I’ve been thinking about distributed systems a lot lately—how we can build applications that not only scale but also remain observable and resilient under pressure. This led me to explore event-driven microservices, a pattern that naturally supports decoupling and scalability. Today, I want to share a practical approach using Go, NATS JetStream, and OpenTelemetry to create production-ready services. If you’ve ever wondered how to handle thousands of events per second while maintaining clear visibility into your system, this is for you.

Let’s start with the basics. Event-driven architecture allows services to communicate asynchronously through events. This means services can operate independently, improving both scalability and fault tolerance. But how do we ensure these events are processed reliably?

NATS JetStream provides persistent messaging with features like exactly-once delivery and message replay. It’s a solid foundation for building resilient systems. Here’s a simple example of connecting to JetStream:

nc, err := nats.Connect("nats://localhost:4222")
if err != nil {
    log.Fatal("Connection failed:", err)
}
js, err := nc.JetStream()
if err != nil {
    log.Fatal("JetStream context failed:", err)
}

But reliable messaging is only part of the story. What happens when something goes wrong? How do we trace a request across multiple services?

OpenTelemetry answers these questions by providing distributed tracing and metrics. It helps us understand the flow of events and identify bottlenecks or failures. Implementing tracing in Go is straightforward:

func StartSpan(ctx context.Context, name string) (context.Context, trace.Span) {
    tracer := otel.Tracer("order-service")
    return tracer.Start(ctx, name)
}

Now, let’s consider data consistency. Using Protocol Buffers for event serialization ensures that our messages are efficient and type-safe. This becomes critical when dealing with high-volume event streams.

Have you thought about how services should handle temporary failures? Circuit breakers and retry mechanisms prevent cascading failures. The gobreaker library offers a simple way to implement this pattern:

cb := gobreaker.NewCircuitBreaker(gobreaker.Settings{
    Name: "payment-service",
    Timeout: 30 * time.Second,
})

Monitoring is another key aspect. Prometheus metrics give us real-time insights into system performance, while health checks ensure our services are ready to handle traffic. A basic health endpoint might look like:

http.HandleFunc("/health", func(w http.ResponseWriter, r *http.Request) {
    w.WriteHeader(http.StatusOK)
    w.Write([]byte("OK"))
})

When deploying to Kubernetes, graceful shutdown becomes essential. Services need to complete ongoing work before terminating. This prevents data loss and maintains system integrity.

What patterns work best for stream processing? Event sourcing allows us to reconstruct system state by replaying events, while idempotent handlers ensure that processing the same event multiple times doesn’t cause issues.

Building these systems requires careful attention to error handling, monitoring, and deployment strategies. Each service must be independently deployable and scalable, with clear contracts between them.

I encourage you to experiment with these concepts. Start small, perhaps with a single service publishing events, and gradually build out your architecture. The combination of Go’s efficiency, NATS JetStream’s reliability, and OpenTelemetry’s observability creates a powerful foundation for modern applications.

What challenges have you faced with event-driven systems? How do you balance consistency and availability in your designs? I’d love to hear your thoughts—please share your experiences in the comments below. If you found this useful, consider sharing it with others who might benefit.