Towards Scala 3

If scala team were to disavow sbt, that'd be the single best thing they could possibly do for the ecosystem.

I used to write a lot of scala, and working with sbt was enough to eventually get under my skin.

I really like some of the OOP aspects of scala, the left-to-right style of thinking matches how my brain works. Scala having a lot to offer can distract new learners, especially with just how overwhelming all the different features can seem for someone not coming from both sides. I had ocaml and ruby under my belt when I found scala, so it was an easier curve, but lots of guys struggle with it. I don't mind showing them, and of course I learn a lot from guys with different experiences and different backgrounds, but that's sometimes a complain I hear, also.

It's a language that doesn't compare very well. What is scala like? We don't know, there's only one language like it, it's a departure from many different paradigms.

I'm back to writing ocaml, all said and done. I enjoyed my time with scala, and interacting with my guys on the other side of the office still doing scala is always enjoyable. Smart guys! I'm not academic enough to grasp some of the new DOT stuff, but they are excited about it.

Anyways, I gave up hope a while back on scala getting rid of sbt. There are other build tools that exist, cbt, mill, maybe more, but without the blessing from lightbend or whatever they call themselves this week, it's not feasible, or responsible to deliver a solution to a client with an unofficial build setup. Of course, after five years of doing sbt I decided it's not ok to deliver sbt either.

Someday! Maybe?!

Python is a good choice for data science even at relatively large scale. I'd question it's suitability for stable, scalable deployment in production (not to say it can't be used then, just that I wouldn't necessarily reach for it first, preferring either C++ or Rust for that).

Scala just doesn't figure into the picture at all. I consider that some "Big" data tools were written in it to be a matter of trivia and not essential to the work of data science.

I liked the idea of Scala and took Odersky's course. But I built a few things in it and it was never not frustrating. What Bruce Eckels said resonates with me: "I’ve come to view Scala as a landscape of cliffs – you can start feeling pretty comfortable with the language and think that you have a reasonable grasp of it, then suddenly fall off a cliff that makes you realize that no, you still don’t get it."

Then I watched this Paul Phillips talk, which convinced me that the problems were deep, not superficial: https://www.youtube.com/watch?v=4jh94gowim0

My hope with Dotty, etc, was that they had learned a little humility and were pruning back enough to make it a decent developer experience. But I could well believe that it was impossible to do enough and still end up with something that could be fairly called Scala.

And maybe not across the board, but my clients are very comfortable with the (very few) F# solutions we've done. Ocaml clients generally use it for stability, or correctness, they certainly aren't competing in the same sphere that kotlin, scala, Java really operate in. Prominent in the financial spade especially, but the trend is the same everywhere.

Reason may eventually help ocaml move closer into those spaces, as people are coming to ML with more openness and are more eager to learn and get involved, so that'll be cool to see in the future.

Anyways, don't count F# out either. I'm no expert, only ever used it with other project leads and with smaller clients, but MS is doing impressive work lately, like NET Core and stuff like that. They at least can show that making net more visible and flexible is part of what they're after.

Java won't be killed. Ever. But people may stop writing it eventually.