I’ve been thinking a lot lately about how to build systems that can handle massive scale while remaining resilient and maintainable. That’s why I want to share my approach to building event-driven microservices using NATS, Go, and gRPC—a combination that has served me well in production environments.

When you’re building distributed systems, communication between services becomes critical. Have you ever considered what happens when one service goes down while others keep running? Traditional request-response patterns can create tight coupling and single points of failure. Event-driven architecture changes this dynamic by making services communicate through events rather than direct calls.

Let me show you how to set up NATS with JetStream for persistent messaging. This gives us both high performance and delivery guarantees. Here’s a basic configuration:

// Connect to NATS with JetStream enabled
nc, err := nats.Connect("nats://localhost:4222")
if err != nil {
    log.Fatal("Failed to connect to NATS:", err)
}

js, err := nc.JetStream()
if err != nil {
    log.Fatal("Failed to get JetStream context:", err)
}

// Create a stream for order events
_, err = js.AddStream(&nats.StreamConfig{
    Name:     "ORDERS",
    Subjects: []string{"orders.*"},
})

Now, what about service-to-service communication? gRPC gives us type-safe, high-performance RPC calls. I prefer using protocol buffers for defining service contracts:

syntax = "proto3";

service UserService {
    rpc CreateUser(CreateUserRequest) returns (UserResponse);
    rpc GetUser(GetUserRequest) returns (UserResponse);
}

message CreateUserRequest {
    string email = 1;
    string name = 2;
}

message UserResponse {
    string id = 1;
    string email = 2;
    string name = 3;
}

But how do we ensure our services remain observable in production? I integrate structured logging, metrics, and distributed tracing from day one. Here’s how I set up basic observability:

func setupTelemetry() (*otel.TracerProvider, error) {
    exporter, err := jaeger.New(jaeger.WithCollectorEndpoint())
    if err != nil {
        return nil, err
    }

    tp := otel.NewTracerProvider(
        otel.WithBatcher(exporter),
        otel.WithResource(resource.NewWithAttributes(
            semanticconv.ServiceNameKey.String("user-service"),
        )),
    )
    
    otel.SetTracerProvider(tp)
    return tp, nil
}

One challenge many developers face is handling graceful shutdowns. When your service needs to stop, how do you ensure in-flight requests complete properly? Here’s my pattern:

func main() {
    ctx, stop := signal.NotifyContext(context.Background(), os.Interrupt)
    defer stop()

    server := grpc.NewServer()
    // Setup your server
    
    go func() {
        <-ctx.Done()
        server.GracefulStop()
    }()

    if err := server.Serve(lis); err != nil {
        log.Fatal("Server failed:", err)
    }
}

For production deployments, I use Docker Compose to manage all services together. This ensures consistent environments from development to production:

version: '3.8'
services:
  nats:
    image: nats:latest
    ports:
      - "4222:4222"
      - "8222:8222"
    command: "-js -sd /data"

  user-service:
    build: ./cmd/user-service
    environment:
      - NATS_URL=nats://nats:4222
    depends_on:
      - nats

Building event-driven microservices requires careful consideration of patterns and tools. By combining NATS for messaging, gRPC for service communication, and Go for implementation, we create systems that are both scalable and maintainable. The key is starting with the right foundations: proper error handling, observability, and resilience patterns.

What patterns have you found effective in your distributed systems? I’d love to hear your thoughts and experiences—feel free to share your comments below, and if you found this useful, please like and share with others who might benefit from this approach.