Async IO is a powerful tool in Python that allows you to write non-blocking code to handle multiple tasks concurrently. But when should you use async instead of threads? Here's a guide on the pros, cons, and best use cases for both.
Threads vs Async: Key Differences
Threads allow parallel execution by running code simultaneously in separate OS threads. Async runs code in a single thread, but uses cooperative multitasking so no one part of the code blocks others from running.
Threads:
Async:
When to Use Async Over Threads
Async shines for I/O-bound workloads involving network or disk operations. Here's why:
For example, here's how to fetch multiple URLs concurrently using async:
import asyncio
import aiohttp
async def fetch_url(url):
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
print(await response.text())
urls = ["https://example.com", "https://python.org"] * 10
asyncio.run(asyncio.gather(*[fetch_url(url) for url in urls]))So in summary, leverage async I/O for non-CPU bound tasks that deal with network, disk, or user interactions for great performance gains. Stick to threads for intensive computational workloads.