I’ve been thinking about how modern applications handle scale and complexity lately. The shift from monolithic systems to distributed architectures isn’t just a trend—it’s becoming essential for building resilient, scalable applications. That’s why I want to share my experience with event-driven microservices using NestJS, RabbitMQ, and Redis. This combination has proven incredibly effective in production environments, and I believe it can transform how you approach system design.

Have you ever wondered how systems maintain responsiveness while handling thousands of concurrent operations?

Let me show you how to build an event-driven foundation. We’ll start with a basic NestJS service setup:

// user-service/src/main.ts
import { NestFactory } from '@nestjs/core';
import { Transport } from '@nestjs/microservices';
import { UserModule } from './user.module';

async function bootstrap() {
  const app = await NestFactory.create(UserModule);
  
  app.connectMicroservice({
    transport: Transport.RMQ,
    options: {
      urls: ['amqp://localhost:5672'],
      queue: 'user_queue',
      queueOptions: {
        durable: true
      },
    },
  });

  await app.startAllMicroservices();
  await app.listen(3001);
}
bootstrap();

The beauty of event-driven architecture lies in its loose coupling. Services communicate through events rather than direct API calls. When a user registers, instead of calling the notification service directly, we publish an event. Any service interested in new user registrations can subscribe and react accordingly.

What happens when your order service needs to scale independently from your user service?

RabbitMQ acts as our message broker, ensuring reliable delivery between services. Here’s how we set up a message publisher:

// shared/src/messaging/publisher.service.ts
import { Injectable } from '@nestjs/common';
import { RabbitMQService } from './rabbitmq.service';

@Injectable()
export class PublisherService {
  constructor(private readonly rabbitMQService: RabbitMQService) {}

  async publishEvent(exchange: string, event: any) {
    await this.rabbitMQService.publish(exchange, event);
  }
}

Redis plays a crucial role in maintaining state across our distributed system. We use it for caching frequently accessed data and managing user sessions:

// user-service/src/services/redis-cache.service.ts
import { Injectable } from '@nestjs/common';
import Redis from 'ioredis';

@Injectable()
export class RedisCacheService {
  private redisClient: Redis;

  constructor() {
    this.redisClient = new Redis({
      host: 'localhost',
      port: 6379,
    });
  }

  async setUserSession(userId: string, sessionData: any) {
    await this.redisClient.setex(
      `session:${userId}`,
      3600, // 1 hour TTL
      JSON.stringify(sessionData)
    );
  }

  async getUserSession(userId: string) {
    const session = await this.redisClient.get(`session:${userId}`);
    return session ? JSON.parse(session) : null;
  }
}

Event sourcing changes how we think about data. Instead of storing current state, we store the sequence of events that led to that state. This approach provides a complete audit trail and enables powerful features like temporal queries.

How do you ensure events are processed in the correct order?

Here’s an example of event handling in our order service:

// order-service/src/handlers/order-created.handler.ts
import { EventsHandler, IEventHandler } from '@nestjs/cqrs';
import { OrderCreatedEvent } from '@shared/events';

@EventsHandler(OrderCreatedEvent)
export class OrderCreatedHandler implements IEventHandler<OrderCreatedEvent> {
  async handle(event: OrderCreatedEvent) {
    const { orderId, userId, items, totalAmount } = event;
    
    // Process the order
    console.log(`Processing order ${orderId} for user ${userId}`);
    
    // Update read models
    // Send to analytics
    // Trigger downstream processes
  }
}

Testing event-driven systems requires a different approach. We need to verify that events are published and handled correctly:

// order-service/test/order.service.spec.ts
describe('OrderService', () => {
  it('should publish OrderCreatedEvent when creating order', async () => {
    const orderData = { userId: '123', items: [] };
    await orderService.create(orderData);
    
    expect(eventBus.publish).toHaveBeenCalledWith(
      expect.objectContaining({
        eventType: 'OrderCreatedEvent',
        userId: '123'
      })
    );
  });
});

Service discovery and health checks become critical in distributed systems. Each service needs to report its status and discover other services:

// shared/src/health/health.controller.ts
import { Controller, Get } from '@nestjs/common';
import { HealthCheckService, HealthCheck } from '@nestjs/terminus';

@Controller('health')
export class HealthController {
  constructor(private health: HealthCheckService) {}

  @Get()
  @HealthCheck()
  check() {
    return this.health.check([
      () => this.redis.pingCheck('redis'),
      () => this.rabbitMQ.pingCheck('rabbitmq'),
    ]);
  }
}

Distributed tracing helps us understand request flow across service boundaries. By correlating logs and metrics, we can identify bottlenecks and troubleshoot issues more effectively.

What patterns emerge when you can see the entire request journey?

Deployment strategies need consideration too. We can scale individual services based on their specific load patterns. The notification service might need more instances during peak hours, while the user service might require consistent capacity.

Remember that event-driven systems introduce eventual consistency. This trade-off enables higher availability and better performance, but requires careful design around data synchronization.

I’ve found that proper error handling and retry mechanisms are essential. Dead letter queues help manage failed messages, while circuit breakers prevent cascade failures.

The combination of NestJS’s structured approach, RabbitMQ’s reliable messaging, and Redis’s performance creates a robust foundation for modern applications. This architecture has served me well in production, handling millions of events daily while maintaining system stability.

If you found this guide helpful or have experiences with event-driven architectures, I’d love to hear your thoughts. Please like, share, or comment below—your feedback helps improve future content and lets me know what topics interest you most.