I’ve been thinking a lot lately about how modern applications handle scale and complexity. The traditional request-response model often falls short when systems need to handle thousands of concurrent operations while remaining responsive and resilient. This led me to explore event-driven architectures, and I want to share how you can build one using NestJS, RabbitMQ, and Redis.

Event-driven architecture changes how services communicate. Instead of direct calls between services, components emit events that others can react to. This approach provides loose coupling, better scalability, and improved fault tolerance. But how do you ensure these events are processed reliably across distributed systems?

Let me show you how to set up the foundation. First, we need a message broker. RabbitMQ excels here with its robust queuing system and delivery guarantees. Here’s a basic setup:

// rabbitmq.service.ts
async connect(): Promise<void> {
  this.connection = await amqp.connect({
    hostname: process.env.RABBITMQ_HOST,
    username: process.env.RABBITMQ_USER,
    password: process.env.RABBITMQ_PASS
  });
  this.channel = await this.connection.createChannel();
}

Now, what happens when services need to share state or cache results? This is where Redis comes in. Its in-memory data store provides lightning-fast access while supporting various data structures. Consider this pattern for caching:

// redis-cache.service.ts
async getWithCache<T>(key: string, fallback: () => Promise<T>): Promise<T> {
  const cached = await this.redisClient.get(key);
  if (cached) return JSON.parse(cached);
  
  const freshData = await fallback();
  await this.redisClient.setex(key, 300, JSON.stringify(freshData));
  return freshData;
}

But how do we ensure messages aren’t lost when services fail? Dead letter queues and retry mechanisms are crucial. RabbitMQ allows us to configure queues that automatically redirect failed messages:

// dlq-setup.ts
await channel.assertQueue('orders.process', {
  durable: true,
  deadLetterExchange: 'orders.dlx',
  deadLetterRoutingKey: 'orders.failed'
});

Monitoring becomes critical in distributed systems. How can you track message flow across services? Implementing correlation IDs helps trace requests through the entire system:

// event-message.interface.ts
interface EventMessage {
  id: string;
  type: string;
  timestamp: Date;
  data: any;
  correlationId: string;
  version: string;
}

Testing event-driven systems requires a different approach. Instead of mocking HTTP calls, you need to verify that events are published and handled correctly. NestJS provides excellent testing utilities for this:

// order.service.spec.ts
it('should publish order.created event', async () => {
  const rabbitMQService = module.get(RabbitMQService);
  jest.spyOn(rabbitMQService, 'publishEvent');
  
  await orderService.createOrder(testOrder);
  expect(rabbitMQService.publishEvent).toHaveBeenCalled();
});

Performance optimization often involves balancing between immediate consistency and eventual consistency. Sometimes it’s better to acknowledge events quickly and process them asynchronously. But how do you decide which approach fits your use case?

Error handling deserves special attention. Transient failures should trigger retries, while permanent failures need proper logging and alerting. Implementing circuit breakers prevents cascading failures:

// circuit-breaker.ts
async executeWithCircuitBreaker<T>(
  operation: () => Promise<T>
): Promise<T> {
  if (this.state === CircuitState.OPEN) {
    throw new Error('Circuit open');
  }
  
  try {
    const result = await operation();
    this.recordSuccess();
    return result;
  } catch (error) {
    this.recordFailure();
    throw error;
  }
}

Building this architecture requires careful consideration of message schemas. How will you handle schema evolution without breaking existing consumers? Using versioned events and backward-compatible changes is essential.

The combination of NestJS’s modular structure, RabbitMQ’s reliable messaging, and Redis’s fast data access creates a powerful foundation for scalable applications. Each service remains focused on its domain while communicating through well-defined events.

I’ve found this approach particularly valuable for systems that need to handle variable loads while maintaining responsiveness. The initial setup requires more thought than traditional architectures, but the long-term benefits in scalability and maintainability are substantial.

What challenges have you faced with distributed systems? I’d love to hear about your experiences and solutions. If this approach resonates with you, please share your thoughts in the comments below.