I was recently working on a microservices project where services kept breaking because of mismatched data types and unreliable communication. This frustration led me to explore how we can build robust, type-safe event-driven systems. If you’ve ever spent hours debugging why one service sent a string when another expected a number, you’ll understand why this matters.

Event-driven architecture lets services communicate by sending and receiving events instead of calling each other directly. This means services can work independently. If the notification service is down, orders can still be created and processed later. Have you considered what happens when services don’t need to wait for each other?

Let me show you how to set this up using NestJS for building services, RabbitMQ for messaging, and Prisma for database interactions. We’ll create a simple e-commerce system with user, order, and notification services.

First, we need our infrastructure. I use Docker Compose to run RabbitMQ and databases locally. This setup ensures everything works the same in development and production.

# docker-compose.yml
services:
  rabbitmq:
    image: rabbitmq:3-management
    ports:
      - "5672:5672"
    environment:
      RABBITMQ_DEFAULT_USER: admin
      RABBITMQ_DEFAULT_PASS: admin123

  postgres-user:
    image: postgres:15
    ports:
      - "5433:5432"
    environment:
      POSTGRES_DB: user_service

Each service has its own database. The user service handles registrations, orders manage purchases, and notifications send emails. They share event types through a common package to avoid mismatches.

Why is type safety crucial here? If an event’s data structure changes, TypeScript will flag errors immediately. Let’s define a base event class.

// shared/events/base-event.ts
export abstract class BaseEvent {
  public readonly eventId: string;
  public readonly timestamp: Date;

  constructor(public readonly eventType: string) {
    this.eventId = Math.random().toString(36);
    this.timestamp = new Date();
  }
}

export class UserCreatedEvent extends BaseEvent {
  constructor(
    public readonly userId: string,
    public readonly email: string
  ) {
    super('user.created');
  }
}

Now, the user service can publish an event when someone registers. The order service listens for this event to create orders for that user. What if the order service is slow? RabbitMQ queues messages so nothing is lost.

In NestJS, I create a module for RabbitMQ. The @nestjs/microservices package makes this straightforward.

// user.service.ts
@Injectable()
export class UserService {
  constructor(private eventBus: EventBus) {}

  async createUser(data: CreateUserDto): Promise<User> {
    const user = await this.prisma.user.create({ data });
    const event = new UserCreatedEvent(user.id, user.email);
    await this.eventBus.publish(event);
    return user;
  }
}

The event bus interface ensures all services use the same methods. Here’s a simple implementation.

// shared/messaging/event-bus.interface.ts
export interface EventBus {
  publish<T extends BaseEvent>(event: T): Promise<void>;
  subscribe(eventType: string, handler: Function): Promise<void>;
}

Prisma helps with type-safe database operations. I define schemas for each service, and Prisma generates TypeScript types. This catches errors at compile time, like trying to save a string where a number is expected.

Error handling is vital. If a message fails, it goes to a dead letter queue. This prevents infinite retries and allows manual inspection.

// order.service.ts
async handleOrderCreated(event: OrderCreatedEvent) {
  try {
    await this.processOrder(event.orderId);
  } catch (error) {
    await this.eventBus.publishToDeadLetter(event);
  }
}

How do you test these flows? I use Docker to spin up dependencies and write integration tests that simulate real event sequences. Monitoring tools like Prometheus track message rates and errors.

Performance can be tuned by adjusting RabbitMQ settings and using connection pooling in Prisma. Always validate events before processing to avoid unnecessary work.

Common mistakes include not versioning events or ignoring message ordering. Start with simple retry logic and add complexity as needed.

Building this system taught me that type safety isn’t just about preventing bugs—it’s about creating systems that are easy to change and understand. When every piece of data is checked, developers can move faster with confidence.

If you found this helpful, please like and share this article. Your comments and experiences would be great to hear—what challenges have you faced with microservices?