What tools or libraries do you use to implement multithreading in your projects?

In Python, the following tools and libraries are often used for implementing multithreading and parallelism:

• threading — a standard module for creating and managing threads. Suitable for I/O-bound tasks where parallelism is important, but inefficient for CPU-bound tasks due to GIL.

• multiprocessing — a module for running processes, bypassing the GIL limitation, suitable for CPU-intensive tasks.

• concurrent.futures — a high-level interface for working with threads (ThreadPoolExecutor) and processes (ProcessPoolExecutor), simplifies managing a pool of workers.

• asyncio — a library for asynchronous programming, useful for scalable I/O-bound tasks.

• third-party libraries:

  • gevent — for cooperative multitasking using green threads.
  • joblib — for parallel execution of tasks, often in scientific computing.

The choice depends on the specific task: for I/O-bound tasks, it's better to use threading or asyncio; for CPU-bound tasks, multiprocessing or concurrent.futures.ProcessPoolExecutor.