I’ve been thinking a lot about how modern applications need to handle massive scale while remaining resilient and responsive. This led me to explore event-driven microservices—a pattern that has transformed how we build distributed systems. If you’re working with Go, you’re in for a treat. Its simplicity and power make it perfect for building production-ready microservices.

Why should you care about event-driven architecture? Imagine building systems where components communicate through events rather than direct calls. This approach gives you loose coupling, better scalability, and improved fault tolerance. When services don’t need to know about each other directly, your system becomes more flexible and easier to maintain.

Let me show you how to build this with Go, NATS for messaging, and MongoDB for data persistence. Here’s a practical example of setting up a basic event publisher:

func publishOrderCreated(event OrderCreatedEvent) error {
    msg, err := json.Marshal(event)
    if err != nil {
        return err
    }
    
    return natsConn.Publish("orders.created", msg)
}

But what happens when you need guaranteed delivery? That’s where NATS JetStream comes in. It provides persistence and exactly-once delivery semantics. Here’s how you can set up a durable consumer:

_, err := js.AddConsumer("ORDERS", &nats.ConsumerConfig{
    Durable: "order-processor",
    FilterSubject: "orders.*",
    AckWait: 30 * time.Second,
})

Have you considered how you’ll handle database operations across services? MongoDB’s support for transactions is crucial here. When processing an order, you might need to update inventory and create a payment record atomically:

session, err := client.StartSession()
if err != nil {
    return err
}
defer session.EndSession(ctx)

err = mongo.WithSession(ctx, session, func(sc mongo.SessionContext) error {
    if err := session.StartTransaction(); err != nil {
        return err
    }
    
    // Update inventory
    // Create payment record
    
    return session.CommitTransaction(sc)
})

Monitoring is non-negotiable in production. Implementing proper observability helps you understand system behavior and troubleshoot issues. Here’s a simple way to add metrics:

func instrumentHandler() gin.HandlerFunc {
    return func(c *gin.Context) {
        start := time.Now()
        c.Next()
        
        duration := time.Since(start)
        metrics.RequestDuration.Observe(duration.Seconds())
        metrics.RequestCount.Inc()
    }
}

What about concurrency patterns? Go’s goroutines and channels are perfect for building worker pools that process events efficiently:

func startWorkers(numWorkers int, workChan chan Event) {
    for i := 0; i < numWorkers; i++ {
        go func(workerID int) {
            for event := range workChan {
                processEvent(event)
            }
        }(i)
    }
}

Deployment considerations are equally important. Using Docker ensures consistency across environments. Here’s a minimal Dockerfile for a Go service:

FROM golang:1.21-alpine AS builder
WORKDIR /app
COPY . .
RUN go build -o service ./cmd/service

FROM alpine:latest
COPY --from=builder /app/service /app/service
EXPOSE 8080
CMD ["/app/service"]

Building production-ready systems requires attention to many details—resilient communication, data consistency, monitoring, and deployment strategies. The combination of Go, NATS, and MongoDB provides a solid foundation for creating scalable, maintainable microservices.

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