Tornado VS Flask

I am not expert in these but I thought Tornado's ioloop was readable enough for me to learn more event loops. Mostly, it was being implemented in pure Python.

https://github.com/tornadoweb/tornado/blob/branch4.5/tornado...

(Had to be in 4.5 because the newer versions 5.x and 6.x, it's switched to Python's stdlib asyncio)

I am a bit confused, Tornado is web server like a apache http server, or a framework like django, or both?

For Python, I use Tornado.

tornado? this https://github.com/tornadoweb/tornado ?

I'm writing a Node app that talks to a server that uses Tornado, a Python networking framework. The server uses WebSocket and structured JSON to expose RPCs, like sign_on called with the following message:

I've been using Python's Tornado (http://www.tornadoweb.org/) for years now.

Now on version 6, it's fast, well maintained, mature, and has good docs with readable code.

@bdarnell has done an excellent job maintaining it.

Here's one example:

Tornado supported async style co-routines before asyncio was a thing. Now it uses asyncio under the hood by default -- and it did so with an exceptionally smooth transition.

Tornado is another micro framework aimed at a specific use case: asynchronous networking applications. Tornado is well-suited for creating services that open a great many network connections and keep them alive that is, anything involving WebSockets or long polling. Moreover, It requires Python 3.5 or higher and drops Python 2 support entirely.

How to fix tornado.util.TimeoutError: Operation timed out after 5 seconds on tornado test debug?

Here's an official demo chat app using websocket: https://github.com/tornadoweb/tornado/tree/master/demos/websocket, though you should read the docs on websocket for understanding the code.

I'd suggest Flask or some of the smaller projects in the Pallets ecosystem:

https://github.com/pallets/flask

#!/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.