Achieving Speed with Asyncio in Python

Python's asyncio library allows you to write asynchronous, non-blocking code that can improve the performance and responsiveness of your applications. But is asyncio truly concurrent or parallel? Let's break it down.

Concurrency vs Parallelism

Concurrency and parallelism may sound similar but have distinct meanings in computer science:

So concurrency is about structure and parallelism is about execution.

How Asyncio Enables Concurrency

Asyncio provides a concurrency framework with an event loop and coroutines. Here's a quick example:

import asyncio

async def task(n):
    print(f'Processing {n}')
    await asyncio.sleep(1)

async def main():
    await asyncio.gather(
        task(1),
        task(2),
        task(3),
    )

asyncio.run(main())

While one coroutine awaits, the event loop can switch to another coroutine to continue processing. So asyncio enables concurrency on a single thread.

The key thing is that asyncio coroutines are non-blocking. Blocking calls (like network I/O) are handled asynchronously via await so that execution can proceed.

Asyncio is Concurrent, Not Parallel

Asyncio allows concurrency by structured switching between tasks. But it does not execute coroutines in parallel across multiple processors/machines.

So while asyncio greatly improves the scalability of Python network programs via concurrent execution, true parallelism requires multiprocessing or multithreading techniques.

The advantage of asyncio is that concurrency enables efficient use of I/O resources within a single thread while avoiding issues like race conditions with shared state.

So in summary, asyncio provides powerful concurrency to make Python more performant, but not parallelism. Structured asynchronous programming opens up new possibilities for writing high-speed networked applications!