In what cases is multithreading not recommended or ineffective?

Multithreading in Python is not always efficient or recommended in the following cases:

1. CPU-bound tasks due to GIL In the standard Python implementation (CPython), there is a GIL (Global Interpreter Lock) that prevents multiple threads from executing Python bytecode simultaneously. Therefore, for CPU-intensive tasks (numerical computations), multithreading will not provide a performance boost.

2. Very short or simple tasks Creating and switching between threads incurs overhead. For very simple or fast operations, multithreading can slow down the program.

3. Synchronization difficulties If tasks require complex synchronization or frequently access shared resources, multithreading can lead to deadlocks, blocking, and reduced performance.

4. Runtime environment limitations In some environments (e.g., certain GUI frameworks), multithreading is limited or requires special handling.

In such cases, it is better to use:

• Multiprocessing (the multiprocessing module) for CPU-bound tasks.
• Asynchronous programming (asyncio) for I/O-bound tasks.

Example: for computing a large array of numbers, it is better to use multiprocessing, and for network requests — multithreading or asyncio.