I’ve been thinking a lot lately about how we build systems that not only work but thrive under real-world pressure. The shift toward event-driven architectures isn’t just theoretical—it’s about creating systems that can handle scale, recover from failures, and provide clear visibility into what’s happening. That’s why I want to share a practical approach using Go, NATS JetStream, and OpenTelemetry.

When you’re building microservices that need to communicate reliably, traditional request-response patterns often fall short. Have you ever wondered how to ensure messages aren’t lost when services restart or fail? NATS JetStream provides persistent messaging that handles these scenarios gracefully.

Let me show you how we set up our event definitions. This foundation ensures all services speak the same language:

type OrderCreated struct {
    BaseEvent
    OrderID    string      `json:"order_id"`
    CustomerID string      `json:"customer_id"`
    Items      []OrderItem `json:"items"`
    TotalAmount float64    `json:"total_amount"`
}

Observability isn’t just nice to have—it’s essential. Without proper tracing, debugging distributed systems becomes guesswork. OpenTelemetry gives us the tools to follow requests across service boundaries:

func InitTracing(cfg TracingConfig) (func(context.Context) error, error) {
    exporter, err := jaeger.New(
        jaeger.WithCollectorEndpoint(jaeger.WithEndpoint(cfg.JaegerEndpoint)),
    )
    if err != nil {
        return nil, fmt.Errorf("failed to create Jaeger exporter: %w", err)
    }
    // ... rest of initialization
}

What happens when a payment service becomes temporarily unavailable? Circuit breakers prevent cascading failures and give overwhelmed services time to recover. The gobreaker package provides this functionality out of the box.

Error handling in event-driven systems requires careful consideration. Unlike synchronous calls, you can’t just return an error to the caller. We implement retry mechanisms with exponential backoff and dead letter queues for messages that repeatedly fail processing.

Containerization with Docker ensures consistency across environments. Each microservice runs in its own container with defined resource limits and health checks. This isolation prevents issues in one service from affecting others.

Monitoring goes beyond just tracing. We implement structured logging with zap for consistent log formats and Prometheus metrics for real-time performance monitoring. Health endpoints allow orchestration tools to verify service status.

Concurrency patterns in Go make it ideal for high-throughput message processing. Goroutines and channels enable efficient parallel processing of events without the overhead of traditional threading models.

The combination of these technologies creates a robust foundation. NATS JetStream ensures message durability, OpenTelemetry provides visibility, and Go delivers performance and reliability. Each component plays a specific role in building systems that can handle production workloads.

Deployment strategies matter too. We use Docker Compose for development and Kubernetes for production deployments. This approach allows us to test locally and deploy with confidence.

Testing event-driven architectures requires simulating various failure scenarios. We create integration tests that verify message flow and error handling under different conditions. This thorough testing prevents surprises in production.

Building production-ready systems involves considering many aspects simultaneously. From message persistence to observability to deployment strategies, each piece must work together seamlessly. The tools and patterns we’ve discussed provide a solid foundation for creating reliable, scalable systems.

I’d love to hear about your experiences with event-driven architectures. What challenges have you faced, and how have you solved them? Share your thoughts in the comments below, and if you found this useful, please like and share with others who might benefit from these approaches.