As a developer who has spent years wrestling with the complexities of microservice communication, I’ve found that traditional request-response patterns often fall short in production environments. The need for scalable, resilient systems led me to explore event-driven architectures, and today I want to share how you can build robust microservices using NestJS, Redis Streams, and PostgreSQL. This approach has transformed how I handle distributed systems, and I believe it can do the same for you.

Why focus on this particular stack? NestJS provides a structured framework that embraces TypeScript’s power, while Redis Streams offer reliable messaging without the overhead of heavier message brokers. PostgreSQL brings transactional integrity to the table. Together, they create a foundation that can handle real-world loads gracefully.

Have you ever wondered what happens to your messages when a service temporarily goes offline? This was a constant concern in my early projects. Redis Streams address this with consumer groups and message persistence, ensuring no event is lost even during failures.

Let me walk you through a practical implementation. We’ll build an e-commerce system with three core services: user management, order processing, and notifications. Each service operates independently but communicates through events.

Starting with the shared event definitions, we establish a common language for our services. This consistency prevents misunderstandings between components.

// Shared event interface
export interface DomainEvent<T = any> {
  id: string;
  type: string;
  timestamp: Date;
  aggregateId: string;
  data: T;
}

Setting up the project structure is crucial. I prefer a monorepo approach using npm workspaces, which keeps related code together while maintaining separation.

# Project structure
mkdir -p packages/{shared,user-service,order-service,notification-service}

The user service handles authentication and profile management. Here’s how I define the user entity in TypeORM:

@Entity('users')
export class User {
  @PrimaryGeneratedColumn('uuid')
  id: string;

  @Column({ unique: true })
  email: string;

  @Column()
  firstName: string;

  @Column()
  lastName: string;
}

When a user registers, we publish an event to Redis Streams. But how do we ensure this event reaches all interested services? Redis consumer groups make this straightforward.

async publishUserCreated(user: User): Promise<void> {
  const event: UserCreatedEvent = {
    id: uuidv4(),
    type: 'USER_CREATED',
    timestamp: new Date(),
    aggregateId: user.id,
    data: {
      userId: user.id,
      email: user.email,
      firstName: user.firstName,
      lastName: user.lastName
    }
  };
  
  await this.redis.xadd('users-stream', '*', 'event', JSON.stringify(event));
}

The order service listens for these events. When a user is created, it can prepare order history for them. This separation allows each service to maintain its own data consistency.

What about error handling? In event-driven systems, we must plan for failures. I implement dead letter queues to capture problematic events for later analysis.

async handleEvent(stream: string, group: string, consumer: string) {
  try {
    const events = await this.redis.xreadgroup(
      'GROUP', group, consumer, 'STREAMS', stream, '>'
    );
    
    for (const event of events) {
      await this.processEvent(event);
      await this.redis.xack(stream, group, event.id);
    }
  } catch (error) {
    await this.moveToDlq(stream, event, error);
  }
}

PostgreSQL plays a key role in maintaining service state. Using transactions, we ensure that database changes and event publishing happen atomically. This prevents inconsistencies where an event is published but the database update fails.

Testing event-driven systems requires a different approach. I focus on contract testing between services, verifying that events conform to expected schemas. This catches breaking changes early.

Monitoring is non-negotiable in production. I instrument services to track event throughput, processing latency, and error rates. This data helps identify bottlenecks before they impact users.

Deployment considerations include configuring Redis for persistence and setting up PostgreSQL replication. I use Docker to containerize each service, making scaling horizontal and straightforward.

A common pitfall I’ve encountered is over-engineered event schemas. Keep events focused on what changed, not the entire state. This reduces payload size and coupling between services.

Another challenge is distributed transactions. While we aim for eventual consistency, sometimes we need sagas to coordinate multi-step processes across services.

As we wrap up, I encourage you to think about how event-driven patterns could improve your current systems. The loose coupling and scalability benefits are substantial, though they require careful design.

I’ve shared the approaches that have worked well in my projects, but I’m curious about your experiences. What challenges have you faced with microservice communication? If this article helped clarify event-driven architectures, please share it with your team and leave a comment below. Your feedback helps me create better content for our community.