Werkzeug VS fastapi

The angle brackets on their own should be a sign that this is not the case, because that's not regular expression syntax. Flask uses werkzeug.routing instead. In fact, the module docs specifically call out the difference:

Flask doesn’t support ASGI; neither does Werkzeug. The official guidance for running Flask with an ASGI server is to wrap it in middleware. The open GitHub issue for Werkzeug to support ASGI is #1322.

But this may be a starting point: https://github.com/pallets/werkzeug/blob/main/src/werkzeug/middleware/http_proxy.py

> In this case, he initially presumed that the problem would be with Flask, Bottle, and Tornado still honoring a semi-colon as a separator in a uri

Flask (or rather Werkzeug what it's based on) removed implicit semicolon support for URL 13 years ago: https://github.com/pallets/werkzeug/commit/0ea28bbc6f5f05eef...

Werkzeug (library used by Flask) had a repr for its sentinel long before getting type annotations.

Flask getting async support is a huge step for the asyncio web ecosystem in Python. I wonder how Flask's ASGI support will evolve in parallel with Quart[1] which bills itself as the ASGI version of Flask and is maintained by a member of the Pallets team. One of the maintainers of Werkzeug even recommended using Quart[2]

[1] https://pgjones.gitlab.io/quart/

[2] https://github.com/pallets/werkzeug/issues/1322#issuecomment...

I’m pretty sure you need to establish a ‘werkzeug’ logger in a logging configuration and make sure that it has a handler for its effective level. But that’s just what I got from reading the source code. I could very well be wrong

You're not able to change the loglevel with werkzeug. It's hardcoded to info: https://github.com/pallets/werkzeug/blob/72b2e48e7d44927b1b7d6b2f940d0691230de893/src/werkzeug/serving.py#L388

The PRs are this initial one and this update. Also note this issue - almost 5 years to close :o.

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.

TiTiler is a dynamic tile server built on FastAPI and Rasterio/GDAL. Its main features include support for Cloud Optimized GeoTIFF(COG), multiple projection methods, various output formats (JPEG, JP2, PNG, WEBP, GTIFF, NumpyTile), WMTS, and virtual mosaic. It also provides Lambda and ECS deployment environments using AWS CDK.

To make it a bit more realistic, we’re going to use a FastAPI route as an example, and we’re also going to use FastAPI’s dependency injection, which can really help with readability (and testability, but more on that later).

2. FastAPI - Turbocharge Your Web APIs with Python ⚡