Multithreading in Python — basic concepts, challenges in a multithreaded environment

Multithreading in Python allows multiple threads to run within a single process, which is useful for I/O-bound tasks.

However, due to the GIL (Global Interpreter Lock), only one thread executes Python bytecode at a time in standard CPython, limiting the efficiency of multithreading for CPU-bound tasks.

Main challenges:

• GIL restricts parallelism in CPU-intensive tasks.
• Thread synchronization — locks (Lock, RLock) are needed to prevent data races.
• Debugging issues — errors in multithreaded code are harder to detect.

Example of creating threads:

import threading

def worker():
    print('Thread is running')

threads = []
for i in range(5):
    t = threading.Thread(target=worker)
    threads.append(t)
    t.start()

for t in threads:
    t.join()

For CPU-intensive tasks, it is better to use multiprocessing or alternative Python implementations without GIL.