I’ve been thinking a lot about how modern applications handle scale and complexity lately. During my work on distributed systems, I’ve seen teams struggle with tightly coupled services that break under load. This led me to explore event-driven architectures with NATS, Go, and Kubernetes—a combination that’s transformed how I build resilient systems. Let me share what I’ve learned about creating production-ready microservices.

When services communicate through events rather than direct calls, the entire system becomes more flexible. Have you ever noticed how a single service failure can cascade through your entire application? Event-driven patterns help prevent this by decoupling services. Each component processes events at its own pace, and if one service goes down, events wait in queues until it recovers.

Let me show you a basic event structure in Go. This foundation ensures all services speak the same language:

type Event struct {
    ID        string    `json:"id"`
    Type      string    `json:"type"`
    Source    string    `json:"source"`
    Data      []byte    `json:"data"`
    Timestamp time.Time `json:"timestamp"`
}

func NewOrderCreatedEvent(orderID string, items []Item) *Event {
    data, _ := json.Marshal(map[string]interface{}{
        "order_id": orderID,
        "items":    items,
    })
    return &Event{
        ID:        uuid.New().String(),
        Type:      "order.created",
        Source:    "order-service",
        Data:      data,
        Timestamp: time.Now().UTC(),
    }
}

Setting up NATS with JetStream gives us persistent messaging. Why does persistence matter? Because losing events means losing business data. Here’s how I configure a connection:

nc, err := nats.Connect("nats://localhost:4222", 
    nats.Name("Order Service"),
    nats.MaxReconnects(5),
    nats.ReconnectWait(2*time.Second),
)
if err != nil {
    log.Fatal("Connection failed:", err)
}
js, err := nc.JetStream()
if err != nil {
    log.Fatal("JetStream failed:", err)
}

// Create a stream for order events
_, err = js.AddStream(&nats.StreamConfig{
    Name:     "ORDERS",
    Subjects: []string{"orders.>"},
})

In Kubernetes, each microservice runs in its own pod. But how do they find each other? Kubernetes services provide DNS names, and NATS handles the messaging. I package services with health checks:

func healthCheck(w http.ResponseWriter, r *http.Request) {
    if nc.Status() != nats.CONNECTED {
        w.WriteHeader(http.StatusServiceUnavailable)
        return
    }
    w.WriteHeader(http.StatusOK)
    w.Write([]byte("OK"))
}

Observability isn’t optional in production. I instrument services to emit metrics, structured logs, and traces. What happens when an event takes too long to process? Distributed tracing shows me the entire journey. Here’s a simple metrics setup:

func recordEventMetrics(eventType string, duration time.Duration) {
    eventCounter.WithLabelValues(eventType).Inc()
    eventDuration.WithLabelValues(eventType).Observe(duration.Seconds())
}

Error handling requires careful planning. Do you retry failed events indefinitely? I implement circuit breakers and dead-letter queues. Services need to handle duplicate events and process them exactly once. This idempotency check saves me from duplicate charges:

func (s *PaymentService) ProcessPayment(event Event) error {
    // Check if payment was already processed
    if s.paymentExists(event.ID) {
        return nil // Idempotent response
    }
    // Process payment logic
    return s.savePayment(event)
}

Testing event-driven systems demands a different approach. I use test containers to spin up NATS and databases for integration tests. How do you verify that services react correctly to events? I publish test events and assert on side effects:

func TestOrderCreation(t *testing.T) {
    // Setup test NATS connection
    nc, js := setupTestNATS(t)
    defer nc.Close()

    // Publish test event
    event := NewOrderCreatedEvent("test123", []Item{})
    js.Publish("orders.created", event)

    // Verify inventory was updated
    assertInventoryReserved(t, "test123")
}

Deploying to Kubernetes involves configuring resource limits, liveness probes, and horizontal pod autoscaling. I’ve found that setting memory limits prevents services from consuming all cluster resources. Here’s a snippet from a deployment manifest:

resources:
  limits:
    memory: "128Mi"
    cpu: "500m"
  requests:
    memory: "64Mi"
    cpu: "250m"
livenessProbe:
  httpGet:
    path: /health
    port: 8080

In production, I monitor queue depths and service latency. Prometheus alerts me when error rates spike or events pile up. Have you considered what happens during deployment? I use rolling updates and ensure new versions can handle old event formats.

Building with these tools has taught me that resilience comes from expecting failures. Services should handle missing dependencies, network timeouts, and invalid messages gracefully. The event-driven approach makes systems more understandable—each service focuses on its domain, and events tell the story of what’s happening.

I’d love to hear about your experiences with microservices. What challenges have you faced with event-driven systems? If this resonates with you, please share your thoughts in the comments below. Feel free to like and share this article with others who might find it helpful. Let’s keep the conversation going about building better distributed systems together.