I’ve spent the last few years designing and deploying microservices in production environments, and one pattern that consistently delivers reliability and scalability is event-driven architecture. When I faced challenges with synchronous communication between services, I turned to Go, NATS, PostgreSQL, and comprehensive observability tools. This combination helped me build systems that handle high loads gracefully while maintaining data consistency. Today, I want to guide you through creating a production-ready user management microservice using these technologies.

Why focus on event-driven systems? Traditional request-response models often create tight coupling between services. If one service goes down, others might fail too. Event-driven approaches decouple components, allowing them to operate independently. Have you considered what happens when your user service needs to notify multiple other services about a new user registration? With events, you can broadcast changes without direct dependencies.

Let’s start with the foundation. I’ll use Go for its excellent concurrency support and performance. NATS provides lightweight messaging, while PostgreSQL ensures data durability. Observability tools like structured logging, metrics, and tracing give me visibility into system behavior.

Here’s how I initialize the project:

go mod init user-service
go get github.com/gin-gonic/gin \
    github.com/jackc/pgx/v5/pgxpool \
    github.com/nats-io/nats.go \
    github.com/prometheus/client_golang \
    go.opentelemetry.io/otel \
    go.uber.org/zap

My project structure separates concerns clearly. The internal directory holds domain logic, while pkg contains shared utilities. This organization makes the codebase maintainable as it grows.

Defining the domain model is crucial. I create a User struct with validation tags:

type User struct {
    ID        uuid.UUID  `json:"id"`
    Email     string     `json:"email" validate:"required,email"`
    FirstName string     `json:"first_name" validate:"required,min=2"`
    LastName  string     `json:"last_name" validate:"required,min=2"`
    CreatedAt time.Time  `json:"created_at"`
}

How do you ensure data integrity when events are published? I use repository interfaces to abstract database operations. The pgx driver for PostgreSQL offers connection pooling, which I configure for optimal performance:

poolConfig, _ := pgxpool.ParseConfig("postgres://user:pass@localhost/db")
poolConfig.MaxConns = 20
conn, err := pgxpool.NewWithConfig(context.Background(), poolConfig)

Event handling forms the core of this architecture. When a user is created, I publish an event to NATS:

func (s *UserService) CreateUser(ctx context.Context, user *User) error {
    if err := s.repo.Create(ctx, user); err != nil {
        return err
    }
    event := domain.NewEvent(domain.UserCreatedEvent, user)
    return s.publisher.Publish(ctx, event)
}

What if a message fails to process? I implement retry logic and dead letter queues. NATS JetStream supports durable consumers, ensuring no events are lost:

js, _ := nats.JetStream()
js.AddStream(&nats.StreamConfig{
    Name:     "USER_EVENTS",
    Subjects: []string{"user.>"},
})

Observability transforms how I debug and monitor services. Structured logging with Zap provides context-rich logs:

logger, _ := zap.NewProduction()
logger.Info("User created",
    zap.String("user_id", user.ID.String()),
    zap.String("email", user.Email))

Metrics collection with Prometheus helps track performance indicators. I expose a /metrics endpoint and instrument key operations:

http.Handle("/metrics", promhttp.Handler())
usersCreated := prometheus.NewCounterVec(
    prometheus.CounterOpts{Name: "users_created_total"},
    []string{"status"},
)
prometheus.MustRegister(usersCreated)

Distributed tracing with OpenTelemetry connects requests across services. How do you trace a user creation flow through multiple components? I propagate trace contexts:

tracer := otel.Tracer("user-service")
ctx, span := tracer.Start(ctx, "CreateUser")
defer span.End()

Error handling must be robust. I use custom error types and handle database conflicts gracefully:

if errors.Is(err, pgx.ErrNoRows) {
    return nil, ErrUserNotFound
}

Testing is non-negotiable. I write unit tests for business logic and integration tests for database and NATS interactions. Docker Compose sets up the environment:

services:
  postgres:
    image: postgres:15
  nats:
    image: nats:jetstream

Deployment involves containerizing the service with health checks. I ensure graceful shutdowns to process in-flight requests:

server := &http.Server{Addr: ":8080"}
go func() {
    if err := server.ListenAndServe(); err != nil {
        logger.Error("Server failed", zap.Error(err))
    }
}()

Wait for interrupt signal sig := <-signalChan server.Shutdown(ctx)

Building this service taught me the importance of designing for failure. Networks partition, databases slow down, and messages get lost. By anticipating these issues, I create systems that recover automatically.

I hope this practical approach helps you implement event-driven microservices with confidence. What challenges have you faced in your projects? Share your experiences in the comments below—I'd love to hear how you've solved similar problems. If this guide was helpful, please like and share it with others who might benefit.