Event-Driven Architecture: Building Systems That React Instead of Poll

Polling is the enemy of scalable systems. Event-driven architecture replaces “check every minute” with “tell me when it happens.” Here’s how we design event-driven systems that are reliable, observable, and a joy to maintain.

Why Event-Driven?

Consider a typical SaaS application: a user signs up, which triggers a welcome email, analytics event, CRM sync, and onboarding flow. In a synchronous system, each step blocks the next. If the CRM is slow, sign-up takes 10 seconds. In an event-driven system, the sign-up publishes a “user.created” event and returns immediately. Downstream services react independently.

The benefits: loose coupling between services, independent scaling, natural audit trail, and resilience to downstream failures.

Event Bus Selection

Redis Pub/Sub + Streams: Best for single-server or small deployments. Redis Streams provide persistence and consumer groups. Simple to set up, good performance, but limited replay capabilities.

RabbitMQ: Best for complex routing patterns. Supports exchanges, queues, dead letter queues, and sophisticated message routing. Slightly more operational overhead than Redis but much more flexible.

Amazon SQS + EventBridge: Best for AWS-native architectures. Fully managed, infinite scale, built-in retry and DLQ. EventBridge for event routing, SQS for processing queues.

Event Design Principles

Events as facts: An event describes something that happened, not a command to do something. “OrderPlaced” not “ProcessOrder”. This distinction makes events reusable by any number of consumers.

Schema versioning: Events must be backward-compatible. Add new fields as optional. Never remove or rename existing fields. Use schema registries for enforcement.

Idempotent consumers: Consumers must handle duplicate events gracefully. Use unique event IDs and track processed events. This makes retry safe and eliminates the need for exactly-once delivery.

Event Sourcing: When to Use It

Event sourcing stores every state change as an immutable event. The current state is derived by replaying events. It’s powerful for audit trails, temporal queries, and debugging. But it adds complexity: event replay, snapshotting, and eventual consistency.

We use event sourcing for: financial transactions (audit requirements), collaborative editing (conflict resolution), and systems requiring full change history. For everything else, traditional CRUD with event notification is simpler and sufficient.

Error Handling and Dead Letters

Every event consumer should have a dead letter queue. Events that fail processing after N retries go to the DLQ for investigation. Monitor DLQ depth and set up alerts. Unprocessed events in the DLQ are a system health indicator.

Observability

Trace events through the system with correlation IDs. Every event carries a trace ID that propagates through all downstream processing. Centralized logging and distributed tracing (Jaeger, X-Ray) make it possible to follow an event from publication through all its handlers.