Is it possible to use multi-process approaches for I/O-bound tasks?

Multithreading (multiprocessing) approaches in Python can be used for I/O-bound tasks, but this is not always efficient. The reason is that I/O operations usually block the thread, and in such cases, it is better to use asynchronous programming or multithreading, as Python releases the GIL (Global Interpreter Lock) during I/O.

Multiprocessing is useful for CPU-intensive tasks, as each process runs in a separate Python interpreter and is not blocked by the GIL. For I/O-bound tasks, multithreading or async/await usually provides better results, as switching between threads is faster and easier.

Example of using multithreading for an I/O-bound task:

import threading
import requests

def fetch_url(url):
    response = requests.get(url)
    print(f"{url}: {len(response.content)} bytes")

threads = []
urls = ["https://example.com", "https://python.org", "https://github.com"]
for url in urls:
    thread = threading.Thread(target=fetch_url, args=(url,))
    thread.start()
    threads.append(thread)

for thread in threads:
    thread.join()

Thus, for tasks with intensive I/O, it is better to use multithreading or asynchronous programming rather than multiprocessing.