I’ve been thinking a lot about how modern applications handle scale and complexity lately. In my work, I’ve seen too many systems struggle with tight coupling and brittle communication patterns. That’s why I want to share my experience building event-driven microservices that can handle real production loads. If you’ve ever dealt with cascading failures or unpredictable traffic spikes, you’ll appreciate why this architecture matters.

Let me show you how to build a robust order processing system using NATS, Go, and Kubernetes. This approach has served me well in production environments, handling millions of events daily. The key is designing services that communicate through events rather than direct calls. Why does this matter? Because it creates systems that can absorb failures and scale independently.

Here’s a basic setup for our Go project structure. I prefer organizing code this way because it separates concerns clearly and makes testing easier.

mkdir event-driven-microservice && cd event-driven-microservice
mkdir -p cmd/{order-service,inventory-service,notification-service}
mkdir -p internal/{config,domain,events,health}

Our domain models define the core business entities. Notice how each event carries metadata for tracing and correlation. This becomes crucial when debugging distributed systems.

type Order struct {
    ID          uuid.UUID   `json:"id"`
    CustomerID  uuid.UUID   `json:"customer_id"`
    Items       []OrderItem `json:"items"`
    Status      OrderStatus `json:"status"`
    TotalAmount float64     `json:"total_amount"`
    CreatedAt   time.Time   `json:"created_at"`
}

When building the event publisher, I always include retry logic and proper context handling. Have you considered what happens when your message broker becomes temporarily unavailable?

func (p *Publisher) PublishWithRetry(ctx context.Context, subject string, event interface{}) error {
    data, err := json.Marshal(event)
    if err != nil {
        return fmt.Errorf("marshal event: %w", err)
    }
    
    // Retry with exponential backoff
    backoff := time.Millisecond * 100
    for attempt := 0; attempt < maxRetries; attempt++ {
        _, err := p.js.Publish(subject, data)
        if err == nil {
            return nil
        }
        select {
        case <-ctx.Done():
            return ctx.Err()
        case <-time.After(backoff):
            backoff *= 2
        }
    }
    return err
}

The inventory service demonstrates how to handle concurrent message processing. Go’s goroutines make this natural, but you need proper synchronization. What patterns do you use for managing concurrent operations?

func (s *InventoryService) StartConsumers(ctx context.Context) error {
    for i := 0; i < s.config.WorkerCount; i++ {
        go s.worker(ctx, i)
    }
    return nil
}

func (s *InventoryService) worker(ctx context.Context, id int) {
    for {
        select {
        case <-ctx.Done():
            return
        default:
            msg, err := s.subscription.NextMsg(time.Second)
            if err != nil {
                continue
            }
            s.processMessage(msg)
        }
    }
}

Observability isn’t an afterthought in this design. Each service emits metrics and traces that give you visibility into the system’s behavior. I’ve found this essential for maintaining reliability in production.

func instrumentedHandler(handler http.Handler) http.Handler {
    return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        start := time.Now()
        defer func() {
            duration := time.Since(start)
            metrics.RequestDuration.Observe(duration.Seconds())
        }()
        handler.ServeHTTP(w, r)
    })
}

Deploying to Kubernetes requires careful resource management. Here’s a snippet from our deployment configuration that ensures proper resource limits and health checks.

containers:
- name: order-service
  image: yourorg/order-service:latest
  resources:
    requests:
      memory: "64Mi"
      cpu: "100m"
    limits:
      memory: "128Mi"
      cpu: "200m"
  livenessProbe:
    httpGet:
      path: /health
      port: 8080
    initialDelaySeconds: 30
    periodSeconds: 10

Testing event-driven systems presents unique challenges. How do you verify that events are processed correctly across service boundaries? I use a combination of unit tests and integration tests with a test NATS instance.

func TestOrderCreation(t *testing.T) {
    nc, err := nats.Connect(testNATSURL)
    require.NoError(t, err)
    defer nc.Close()
    
    service := NewOrderService(nc)
    order := createTestOrder()
    
    err = service.CreateOrder(context.Background(), order)
    assert.NoError(t, err)
    
    // Verify event was published
    msg, err := nc.Request("orders.created", nil, time.Second)
    assert.NoError(t, err)
    assert.NotNil(t, msg)
}

Building production-ready systems means anticipating failure. Circuit breakers prevent cascading failures when downstream services become unresponsive. I’ve seen this save systems from complete collapse during partial outages.

func (s *InventoryService) ReserveStock(orderID uuid.UUID, items []OrderItem) error {
    return s.breaker.Execute(func() error {
        return s.reserveStock(orderID, items)
    })
}

The beauty of this architecture lies in its flexibility. Services can be updated, scaled, or replaced independently. New features can be added by simply subscribing to relevant events. Have you considered how this might simplify your own system evolution?

As we wrap up, I hope this gives you a practical foundation for building your own event-driven systems. The combination of NATS for messaging, Go for performance, and Kubernetes for orchestration creates a powerful platform for modern applications. If you found this helpful, I’d love to hear about your experiences—please share your thoughts in the comments and pass this along to others who might benefit. Your feedback helps me create better content for our community.