cats-effect VS mypy

Those benchmarks are using a snapshot version of cats-effect. I don't know where that one comes from, but previously they were using a snapshot from https://github.com/typelevel/cats-effect/pull/3332 which had some issues (3.5-6581dc4, 70% performance degradation), which have since been resolved (see that PR for more info and comparative benchmarks).

Recently, CE3 has had similar issues reported across multiple repositories, almost an epidemic of reports!

The one advantage Rust has over Scala is that it detects data races at compile time, and that's a big time saver if you use low level thread synchronization. However, if you write pure FP code with ZIO or Cats Effect that's basically a non-issue anyway.

See also cats-effect and fs2. cats-effect gives you your IO Monad (and IOApp to run it with on supported platforms). fs2 is the ecosystem’s streaming library, which is much more pervasive in functional Scala than in Haskell. For example, http4s and Doobie are both based on fs2.

On the contrary, switching to the functional mindset, with something like Typelevel Scala3 and respective cats and cats-effect fs2 frameworks, helps to rethink a lot of designs and development approaches.

I think "better Haskell on JVM" (in contrast to "worse Haskell") is a good identity for Scala to have. (Please note that this is an intentional hyperbole.)

Of course, there are areas where Haskell is stronger than Scala (hint: modularity, crucial for good Software Engineering, is not one of them). And Scala has its own way of doing things, so just imitating Haskell won't work well.

Examples of this "better Haskell" are https://typelevel.org/cats-effect/ and https://zio.dev/ .

All together, Scala may be a better choice for you if you want to do Pure Functional Programming. And is definitely less risky (runs on JVM, Java libraries interop, IntelliJ, easy debugging, etc...).

None of the other languages you mentioned are viable in this sense (if also you want a powerful type system, which rules out Clojure).

I agree that Rust's identity is pretty clear: a modern language for use cases where only C or C++ could have been used before.

I would use Scala. I like FP and Scala comes with some awesome libraries for concurrent/async programming like Cats Effect or ZIO. Good choice for creating modern style micro-services to be run in the cloud (or even macro-services, Scala has a powerful module system, so it's made to handle large codebases).

https://typelevel.org/cats-effect/

https://zio.dev/

The language, the community and customs are great. You don't have to worry about nulls, things are immutable by default, domain modelling with ADTs and patter matching is pure joy.

The tooling available is from good to great and Scala is big enough that there are good libraries for typical if not vast majority of stuff and Java libs as a reliable fallback.

What took my interest is this (for both JVM and future multithreaded Scala native): https://github.com/typelevel/cats-effect/discussions/3070 Having the same threads poll available IO events and execute callbacks should improve performance greatly

The author is the creator of Monix and implemented the first version of cats-effect. He knows what he is doing.

You want Kernmantle, which quite honestly shouldn't be hard to implement around Cats and cats-effect. In particular, although Kernmantle doesn't require the use of the Arrow typeclass, there happen to be Arrow (actually ArrowChoice) instances for both Function1 from the standard library and Kleisli from Cats itself, given a Monad instance for the Kleilsi's F[_] type parameter. In other words, we should be able to port Kernmantle from Haskell to Scala (with the Typelevel ecosystem) and instantly be able to use pretty much anything else from the Typelevel ecosystem, or wrapped with it, in our workflow graphs. Pure functions, monadic functions, applicative functions, GADTs with hand-written interpreters, any of it. I think this would be eminently worth doing.

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.