Python Threading vs Multiprocessing: Choosing the Right Concurrency Model

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Python developers often slow down their programs by picking the wrong concurrency approach, and understanding the difference is key to unlocking full performance. The Global Interpreter Lock (GIL) limits threading to one executing thread at a time within a process, making threads suitable for I/O-bound tasks like web scraping, API calls, and file operations where waiting time dominates. Multiprocessing spawns separate interpreter instances, each with its own GIL, enabling true parallel execution across multiple CPU cores for CPU-intensive work such as data processing, image manipulation, and machine learning. A practical rule of thumb guides the choice: use threads when a program spends most of its time waiting for external resources, and use processes when it spends most of its time computing. Selecting the correct model can mean the difference between a program running at baseline speed versus achieving near-linear speedup on multi-core hardware.

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