I’ve been thinking a lot lately about how modern applications handle complexity at scale. When you’re dealing with dozens of services that all need to communicate reliably, traditional request-response patterns start to show their limitations. That’s why I want to share my approach to building event-driven systems using NATS, Go, and distributed tracing—a combination that has transformed how I think about scalable architecture.

Have you ever wondered what happens when a service goes down in the middle of processing a critical business operation? Event-driven architecture addresses this by decoupling services through messaging. Instead of services calling each other directly, they publish events that other services can consume when they’re ready. This approach brings resilience, scalability, and flexibility to complex systems.

Let me show you how to set up a basic NATS connection in Go. This simple code establishes a persistent connection to our messaging backbone:

conn, err := nats.Connect("nats://localhost:4222")
if err != nil {
    log.Fatal("Connection failed:", err)
}
defer conn.Close()

But why stop at basic connectivity? JetStream, NATS’ persistence layer, ensures messages aren’t lost if services restart. Here’s how you create a stream that retains order events:

js, _ := conn.JetStream()
js.AddStream(&nats.StreamConfig{
    Name:     "ORDERS",
    Subjects: []string{"orders.>"},
})

Now, let’s talk about events themselves. Defining clear event schemas is crucial. I structure my events with a base containing common metadata and specific data payloads:

type OrderCreatedEvent struct {
    ID        string    `json:"id"`
    Type      string    `json:"type"`
    Timestamp time.Time `json:"timestamp"`
    Data      OrderData `json:"data"`
}

What happens when services need to understand the context of operations across multiple events? This is where distributed tracing shines. By propagating trace context through events, we can follow a single operation across service boundaries. Here’s how you inject tracing information into your messages:

carrier := propagation.HeaderCarrier{}
otel.GetTextMapPropagator().Inject(ctx, carrier)
msg.Header.Set("TraceContext", carrier.Get("traceparent"))

Error handling in event-driven systems requires special attention. I implement retry mechanisms with exponential backoff and dead letter queues for problematic messages:

sub, _ := js.QueueSubscribe("orders.process", "order-processors", 
    func(msg *nats.Msg) {
        if err := processOrder(msg); err != nil {
            if shouldRetry(err) {
                msg.Nak() // Negative acknowledgment
            } else {
                moveToDLQ(msg) // Dead letter queue
            }
        }
        msg.Ack() // Positive acknowledgment
    },
)

Testing event-driven services presents unique challenges. I’ve found that creating test containers with pre-loaded messages helps verify service behavior under various conditions. Mocking NATS connections in unit tests ensures your business logic remains isolated from infrastructure concerns.

Deployment considerations include configuring appropriate retention policies, monitoring consumer lag, and setting up alerts for processing delays. Health checks should verify both service status and connectivity to NATS:

func healthCheck() bool {
    return conn.Status() == nats.CONNECTED
}

As we build these systems, questions naturally arise. How do we handle schema evolution without breaking existing services? What’s the best way to monitor message throughput and latency? These considerations become part of our ongoing design process.

The combination of Go’s performance characteristics, NATS’ lightweight messaging, and comprehensive tracing creates a powerful foundation for building resilient systems. Each component plays a specific role: Go provides the execution engine, NATS handles message distribution, and tracing gives us operational visibility.

I encourage you to experiment with these patterns in your own projects. Start small with a single event type and gradually expand as you become comfortable with the patterns. The initial investment in setting up the infrastructure pays dividends as your system grows in complexity.

What challenges have you faced with microservice communication? I’d love to hear your experiences and solutions. If you found this useful, please share it with others who might benefit, and feel free to leave comments or questions below.