I’ve been thinking a lot about how modern applications handle scale and complexity. The shift toward distributed systems brings both power and challenges. How do we maintain clarity when our services span multiple machines and processes? This question led me to explore event-driven architectures with NATS and Go, combined with robust tracing to maintain visibility.

Event-driven design changes how services communicate. Instead of direct calls, services publish events that others can react to. This loose coupling allows systems to scale independently and handle failures gracefully. But it also introduces new questions: How do we track a request across service boundaries? What happens when messages get lost?

NATS JetStream provides reliable message streaming with persistence and delivery guarantees. It’s a natural fit for Go microservices due to its performance and simplicity. Combined with distributed tracing, we can build systems that are both scalable and observable.

Let me show you a practical implementation. Here’s how to set up a basic event structure:

type Event struct {
    ID        string                 `json:"id"`
    Type      string                 `json:"type"`
    Data      map[string]interface{} `json:"data"`
    Timestamp time.Time              `json:"timestamp"`
}

func publishOrderEvent(nc *nats.Conn, event Event) error {
    data, err := json.Marshal(event)
    if err != nil {
        return err
    }
    return nc.Publish("orders.events", data)
}

This simple structure forms the foundation of our event-driven system. Each service can publish events without knowing which other services might consume them. But how do we ensure these events are processed reliably?

JetStream adds persistence and delivery guarantees to NATS. Here’s how to create a stream that retains messages:

js, _ := jetstream.New(nc)
stream, _ := js.CreateStream(context.Background(), jetstream.StreamConfig{
    Name:     "ORDERS",
    Subjects: []string{"orders.>"},
    MaxAge:   time.Hour * 24,
})

Now events published to “orders.*” subjects will be stored and available for consumers even if they’re temporarily offline. This reliability is crucial for production systems.

Distributed tracing helps us understand the flow of events across services. With OpenTelemetry, we can instrument our code to generate trace data:

func processOrder(ctx context.Context, event Event) {
    tracer := otel.Tracer("order-service")
    ctx, span := tracer.Start(ctx, "processOrder")
    defer span.End()
    
    // Process the order event
    span.SetAttributes(attribute.String("order.id", event.ID))
}

This tracing allows us to see the complete path of a request through our system, even when it crosses multiple service boundaries. Have you ever wondered how to track a specific user action through dozens of microservices?

Error handling becomes more complex in distributed systems. We need strategies for retries, dead-letter queues, and monitoring:

func withRetry(fn func() error, maxAttempts int) error {
    var err error
    for i := 0; i < maxAttempts; i++ {
        if err = fn(); err == nil {
            return nil
        }
        time.Sleep(time.Second * time.Duration(math.Pow(2, float64(i))))
    }
    return err
}

This exponential backoff strategy helps handle temporary failures without overwhelming the system. But what happens when failures persist? We need monitoring to alert us to problems.

Metrics collection gives us insight into system health and performance. Prometheus integration helps track important indicators:

var ordersProcessed = prometheus.NewCounterVec(
    prometheus.CounterOpts{
        Name: "orders_processed_total",
        Help: "Total number of processed orders",
    },
    []string{"status"},
)

func init() {
    prometheus.MustRegister(ordersProcessed)
}

These metrics help us understand throughput, error rates, and system behavior under load. They’re essential for maintaining reliability as our system grows.

Deployment considerations include containerization and orchestration. Docker Compose helps us manage the various components:

version: '3.8'
services:
  nats:
    image: nats:jetstream
    ports:
      - "4222:4222"
  jaeger:
    image: jaegertracing/all-in-one:1.48
    ports:
      - "16686:16686"

This setup gives us a complete development environment with messaging and tracing infrastructure. But how do we ensure this works equally well in production?

Testing distributed systems requires careful planning. We need to verify not just individual components but their interactions:

func TestOrderFlow(t *testing.T) {
    // Setup test NATS connection
    // Publish test event
    // Verify all services processed the event
    // Check tracing data was captured
}

These integration tests help catch issues that unit tests might miss. They’re time-consuming to write but invaluable for catching distributed system bugs.

The combination of NATS, Go, and distributed tracing creates a powerful foundation for building scalable systems. Each technology brings strengths that complement the others. NATS provides reliable messaging, Go offers performance and simplicity, while tracing gives us visibility into complex interactions.

I hope this exploration of event-driven architectures with NATS and Go has been helpful. These patterns have served me well in building resilient, scalable systems. What challenges have you faced with distributed systems? I’d love to hear about your experiences and solutions.

If you found this useful, please share it with others who might benefit. Comments and questions are always welcome—let’s continue the conversation about building better distributed systems.