I was recently working on a Go application that needed to handle thousands of concurrent tasks efficiently. The system kept crashing under heavy load, and I realized I needed a better way to manage resources. That’s when I decided to build a production-ready worker pool system. If you’ve ever struggled with concurrency in Go, this might help you too.

Worker pools are a powerful pattern for controlling how many tasks run at once. They use goroutines and channels to process jobs in parallel while keeping resource usage in check. Why is this important? Because without limits, your app could exhaust memory or CPU, leading to downtime.

Let me start with a simple code example. Here’s how you can define a basic job interface.

type Job interface {
    Execute(ctx context.Context) (interface{}, error)
    ID() string
}

This interface lets any task be processed as long as it has an Execute method. I use contexts to handle timeouts and cancellations, which is crucial for real-world apps.

Now, imagine you have a stream of incoming requests. How do you ensure they’re processed without overwhelming your database? A worker pool can queue jobs and distribute them to a fixed number of workers. Here’s a snippet to create a pool.

pool := workerpool.NewPool(workerpool.Config{
    NumWorkers:   5,
    JobQueueSize: 100,
})
pool.Start()

This sets up 5 workers and a job queue that can hold 100 tasks. When you submit a job, it goes into the queue. If the queue is full, you can handle backpressure by rejecting new jobs or implementing retries.

But what happens when you need to shut down the app? A hard stop could lose active jobs. Graceful shutdown ensures all current tasks finish before exiting. I use context and signal handling for this.

ctx, cancel := context.WithCancel(context.Background())
defer cancel()

// Handle OS signals for shutdown
go func() {
    sigchan := make(chan os.Signal, 1)
    signal.Notify(sigchan, syscall.SIGINT, syscall.SIGTERM)
    <-sigchan
    cancel()
}()

This code listens for interrupt signals and cancels the context, which notifies all workers to stop. Have you ever had a deployment where running jobs were cut off mid-process? This approach prevents that.

Error handling is another key area. Workers might panic or return errors, but the pool should keep running. I add recovery mechanisms to log issues without crashing.

func (w *Worker) Start(ctx context.Context) {
    defer func() {
        if r := recover(); r != nil {
            log.Printf("Worker panicked: %v", r)
        }
    }()
    for {
        select {
        case job := <-w.jobs:
            result, err := job.Execute(ctx)
            // Handle result or error
        case <-ctx.Done():
            return
        }
    }
}

This way, if one job fails, others continue. How do you monitor such a system? I integrate metrics to track submitted, processed, and failed jobs. Tools like Prometheus can scrape these metrics for dashboards.

In production, you might need rate limiting or timeouts. For instance, when calling external APIs, you don’t want to exceed rate limits. I use tickers or token buckets to control the pace.

Testing concurrent code can be tricky. I write tests that simulate high load and verify shutdown behavior. Go’s testing package supports parallel execution, which helps catch race conditions.

One common pitfall is not setting queue sizes properly. Too small, and jobs get rejected; too large, and memory usage spikes. I adjust based on monitoring data.

Building this system taught me the importance of designing for failure. What if a worker hangs? Timeouts and health checks can detect and restart stuck workers.

I hope this gives you a solid foundation. Worker pools are versatile—use them for batch processing, API rate limiting, or any task that benefits from controlled concurrency.

If you found this helpful, please like and share this article. Your comments and experiences are valuable, so feel free to discuss below!