Lately, I’ve been thinking about how modern systems need to handle massive scale while staying resilient. That’s what led me to explore event-driven microservices. In this guide, I’ll show you how to build production-ready services using Go, NATS, and MongoDB—technologies I’ve found incredibly effective for creating scalable, maintainable systems.

Setting up the foundation is crucial. I start with a clear project structure that separates concerns and makes maintenance easier. Here’s how I organize my Go modules and dependencies:

go mod init event-driven-ecommerce
go get github.com/nats-io/stan.go
go get go.mongodb.org/mongo-driver/mongo
go get github.com/spf13/viper

Configuration management often gets overlooked, but it’s vital for production systems. I use Viper to handle different environments seamlessly. Have you considered how your configuration might change between development and production?

type Config struct {
    Server   ServerConfig   `mapstructure:"server"`
    MongoDB  MongoDBConfig  `mapstructure:"mongodb"`
    NATS     NATSConfig     `mapstructure:"nats"`
}

func LoadConfig(path string) (*Config, error) {
    viper.AddConfigPath(path)
    viper.SetConfigName("config")
    viper.SetConfigType("yaml")
    // ... configuration loading logic
}

The event system forms the backbone of our architecture. I design events with clear types and consistent structure:

type EventType string

const (
    OrderCreated    EventType = "order.created"
    OrderCancelled  EventType = "order.cancelled"
)

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

When building the order service, I focus on clean separation between handlers, services, and repositories. This approach makes testing easier and keeps code maintainable. How would you handle concurrent order processing without losing data consistency?

type OrderService struct {
    repo        OrderRepository
    natsConn    stan.Conn
    logger      *logrus.Logger
}

func (s *OrderService) CreateOrder(ctx context.Context, order models.Order) error {
    // Validate order
    if err := s.validateOrder(order); err != nil {
        return err
    }
    
    // Persist to MongoDB
    if err := s.repo.CreateOrder(ctx, order); err != nil {
        return err
    }
    
    // Publish event
    event := events.OrderCreatedEvent{
        BaseEvent: events.BaseEvent{
            ID:        uuid.New().String(),
            Type:      events.OrderCreated,
            Timestamp: time.Now(),
        },
        Data: events.OrderCreatedData{
            OrderID:     order.ID,
            CustomerID: order.CustomerID,
            Items:      order.Items,
        },
    }
    
    return s.publishEvent(event)
}

Error handling in distributed systems requires special attention. I implement retry mechanisms and circuit breakers to prevent cascading failures:

func (s *OrderService) publishEvent(event events.BaseEvent) error {
    data, err := json.Marshal(event)
    if err != nil {
        return fmt.Errorf("failed to marshal event: %w", err)
    }

    // Implement retry with exponential backoff
    retryOp := func() error {
        return s.natsConn.Publish("orders.events", data)
    }

    return retry.Do(retryOp, retry.Attempts(3), retry.Delay(time.Second))
}

Testing event-driven systems presents unique challenges. I use Docker Compose to spin up NATS and MongoDB for integration tests:

version: '3.8'
services:
  nats:
    image: nats-streaming:latest
    ports:
      - "4222:4222"
      - "8222:8222"
  
  mongodb:
    image: mongo:5.0
    ports:
      - "27017:27017"

Monitoring production services is non-negotiable. I integrate Prometheus metrics and structured logging to gain visibility:

func NewOrderHandler(service OrderService, metrics *prometheus.Registry) *OrderHandler {
    return &OrderHandler{
        service: service,
        metrics: metrics,
        ordersCreated: prometheus.NewCounterVec(
            prometheus.CounterOpts{
                Name: "orders_created_total",
                Help: "Total number of orders created",
            },
            []string{"status"},
        ),
    }
}

Deployment considerations include health checks, resource limits, and proper service discovery. I use Docker multi-stage builds to create minimal container images:

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

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

Building event-driven microservices requires careful thought about data consistency, error handling, and observability. The patterns I’ve shared here have served me well in production environments, but every system has unique requirements.

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