I’ve been thinking a lot about how modern systems handle the constant flow of events and data. It’s not just about processing information quickly—it’s about doing it reliably, with full visibility into what’s happening across distributed services. That’s why I want to share my approach to building production-ready event-driven microservices using Go, NATS JetStream, and OpenTelemetry.

Event-driven architecture fundamentally changes how services communicate. Instead of direct API calls, services emit events that others can react to. This creates systems that are more resilient, scalable, and flexible. But how do we ensure these events are processed reliably, even when components fail?

Go’s simplicity and powerful concurrency model make it ideal for building high-performance microservices. Its lightweight goroutines and channels allow us to handle thousands of concurrent events efficiently. Have you considered how Go’s type safety helps prevent common errors in distributed systems?

NATS JetStream provides the persistence and delivery guarantees we need for production systems. Unlike traditional message queues, JetStream offers at-least-once delivery, message replay, and durable subscriptions. This means our services won’t lose important events, even during outages or restarts.

Here’s how we set up a JetStream connection with proper error handling:

func ConnectJetStream(url string) (nats.JetStreamContext, error) {
    nc, err := nats.Connect(url)
    if err != nil {
        return nil, fmt.Errorf("NATS connection failed: %w", err)
    }
    
    js, err := nc.JetStream(nats.MaxWait(10*time.Second))
    if err != nil {
        return nil, fmt.Errorf("JetStream context failed: %w", err)
    }
    
    return js, nil
}

Observability is crucial in distributed systems. Without proper tracing and metrics, debugging becomes nearly impossible. OpenTelemetry gives us a standardized way to collect telemetry data across all our services. What happens when an order processing pipeline suddenly slows down? Distributed tracing shows us exactly where the bottleneck occurs.

Implementing OpenTelemetry tracing in our event handlers:

func processOrderEvent(ctx context.Context, msg *nats.Msg) error {
    ctx, span := tracer.Start(ctx, "process_order_event")
    defer span.End()
    
    var event events.OrderCreatedEvent
    if err := json.Unmarshal(msg.Data, &event); err != nil {
        span.RecordError(err)
        return fmt.Errorf("failed to unmarshal event: %w", err)
    }
    
    span.SetAttributes(
        attribute.String("order.id", event.Data.OrderID),
        attribute.Int("item.count", len(event.Data.Items)),
    )
    
    // Process the order...
    return nil
}

Error handling and resilience patterns are non-negotiable in production systems. Circuit breakers prevent cascading failures, while retry mechanisms with exponential backoff handle temporary issues. These patterns ensure our system remains stable under load or partial failures.

Deployment considerations include proper health checks, resource limits, and monitoring. Containerizing our services with Docker ensures consistent environments from development to production. But have you thought about how to test event-driven systems effectively?

Testing event-driven services requires simulating various scenarios—message ordering, duplicate delivery, and service failures. We need to verify that our services handle these edge cases correctly. Integration tests that spin up the entire system with test data are essential for confidence in production deployments.

The combination of Go’s performance, NATS JetStream’s reliability, and OpenTelemetry’s observability creates a powerful foundation for building modern distributed systems. Each component plays a crucial role in ensuring our services are robust, scalable, and maintainable.

What challenges have you faced when building event-driven systems? I’d love to hear your experiences and solutions. If you found this useful, please share it with others who might benefit, and let me know your thoughts in the comments below.