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Comment by dkarl | Hacker News Reader

I'm on exactly that kind of team now. They've been working on a very simple problem for several years now and have an enormous, sophisticated, but still buggy and unstable solution.

They're really smart people, and they had to be extremely capable programmers to get this far, but the embarrassing question is, how would a team of mediocre developers have tackled this problem? They would have picked a mediocre language and probably written a naive, straightforward solution, and after a few months of squashing bugs and developing production workarounds, it would have been a workable, tolerable system. They would have spent the last year working on something else.

The naive solution would have more inherent unreliability, but our vast and intricate solution, designed to scale to the moon and be more self-healing than the T2000, is never going to approach the same reliability as a naive solution, because it's too complex (and the code is too hard for mortals to read and reason about) for us ever to iron out the bugs.

They make a huge deal out of how much safer and easier to reason about our code is thanks to the FP discipline, but why do we have just as many concurrency-related production issues as a typical team using blocking operations and threadpools? Why do we have _more_ incorrectness caused by swallowed errors than I came to expect in projects that relied on exceptions?

I'm still keen on mastering this style of Scala, because I think the benefits can be had, but it annoys me that some of my teammates are happy to use those stated benefits to justify their programming adventures without seeming to care if they actually materialize.

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"I'm on exactly that kind of team now. They've been working on a very simple problem for several years now and have an enormous, sophisticated, but still buggy and unstable solution. They're really smart people, and they had to be extremely capable programmers to get this far, but the embarrassing question is, how would a team of mediocre developers have tackled this problem? They would have picked a mediocre language"

I don't mean to be rude to your colleagues, but almost by implication in what you've said, they're not good programmers. Being a good programmer is nothing to do with leveraging a fancy FP language. It's about delivering working, maintainable, testable code. Whether you write buggy spaghetti code in Go or misuse Scala, its the same root problem.

I've been a Scala dev for five years, worked on some massive projects and it's been a dream. As long as you agree on a style, don't go crazy with the language and use it with discernment, it's a joy to work with. It's a sharp tool though and it takes discernment to know how to wield it appropriately. I don't know if that's a downside, it's more a warning.

I think people can be good programmers and poor software architects at the same time. Sounds like that's what has happened in this case: brilliant abstractions that are misused and result in a buggy product and less productive team.

"I think people can be good programmers and poor software architects at the same time. Sounds like that's what has happened in this case: brilliant abstractions that are misused and result in a buggy product and less productive team."

That's fair. I guess if by good programmer we mean, fluency with the language and algorithms / abstractions /types etc then perhaps they qualify. I really meant something more like "software engineer" ie. someone who can architect a simple, sane, working solution adhering to the usual best practices of good software construction. Maybe there's a missing piece between being a good coder vs scaling this knowledge up to the application level. The latter is indeed a much rarer skill and too often, people without it influence major decisions.

I would guess that it's mainly a question of experience. When you have smart kids fresh out of school, they are going to be eager to use every tool in their toolbox. Eventually (hopefully) they will develop the wisdom to know when to deploy what.

Maybe the issue is that Scala requires a bit more wisdom that some languages. The problem is that the profession is filled with inexperienced people because of the rate at which new programmers are appearing. In that world you either want safe and, dare I say it, lowest common denominator languages, or you need more hierarchy where senior people take a stronger lead and set the rules. In other words, if you're going to use Rust, Scala, Haskell etc acknowledge the tradeoffs and potential footguns and insist that someone who knows what they're doing, leads the project in a very hands on way. Properly mentored juniors or mid level devs will have that moment of enlightenment where they see the point of these languages and don't just succumb to the blub paradox https://en.wiktionary.org/wiki/Blub_paradox.

The issue I've seen is that the help venues are overwhelmingly filled with language-"extremists" that have too much time and lead beginners and those looking for help into the rabbit hole. A bit like people that spend more time telling people to TDD-everything than coding...

Frankly, they are bad programmers. They are smart highly intelligent people who don't have either knowledge or aptitude or willingness to be good programmers.

My pet peeve in this business are supposedly good programmers who get praised despite never having actual results. And it is not like they would be rare.

True, and with people switching jobs every 12 to 24 months (at least in the Bay Area) many of them never get to see the outcome of their choices, and don't learn from experience.

The companies in turn don't realize the outcome of their choice either and just consider the inefficiencies to be the normal status... I've never seen such a low productivity than in large companies with a lot of churn... Thankfully they have good people that can hold and patch the walls and are the real not-recognized "heroes"...

Unfortunately the leetcode whiteboard interviewing strongly encourages this type. Even almost requires it. If all energy is spent on that, it is time not spent learning how to build stable products which can be maintained long term.

Because turns out that popping out algorithm exercise code after another into source control doesn't produce a good product. But that's what current interview fad measures, thus it's what it produces.

I have the same feeling with Kotlin. If you stay away from overusing DSLs and over FPing things (traduction, using Arrow or most of it) it is beautiful and easy to maintain and understand even by junior devs.

And my feeling is the same with Clojure and Scala, I'm not a specialist, but code can be written in such a way that I can even contribute to it.

I am fairly certain the jury is out on this question:

It's always better to write simple, almost dumb code that anyone can understand than to use advanced abstractions from category theory or whatever.

Why? Because every single study or anedocte I've ever heard is like yours: adding complex abstractions to code do NOT make it more reliable. But they do make it much harder to modify, understand, and fix.

FP tought us that unconstrained mutable state is very bad, and that has helped us immensely in the mundane languages of the world (Java, C#, C++, Go). But I think it's time to learn the other lesson from FP: overly complex abstractions are also very bad and should NOT be used willy nilly as they simply have a very low or negative cost-benefit.

i think it depends if you need it, rust for example is quite complex, but it solves a class of issue that other language have trouble solving while maintaining speed

> adding complex abstractions to code do NOT make it more reliable.

Sometimes false. If the (more) complex abstractions are in well-tested libraries that haven't been written solely for your code.

> But [complex abstractions] do make [your code] much harder to modify,

Not necessarily. You can separate concerns; you can isolate changes; and you hopefully reduce the amount of code you've written yourself, significantly even.

> [complex abstractions] do make [your code] much harder to understand

This is as likely false as it is true.

> [complex abstractions] do make [your code] much harder to fix

Again, this very much depends. If you're using a well-tested, widely-used external library with those abstractions, it may well be easier to fix your own code.

When I was young I though that good writers are the ones who write such complex sentences that nobody can understand. Later I learned that good writers are clear and understandable.

I used to write complex code because I found it elegant. Now I write simple code because I enjoy breaking down a complex problem into its simplest form, which for me means I fully understand the problem.

I think the writing metaphor is a good one; we are writing source code for other humans to read after all.

We are also writing code so we can read it ourselves in a few months or years and still understand what it does!

Finding the right level of abstraction is basically the one and only skill that matters for a software engineer IMO. I've got about 15 years of experience in programming and it's still something I'm actively working on.

I disagree with the general take that "adding complex abstractions to code do NOT make it more reliable". Good use of abstraction makes code easier to write, easier to maintain and easier to extend. Good use of abstraction can make code more concise and more regular while at the same time allowing better diagnostics when you do something wrong.

As a quick example: a generic JSON serialization library that can work with any type is probably a lot nicer to use that one that requires manual reimplementation for every class in your program. It'll be more complicated to write but it's probably well worth it in the end. Similarly a logging system that can abstract over several backends and log levels depending on the environment probably beats having a bunch of if/else every time you want to log a message.

But one needs to remember the old saying: debugging code is harder than writing it, so if you write code that's as smart as you're capable of producing then by definition you're not smart enough to debug it.

You are completely right: finding "THE RIGHT" level of abstraction is extremely important. Without abstractions we would be writing code in Assembly.

This is the reason I used the term "overly complex abstractions", not just "abstractions" in general, of course. Overly complex meaning it's a failure to use "the right abstraction" where you instead go for something much more complex than the proble called for... this is what I understand most people in this thread are referring to when they refer to previous Scala projects they've worked on.

“Complex abstraction” is a contradiction in terms since an abstraction is a relation A -> B where B needs to be simpler than A.

From the client's point of view that may be true; abstraction should be the process of pushing complexity down. But in this case I think we're talking about code that has inadvertently introduced unnecessary complexity in an attempt to be hyper-generic.

I agree 100%, and I always bring it back to one simple observation that has been true of almost all the projects I've worked on: the limiting resource that programmers work with is their own time and brainpower. To my mind, much of the discipline of programming is centered around making frugal use of that scarce resource.

I still think programmers should work with powerful tools, because "dumb" tools often force you to express things in convoluted ways, and you don't need a powerful language to make a huge mess. (Java proves that you only need single inheritance to produce virtually unlimited amounts of spaghettiness in real-world projects. The difference between Java and Scala is not that Scala enables teams to produce epic piles of FP crap, it's that we long ago stopped being shocked when teams produce epic piles of OO crap.)

I think the programmers doing terrible things with Scala would be doing terrible things in any language, given the chance. What they need is hands-on technical management. When they have a wild idea, they need to be walked through an appropriate engineering decision-making process. They need somebody with the authority to tell them, "Ha ha, yeah, that design with etcd and Kafka and the robot space lasers would friggin' rock, how cool would that be, but on the other hand, we could just stick a REST API in front of a database and you'd be done in two weeks, so let's compare pros and cons."

> the other lesson from FP: overly complex abstractions are also very bad

Lisp, in its persistent failure to achieve world domination, has been teaching us this lesson for over 60 years now!

Infinitely powerful languages are fun and, yes, powerful. But as soon as the project has more than one or two people in it, they also result in runaway complexity that will hurt the product far more than any gains in expressibility.

That's a common falsehood though.

It does not matter how "simple" each line of code is.

What matters is how "simple" the whole application/solution is.

You can argue that you can write very simple code in Assembler - all instructions are very clearly defined and very simple (well, maybe not anymore in CISC...). Someone can learn them all in a day. The problem is just that now you end up with a lot of code and overall that will be hard to maintain.

Compare it to Javascript: certainly not the language where code is most maintainable or simple, but the end-result is easier to maintain then assembler.

In the end, the choice of programming language is like a the choice of a compression-tool like gzip. The more complex, the more difficult to learn and use (setting library size and various compression settings) but the output will be smaller compared to a simple tool or no compression (Assembler) while containing the same amount of information.

You should always reach for the simplest solution first (and Haoyi has a great set of guidelines about that: https://www.lihaoyi.com/post/StrategicScalaStylePrincipleofL... ). The point of those complex FP abstractions is to let you keep writing dumb code even when you want to do something fiddly (such as async operations with error recovery). But don't cargo-cult the complex solution if you don't have the complex problem! A plain function whose result is a plain value should be expressed that way.

Counter-point:

I write lots of F#, and I find that computation expressions (a fancy version of do-notation) makes my code more "dumb" and "simple", despite being an advanced "FP" feature. This is because it pushes the complexity from my business logic and into library code.

With computation expressions:

    async {
      let! foos = fetchFoos

      for foo in foos do
        do! launchFoo foo

      do! clearFoos
    }

Without computation expressions (this is probably wrong but you get the idea):

    Async.bind 
      fetchFoos
      (fun foos -> 
        Async.bind 
          (
            foos
            |> Seq.fold (Async.bind (fun foo -> launchFoo foo)) (Async.just ())
          )
          (fun () -> clearFoos)
      )

Notice how my fancy non-blocking code reads like straight-forward blocking code?

I could not manage complexity without this feature.

Reading this thread is giving me some catharsis. I worked on a large project in a big tech company where the org’s leadership was ideologically determined to build everything in Scala. We ended up with dozens of engineers and failed to deliver basic functionality. The leadership eventually left the company and now they are running a startup with a few of the Scala savants from the old team. They got lots of funding and they invited me to visit to get me to join. They showed me the awesome type system they had implemented and tried to demo it but the product was broken in five ways.

I don't think it is a Scala problem but more of a leadership/organizational problem. If you look at Jane street, they do everything in OCaml which is an interesting choice to say the least, but their stuff works, evolve and they can demonstrate...

Yeah, I don’t think Scala itself caused the problem. There was this mindset: “if we just properly model the universe in the type system, the solution will just fall out!”

Because that was the obsession, they ended up spending all their time iterating on this perfect type system and not enough on the product. I think people with that issue are more likely to want to use Scala.

I am continually perplexed at the swallowing of exceptions in certain FP communities.

Result types, Either et cetera make it extremely easy to swallow errors by flatMapping thoughtlessly losing the context of where they were - you typically /want/ the call stack when you hit into an exceptional flow.

It makes the code impure (that is with side-effects). One can also argue that if you can encode the potential errors in the type they are not exceptional.

It's not that bad though, because you get much less of these kind of errors than in a typical Java program (for example).

Yes, it is a side-effect - and does make the code impure; but it's an /exceptional/ flow like OOM, infinite looping, stack overflows which also break equational reasoning.

My argument is that as a programmer you /choose/ the base lemmas which you're comfortable with - with an exception you're saying for a large swathe of your code, it will assume a lemma. When is /does/ break you get to point the finger at it with a stack trace saying here is where the lemma was broken. As an aside there are nicer ways to do this ala contracts, but exceptions aren't a /bad/ way.

The equivalent of try.toOption is try catch everything and discarding. The equivalent of flatMapping without adding surrounding context is equivalent to try catch rethrowing. Both of these are /much/ too common in FP codebases.

This is spot on! Way too often in Scala codebases, which tend to wrap IO calls in Future or similar is that you'll have chains of side effecting futures like

    readFromDb()
      .flatMap(x => doSomethingElse(x))
      .flatMap(x => doSomethingElseAgain(x))
      .flatMap(x => onceAgain(x))

And this all gets passed up the call stack to one generic thing that basically does nothing in the case of error, maybe logging it and that's it. It's tempting to write code like this because it's so easy and looks so clean and readable, but in reality it is not how you build reliable software because what one must do in response to a failure from the first call is not the same as what one must do for a failure of the second call, or third or fourth, but this flatMap().flatMap().flatMap() style seduces the programmer into thinking they've handled errors when really they've just lumped the entire workflow as one big chain that can fail at any point and in practice usually just ignore all error cases.

One strategy I find does slightly mitigate this is to use future.transformWith[S](f: Try[T] => Future[S]). This at least presents to you the opportunity to think about error handling a bit more consciously, but I still find that the chaining operator approach nudges you away from thinking about error handling because it becomes quickly difficult to read.

It's probably to do with how limited the control structures are when dealing in Futures. You don't have while/if-else/etc, you must lift all your control into flatMaps and iterations are only doable via recursive calls with explicit accumulator state (yuck!) and nesting. So whereas a properly thoughtful treatment of error cases in synchronous code may take 20 or 30 lines of legible code, this gets tranformed in the async style to 20 or 30 levels of nested callbacks and recursive calls which becomes unreadable.

Doesn't Scala have transformer libraries like ExceptT in Haskell. Aside, much of what this comment thread is about, is similar in Haskell-land as well.

Anyway, a pattern I've started to use in Haskell with ExceptT is along the lines of

    handledIo = runExceptT $ do
      failable <- someIo
      can_handle <- anotherIo failable `catchError` handlerOfSubsetOfExceptionsOrRethrows
      return $ updateState can_handle

So the code still remains legible (minus the plethora of Haskell operators, data wrappers and unwrappers :)), I can trap individual exceptions along the way, which I can handle. And at the end unhandled exceptions are returned by the function as Left.

Amen. I saw exactly the same problem with over-reliance on flatMap, either explicitly or in for expressions, in future-heavy Akka code that I do in cats and cats-effect code.

People love the unifying abstractions underneath these types, and they love developing instincts about how to write code based on them, but from an application programming point of view, their instincts are often counterproductive. I don't think people take a mathematical enough viewpoint. The abstractions can't tell you what is important and unimportant. They can't tell you how your code should be shaped. They can't tell you which values should be transformed further and which should be short-circuited. They can't tell you which values deserve to be given a name for readability and which values should be anonymous.

"All these values are monads, so I can combine them with a for expression" is a meaningless statement of a trivial mathematical fact, not a clue about how you should write your code.

I think error handling code can be written in a straightforward style, but it isn't as pretty as people would like. I think the trap they fall into is holding onto elegance while they reach for correctness, instead of holding fast to correctness while they reach for elegance.

That's a great observation about for-comprehensions. It's an other one of these cute niceties that in the most basic cases can come in handy but again nudges users away from consciously thinking through what they're doing (good luck handling the error path in a for-comprehension over Futures).

I dunno. You see the same pattern with chained class methods in Python and it suffers from the same exception specificity problems, but doing it is still a fair judgment call. Sometimes all you need to know is that there was an error in the chain. Like if an exception is raised by dataframe.transpose().to_dict().values() I don’t ask myself where in the chain the error occurred, I just think, “oh that’s weird why couldn’t I turn that into a list of dicts? Did I call those methods on the wrong object?”

Your point's well taken. I'd say that there is a bigger issue in the case of flatMapping side-effectful monads because they're typically dealing with things like writing/reading from databases and the error cases should be thoughtfully considered (do I have to roll something back or perform compensating actions, or clean up anything?)

If understood “flatmapping monads” as anything other than the name of a band I’m sure I would agree.

How is it different from

  try {
    doSomethingElse(x)
    doSomethingElseAgain(x)
    onceAgain(x)
  } catch {
    ...
  }

where each method just returns Unit or throws exception? The version with IO/Future is superior since it at least explicitly states that you can get an error here. If you want to say that Go-style error handling is better because if forces errors handling, I can kind of buy it, but it also has some cost.

>You don't have while/if-else/etc

Maybe you are looking for ifM/whileM functions from e.g. here: typelevel.org/cats/api/cats/Monad.html.

You shouldn't swallow errors like that in FP either. You can trap the error in an effect type and throw it at the top level of your app when your effect type gets run after all of the code that processes that error value has a chance to recover. See something like Zio for an example, though you can do similar things without Zio.

If the team uses Zio, or maybe they’ve gotten lost in the religious war of Scalaz, vs Cats, vs Zio... One of the major issues I have with Scala is how fragmented the community is. Everyone has an opinion on how something should be done and everyone thinks that everyone else is wrong.

Well you have extremist who believe that you have to cull the community due to a few bad (but still contributing) apples. Additionally you have a big lightbending company that produces some questionable contributions to the community as if it's the "scala-endorsed way".

Don't get me started with the whole forcing of "lets be python like" initiatives as of late.

And will you get a call trace when you do that?

we use error types that extend Exception specifically for the purpose of getting a call trace. A lot of people hate doing this but it's been extremely helpful on our project

We do this too - but the other problem you get is that Exception aren't treated specially (like Any and Nothing) in the inference hierarchy so errors are liable to "collapse" to Throwable or Exception. I like wrapping them with a new type if possible to stop this behaviour.

As I am slowly acquiring the habits of thought that make it possible to read and write FP code at a reasonable speed, I'm horrified by how much idiomatic Scala FP code relies on projecting certain assumptions onto types and operations that seem, at first glance, to be neutral mathematical abstractions.

For example, I find myself hating the Either type, because I feel like there is a socially established convention that one half of the Either is the type that matters, the value that you want, the value that is the point of the computation you're doing, and the other half is a garbage value that should never be directly handled. So I really feel like I should conform to the convention and reserve Either for cases where one of the possible types doesn't matter. But how often is it true that one side of the Either doesn't matter? People want me to encode success/failure in an Either type, but if I do that, are they going to treat failure with the care it deserves?

I often handle Either (and Option) using pattern matching when I feel it's important to give both code paths equal importance and equal visibility in the code, but people change it because flatMap is supposedly more idiomatic, and they believe that eliminating pattern matching from their code is a sign of sophistication.

I feel like this stems from a strong desire among FP folks for the happy path to be the only one visible in the code, and the non-happy path to work by invisible magic. Maybe there are some brilliant programmers who achieve this by careful programming, but there are people mimicking them who seem to rely more on faith than logical analysis. They just flatMap their way through everything and trust that this results in correct behavior for the "less important" cases.

I'm sorry that this turned into a bit of a rant, but I'm entirely fed up with it, and it accounts for a lot of what I dislike about the code I work with on a daily basis.

I've been working through 'Haskell From First Principles', and it turned on a lightbulb: the 'right' half of Either is the 'important' one because its type variable is free to change.

  instance Functor (Either a) where    -- a is fixed here!
    fmap :: (b -> c) -> Either a b -> Either a c
    fmap _ (Left l)  = Left l
    fmap f (Right r) = Right (f r)

As a general rule, the last type parameter of a type carries special significance: the same applies to Tuples.

You _can_ trivially construct a type where the two labels are swapped; it's just labels, Left and Right aren't intrinsically important, except insofar as they reflect the positions of the type arguments in written text.

  data Either' a b = Left b | Right a

This is also the reason we have the convention in Scala as well - the inference to partially apply the type works in a certain way. But I agree with the parent post, a more descriptive name would be better.

Right; ostensibly you could create a language that lets you easily poke holes in any slot of a type, but I'm not sure you necessarily /gain/ a lot in doing so except for confusion. It would take a lot more convolution to specify types and instances for every function application.

The obvious (and obviousyl better/correct) name for that type is Result; instead, Either should be unbiased.

This was great, actually. I don't program in Scala, but it was very interesting to hear about the difference between types as abstractions vs types as they are used.

For unfamiliar topics or when presented with uncommon insight, I believe rants, monologues, even diatribes are actually some of the best things to read.

> I often handle Either (and Option) using pattern matching when I feel it's important to give both code paths equal importance and equal visibility in the code, but people change it because flatMap is supposedly more idiomatic, and they believe that eliminating pattern matching from their code is a sign of sophistication.

Isn't this the same as letting exceptions bubble up in a non-FP language?

It's supposed to work like that, but it's a lot easier to screw up. Smart people screw it up all the time, and it's hard to spot in code review, whereas average programmers have no problem avoiding swallowing exceptions once they realize it's important, and if they do mess up it stands out like a sore thumb in the code.

Yes - but you /also/ lose the stack trace and it's /far/ too easy to do; hence my OP.

It is /infuriating/ to be knee-deep trying to work out which of your 30 Eithers failed with a non-descript error.

You don't necessarily lose the stack trace. Typically the left side of an either is an Exception (or an error ADT that wraps one). When you want to handle the left case, you can log out the full trace as you would without Either.

The Monad instance for Either means that chaining them together with flatMap has a short-circuiting effect and the first failure will stop the rest of the chain from being evaluated. I find this actually makes it easier to know where your errors are happening, and also allows you to centralise your error handling logic.

Hmm, when I first learnt Scala, I haven’t had too advanced FP knowledge, so I am yet to have first-hand experience with this sort of exception-handling and I’m yet to decide how good it is.

Compared to Haskell, it is probably better in some way because you have the proper stacktrace; but it “feels” impure a bit.. In a way Java’s exceptions are already an Either type with the result type and the thrown Exception (with “auto-decomposition”, unless checked exceptions) —- is the advantages like manual management of when mapping/flatmapping happens worth it in your opinion? Nonetheless thanks for the heads up, I might try out Scala again with the exception handling model you mentioned!

Sure - you can implicit srcloc or capture the Exception, both of which preserve it; but that's not the default behaviour and it's not what we recommend to beginners.

If you go onto the scaladoc for Either today, you see a stringly-typed Either where they discard the Exception.

> For example, I find myself hating the Either type, because I feel like there is a socially established convention that one half of the Either is the type that matters, the value that you want, the value that is the point of the computation you're doing, and the other half is a garbage value that should never be directly handled. So I really feel like I should conform to the convention and reserve Either for cases where one of the possible types doesn't matter. But how often is it true that one side of the Either doesn't matter? People want me to encode success/failure in an Either type, but if I do that, are they going to treat failure with the care it deserves?

There's always a tradeoff between making the happy path clear and making the error handling explicit. The whole point of Either is to be a middle ground between "both cases are equal weight and you handle them by pattern matching" (custom ADTs) and "only the happy path is visible, the error path is completely invisible magic" (exceptions). Given that people in Python or Java tend to use exceptions a lot more than they use datatypes, I'd argue that a typical Scala codebase puts more emphasis on actually handling errors than a typical codebase in other languages.

Where each case really is of equal weight, consider using a custom datatype (it's only a couple of lines: sealed trait A, case class B(...) extends A, case class C(...) extends A) rather than Either.

I think the solution is to have typed exception modelled as polymorphic variants. Something similar to Swift.

How do you mean? If your either turned left you should be stuck with that exact left and no amount of flatmapping should effect your stack, it simply does not kick in unless you added something like leftmap that swallows the exception

The call stack is "magic" built into the language, so as soon as you're not using the "blessed" way of error handling, you lose it and need to rebuild the same functionality "by hand".

I agree that some kind of logical call stack is a very useful thing to have, and I'd recommend implementing something along the lines of https://github.com/lancewalton/treelog that provides it.

> how would a team of mediocre developers have tackled this problem?

In my experience, mediocre developers would stick to a simple language like Python and only use simple Python features... and write a completely impenetrable, 100% coupled and wholly unreadable mess that would make you wish for over-engineered Scala. Don't even get me started on what happens with "simple" distributed computing tools like Hive...

I see people complain about languages like Scala leading to over-engineering and I just don't get it: have you not worked with sub-par Python or JavaScript code? Sure, poorly designed complex solutions have problems, but so do under-abstracted codebases! Bad code is bad code, and I don't think it's meaningful to say that "over-engineering" is categorically worse than "under-engineering".

I actually worked with somebody who wrote pathologically over-complicated Haskell. Had some friction between us over that. His Python code? Somehow even worse.

No serious language I've seen at either end of the abstraction-friendly spectrum (not Haskell/Scala nor Go/Java/Python/etc) puts a meaningful floor on code quality. An inexpressive language can have a low ceiling for how good code is, but restrict or simplify the language however you like and people will still happily write utterly unworkable code.

I've found that languages—especially less common languages thanks to the "nobody gets fired for IBM" effect—get used as scapegoats for not talking about cultural issues. If people on the team are writing clearly poor, over-complicated and bug-riddled code, it means there is some sort of technical leadership shortcoming, and it would not be any better if they were using Java instead. (I mean, have you seen Enterprise™ Java™ codebases?) And, at the same time, it's clear that a Scala codebase with tasteful technical direction and leadership—conveyed through team culture, code reviews, shared expectations... etc—can be absolutely great to work in.

At this point, I'm leery of blaming languages for programming problems without a clear mechanism. "The language lets people do bad things" isn't a real mechanism because all languages do bad things. "The language attracts people who like complexity" seems specious too. (Again, let's not blame languages for hiring and cultural problems!) "The languages defaults incentivize poor code" is a better argument, although it can be a bit fuzzy. And arguments like "language A doesn't have capability B that we need" or "language C allows classes of bugs other languages don't which empirically cause problems" are the most compelling of all.

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