graphjin VS fastapi

This one looks interesting if you like GraphQL… https://graphjin.com

You're right. Django made a ton of tables, and it's pretty insightful to think about all the stuff it's trying to consider and a nightmare to migrate that framework. Django might have simplified some stuff, but it's still too opaque for my taste and the overall end goals of the project. Currently, I'm testing using graphjin (it doesn't have the best documentation, but it does boast an SQL generation capability that I philosophically think is the best way to do things).

Checkout GraphJin it's an automatic GraphQL to SQL compiler, you write your database queries in GraphQL instead of SQL and it compiles them into efficient SQL on the fly. Works as a library in GO and NodeJS. https://github.com/dosco/graphjin

In GraphJin an automatic GraphQL to SQL compiler we use the gnomock library it startups a database instance (docker) then create the schema and tests data and finally our code connects to it and runs a series of tests. We run these across Mysql, Postgres and a few other DB's. Gnomock supports a wide range of them. Right now we don't take down the db for every test only between test runs but its fast enough that we could. This whole thing runs of a simple `go test -v .` command and we run it on every commit using a githook. https://github.com/dosco/graphjin/blob/master/tests/dbint_te...

This was a bug in GraphJin it has since been fixed. The issue was that you were trying to have multiple subscriptions over a single websocket this did not work (it should) and now it does. https://github.com/dosco/graphjin/commit/43e619b2ff392dd42b99a4d56074a041a54b0e1c

Not exactly GraphQL is just a format to define what data you want back from someplace. You can use it in a browser app to request data from a backend GraphQL server or you can use it as an ORM within your backend app to fetch data from a database using a library like GraphJin. https://github.com/dosco/graphjin

FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.

In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.

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: