I’ve been working with distributed systems for years, and I keep seeing the same challenges pop up: services that are too tightly coupled, systems that can’t scale, and failures that cascade through an entire application. That’s why I decided to write about building event-driven microservices with NestJS, RabbitMQ, and MongoDB. This combination has helped me create systems that are resilient, scalable, and much easier to maintain. If you’re tired of dealing with brittle architectures, this might be the approach you’ve been looking for.

Event-driven architecture changes how services communicate. Instead of services calling each other directly, they publish events when something important happens. Other services listen for those events and react accordingly. This loose coupling means that if one service goes down, the others can keep working independently. Have you ever had a payment service failure bring down your entire order processing? With events, that doesn’t happen.

Let me show you how this works in practice. Here’s a simple example using NestJS:

// Order service publishing an event
@Injectable()
export class OrderService {
  constructor(private eventPublisher: EventPublisher) {}

  async createOrder(orderData: any) {
    const order = await this.saveOrder(orderData);
    await this.eventPublisher.publish('order.created', {
      orderId: order.id,
      customerId: order.customerId,
      items: order.items
    });
    return order;
  }
}

RabbitMQ acts as our message broker, ensuring events get delivered reliably. Setting it up with NestJS is straightforward. We use the built-in microservices support to connect to RabbitMQ:

// app.module.ts configuration
@Module({
  imports: [
    ClientsModule.register([
      {
        name: 'EVENT_BUS',
        transport: Transport.RMQ,
        options: {
          urls: ['amqp://localhost:5672'],
          queue: 'events_queue'
        }
      }
    ])
  ]
})

What happens when you need to query data across multiple services? This is where CQRS (Command Query Responsibility Segregation) comes in handy. We separate the write operations from the read operations. Commands change state, while queries read data. This separation makes our system more flexible and performant.

MongoDB works well for storing events because it’s schema-less and scales horizontally. We can store every state change as an event, which gives us a complete history of what happened in our system. Here’s how we might model an event in MongoDB:

// Event schema in MongoDB
@Schema()
export class EventStore {
  @Prop({ required: true })
  eventType: string;

  @Prop({ required: true })
  aggregateId: string;

  @Prop({ type: Object })
  data: any;

  @Prop({ default: Date.now })
  timestamp: Date;
}

Handling distributed transactions requires a different mindset. Instead of trying to maintain ACID properties across services, we accept eventual consistency. Events propagate through the system, and services update their state independently. If a service misses an event, RabbitMQ’s dead letter queues help us handle failures gracefully.

Monitoring distributed systems is crucial. I use a combination of logging, metrics, and tracing to understand what’s happening. Have you ever struggled to debug an issue that spans multiple services? Proper observability tools make this much easier.

Testing event-driven systems involves verifying that events are published and handled correctly. We test each service in isolation and use contract testing to ensure events maintain compatibility. Here’s a simple test for our order service:

// Testing event publication
describe('OrderService', () => {
  it('should publish order.created event', async () => {
    const eventPublisher = { publish: jest.fn() };
    const service = new OrderService(eventPublisher as any);
    
    await service.createOrder(testOrderData);
    
    expect(eventPublisher.publish).toHaveBeenCalledWith(
      'order.created',
      expect.objectContaining({
        orderId: expect.any(String)
      })
    );
  });
});

Deploying these services with Docker makes scaling simple. We can run multiple instances of each service behind a load balancer. RabbitMQ handles the message distribution, and MongoDB replicates our data. The entire system can handle increased load without major changes.

Common pitfalls include designing events that are too specific or too generic. Events should represent business facts that multiple services care about. Another mistake is not planning for schema evolution. How will you handle changes to event structures over time?

I’ve found that starting small and iterating works best. Begin with a few key services and events, then expand as you understand the patterns better. The investment in learning this architecture pays off quickly in reduced complexity and improved reliability.

Building distributed systems is challenging, but the right patterns and tools make it manageable. I hope this gives you a solid foundation for your own projects. If this approach resonates with you, I’d love to hear about your experiences. Please share this with others who might benefit, and leave a comment with your thoughts or questions.