Introduction

Processing many tasks concurrently is a common need, but creating unlimited goroutines can overwhelm your system. Worker pools solve this by using a fixed number of workers to process tasks from a queue.

The Problem with Unlimited Goroutines

Consider processing 10,000 images:

// Don't do this - creates 10,000 goroutines
for _, image := range images {
    go processImage(image)
}

This approach can:

• Exhaust memory with too many goroutines
• Overwhelm the CPU with context switching
• Crash your system under high load
• Make it hard to control resource usage

Worker Pool Solution

Instead, use a fixed number of workers:

// Create a pool of 10 workers
jobs := make(chan Image, 100)
results := make(chan Result, 100)

// Start workers
for i := 0; i < 10; i++ {
    go worker(jobs, results)
}

// Send work
for _, image := range images {
    jobs <- image
}

Benefits of Worker Pools

Worker pools provide:

• Resource Control: Limit memory and CPU usage
• Backpressure Handling: Queue work when workers are busy
• Graceful Shutdown: Stop processing cleanly
• Error Isolation: Handle failures without crashing everything
• Monitoring: Track progress and performance

Common Use Cases

Worker pools work well for:

• Web Scraping: Process URLs without overwhelming servers
• Image Processing: Resize, compress, or transform images
• Log Analysis: Parse and analyze log files
• API Clients: Make HTTP requests with rate limiting
• Database Operations: Batch process database updates

Worker Pool Patterns

This guide covers different worker pool architectures:

1. Basic Worker Pool: Simple fixed-size pool
2. Dynamic Worker Pool: Scales workers based on load
3. Priority Worker Pool: Handles high-priority tasks first
4. Staged Worker Pool: Multi-stage processing pipeline
5. Resilient Worker Pool: Handles failures gracefully

Each pattern solves specific problems and has different trade-offs.