I’ve spent years building microservices that scale, and nothing has transformed my approach more than event-driven architectures. In production systems, handling high-throughput events while maintaining reliability and observability is crucial. That’s why I’m sharing my experience with Go, NATS JetStream, and OpenTelemetry—a combination that’s helped me build systems that not only perform but are also resilient and easy to monitor.

Setting up the foundation starts with NATS JetStream. It provides durable message storage and exactly-once delivery semantics, which are essential for production systems. Here’s a basic setup in Go to connect and create a stream:

// Initialize NATS JetStream client
nc, err := nats.Connect("nats://localhost:4222")
if err != nil {
    log.Fatal().Err(err).Msg("Failed to connect to NATS")
}
js, err := nc.JetStream()
if err != nil {
    log.Fatal().Err(err).Msg("Failed to create JetStream context")
}

// Create a stream for order events
_, err = js.AddStream(&nats.StreamConfig{
    Name:     "ORDERS",
    Subjects: []string{"orders.*"},
    MaxAge:   24 * time.Hour,
})
if err != nil {
    log.Fatal().Err(err).Msg("Failed to create stream")
}

Have you considered how your services would handle a sudden spike in events without losing data?

Event sourcing forms the backbone of reliable systems. Each state change is captured as an immutable event, allowing you to replay history if needed. In Go, I structure events with clear types and metadata:

type OrderEvent struct {
    ID        string    `json:"id"`
    Type      string    `json:"type"` // e.g., "order_created"
    Data      []byte    `json:"data"`
    Timestamp time.Time `json:"timestamp"`
}

func (s *OrderService) PublishOrderCreated(order Order) error {
    event := OrderEvent{
        ID:        uuid.New().String(),
        Type:      "order_created",
        Data:      order.MarshalJSON(),
        Timestamp: time.Now().UTC(),
    }
    data, _ := json.Marshal(event)
    _, err := s.js.Publish("orders.created", data)
    return err
}

What happens when a service goes down mid-processing? JetStream’s durable subscriptions ensure messages aren’t lost.

Integrating OpenTelemetry gives you a clear view across services. I instrument my Go services to trace events from start to finish:

func ProcessOrder(ctx context.Context, order Order) error {
    ctx, span := tracer.Start(ctx, "process_order")
    defer span.End()
    span.SetAttributes(
        attribute.String("order.id", order.ID),
        attribute.String("event.type", "processing"),
    )
    // Business logic here
    return nil
}

This tracing helps pinpoint bottlenecks when orders slow down during peak sales.

Resilience isn’t optional—it’s a requirement. I use circuit breakers and retries to handle failures gracefully. Here’s a simple circuit breaker in Go:

cb := gobreaker.NewCircuitBreaker(gobreaker.Settings{
    Name:        "payment_service",
    MaxRequests: 5,
    Timeout:     30 * time.Second,
})

result, err := cb.Execute(func() (interface{}, error) {
    return s.processPayment(ctx, payment)
})
if err != nil {
    log.Error().Err(err).Msg("Payment processing failed")
}

How do you ensure your services degrade gracefully under load?

Concurrency in Go makes high-throughput event processing efficient. I use goroutines with proper context propagation:

func (s *InventoryService) HandleEvents(ctx context.Context) {
    sub, _ := s.js.PullSubscribe("inventory.>", "inventory-group")
    for {
        msgs, _ := sub.Fetch(10, nats.Context(ctx))
        for _, msg := range msgs {
            go func(m *nats.Msg) {
                ctx := otel.GetTextMapPropagator().Extract(ctx, m.Header)
                s.processInventoryEvent(ctx, m)
                m.Ack()
            }(msg)
        }
    }
}

Deploying this in containers with Docker Compose ensures consistency. I define services, NATS, and monitoring tools in a single file, making local development and production deployments seamless.

Monitoring is where OpenTelemetry shines, exporting traces to Jaeger and metrics to Prometheus. I set up alerts for error rates and latency spikes, so I’m notified before users are affected.

Building event-driven microservices isn’t just about technology—it’s about creating systems that adapt and grow. I’ve seen teams reduce incident response times by over 50% with proper observability. What challenges have you faced in your microservices journey?

I hope this guide gives you a solid starting point. If you found these insights helpful, please like, share, and comment with your experiences—I’d love to hear how you’re implementing these patterns in your projects.