I’ve been thinking about distributed systems a lot lately. The challenges of building scalable, resilient applications that can handle real-world demands are more pressing than ever. Recently, I designed an e-commerce platform where orders needed to flow seamlessly between services without bottlenecks. That’s when event-driven architecture with Go and NATS JetStream became my solution. Let me show you how I built this production-ready system running on Kubernetes.

Our architecture centers around events. When something important happens - like an order being placed - services communicate through messages rather than direct API calls. This approach avoids tight coupling and gives us flexibility. We define our events with clear structures:

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

Why NATS JetStream? It provides persistent messaging with exactly-once delivery guarantees. Setting up our streaming backbone requires proper configuration. Notice how we define separate streams for different event categories with appropriate storage policies:

jetstream {
    store_dir: "/data/jetstream"
    max_memory_store: 1GB
    max_file_store: 10GB
}

For publishers, we ensure reliability through connection handling. What happens if the network blips? Our implementation automatically reconnects while logging state changes:

opts := []nats.Option{
    nats.MaxReconnects(config.MaxReconnects),
    nats.ReconnectWait(config.ReconnectWait),
    nats.DisconnectErrHandler(func(nc *nats.Conn, err error) {
        logger.Warn("NATS disconnected", "error", err)
    })
}

Consumers need special attention too. How do we prevent message loss during failures? We use acknowledgment modes and durable subscriptions:

_, err = js.Subscribe(subject, func(msg *nats.Msg) {
    if err := process(msg.Data); err != nil {
        msg.Nak() // Not acknowledged - redeliver later
    } else {
        msg.Ack()
    }
}, nats.Durable(durableName))

Go’s concurrency primitives shine in event processing. I use worker pools to parallelize message handling without overwhelming systems. The key is controlling throughput with channels and goroutines:

jobs := make(chan *nats.Msg, 100)
for i := 0; i < workerCount; i++ {
    go func() {
        for msg := range jobs {
            processMessage(msg)
        }
    }()
}

Errors will happen - that’s guaranteed. We implement retries with exponential backoff and circuit breakers to prevent cascading failures. When payment service responds slowly, the circuit opens temporarily:

breaker := gobreaker.NewCircuitBreaker(gobreaker.Settings{
    ReadyToTrip: func(counts gobreaker.Counts) bool {
        return counts.ConsecutiveFailures > 5
    }
})

Visibility is crucial. I instrument everything with OpenTelemetry traces and Prometheus metrics. Ever wondered how long inventory checks actually take? Distributed tracing shows you:

_, span := tracer.Start(ctx, "inventory-reserve")
defer span.End()
// Business logic here
span.SetAttributes(attribute.Int("items.reserved", count))

Kubernetes deployments bring it all together. Our Helm charts configure NATS clusters and microservices with proper resource limits. StatefulSets run JetStream with persistent volumes, while HPA scales consumers during peak loads. The manifest snippet shows service discovery configuration:

env:
- name: NATS_URL
  value: "nats://nats-jetstream:4222"

Performance tuning requires measurement. I benchmark components using realistic loads and optimize bottlenecks. Sometimes simple changes yield big gains - like increasing batch sizes or adjusting Go’s GOMAXPROCS. Remember to profile before optimizing!

Testing event-driven systems demands creativity. We use contract tests to verify event schemas and integration tests with Testcontainers. My favorite technique? Sending poison pills to validate error handling:

// Test dead-letter queue handling
invalidMsg := []byte(`{"malformed": data`)
publishTestMessage(invalidMsg)
assert.Eventually(t, func() bool {
    return dlqCount() > 0
}, 5*time.Second, 100*time.Millisecond)

Building this changed how I view distributed systems. The combination of Go’s efficiency, JetStream’s reliability, and Kubernetes’ orchestration creates something greater than the sum of its parts. What challenges have you faced with microservices? Share your experiences below - I’d love to hear what solutions you’ve discovered. If this approach resonates with you, consider sharing it with others facing similar architectural decisions.