Graphene VS fastapi

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).

That’s when I found FastAPI.

FastAPI & Uvicorn

Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696

An API application framework (such as FastAPI)

It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.

This ends up made worse if you’re using sync routes.

If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.

This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759

Fast API.

We will create our API using FastAPI, a modern high-performance web framework for building fast APIs with Python. It is designed to be easy to use, efficient, and highly scalable. Some key features of FastAPI include:

FastAPI is a modern, fast, web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use, fast to run, and secure. In this blog post, we’ll explore the key features of FastAPI and walk through the process of creating a simple API using this powerful framework.

FastAPI is a modern, fast web framework for building APIs with Python 3.7+ that automatically generates OpenAPI and JSON Schema documentation. While FastAPI simplifies API development, manually creating and updating API documentation can still be a time-consuming task. In this blog post, we’ll explore how to leverage FastAPI’s automatic documentation generation capabilities, specifically focusing on Swagger and ReDoc, and how to streamline the process of documenting your APIs.