Python Fire VS fastapi

The cli tool [fire](https://github.com/google/python-fire/blob/master/docs/guide...) has a nifty feature where it can generate a cli for any file for you.

So random and math are somewhat usable that way

    $ python -m fire random uniform 0 1

With python-fire you can use either function or class to create your subcommands. But I find working with classes more intuitive and manageable. Our first command is going to be a sub-command that shows us the UTC time.

Have you checked out fire? Personally, I think it's a really elegant solution to turning a callable object into command line. Plus, the chaining function calls feature lets you build some pretty complex command line patterns likes you never seen with other frameworks. Definitely worth giving it a try!

I started with click but found python fire to be so much easier to use.

I highly recommend checking out fire for adding a CLI quickly to little utility scripts that aren't going to be published to the world but just for you.

python fire autogenerates CLI wrappers for python modules, which really synergizes with method-chaining APIs like pandas.

Fire can basically do the first step (object -> CLI):

https://github.com/google/python-fire

Gooey can do (CLI -> GUI):

https://github.com/chriskiehl/Gooey

also, while we're on the subject, fire may not be the same kind of workhorse as argparse or click, but for really simple stuff it's pretty awesome

I'm relatively new to Go (coming from Python) so I haven't been using Cobra (or Go, for that matter) for long but it's clearly very polished -- only friction I was experiencing with it is there's a lot of boilerplate to creating commands and subcommands, that IMO (idea as proven by python-fire) can be naturally (better) expressed as types / fields / methods that are already built into the language.

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 ⚡