I’ve been thinking a lot about how modern applications handle complex workflows while staying responsive and scalable. That’s what led me to explore event-driven microservices—a pattern that transforms how services communicate and coordinate. When you build systems that need to react to changes in real-time, handle high loads gracefully, and remain resilient under failure, this architecture becomes essential.

Let me show you how to build a production-ready event-driven microservice using NestJS, RabbitMQ, and MongoDB. We’ll create a system where services communicate through events rather than direct calls, enabling loose coupling and better scalability.

First, we set up our project structure. I prefer organizing services and shared components clearly from the start.

mkdir event-driven-microservices
cd event-driven-microservices
mkdir services/order-service services/inventory-service services/notification-service
mkdir shared/events shared/types

Our architecture uses RabbitMQ as the message broker. Services publish events when something important happens, and other services subscribe to those events. This approach means services don’t need to know about each other directly—they just react to events.

Why is this powerful? Because it allows each service to focus on its specific domain without being tightly coupled to others. If one service goes down, others can continue processing events once it’s back online.

Here’s how we define a base event class that all our events will extend:

export abstract class BaseEvent {
  id: string;
  aggregateId: string;
  aggregateType: string;
  timestamp: Date;
  eventType: string;
  version: string;

  constructor(aggregateId: string, aggregateType: string, eventType: string) {
    this.id = crypto.randomUUID();
    this.aggregateId = aggregateId;
    this.aggregateType = aggregateType;
    this.eventType = eventType;
    this.timestamp = new Date();
    this.version = '1.0.0';
  }
}

Now, let’s create a specific event for when an order is created. Notice how we extend the base event and add order-specific properties:

export class OrderCreatedEvent extends BaseEvent {
  customerId: string;
  items: OrderItem[];
  totalAmount: number;
  status: string;

  constructor(aggregateId: string, customerId: string, items: OrderItem[], totalAmount: number) {
    super(aggregateId, 'Order', 'OrderCreated');
    this.customerId = customerId;
    this.items = items;
    this.totalAmount = totalAmount;
    this.status = 'PENDING';
  }
}

In our order service, when a new order is created, we publish this event. Other services can then react to it. For example, the inventory service might update stock levels, while the notification service sends a confirmation email.

But how do we ensure these events are processed reliably? That’s where RabbitMQ comes in. We use it to queue messages and ensure they’re delivered even if services restart.

Here’s how we set up a RabbitMQ connection in NestJS:

const rabbitMqOptions: RabbitMQConfig = {
  transport: Transport.RMQ,
  options: {
    urls: ['amqp://localhost:5672'],
    queue: 'order_queue',
    queueOptions: {
      durable: true,
    },
  },
};

MongoDB stores our application data and can also serve as an event store. By storing all events that occur, we create an audit trail and enable event sourcing—reconstructing application state by replaying events.

What happens when something goes wrong? We implement retry mechanisms and dead letter queues. If processing fails, messages move to a separate queue for investigation.

@MessagePattern('order.created')
@UseInterceptors(RetryInterceptor)
async handleOrderCreated(data: OrderCreatedEvent) {
  try {
    await this.inventoryService.updateStock(data.items);
  } catch (error) {
    throw new RpcException('Failed to update inventory');
  }
}

Testing becomes crucial in distributed systems. We need to verify that events are published correctly and handlers respond as expected. I often use Docker to spin up RabbitMQ and MongoDB for integration tests.

Monitoring is another key aspect. We track message rates, processing times, and error rates to understand system health. Tools like Prometheus and Grafana help visualize these metrics.

As we deploy to production, we containerize our services with Docker and implement health checks. Kubernetes can orchestrate these containers, ensuring they’re always available and scaled appropriately.

The beauty of this architecture lies in its flexibility. New services can be added without modifying existing ones—they just subscribe to relevant events. This makes the system adaptable to changing requirements.

Building event-driven microservices requires careful thought about message formats, error handling, and data consistency. But the payoff is a system that scales well, handles failures gracefully, and evolves easily.

What patterns have you found effective in your distributed systems? I’d love to hear about your experiences and challenges.

If this approach resonates with you, please share your thoughts in the comments. Your feedback helps all of us learn and improve our craft. Don’t forget to like and share if you found this useful!