I’ve been thinking a lot about how modern applications handle scale and complexity lately. Traditional request-response architectures often struggle under heavy loads, leading to cascading failures and poor user experiences. That’s why I want to share my approach to building robust event-driven microservices using Go, NATS JetStream, and Kubernetes - a combination that has proven incredibly effective in production environments.

Have you ever wondered how large systems maintain responsiveness while processing thousands of transactions simultaneously? The answer often lies in event-driven architecture, where services communicate through events rather than direct calls. This approach provides better scalability, resilience, and flexibility.

Let me show you how to set up a solid foundation. We’ll create a structured project that separates concerns while maintaining clear communication paths between services. The key is establishing a shared events package that defines our domain events consistently across all services.

Here’s how we configure NATS JetStream for reliable messaging:

streamConfig := jetstream.StreamConfig{
    Name:        "ORDERS",
    Subjects:    []string{"order.>"},
    Retention:   jetstream.WorkQueuePolicy,
    MaxAge:      24 * time.Hour,
    Replicas:    3,
    Storage:     jetstream.FileStorage,
}

This configuration ensures message persistence, replication across nodes, and automatic cleanup after 24 hours. But what happens when a service goes down temporarily? JetStream’s consumer configuration handles this gracefully with retry mechanisms and explicit acknowledgments.

Now, let’s look at event definition. Consistency here is crucial for maintaining a clear understanding across services:

type OrderCreatedEvent struct {
    EventID        string    `json:"event_id"`
    OrderID        string    `json:"order_id"`
    CustomerID     string    `json:"customer_id"`
    TotalAmount    float64   `json:"total_amount"`
    CreatedAt      time.Time `json:"created_at"`
    Items          []OrderItem `json:"items"`
}

func PublishOrderCreated(ctx context.Context, js jetstream.JetStream, event OrderCreatedEvent) error {
    data, _ := json.Marshal(event)
    _, err := js.Publish(ctx, "order.created", data)
    return err
}

How do we ensure these events are processed reliably? Each service needs to implement proper error handling and retry logic. The circuit breaker pattern becomes essential here to prevent cascading failures:

func ProcessPayment(ctx context.Context, orderEvent OrderCreatedEvent) error {
    breaker := gobreaker.NewCircuitBreaker(gobreaker.Settings{
        Name:        "payment-service",
        Timeout:     10 * time.Second,
        ReadyToTrip: func(counts gobreaker.Counts) bool {
            return counts.ConsecutiveFailures > 5
        },
    })
    
    _, err := breaker.Execute(func() (interface{}, error) {
        return nil, processPaymentInternal(orderEvent)
    })
    
    return err
}

Deploying to Kubernetes requires careful consideration of resource allocation and health checks. Our deployment configuration includes liveness and readiness probes that understand the event-driven nature of our services:

livenessProbe:
  httpGet:
    path: /health
    port: 8080
  initialDelaySeconds: 30
  periodSeconds: 10

readinessProbe:
  httpGet:
    path: /ready
    port: 8080
  initialDelaySeconds: 5
  periodSeconds: 5

Observability is non-negotiable in distributed systems. We implement structured logging, metrics collection, and distributed tracing to maintain visibility into our event flows:

func ProcessOrder(ctx context.Context, event OrderCreatedEvent) {
    ctx, span := tracer.Start(ctx, "process_order")
    defer span.End()
    
    logger.Info().
        Str("order_id", event.OrderID).
        Float64("amount", event.TotalAmount).
        Msg("Processing order")
    
    // Processing logic here
}

Testing event-driven systems requires a different approach. We use containerized testing with TestContainers to spin up real NATS and database instances:

func TestOrderProcessing(t *testing.T) {
    ctx := context.Background()
    
    natsContainer, err := testcontainers.StartNATS(ctx)
    if err != nil {
        t.Fatal(err)
    }
    defer natsContainer.Terminate(ctx)
    
    // Test logic using real NATS connection
}

Building production-ready event-driven microservices requires attention to reliability patterns, observability, and deployment strategies. The combination of Go’s performance characteristics, NATS JetStream’s messaging capabilities, and Kubernetes’ orchestration power creates a foundation that can handle real-world demands.

What challenges have you faced with microservices communication? I’d love to hear about your experiences and solutions. If you found this useful, please share it with others who might benefit, and feel free to leave comments with your thoughts or questions.