What are the common performance issues with Python's threading module?
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What are the common performance issues with Python's threading module?
Common performance issues with Python's threading module include the Global Interpreter Lock (GIL), context switching overhead, and thread contention.
How does the GIL affect multi-threading performance?
The GIL allows only one thread to execute Python bytecode at a time, which can limit the performance of CPU-bound multi-threaded programs.
Are there any alternatives to using threads in Python?
Yes, alternatives include using the multiprocessing module, which bypasses the GIL by using separate memory spaces for each process.
What are some best practices for optimizing threading in Python?
Best practices include minimizing shared data, using thread pools, and profiling your application to identify bottlenecks.
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