I’ve been building distributed systems for years, and I keep coming back to event-driven architectures because they solve real-world problems in elegant ways. Recently, I worked on a project where traditional request-response patterns were causing bottlenecks and tight coupling between services. That’s when I decided to dive into NATS with Go, and the results transformed how I think about microservice communication. If you’re dealing with similar challenges, this approach might change your perspective too.

Setting up our foundation starts with thoughtful configuration management. I prefer using environment variables for configuration because it makes deployment straightforward across different environments. Here’s how I structure my config package:

type Config struct {
    ServiceName string `envconfig:"SERVICE_NAME" required:"true"`
    NATSUrl     string `envconfig:"NATS_URL" default:"nats://localhost:4222"`
    Port        string `envconfig:"PORT" default:"8080"`
}

func Load() (*Config, error) {
    var cfg Config
    if err := envconfig.Process("", &cfg); err != nil {
        return nil, fmt.Errorf("config loading failed: %w", err)
    }
    return &cfg, nil
}

Why do you think proper configuration handling is often overlooked in early development stages?

Logging is more than just printing messages; it’s about creating a narrative of what’s happening in your system. I’ve found that structured logging with correlation IDs makes debugging distributed systems much simpler. Here’s a snippet from my logging setup:

func (l *Logger) WithContext(ctx context.Context) *zap.SugaredLogger {
    logger := l.SugaredLogger
    if traceID := ctx.Value("traceID"); traceID != nil {
        logger = logger.With("trace_id", traceID)
    }
    return logger
}

When events drive your system, defining them clearly becomes crucial. I model events as immutable facts that have occurred in the system. This mindset shift from commands to events has helped me build more resilient systems. Here’s how I define a base event:

type BaseEvent struct {
    ID        string    `json:"id"`
    Type      EventType `json:"type"`
    Timestamp time.Time `json:"timestamp"`
    Source    string    `json:"source"`
}

Have you considered how event schemas evolve over time without breaking existing consumers?

Connecting to NATS is straightforward, but production systems need more than basic connections. I always implement connection error handling and reconnection logic. The NATS Go client provides excellent support for this out of the box:

nc, err := nats.Connect(cfg.NATSUrl,
    nats.MaxReconnects(5),
    nats.ReconnectWait(2*time.Second),
    nats.DisconnectErrHandler(func(nc *nats.Conn, err error) {
        log.Error("NATS disconnected", "error", err)
    }))

What happens when your message broker becomes unavailable? How do your services respond?

For high-throughput scenarios, I leverage Go’s concurrency primitives. Using goroutines and channels with NATS subscriptions can significantly improve message processing rates. But remember, uncontrolled concurrency can be dangerous. Here’s a pattern I use for safe concurrent message processing:

go func() {
    for msg := range messageChan {
        go func(m *nats.Msg) {
            ctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)
            defer cancel()
            if err := processMessage(ctx, m); err != nil {
                log.Error("Message processing failed", "error", err)
            }
        }(msg)
    }
}()

Observability isn’t just about monitoring; it’s about understanding system behavior from the outside. I instrument everything with metrics, using Prometheus for collection. This histogram tracks message processing latency:

var processingDuration = prometheus.NewHistogramVec(
    prometheus.HistogramOpts{
        Name: "message_processing_duration_seconds",
        Help: "Time spent processing messages",
    },
    []string{"service", "event_type"},
)

Circuit breakers prevent cascading failures when dependencies become unstable. I use the gobreaker library to implement this pattern. It’s surprising how few services implement proper circuit breaking until they face production issues:

cb := gobreaker.NewCircuitBreaker(gobreaker.Settings{
    Name:        "payment-service",
    MaxRequests: 3,
    Timeout:     60,
})

Testing event-driven systems requires a different approach. I use embedded NATS servers for integration tests, which provides realistic testing without external dependencies. This has caught numerous issues before deployment:

func TestOrderProcessing(t *testing.T) {
    nc, err := natstest.StartServer()
    require.NoError(t, err)
    defer nc.Stop()
    
    // Test logic here
}

Dockerizing services ensures consistent environments from development to production. I always include health checks and graceful shutdown handling. This Dockerfile snippet shows the essentials:

FROM golang:1.21-alpine
WORKDIR /app
COPY go.mod ./
RUN go mod download
COPY . ./
RUN go build -o /order-service ./cmd/order-service
EXPOSE 8080
HEALTHCHECK --interval=30s CMD curl -f http://localhost:8080/health
CMD ["/order-service"]

Graceful shutdown in Go is straightforward but crucial for preventing message loss. I handle SIGTERM and SIGINT signals to clean up resources properly:

quit := make(chan os.Signal, 1)
signal.Notify(quit, syscall.SIGINT, syscall.SIGTERM)
<-quit
log.Info("Shutting down gracefully")
nc.Drain()

Building production-ready systems requires thinking about failure scenarios from the start. Event-driven architectures with NATS and Go have served me well in creating systems that are both scalable and maintainable. The combination of Go’s simplicity and NATS’s performance creates a powerful foundation for modern applications.

I’d love to hear about your experiences with event-driven architectures. What challenges have you faced, and how did you overcome them? If you found this useful, please share it with others who might benefit, and leave a comment with your thoughts or questions.