I’ve been thinking a lot about scalable systems lately. As applications grow, traditional request-response patterns often become bottlenecks. That’s why I started exploring event-driven architectures using NestJS and Redis Streams. The combination offers reliability while maintaining flexibility - crucial when systems need to evolve without downtime. Let’s explore how these technologies work together to create robust, scalable systems.

Setting up our project requires careful organization. I begin with a clean NestJS structure that separates concerns:

npm install @nestjs/{core,common,config} ioredis class-validator

The core directory organizes events, decorators, and interfaces. This separation proves valuable as the system expands. For event definitions, I create a base structure:

// base-event.ts
export abstract class DomainEvent {
  public readonly id: string;
  public readonly timestamp: Date = new Date();
  
  constructor(
    public readonly aggregateId: string,
    public readonly eventType: string,
    public readonly data: Record<string, any>
  ) {
    this.id = `${Date.now()}-${Math.random().toString(36).slice(2, 11)}`;
  }
}

Redis Streams integration forms the backbone of our messaging. Why choose this over traditional pub/sub? Persistent storage and consumer groups change how we handle events. Here’s my Redis configuration:

// redis.config.ts
@Injectable()
export class RedisConfigService {
  createRedisConnection(): Redis {
    return new Redis({
      host: configService.get('REDIS_HOST'),
      port: configService.get('REDIS_PORT'),
      maxRetriesPerRequest: 3,
      lazyConnect: true
    });
  }
}

Event producers need to reliably publish messages. In our order service, publishing an event becomes straightforward:

// order.service.ts
async createOrder(orderData) {
  const order = await this.saveOrder(orderData);
  const event = new OrderCreatedEvent(order.id, orderData);
  await this.eventBus.publish('orders_stream', event);
  return order;
}

But what happens when consumers fail? Building robust consumers requires more than basic handlers. Here’s a consumer setup that includes acknowledgment:

// payment.consumer.ts
@EventHandler('orders_stream')
async handleOrderCreated(event: OrderCreatedEvent) {
  try {
    await this.paymentService.process(event.data);
    await this.eventBus.ack('orders_stream', event.id);
  } catch (error) {
    this.logger.error(`Payment failed: ${error.message}`);
  }
}

Scaling becomes essential under load. Consumer groups allow horizontal scaling with automatic load balancing. Implementing them in Redis Streams is surprisingly simple:

await redis.xgroup('CREATE', 'orders_stream', 'payments_group', '0', 'MKSTREAM');

Error handling separates amateur from production-ready systems. Dead letter queues capture failed events for later analysis:

// event-bus.service.ts
async handleFailedEvent(stream: string, eventId: string, error: any) {
  const event = await this.getEvent(stream, eventId);
  await this.redis.xadd('dead_letter_queue', '*', ...this.serializeEvent(event));
}

Monitoring event flows provides crucial insights. I integrate OpenTelemetry to trace events across services:

// tracing.config.ts
const tracer = new NodeTracerProvider();
tracer.addSpanProcessor(new BatchSpanProcessor(new ConsoleSpanExporter()));
tracer.register();

Testing event-driven systems presents unique challenges. I use Docker containers for integration tests:

docker run -p 6379:6379 redis/redis-stack-server:latest

Deployment considerations significantly impact reliability. Kubernetes deployments with proper resource limits ensure stability:

# payment-deployment.yaml
resources:
  limits:
    memory: "512Mi"
    cpu: "500m"

Common pitfalls often surprise developers. Did you know that unacknowledged messages can accumulate, causing memory pressure? Setting TTLs prevents this:

CONFIG SET stream-node-max-bytes 4096
CONFIG SET stream-node-max-entries 100

Another frequent issue involves unordered processing. Using Redis Streams’ natural ordering maintains sequence integrity without complex logic.

Through this journey, I’ve learned that resilience comes from thoughtful design, not complexity. The combination of NestJS and Redis Streams creates systems that scale gracefully while remaining understandable. What challenges have you faced with event-driven architectures?

If you found this exploration helpful, share it with others facing similar architectural decisions. Your comments and experiences enrich our collective knowledge - join the conversation below!