I’ve spent the last few months pulling my hair out over a monolithic backend that couldn’t keep up. Every new feature felt like adding another room to a house built on sand. The system was slow, tightly wound, and a nightmare to scale. This frustration is what led me down a new path, one built on small, independent services that talk through events. I want to show you how to build something better. If you’re tired of the same old problems, stick with me.

We’re going to create a system of services that work together not by calling each other directly, but by announcing what they’ve done. Think of it like a town crier. One service shouts, “A user just registered!” Another service listening to that shout can then start its own work, like sending a welcome email. This is the heart of event-driven design. It keeps services separate and focused. A problem in the email service won’t crash the user registration process.

To make this work, we need a trustworthy messenger. This is where RabbitMQ comes in. It’s a message broker that ensures these announcements (or events) are delivered, even if a service is briefly offline. We’ll set it up using Docker, which makes the infrastructure simple to run on your machine.

Let’s look at a piece of the glue that holds this together. First, we define what our events look like in a shared library that all our services can understand.

// A common definition for an 'Order Created' event
export interface OrderCreatedEvent {
  type: 'ORDER_CREATED';
  payload: {
    orderId: string;
    userId: string;
    totalAmount: number;
  };
}

Now, how does a service publish this event? In our order service, after successfully saving an order to its own database, it would send this message. Here’s a simplified look at how that might work in a NestJS controller.

// Inside the Order Service
@Post()
async createOrder(@Body() createOrderDto: CreateOrderDto) {
  const newOrder = await this.ordersService.create(createOrderDto);

  // Publish an event about what just happened
  await this.eventBus.publish({
    type: 'ORDER_CREATED',
    payload: {
      orderId: newOrder.id,
      userId: newOrder.userId,
      totalAmount: newOrder.total,
    },
  });

  return newOrder;
}

See what happened there? The order service did its job and sent out a signal. It doesn’t know or care who is listening. So, who is listening? A separate notification service could be waiting for that exact event. When it receives the ORDER_CREATED message, it automatically fires off a confirmation email to the user. But what if multiple services need to react? No problem. Five services can listen to one event without the publisher needing any changes.

This is powerful, but it introduces a big question: how do we handle a business process that spans multiple services, like taking payment and updating inventory? We can’t use a traditional database transaction across different services. This is a classic distributed systems problem.

The solution often involves a pattern called Saga. Instead of one big transaction, you manage the process through a series of events and compensating actions. If the payment fails later in the chain, the saga triggers events to undo the earlier steps, like releasing the reserved inventory. It’s more work but essential for reliability. Have you considered how your application would undo a complex action across service boundaries?

Now, let’s talk about speed and state. Constantly asking the user service for profile data will slow everything down. This is where Redis shines. It’s an in-memory data store, perfect for caching frequently accessed information. You can also use it to manage user sessions in a way that any instance of your service can access.

Imagine storing a user’s session after they log in. With Redis, any service can check if that session is valid in a fraction of the time it would take to query a main database.

// Caching a user profile in Redis
async getUserProfile(userId: string) {
  const cacheKey = `user:${userId}`;
  let profile = await this.redisClient.get(cacheKey);

  if (!profile) {
    // Fetch from the main database if not in cache
    profile = await this.userDbRepository.findById(userId);
    // Store it in Redis for next time, expire in 1 hour
    await this.redisClient.setex(cacheKey, 3600, JSON.stringify(profile));
  }

  return JSON.parse(profile);
}

Building this way requires a shift in thinking. You move from writing code that controls a linear flow to designing systems that react to change. Testing becomes about ensuring services respond correctly to events. Monitoring needs to track messages flowing through RabbitMQ and cache hit rates in Redis.

It’s not a silver bullet. You have to think about duplicate messages, event ordering, and designing events that are clear and stable. But the payoff is a system that is resilient, scalable, and a joy to extend. You can deploy a new service that listens to existing events without touching the old, running code.

I went from frustration to building a system where services are good neighbors—they communicate well but mind their own business. It changed how I design software. If this journey from a tangled monolith to a clear, event-driven system makes sense to you, let me know what you think. Have you tried a similar approach? Share your thoughts or questions in the comments. If you found this guide helpful, please like and share it with another developer who might be facing the same scaling walls.