I’ve been thinking about event-driven microservices lately because I keep seeing teams struggle with tightly coupled systems that can’t scale. The complexity grows exponentially when you add more services, and suddenly your beautiful microservices architecture becomes a distributed monolith. This realization led me to explore how we can build truly decoupled systems that handle real production workloads.

Event-driven architecture with NATS, Go, and Kubernetes offers a compelling solution. Why do I believe this combination works so well? NATS provides lightweight messaging, Go delivers exceptional performance for concurrent workloads, and Kubernetes handles the operational complexity. Together, they create systems that are both resilient and scalable.

Let me show you how to set up NATS JetStream for persistent messaging. This ensures your events won’t disappear when services restart:

// Initialize JetStream with proper configuration
js, err := jetstream.New(nc)
if err != nil {
    return nil, fmt.Errorf("jetstream init failed: %w", err)
}

// Create a stream for order events
_, err = js.CreateStream(ctx, jetstream.StreamConfig{
    Name:     "ORDERS",
    Subjects: []string{"orders.>"},
    Retention: jetstream.WorkQueuePolicy,
    MaxMsgs:  1000000,
})

Building the event infrastructure requires careful planning. I define clear event schemas that capture both business data and technical metadata. This approach makes debugging distributed workflows much simpler. Have you ever tried tracing a request through multiple services without proper correlation IDs?

Here’s how I structure domain events:

type OrderEvent struct {
    ID            string                 `json:"id"`
    Type          string                 `json:"type"`
    AggregateID   string                 `json:"order_id"`
    Timestamp     time.Time              `json:"timestamp"`
    Data          OrderData              `json:"data"`
    Metadata      map[string]string      `json:"metadata"`
    CorrelationID string                 `json:"correlation_id"`
}

func (s *OrderService) CreateOrder(ctx context.Context, req CreateOrderRequest) error {
    event := OrderEvent{
        ID:          uuid.New().String(),
        Type:        "order.created",
        AggregateID: req.OrderID,
        Timestamp:   time.Now().UTC(),
        Data:        req.ToOrderData(),
        CorrelationID: getCorrelationID(ctx),
    }
    
    return s.publishEvent(ctx, "orders.created", event)
}

Implementing core microservices involves more than just publishing events. Each service needs to handle failures gracefully and maintain data consistency. I use the saga pattern for managing distributed transactions across service boundaries. What happens when payment fails after inventory reservation?

Here’s a resilient event consumer with retry logic:

func (s *PaymentService) processPaymentEvents(ctx context.Context) error {
    consumer, err := s.js.CreateOrUpdateConsumer(ctx, "ORDERS", jetstream.ConsumerConfig{
        FilterSubjects: []string{"orders.created"},
        AckWait:        30 * time.Second,
        MaxDeliver:     5,
    })
    
    for {
        msgs, err := consumer.Fetch(10)
        if err != nil {
            return err
        }
        
        for msg := range msgs.Messages() {
            if err := s.handlePayment(msg); err != nil {
                s.logger.Error("payment processing failed", 
                    zap.Error(err),
                    zap.String("message_id", msg.ID()))
                continue
            }
            msg.Ack()
        }
    }
}

Observability becomes crucial in distributed systems. I instrument everything with metrics, structured logging, and distributed tracing. This visibility helps identify bottlenecks and understand system behavior under load.

Deploying to Kubernetes requires proper configuration for service discovery and resilience. Here’s a sample deployment that includes health checks and graceful shutdown:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: order-service
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: order-service
        image: orders:latest
        ports:
        - containerPort: 8080
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 30
        readinessProbe:
          httpGet:
            path: /ready
            port: 8080
        env:
        - name: NATS_URL
          value: "nats://nats-cluster:4222"

Graceful shutdown ensures your services don’t drop messages during deployment or scaling events. I implement proper signal handling in Go:

func (s *OrderService) Start() error {
    ctx, cancel := context.WithCancel(context.Background())
    defer cancel()
    
    // Handle OS signals for graceful shutdown
    sigChan := make(chan os.Signal, 1)
    signal.Notify(sigChan, syscall.SIGINT, syscall.SIGTERM)
    
    go func() {
        <-sigChan
        s.logger.Info("shutdown signal received")
        cancel()
    }()
    
    return s.processEvents(ctx)
}

Testing event-driven systems requires a different approach. I focus on testing event contracts and integration points. How can you be sure your services will understand each other’s events in production?

Performance optimization comes from understanding your workload patterns. I monitor message processing rates, error patterns, and resource utilization. Sometimes the bottleneck isn’t where you expect - is it the message broker, the service logic, or the database?

Building production-ready event-driven microservices requires thinking about the entire lifecycle - from development through deployment to monitoring. The patterns I’ve shared here have served me well across multiple projects, helping teams build systems that scale reliably.

What challenges have you faced with microservices communication? I’d love to hear about your experiences - please share your thoughts in the comments below, and if you found this useful, consider sharing it with others who might benefit from these patterns.