I’ve been thinking about how modern applications need to handle massive scale while staying responsive and resilient. The shift toward event-driven architectures isn’t just a trend—it’s becoming essential for systems that must process thousands of operations per second without breaking. That’s why I want to share my approach to building production-ready microservices using NATS, Go, and Kubernetes.

How do you ensure your services can handle sudden traffic spikes while maintaining data consistency across distributed systems? This question kept me up at night until I found the right combination of tools and patterns.

Let me show you how to set up the foundation. First, we establish a robust connection to NATS JetStream with proper error handling and reconnection logic. This isn’t just about connecting—it’s about creating a reliable messaging backbone that won’t let you down when things get tough.

// Establishing a production-grade NATS connection
config := messaging.NATSConfig{
    URL:           "nats://nats-cluster:4222",
    MaxReconnect:  25,
    ReconnectWait: 2 * time.Second,
    StreamName:    "production-events",
    AckWait:       30 * time.Second,
}

manager, err := messaging.NewNATSManager(&config)
if err != nil {
    log.Fatalf("Failed to initialize NATS: %v", err)
}

The heart of any event-driven system is how you structure your events. I’ve found that a consistent event format pays dividends throughout the development lifecycle. Here’s what works well in practice:

// Core event structure
type OrderCreated struct {
    OrderID    string    `json:"order_id"`
    CustomerID string    `json:"customer_id"`
    Amount     float64   `json:"amount"`
    Timestamp  time.Time `json:"timestamp"`
}

event := events.BaseEvent{
    ID:          uuid.New().String(),
    Type:        "OrderCreated",
    AggregateID: "order-123",
    Data:        json.RawMessage(`{"order_id":"123","amount":99.99}`),
}

But what happens when a service goes down or messages get processed out of order? This is where consumer idempotency and proper error handling become critical. I implement consumers that can handle duplicates and temporary failures gracefully.

// Resilient message consumer
consumer, err := js.CreateOrUpdateConsumer(ctx, "orders", jetstream.ConsumerConfig{
    Durable:   "order-processor",
    AckPolicy: jetstream.AckExplicitPolicy,
    AckWait:   30 * time.Second,
    MaxDeliver: 5,
})

msgs, _ := consumer.Messages()
for msg := range msgs {
    if err := processOrder(msg); err != nil {
        msg.Nak() // Negative acknowledgment
        continue
    }
    msg.Ack() // Positive acknowledgment
}

When deploying to Kubernetes, health checks and proper resource management are non-negotiable. I’ve learned the hard way that without good monitoring, you’re flying blind in production.

# Kubernetes deployment with proper probes
livenessProbe:
  httpGet:
    path: /health
    port: 8080
  initialDelaySeconds: 5
  periodSeconds: 10

readinessProbe:
  httpGet:
    path: /ready  
    port: 8080
  initialDelaySeconds: 2
  periodSeconds: 5

Testing event-driven systems requires a different mindset. I focus on testing the behavior rather than implementation details, using techniques like contract testing and simulating failure scenarios.

The beauty of this architecture is how naturally it scales. When traffic increases, Kubernetes can spin up more consumer instances, and NATS automatically distributes the load. The system self-heals when components fail, and new features can be added without disrupting existing services.

Have you considered how your current architecture would handle a tenfold increase in traffic tomorrow? The patterns I’ve shared here have helped me sleep better at night, knowing the systems can handle whatever gets thrown at them.

I’d love to hear about your experiences with event-driven architectures. What challenges have you faced, and how did you overcome them? 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.