Go Goroutine Pool: Too Many Goroutines
In this tutorial, you'll learn about Go Goroutine Pool: Too Many Goroutines. We cover key concepts, practical examples, and best practices.
Goroutine pool -- Use worker pools with bounded goroutines to prevent resource exhaustion under load.
The Problem
Spawning unlimited goroutines for incoming tasks exhausts memory and degrades performance. Use a worker pool pattern with a buffered channel and fixed number of workers.
Wrong
func handleRequests(requests <-chan Request) {
for req := range requests {
go req.Process() // Unlimited goroutines!
}
}
Output:
// Under load, thousands of goroutines created
// Memory exhaustion, performance collapse
Right
func workerPool(requests <-chan Request, numWorkers int) {
var wg sync.WaitGroup
for i := 0; i < numWorkers; i++ {
wg.Add(1)
go func() {
defer wg.Done()
for req := range requests {
req.Process()
}
}()
}
wg.Wait()
}
// Create 10 workers:
workerPool(requests, 10)
Output:
// Only 10 goroutines regardless of load
// Requests queued in channel
Prevention
- Use bounded worker pools for task processing
- Number of workers based on workload type (CPU vs IO)
- CPU-bound: GOMAXPROCS workers. IO-bound: more workers
- Use buffered channels for task queue
- Close channel to signal workers to stop
Common Mistakes with goroutine pool
- Non-exhaustive pattern matches that compile with warnings then crash at runtime
- Misunderstanding that
Stringis[Char]with poor performance for large text operations - Using
foldlinstead offoldl'causing stack overflow on large lists
These mistakes appear frequently in real-world GO code. DodaTech's contributors have identified these patterns through analysis of open-source projects and production systems.
Practice Exercise
Write a pure function that safely divides two integers using Maybe, then test it with edge cases like division by zero and negative numbers.
This exercise reinforces the concepts covered in this guide. Try implementing it before checking online solutions.
FAQ
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