[david422]

I love static typing/type hints if for only 1 thing - code maintenance.

Even code I wrote six months ago.

Not having to dig through 6 functions deep to try to figure out whether "person" is a string, or an object, and if it's an object what attributes it has on it etc. is huge. And not to mention that some clever people decide - hey, if you pass a string I'll look up the person object - so you can pass an object or a string - which makes all sorts of convoluted code paths when someone else was looking at "person" and only saw one type so now their function doesn't work on both types etc.

I hate having to waste time figuring out the type of every variable and hold it in my head every single time I read a piece of code.

[wvenable]

The main argument for dynamic typing is speed in prototyping but I find that's opposite for me. I'm much more comfortable rapid prototyping and ripping stuff apart when I have a strongly static typed environment telling me what I just broke.

Doing radical refactoring often involves just making those changes and then fixing all the IDE or compiler errors until it runs again.

[tetha]

Hm, growing somewhat experienced, I find myself adapating an old quote more and more: Sufficiently advanced static typing is indistinguishable from dynamic typing.

Now, I know, it's not true. It's entirely possible to build weird things in python that are provably impossible to typecheck statically. But modern language servers and their type inference capabilities in rust, terraform, or even straight up python are very impressive.

[Jensson]

> Sufficiently advanced static typing is indistinguishable from dynamic typing.

Static typing type checks are compile time, dynamic typing doesn't. I don't see how these two could be indistinguishable, in one you can't run the program with type errors, in the other you can.

[acdha]

This is true if you're focused on the compiler but if you include the adjacent question of what is reported to you in your editor while working it's a bit blurrier. If I write some Rust code and pass a string into something which expects an integer, I get a hard error preventing compilation. If I do the same thing in Python, however, and there's a prominent error displayed in my editor before I even run the code, how different is that from the perspective of anyone who isn't watching my screen? In either case the bug was caught before the code even ran and I likely have type-aware autocompletion to reduce the odds of making that mistake in the first place.

That's not to say that there aren't quite reasonable questions about how effective the different approaches are or how easy it is to fix the error, how complete the checks are, how deep the checks go, etc. A lot of how you feel about that is going to be subjective based on the languages you use and the quality of the code you work with — Rust has an advanced type system and great developer ergonomics providing unusually helpful error messages, Python has weaker typing but also a culture about simplicity which discourages some classes of bugs, Java has a lot of mushy-typed code where people got tired of language / compiler drawbacks and came up with ways to improve ergonomics at the expense of defeating the type checker, etc.

[madeofpalk]

Both your examples are the same static typing. One just has better IDE integration than the other.

[acdha]

In the first case, there would be a hard compiler error — rustc would refuse to compile the code until I fixed it.

In the second case, Python would allow the code to run but would potentially produce a runtime TypeError when it reached that point depending on exactly the code does. It might also run fine (e.g. I'm just passing that variable to json.dump()) or produce unexpected output (e.g. I'm passing that code to print() and that worked for int, and str, but then someone called it with None and I didn't want "None" in the output).

The point was that while those differ in how they're implemented, the experience can be fairly similar when you're in the middle of the code-test cycle. My example wasn't the most complicated dynamic typing scenario but it's an example of why this works pretty well: most Python code isn't highly dynamic or dynamic everywhere — typically there are a few places which might be challenging for analysis but there's also a LOT of code which only ever works with a single input type. If your IDE provides feedback on all of that code, you're going to avoid a fair number of other bugs and free up time for the hard parts.

[wvenable]

I believe the author was talking about the act of writing the software. Modern type inference means that you can mostly code without needing to write down the types in many cases. This line of code is the same in JavaScript or C#:

    var instance = new SomeClass();

In function definitions, where you are definitely going to need to provide parameter types it's extremely common to document those types in a docblock in a dynamically typed language. At least I always did. So making the types part of the definition is not a significant difference while writing the code.

[merb]

c# even has target-typed expressions which might be useful aswell, i.e. instead of var instance = you write SomeClass instance = new(); this might be preferable so that you have all types on the left.

[slaymaker1907]

Please write a type for the following function:

  def compose(start, *args):
    def helper(x):
      for func in reversed(args):
        x = func(x)
      return start(x)
    return helper

There is no mainstream typed language which can write a fully general type for the vararg compose function. TypeScript is probably the one that comes closest, but last I checked it still was unable to write a sufficiently powerful array type. You can write a type for a version of compose with a fixed number of arguments, but not for one working over an arbitrary number of arguments.

[loup-vaillant]

Vararg functions also have limited use. Especially considering that most of the time, your args will all have the same type, and therefore could just be passed in an array or similar. The one mainstream exception I know of is print functions, and we have* ways to statically check those.

Your toy example, even generalised, has no practical use. If I can write this:

  compose(f, f1, f2, f3)

Then I can write that instead (Haskell):

  f . f3 . f2 . f1

Or this (F#):

  f1 |- f2 |- f3 |- f

And now we’ve reduced the problem to a simple function composition, which is very easy to define (Ocaml):

  let (|-) f g = fun x -> g (f x)
  (|-): (’a -> ’b) -> (’b -> ’c) -> (’a -> ’c)

This generalises to any fold where the programmer would provide the list statically (as they always would for a vararg function): instead of trying to type the whole thing, just define & type the underlying binary operation.

[skrimp]

This still technically reduces the generality of the given function since you are specifying that each function cannot have multiple overloads.

let f be a overload set matching the signatures {a -> b, i -> j} let g be a overload set matching the signatures {b -> c, j -> k}

compose(g, f) could be given a to return c or i to return k

[loup-vaillant]

> This still technically reduces the generality of the given function

My point was that we are almost never hurt by that reduction.

> you are specifying that each function cannot have multiple overloads

Haskell has type classes, and if we restrict ourselves to local type inference it's fairly easy to have C++ style overloads without even that. So no, I'm not specifying such a thing.

[fiddlerwoaroof]

> Vararg functions also have limited use.

This is only because people are using statically typed language that place arbitrary restrictions on such functions and make them harder to use. In dynamically typed languages, vararg functions are widely used and enable patterns that are pretty nice.

[loup-vaillant]

Perhaps. But then I want to know what those patterns are, what are their actual benefits compared to not using them, and most of all I want to know if such benefits outweigh the significant costs that comes with the lack of static analysis¹.

[1] The need to test much more, the need for a better, more accurate documentation, the higher cost of refactoring, even the higher prototyping times (I prototype faster with a REPL that has static typing, because I don't to debug type errors).

[skrimp]

C++ is an extremely mainstream language that can write a fully general version of compose with variable arguments.

https://godbolt.org/z/h7n8Y7qf1

Like sure, you can't write out a type for the entire overload set. Overload sets don't have types, but functions do. However, I don't think you'd ever actually want to write out the type of the compose function. Instead, I think it would be more reasonable to request that every intermediate function call is type-checked with fully specified types. In C++ this is the case.

[Matheus28]

Great implementation of what the parent comment asked. Not only that, but the compiler managed remove all the abstractions.

(very minor nitpick: I'd pick `auto&& x` over `auto x`)

[munificent]

> There is no mainstream typed language which can write a fully general type for the vararg compose function.

True. A more interesting question might be what level of static safety and performance benefits you'd be willing to sacrifice to be able to write functions like this.

Personally, I don't find the kind of code I can't fit into static types particularly appealing, but I find the code navigation, error checking, and optimizations of static types to be priceless.

[tetha]

As I said, there are infinite possible programs that cannot be type checked statically. This is derived from the halting theorem trivially - lambda(p) = if p.halts() then A() else B().

However, the intersection of programs I encounter in practice with the number of programs that can be statically checked is rather large.

[phoneperson]

You are probably thinking of Gödel's incompleteness theorems. I do not think that you can trivially derive it from the halting problem.

[tehnub]

He just did derive it from the halting problem. In words: It's impossible to statically check the type of a function that takes a program P as its input, and returns the integer 1 as its output if P halts, and returns the string "1" if it does not halt. It would require solving the halting problem, which is undecidable.

[nybble41]

This is really a fairly trivial exercise in Haskell, provided you pass the arguments as a heterogeneous list—which is semantically equivalent to a variable argument list. Here is my implementation: https://gist.github.com/nybble41/c459c6927a3bad8ec350d227193...

Here I defined a simple `Pipeline` GADT for the argument list, which is just a list of functions with some extra type constraints to ensure that they can be composed. You could do the same thing with a more general type like HList but the type signature for the `compose` function would be much more verbose since you would need to define the relationships between each pair of adjacent function types through explicit constraints involving type families, whereas the `Pipeline` type handles that internally.

Perhaps you don't consider Haskell "mainstream" enough?

[albrewer]

I took a stab at it, there's not enough information to figure out anything more specific:

  from typing import Callable, Any

  def compose(start: Callable[[Any], Any], *args: Callable[[Any], Any] -> Callable[[Any], Any]:
    def helper(x: Any) -> Any:
      for func in reversed(args):
        x = func(x)
      return start(x)
    return helper

[slaymaker1907]

Sure, that's probably as close as you can get, but ideally it would be possible to write a type which guarantees the input functions are compatible as well as knowing what the type is of the returned function.

[iamevn]

I think you can get close implementing it as a macro in typed racket, expanding the type out based on how many arguments you give it. But then it's not a first class function until you expand it.

Found this implementation which also provides pre-expanded forms that are first class functions for specific lengths of arguments docs: https://docs.racket-lang.org/typed-compose/index.html implementation: https://git.marvid.fr/scolobb/typed-compose/src/branch/maste...

[monocasa]

I think you can in rust as long as args is a slice. Rust doesn't have varargs except for c interop. A slice or Vec of function pointers is the idiomatic way to do the same thing.

Something like:

  fn compose<X, T>(start: Box<Fn(T) -> X>, args: Vec<Box<Fn(T) -> T>>) -> Fn(T) -> X {
    move |x: X| {
      let mut x = x;
      for func in args.iter(). reversed() {
        x = func(x);
      }
      start(x)
    }
  }

[slaymaker1907]

This only works if all of the functions return the same type. However, you can write a compose macro which operates as expected.

[plmpsu]

I don't understand your argument. Could you please explain it?

[tetha]

Type systems simply have matured a lot.

It's not too long ago that you either had very clumsy type systems - C, Java. These type systems were more of a chore than anything else. Especially the generic transition in java was just tedious, you had to type cast a lot of stuff, and the compiler would still yell at you, and things would still crash.

Or you had very powerful and advanced type systems - Haskell and C++ with templates for example. However, these type systems were just impenetrable. C++ template errors before clang error messages are something. They are certainly an error message. But fixing those without a close delta what happened? Pfsh. Nah.

In those days, dynamic typing was great. You could shed the chore of stupid types, and avoid the really arcane work of making really strong types work.

However, type systems have matured. Today, you can slap a few type annotations on a python function and a modern type inference engine can give you type prediction, accurate tab-completion and errro detection. In something like rust, you define a couple of types in important locations and everything else is inferred.

This in turn gives you the benefit of both: You care about types in a few key locations, but everything else is as simple as a dynamically typed language. And that's when statically typed languages can end up looking almost - or entirely - like a dynamically typed language. Except with less error potential.

[musicale]

> a modern type inference engine can give you type prediction, accurate tab-completion and errro detection (emphasis mine)

"errros" are my nemesis in languages which automatically create a new symbol with every typo!

[tabbott]

I would argue that the really big benefit of dynamic typing is that it enables a really nice interactive interpreter shell experience. I think it's also important from a prototyping standpoint that Python's static typing model does a lot of inference -- you don't have to add an explicit type annotation on every single variable.

[nyberg]

This is possible with statically typed languages with Haskell being one of the examples where it's encouraged to use the REPL to work towards a solution and/or qucikly test ideas without having to write unit tests or full program tests. Ocaml and friends fall within this category too and none require extensive type annotation due to type inference.

I feel that too much focus is on static languages like C/C++ where types become a chore and judging it on that rather than looking at the plenty of languages with type inference brought by ML-style languages.

[BeFlatXIII]

> languages like C/C++ where types become a chore

It's been a long while since I used those languages, but I remember the chore part wasn't so much of typing Int or String and more so having to care if it's an Int, Short, or Long or if the float is single or double precision. I believe that those micro-optimizations are no longer popular, but manually thinking low-level is not something I enjoy.

[loup-vaillant]

> I would argue that the really big benefit of dynamic typing is that it enables a really nice interactive interpreter shell experience.

I use the Python REPL quite often, and have non trivial experience with Lua’s. But the best experience I’ve ever got was with OCaml: I type the expression, or function definition, without giving type annotations, and I get the type of the result in the response.

You wouldn’t believe the number of bugs I caught just by looking at the type. Before I even start testing the function. And that’s when I don’t have an outright type error, which a dynamic language wouldn’t have caught — not before I start testing anyway.

[munk-a]

When I'm prototyping I tend to go inside out in a layered fashion - some days I am really feeling the data layer - other days I like to work closer to the fringes. To this end type hinting serves as a quick and dirty code contract before all my pieces are in place. I can splat out a bunch of low level definitions that I know I'm going to need and then come back the next day to add in struts - remembering my choices easily as I go.

I know this isn't the approach of choice for most folks but hey - I'm working with ADHD so I've got to make some allowances for some neurodiversity.

[tiew9Vii]

It depends on the language but I find I'm also far more productive with strong types.

When I use a dynamic language I get no errors in dev, I need to run/invoke the program to see if it works. It may appear to work fine as I haven't executed a specific code path hence dynamic languages have extremely high test coverage. With dynamic languages I am delaying my feedback loop, I may get some visual output quicker but that doesn't mean my program is correct.

With a strongly typed language and utilizing types you use the compiler to guide you. The compiler says hey, this isn't correct, fix it, you go fix the error and recompile and repeat.

I've used Elm before and it's the only time I had a complex Javascript UI just compile and work first time. It's like a wow, did that just happen.

With Typescript it's not quite to the level of Elm but find my experience working with React etc far more productive. Typescript says hey, that's wrong, I expect ... you gave ..., you work through the errors and when it runs generally there's less silly mistakes than when I just use Javascript.

I'm learning Rust, the compiler error messages have greatly helped. When you compile it says hey, you tried to do ..., maybe you want ... instead. Not to sure what the suggestion is I try it and 9 times out of 10 it works, compiles, program runs.

With types you generally get better IDE auto complete support etc.

Now i'm using Python for my day job. My experience has been painful, discovering what arguments functions take, passing in wrong values, needing to run slow test suites, finding errors at runtime. Yes you can use type hints and I do but I find them far less reliable.

I guess I'm not a very good programmer so learnt to lean on a compiler to do the hard work for me, and if you have good type support you can lean on types more to get the compiler to help you more.

In Haskell I can write complex logic by writing out the types and ADT's. I've written whole programs with tests to verify the logic without writing a program. I find this incredible efficient for prototyping ideas, just write the types, the functions signitures etc etc. Once that is done you implement the functions, hit compile then boom, your shocked it just worked first time running.

[orwin]

The biggest reason why i like type hints is because it force me to reflect on the datatype i want to use before implementing my code.

Last week, i could've done either a dataframe, a list of list, a list of tuple, a dict of tuples, a dict of lists (this was a bad idea that did not survive more than 2s in my head) or a list of dict. I started coding with a dataframe in mind (i guess i wanted to show off my numpy/pandas skills to my devops colleagues), but adding type hints to my prototypes shut down the idea pretty quick: lot of complexity for nothing.

[steve_adams_86]

> when I have a strongly static typed environment telling me what I just broke.

Yes, I'm a total scatterbrain. Types let me remind myself later that I did in fact forget what I'm doing and what I did. It lets past-me protect future-me.

[agumonkey]

I used to have loads of fun abusing Eclipse real time type checker, imagining software live with typed interfaces.

The IDE was my logical buddy, and every idea's possibility was rapidly shown with it. And I need to massage things a bit, I go faster because I know what's missing.

The only time I liked eclipse/java :)

[Enginerrrd]

Dynamic typing was great before I knew anything about programming. I'm talking like, at a middle school level. Fewer "Silly" errors.

After university, the opposite became true. No difficult to diagnose undefined behavior because of ambiguity in typing.

[ttymck]

I agree with your point on prototyping. I've never been more productive than when I have the (Scala) compiler acting as a second set of eyes, essentially looking over my shoulder, checking my business logic.

[tshaddox]

I think the only reason dynamic typing can speed up prototyping is that it allows you to make certain type errors that you may never encounter at runtime while prototyping.

[mbrodersen]

Agree. I usually think types first and quickly sketch the whole application without writing any code. So,when I start writing code it just works end-to-end.

[Barrin92]

the main argument for dynamic typing in particular in the context of object oriented programming is decoupling. It is always the receiving object's responsibility to handle whatever they get.

if you write dynamic OO languages with a static mentality in mind, i.e. you try to enforce some sort of global type expectation before the program runs, then obviously static languages are better, because you're trying to write static code.

Benefiting from dynamic languages means ditching that mindset altogether.

[handrous]

> I hate having to waste time figuring out the type of every variable and hold it in my head every single time I read a piece of code.

If a codebase doesn't have static types, it damn well better be set up to be highly grep-able. Including dependencies and frameworks.

This is why Rails pisses me off so much. No static types to help you out, and you can't grep (can barely google, even!) methods and properties that aren't defined anywhere until runtime. Is this from core? Is it from some 3rd party gem? Well fuck me, this file doesn't even tell me which gems it's relying on, so it could be literally anything in the entire goddamn dependency tree.

[rightbyte]

> ... grepable ...

This is so important.

It is also the reason why I like global variables. They are accused of making a spaghetti mess but ... in my experience the opposite is true.

Fancy patterns are way worse to reverse engineer than simple flat long functions accessing globals. Easy to debug too!

[mewpmewp2]

I agree with that. I despise all the DI things where I can't "goto" to the definition of the actual dependency that was injected, but only to the interface. So frustrating. It makes understanding what is going on so difficult for me.

[blacktriangle]

That's some Rails stupidity there, not a dynamic language problem. Autoloading symbols by name is straight up dumb.

As for greppable though...then you may as well be using a static language. The point of a dynamic language is to be dynamic, ie you can do those things at runtime.

[nitrogen]

The point of a dynamic language is to be dynamic, ie you can do those things at runtime.

With Rails you have the option of pry-rails, and you can get a list of descendants of important parent classes like ActiveRecord with this: https://apidock.com/rails/Class/descendants

With the combination of vim, rspec, pry, fzf, and ripgrep, it's possible to become quite comfortable refactoring pure Ruby and Ruby+Rails code. But it does take some time to learn how to navigate the Rails runtime code generation magic. The more magic the code, the more you might have to use a debugger to break on method definition, but Ruby's dynamicism lets you do that.

On the topic of frameworks with a lot of magic, having used both Rails and Spring Boot (with Java and Kotlin), I'll take Rails any day. It was way easier to introspect Rails codegen magic with Pry, than Spring's codegen magic with IntelliJ. With Spring Boot, even with Kotlin, we had the burden of semi-manual typing, but lost a lot of the benefits because a lot of DB interaction and API payload handling was still only runtime checked.

[sseagull]

This is absolutely how I feel. I've mentioned previously taking over a project, and just not knowing the type of anything took me months to overcome.

Also, type hints really help your IDE, even catching errors before you even run tests.

There's also a visual cue that you are doing something wrong: If a function returns 4 levels of Union[Tuple[List[int]], Optional[str]........ Then you are doing something too complex and the function should be broken up.

[karmakaze]

I learned the same thing on a project that was using Java 1 non-generics. Not exactly untyped to typed but an analogous experience. Everyone I asked said that it was too big to do. I started anyway by enabling the warnings for nongeneric use. I turned down the reporting limit to 1000 (I think) so as not to be discouraged. After months and months of incremental work alongside my main work, I got under the 1000 warnings. It got a bit trickier after that. In the end, there was exactly 1 bug, where an object.toString was being added to a dropdown box and we'd see it from time to time as Class@hexhash. What I learned then is that it isn't strictly about the bugs, it's the confident way you can navigate the codebase and understand and add in consistent ways. Now I add types to all my Ruby and it's seems normal again.

[angelzen]

This is doubly true as experienced programmers argue that designing the data structures is the hardest part of coding. Code follows semi-automatically.

[nitrogen]

I'd add data flows as another level above data structures. It helps to think about how data flows into, through, and out of a system, then it's more clear how the data needs to be packaged, and from there, the code follows semi-automatically.

Tangentially related: I think it'd be cool if there was a development environment that combined a node-based dataflow editor with normal text editing, so pure plumbing could be implemented visually, but embedded within (and translated to) textual code.

[BeFlatXIII]

> some clever people decide - hey, if you pass a string I'll look up the person object - so you can pass an object or a string - which makes all sorts of convoluted code paths

Do you have hints on how to avoid being one of those 10x clever programmers while programming a prototype? I find that I am most likely to write functions like that when there's some variables that I don't want to pass 5 layers down the call stack and then, in your example, would accept either a string (in which case those variables use their default values) or the Person object, where the variables are pulled from the Person's attributes.

[david422]

I don't really, but I guess I could say that I have developed in statically typed languages and dynamically typed languages (professionally) for over a decade and I've always found that using the "power" of dynamic languages always ends up causing (me) more frustration in the long run- basically classes of bugs or time wasted that simply doesn't occur with statically typed languages. So for me, I tend to spend a little more time up front to try not to waste (my) time in the future.

> I find that I am most likely to write functions like that when there's some variables that I don't want to pass 5 layers down the call stack

I agree for a prototype, there are some tradeoffs to be made. However, very often prototypes can end up becoming production. Temporary decisions often become permanent ones. Just something to keep in mind.

[didibus]

I've been working in Clojure for the last few years, and what I learned is that the trick is to reverse the data dependencies, so that instead of your function asking: "What is a "person" and what attributes does it have if an object?". You have your function declaring: "I take a person as a map of keys :name and :age". And it is the caller who needs to ask itself: "What am I supposed to provide to this function?"

This is a very different mindset, but once you adopt this style, the lack of static types isn't as big an issue.

The reason you can do this in a dynamic language is that you can very easily adapt one structure to another, so its okay if not all your functions work directly on the same shared structures.

It also has the advantage that this style really favors making modular independent granular components that can be reused easily, because they aren't coupled to an application's shared domain structures, but to their own set of structures, creating a natural sub-domain.

There are other aspects to make this style work well, like keeping call-stacks shallow, and having a well defined domain model at the edge of your app with good querying capabilities for it.

Concretely it means say you need to add some feature X to the code, you might think, ok this existing function is one place where I could add the behavior, but for my new feature I need to have :age of "person", but I don't know if the "person" argument of this existing function would contain :age or not. Dammit, I wish I had static types to tell me.

Well, in this scenario, instead, what you do is that you don't add the behavior to that function. Instead, in my style you would have:

    A -> B
    A -> C

instead of:

    A -> B -> C

That means if after B is the right place for your logic, you don't do:

    A -> B -> B' -> C

And hope that the "person" passed to B had the :age key which is needed by B'.

Instead you would do:

    A -> B
    A -> B'
    A -> C

And when you implement B', you don't even care about "person", you can just say you need person-age, or that you need a Person object with key :age (which you don't care if it is the Person object shared in other places or not).

Finally, you modify A, where A was the function that creates the Person object in the first place, it has direct access to your actual database/payload and so finding whatever data you need is trivial in it.

[jfabre]

I never understood this argument. In what kind of shop are you working that passing a string named person to a method expecting an object is tolerated. Or even passing different types that don't share a common interface.

This would never fly in a code review in any of the companies I've worked for.

[kennywinker]

I've seen essentially this code in so many organically grown codebases (when they grew up without types). It's usually close the the UI, because someone had to quickly add an alternate path to support some new user interaction

    function find_user(person) {
        if user is string {
            query_by_name(person)
        } else {
            query_by_name(person.name)
        }
    }

and yeah, we all know it's kinda messy, but also that logic has to live somewhere and we need this feature asap so it passes code review. I wrote a test for it, ship it.

[sseagull]

I came very close to writing almost this exact code just the other day (except it was username or user id for me), but came to my senses. It's just so tempting in a dynamic language...

In a static language, you either can't do it, have to really go out of your way to do it, or at least do function overloading (which is a bit cleaner)

[fiddlerwoaroof]

Calling a function like this “ensureUser” is pretty idiomatic and useful, in lisp-style code bases. I think it’s a pattern related to “parse, don’t validate” in static-type lands: rather than _checking_ what the type is and throwing an error, you define a function that knows how to turn various representations of your type into its canonical shape.

[freemint]

Sounds like a brilliant case for multiple-dispatch.

[kennywinker]

Right so we have:

    function find_user(person: string)

and also:

    function find_user(person: Object)

how long before someone writes this:

    find_user(person: { name: "dave" })

meanwhile, someone else, not suspecting that they'll be handed a weird half-formed `User` object adds `person.id` somewhere in the body of the Object version of `find_user` and now we have a weird edge-case where very rarely `find_user` panics because the user object we're handed doesn't have an id??? Great, I just lost an hour trying to dig that out of the logs, and the users are starting to think of the product as flakey because the bug has been in prod for over a month before we finally believed them enough to look into it.

Just. Use. Types. Multiple dispatch won't save you on its own. You NEED compile-time types.

[david422]

Somebody downvoted you, I'm guessing because they think this is a silly example and have never actually seen something like this. I have, in a production code base.

[freemint]

Multiple dispatch and compile time times are not exclusive at all.

[kennywinker]

I'm saying the problem isn't solved by multiple dispatch alone, but it is solved by compile time types alone. You can use both together, of course.

[elzbardico]

This was probably just a silly example for a quick explanation.

  But all it takes is a method that expects an integer Id to receive a string representation of said id because of some obscure path in code that notwithstanding your 100% line coverage the team is so proud of, was never exercised on tests because nobody can have 100% branch coverage

[tialaramex]

In C++ you're only ever one missing "explicit" from introducing such problems.

Suppose I call fire(bob). Programmers from other languages might reason that since fire is a function which takes a Person, bob must be a Person. Not in C++. In C++ the compiler is allowed to go, oh, bob is a string and I can see that there's a constructor for Person which takes a string as its only argument, therefore, I can just make a Person from this string bob and use that Person then throw it away.

To "fix" the inevitable cascade of misery caused by this "feature" C++ then introduces more syntax, an "explicit" keyword which means "Only use this when I actually ask you to" rather than as a sane person might, requiring an implicit keyword to flag any places you actually want this behaviour to just silently happen.

This way, hapless, lazy or short-sighted programmers cause the maximum amount of harm, very on-brand for C++. See also const.

[cyral]

If only there was a way to enforce these parameter types automatically

[dec0dedab0de]

I personally love it, and wish every library worked this way. My argument is why go out of my way to make it not work, when it would be easy to make it work. This is because I think of modules/packages as user facing programs that are easy to tie together, instead of simple building blocks.

What I really wish existed was a built in way to cast and validate, or normalize and validate. I never care if something is a string. I care that if I wrap it in str(), or use it in a fstring, the result matches a regex. Or if I run a handful of functions one of them returns what I need.

The only benefit I can see of type hints on their own is it makes it easy to change a callable's signature, but I think that's best avoided to begin with.

[nybble41]

> why go out of my way to make it not work, when it would be easy to make it work

The problem the DWIM approach to APIs is that when you go out of your way to "do something reasonable" with absolutely any kind of argument type, leaving the caller's intent implicit, you will sometimes run into combinations that "work" in unexpected—and often unwanted—ways.

For example, say you have a function which returns either a Person object or, in very rare cases, an error string. Moreover, you fail to check for the error string, and pass the result into another function which expects a Person object but will also take a name and look up the corresponding Person object in a table. Now if the first function fails you're left trying to look up an error string as a name, with no obvious signs (such as a type mismatch error) to show that anything is amiss.

It's important to make the intent explicit, and not just let the function guess. One option compatible with both statically- and dynamically-typed languages is to provide two functions, one requiring a Person object and another taking a name string. This is still perfectly ergonomic for the user and mitigates most of the potential for confusion.

[dec0dedab0de]

For example, say you have a function which returns either a Person object or, in very rare cases, an error string. Moreover, you fail to check for the error string, and pass the result into another function which expects a Person object but will also take a name and look up the corresponding Person object in a table. Now if the first function fails you're left trying to look up an error string as a name, with no obvious signs (such as a type mismatch error) to show that anything is amiss.

Well I only ever return one type from a function, I'm not a total madman. Sometimes I'll do one type or a None, if I'm trying to replicate the functionality of dict.get(). Any error string would be within an Exception, so that wouldn't be an issue, but even in your example it would show a stack trace to the function looking up the user, and would be much more valuable to troubleshoot than a type mismatch.

One option compatible with both statically- and dynamically-typed languages is to provide two functions, one requiring a Person object and another taking a name string. This is still perfectly ergonomic for the user and mitigates most of the potential for confusion.

In practice that is usually what I end up doing, but with a 3rd function that takes either and returns a Person object. In this particular case I would probably make the function be a method on the Person object, and have a class method to look up the Person.

Here is the scenario that annoyed me enough to turn me off static typing. I had a class that stored the IP address of a network device as an ipaddr.IPAddress object (now ipaddress in the standard library) and there were various subclasses for specific device types. One of the device types needed an SDK, and the init for the SDK class looked something like this

  def __init__(self, host, port=1234, scheme='https'):

      if not isinstance(ip, str):
         raise TypeError('invalid host')

      self.url = f"{scheme}://{host}:{port}"

If they didn't check the type it would have worked fine. Just like every other library we were using to connect to devices.

So after a bit of frustration we changed our base class

  def original__init__(self, ip_address):
      self.ip_address = ipaddr.IPAddress(ip_address)
  def new__init__(self, ip_address):
      ipaddr.IPAddress(ip_address) # just to validate
      self.ip_address = ip_address 

and all was well with the world, but there was a dumb mistake waiting for us. A year or two later, after upgrading to 2.7 we started passing around unicode objects instead of strings to get ready for 3.x, as was the style at the time. Again that SDK broke, and only that SDK, because it insisted on checking the type. Sure it was our mistake this time for not having the original fix to be just casting it to str right before passing it to the SDK, but it was annoying and should have been unnecessary.

I understand that type hints are much better in this regard because it would only show an error in your tooling. But that brings me to another point.

I write my packages/classes/modules to mostly be used in a web app, or as scripts that run on a schedule. However, I also need to be able to write one-offs very quickly. When that happens my code that was previously a library for different applications, now becomes an application itself. Using the REPL, a jupyter notebook, or bpython, I will need to quickly get something done. In these scenarios I don't want to waste time remembering how to normalize the data being given to me. Especially If the code that provides such niceties is tucked away at a higher level for end users of the web app.

Like I said, I tend to just make a lookup function, and then have everything else be methods on the object. But that doesn't really help when it's parameters to a function. I really don't know what would make it better. Perhaps some kind of mix between function overloading and interfaces from other languages, and the magic *_validate() methods that Django uses. Maybe instead of type hints for return values we need value hints, that give an idea of what actual objects might look like. Then tooling could take into account if it would still work after validation and normalization. Of course it could be that there is no elegant and reliable way to do what I really want, but I can dream.

[nybble41]

> Well I only ever return one type from a function, I'm not a total madman.

I'm sure your APIs are sane (at least to you). It's all the other developers you have to watch out for.

> … even in your example it would show a stack trace to the function looking up the user, and would be much more valuable to troubleshoot than a type mismatch.

A type mismatch would be caught earlier (even in a dynamic language) and the runtime exception should report the specific objects involved, so you still get the string which caused the problem.

> Here is the scenario that annoyed me enough to turn me off static typing.

To begin with, this example has nothing to do with static typing. It involves a runtime time check. In this case I would agree that the type check is too strict. Some languages have an interface or protocol for "string-like" objects (e.g. the to_str method in Ruby), and it would be better to use that rather than checking specifically for an instance of str. Objects which shouldn't be treated as strings just don't implement the protocol. Python has the __str__ magic method, but unfortunately it's not very useful in this regard since all objects implement it, even ones that are nothing like strings. It's more like Ruby's to_s method, used for formatting and debugging rather than as an indication that you have an actual string. The best recommendation I've seen for checking for "string-like" objects in Python is something like `str(x) == x`, though the extra comparison adds some overhead.

Of course that doesn't really help you since you were trying to pass an arbitrary non-string-like object (IPAddress) to a function expecting a string; the looser `str(x) == x` check would also have failed. The call might have "just worked" without the condition, or it might have failed spectacularly. In assuming that it would work without the type check you're depending on the implementation using string interpolation rather than, say, concatenating the strings with the + operator, which requires actual strings and not IPAddress objects since the + operator doesn't do implicit conversion like f-strings would. Static typing would have helped to limit these dependencies on unstable implementation details, letting you know that you need to fix the issue at the call site by passing `str(self.ip_address)` for the host parameter.

[madeofpalk]

We have tests, and static types, because developers are people and people make mistakes.

You can't say "we simply don't allow bugs!" because it's a lie. Why rely on a another person manually checking for silly mistakes when the computer can do it for you?

[dboreham]

You'd think. But I've seen many many many examples of this pattern in production JS code.

[layer8]

> I hate having to waste time figuring out the type of every variable and hold it in my head every single time I read a piece of code.

For the same reason, I’m not a fan of type-inferring variable declarations.

[lamontcg]

I'm okay with "var = new FooBarBazThingyWithALongName()" because I don't need to see the type name twice there.

In an IDE you can get the type annotation from the IDE over every inferred var type, but I don't like requiring an IDE to see that information and like it showing up in 'less' as well.

[layer8]

I agree it's redundant if the type name occurs twice in the same statement. However, further evolution of the code often causes the instantiation to be moved elsewhere, and I wouldn't have confidence that the one doing that change then also changes `var` back to the type name. Instead, it would be nice to have syntax avoiding the duplication in the fashion of `FooBarBazThingyWithALongName thingy = new(...constructor parameters...);`.

[Serow225]

C# 9 now has "target-typed new expressions" https://www.thomasclaudiushuber.com/2020/09/08/c-9-0-target-...

[lamontcg]

Ideally linting tools on PRs show the refactored code as a violation, and it should be easy to rip a cleanup refactoring across the files before submitting a PR to avoid that.

[dmart]

Yeah, when writing type inference is obviously nice, but it can be annoying to try to go back and read.

I think the best experience is having a language server annotate the inferred types (like how rust-analyzer does it.) But even then, it can become hard to read code on GitHub or somewhere where tools are not available. Granted that's becoming less and less of a problem, and even GitHub allows using some VS Code extensions now.

[layer8]

With good IDE support, writing types isn’t that much of a burden. Either write a function call first and use "assign return value to new variable", or use autocompletion where you only type the initials of a multi-word type name. Plus IDE refactoring actions when a parameter or return type needs to be changed.

[blacktriangle]

I would argue that any function that branches on argument type is straight up doing dynamic typing wrong. Well branching may not be the right word. Something resembling pattern maching is fine, but like you say having a function that takes a string for lookup OR the object is just a disaster, particularly when you start stacking function calls. Dynamic types should closer resemble things that all share an interface, not totally different representations of that data based on the shape of your code.

Javascript is by far the worst offender here with its ignoring extra arguments. Javascript functions that totally change effective type signatures based on number of args are the devil's work.

I'd argue that if the types that a function accepts are not easily defineable than you're doing dynamic typing wrong.