Lately, I’ve been thinking a lot about how we build systems that are not only scalable but truly resilient under real-world pressure. This led me to explore event-driven microservices with NATS, Go, and Kubernetes—a combination that feels both powerful and practical. Why now? Because the need for systems that can handle unpredictability, scale efficiently, and remain observable is more critical than ever.

Let’s start with the basics: event-driven architecture shifts the way services communicate. Instead of direct API calls, services publish and subscribe to events. This decouples components, making the system more flexible and fault-tolerant. But how do you ensure these events are processed reliably, especially at scale?

NATS JetStream provides a robust messaging backbone. It handles persistence, guarantees message delivery, and supports complex patterns like event sourcing. Here’s a simple example of setting up a JetStream connection in Go:

nc, err := nats.Connect("nats://localhost:4222")
if err != nil {
    log.Fatal("Connection failed:", err)
}
js, err := nc.JetStream()
if err != nil {
    log.Fatal("JetStream context failed:", err)
}

Once connected, you can define streams and consumers. Think of a stream as a log of events, and consumers as subscribers processing those events. For instance, in an e-commerce system, an order creation event might be published to a stream, and services like payment or inventory would consume it.

Building services in Go brings efficiency and simplicity. Go’s concurrency model, with goroutines and channels, aligns perfectly with event-driven patterns. Here’s a snippet for a basic event handler:

func handleOrderCreated(msg *nats.Msg) {
    var event OrderCreatedEvent
    if err := json.Unmarshal(msg.Data, &event); err != nil {
        log.Printf("Failed to unmarshal event: %v", err)
        msg.Nak() // Negative acknowledgment for retry
        return
    }
    // Process the event
    if err := processOrder(event); err != nil {
        msg.Nak()
        return
    }
    msg.Ack() // Acknowledge successful processing
}

But what happens when things go wrong? Production systems need resilience. Dead letter queues and retry mechanisms are essential. If a message fails processing after several attempts, it can be moved to a dead letter queue for later analysis.

Deploying on Kubernetes adds another layer of reliability. Each microservice runs in its own container, with health checks and scaling policies. Here’s a minimal Kubernetes deployment for a Go service:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: order-service
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: order-service
        image: order-service:latest
        ports:
        - containerPort: 8080
        livenessProbe:
          httpGet:
            path: /health
            port: 8080

Observability is key. Integrating metrics with Prometheus and logging with structured formats like JSON helps you understand system behavior. Have you ever wondered how to trace a request across multiple services? Distributed tracing tools like Jaeger can provide that visibility.

Testing event-driven systems requires a different approach. Instead of mocking APIs, you simulate events and verify outcomes. Tools like Testcontainers help spin up NATS and databases for integration tests.

As we wrap up, I encourage you to think about how event-driven patterns could improve your current systems. What challenges have you faced with microservices? Share your thoughts in the comments—I’d love to hear your experiences. If this article resonated with you, please like and share it with others who might find it useful.