
Build read models from event stream
Time Travel
Query state at any point in time
When to use EDA
Architecture Fit by Use Case
Implementation checklist
EDA Implementation Priorities
FAQ
Q: When should we use event-driven vs request-response? A: Use EDA for: loose coupling between services, handling high throughput, real-time processing, audit trails. Use request-response for: simple CRUD, when immediate response is required, synchronous workflows.
Q: How do we handle event ordering? A: Use partitioning keys to ensure related events go to the same partition. Within a partition, ordering is guaranteed. For global ordering, use a single partition (but this limits scalability).
Q: What about exactly-once processing? A: True exactly-once is hard. Most systems achieve "effectively once" through idempotent consumers. Design consumers to handle duplicates safely.
Q: How do we debug distributed event flows? A: Use correlation IDs in all events, implement distributed tracing, maintain event catalogs, and invest in observability tooling.
Sources and further reading
- Confluent State of Data Streaming
- Martin Fowler: Event-Driven Architecture
- Building Event-Driven Microservices by Adam Bellemare
- Designing Data-Intensive Applications
- Enterprise Integration Patterns
Build Event-Driven Systems: Implementing event-driven architecture requires expertise in distributed systems, messaging platforms, and domain modeling. Our team helps organizations design and build scalable event-driven systems. Contact us to discuss your architecture needs.
Ready to implement event-driven architecture? Connect with our architects to develop a tailored event-driven strategy.
