The Global Interpreter Lock (GIL) in Python limits concurrency performance by allowing only one thread to execute Python bytecodes at a time, impacting CPU-bound tasks. While threads struggle under the GIL for CPU-heavy workloads, I/O-bound tasks fare better as the GIL is released during I/O operations. Multiprocessing bypasses the GIL, enabling parallel execution across CPU cores. Utilizing threading, multiprocessing, or asyncio can optimize Python backend workloads depending on their nature.
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What’s the GIL?CPU-Bound vs I/O-Bound: Why It MattersExample #1: CPU-Bound Task (With Threads)Example #2: CPU-Bound Task (With Multiprocessing)Example #3: I/O-Bound Task (With Threads)Threads Work Well With I/Oasyncio vs multiprocessing vs threadingSummarySort: