I’ve been thinking a lot about how modern applications handle scale and reliability, especially in distributed systems. Recently, I found myself building a system that needed to process thousands of orders per second without dropping a single message. That’s when I dove into event-driven microservices with Go, NATS JetStream, and OpenTelemetry. In this article, I’ll share my journey and how you can build production-ready systems that are scalable, observable, and resilient.

Event-driven architecture changes how services communicate. Instead of direct API calls, services emit events when something significant happens. Other services listen and react. This approach decouples components, making systems more flexible and scalable. Have you ever faced issues with tightly coupled services that break when one part fails? Event-driven patterns can help.

Go is an excellent choice for building these systems. Its simplicity, performance, and strong concurrency support make it ideal for high-throughput applications. I’ve used it to handle millions of events daily with minimal resource usage. The static typing and fast compilation reduce errors and speed up development.

NATS JetStream provides persistent messaging with at-least-once delivery guarantees. It ensures messages aren’t lost even if services restart. Setting up a JetStream client in Go is straightforward. You define streams and consumers to manage event flow.

js, err := nc.JetStream()
if err != nil {
    log.Fatal("JetStream setup failed:", err)
}

streamConfig := &nats.StreamConfig{
    Name:     "ORDERS",
    Subjects: []string{"orders.>"},
    MaxAge:   24 * time.Hour,
}
_, err = js.AddStream(streamConfig)

Why is observability crucial in distributed systems? Without it, debugging issues feels like searching for a needle in a haystack. OpenTelemetry offers distributed tracing to track requests across services. I instrumented my Go services to capture spans and export them to Jaeger.

tracer := otel.Tracer("order-service")
ctx, span := tracer.Start(ctx, "process-order")
defer span.End()
span.SetAttributes(attribute.String("order.id", orderID))

Event schema design requires careful planning. I define events with metadata for correlation and versioning. This helps track event flow and handle schema evolution. What happens when you need to change an event structure? Versioning prevents breaking existing consumers.

type Event struct {
    Metadata EventMetadata `json:"metadata"`
    Data     interface{}   `json:"data"`
}

func NewOrderCreated(orderID string, items []OrderItem) *Event {
    return NewEvent("order.created", "1.0", "order-service", 
        OrderCreatedV1{OrderID: orderID, Items: items})
}

Error handling and resilience are non-negotiable. I use circuit breakers to prevent cascading failures. The gobreaker library in Go helps implement this pattern. When a service repeatedly fails, the circuit opens, giving it time to recover.

cb := gobreaker.NewCircuitBreaker(gobreaker.Settings{
    Name: "payment-service",
    Timeout: 30 * time.Second,
})
result, err := cb.Execute(func() (interface{}, error) {
    return processPayment(order)
})

Saga patterns manage distributed transactions across services. Instead of a two-phase commit, I choreograph sagas where each step emits events. If one step fails, compensating actions roll back changes. This maintains consistency without tight coupling.

Deploying these systems requires containerization and orchestration. I use Docker Compose for local development and Kubernetes for production. Monitoring with Prometheus and Grafana ensures I can track performance and set alerts.

Building event-driven microservices has taught me the importance of idempotency. Services must handle duplicate events without side effects. I design handlers to check if an action was already processed using event IDs.

What strategies do you use for event ordering? JetStream supports ordered consumption, but I sometimes add sequence numbers to events for critical workflows.

In conclusion, combining Go, NATS JetStream, and OpenTelemetry creates robust event-driven systems. Start small, focus on observability, and gradually add complexity. I’d love to hear about your experiences—please like, share, and comment if this resonates with your projects!