I’ve been building distributed systems for over a decade, and I keep seeing the same pattern: teams struggle with service communication that’s either too tightly coupled or too unreliable. That frustration led me to explore event-driven architecture with TypeScript, Redis Streams, and NestJS—a combination that’s transformed how I design scalable applications. Today, I want to share this approach with you, focusing on type safety and reliability from the ground up.

Event-driven architecture changes how services communicate by using events as the primary mechanism. Services produce events when something meaningful happens, and other services consume those events without direct dependencies. This approach naturally leads to systems that can scale independently and recover from failures gracefully. But have you ever wondered how to maintain type safety across these distributed boundaries?

Let me show you how I set up the foundation. We start with a base event class that ensures every event has essential properties like ID, type, and timestamp. TypeScript’s type system helps catch errors at compile time rather than runtime.

abstract class Event {
  public readonly id: string;
  public readonly type: string;
  public readonly timestamp: Date;

  constructor() {
    this.id = uuidv4();
    this.type = this.constructor.name;
    this.timestamp = new Date();
  }

  abstract serialize(): Record<string, any>;
}

Why Redis Streams over other message brokers? Redis Streams provide persistence and consumer groups out of the box, making them ideal for event sourcing. Events stay in the stream until explicitly acknowledged, which prevents data loss. I’ve found this particularly useful for audit trails and replay scenarios.

Here’s how I configure Redis in a NestJS application:

@Module({
  imports: [
    RedisModule.forRoot({
      host: 'localhost',
      port: 6379,
    }),
  ],
})
export class AppModule {}

Creating type-safe event handlers involves decorators that automatically register handlers for specific event types. This pattern ensures that the right method gets called for each event, with full TypeScript type checking.

@EventHandler(UserCreatedEvent)
async handleUserCreated(event: UserCreatedEvent) {
  // TypeScript knows event has userId, email, etc.
  await this.userService.createProfile(event.userId, event.email);
}

What happens when an event fails processing? We need dead letter queues for error recovery. I implement this by catching exceptions and moving failed events to a separate stream for later analysis.

async handleEvent(stream: string, event: Event) {
  try {
    await this.eventHandlerRegistry.handle(event);
    await this.redis.xack(stream, 'consumers', event.id);
  } catch (error) {
    await this.redis.xadd('dead-letter-stream', '*', 'event', JSON.stringify(event));
  }
}

Testing event-driven systems requires simulating event flows. I use Jest to create integration tests that publish events and verify consumers react correctly. Mocking Redis streams helps isolate tests from infrastructure dependencies.

In production, monitoring becomes crucial. I add metrics for event processing times, failure rates, and consumer lag. Distributed tracing helps track events across service boundaries, making debugging much easier.

Did you know that proper event versioning can prevent breaking changes? I include a version field in every event and use migration strategies when schemas evolve. This practice has saved me from numerous deployment issues.

Here’s a complete example of publishing an event:

@Injectable()
export class UserService {
  constructor(private eventPublisher: EventPublisher) {}

  async createUser(email: string, username: string) {
    const userId = generateId();
    const event = new UserCreatedEvent(userId, email, username);
    await this.eventPublisher.publish('user-stream', event);
    return userId;
  }
}

Building this architecture requires careful consideration of serialization. I use class-transformer to ensure events serialize and deserialize properly, maintaining type information across process boundaries.

What if you need strict ordering? Redis Streams guarantee order within a partition, but for global ordering, you might need additional techniques like version vectors or consensus algorithms.

I’ve deployed this pattern in production across multiple services, handling millions of events daily. The type safety catches potential issues during development, while Redis Streams provide the reliability needed for critical business processes.

Remember that event-driven systems shift complexity from direct service calls to event management. Proper documentation and schema registries help teams understand event contracts and dependencies.

I hope this walkthrough gives you a solid foundation for building your own type-safe event-driven systems. The combination of TypeScript, Redis Streams, and NestJS has proven incredibly powerful in my projects. If you found this helpful, I’d love to hear about your experiences—please share your thoughts in the comments, and don’t forget to like and share this with others who might benefit from it.