I’ve been thinking a lot about how modern applications handle scale and complexity lately. The shift from monolithic architectures to distributed systems isn’t just a trend—it’s becoming essential for building resilient, scalable software. Recently, I helped a client transition their e-commerce platform to microservices, and the event-driven approach we implemented using NestJS and Redis transformed their system’s performance and maintainability. This experience inspired me to share a practical guide that you can apply to your own projects.

Event-driven microservices communicate through events rather than direct API calls. When a service performs an action, it emits an event that other services can react to. This creates loose coupling—services don’t need intimate knowledge of each other, making the system more flexible and robust. Have you ever considered how much simpler debugging becomes when services operate independently?

Let me show you how to set this up. We’ll use Redis as our message broker because it’s fast, reliable, and perfect for this use case. First, define your events in a shared package that all services can access.

// Base event structure
export abstract class BaseEvent {
  public readonly eventId: string;
  public readonly timestamp: Date;
  
  constructor() {
    this.eventId = crypto.randomUUID();
    this.timestamp = new Date();
  }
}

// Specific event for user registration
export class UserRegisteredEvent extends BaseEvent {
  constructor(
    public readonly userId: string,
    public readonly email: string
  ) {
    super();
  }
}

Now, implement a Redis event bus to handle publishing and subscribing to events. This service will be injected into your NestJS modules.

@Injectable()
export class RedisEventBusService {
  private publisher: RedisClientType;
  private subscriber: RedisClientType;

  async publish<T extends BaseEvent>(event: T): Promise<void> {
    await this.publisher.publish(
      event.constructor.name,
      JSON.stringify(event)
    );
  }

  async subscribe<T extends BaseEvent>(
    eventType: string,
    handler: (event: T) => Promise<void>
  ): Promise<void> {
    await this.subscriber.subscribe(eventType, async (message) => {
      const event = JSON.parse(message) as T;
      await handler(event);
    });
  }
}

In your user service, when someone registers, publish an event instead of calling other services directly. This keeps the user service focused on its single responsibility.

@Injectable()
export class UserService {
  constructor(private eventBus: RedisEventBusService) {}

  async registerUser(createUserDto: CreateUserDto): Promise<User> {
    const user = await this.userRepository.save(createUserDto);
    
    // Publish event instead of direct service calls
    await this.eventBus.publish(
      new UserRegisteredEvent(user.id, user.email)
    );
    
    return user;
  }
}

Other services can now listen for this event. The order service might create a welcome discount, while the notification service sends a confirmation email. What happens if the notification service is temporarily down? With event-driven architecture, the user registration still completes, and the notification will be handled when the service recovers.

Error handling is crucial in distributed systems. Implement retry mechanisms and dead letter queues for failed events.

async handleUserRegistered(event: UserRegisteredEvent): Promise<void> {
  try {
    await this.sendWelcomeEmail(event.email);
  } catch (error) {
    // Retry logic or move to dead letter queue
    await this.retryOrQueue(event, error);
  }
}

Docker makes deployment straightforward. Here’s a sample docker-compose file to run your services and Redis.

version: '3.8'
services:
  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
  
  user-service:
    build: ./packages/user-service
    environment:
      - REDIS_URL=redis://redis:6379
  
  order-service:
    build: ./packages/order-service
    environment:
      - REDIS_URL=redis://redis:6379

Testing event-driven systems requires simulating event flows. Use in-memory Redis for testing to avoid dependencies on external services.

Monitoring is your best friend in production. Track event throughput, latency, and error rates. Tools like Prometheus and Grafana can visualize how events flow through your system. Did you know that proper monitoring can help you identify bottlenecks before they affect users?

Common pitfalls include over-engineering early on and not planning for event schema evolution. Start simple, version your events, and use contracts between services. Remember that eventual consistency might require different thinking about data validation.

I’ve found that this architecture scales beautifully as requirements grow. Adding a new service? Just have it subscribe to relevant events without modifying existing code. The isolation makes teams more productive and deployments safer.

What challenges have you faced with microservices communication? I’d love to hear about your experiences in the comments. If this guide helps you build better systems, please like and share it with your colleagues who might benefit from these patterns. Your feedback helps me create more relevant content for our community.