Lately, I’ve been thinking a lot about how modern applications need to handle massive scale while staying responsive and resilient. That’s what led me to explore event-driven microservices. In this article, I’ll share my approach to building production-ready systems using Go, NATS JetStream, and OpenTelemetry. Let’s get started.

Event-driven architecture helps systems handle complexity by breaking them into smaller, focused services that communicate through events. This approach improves scalability and fault tolerance. But how do we ensure these events are processed reliably without losing data?

NATS JetStream provides persistent messaging with features like exactly-once delivery and message retention. Here’s how to set up a JetStream connection in Go:

func setupJetStream(cfg *config.Config) (nats.JetStreamContext, error) {
    opts := []nats.Option{
        nats.Name(cfg.ServiceName),
        nats.Timeout(cfg.NATSTimeout),
        nats.ReconnectWait(cfg.NATSReconnectWait),
        nats.MaxReconnects(cfg.NATSMaxReconnects),
    }
    
    nc, err := nats.Connect(strings.Join(cfg.NATSURLs, ","), opts...)
    if err != nil {
        return nil, fmt.Errorf("failed to connect to NATS: %w", err)
    }
    
    js, err := nc.JetStream()
    if err != nil {
        return nil, fmt.Errorf("failed to get JetStream context: %w", err)
    }
    
    return js, nil
}

When building microservices, observability becomes crucial. How can we trace a request as it flows through multiple services? OpenTelemetry provides distributed tracing that helps us understand system behavior.

Implementing tracing in Go services is straightforward:

func initTracer(cfg *config.Config) (*sdktrace.TracerProvider, error) {
    exporter, err := jaeger.New(jaeger.WithCollectorEndpoint(
        jaeger.WithEndpoint(cfg.JaegerEndpoint),
    ))
    if err != nil {
        return nil, err
    }

    tp := sdktrace.NewTracerProvider(
        sdktrace.WithSampler(sdktrace.TraceIDRatioBased(cfg.TracingSampleRate)),
        sdktrace.WithBatcher(exporter),
    )
    
    otel.SetTracerProvider(tp)
    return tp, nil
}

Error handling in distributed systems requires careful consideration. Circuit breakers prevent cascading failures when downstream services become unavailable. The gobreaker package offers a robust implementation:

func createCircuitBreaker(name string, cfg *config.Config) *gobreaker.CircuitBreaker {
    return gobreaker.NewCircuitBreaker(gobreaker.Settings{
        Name:        name,
        Timeout:     cfg.CircuitBreakerTimeout,
        MaxRequests: cfg.CircuitBreakerMaxRequests,
        Interval:    cfg.CircuitBreakerInterval,
        ReadyToTrip: func(counts gobreaker.Counts) bool {
            return counts.ConsecutiveFailures > 3
        },
    })
}

Event schema design is another critical aspect. How do we ensure events remain compatible as services evolve? Using versioned schemas and careful field management helps maintain backward compatibility.

Here’s an example of publishing an event with proper metadata:

func publishOrderEvent(js nats.JetStreamContext, event *events.BaseEvent) error {
    data, err := json.Marshal(event)
    if err != nil {
        return fmt.Errorf("failed to marshal event: %w", err)
    }

    subject := fmt.Sprintf("events.orders.%s", event.Metadata.EventType)
    _, err = js.Publish(subject, data, nats.MsgId(event.Metadata.EventID))
    if err != nil {
        return fmt.Errorf("failed to publish event: %w", err)
    }

    return nil
}

Deployment considerations include containerization and orchestration. Docker helps package services consistently, while Kubernetes manages their lifecycle. Monitoring with Prometheus and structured logging complete the production readiness picture.

Building event-driven systems requires thinking about failure scenarios. What happens if a service goes down during event processing? JetStream’s durable consumers and acknowledgment mechanisms help ensure no events are lost.

The combination of Go’s performance characteristics, NATS JetStream’s reliability, and OpenTelemetry’s observability creates a solid foundation for production systems. Each service remains focused on its domain while communicating through well-defined events.

I hope this gives you a practical starting point for your own event-driven architectures. What challenges have you faced when building distributed systems? Share your experiences in the comments below, and don’t forget to like and share this article if you found it helpful.