exhaustive VS ZIO

This is an analyzer that will catch this: https://github.com/nishanths/exhaustive

I believe it's in golangci-lint.

I agree linters in general are quite useful for Go though. The default suite from golangci-lint is quite good. I would also recommend enabling exhaustive if you're working with a codebase that uses "enums" (full disclosure, I contributed a bit to that project).

there’s a linter for exhaustive matching: https://github.com/nishanths/exhaustive

I tried to find that linter and found this: exhaustive

And in Go you'd use a linter, like this one.

this is AST go vet analyzer that performs just that: https://github.com/nishanths/exhaustive (too bad it can not do struct based enums..)

>> the main thing missing from Go is ADT's. After using these in Rust and Swift, a programming language doesn't really feel complete without them

What are the differences between an ADT (plus pattern matching i’d reckon?) in Rust/Swift vs the equiv in Go (tagged interfaces + switch statement)?

One has exhaustive matching at compile time, the other has a default clause (non exhaustive matching), although there’s an important nub here with respect to developer experience; it would be idiomatic in Go to use static analysis tooling (e.g. Rob Pike is on record saying that various checks - inc this one - don’t belong in the compiler and should live in go vet). I’ve been playing with Go in a side project and using golint-ci which invokes https://github.com/nishanths/exhaustive - net result, in both go and rust, i get a red line of text annotated at the switch in vscode if i miss a case.

Taking a step back, there isn’t a problem you can solve with one that you can’t solve with the other, or is there?

To take a step further back, why incomplete?

Use a linter.

For an exhaustive linter, were you referring to this? It looks pretty nice. If it's possible to check this with static analysis, is it something that could be in the compiler itself in the future?

https://github.com/nishanths/exhaustive

here, have fun. You’re gonna write some tests, make new types to satisfy interfaces for testing, and then wind up with branches for your test paths in your live code, but go for it, I guess. You know everything! I am but a simple blubbite, too dim, too dim to get it.

scala has 2 healthy and pretty complete lib ecosystems : check out typelevel and ZIO. Both are FP oriented, which might not be your cup of tea at first glance but I would encourage you to try em out ! Softest introduction would be to start with the typelevel cats library and build up from there. The excellent Scala with Cats will ease you softly into an FP mindset. It's a bit dated and for scala 2 only but translating to Scala 3 is a very good exercise if you feel so inclined !

Last but not least, Scala is currently the language with one of the best effect systems in my opinion (https://zio.dev/). Kotlin for example has copied the approach with https://arrow-kt.io/ which I think is great actually. But when comparing Scala and Kotlin here, Scala wins by a large margin, it is a completely different world. It's like building a highly concurrent system in Erlang vs C.

Of course, if you don't want to learn things like union types, traits/typeclasses and effects (similar to async/await but more powerful) you will be annoyed by Scala. But once you learned them, you can never go back.

ZIO

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

For the Rx approach, The ZIO framework for Scala has a streaming API that can meet those sorts of requirements. e.g.

This tutorial will introduce how to build from scratch, a REST microservice using the ZIO framework, and examples of ZIO dependency injection, ZIO HTTP, JSON, JDBC, and others from the ZIO environment. The source code is available here

I use ZIO (http://zio.dev) for Scala which makes parallel programming trivial.

Wraps different styles of asynchronicity e.g. callbacks, futures, fibers into one coherent model. And has excellent resource management so you can be sure that when you are forking a task that it will always clean up after itself.

Have yet to see anything that comes close whilst still being practical i.e. you can leverage the very large ecosystem of Java libraries.

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.