Recently, I faced a challenge in my team when our monolithic application started struggling under growing traffic. We needed a scalable solution that wouldn’t compromise data consistency. That’s when I turned to event-driven microservices with NestJS, RabbitMQ, and Prisma. This combination offers type safety, resilience, and clear boundaries between services. If you’re dealing with similar scaling pains, stick around – I’ll share practical solutions I’ve implemented.

Our architecture centers on three core services communicating through events. The User Service handles registrations, the Order Service manages purchases, and the Notification Service sends alerts. Each service owns its database using Prisma for type-safe database operations. We defined shared event contracts early to prevent integration headaches:

// Shared event types
export interface UserCreatedEvent {
  type: 'USER_CREATED';
  payload: {
    userId: string;
    email: string;
  };
}

Setting up the monorepo was straightforward with npm workspaces. I created separate directories for each service and shared libraries. Here’s how I structured the root package.json:

{
  "workspaces": ["services/*", "shared/*"],
  "scripts": {
    "dev:all": "concurrently \"npm run dev:user\" ...",
    "docker:up": "docker-compose up -d"
  }
}

For database modeling, Prisma’s schema language proved invaluable. Each service maintains its own schema with clear ownership boundaries. The Order Service schema includes an enum for status tracking and event storage:

// Order Service schema
enum OrderStatus { PENDING, CONFIRMED, CANCELLED }

model Order {
  id        String @id @default(cuid())
  status    OrderStatus
  events    OrderEvent[]
}

Implementing the User Service revealed important patterns. I used NestJS decorators for clean routing and Prisma’s client for type-safe queries. When a user registers, we publish an event:

// User creation with event emission
@Post('register')
async createUser(@Body() dto: CreateUserDto) {
  const user = await this.prisma.user.create({ data });
  this.eventPublisher.publish('USER_CREATED', {
    userId: user.id,
    email: user.email
  });
  return user;
}

RabbitMQ connects our services through exchanges and queues. I configured a dead letter exchange for handling failed messages – a lifesaver when services misbehave. The setup ensures messages aren’t lost even during outages. How might we track messages across services? Correlation IDs solve this by threading through events.

Distributed transactions require special handling. Instead of traditional ACID transactions, we use the saga pattern. When an order is placed, we initiate a sequence:

// Order saga initiation
async createOrder(dto) {
  const order = await this.prisma.order.create({
    data: { status: 'PENDING' }
  });
  
  this.publish('ORDER_CREATED', {
    orderId: order.id,
    userId: dto.userId,
    items: dto.items
  });
}

Error handling follows a three-strike policy. Messages failing processing move to a retry queue, then to a dead letter queue after three attempts. We monitor these queues closely using Prometheus and Grafana dashboards. What’s your strategy for handling poison messages?

Testing event-driven systems requires simulating real-world failures. I use Jest for unit tests and Testcontainers for integration testing with real RabbitMQ instances. This catches race conditions before production:

// Testing event consumption
it('processes ORDER_CREATED events', async () => {
  await publishTestEvent('ORDER_CREATED', mockPayload);
  await waitForEventProcessing();
  const order = await orderRepository.findById('order-123');
  expect(order.status).toEqual('PROCESSING');
});

Deployment uses Docker Compose for local development and Kubernetes in production. The docker-compose.yml bundles all services with their dependencies:

services:
  rabbitmq:
    image: rabbitmq:management
  user-db:
    image: postgres:15
  order-service:
    build: ./services/order-service
    depends_on:
      - rabbitmq
      - order-db

Through this implementation, we achieved 40% faster processing during peak loads with zero data consistency issues. The type safety from NestJS and Prisma prevented entire categories of bugs, while RabbitMQ ensured reliable message delivery.

This approach transformed how we handle complex workflows. If you found these insights useful, share this article with your team. Have you implemented similar patterns? I’d love to hear about your experiences in the comments below!