I’ve been thinking a lot lately about how modern applications handle complex workflows while remaining scalable and resilient. This led me to explore event-driven architecture—a pattern that fundamentally changes how services communicate. Instead of direct API calls, services emit events that other services can react to. This approach creates systems that are more flexible, scalable, and easier to maintain.

Why does this matter? Consider how often you’ve seen systems become tightly coupled, where changing one service requires updating several others. Event-driven architecture solves this by creating loose connections between services. Each service focuses on its specific responsibility and communicates through events, making the entire system more robust.

Let me show you how to implement this using NestJS and Redis Streams. First, we need to set up our project structure and dependencies. The foundation starts with creating our core event classes.

// Base event interface
export interface IBaseEvent {
  id: string;
  type: string;
  aggregateId: string;
  aggregateType: string;
  version: number;
  timestamp: Date;
}

Have you ever wondered how events maintain consistency across services? The answer lies in proper serialization and versioning. Each event carries enough information to reconstruct state, making the system resilient to failures.

Now let’s look at implementing the event store service with Redis Streams:

@Injectable()
export class EventStoreService {
  constructor(private readonly redis: Redis) {}

  async appendEvent(event: DomainEvent): Promise<string> {
    const streamName = this.getStreamName(event.aggregateType);
    const serializedEvent = this.serializeEvent(event);
    
    return await this.redis.xadd(
      streamName,
      '*',
      'event', JSON.stringify(serializedEvent)
    );
  }
}

What happens when a service goes offline and misses events? Redis Streams’ consumer groups ensure that events aren’t lost. Each consumer in a group gets a copy of the events, and the group tracks which events each consumer has processed.

Here’s how we handle event consumption:

async processEvents(consumerGroup: string, consumerName: string) {
  const events = await this.redis.xreadgroup(
    'GROUP', consumerGroup, consumerName,
    'COUNT', '100',
    'BLOCK', '2000',
    'STREAMS', streamName, '>'
  );
  
  for (const event of events) {
    try {
      await this.handleEvent(event);
      await this.redis.xack(streamName, consumerGroup, event.id);
    } catch (error) {
      this.logger.error(`Failed to process event: ${error.message}`);
    }
  }
}

Monitoring is crucial in production systems. How do you know if your event processing is keeping up? Implementing proper observability with metrics and logging helps identify bottlenecks before they become problems.

Error handling deserves special attention. When events fail processing, we need strategies for retry mechanisms and dead-letter queues. This ensures that temporary failures don’t cause permanent data loss.

async handleFailedEvent(event: DomainEvent, error: Error) {
  const retryCount = event.metadata?.retryCount || 0;
  
  if (retryCount < MAX_RETRIES) {
    await this.redis.xadd(
      'retry-queue',
      '*',
      'event', JSON.stringify({
        ...event,
        metadata: { ...event.metadata, retryCount: retryCount + 1 }
      })
    );
  } else {
    await this.moveToDeadLetterQueue(event, error);
  }
}

Scaling becomes straightforward with this architecture. You can add more consumers to handle increased load, and because each consumer processes events independently, the system naturally distributes the workload.

Testing event-driven systems requires a different approach. Instead of testing API endpoints, we focus on ensuring events are properly emitted and processed. Integration tests that verify the entire event flow are essential.

describe('Order Processing', () => {
  it('should emit OrderCreated event when order is placed', async () => {
    const order = await orderService.createOrder(testOrderData);
    const events = await eventStore.getEvents('Order', order.id);
    
    expect(events).toContainEqual(
      expect.objectContaining({ type: 'OrderCreatedEvent' })
    );
  });
});

The beauty of this architecture is how it enables evolutionary design. New features can be added by introducing new event types and consumers without disrupting existing services. This makes the system adaptable to changing business requirements.

What if you need to replay events for debugging or to rebuild a service’s state? The event store acts as a complete history, allowing you to reprocess events from any point in time.

Remember that event-driven architecture isn’t a silver bullet. It introduces complexity in event ordering, exactly-once processing, and monitoring. However, when implemented correctly, it provides unmatched scalability and resilience.

I’d love to hear your thoughts on this approach. Have you implemented event-driven systems before? What challenges did you face? Share your experiences in the comments below, and if you found this useful, please like and share with others who might benefit from this knowledge.