Lately, I’ve been thinking a lot about building systems that don’t just work, but work reliably under real-world conditions. The shift from monoliths to microservices is more than a trend—it’s a necessity for scaling modern applications. But with distributed systems come distributed problems. How do we ensure these services communicate efficiently, handle failures gracefully, and remain observable when things go wrong? This led me to explore event-driven architectures with NATS, Go, and robust observability practices.

Let’s build a production-ready system. We’ll use NATS JetStream as our messaging backbone because it offers persistence, exactly-once delivery semantics, and horizontal scaling. Go serves as our language of choice for its simplicity, performance, and excellent concurrency primitives.

First, we define our core event structures. Clear event definitions form the foundation of our system.

type OrderCreatedEvent struct {
    BaseEvent
    OrderID     string  `json:"order_id"`
    CustomerID  string  `json:"customer_id"`
    Items       []Item  `json:"items"`
    TotalAmount float64 `json:"total_amount"`
}

Establishing a reliable messaging layer is crucial. Our NATS setup includes proper connection handling and stream configuration.

func setupJetStream(nc *nats.Conn, streamName string, subjects []string) error {
    js, err := nc.JetStream()
    if err != nil {
        return err
    }
    
    _, err = js.AddStream(&nats.StreamConfig{
        Name:     streamName,
        Subjects: subjects,
        MaxAge:   24 * time.Hour,
    })
    return err
}

What happens when a service becomes temporarily unavailable? We implement circuit breakers to prevent cascading failures.

func NewPaymentService(circuit *gobreaker.CircuitBreaker) *PaymentService {
    return &PaymentService{
        circuitBreaker: circuit,
    }
}

func (ps *PaymentService) ProcessPayment(ctx context.Context, amount float64) error {
    result, err := ps.circuitBreaker.Execute(func() (interface{}, error) {
        return ps.processPaymentInternal(ctx, amount)
    })
    return err
}

Observability isn’t an afterthought—it’s built into every service from the start. We integrate OpenTelemetry for distributed tracing, ensuring we can follow a request across service boundaries.

func instrumentRouter(router *gin.Engine, serviceName string) {
    router.Use(otelgin.Middleware(serviceName))
    router.Use(prometheusMiddleware())
    router.Use(loggingMiddleware())
}

Handling high-throughput events requires careful concurrency management. Go’s goroutines and channels provide excellent tools for this.

func (s *OrderProcessor) StartWorkers(ctx context.Context, numWorkers int) {
    for i := 0; i < numWorkers; i++ {
        go s.worker(ctx)
    }
}

func (s *OrderProcessor) worker(ctx context.Context) {
    for {
        select {
        case msg := <-s.messageChan:
            s.processMessage(ctx, msg)
        case <-ctx.Done():
            return
        }
    }
}

Testing event-driven systems presents unique challenges. We need to verify not just function outputs, but also message publications and side effects.

func TestOrderCreation(t *testing.T) {
    mockJS := NewMockJetStream()
    service := NewOrderService(mockJS)
    
    order := createTestOrder()
    err := service.CreateOrder(context.Background(), order)
    
    assert.NoError(t, err)
    assert.Equal(t, 1, mockJS.PublishedCount("orders.created"))
}

Deployment considerations include health checks, graceful shutdown, and proper configuration management. Each service exposes health endpoints and handles termination signals correctly.

func main() {
    ctx, stop := signal.NotifyContext(context.Background(), syscall.SIGINT, syscall.SIGTERM)
    defer stop()
    
    server := setupServer()
    go func() {
        <-ctx.Done()
        server.Shutdown(context.Background())
    }()
    
    server.ListenAndServe()
}

Building this system taught me that production readiness isn’t about any single technology—it’s about how all components work together. The combination of NATS for messaging, Go for implementation, and comprehensive observability creates a foundation that can handle real production loads while remaining maintainable and debuggable.

What challenges have you faced with microservices? 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.