What is multithreading and how does it work in Python?

Multithreading allows executing multiple threads (independent sequences of execution) within a single process. This is useful for tasks that involve waiting for input/output (e.g., reading from a file, network requests), as threads can switch while one thread is waiting.

In Python, multithreading is implemented using the threading module.

import threading
import time

def worker(num):
    """Worker thread function"""
    print(f'Thread {num} started.')
    time.sleep(1) # Simulate work
    print(f'Thread {num} finished.')

threads = []
for i in range(5):
    t = threading.Thread(target=worker, args=(i,)) # Create thread
    threads.append(t)
    t.start() # Start thread

for t in threads:
    t.join() # Wait for thread to finish

print('All threads are finished.')

Component	Description
Thread	Class for creating a new thread
target	Function to be executed in the thread
args	Tuple of arguments for the target function
start()	Starts the thread execution
join()	Waits for the thread to finish

However, Python has the Global Interpreter Lock (GIL). GIL is a mutex that protects access to Python objects, preventing multiple native, non I/O blocking (CPU-bound) threads from executing simultaneously. This means that for CPU-intensive tasks, multithreading in Python will not lead to parallel execution on multi-core processors. For such tasks, multiprocessing is often used, which creates separate processes.

GIL does not affect threads that are waiting for input/output operations, as GIL is released during waiting. Therefore, multithreading is effective for I/O-bound tasks.