Lately, I’ve been thinking a lot about building systems that are not just functional but truly resilient and scalable. In my work, I often see teams struggle with distributed systems that become fragile under load or fail to handle unexpected errors gracefully. This led me to explore event-driven architectures using Go, NATS, and PostgreSQL—a combination that offers both performance and reliability. If you’ve ever wondered how to build services that can handle real-world traffic without falling apart, you’re in the right place.

Let me show you how to set up a production-ready event-driven system. We’ll create three microservices that communicate through events, ensuring loose coupling and independent scalability. Each service maintains its own event store in PostgreSQL, providing a single source of truth while NATS JetStream handles the message flow between them.

Why use Go for this? Go’s simplicity and built-in concurrency features make it ideal for high-throughput event processing. Its standard library and rich ecosystem provide everything we need for building robust network services. Combined with NATS for messaging and PostgreSQL for persistence, we get a stack that’s both powerful and practical.

Here’s a basic setup for our event structure:

type Event struct {
    ID            uuid.UUID       `json:"id"`
    AggregateID   uuid.UUID       `json:"aggregate_id"`
    EventType     string          `json:"event_type"`
    EventData     json.RawMessage `json:"event_data"`
    Timestamp     time.Time       `json:"timestamp"`
}

This simple structure allows us to store any domain event in a consistent format. Notice how we use JSONB in PostgreSQL for the event data? This gives us flexibility while maintaining query capabilities.

But how do we ensure events are processed reliably? That’s where NATS JetStream comes in. It provides persistent storage for messages, ensuring they’re not lost if a service goes down temporarily. Here’s how you might configure a JetStream consumer:

js.AddConsumer("ORDERS", &nats.ConsumerConfig{
    Durable: "order-processor",
    AckWait: 30 * time.Second,
    MaxDeliver: 5,
})

This configuration allows up to five delivery attempts with a 30-second acknowledgement window. If processing fails, the message will be retried automatically.

What happens when something goes wrong despite our best efforts? We need proper error handling and monitoring. Structured logging and metrics collection are essential for understanding system behavior in production. Consider implementing something like this:

func processOrderEvent(msg *nats.Msg) {
    ctx := context.Background()
    logger := log.WithFields(log.Fields{
        "event_id": extractEventID(msg.Data),
        "service":  "order-processor",
    })
    
    defer func() {
        if r := recover(); r != nil {
            logger.Error("panic in event processing", r)
            metrics.Panics.Inc()
        }
    }()
    
    // Process message here
}

This approach gives us visibility into errors while preventing single failures from bringing down the entire service.

Building the actual services involves creating independent components that react to events. The order service might publish an “order.created” event, which the inventory service consumes to reserve stock. The notification service could listen for both events to keep users informed. Each service remains focused on its specific domain while contributing to the overall workflow.

Have you considered how you’ll handle schema evolution? As your system grows, event structures will change. Using versioned events and backward-compatible changes ensures smooth transitions without breaking existing consumers.

Deployment deserves careful attention too. Containerizing each service with Docker allows consistent environments across development and production. Implementing graceful shutdown ensures in-flight events are processed before termination:

func main() {
    // Setup code here
    
    stop := make(chan os.Signal, 1)
    signal.Notify(stop, syscall.SIGINT, syscall.SIGTERM)
    
    <-stop
    log.Info("shutting down gracefully")
    
    // Close connections and wait for processing to complete
    ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
    defer cancel()
    
    if err := server.Shutdown(ctx); err != nil {
        log.Error("forced shutdown", err)
    }
}

This pattern prevents data loss during deployments or scaling operations.

Building event-driven microservices requires thinking differently about data flow and error handling. The payoff is a system that scales naturally, handles failures gracefully, and evolves easily over time. By combining Go’s efficiency with NATS’ reliability and PostgreSQL’s durability, we create foundations that support growth and change.

I hope this gives you a solid starting point for your own event-driven journey. What challenges have you faced with microservices architecture? I’d love to hear about your experiences—feel free to share your thoughts in the comments below, and if you found this useful, please pass it along to others who might benefit.