8 docs tagged with "asyncio"

We've learned about the core components of asyncio async and await.

We've seen how asyncio is perfect for I/O-bound tasks and how aiohttp solves the problem of blocking network requests. But what about the other major type of I/O: reading and writing files?

This is the final article in our comprehensive series on asynchronous programming. We've learned how to create and run concurrent tasks, but with this power comes new challenges. One of the most notorious problems in any concurrent system is the deadlock.

Asynchronous programming introduces new complexities, and error handling is one of the most critical. When you are running dozens of tasks concurrently, what happens if one of them fails? How do you prevent a single failure from crashing your entire application?

Welcome to the final chapter of our advanced Python section. We are about to tackle asynchronous programming, a powerful paradigm for writing concurrent code that can significantly boost the performance of certain types of applications.

We've seen how to create individual tasks with asyncio.create_task() and then await them. This is a great way to start multiple background jobs. However, it can be a bit verbose if you just want to run a list of coroutines concurrently and wait for all of them to finish.

In our introduction to asynchronous programming, we saw how async and await can dramatically improve the performance of I/O-bound applications. Now, let's dive deeper into the three fundamental components that make this possible within the asyncio module: the Event Loop, Coroutines, and Tasks.

The most common use case for asyncio is to handle I/O-bound tasks, and the most common I/O task in modern software is making network requests. However, the popular requests library is synchronous. If you use it in an async function, it will block the entire event loop, defeating the purpose of asyncio.