Concurrency in Python

The GIL: Python has a Global Interpreter Lock — only one thread executes Python bytecode at a time. Threading helps with I/O-bound tasks, not CPU-bound.

Threading (I/O-bound tasks):

import threading

def download(url):
    # simulate download
    print(f"Downloading {url}")

threads = []
for url in urls:
    t = threading.Thread(target=download, args=(url,))
    threads.append(t)
    t.start()

for t in threads:
    t.join()

asyncio (modern I/O concurrency):

import asyncio

async def fetch(url):
    # async code here
    await asyncio.sleep(1)  # non-blocking wait
    return f"Data from {url}"

async def main():
    results = await asyncio.gather(
        fetch("url1"),
        fetch("url2")
    )

asyncio.run(main())

multiprocessing (CPU-bound): Bypasses the GIL. Use for heavy computation.

Reference:

Wikipedia: Concurrency

https://en.wikipedia.org/wiki/Concurrency_(computer_science)