I’ve spent the last few years building distributed systems, and one question kept resurfacing: how can we create microservices that truly scale while remaining resilient? That’s what led me to explore event-driven architecture with NestJS, RabbitMQ, and Redis. In this guide, I’ll walk you through building a production-ready system step by step.

Event-driven architecture fundamentally changed how I approach microservices. Instead of services tightly coupled through direct API calls, they communicate through events. This means when an order is created, it publishes an event that other services can react to independently. Does that sound more flexible than traditional request-response patterns?

Let me show you how to set up the foundation. First, we need to define our event types. I prefer using TypeScript interfaces for type safety.

// Event type definitions
interface DomainEvent {
  id: string;
  aggregateId: string;
  eventType: string;
  timestamp: Date;
  data: any;
}

interface OrderCreatedEvent extends DomainEvent {
  eventType: 'OrderCreated';
  data: {
    customerId: string;
    items: Array<{ productId: string; quantity: number }>;
  };
}

Setting up the development environment is straightforward with Docker. I use a simple docker-compose file to spin up RabbitMQ and Redis. This approach ensures consistency across development and production.

# Docker Compose for infrastructure
services:
  rabbitmq:
    image: rabbitmq:3-management
    ports: ["5672:5672", "15672:15672"]
  
  redis:
    image: redis:alpine
    ports: ["6379:6379"]

Now, here’s a question I often get: why combine RabbitMQ and Redis? RabbitMQ handles message queuing reliably, while Redis excels at caching and fast data access. In our order processing system, we use RabbitMQ for event distribution and Redis for caching product details and session data.

Building the event store was a game-changer in my projects. It captures every state change as an immutable event. This pattern enables event sourcing - you can reconstruct any entity’s state by replaying its events.

// Event store implementation
@Injectable()
export class EventStoreService {
  async saveEvent(event: DomainEvent): Promise<void> {
    // Save to database
    await this.eventCollection.insertOne(event);
    
    // Cache in Redis
    await this.redis.set(
      `event:${event.id}`, 
      JSON.stringify(event)
    );
  }
}

When implementing event publishers, I learned the importance of idempotency. Services might receive the same event multiple times, so handlers must handle duplicates gracefully. How would you ensure your service processes events only once?

Here’s how I configure RabbitMQ in NestJS:

// RabbitMQ configuration
const microserviceOptions: RabbitMQConfig = {
  transport: Transport.RMQ,
  options: {
    urls: ['amqp://localhost:5672'],
    queue: 'order_queue',
    queueOptions: { durable: true }
  }
};

For event consumers, I use NestJS message patterns. They make it simple to handle different event types.

// Event consumer example
@Controller()
export class OrderConsumer {
  @EventPattern('OrderCreated')
  async handleOrderCreated(data: OrderCreatedEvent) {
    // Process order creation
    await this.inventoryService.reserveItems(data.items);
  }
}

One of the most challenging aspects was handling distributed transactions. The Saga pattern became my go-to solution. Instead of a single database transaction, we break it into a series of events with compensation actions.

What happens if payment fails after inventory is reserved? We need to reverse the reservation. Here’s a simplified Saga implementation:

// Saga pattern example
@Injectable()
export class OrderSaga {
  @Saga()
  orderCreated = (events$: Observable<any>): Observable<ICommand> => {
    return events$.pipe(
      ofType(OrderCreatedEvent),
      map((event) => new ReserveInventoryCommand(event.data))
    );
  }
}

Error handling requires careful planning. I implement retry mechanisms with exponential backoff and dead letter queues for problematic messages. Redis helps here by storing retry counts and circuit breaker states.

Monitoring event flows is crucial in production. I use structured logging and correlate events using unique identifiers. This makes debugging much easier when something goes wrong.

// Adding correlation IDs
const event = {
  id: uuidv4(),
  correlationId: requestId,
  data: orderData
};

Deployment strategies evolved in my practice. I now use blue-green deployments for microservices, with careful versioning of event schemas. Breaking changes in events can cause cascading failures, so I always maintain backward compatibility.

Common pitfalls? I’ve seen teams struggle with event versioning and schema evolution. My advice: use schema registries and validate events before processing. Also, watch out for event storming - too many events can make the system hard to reason about.

I remember one project where we missed monitoring event latency. It caused delayed order processing until we added proper metrics. What monitoring tools would you integrate?

Building event-driven microservices requires shifting your mindset from synchronous to asynchronous communication. The initial complexity pays off in scalability and resilience. Services can evolve independently, and the system handles failures more gracefully.

I hope this guide helps you avoid the mistakes I made early on. The combination of NestJS, RabbitMQ, and Redis provides a solid foundation for robust microservices. If you found this useful, please share it with others who might benefit. I’d love to hear about your experiences in the comments - what challenges have you faced with event-driven systems?