I’ve been thinking a lot about how modern applications handle scale and complexity. Traditional request-response architectures often struggle under heavy loads, leading me to explore event-driven patterns. The combination of Go’s concurrency model, NATS JetStream’s reliable messaging, and OpenTelemetry’s observability capabilities creates a powerful foundation for building resilient systems.

Let me show you how we can construct production-ready microservices that handle real-world demands. We’ll build an order processing system that demonstrates practical event-driven patterns.

What makes Go particularly suited for this architecture? Its lightweight goroutines and built-in concurrency primitives allow us to handle thousands of simultaneous events efficiently. Combined with NATS JetStream’s persistent streaming capabilities, we create systems that can process events at scale while maintaining data consistency.

Here’s how we set up our core event structure:

type OrderCreatedEvent struct {
    ID          string
    CustomerID  string
    Items       []OrderItem
    TotalAmount float64
    Timestamp   time.Time
}

func publishOrderEvent(js nats.JetStreamContext, event OrderCreatedEvent) error {
    data, err := json.Marshal(event)
    if err != nil {
        return err
    }
    
    _, err = js.Publish("orders.created", data)
    return err
}

Observability is crucial in distributed systems. Have you ever tried debugging a complex microservice interaction without proper tracing? OpenTelemetry provides the visibility we need:

func processOrder(ctx context.Context, event OrderCreatedEvent) error {
    ctx, span := tracer.Start(ctx, "processOrder")
    defer span.End()
    
    span.SetAttributes(
        attribute.String("order.id", event.ID),
        attribute.Float64("order.amount", event.TotalAmount),
    )
    
    // Processing logic here
    return nil
}

Error handling requires careful consideration. What happens when a service becomes unavailable or processes messages out of order? We implement dead letter queues and circuit breakers to maintain system stability:

func createDurableConsumer(js nats.JetStreamContext) error {
    _, err := js.AddConsumer("ORDERS", &nats.ConsumerConfig{
        Durable:       "order-processor",
        FilterSubject: "orders.created",
        AckWait:       30 * time.Second,
        MaxDeliver:    5,
    })
    return err
}

Testing event-driven systems presents unique challenges. How do we verify that events are processed correctly across service boundaries? We use integration tests that simulate real event flows:

func TestOrderProcessing(t *testing.T) {
    testEvent := OrderCreatedEvent{
        ID:         "test-123",
        CustomerID: "cust-456",
        TotalAmount: 99.99,
    }
    
    err := publishTestEvent(testEvent)
    assert.NoError(t, err)
    
    // Verify downstream effects
    assert.Eventually(t, func() bool {
        return orderWasProcessed("test-123")
    }, 5*time.Second, 100*time.Millisecond)
}

Deployment considerations include health checks, resource limits, and proper monitoring configuration. Each service needs to expose metrics that help us understand system behavior under load:

func healthCheckHandler(w http.ResponseWriter, r *http.Request) {
    if isHealthy() {
        w.WriteHeader(http.StatusOK)
        return
    }
    w.WriteHeader(http.StatusServiceUnavailable)
}

Performance optimization involves understanding bottlenecks through careful measurement. We monitor message processing rates, error rates, and resource utilization to identify areas for improvement.

Building these systems requires attention to both technical details and architectural patterns. The combination of Go’s efficiency, NATS JetStream’s reliability, and OpenTelemetry’s visibility creates a robust foundation for modern applications.

I’d love to hear your thoughts on event-driven architectures. What challenges have you faced when building distributed systems? Share your experiences in the comments below, and if you found this useful, please consider sharing it with others who might benefit from these patterns.