I’ve been thinking a lot lately about how modern applications need to handle massive scale while remaining reliable. The shift toward event-driven architectures isn’t just a trend—it’s becoming essential for building systems that can handle real-world complexity. That’s why I want to share my approach to creating production-ready microservices using Go, NATS JetStream, and PostgreSQL.

Why do event-driven systems matter so much today? Because they let us build applications that are both scalable and resilient. When services communicate through events rather than direct calls, they become loosely coupled. This means one service can fail without bringing down the entire system. Have you ever wondered how large e-commerce platforms handle thousands of orders per second without collapsing? This architecture is often the answer.

Let me show you how I structure these systems. The foundation starts with clear event schemas that define how services communicate. Here’s a basic example of how I define events:

type OrderCreated struct {
    ID          uuid.UUID `json:"id"`
    OrderID     uuid.UUID `json:"order_id"`
    CustomerID  uuid.UUID `json:"customer_id"`
    Items       []OrderItem `json:"items"`
    TotalAmount float64   `json:"total_amount"`
    Timestamp   time.Time `json:"timestamp"`
}

This structure ensures every service understands the data format, making system evolution much smoother. But what happens when you need to change an event schema? That’s where versioning becomes critical.

Connecting to NATS JetStream requires careful configuration. I always include proper error handling and reconnection logic:

func ConnectJetStream(url string) (nats.JetStreamContext, error) {
    opts := []nats.Option{
        nats.ReconnectWait(2 * time.Second),
        nats.MaxReconnects(10),
        nats.DisconnectErrHandler(logDisconnect),
    }
    
    nc, err := nats.Connect(url, opts...)
    if err != nil {
        return nil, err
    }
    
    return nc.JetStream()
}

Database operations need equal attention. PostgreSQL with connection pooling handles high concurrency beautifully:

func NewDBPool(connString string) (*pgxpool.Pool, error) {
    config, err := pgxpool.ParseConfig(connString)
    if err != nil {
        return nil, err
    }
    
    config.MaxConns = 25
    config.MinConns = 5
    config.HealthCheckPeriod = time.Minute
    
    return pgxpool.NewWithConfig(context.Background(), config)
}

Monitoring and observability can’t be afterthoughts. I integrate structured logging and metrics from day one:

func NewLogger() (*zap.Logger, error) {
    config := zap.NewProductionConfig()
    config.OutputPaths = []string{"stdout", "/var/log/service.log"}
    return config.Build()
}

What separates production-ready code from simple prototypes? It’s the attention to failure scenarios. Circuit breakers prevent cascading failures:

func NewPaymentClient() *gobreaker.CircuitBreaker {
    return gobreaker.NewCircuitBreaker(gobreaker.Settings{
        Name:    "payment-service",
        Timeout: 30 * time.Second,
        ReadyToTrip: func(counts gobreaker.Counts) bool {
            return counts.ConsecutiveFailures > 5
        },
    })
}

Deployment matters just as much as development. Docker multi-stage builds keep images lean and secure:

FROM golang:1.21 as builder
WORKDIR /app
COPY go.mod go.sum ./
RUN go mod download
COPY . .
RUN CGO_ENABLED=0 go build -o service ./cmd/service

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

The real test comes when handling graceful shutdowns. Services must complete current work before terminating:

func StartServerWithGracefulShutdown(srv *http.Server) {
    quit := make(chan os.Signal, 1)
    signal.Notify(quit, syscall.SIGINT, syscall.SIGTERM)
    
    <-quit
    ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
    defer cancel()
    
    if err := srv.Shutdown(ctx); err != nil {
        log.Fatal("Server forced to shutdown:", err)
    }
}

Building these systems requires thinking about both success and failure paths. How do you ensure data consistency across services? Event sourcing patterns help maintain system state even when components fail.

The combination of Go’s performance, NATS JetStream’s reliability, and PostgreSQL’s robustness creates a foundation that can handle real production loads. Each component plays a specific role, and their integration points require careful design.

I’d love to hear your thoughts on this approach. What challenges have you faced with event-driven architectures? Share your experiences in the comments below, and if you found this useful, please consider liking and sharing this article with others who might benefit from it.