I’ve been thinking a lot about how modern applications handle complexity while remaining responsive and reliable. Recently, I worked on a system that needed to process thousands of simultaneous user actions without slowing down or breaking. That’s when I truly appreciated the power of event-driven microservices. If you’re building systems that need to scale gracefully while maintaining clear separation of concerns, this approach might transform how you think about architecture.

Have you ever wondered how services can communicate without creating tight dependencies? Event-driven architecture answers this by letting services broadcast events without knowing who’s listening. When a user registers, the user service publishes an event. The order service might listen to update user profiles, while the notification service sends a welcome email. Each service focuses on its job without direct calls to others.

Let me show you how to set this up. First, ensure you have Node.js, Docker, and the NestJS CLI installed. We’ll use a monorepo structure to keep our services organized while allowing independent development.

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

Why start with a base event class? It ensures consistency across all events in your system. Every event gets a unique ID and timestamp, which becomes crucial for debugging and auditing later.

RabbitMQ acts as our message broker. It’s like a postal service for events—services send messages to exchanges, and queues receive copies based on routing rules. Here’s a basic Docker setup:

# docker-compose.yml for RabbitMQ
services:
  rabbitmq:
    image: rabbitmq:3-management
    ports:
      - "5672:5672"
      - "15672:15672"
    environment:
      RABBITMQ_DEFAULT_USER: admin
      RABBITMQ_DEFAULT_PASS: password

What happens if a service goes down while processing messages? Dead letter queues handle failed messages. If the order service can’t process an event after several attempts, RabbitMQ moves it to a separate queue for manual inspection.

Now, let’s create a user service that publishes events. When a user registers, we emit a UserCreatedEvent:

// In user service
@Injectable()
export class UserService {
  constructor(private eventPublisher: EventPublisher) {}

  async createUser(userData: CreateUserDto): Promise<User> {
    const user = await this.userRepository.save(userData);
    
    // Publish event without waiting for consumers
    await this.eventPublisher.publish(
      new UserCreatedEvent(user.id, user.email)
    );
    
    return user;
  }
}

Notice how the user service doesn’t care who listens to this event. It simply announces that a user was created. This loose coupling means we can add new consumers without modifying the user service.

How do other services react to these events? The notification service subscribes to UserCreatedEvent and sends welcome emails:

// In notification service
@EventHandler(UserCreatedEvent)
export class UserCreatedHandler {
  async handle(event: UserCreatedEvent): Promise<void> {
    await this.emailService.sendWelcomeEmail(event.email);
  }
}

But what if the email service is temporarily unavailable? Circuit breakers prevent cascading failures. After a certain number of failures, the circuit opens, and requests fail fast without overloading the struggling service.

Distributed tracing helps you follow a request across service boundaries. When a user places an order, you can trace the journey from the API gateway through the order service to the notification service. I use OpenTelemetry with Jaeger to visualize these flows.

Containerization makes deployment consistent. Each service runs in its own Docker container, and we use Docker Compose to manage them together:

# Dockerfile for a typical service
FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY dist/ ./dist/
CMD ["node", "dist/main"]

Testing event-driven systems requires a different approach. I often use contract testing to verify that events contain expected data. This catches breaking changes before they reach production.

In production, monitor queue depths and processing times. If the order queue grows faster than it’s consumed, you might need to scale the order service. Tools like Prometheus and Grafana provide these insights.

Have you considered what happens when business rules span multiple services? Saga patterns help manage distributed transactions. If payment fails after order creation, a compensating action reverses the order.

Remember that event-driven systems trade immediate consistency for eventual consistency. Users might see temporary inconsistencies, but the system remains available and responsive.

I’ve found that proper error handling separates hobby projects from production systems. Always implement retry logic with exponential backoff and have dead letter queues for problematic messages.

What questions should you ask when designing events? Focus on “what happened” rather than “what to do.” Events like “user.registered” are better than “send.welcome.email” because they’re reusable.

If you found this walkthrough helpful, I’d love to hear about your experiences. What challenges have you faced with microservices? Share your thoughts in the comments below, and if this resonated with you, please like and share it with others who might benefit.