scala-cli VS mypy

That has not much to do with the JVM. See Scala CLI[1] for instance, the developer experience is pretty similar to Cargo.

The thing is, with any non-trivial project, zero to hello world isn't a very useful metric. Gradle (and Maven, sbt, ...) do a lot more than Cargo, and their usage is primarily optimized for complex multi-modules projects.

[1] https://scala-cli.virtuslab.org

Scala CLI v1.0.5 was released. https://github.com/VirtusLab/scala-cli/releases/tag/v1.0.5 This includes:

The new official Scala build tool / compiler front end (scala-cli) is amazing,

https://scala-cli.virtuslab.org/

The thing that really struck me after years of python is how it lets you out dependencies directly in a comment on top of a script and it will download and run with them automatically, without poisoning any system settings. It's so simple!

sbt can indeed be a bit harsh for beginners. If your aim is not to build a big project, you might want to use scala-cli instead : no complex build script, only command line goodness to run, test, compile and package your code. Yes it supports dockerization. No need for a dockerfile.

Imo the tooling has to become way more user friendly. The Scala community has picked up on this and made Scala-CLI the official running tool for Scala. It's a great tool for single module projects and makes everything from adding dependencies to building fat jars very easy, also the runner comes as a native image. The reason I'm mentioning is because sometimes we forget how hard it can be as a beginner, especially when younger people are used to simpler CLIs from newer languages.

We even have a ticket for something similar right here. Feel free to upvote and/or comment on it.

Links: - https://dotty.epfl.ch/ - https://scala-native.org/en/stable/ - https://www.scala-js.org/ - https://typelevel.org/ - https://zio.dev/ - https://github.com/scala-native/scala-native/pull/3120 - https://github.com/lampepfl/dotty/pull/16517 - https://dotty.epfl.ch/docs/reference/experimental/index.html - https://scala-cli.virtuslab.org/ - https://scalameta.org/metals/ - https://docs.scala-lang.org/scala3/guides/migration/compatibility-intro.html - https://www.scala-lang.org/blog/2023/04/18/faster-scalajs-development-with-frontend-tooling.html - https://www.scala-lang.org/blog/2022/08/17/long-term-compatibility-plans.html

Code gens are not that far away: https://github.com/VirtusLab/scala-cli/issues/610

Check out Scala CLI as it will very soon be the one true and sanctioned way to get started.

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.