I’ve been thinking a lot lately about how modern systems need to handle massive scale while staying resilient. That’s why I want to share my approach to building event-driven microservices that can handle real production workloads. If you’re working with Go and Kubernetes, this might change how you think about system architecture.

Event-driven architecture isn’t just another pattern—it’s a fundamental shift in how services communicate. Instead of services constantly polling each other, they react to events as they happen. This approach scales beautifully and handles failures more gracefully than traditional request-response models.

Let me show you how I structure events in Go:

type Event struct {
    ID        string                 `json:"id"`
    Type      string                 `json:"type"`
    Source    string                 `json:"source"`
    Subject   string                 `json:"subject"`
    Time      time.Time              `json:"time"`
    Data      interface{}            `json:"data"`
    Metadata  map[string]string      `json:"metadata"`
}

This structure gives us flexibility while maintaining consistency across our services. Notice how we include metadata for tracing and versioning—crucial for debugging in production.

Why use NATS JetStream over other messaging systems? Its simplicity and performance characteristics make it perfect for cloud-native environments. The persistence guarantees and built-in replication mean we don’t have to build these features ourselves.

Here’s how I set up a durable consumer:

consumer, err := js.CreateOrUpdateConsumer(ctx, "ORDERS", jetstream.ConsumerConfig{
    Durable:       "inventory-service",
    FilterSubject: "orders.created",
    AckPolicy:     jetstream.AckExplicitPolicy,
    MaxDeliver:    5,
    AckWait:       30 * time.Second,
})

This configuration ensures that even if our service restarts, we don’t lose track of where we were in processing messages. The explicit acknowledgment policy gives us control over when we consider a message fully processed.

But what happens when things go wrong? Error handling becomes critical in event-driven systems. I implement retry mechanisms with exponential backoff:

func withRetry(operation func() error, maxAttempts int) error {
    backoff := 1 * time.Second
    for attempt := 1; attempt <= maxAttempts; attempt++ {
        err := operation()
        if err == nil {
            return nil
        }
        time.Sleep(backoff)
        backoff *= 2
    }
    return fmt.Errorf("operation failed after %d attempts", maxAttempts)
}

This pattern prevents cascading failures when downstream services are temporarily unavailable.

Observability is non-negotiable in production systems. I instrument everything with metrics and tracing:

func instrumentHandler(handler EventHandler) EventHandler {
    return &instrumentedHandler{
        handler: handler,
        metrics: prometheus.NewCounterVec(
            prometheus.CounterOpts{
                Name: "events_processed_total",
                Help: "Total events processed",
            },
            []string{"event_type", "status"},
        ),
    }
}

These metrics help us understand our system’s behavior and quickly identify bottlenecks.

Kubernetes deployment requires careful consideration of resource limits and health checks. Here’s my typical health check implementation:

func healthCheck(w http.ResponseWriter, r *http.Request) {
    if natsClient.IsConnected() && db.IsConnected() {
        w.WriteHeader(http.StatusOK)
        return
    }
    w.WriteHeader(http.StatusServiceUnavailable)
}

This simple check ensures our service is truly ready to handle traffic before Kubernetes sends requests its way.

Testing event-driven systems requires a different approach. I focus on contract testing and integration tests:

func TestOrderCreatedEvent(t *testing.T) {
    event := Event{
        Type:    "order.created",
        Subject: "orders/123",
        Data:    OrderData{ID: "123", Amount: 100},
    }
    
    err := testHandler.Handle(context.Background(), event)
    require.NoError(t, err)
    // Verify side effects
}

These tests verify that our services understand the same event contracts without requiring full environment setup.

The real power comes when we combine these patterns. Our services become resilient, scalable, and maintainable. They can handle traffic spikes gracefully and recover from failures without human intervention.

Remember that event-driven architecture isn’t just about technology—it’s about designing systems that reflect how your business actually operates. Events represent things that happen in your domain, and services react to those changes.

I’d love to hear about your experiences with event-driven architectures. What challenges have you faced? What patterns have worked well for your team? Share your thoughts in the comments below, and if you found this useful, please like and share with others who might benefit from these approaches.