hatch VS cats-effect

Exciting stuff! I view Hatch [1] as becoming the Cargo for Python because it's already close and has an existing (and growing) user base but I can definitely see depending on this for resolution and potentially not even using pip after it becomes more stable.

[1]: https://hatch.pypa.io/latest/

I was inspired enough by the hatch sync idea that I created a PR to add that functionality to hatch: https://github.com/pypa/hatch/pull/1094

Another concept I learned was about build backends, an import step which is used to initialize and install any dependencies of the app you're packaging. Since the tutorial went with using Hatch that is also what I went with, though it didn't provide a lot of useful details especially because it didn't show how to add any dependencies, so I took a look at the docs which were very nice and simple to follow.

Try using hatch: https://hatch.pypa.io/latest/

I'm trying to learn Hatch, I currently use [Poetry](python-poetry.org/) to manage my dependencies, and while I'm overall happy with it, I really like the features I'm reading about with Hatch. I'm also working on learning CI pipelines & Dockerizing Python applications, and Hatch seems like a really useful tool to learn for this (and just as a general use tool).

Poetry 1.2 has been a pain. Which was the dev's fault though. Switching to something new while deprecating a related feature is just plain bad. I've been looking into modern alternatives like PDM and Hatch, but haven't used them (yet).

For example, when it comes to Python, one option is to use the same packaging system that a huge number of open-source libraries and tools are published with. You can use setuptools or Hatch to build a "packaged" version of your code, and publish it to either the public PyPi repository or an internal one that you set up. Then your users can use pip to install your package, automatically fetch its dependencies, and keep it up to date, just like any other Python module.

Don't forget the new PyPa tool on the block: Hatch.

See also: https://github.com/pypa/hatch

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.