As a developer who has spent years wrestling with monolithic applications that crumbled under load, I found myself constantly searching for better ways to build scalable systems. The turning point came when I inherited a legacy e-commerce platform that struggled during peak sales events. Orders would get lost, inventory counts went haywire, and debugging felt like finding needles in haystacks. This frustration sparked my journey into event-driven microservices, and today I want to share how you can build robust systems using Go, NATS JetStream, and OpenTelemetry.

Have you ever wondered what happens to an order when your payment processor takes too long to respond? Event-driven architectures handle these scenarios gracefully by decoupling services through message passing. Let me show you how to set up the foundation.

First, we need to structure our project properly. I organize my Go modules to separate concerns clearly, with internal packages for business logic and pkg for shared protocols. Here’s a snippet from my go.mod file that includes essential dependencies:

module github.com/yourorg/event-driven-services

go 1.21

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

Configuring NATS JetStream is crucial for reliable messaging. I prefer using a configuration file that specifies memory and file storage limits to prevent resource exhaustion. In my services, I initialize the JetStream connection with proper error handling and reconnection logic. What if your message broker goes down temporarily? JetStream’s persistence ensures no events are lost during outages.

func NewJetStreamManager(config *JetStreamConfig) (*JetStreamManager, error) {
    opts := []nats.Option{
        nats.Name(config.ConnectionName),
        nats.MaxReconnects(config.MaxReconnects),
        nats.ReconnectWait(config.ReconnectWait),
    }
    nc, err := nats.Connect(config.URL, opts...)
    if err != nil {
        return nil, fmt.Errorf("connection failed: %w", err)
    }
    js, err := nc.JetStream()
    return &JetStreamManager{nc: nc, js: js}, nil
}

Observability transforms how we understand system behavior. I integrate OpenTelemetry from day one, adding traces to track requests across services. Imagine trying to debug a delayed notification without knowing which service caused the bottleneck. Distributed tracing reveals these insights instantly.

func ProcessOrder(ctx context.Context, order Order) error {
    ctx, span := tracer.Start(ctx, "process_order")
    defer span.End()
    // Business logic here
}

Designing event schemas requires careful thought. I use protocol buffers for type safety and versioning. When the inventory service publishes a “StockReserved” event, other services consume it without tight coupling. How do you handle schema changes without breaking existing consumers? I include version fields and avoid removing fields in updates.

Error handling separates amateur implementations from production-ready systems. I implement retry mechanisms with exponential backoff and dead-letter queues for problematic messages. For instance, if payment processing fails temporarily, the service retries before moving the message to a quarantine stream.

func HandlePaymentEvent(msg *nats.Msg) {
    if err := processPayment(msg.Data); err != nil {
        if shouldRetry(err) {
            msg.Nak() // Negative acknowledgment for retry
        } else {
            moveToDLQ(msg) // Dead-letter queue
        }
    }
    msg.Ack()
}

Testing event-driven services involves mocking dependencies and verifying message flows. I write integration tests that spin up a test NATS server and validate end-to-end scenarios. Can you trust your system if you haven’t tested failure modes?

Deployment in Kubernetes with proper resource limits and health checks ensures stability. I configure liveness probes that check JetStream connections and OpenTelemetry exporters. Monitoring dashboards display error rates and latency percentiles, helping me spot issues before users do.

Performance optimization often involves tuning JetStream stream configurations and optimizing Go code. I use connection pooling and batch processing where appropriate to reduce overhead.

Building these systems has taught me that resilience comes from anticipating failures. Every component should handle disruptions gracefully, whether it’s a network partition or a downstream service outage.

I hope this practical guide helps you avoid the pitfalls I encountered. If you’ve faced similar challenges or have questions about implementing these patterns, I’d love to hear your experiences. Please like, share, and comment below to continue the conversation!