I’ve spent countless hours debugging messy event systems where type errors and race conditions caused production outages. That frustration led me to build robust, type-safe event-driven architectures that actually work in real-world scenarios. Today, I’ll show you how to combine TypeScript’s type system with NestJS’s structure and Redis Streams’ reliability to create something truly maintainable.

Setting up the foundation is crucial. I start with a fresh NestJS project and add Redis support. The package installation is straightforward, but the project structure matters immensely. I organize events by domain, with clear separation between contracts, handlers, and the bus implementation. This makes the system predictable and easy to navigate as it grows.

Have you ever wondered how to ensure events are always valid at compile time? Type-safe event contracts are the answer. I define base interfaces for all events, ensuring every event has essential metadata like IDs, timestamps, and versioning. Using class-validator decorators, I can validate event data automatically before it even reaches the bus.

@RegisterEvent({
  type: 'user.created',
  version: 1
})
export class UserCreatedEventData {
  @IsString()
  @IsNotEmpty()
  userId: string;

  @IsEmail()
  email: string;
}

What happens when events need to evolve? Versioning becomes your best friend. I register events in a central registry that tracks versions and data schemas. This prevents breaking changes from cascading through your system and makes event evolution manageable.

Redis Streams changed how I think about event persistence. Unlike traditional message queues, streams provide built-in persistence, consumer groups, and exactly-once processing semantics. I configure the event bus to use Redis Streams with consumer groups, ensuring multiple services can process events independently while maintaining order.

const result = await this.redis.xadd(
  `${this.config.streamPrefix}:${event.type}`,
  '*',
  'event',
  JSON.stringify(event)
);

Error handling often gets overlooked until it’s too late. I implement dead letter queues for failed events and automatic retry mechanisms. When an event fails processing after multiple attempts, it moves to a separate stream for manual inspection. This prevents one bad event from blocking the entire system.

How do you test event-driven systems effectively? I use a combination of unit tests for individual handlers and integration tests that verify the entire flow. Mocking the Redis connection during tests ensures fast, reliable test execution without external dependencies.

Monitoring event flows is non-negotiable in production. I add structured logging to track event processing latency, error rates, and throughput. Combining this with distributed tracing helps pinpoint bottlenecks when issues arise across multiple services.

Performance optimization comes down to smart batching and parallel processing. I configure the event bus to process events in batches where possible, reducing Redis round trips. For CPU-intensive handlers, I use worker threads to prevent blocking the main event loop.

What about event replay capabilities? I design the system to support replaying events from specific points in time. This is invaluable for debugging production issues or rebuilding read models after schema changes. Redis Streams’ native support for reading historical data makes this straightforward.

The beauty of this architecture shines during system evolution. When I need to add new features, I can introduce new event handlers without modifying existing code. The loose coupling means services can evolve independently, reducing coordination overhead across teams.

In my experience, the initial investment in type safety pays dividends during maintenance. Catching event schema issues at compile time prevents runtime errors that are notoriously difficult to debug. TypeScript’s advanced type features like conditional types and mapped types help create flexible yet safe event contracts.

Building this architecture requires careful consideration of trade-offs. While Redis Streams provide excellent durability, they might not be the best fit for extremely high-throughput scenarios where Kafka would excel. However, for most applications, Redis offers the perfect balance of simplicity and reliability.

I’d love to hear about your experiences with event-driven systems. What challenges have you faced, and how did you overcome them? If you found this approach helpful, please share it with your team and leave a comment about how you’ve implemented similar patterns. Your feedback helps me create better content for everyone.