I’ve been thinking a lot about how modern applications handle massive scale while remaining resilient and responsive. In my work with distributed systems, I’ve seen firsthand how traditional request-response architectures can become bottlenecks as systems grow. That’s what led me to explore event-driven architecture—a pattern that has transformed how I build scalable applications. Today, I want to share my approach to building distributed systems using Node.js, EventStore, and Docker.

Have you ever considered what happens when your application needs to process thousands of operations simultaneously without slowing down? Event-driven architecture addresses this by treating every state change as an immutable event. Instead of services calling each other directly, they emit events that other services can react to. This loose coupling means your system can scale horizontally and handle failures gracefully.

Let me show you how to set this up. We’ll use Docker to containerize our services and infrastructure. Here’s a basic docker-compose file to get EventStore running:

services:
  eventstore:
    image: eventstore/eventstore:22.10.0-buster-slim
    environment:
      - EVENTSTORE_INSECURE=true
    ports:
      - "2113:2113"

Why is EventStore so crucial? It’s designed specifically for storing sequences of events, making it perfect for event sourcing. In event sourcing, we don’t just save the current state—we keep the entire history of changes. This allows us to reconstruct past states and debug issues more effectively.

Now, let’s define our events in TypeScript. Events are the heart of our system. They represent something that has happened and contain all the relevant data.

interface OrderCreatedEvent {
  eventType: 'OrderCreated';
  data: {
    orderId: string;
    customerId: string;
    totalAmount: number;
  };
}

When you emit an event, how do other services know to react? That’s where event handlers come in. Each service listens for specific events and performs actions when they occur. For example, an inventory service might listen for OrderCreated events to reserve stock.

What if you need to query data efficiently while handling high write volumes? This is where CQRS (Command Query Responsibility Segregation) shines. It separates read and write operations into different models. Commands change state, while queries read data. Here’s a simple command handler:

class CreateOrderHandler {
  async handle(command: CreateOrderCommand) {
    const events = OrderAggregate.create(command);
    await eventStore.appendToStream(`order-${command.orderId}`, events);
  }
}

Building aggregates is key to maintaining consistency. An aggregate is a cluster of domain objects that can be treated as a single unit. It ensures that business rules are enforced when events are applied.

class OrderAggregate {
  static create(command: CreateOrderCommand): OrderCreatedEvent {
    return {
      eventType: 'OrderCreated',
      data: { ...command },
      timestamp: new Date()
    };
  }
}

Handling eventual consistency can be challenging. Since events are processed asynchronously, different parts of your system might be temporarily inconsistent. I’ve found that designing for idempotency—making sure operations can be repeated safely—helps mitigate issues.

What about monitoring all these event flows? Tools like Grafana can visualize event streams and help identify bottlenecks. You can set up dashboards to track event counts, processing times, and error rates.

Deploying with Docker ensures consistency across environments. Each microservice runs in its own container, making it easy to scale independently. Here’s how you might structure a service:

// order-service/src/index.ts
import { EventStoreClient } from '../shared/infrastructure';

const eventStore = new EventStoreClient();
eventStore.subscribeToStream('orders', handleOrderEvent);

Testing event-driven systems requires a different approach. Instead of mocking dependencies, you can replay event streams to verify behavior. I often use snapshot testing to ensure events are emitted as expected.

In my experience, the biggest pitfall is not designing events carefully. Events should be immutable and represent business facts—not implementation details. Once emitted, they can’t be changed, so plan your schema evolution.

Have you thought about how event-driven systems handle failures? By using persistent event stores and retry mechanisms, events can be replayed if processing fails. This built-in resilience is one reason I prefer this architecture for critical systems.

As we wrap up, I encourage you to experiment with these patterns in your projects. Start small—perhaps with a single service emitting events—and gradually expand. The flexibility and scalability you gain are worth the initial learning curve.

If you found this helpful, please like and share this article. I’d love to hear about your experiences with event-driven architecture in the comments—what challenges have you faced, and how did you overcome them?