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What does the GIL actually prevent, and how do you decide between threads, processes, and asyncio?
The Python GIL serializes CPython bytecode execution, but threads still help I/O-bound work; choose processes for pure Python CPU work and asyncio for many concurrent waits.
4 min readUpdated 2026-08-15
Answer
Short answer
The GIL prevents multiple threads in one CPython interpreter from running Python bytecode at the same time. Use threads for blocking I/O, processes for pure-Python CPU work, and asyncio when you need to coordinate many waiting operations.
Mention threading where it changes the risk, the owner, or the next check. A useful threading point should make the answer more testable, not merely longer. Tie threading back to the scenario so the interviewer can see why it matters here.
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Likely follow-ups
- If the GIL serialises bytecode, why does a pure-Python CPU-bound function still get slower when you add threads?
- How would you call a blocking library from inside an asyncio event loop without stalling it?
- Why is a race condition still possible on `counter += 1` even with the GIL held?
- When would you reach for a C extension or a subinterpreter instead of multiprocessing?
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