Graphene VS Flask

And gqlgen is not alone in this. We found several more GraphQL frameworks that don’t take it upon themselves to address this problem. Widely used GraphQL server implementations, such as graphql-go/graphql and Python’s graphene, have the exact same gap of exposing messages of unexpected errors by default.

There are multiple Python-based GraphQL libraries and they all vary slightly from each other. For the longest time, Graphene was a natural choice as it was the oldest and was used in production at different companies, but now other newer libraries have also started gaining some traction.

> But you need to know that Wasmer and its sibling projects will stay free forever. We are open source lovers, all of us, and we have a strong background on working on open source projects before joining Wasmer too.

That's great. I'm sure the closed source paid 10x faster editions of the graphene Python GraphQL server written by Syrus "CEO of Graphene" and CEO of wasmer will be open sourced in this spirit.

https://github.com/graphql-python/graphene/issues/268#issuec...

http://graphql-quiver.com/

CloudinaryField is a custom model field not a django built in one and graphene-python doesn't know what do with it. See the list of types it does https://github.com/graphql-python/graphene/tree/master/graphene/types

Thanks for the reply, I was looking at Strawberry a bit but not enough documentation for a graphql noob like me... Hate to turn this into tech support but could you potentially answer a question for me? I'm having a really hard time figuring out how to do this. Given this example: https://github.com/graphql-python/graphene/blob/master/examples/simple_example.py and a slight modification:

Graphene was one of the first (if not the first) lib built on top of graphql-core to provide an easy to use api to build graphql applications. It's been around since 2015 and has a lot of integrations built for it (e.g. Django, SQL Alchemy, etc).

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.