I’ve been thinking a lot about building resilient systems lately. In my work with distributed architectures, I’ve found that event-driven microservices offer incredible scalability and flexibility, but they also introduce new challenges around reliability and observability. That’s why I want to share a practical approach using Go, NATS JetStream, and OpenTelemetry—technologies that have proven themselves in production environments.

Let me show you how to build a system that can handle real-world demands while remaining observable and maintainable. The key is starting with a solid foundation: clear event definitions that serve as contracts between services.

type OrderCreatedEvent struct {
    BaseEvent
    Data OrderData `json:"data"`
}

This structure ensures every event carries essential metadata like trace IDs and correlation IDs. Have you ever tried debugging a distributed system without proper tracing? It’s like finding a needle in a haystack.

Connecting to NATS JetStream requires careful configuration for production readiness. Here’s how I typically set up the connection:

func NewClient(config Config, logger *zap.Logger) (*Client, error) {
    opts := []nats.Option{
        nats.ReconnectWait(config.ReconnectWait),
        nats.MaxReconnects(config.MaxReconnects),
        nats.Timeout(config.Timeout),
    }
    // Connection logic continues...
}

This configuration handles network instability gracefully—something you’ll appreciate when things inevitably go wrong in production.

What separates a prototype from a production system? Often it’s how you handle failures. Implementing retry mechanisms with exponential backoff has saved me countless times:

func withRetry(operation func() error, maxAttempts int) error {
    for attempt := 1; attempt <= maxAttempts; attempt++ {
        err := operation()
        if err == nil {
            return nil
        }
        time.Sleep(time.Duration(math.Pow(2, float64(attempt))) * time.Second)
    }
    return errors.New("max retries exceeded")
}

OpenTelemetry transforms debugging from guesswork to science. Integrating tracing into your event handlers provides visibility across service boundaries:

func processOrder(ctx context.Context, event events.OrderCreatedEvent) {
    ctx, span := tracer.Start(ctx, "process_order")
    defer span.End()
    
    // Your business logic here
    span.SetAttributes(attribute.String("order.id", event.Data.OrderID))
}

When you can see the entire flow of a request through multiple services, issues become much easier to identify and resolve.

Testing event-driven systems requires a different approach. I’ve found that testing at the event contract level provides the best balance between coverage and maintainability:

func TestOrderCreatedEventMarshaling(t *testing.T) {
    original := events.NewOrderCreatedEvent(orderData, "corr-123", "trace-456")
    marshaled, err := original.Marshal()
    require.NoError(t, err)
    
    unmarshaled, err := events.UnmarshalOrderCreatedEvent(marshaled)
    require.NoError(t, err)
    require.Equal(t, original, unmarshaled)
}

Deployment considerations often get overlooked until it’s too late. Using Docker Compose for local development and testing ensures your production environment doesn’t hold surprises:

services:
  nats:
    image: nats:jetstream
    ports:
      - "4222:4222"
    command: "-js"

Monitoring your event flows is crucial. I instrument key metrics like event processing latency and error rates—these numbers tell the health story of your system.

Building production-ready systems isn’t about using the trendiest technologies; it’s about making thoughtful choices that lead to reliability and maintainability. The combination of Go’s performance characteristics, NATS JetStream’s persistence guarantees, and OpenTelemetry’s observability capabilities creates a foundation you can build upon with confidence.

What challenges have you faced with event-driven architectures? I’d love to hear about your experiences and solutions. If this approach resonates with you, please share it with others who might benefit, and let me know your thoughts in the comments.