asgiref VS fastapi

uvicorn is an ASGI server that is recommended for running FastAPI applications.

I can't seem to be able to edit on mobile. OP either meant this, or its variation, such as async_to_sync and sync_to_async.

https://github.com/django/asgiref/blob/main/asgiref/sync.py

Ofc this is a python example. I have no idea how it works in different languages.

FastAPI is a popular Python Web framework that developers use to create RESTful APIs. It is based on Pydantic and Python-type hints that assist in the serialization, deserialization, and validation of data. In this tutorial, we will use FastAPI to create a simple "Hello World" application. We test and run the application locally. FastAPI requires a ASGI server to run the application production such as Uvicorn.

Look at the intended semantics [1], and then read the implementation [2]. Can you figure out if the implementation is correct? Can you infer the possible limitations of the approach at glance? Can your async library actually handle being called with multiple event loops installed?

I have zero trust in this code and I have been bitten by fixes to this library that introduced deadlocks in my own code.

[1] https://github.com/django/asgiref#synchronous-code--threads.

[2] https://github.com/django/asgiref/blob/main/asgiref/sync.py#...

Using WebSockets in Django utilizes asynchronous Python and Django channels, making the process straightforward. Using Django channels, you can create an ASGI server, and then create a group where users can send text messages to all the other users in the group in real time. This way, you are not communicating with a particular user, but with a group, multiple users can be added.

If you're interested in the architecture itself I recommend you start by making yourself familiar with [ASGI specification](https://asgi.readthedocs.io/en/latest/) .

We switched to using strong typing derived from the asgiref for typing ASGI types, which makes Starlite the strongest type framework of its kind.

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.