Flask VS AIOHTTP

#!/usr/bin/python # # https://flask.palletsprojects.com/en/3.0.x/installation/ # from flask import Flask, jsonify, request contacts = [ { "id": "1", "firstname": "Lorem", "lastname": "Ipsum", "email": "[email protected]", }, { "id": "2", "firstname": "Mauris", "lastname": "Quis", "email": "[email protected]", }, { "id": "3", "firstname": "Donec Purus", "lastname": "Purus", "email": "[email protected]", } ] app = Flask(__name__, static_url_path='', static_folder='public',) @app.route("/contact//save", methods=["PUT"]) def save_contact(id): data = request.json contacts[id - 1] = data return jsonify(contacts[id - 1]) @app.route("/contact/", methods=["GET"]) @app.route("/contact//edit", methods=["GET"]) def get_contact(id): return jsonify(contacts[id - 1]) @app.route('/') def root(): return app.send_static_file('index.html') if __name__ == '__main__': app.run(debug=True)

I was looking at all the big projects like numpy, pytorch, flask, etc.

Explore here.

I may use Flask-Admin initially to offload the "CRUD" operations to have an initial prototype fast but then drop it ASAP because I don't want to write a "flask-admin application" to fight against later on. If the application is mainly "CRUD", then Flask-Admin is suitable.

Now...

Would you do a breakdown/list of all the jobs you've done by sector/vertical and by function/role and by application functionality?

- [0]: https://flask.palletsprojects.com

- [1]: https://flask-admin.readthedocs.io/en/latest

- [2]: https://flask.palletsprojects.com/en/2.3.x/patterns/celery

- [3]: https://sentry.io

- [4]: https://posthog.com

- [5]: https://www.docker.com

In the lab to follow, we will be setting up an end-to-end DevOps workflow for a Flask microservice with GitHub Actions, using a self-managed custom runner for maximal control over the pipeline execution environment and automating deployments to a local Kubernetes cluster. Furthermore, we will construct separate pipelines for our "development" and "production" environments to further elaborate on the concepts of continuous deployment and delivery.

Flask Micro web Framework: You will use Flask to build a Flask web application.

In an earlier post I mentioned a bunch of Open Source web applications. Let's now focus on the ones written in Python using Flask the light-weight web framework.

I also suggest that once you've gained familiarity with the basics, you look at micro-frameworks, such as FastAPI and flask, before switching to a full fat framework like django, which will give you an appreciation of what opinionated frameworks such as django can do for you.

> AIOHTTP: The Python int constructor is used to parse Content-Lengths and chunk-sizes, so _, +, and - are misinterpreted.

Fun. I believe the fix is incomplete here[1]. Python's int() constructor accepts integers comprised of any unicode numeral, for example, int("٦٦٦") == 666, and "٦٦٦".isdecimal() == True.

[1] https://github.com/aio-libs/aiohttp/pull/7663/files#diff-197...

aiohttp

If you want speed, I would try to rewrite everything using an asynchronous library like aiohttp.

requests does not support async out-of-the-box, so we will use aiohttp to avoid complications. requests can do the job, and there is no substantial performance difference. But the code is more readable using aiohttp.

I'm using aiohttp to build an API server that sends TCP requests off to a seperate server. The module that sends the TCP requests is synchronous and a black box for my purposes. So my problem is that these requests are blocking the entire API. I need a way to wrap the module requests in an asynchronous coroutine that won't block the rest of the API.

aiohttp - Everything that I need and nothing that I don't.

Use aiohttp to download the content of each page of lists. As of this Code Review post, this is the only step we execute.

Reminds me of this post from 5 months ago, but it was for a Python library. I made a discussion thread on the aiohttp repo and someone ended up contacting the reddit admins about it. The problem seems to be resolved now.