Understanding Why Python Async IO is Not Speeding Up Your Code

Learn about the issues you may face when using `Python Async IO` for I/O operations and how to fix them using async-aware libraries.
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Understanding Why Python Async IO is Not Speeding Up Your Code

In the world of programming, achieving efficiency and speed is paramount, especially with I/O operations where waiting for responses can slow down the whole process. For many Python developers, asyncio has become a go-to solution for handling asynchronous operations. However, what happens when you implement asyncio but still find that your code is running slower than expected? This guide will explore the issue of not seeing speed improvements with Python Async IO, especially in the context of I/O operations like HTTP requests.

The Problem: Slow Performance with Async IO

You've written your asynchronous code, but the performance isn't where you expect it to be. Let's illustrate this with two samples of code.

Sample 1: Non-Async Code

[[See Video to Reveal this Text or Code Snippet]]

Sample 2: Correct Async Usage

[[See Video to Reveal this Text or Code Snippet]]

The Key Takeaway: Use Async-Aware Libraries

When working with HTTP requests in Python, it's crucial to remember that not all libraries support asynchronous operations. For instance, the requests library is not async-aware, which means if you attempt to use it within an async function, it will block the event loop—leading to performance issues similar to the first code sample. To make non-blocking HTTP requests, you can use libraries such as:

httpx: A fully featured HTTP client for Python 3 that supports asynchronous requests.

aiohttp: An asynchronous HTTP client that integrates smoothly with the async features of Python.

Summary of Best Practices

Avoid Blocking Calls: Always use await with functions that need to be asynchronous.

Choose Async Libraries: For tasks like HTTP requests, opt for libraries designed to be async-aware, like httpx or aiohttp.

Understand the Framework: Know that async does not execute functions in parallel by default. It merely allows for better time management, letting your program handle other tasks while awaiting results.

By adhering to these practices, you can fully leverage the power of asyncio and drastically improve the performance of your I/O-bound Python applications.

In conclusion, when tapping into asynchronous programming, it’s not just about making your code asynchronous—but ensuring every aspect of it, including external libraries, aligns with that asynchronous model. Happy coding!