I’ve been thinking a lot lately about how modern applications handle scale and complexity. It’s one thing to build a simple service, but what happens when you need to coordinate multiple services, process background tasks efficiently, and keep everything responsive? That’s where event-driven microservices come in—they let you build systems that are resilient, scalable, and easy to maintain. So I decided to put together a practical guide on building them using NestJS, Redis, and Bull Queue. If you’re looking to build something that can grow with your needs, this is for you.

Let’s start with the basics. Event-driven architecture means services communicate by emitting and listening to events rather than calling each other directly. This approach reduces tight coupling and makes it easier to scale or modify individual parts of your system. For example, when an order is placed, the order service can emit an event, and other services—like inventory or notifications—can react without being tightly linked.

Here’s a simple event emitter setup in NestJS:

// event-emitter.service.ts
import { Injectable } from '@nestjs/common';
import { EventEmitter2 } from '@eventemitter2';

@Injectable()
export class EventEmitterService {
  constructor(private eventEmitter: EventEmitter2) {}

  async emit(event: string, data: any): Promise<void> {
    this.eventEmitter.emit(event, data);
  }
}

Why use events instead of direct API calls? Think about it: if one service goes down, should it bring everything else to a halt? Events let services work independently, improving fault tolerance.

Now, let’s talk about Redis. It’s not just a cache—it’s a powerful tool for pub/sub messaging and job queuing. By using Redis with Bull Queue, you can manage background jobs like sending emails, processing payments, or updating inventory without blocking your main application flow.

Here’s how you might set up a Bull queue in a NestJS service:

// order.service.ts
import { Injectable } from '@nestjs/common';
import { InjectQueue } from '@nestjs/bull';
import { Queue } from 'bull';

@Injectable()
export class OrderService {
  constructor(@InjectQueue('order-processing') private orderQueue: Queue) {}

  async createOrder(orderData: any) {
    await this.orderQueue.add('process-order', orderData, {
      attempts: 3,
      backoff: 5000,
    });
  }
}

What happens if a job fails? Bull lets you configure retries with exponential backoff, so transient issues don’t derail your process.

Handling errors gracefully is crucial. You don’t want a single failing job to pile up and clog your queue. With Bull, you can set up failure handlers to move jobs to a dead-letter queue or trigger alerts.

Here’s an example of a job processor with error handling:

// order-processor.consumer.ts
import { Process, Processor } from '@nestjs/bull';
import { Job } from 'bull';

@Processor('order-processing')
export class OrderProcessor {
  @Process('process-order')
  async handleOrder(job: Job) {
    try {
      // Process the order
      console.log(`Processing order: ${job.data.id}`);
    } catch (error) {
      console.error(`Job ${job.id} failed: ${error.message}`);
      throw error; // Bull will handle retries
    }
  }
}

How do you ensure events are delivered even during high load? Redis pub/sub helps here, but you still need to design your services to handle bursts. Using queues decouples processing from event ingestion, so you can scale workers independently.

Monitoring is another key piece. Without visibility, it’s hard to know if your system is healthy. Tools like Bull Board can help you visualize queues, and integrating logging and metrics lets you track performance and errors.

Deploying everything with Docker Compose simplifies running multiple services. Here’s a snippet for a basic setup:

# docker-compose.yml
version: '3.8'
services:
  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"

  order-service:
    build: ./apps/order-service
    environment:
      REDIS_URL: redis://redis:6379
    depends_on:
      - redis

This approach keeps your services isolated and easy to scale. Want to add a new service? Just define it in your compose file and connect it to Redis.

Building event-driven microservices isn’t just about the tools—it’s about designing for change. By using events and queues, you create a system that can evolve without constant rewrites. Have you considered how this might simplify your current architecture?

I hope this gives you a solid starting point. Experiment with these patterns, and you’ll find they make your applications more robust and easier to extend. If you found this helpful, feel free to share your thoughts or questions in the comments—I’d love to hear how you’re using these techniques in your projects.