I’ve been building distributed systems for years, and one challenge that consistently arises is how to keep microservices loosely coupled while maintaining data consistency. After wrestling with tightly coupled REST APIs and their limitations, I started exploring event-driven architecture with Node.js. The shift transformed how I design systems, making them more resilient and scalable. In this guide, I’ll walk you through implementing event-driven microservices using EventStore and domain events, drawing from extensive research and hands-on experience.

Event-driven architecture centers around events—meaningful business occurrences like “OrderPlaced” or “PaymentProcessed.” Instead of services calling each other directly, they publish and subscribe to events. This approach reduces dependencies, allowing each service to evolve independently. Have you ever faced a situation where a small change in one service caused cascading failures in others? Event-driven design helps prevent that.

Let’s start with the setup. I prefer using a monorepo with TypeScript for better type safety and organization. Here’s a basic package.json structure for our project:

{
  "name": "event-driven-ecommerce",
  "private": true,
  "workspaces": ["packages/*"],
  "scripts": {
    "dev": "concurrently \"npm run dev:order\" \"npm run dev:inventory\" \"npm run dev:payment\"",
    "build": "npm run build --workspaces"
  }
}

We’ll use Docker to run EventStore, Redis, and PostgreSQL. This docker-compose.yml gets the infrastructure running quickly:

services:
  eventstore:
    image: eventstore/eventstore:23.10.0-bookworm-slim
    ports: ["1113:1113", "2113:2113"]
    environment:
      - EVENTSTORE_INSECURE=true

Domain events are the heart of this architecture. They represent something that happened in the business, carrying all the context needed for other services to react. I define them using Zod for validation, which catches errors early. Here’s a base event structure:

import { z } from 'zod';

export const BaseEventSchema = z.object({
  id: z.string().uuid(),
  aggregateId: z.string().uuid(),
  eventType: z.string(),
  occurredAt: z.date()
});

When you store only events rather than current state, you gain a complete audit trail. How might replaying past events help you debug a production issue? Event sourcing allows reconstructing state at any point in time, which I’ve found invaluable for troubleshooting.

Implementing the core infrastructure involves setting up EventStore connections and event handlers. In Node.js, I use the @eventstore/db-client package. Here’s a simplified event store service:

import { EventStoreDBClient } from '@eventstore/db-client';

const client = EventStoreDBClient.connectionString('esdb://localhost:2113?tls=false');

export async function appendEvent(streamName: string, event: any) {
  await client.appendToStream(streamName, event);
}

Building an order service demonstrates how events flow. When a user places an order, the service emits an “OrderPlaced” event. Other services like inventory and payment listen and act accordingly. This separation means the order service doesn’t need to know about inventory logic.

What happens if the payment service is temporarily down? With event-driven systems, events can be retried, ensuring eventual consistency. I implement sagas to manage distributed transactions—a series of steps where each triggers the next through events.

Here’s a snippet from a saga orchestrator handling an order process:

class OrderSaga {
  async start(orderId: string) {
    await this.emit('OrderProcessingStarted', { orderId });
    // Subsequent steps handled by other services
  }
}

Error handling is crucial. I add retry mechanisms with exponential backoff and dead-letter queues for problematic events. Monitoring with tools like Prometheus helps track event flows and identify bottlenecks.

Testing event-driven systems requires simulating event sequences. I use Jest to verify that services emit correct events and handle them appropriately. For example:

test('order placement emits event', async () => {
  const orderService = new OrderService();
  await orderService.placeOrder({ items: ['item1'] });
  expect(eventStore.getEvents()).toContain('OrderPlaced');
});

Deploying to production involves scaling services based on event load. Kubernetes works well for this, with horizontal pod autoscaling. I’ve seen systems handle millions of events daily by adjusting replica counts dynamically.

Common pitfalls include overcomplicating event schemas or neglecting idempotency. Always version your events and design handlers to process the same event multiple times safely. How would you handle a duplicate “PaymentProcessed” event?

Throughout this journey, I’ve learned that event-driven architecture isn’t a silver bullet—it introduces complexity in exchange for scalability and resilience. Start small, focus on clear domain boundaries, and iterate.

If this guide helped you grasp event-driven systems, I’d love to hear your thoughts! Please like, share, or comment with your experiences or questions. Let’s build more robust systems together.