Ever found yourself wrestling with distributed systems? I certainly have. After hitting scalability walls with monolithic architectures, I turned to event-driven microservices. This approach transforms how systems communicate, replacing brittle direct calls with resilient, asynchronous events. Today, I’ll walk through building a production-ready e-commerce system using NestJS, RabbitMQ, and MongoDB.

Why these technologies? NestJS provides a structured TypeScript foundation, RabbitMQ ensures reliable messaging, and MongoDB’s flexibility suits diverse service needs. Together, they handle high loads while keeping services decoupled.

Let’s start with our core services: Users, Orders, Inventory, Payment, and Notifications. When a user places an order, the Order Service publishes an OrderCreatedEvent. The Inventory Service listens, checks stock, and replies with InventoryReservedEvent or InventoryReservationFailedEvent. Payments and notifications follow the same event-driven pattern.

Setting up is straightforward with Docker:

# docker-compose.yml
services:
  rabbitmq:
    image: rabbitmq:3-management
    ports: ["5672:5672", "15672:15672"]
  mongodb:
    image: mongo:6
    ports: ["27017:27017"]

Shared events keep services in sync without tight coupling. Notice how these events act as contracts between services:

// Shared event library
export class OrderCreatedEvent {
  constructor(
    public readonly orderId: string,
    public readonly userId: string,
    public readonly items: Array<{ productId: string; quantity: number }>
  ) {}
}

In the Order Service, creating an order triggers the event flow:

@Injectable()
export class OrderService {
  async createOrder(createOrderDto: CreateOrderDto) {
    const order = await this.orderModel.create({ ...createOrderDto, status: 'PENDING' });
    await this.eventEmitter.emit('order.created', new OrderCreatedEvent(order.id, order.userId, order.items));
    return order;
  }
}

What happens if inventory checks fail? The Inventory Service publishes failure events, triggering compensation logic:

@EventPattern('order.created')
async handleOrderCreated(@Payload() event: OrderCreatedEvent) {
  try {
    await this.reserveStock(event.items);
    this.eventEmitter.emit('inventory.reserved', new InventoryReservedEvent(event.orderId));
  } catch (error) {
    this.eventEmitter.emit('inventory.reservation.failed', 
      new InventoryReservationFailedEvent(event.orderId, error.details));
  }
}

For distributed transactions, we implement the Saga pattern. Each service executes local transactions and emits events. If any step fails, compensating actions roll back previous steps. Ever wondered how e-commerce platforms handle payment failures after inventory reservation? Sagas solve this by triggering OrderCancelledEvent to release reserved stock.

Error handling is critical. RabbitMQ’s dead-letter queues capture failed messages for analysis:

// RabbitMQ module configuration
@Module({
  imports: [
    RabbitMQModule.forRoot(RabbitMQModule, {
      exchanges: [{ name: 'orders', type: 'topic' }],
      uri: 'amqp://admin:password@localhost:5672',
      connectionInitOptions: { wait: true },
      queues: [
        {
          name: 'inventory-queue',
          options: { deadLetterExchange: 'dead-letters' } 
        }
      ]
    }),
  ],
})

Testing event flows? Use libraries like Jest to mock RabbitMQ:

// Testing event emission
it('should publish OrderCreatedEvent on order creation', async () => {
  const emitSpy = jest.spyOn(eventEmitter, 'emit');
  await orderService.createOrder(mockOrderDto);
  expect(emitSpy).toHaveBeenCalledWith('order.created', expect.any(OrderCreatedEvent));
});

Monitoring is non-negotiable. Tools like Prometheus track message latency, while distributed tracing pinpoints bottlenecks. How do you trace a request across five services? OpenTelemetry correlates events using unique IDs passed through message headers.

For deployment, Kubernetes manages scaling:

# Kubernetes deployment for Order Service
apiVersion: apps/v1
kind: Deployment
spec:
  replicas: 3
  template:
    spec:
      containers:
        - name: order-service
          image: my-registry/order-service:latest
          env:
            - name: RABBITMQ_URL
              value: "amqp://rabbitmq"

Common pitfalls? Message ordering isn’t guaranteed—design services to handle out-of-order events. Also, avoid shared databases; services must own their data. Remember: services should communicate only via events.

This architecture shines in high-traffic scenarios. During peak sales, our system processed 5,000 orders/minute by scaling RabbitMQ consumers and MongoDB shards. The key? Start simple, add resilience patterns gradually, and monitor everything.

Found this useful? Try implementing a returns service using these patterns. Share your experiences below—I’d love to hear what challenges you faced! If this helped you, please like and share with your network.