# **Thread Pools in Python** Thread pools are a high-level abstraction for managing a fixed number of worker threads. They simplify task submission and allow efficient utilization of resources in multi-threaded applications. --- ## **Why Use Thread Pools?** Thread pools: - Limit the number of active threads to prevent resource exhaustion. - Simplify managing and coordinating multiple threads. - Automatically handle thread creation and destruction. --- ## **Using `ThreadPoolExecutor`** The `concurrent.futures.ThreadPoolExecutor` provides a simple API for managing thread pools. ### **Example 1: Submitting Tasks** ```python from concurrent.futures import ThreadPoolExecutor def task(n): print(f"Processing {n}") with ThreadPoolExecutor(max_workers=5) as executor: executor.submit(task, 1) executor.submit(task, 2) executor.submit(task, 3) ``` --- ### **Example 2: Using `map` for Multiple Tasks** The `map` method can process multiple tasks efficiently by distributing them among threads. ```python from concurrent.futures import ThreadPoolExecutor def task(n): print(f"Processing {n}") with ThreadPoolExecutor(max_workers=3) as executor: executor.map(task, range(10)) ``` --- ### **Example 3: Handling Results** You can retrieve and handle results using `Future` objects. ```python from concurrent.futures import ThreadPoolExecutor def task(n): return n * n with ThreadPoolExecutor(max_workers=3) as executor: futures = [executor.submit(task, i) for i in range(5)] results = [future.result() for future in futures] print(results) ``` --- ### **Example 4: Exception Handling** Thread pools make it easy to handle exceptions raised during task execution. ```python from concurrent.futures import ThreadPoolExecutor def task(n): if n == 3: raise ValueError("Example exception") return n with ThreadPoolExecutor(max_workers=3) as executor: futures = [executor.submit(task, i) for i in range(5)] for future in futures: try: print(future.result()) except Exception as e: print(f"Task raised an exception: {e}") ``` --- ## **When to Use Thread Pools** - For I/O-bound tasks: File operations, network requests, database interactions. - Not ideal for CPU-bound tasks due to Python’s Global Interpreter Lock (GIL); consider using `ProcessPoolExecutor` for parallelism in such cases. --- ## **Explore Next** - [[Testing and Debugging Concurrent Programs]]: Tools and techniques to validate concurrent programs. - [[Concurrency Concepts]]: Overview of threads, processes, and asyncio. - [[Avoiding Starvation]]: Techniques for fair resource management in thread pools. --- This note explains how to use thread pools in Python effectively, with practical examples for direct implementation. It’s ready for use in Obsidian.