I’ve been thinking a lot about microservices lately. After struggling with a monolithic application that couldn’t handle our growing user base, I knew we needed a more resilient approach. That’s when event-driven architecture caught my attention - a way to build systems that scale gracefully and handle failures without collapsing. Let me walk you through how I implemented this using NestJS, RabbitMQ, and MongoDB.

When building microservices, communication is everything. Traditional request-response patterns create fragile dependencies. What if we flipped this model? Instead of services calling each other directly, they could broadcast events when something important happens. Other services then react to these events independently. This creates systems that keep working even when individual components fail.

Here’s how I set up our core services:

// User service creates users and publishes events
async createUser(createUserDto) {
  const newUser = await this.userModel.create(createUserDto);
  
  const event = {
    id: uuidv4(),
    type: 'UserCreated',
    data: {
      userId: newUser.id,
      email: newUser.email
    }
  };
  await this.eventBus.publish(event);
  return newUser;
}

The magic happens with RabbitMQ acting as our central nervous system. Services publish events to exchanges without knowing who might listen. This separation is powerful - we can add new functionality without touching existing services. For instance, when we introduced a loyalty points system, it simply started listening for existing OrderPlaced events. How might this approach simplify your next feature addition?

Message reliability is critical. We implemented dead letter queues to handle processing failures:

// RabbitMQ setup with dead letter exchange
ch.assertExchange('events', 'topic', { durable: true });
ch.assertQueue('order_events', { 
  durable: true,
  deadLetterExchange: 'failed_events'
});
ch.bindQueue('order_events', 'events', 'order.*');

When an event fails processing after several retries, it moves to a special queue for investigation. This pattern has saved us countless times - like when our notification service had a temporary outage, but no orders were lost. Events simply waited in the queue until the service recovered.

For data persistence, we combined MongoDB with event sourcing. Each service maintains its own data store, but we also keep an immutable log of all events:

// Event storage in MongoDB
const eventSchema = new Schema({
  _id: String,
  type: String,
  aggregateId: String,
  timestamp: Date,
  data: Object
}, { versionKey: false });

This event log became invaluable for debugging. We can replay events to reconstruct state or diagnose issues. It also enabled new reporting features we hadn’t originally planned for. What unexpected benefits might event sourcing bring to your project?

Monitoring distributed systems requires special tools. We implemented OpenTelemetry to trace requests across services:

# Docker compose for monitoring stack
services:
  jaeger:
    image: jaegertracing/all-in-one
    ports:
      - "16686:16686"

The visualization of request flows helped us identify bottlenecks - like when order processing was delayed due to an unoptimized database query. We also added health checks:

// NestJS health check endpoint
@Get('health')
@HealthCheck()
checkHealth() {
  return this.health.check([
    () => this.db.pingCheck('mongodb'),
    () => this.rabbitmq.pingCheck('rabbitmq')
  ]);
}

For deployment, we containerized everything with Docker. Each service runs in its own container, scaled independently based on load. Our CI/CD pipeline runs integration tests against a staging environment that mirrors production.

Testing event-driven systems requires a different approach. We focus on:

1. Service contract tests - verifying event formats
2. Component tests - ensuring services react properly to events
3. End-to-end tests - validating complete workflows

// Sample integration test
it('should process order when payment succeeds', async () => {
  await publishTestEvent('PaymentApproved', paymentData);
  const order = await waitForOrderStatus(orderId, 'completed');
  expect(order).toBeDefined();
});

Performance optimization became an ongoing process. We implemented:

• RabbitMQ consumer prefetch limits
• MongoDB indexing for frequent queries
• Event versioning for schema evolution
• Bulkhead pattern to isolate failures

The journey to production readiness taught me valuable lessons. Start small - implement one event flow completely before expanding. Document your event contracts religiously. And invest in observability early - it pays dividends when debugging complex issues.

What challenges have you faced with distributed systems? I’d love to hear about your experiences. If you found this useful, please share it with your network - these patterns have transformed how we build reliable systems at scale. Let me know in the comments what other aspects of microservices you’d like me to cover!