I’ve been thinking about microservices architecture a lot lately, especially as our systems grow more complex. Traditional request-response patterns start showing their limitations when you need to scale. That’s why I want to share my journey with event-driven architectures using NATS and Go - a combination that’s transformed how we build resilient systems.

When I first started with microservices, I noticed something interesting: services would fail silently, and tracing issues felt like finding needles in haystacks. Have you ever faced that moment when a payment processes but the inventory never updates? That frustration led me to explore event-driven patterns with proper observability.

Let me show you how we can build something better. We’ll create an order processing system where services communicate through events rather than direct calls. This approach gives us loose coupling and better fault tolerance.

Starting with NATS setup, here’s how we configure a clustered JetStream instance:

// Connecting to NATS with resilience
config := messaging.NATSConfig{
    URLs:           []string{"nats://nats-1:4222", "nats://nats-2:4222"},
    MaxReconnects:  10,
    ReconnectWait:  time.Second * 2,
    ConnectTimeout: time.Second * 5,
}

client, err := messaging.NewNATSClient(config)

This connection handles network issues gracefully. But what happens when messages get lost during failures? That’s where JetStream’s persistence comes in.

Now, let’s look at event publishing. Notice how we include tracing information right from the start:

// Publishing an event with tracing context
func (s *OrderService) CreateOrder(ctx context.Context, order Order) error {
    span := trace.SpanFromContext(ctx)
    event, err := events.NewEvent(
        events.OrderCreated,
        "order-service",
        order,
        span.SpanContext().TraceID().String(),
    )
    
    return s.nats.Publish("orders.created", event)
}

Every event carries its trace ID, creating a breadcrumb trail across services. When the payment service processes this order, it continues the same trace.

Speaking of payments, how do we ensure they’re processed exactly once? We use NATS’ durable consumers:

// Reliable event consumption
sub, err := js.Subscribe("orders.created", func(msg *nats.Msg) {
    // Process payment
    msg.Ack() // Only acknowledge after successful processing
}, nats.Durable("payment-processor"))

This pattern prevents lost messages while maintaining order. But what about errors? We implement retry logic with exponential backoff:

// Resilient message handling
func processWithRetry(msg *nats.Msg, maxAttempts int) {
    for attempt := 1; attempt <= maxAttempts; attempt++ {
        if err := processPayment(msg); err == nil {
            msg.Ack()
            return
        }
        time.Sleep(time.Duration(attempt*attempt) * time.Second)
    }
    msg.Nak() // Let another instance try
}

Monitoring becomes crucial in distributed systems. We export metrics for every important operation:

// Tracking message processing latency
func recordProcessingTime(start time.Time, eventType string) {
    duration := time.Since(start).Seconds()
    processingTime.WithLabelValues(eventType).Observe(duration)
}

These metrics help us set alerts for abnormal behavior. When we see payment processing times spike, we know to investigate before users notice.

Deployment-wise, we package each service in Docker containers with health checks:

# Dockerfile for order service
FROM golang:1.19-alpine
COPY cmd/order-service/ /app/
HEALTHCHECK --interval=30s --timeout=3s \
  CMD curl -f http://localhost:8080/health || exit 1

In Kubernetes, we use readiness probes to ensure traffic only reaches healthy instances. But how do we handle database migrations in this setup? We run them as init containers before the main application starts.

Testing event-driven systems requires a different approach. We use contract testing to verify events match what consumers expect:

// Contract test for order events
func TestOrderCreatedEvent(t *testing.T) {
    event := OrderCreatedData{
        OrderID:    "test-123",
        CustomerID: "cust-456",
        TotalAmount: 99.99,
    }
    
    jsonData, err := json.Marshal(event)
    assert.NoError(t, err)
    
    // Verify schema compliance
    assert.JSONEq(t, expectedSchema, string(jsonData))
}

Performance optimization comes last. We tune NATS settings based on our workload patterns:

// Optimized JetStream configuration
jsConfig := nats.StreamConfig{
    Name:         "ORDERS",
    Subjects:     []string{"orders.>"},
    Retention:    nats.WorkQueuePolicy,
    MaxAge:       time.Hour * 24,
    Storage:      nats.FileStorage,
    Replicas:     3,
}

The real beauty emerges when you see the entire system working together. Orders flow through services seamlessly, traces connect the dots across failures, and metrics provide real-time visibility.

What surprised me most was how this architecture handles peak loads. During Black Friday, our system processed 10x the normal traffic without breaking a sweat. The event-driven approach with proper observability made all the difference.

I’d love to hear about your experiences with microservices. Have you tried event-driven architectures? What challenges did you face? Share your thoughts in the comments below, and if this helped you, please like and share with others who might benefit.