Lately, I’ve been thinking a lot about how modern applications handle scale and complexity. It’s one thing to build a service that works on your machine, but another entirely to design one that stays reliable under real-world pressure. That’s why I decided to explore building production-ready event-driven microservices—a system that can grow, adapt, and remain observable even when things go wrong. If you’ve ever wondered how to keep services communicating smoothly without creating a tangled web of dependencies, you’re in the right place.
Event-driven architectures help decouple services, allowing each part of your system to work independently while still collaborating through events. I chose Go for its simplicity and performance, NATS JetStream for durable messaging, and OpenTelemetry to keep track of what’s happening across services. This combination isn’t just powerful—it’s practical.
Let’s start with the basics. How do we ensure these events reach their destination reliably? JetStream provides persistence, so messages aren’t lost if a service restarts. Here’s a simple way to set up a connection:
nc, err := nats.Connect("nats://localhost:4222")
if err != nil {
log.Fatal("Connection failed:", err)
}
defer nc.Close()
js, err := nc.JetStream()
if err != nil {
log.Fatal("JetStream context failed:", err)
}Once connected, we can create a stream to hold our events. Think of a stream as a durable log where events are stored until processed.
_, err = js.AddStream(&nats.StreamConfig{
Name: "ORDERS",
Subjects: []string{"orders.>"},
})
if err != nil {
log.Fatal("Stream creation failed:", err)
}Now, what happens when you need to share data between services in a structured way? Protocol Buffers offer a compact, efficient format. Here’s how you might publish an event:
orderCreated := &events.OrderCreated{
Metadata: &events.EventMetadata{
EventId: uuid.New().String(),
CorrelationId: "corr-123",
ServiceName: "order-service",
Timestamp: timestamppb.Now(),
},
OrderId: "order-456",
CustomerId: "cust-789",
}
data, _ := proto.Marshal(orderCreated)
_, err = js.Publish("orders.created", data)But building services isn’t just about sending messages—it’s also about understanding what’s happening as those messages travel. Have you ever tried debugging a distributed system without proper traces? It’s like finding a needle in a haystack. OpenTelemetry provides those traces, giving you a clear view of each event’s journey.
Integrating tracing is straightforward. Here’s a basic setup:
tp := trace.NewTracerProvider()
otel.SetTracerProvider(tp)
ctx, span := tp.Tracer("order-service").Start(context.Background(), "ProcessOrder")
defer span.End()
// Now pass this ctx through your callsErrors are inevitable. How do you keep a system resilient? Retries with exponential backoff help manage temporary issues without overwhelming your services.
retryPolicy := backoff.NewExponentialBackOff()
retryPolicy.MaxElapsedTime = time.Minute
err := backoff.Retry(func() error {
return processEvent(ctx, event)
}, retryPolicy)
if err != nil {
log.Printf("Final error after retries: %v", err)
}And what about monitoring? Prometheus metrics can be added to track everything from message rates to error counts, giving you real-time insight into system health.
Deploying all this with Docker ensures consistency across environments. Here’s a snippet from a Dockerfile for a Go service:
FROM golang:1.21-alpine AS builder
WORKDIR /app
COPY . .
RUN go build -o /order-service ./cmd/order
FROM alpine:latest
COPY --from=builder /order-service .
CMD ["./order-service"]Building event-driven microservices is more than just coding—it’s about designing for failure, observing behavior, and ensuring reliability at every step. The tools and patterns we’ve covered form a solid foundation, but the real value comes from adapting them to your unique challenges.
I hope this gives you a useful starting point. If you found this helpful, feel free to share your thoughts in the comments or pass it along to others who might benefit. Let’s keep the conversation going.
