I’ve been thinking about how modern applications need to handle complexity while maintaining reliability. Recently, I worked on a project where traditional monolithic architecture became our bottleneck. That experience led me to explore event-driven microservices with NestJS, RabbitMQ, and Prisma—a combination that brings type safety and scalability to distributed systems.
Why consider this approach? When services communicate through events rather than direct API calls, they become more resilient and loosely coupled. Each service can evolve independently while maintaining clear communication channels.
Let me walk you through building a robust foundation. We start with a monorepo structure that keeps our services organized while sharing common types and utilities.
// Shared event base class
export abstract class BaseEvent {
abstract readonly type: string;
readonly timestamp: Date = new Date();
}Have you ever faced issues where services expect different data formats? TypeScript helps prevent these problems at compile time rather than runtime.
RabbitMQ serves as our message broker, ensuring events are delivered reliably even when services are temporarily unavailable. The setup is straightforward with Docker:
# docker-compose.yml
services:
rabbitmq:
image: rabbitmq:3-management
ports:
- "5672:5672"
- "15672:15672"Each microservice maintains its own database using Prisma. This separation prevents tight coupling between data models. Here’s how we define a user model:
// User service Prisma schema
model User {
id String @id @default(uuid())
email String @unique
name String
createdAt DateTime @default(now())
}When a user registers, the user service publishes an event that other services can react to. The order service might listen for this event to prepare user-specific data, while the notification service could send a welcome email.
How do we ensure events are handled correctly across services? Let’s look at event publishing:
// In user service
async createUser(userData: CreateUserDto) {
const user = await this.prisma.user.create({
data: userData
});
await this.eventBus.publish(
new UserCreatedEvent(user.id, user.email, user.name)
);
return user;
}The beauty of this pattern emerges when we add new functionality. Need to update a loyalty program when users place orders? Simply create a new service that listens to order events without modifying existing services.
Error handling deserves special attention. What happens when a service fails to process an event? RabbitMQ’s acknowledgment system allows us to retry failed messages or move them to dead-letter queues for investigation.
// Handling events with retry logic
async handleOrderCreated(event: OrderCreatedEvent) {
try {
await this.processOrder(event);
} catch (error) {
await this.retryService.scheduleRetry(event);
}
}Monitoring becomes crucial in distributed systems. We need to track event flow and identify bottlenecks. Structured logging and distributed tracing help maintain visibility across service boundaries.
Testing strategies should verify both individual service behavior and cross-service interactions. Event-driven architectures allow us to test services in isolation while ensuring they communicate correctly through contract tests.
As your system grows, you might wonder about deployment complexity. Containerization with Docker and orchestration with Kubernetes provide the scaffolding for reliable deployments. Each service can be scaled independently based on its specific load patterns.
The journey from monolith to microservices requires careful planning, but the payoff in scalability and maintainability is substantial. Type safety with TypeScript and Prisma reduces runtime errors, while RabbitMQ ensures reliable communication.
What challenges have you faced with microservices? I’d love to hear about your experiences and solutions. If this approach resonates with you, please share your thoughts in the comments and pass this along to others who might benefit from these patterns.
