Python Global Interpreter Lock (GIL)

Some notes on Python’s GIL:

  1. It is basically a mechanism in the CPython interpreter (the most common Python implementation) that ensures only one thread can execute Python bytecode at a time.
  2. The GIL prevents multiple threads from modifying reference counts at the same time, which could lead to memory corruption.
  3. This limits the performance gains you can achieve with multithreading for CPU-intensive tasks.
  4. But threads often spend time waiting for I/O operations (like network requests or file access), which can allow other threads to run even with the GIL. So, multithreading can still be beneficial for I/O-bound tasks.
  5. If you need true parallelism for CPU-bound tasks, use the multiprocessing module to create multiple processes, each with its own interpreter and GIL.
  6. Some libraries, like NumPy and Cython, release the GIL during certain operations, allowing better performance for multithreaded code.
  7. Alternative Python implementations like Jython and IronPython don’t have a GIL, but they have their own limitations and trade-offs.