Async IO in Python: When and Why to Use It Over Threads

Mar 17, 2024 ยท 2 min read

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.


  • True parallelism
  • Blocking calls pause all threads
  • Higher memory usage
  • Locks and semaphores needed for shared state
  • Async:

  • Pseudo-parallelism through cooperative multitasking
  • Non-blocking calls avoid halting the event loop
  • Lower memory footprint
  • Async-native primitives like queues
  • When to Use Async Over Threads

    Async shines for I/O-bound workloads involving network or disk operations. Here's why:

  • Avoid thread blocking: Calls like requests.get() won't halt the event loop since libraries use async under the hood.
  • Scale thousands of tasks: Async can handle thousands of tasks with a fraction of the memory compared to threads.
  • Improve perceived performance: Overlap I/O with other processing for faster overall throughput.
  • 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 = ["", ""] * 10*[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.

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