I’ve been thinking a lot lately about how we build systems that can handle real-world complexity while remaining maintainable and observable. The transition from monolithic applications to distributed microservices brings both power and challenges—how do we ensure these services communicate reliably? How do we trace a request as it flows through multiple components? These questions led me to explore event-driven architectures with modern tools.
Go’s simplicity and performance make it an excellent choice for building microservices. When combined with NATS JetStream for reliable messaging and OpenTelemetry for observability, we create systems that are both robust and transparent. This combination addresses critical needs in distributed systems: reliable communication, clear visibility, and straightforward error handling.
Consider this basic setup for connecting to NATS JetStream:
func connectToJetStream(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()
if err != nil {
return nil, fmt.Errorf("JetStream context failed: %w", err)
}
return js, nil
}This simple function establishes a connection, but what happens when we need to ensure message delivery even during network issues? JetStream’s persistence capabilities provide exactly that reliability.
OpenTelemetry transforms how we understand our systems. Instead of guessing where bottlenecks occur, we can see exactly how long each operation takes and how services interact. The ability to trace a request across service boundaries changes everything about debugging and optimization.
Here’s how you might instrument a simple event handler:
func processOrder(ctx context.Context, order events.OrderCreated) error {
ctx, span := tracer.Start(ctx, "processOrder")
defer span.End()
span.SetAttributes(
attribute.String("order.id", order.OrderID),
attribute.Float64("order.amount", order.TotalAmount),
)
// Business logic here
if err := validateOrder(order); err != nil {
span.RecordError(err)
return err
}
return nil
}Did you notice how the span records both the operation details and any errors? This level of visibility becomes invaluable when diagnosing production issues.
Error handling in distributed systems requires careful consideration. Transient failures should trigger retries, while permanent failures need different handling. NATS JetStream’s acknowledgment mechanisms help here—messages are only removed from the stream when explicitly acknowledged, preventing data loss during processing.
What happens when services need to maintain their own state while reacting to events? This is where patterns like event sourcing shine, providing both auditability and the ability to reconstruct state at any point in time.
The real power emerges when these tools work together. Go provides the performance foundation, NATS JetStream ensures reliable messaging, and OpenTelemetry offers complete visibility. This combination handles the complexities of distributed systems while maintaining developer productivity.
Building these systems requires thinking differently about application architecture. Instead of synchronous request-response patterns, we design around events and state changes. This approach leads to more decoupled, scalable systems that can evolve independently.
As you explore these technologies, remember that the goal isn’t just technical implementation—it’s about creating systems that are understandable, maintainable, and reliable. The tools provide the foundation, but thoughtful design makes the difference between a working system and an excellent one.
I’d love to hear about your experiences with event-driven architectures. What challenges have you faced? What patterns have worked well for your teams? Share your thoughts in the comments below, and if you found this useful, please like and share with others who might benefit from this approach.
