requests-html VS fastapi

requests-html

Ya, thanks for advice. When using requests_html library, I am trying to lower down the speed using response.html.render(timeout=1000), but it raise Runtime error instead on Google Colab: https://github.com/psf/requests-html/issues/517.

For dynamic js, prefer requests-html with xpath selection.

Example: requests-html which has a rather exhaustive README.md, but their dedicated page is not that helpful, if I remember correctly, and currently the domain is suspended.

When it comes to web scraping, the usual people recommend is beautifulsoup, lxml, or selenium. But I highly recommend people check out requests-html also. Its a library that is a happy medium between ease of use as in beautifulsoup and also good enough to be used for dynamic, javascript data where it would be overkill to use a browser emulator like selenium.

Quite possible. If you look at requests-html source code, it is simply one single python file that acts as a wrapper around a bunch of other packages, like requests, chromium, parse, lxml, etc., plus a couple convenience functions. So it could easily be some sort of bad dependency resolution.

What I do is try to see if I can use requests_html first before trying selenium. requests_html is usually enough if I dont need to interact with browser widgets or if the authentication isnt too difficult to reverse engineer.

He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.

It has been an interesting exercise developing this wrapper component. The fact that it seamlessly integrates with the FastAPI framework is just a bonus for me; I didn't plan for it since I hadn't learned FastAPI at the time. I hope you find this post useful. Thank you for reading, and stay safe as always.

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.