I’ve been building web applications for years, and I keep hitting the same wall. As user bases grow and business logic becomes more complex, traditional architectures start to creak under the pressure. That’s why I’ve become fascinated with event-driven systems—they offer a way to build software that scales naturally and remains understandable as complexity increases.

Have you ever wondered what happens to data consistency when multiple services need to react to the same business action? This question haunted me until I discovered the power of combining NestJS with EventStore and Redis. Let me show you how these technologies work together to create systems that handle complexity with grace.

Event-driven architecture fundamentally changes how services communicate. Instead of services calling each other directly, they emit events that other services can react to. This loose coupling means you can scale individual parts of your system independently. When a user places an order, for example, you don’t need the inventory service to be available immediately—it can process the order event when it’s ready.

Here’s how I structure events in my TypeScript projects:

interface OrderCreatedEvent {
  eventId: string;
  eventType: 'OrderCreated';
  aggregateId: string;
  aggregateVersion: number;
  timestamp: Date;
  data: {
    orderId: string;
    customerId: string;
    items: Array<{
      productId: string;
      quantity: number;
      price: number;
    }>;
  };
}

Notice how each event captures the complete context of what happened. This becomes incredibly valuable when you need to understand why a particular decision was made months later.

Setting up the foundation starts with a well-organized NestJS project. I begin by installing the essential packages:

npm install @nestjs/cqrs @eventstore/db-client redis

Then I create a module structure that separates concerns cleanly. Commands handle actions that change state, while queries handle data retrieval. Events represent things that have already happened. This separation might feel unfamiliar at first, but it pays dividends in maintainability.

What if you could replay your entire system’s history to understand a bug? That’s exactly what event sourcing enables. Instead of storing the current state, you store the sequence of events that led to that state. EventStore is perfect for this—it’s built from the ground up for storing events in an append-only log.

Here’s how I configure EventStore in a NestJS module:

@Module({
  providers: [{
    provide: 'EVENT_STORE',
    useFactory: () => EventStoreDBClient.connectionString(
      process.env.EVENTSTORE_URL || 'esdb://localhost:2113'
    )
  }]
})
export class EventStoreModule {}

The beauty of this approach is that events become your source of truth. When you need to create new read models or analytics, you can process existing events without changing your core application logic.

Redis enters the picture for managing read models and caching. Since events are immutable, I can project them into optimized read models that serve queries efficiently. Redis’s speed makes it ideal for this purpose.

Imagine handling thousands of concurrent orders while maintaining data consistency. Here’s a command handler that demonstrates the pattern:

@CommandHandler(CreateOrderCommand)
export class CreateOrderHandler {
  constructor(private readonly eventStore: EventStoreRepository) {}

  async execute(command: CreateOrderCommand) {
    const order = OrderAggregate.create(
      command.orderId,
      command.customerId,
      command.items
    );
    
    await this.eventStore.saveEvents(
      `order-${command.orderId}`,
      order.getUncommittedEvents(),
      order.version
    );
  }
}

The CQRS pattern might seem like overkill for simple applications, but it shines as complexity grows. Separating reads from writes allows you to optimize each path independently. Your write model can focus on business rules and consistency, while your read model delivers data in the most efficient format for consumption.

Error handling requires a different mindset in event-driven systems. Since operations are asynchronous, I’ve learned to implement comprehensive retry mechanisms and dead letter queues. The system must be resilient to temporary failures without losing events.

Testing becomes more straightforward when you can verify the events emitted by each command. I often write tests that look like this:

it('should emit OrderCreated event when valid order is placed', async () => {
  const command = new CreateOrderCommand(testOrderData);
  await handler.execute(command);
  
  const events = await eventStore.getEvents(`order-${testOrderId}`);
  expect(events[0].eventType).toBe('OrderCreated');
});

Have you considered how this architecture affects database choices? With event sourcing, your write database (EventStore) and read database (Redis) have different requirements and can be optimized separately. This separation prevents the common antipattern of designing databases that try to be good at everything but excel at nothing.

Performance optimization in this architecture often involves tuning your projections. Since events are the source of truth, you can rebuild read models from scratch if needed. This flexibility is liberating when you need to change how data is presented to users.

In my experience, the initial learning curve pays off quickly. The system becomes more observable—you can see exactly what happened and when. Debugging production issues becomes easier when you can replay events to reproduce the exact state when an error occurred.

Common pitfalls? Underestimating the importance of event versioning. As your business evolves, events might need to change. I always include version information in events and write migration scripts for old events when necessary.

Another challenge is ensuring eventual consistency. Since reactions to events happen asynchronously, users might see slightly stale data. I address this by designing UIs that handle this gracefully and providing clear feedback.

The combination of NestJS, EventStore, and Redis has transformed how I approach system design. It encourages thinking in terms of business events rather than technical implementation details. This mindset shift leads to software that better models real-world processes.

What questions do you have about implementing this in your own projects? I’d love to hear about your experiences and challenges. If this approach resonates with you, please share this article with your team—these concepts work best when everyone understands them. Leave a comment below with your thoughts or questions!