patterns

Go Concurrency Patterns Series: ← Mutex Patterns | Series Overview | Once Pattern → What is the WaitGroup Pattern? The WaitGroup pattern uses sync.WaitGroup to coordinate the completion of multiple goroutines. It acts as a counter that blocks until all registered goroutines have finished executing, making it perfect for implementing barriers and waiting for parallel tasks to complete. Key Operations: Add(n): Increment the counter by n Done(): Decrement the counter by 1 (usually called with defer) Wait(): Block until counter reaches zero Real-World Use Cases Parallel Processing: Wait for all workers to complete Batch Operations: Process multiple items concurrently Service Initialization: Wait for all services to start Data Collection: Gather results from multiple sources Cleanup Operations: Ensure all cleanup tasks finish Testing: Coordinate test goroutines Basic WaitGroup Usage package main import ( "fmt" "math/rand" "sync" "time" ) // Task represents work to be done type Task struct { ID int Name string } // processTask simulates processing a task func processTask(task Task, wg *sync....

Go Concurrency Patterns Series: ← Request/Response | Series Overview | Mutex Patterns → What is the Worker Pool Pattern? The Worker Pool pattern manages a fixed number of worker goroutines that process jobs from a shared queue. This pattern is essential for controlling resource usage, preventing system overload, and ensuring predictable performance under varying loads. Key Components: Job Queue: Channel containing work to be processed Worker Pool: Fixed number of worker goroutines Result Channel: Optional channel for collecting results Dispatcher: Coordinates job distribution to workers Real-World Use Cases Image Processing: Resize/compress images with limited CPU cores Database Operations: Limit concurrent database connections API Rate Limiting: Control outbound API call rates File Processing: Process files with bounded I/O operations Web Scraping: Limit concurrent HTTP requests Background Jobs: Process queued tasks with resource limits Basic Worker Pool Implementation package main import ( "fmt" "math/rand" "sync" "time" ) // Job represents work to be processed type Job struct { ID int Data interface{} } // Result represents the outcome of processing a job type Result struct { JobID int Output interface{} Error error } // WorkerPool manages a pool of workers type WorkerPool struct { workerCount int jobQueue chan Job resultQueue chan Result quit chan bool wg sync....