Concurrency Patterns in Go You Should Actually Use in Production

Why Most Go Concurrency Tutorials Fail You

Tutorials show you goroutines and channels. Production shows you goroutine leaks at 3 AM. Here are the patterns that actually matter when you're running high-throughput services.

1. Always Use Context for Cancellation

Never launch a goroutine without a cancellation path.

func processStream(ctx context.Context, stream <-chan Event) error {
    for {
        select {
        case <-ctx.Done():
            return ctx.Err()
        case event, ok := <-stream:
            if !ok {
                return nil
            }
            if err := handle(event); err != nil {
                return fmt.Errorf("handle event: %w", err)
            }
        }
    }
}

2. Worker Pool — The Right Way

func workerPool(ctx context.Context, jobs <-chan Job, concurrency int) error {
    g, ctx := errgroup.WithContext(ctx)
    for range concurrency {
        g.Go(func() error {
            for {
                select {
                case <-ctx.Done():
                    return ctx.Err()
                case job, ok := <-jobs:
                    if !ok {
                        return nil
                    }
                    if err := job.Process(ctx); err != nil {
                        return err
                    }
                }
            }
        })
    }
    return g.Wait()
}

3. Semaphore for Resource Limiting

type Semaphore chan struct{}

func NewSemaphore(n int) Semaphore { return make(chan struct{}, n) }

func (s Semaphore) Acquire(ctx context.Context) error {
    select {
    case s <- struct{}{}:
        return nil
    case <-ctx.Done():
        return ctx.Err()
    }
}

func (s Semaphore) Release() { <-s }

Key Takeaways

• Every goroutine needs an exit condition
• errgroup > sync.WaitGroup for error propagation
• Bounded pools > unbounded goroutines
• Test for leaks with go.uber.org/goleak