I’ve been thinking a lot about how modern applications handle massive scale while staying reliable. Recently, I worked on a project where traditional request-response systems kept failing under load. That experience pushed me toward event-driven architectures. Today, I want to share how you can build robust microservices using Go, NATS JetStream, and OpenTelemetry. This combination has transformed how I design systems, making them more resilient and observable.

Event-driven architecture changes how services communicate. Instead of direct calls, services emit events when something important happens. Other services listen and react. This approach reduces coupling and improves scalability. But how do you ensure messages aren’t lost when services restart? That’s where persistent messaging comes in.

NATS JetStream provides durable message storage. It remembers events even if consumers go offline. Setting it up in Go is straightforward. Here’s a basic event structure I often use:

type Event struct {
    ID        string            `json:"id"`
    Type      string            `json:"type"`
    Source    string            `json:"source"`
    Data      json.RawMessage   `json:"data"`
    Timestamp time.Time         `json:"timestamp"`
}

Have you considered what happens when multiple services need the same event? JetStream’s stream configurations handle this elegantly. You define streams that store events and consumers that process them. Each service can have its own consumer with separate progress tracking.

Connecting to JetStream from Go involves a few steps. I initialize the connection with retry logic for production reliability:

func NewEventBus(url string) (*NATSEventBus, error) {
    opts := []nats.Option{
        nats.ReconnectWait(2 * time.Second),
        nats.MaxReconnects(-1),
    }
    conn, err := nats.Connect(url, opts...)
    if err != nil {
        return nil, err
    }
    js, err := conn.JetStream()
    return &NATSEventBus{conn: conn, js: js}, nil
}

Publishing events requires careful attention to context and tracing. I wrap each publish operation with OpenTelemetry spans to track performance. This helps identify bottlenecks when events move through the system.

What about processing events efficiently? Go’s concurrency model shines here. I use goroutines with careful synchronization to handle multiple events simultaneously. Here’s a pattern I frequently implement:

func (s *Service) StartConsumers(ctx context.Context) {
    for i := 0; i < s.workerCount; i++ {
        go s.consumeEvents(ctx)
    }
}

func (s *Service) consumeEvents(ctx context.Context) {
    sub, _ := s.js.PullSubscribe("orders.>", "order-processor")
    for {
        select {
        case <-ctx.Done():
            return
        default:
            msgs, _ := sub.Fetch(10)
            for _, msg := range msgs {
                s.processMessage(ctx, msg)
            }
        }
    }
}

Observability isn’t optional in production systems. OpenTelemetry provides tracing and metrics out of the box. I instrument every event publish and consume operation. This creates a detailed map of how events flow between services. When something slows down, I can pinpoint the exact cause.

Error handling requires thoughtful design. Sometimes events fail processing. I implement retry mechanisms with exponential backoff. For persistent failures, events move to dead-letter queues for investigation. This prevents one faulty event from blocking others.

Testing event-driven services presents unique challenges. I run integration tests with real NATS instances in Docker. Each test verifies that events are published and consumed correctly. Mocking the event bus helps with unit tests for business logic.

Deploying to production involves monitoring key metrics. I track event throughput, processing latency, and error rates. Alerting rules notify me when thresholds are breached. This proactive approach catches issues before they affect users.

Scaling these services horizontally is straightforward. Since JetStream handles message distribution, I can add more consumer instances without configuration changes. The system automatically balances load across available workers.

What strategies do you use for ensuring data consistency across services? I find that idempotent consumers solve many challenges. They process the same event multiple times without side effects. This handles duplicate deliveries gracefully.

Building this infrastructure requires initial effort, but the long-term benefits are substantial. Systems become more fault-tolerant and easier to maintain. Development teams work independently on different services.

I encourage you to experiment with these patterns in your projects. Start with a simple event flow and gradually add complexity. The learning curve is manageable, and the results are rewarding.

If you found this helpful, please share it with others who might benefit. I’d love to hear about your experiences in the comments. What challenges have you faced with event-driven systems? Let’s continue the conversation.