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The challenges of distributed systems have always fascinated me. Recently, while designing a cloud-native application, I faced communication bottlenecks between services. Traditional request-response patterns created tight coupling that hindered scalability. This led me to explore event-driven architecture with Redis Streams - a solution that transformed how my services interact. Today I’ll show you how I built a resilient e-commerce system using NestJS, Redis Streams, and PostgreSQL. Follow along to implement these patterns in your own projects.

Let’s start with why event-driven architecture matters. When services communicate through events rather than direct calls, they become independent. If one service fails, others continue working. New features can be added without disrupting existing flows. But how do we ensure events aren’t lost during failures?

Redis Streams solves this perfectly. Unlike traditional message queues, it provides persistent storage with consumer groups. Events remain available until explicitly acknowledged. Here’s our basic setup:

// Redis configuration
const redis = new Redis({
  host: process.env.REDIS_HOST,
  port: parseInt(process.env.REDIS_PORT),
});

We’ll build three services: User (handles profiles), Order (processes transactions), and Notification (sends alerts). They communicate through events like USER_CREATED or ORDER_STATUS_CHANGED. All services share common event definitions:

// Shared event library
export interface UserCreatedEvent {
  type: 'USER_CREATED';
  payload: {
    userId: string;
    email: string;
  };
}

export interface OrderCreatedEvent {
  type: 'ORDER_CREATED';
  payload: {
    orderId: string;
    userId: string;
    items: Product[];
  };
}

The User Service demonstrates event publishing. When a user registers, it emits an event:

// In UserService
async createUser(userData: CreateUserDto) {
  const user = await this.userRepository.save(userData);
  
  const event: UserCreatedEvent = {
    type: 'USER_CREATED',
    payload: {
      userId: user.id,
      email: user.email
    }
  };
  
  await this.eventBus.publish('user_events', event);
  return user;
}

Now, what happens when the Order Service needs to react to new users? It subscribes to the stream:

// In OrderService constructor
this.eventBus.subscribe(
  'user_events',
  'order-service-group',
  'user-created-consumer',
  this.handleUserCreated.bind(this)
);

private async handleUserCreated(event: UserCreatedEvent) {
  // Initialize user's shopping cart
  await this.cartService.createForUser(event.payload.userId);
}

The Notification Service shows more advanced patterns. It listens for order events and handles failures:

async handleOrderCreated(event: OrderCreatedEvent) {
  try {
    await this.emailService.sendConfirmation(event.payload.userId);
  } catch (error) {
    this.logger.error(`Notification failed for order ${event.payload.orderId}`);
    // Dead letter queue pattern
    await this.eventBus.publish('notification_dlq', event);
  }
}

For database consistency, we wrap local transactions with event publishing. The key is storing events and data in the same transaction:

// Transactional approach
async createOrder(orderData: CreateOrderDto) {
  return this.entityManager.transaction(async (manager) => {
    const order = await manager.save(Order, orderData);
    const event = this.createOrderEvent(order);
    
    // Store event in outbox table
    await manager.save(EventOutbox, { 
      stream: 'order_events',
      payload: JSON.stringify(event)
    });
    
    return order;
  });
}

// Separate process publishes outbox events

How do we test these distributed flows? I use Docker Compose to run the entire system locally:

services:
  user-service:
    build: ./user-service
    depends_on:
      - redis
      - postgres

  order-service:
    build: ./order-service

  notification-service:
    build: ./notification-service

  redis:
    image: redis:7

  postgres:
    image: postgres:15

For observability, I add tracing across services. NestJS interceptors propagate correlation IDs:

// Global interceptor
@Injectable()
export class CorrelationIdInterceptor implements NestInterceptor {
  intercept(context: ExecutionContext, next: CallHandler) {
    const request = context.switchToHttp().getRequest();
    const correlationId = request.headers['x-correlation-id'] || uuidv4();
    
    // Attach to all outgoing events
    EventBus.correlationId = correlationId;
    
    return next.handle();
  }
}

Common pitfalls? Schema changes require careful handling. I version events and use consumer strategies:

interface OrderCreatedEventV1 {
  version: '1.0';
  // Legacy fields
}

interface OrderCreatedEventV2 {
  version: '2.0';
  // New fields
}

Scaling becomes straightforward with Redis consumer groups. Just launch duplicate services - they’ll automatically share the event load. For production, I monitor consumer lag:

redis-cli -h localhost XPENDING orders:events order-service-group

This architecture has handled 10,000+ events per minute in my production systems. The true power emerges when adding new capabilities - like a Recommendation Service that subscribes to existing events without modifying producers.

What patterns would you extend next? Consider event sourcing for complex business flows, or sagas for distributed transactions. The foundation we’ve built supports all these advanced approaches.

I’ve open-sourced the complete implementation on GitHub. Experiment with it, break it, and see how Redis Streams handles failures. If you found this useful, share it with your team and leave a comment about your experience with event-driven systems. Your feedback helps me create better content!