I’ve been thinking a lot about how modern applications need to handle massive scale while remaining resilient. Last month, I watched a popular e-commerce platform struggle during a flash sale—orders were lost, payments timed out, and customers grew frustrated. That experience solidified my belief in event-driven architectures. Today, I want to share how we can build systems that not only survive but thrive under pressure using NATS, Go, and Kubernetes.

Event-driven microservices transform how we handle complex workflows. Instead of services calling each other directly, they communicate through events. This loose coupling means one service can fail without bringing down the entire system. But how do we ensure these events are processed reliably? That’s where NATS JetStream comes in.

type OrderEvent struct {
    ID        string    `json:"id"`
    UserID    string    `json:"user_id"`
    Items     []Item    `json:"items"`
    Total     float64   `json:"total"`
    Timestamp time.Time `json:"timestamp"`
}

func (eb *NATSEventBus) PublishOrderCreated(ctx context.Context, event OrderEvent) error {
    data, err := json.Marshal(event)
    if err != nil {
        return fmt.Errorf("failed to marshal event: %w", err)
    }
    
    _, err = eb.js.PublishAsync(eb.subjects["OrderCreated"], data)
    if err != nil {
        return fmt.Errorf("failed to publish event: %w", err)
    }
    
    return nil
}

Have you ever considered what happens when a payment service becomes unavailable? In traditional architectures, the entire order process would stall. With event-driven design, orders continue flowing into the system, and payments are retried once the service recovers. This resilience comes from treating every action as an event that can be replayed.

Go’s concurrency model makes it ideal for handling high-throughput event processing. Goroutines and channels allow us to build efficient worker pools that scale with demand. Here’s how I implement a simple worker pool in the notification service:

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

func (ns *NotificationService) worker(ctx context.Context) {
    for {
        select {
        case <-ctx.Done():
            return
        case msg := <-ns.messageQueue:
            if err := ns.processNotification(msg); err != nil {
                ns.logger.Error("failed to process notification",
                    zap.Error(err),
                    zap.String("message_id", msg.ID))
                // Implement retry logic here
            }
        }
    }
}

What separates production-ready systems from prototypes? It’s the attention to observability and graceful degradation. I always instrument my services with Prometheus metrics and distributed tracing. This allows me to understand exactly where bottlenecks occur and how events flow through the system.

Deploying to Kubernetes introduces its own challenges. How do we ensure our services start in the correct order? I use readiness probes and init containers to manage dependencies. Here’s a snippet from my Kubernetes deployment configuration:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: order-service
spec:
  replicas: 3
  template:
    spec:
      initContainers:
      - name: wait-for-nats
        image: busybox
        command: ['sh', '-c', 'until ncat -z nats-server 4222; do echo waiting for nats; sleep 2; done']
      containers:
      - name: order-service
        image: order-service:latest
        ports:
        - containerPort: 8080
        readinessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 10

Testing event-driven systems requires a different approach. Instead of mocking every dependency, I focus on integration tests that verify the entire event flow. This catches issues that unit tests might miss, like serialization problems or network timeouts.

One lesson I’ve learned the hard way: always implement circuit breakers for external service calls. When the payment gateway starts failing, the circuit breaker prevents cascading failures by failing fast and giving the system time to recover.

func (ps *PaymentService) ProcessPayment(ctx context.Context, payment PaymentRequest) error {
    result, err := ps.circuitBreaker.Execute(func() (interface{}, error) {
        return ps.paymentGateway.Process(ctx, payment)
    })
    
    if err != nil {
        if errors.Is(err, gobreaker.ErrOpenState) {
            ps.metrics.CircuitOpen.Inc()
            return fmt.Errorf("circuit breaker open: %w", err)
        }
        return fmt.Errorf("payment processing failed: %w", err)
    }
    
    paymentResult := result.(PaymentResult)
    return ps.handlePaymentResult(ctx, paymentResult)
}

Building these systems has taught me that reliability isn’t an afterthought—it’s built into every design decision. From how we handle messages to how we deploy services, each choice either strengthens or weakens the system’s resilience.

What patterns have you found most effective in your distributed systems? I’d love to hear about your experiences. If this approach resonates with you, please share this article with your team and leave a comment about your biggest challenge in building event-driven systems. Your insights could help others navigating similar journeys.