I’ve been thinking about microservices lately, specifically how we can build systems that are both resilient and responsive. Traditional request-response architectures often create tight coupling between services. When one service goes down, the whole chain can break. That’s why I want to share my experience with event-driven architecture using NestJS, RabbitMQ, and MongoDB. This approach transforms how services communicate, making our systems more robust and scalable.

Have you ever wondered what happens when services can communicate without direct dependencies?

Let me walk you through building a complete event-driven system. We’ll create independent services that exchange messages through events, allowing them to operate autonomously while maintaining system-wide consistency.

Our foundation begins with the shared event infrastructure. This is the nervous system connecting all our microservices. We define a standard event structure that every service understands, ensuring consistent communication across the entire architecture.

// Event interface definition
export interface DomainEvent {
  id: string;
  aggregateId: string;
  eventType: string;
  eventData: any;
  version: number;
  timestamp: Date;
}

The event bus becomes our message router. It handles publishing events to RabbitMQ and manages retries when things go wrong. This abstraction means services don’t need to know about message broker implementation details.

What happens when a message fails to deliver multiple times?

We implement a dead letter queue strategy. Failed events get redirected to a separate queue for later analysis and manual processing. This prevents system-wide failures when individual messages encounter problems.

// Event publishing with retry logic
async publishEventWithRetry(event: DomainEvent, maxRetries = 3) {
  let attempt = 0;
  while (attempt < maxRetries) {
    try {
      await this.publishEvent(event);
      return;
    } catch (error) {
      attempt++;
      if (attempt === maxRetries) {
        await this.sendToDeadLetterQueue(event, error);
      }
    }
  }
}

Now, let’s consider the user service. When a user registers, instead of directly calling other services, it publishes a UserRegistered event. The order service and inventory service listen for this event and react accordingly. This loose coupling means we can add new subscribers without modifying the user service.

The order processing service demonstrates event sourcing beautifully. Rather than just storing the current state of an order, we store every state change as an event. This gives us a complete audit trail and the ability to reconstruct any past state.

// Order aggregate using event sourcing
export class Order extends AggregateRoot {
  private status: OrderStatus;
  private items: OrderItem[] = [];

  createOrder(userId: string, items: OrderItem[]) {
    this.addEvent('OrderCreated', { userId, items });
  }

  private applyOrderCreated(event: DomainEvent) {
    const { userId, items } = event.eventData;
    this.status = OrderStatus.CREATED;
    this.items = items;
  }
}

How do we ensure data consistency across services?

We embrace eventual consistency. Services update their local data stores independently after processing events. While there might be brief moments where data appears inconsistent, the system eventually reaches a consistent state across all services.

The API Gateway serves as our system’s front door. It handles incoming HTTP requests and translates them into commands that get routed to the appropriate services. It doesn’t contain business logic but coordinates the flow of operations.

Monitoring becomes crucial in distributed systems. We implement comprehensive logging and health checks. Each service reports its status, and we use distributed tracing to follow requests across service boundaries. This visibility helps us quickly identify and resolve issues.

// Health check implementation
@Controller('health')
export class HealthController {
  @Get()
  checkHealth() {
    return {
      status: 'healthy',
      timestamp: new Date(),
      service: process.env.SERVICE_NAME
    };
  }
}

Deployment brings its own considerations. We package each service in its own Docker container. This isolation allows independent scaling and deployment. RabbitMQ and MongoDB run as separate containers, providing the backbone for our event-driven architecture.

Testing requires a different mindset. We test each service in isolation and verify event interactions between services. Integration tests ensure that events flow correctly and services react as expected to various scenarios.

What about performance under heavy load?

The event-driven approach naturally handles load spikes. Messages queue up in RabbitMQ, and services process them at their own pace. We can scale individual services based on their specific load patterns without affecting the entire system.

I’ve found this architecture particularly valuable for systems that need to evolve over time. New features often mean adding new services that subscribe to existing events, rather than modifying existing code. This reduces regression risks and speeds up development.

The combination of NestJS’s modular structure, RabbitMQ’s reliable messaging, and MongoDB’s flexible document storage creates a powerful foundation for building scalable applications. Each technology brings specific strengths that complement the others in this architecture.

Remember that event-driven systems require careful design thinking. We need to consider event schemas, versioning strategies, and error handling from the beginning. Proper planning prevents many common pitfalls in distributed systems.

I hope this exploration of event-driven microservices gives you practical insights for your next project. The patterns we’ve discussed can transform how you think about building scalable, maintainable systems. What challenges have you faced with microservices architecture? Share your experiences in the comments below, and if you found this helpful, please like and share with others who might benefit from this approach.