Why Event-Driven Architecture

In a microservices architecture, services need to communicate. Synchronous REST calls create tight coupling — if service B is down, service A fails. Event-driven architecture decouples services by communicating through events: service A publishes an event, and any interested service can consume it independently. At Nexis Limited, Bondorix uses event-driven architecture for shipment lifecycle management, where events flow between bidding, tracking, documentation, and notification services.

Core Concepts

Events vs Commands vs Queries

  • Events: Record something that happened. "OrderPlaced", "ShipmentDelivered", "UserRegistered". Events are immutable and past tense.
  • Commands: Request something to happen. "PlaceOrder", "CancelShipment". Commands may be rejected.
  • Queries: Request information. "GetOrderStatus", "ListShipments". Queries do not modify state.

Event Brokers

Apache Kafka is the most popular event broker for enterprise systems. Kafka provides durable, ordered, replayable event streams with high throughput. Alternative brokers include RabbitMQ (for simpler messaging patterns), AWS SNS/SQS, and Redis Streams.

Key Patterns

Event Sourcing

Instead of storing current state, store the sequence of events that led to the current state. The current state is derived by replaying events. Benefits include a complete audit trail, ability to reconstruct state at any point in time, and natural fit for event-driven systems. Drawbacks include complexity, eventual consistency, and the need for snapshots to avoid replaying millions of events.

CQRS (Command Query Responsibility Segregation)

Separate the write model (commands) from the read model (queries). The write side processes commands and emits events. The read side consumes events and builds optimized read models (projections). This allows independent scaling and optimization of reads and writes.

Saga Pattern

Coordinate multi-service transactions through a sequence of local transactions. Each service performs its local transaction and publishes an event. If any step fails, compensating transactions are executed to undo previous steps. This replaces traditional distributed transactions (2PC) with eventual consistency.

Practical Implementation

  • Design events carefully: Events are your API contract. Version them and avoid breaking changes.
  • Idempotent consumers: Events may be delivered more than once. Consumers must handle duplicates gracefully.
  • Dead letter queues: Route failed events to a dead letter queue for investigation rather than blocking the consumer.
  • Schema registry: Use a schema registry (Confluent Schema Registry) to manage event schema evolution.
  • Monitoring: Track consumer lag (how far behind a consumer is from the latest event) and processing errors.

When NOT to Use Event-Driven Architecture

  • Simple CRUD applications with few services.
  • When strong consistency is required for every operation.
  • Teams without experience in distributed systems (start simpler and evolve).

Conclusion

Event-driven architecture enables loosely coupled, independently scalable microservices. It introduces complexity (eventual consistency, event ordering, idempotency), but the benefits — decoupling, auditability, and resilience — make it essential for complex distributed systems.

Building a microservices architecture? Our team has production experience with event-driven systems.