Merging asyncio with Flask or aiohttp: A Guide to Building High-Performance Python Web Applications

Discover how to integrate `asyncio` with Flask or Quart for efficient web server capabilities in your Python application, enhancing your data processing capabilities.
---
This video is based on the question https://stackoverflow.com/q/62157400/ asked by the user 'All3' ( https://stackoverflow.com/u/11374103/ ) and on the answer https://stackoverflow.com/a/62216853/ provided by the user 'All3' ( https://stackoverflow.com/u/11374103/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: asyncio application with flask or aiohttp server inside

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Merging asyncio with Flask or aiohttp: A Guide to Building High-Performance Python Web Applications

In the world of web development, performance can often be a critical factor in the success of your application. If you've worked with Python before, you're likely familiar with Flask—a lightweight web framework that is simple to use but may not always cater to asynchronous operations out of the box. If you're looking to integrate asynchronous capabilities into your Python web application, particularly using asyncio, you’re in for a treat. In this guide, we'll discuss the problem of merging an asyncio application with a web server, and how you can effectively achieve this with the Quart framework, which is designed to work with asyncio.

Understanding the Problem

You've already taken the first steps in developing your application that processes data and serves HTTP requests. You’ve created a robust data processing system using asyncio that retrieves data from a third-party database and processes statistics before storing them in PostgreSQL. Now, you’re ready to add web server functionality, enabling your application to respond to GET requests.

Your Requirements

Combine two separate Python applications: One for counting statistics and another web server for handling GET requests.

Use asynchronous programming with asyncio for your data processing and your web server.

To achieve this, you have two prominent choices: integrate with Flask or reason through using the aiohttp server. However, you will find that using the Quart framework may actually offer the best of both worlds: the simplicity of Flask with the power of asyncio.

The Solution: Integrating Quart

Step 1: Setting Up Quart

If you're looking to run an asynchronous HTTP server, we initially suggest using Quart, which operates similarly to Flask but is built with asynchronous capabilities in mind. Below is a simple example to setup a basic web server using Quart.

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

Step 2: Define Your Routes

Next, define the routes that will handle your HTTP GET requests. Here’s an example implementation:

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

Step 3: Combine Async Functions

Now, adapt your existing asynchronous data-processing functions to run alongside your Quart server. Instead of running the server in its typical blocking manner, you will use await asyncio.gather() and app.run_task() for non-blocking execution.

Here’s how you could structure it:

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

Step 4: Access Shared Data

Finally, if you want to access the data processed in your application, you can store your required variables in the Quart configuration. Here’s an example of how to make your format dataframe accessible:

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

Conclusion

Integrating asyncio with a web server like Quart provides a seamless way to handle both data processing and web requests without blocking. You can now leverage the asynchronous features to enhance your data handling capabilities while serving requests efficiently.

In summary, you can:

Utilize Quart, an asynchronous web framework that works similarly to Flask but with support for asyncio.

Use await asyncio.gather() to launch both your data processing and web server concurrently.

Store shared resources in Quart's config for easy access across your routes.

With this guide, you should be well-equipped to scale and improve the performance of your Python applications by merging asynchronous data processing with a flexible web server. Happy coding!