urllib3 VS mypy

As opposed to what the article says, urllib3 now has experimental support for browser as of Jan 30th.

Source: https://github.com/urllib3/urllib3/releases/tag/2.2.0

Then, I tried to get a firm grip on urllib3 base code, contributing this and there until I was ready to kick things up with a proof of concept that would have put urllib3 far ahead. Without any breaking changes. I was delusional. This was a bit of a shock, but six months passed between my initial kick off and my formal give up, and here's why in a nutshell:

Oh wow, thanks for this story! Would love to hear more if you have time :) Good luck with testing it out.

Note that we found an issue w/ emitting an InsecureRequestWarning by default. The request is perfectly secure, it's just we aren't telling the ConnectionPool that information (see: https://github.com/urllib3/urllib3/issues/3331)

I've had a good experience doing a couple of bug fix bounties for urllib3 https://github.com/urllib3/urllib3/issues . I'd be interested in how the maintainers how found running the bug bounty and if it's given them more useful fixes or if it just adds more noise to deal with

ImportError: urllib3 v2.0 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with LibreSSL 2.8.3. See: https://github.com/urllib3/urllib3/issues/2168

> Could not import extension sphinx.builders.linkcheck (exception: urllib3 v2.0 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with OpenSSL 1.0.2n 7 Dec 2017. See: https://github.com/urllib3/urllib3/issues/2168)

This error is coming from Python, it's telling us Python is failing to import the urllib3 library, these lines here are important:

Requests allows you to send HTTP/1.1 requests extremely easily. There’s no need to manually add query strings to your URLs, or to form-encode your POST data. Keep-alive and HTTP connection pooling are 100% automatic, thanks to urllib3.

Judging projects based on stars is really immature. for example everyone knows requests https://github.com/psf/requests the python package that is used in every python project out there. it has 47k star too WOW. but the thing that less people know is urllib3. https://github.com/urllib3/urllib3. it has only 3k stars. It basically does the heavy lifting for requests!!

urllib3 – Python HTTP library with thread-safe connection pooling, file post support, user friendly, and more

It's got type annotations and mypy has a discussion about it here as well: https://github.com/python/mypy/issues/1282

There is already an AOT compiler for Python: Nuitka[0]. But I don't think it's much faster.

And then there is mypyc[1] which uses mypy's static type annotations but is only slightly faster.

And various other compilers like Numba and Cython that work with specialized dialects of Python to achieve better results, but then it's not quite Python anymore.

[0] https://nuitka.net/

[1] https://github.com/python/mypy/tree/master/mypyc

In Python there is MyPy that can help with this. https://www.mypy-lang.org/

It's funny you should say this.

Reading this article prompted me to future-proof a program I maintain for fun that deals with time; it had one use of utcnow, which I fixed.

And then I tripped over a runtime type problem in an unrelated area of the code, despite the code being green under "mypy --strict". (and "100% coverage" from tests, except this particular exception only occured in a "# pragma: no-cover" codepath so it wasn't actually covered)

It turns out that because of some core decisions about how datetime objects work, `datetime.date.today() < datetime.datetime.now()` type-checks but gives a TypeError at runtime. Oops. (cause discussed at length in https://github.com/python/mypy/issues/9015 but without action for 3 years)

One solution is apparently to use `datetype` for type annotations (while continuing to use `datetime` objects at runtime): https://github.com/glyph/DateType

PEP 695 is great. I've been using mypy every day at work in last couple years or so with very strict parameters (no any type etc) and I have experience writing real life programs with Rust, Agda, and some Haskell before, so I'm familiar with strict type systems. I'm sure many will disagree with me but these are my very honest opinions as a professional who uses Python types every day:

* Some types are better than no types. I love Python types, and I consider them required. Even if they're not type-checked they're better than no types. If they're type-checked it's even better. If things are typed properly (no any etc) and type-checked that's even better. And so on...

* Having said this, Python's type system as checked by mypy feels like a toy type system. It's very easy to fool it, and you need to be careful so that type-checking actually fails badly formed programs.

* The biggest issue I face are exceptions. Community discussed this many times [1] [2] and the overall consensus is to not check exceptions. I personally disagree as if you have a Python program that's meticulously typed and type-checked exceptions still cause bad states and since Python code uses exceptions liberally, it's pretty easy to accidentally go to a bad state. E.g. in the linked github issue JukkaL (developer) claims checking things like "KeyError" will create too many false positives, I strongly disagree. If a function can realistically raise a "KeyError" the program should be properly written to accept this at some level otherwise something that returns type T but 0.01% of the time raises "KeyError" should actually be typed "Raises[T, KeyError]".

* PEP 695 will help because typing things particularly is very helpful. Often you want to pass bunch of Ts around but since this is impractical some devs resort to passing "dict[str, Any]"s around and thus things type-check but you still get "KeyError" left and right. It's better to have "SomeStructure[T]" types with "T" as your custom data type (whether dataclass, or pydantic, or traditional class) so that type system has more opportunities to reject bad programs.

* Overall, I'm personally very optimistic about the future of types in Python!

[1] https://github.com/python/mypy/issues/1773

# is fixed: https://github.com/python/mypy/issues/12987.

You probably already know but you can add type hints and then check for consistency with https://github.com/python/mypy in python.

Modern Python with things like https://learnpython.com/blog/python-match-case-statement/ + mypy + Ruff for linting https://github.com/astral-sh/ruff can get pretty good results.

I found typed dataclasses (https://docs.python.org/3/library/dataclasses.html) in python using mypy to give me really high confidence when building data representations.