Lately, I’ve been thinking about how modern systems handle high traffic without breaking. It struck me during a late-night debugging session when our team faced cascading failures in a monolithic application. That’s when event-driven microservices became my focus. They promise resilience and scalability, but how do we build them properly? Today, I’ll walk through creating production-ready services using Go, NATS JetStream, and distributed tracing. Stick with me—you’ll gain practical skills for building systems that withstand real-world pressure.

First, let’s establish our foundation. We’re building an e-commerce system with four microservices: orders, payments, inventory, and notifications. Go serves as our language because of its concurrency support and efficiency. For messaging, NATS JetStream provides persistence and exactly-once delivery guarantees. Why JetStream specifically? It handles stream persistence and consumer groups natively, eliminating the need for external brokers.

Setting up the project begins with module initialization:

go mod init github.com/yourusername/event-driven-ecommerce

Dependencies include NATS for messaging, OpenTelemetry for tracing, and gobreaker for circuit breaking. This snippet from go.mod shows critical imports:

require (
    github.com/nats-io/nats.go v1.31.0
    go.opentelemetry.io/otel v1.21.0
    github.com/sony/gobreaker v0.5.0
)

Event schemas form our communication backbone. We define types like OrderCreated or PaymentFailed with careful versioning. Notice the BaseEvent structure—it includes tracing IDs and metadata for cross-service correlation:

type BaseEvent struct {
    ID            string 
    Type          EventType 
    TraceID       string  // For distributed tracing
    // ... other fields
}

How do we ensure services understand each other as schemas evolve? We use backward-compatible changes: adding optional fields, never removing existing ones. JSON serialization handles unknown fields gracefully.

Connecting services requires robust messaging. Here’s how we initialize NATS JetStream with reconnection logic:

nc, _ := nats.Connect("nats://localhost:4222",
    nats.MaxReconnects(-1), // Infinite retries
    nats.DisconnectErrHandler(func(_ *nats.Conn, err error) {
        logger.Error("Disconnected", zap.Error(err))
    }))
js, _ := jetstream.New(nc)

We create a stream with specific retention policies:

stream, _ := js.CreateStream(ctx, jetstream.StreamConfig{
    Name:     "ORDERS",
    Subjects: []string{"order.>"}, 
    Retention: jetstream.WorkQueuePolicy,
})

For message processing, we use pull consumers with retry logic. This snippet shows how to fetch messages with a timeout:

msgs, _ := consumer.Fetch(10, jetstream.FetchMaxWait(5*time.Second))
for msg := range msgs.Messages() {
    if err := process(msg); err != nil {
        msg.Nak() // Negative acknowledgment for retry
    } else {
        msg.Ack()
    }
}

What happens when messages repeatedly fail? Dead letter queues (DLQ) come to the rescue. We configure JetStream to route unprocessable messages to a separate stream after three attempts.

Distributed tracing ties operations across services. We instrument handlers using OpenTelemetry:

func handleOrder(ctx context.Context, event OrderEvent) {
    ctx, span := tracer.Start(ctx, "process_order")
    defer span.End()
    // ... business logic
    span.SetAttributes(attribute.String("order.id", event.ID))
}

Traces appear in Jaeger, showing cascading events from order creation to notification. Ever wondered why a payment timed out? Traces reveal latency bottlenecks between services.

Resilience patterns prevent localized failures from spreading. Circuit breakers halt requests to failing dependencies:

breaker := gobreaker.NewCircuitBreaker(gobreaker.Settings{
    Name: "PaymentService",
    ReadyToTrip: func(counts gobreaker.Counts) bool {
        return counts.ConsecutiveFailures > 5
    },
})
breaker.Execute(func() (interface{}, error) {
    return paymentClient.Process(order)
})

Bulkheads isolate resources using Go’s semaphores. This limits concurrent payment processing to prevent resource exhaustion:

sem := make(chan struct{}, 10) // Allow 10 concurrent
for msg := range messages {
    sem <- struct{}{}
    go func(m jetstream.Msg) {
        defer func() { <-sem }()
        processPayment(m)
    }(msg)
}

Deployment uses Docker Compose with Jaeger, Prometheus, and NATS. We expose metrics via /metrics endpoints and visualize them in Grafana. Alerts trigger when error rates exceed thresholds.

In closing, these patterns transform fragile systems into resilient architectures. But remember—no solution is universal. What challenges have you faced with microservices? Share your experiences below! If this guide helped you, consider liking or sharing it. Your feedback fuels deeper explorations. Let’s build robust systems together.