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Lately, I’ve noticed many teams struggling with monolithic applications that can’t keep up with modern demands. Scalability bottlenecks, tight coupling, and deployment nightmares – sound familiar? That’s what pushed me to explore event-driven microservices. After extensive research and practical experiments, I want to share how to build a resilient system using NestJS, RabbitMQ, and Redis. Stick with me, and you’ll see how these technologies solve real-world distributed system challenges. Let’s dive right in.

Our architecture connects independent services through events. When a user places an order, the Order Service publishes an event instead of calling other services directly. RabbitMQ routes this event to interested services: Inventory reserves items, Payments processes transactions, and Notifications alerts the user. This loose coupling allows each service to scale independently.

Setting up is straightforward with Docker. Here’s our infrastructure foundation:

# docker-compose.yml
services:
  rabbitmq:
    image: rabbitmq:3.11-management
    ports: ["5672:5672", "15672:15672"]
  redis:
    image: redis:7-alpine
    ports: ["6379:6379"]
  postgres:
    image: postgres:15
    environment: 
      POSTGRES_DB: microservices

Run docker-compose up and we’ve got messaging, caching, and databases ready. Now, how do we make services communicate without direct dependencies?

RabbitMQ handles that via AMQP protocol. In NestJS, we configure a microservice like this:

// main.ts (Order Service)
const app = await NestFactory.createMicroservice(AppModule, {
  transport: Transport.RMQ,
  options: {
    urls: ['amqp://localhost:5672'],
    queue: 'orders_queue',
  },
});

Services publish events when state changes:

// Order Service
@Injectable()
export class OrderService {
  constructor(
    @Inject('RABBITMQ_CLIENT') private client: ClientProxy
  ) {}

  async createOrder(dto: CreateOrderDto) {
    const order = await this.orderRepo.save(dto);
    this.client.emit('order_created', new OrderCreatedEvent(order.id, ...));
    return order;
  }
}

Meanwhile, the Notification Service listens:

// Notification Service
@EventPattern('order_created')
async handleOrderCreated(data: OrderCreatedEvent) {
  await this.mailService.sendOrderConfirmation(data.userId, data.orderId);
}

But what happens if Redis goes down during high traffic? We implement fallbacks. Redis caching boosts performance dramatically. Here’s how we cache product data:

// Product Service
async getProduct(id: string) {
  const cached = await this.redisClient.get(`product:${id}`);
  if (cached) return JSON.parse(cached);

  const product = await this.productRepo.findOne(id);
  await this.redisClient.set(`product:${id}`, JSON.stringify(product), 'EX', 3600);
  return product;
}

Distributed transactions require special handling. The Saga pattern coordinates multi-step processes using events. Consider order processing:

// Saga Coordinator
@Saga()
orderProcessing = (events$: Observable<any>): Observable<CommandMessage> => {
  return events$.pipe(
    ofType(OrderCreatedEvent),
    map(event => new ReserveInventoryCommand(event)),
    timeout(5000),
    catchError(() => [new CancelOrderCommand(event)])
  );
}

Services emit events for each step: InventoryReserved, PaymentProcessed, OrderCompleted. If any step fails, compensating actions trigger: ReleaseInventory, RefundPayment. This keeps data consistent across services.

Service discovery is crucial. We use a simple HTTP health check endpoint:

@Get('health')
healthCheck() {
  return { 
    status: 'up',
    services: ['rabbitmq', 'redis', 'db']
  };
}

For fault tolerance, we implement retry queues in RabbitMQ. Messages that fail processing go to a dead-letter queue for analysis:

channel.assertQueue('orders_queue', {
  durable: true,
  deadLetterExchange: 'dlx_exchange'
});

Testing event flows is critical. We use NestJS testing utilities to verify events:

it('should publish OrderCreatedEvent on order creation', async () => {
  const client = app.get<ClientProxy>('RABBITMQ_CLIENT');
  const emitSpy = jest.spyOn(client, 'emit');
  
  await orderService.createOrder(mockOrderDto);
  expect(emitSpy).toHaveBeenCalledWith('order_created', expect.any(OrderCreatedEvent));
});

Deployment to production requires careful planning. We configure resource limits in Docker:

# production.yml
services:
  order-service:
    deploy:
      resources:
        limits:
          cpus: '0.50'
          memory: 512M

For zero-downtime deployments, we use rolling updates. RabbitMQ’s message persistence ensures no events are lost during deployments.

I’ve seen this architecture handle 10x traffic spikes without breaking. Services scale horizontally – just add more instances. Maintenance becomes easier too; update one service without redeploying everything.

What surprises developers most? How clean the code stays. Services focus on their domain without entanglement. Debugging is simpler with distributed tracing.

Building this requires thoughtful design, but the payoff is huge. Scalable, resilient systems that evolve with business needs. I encourage you to try this approach in your next project. If you found this useful, share it with your team, leave a comment about your experience, or connect with me to discuss more. Happy coding!