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Comment by scott_w | Hacker News Reader

I don’t think that’s strictly true. Python developers are finding there are advantages to having a type system in larger programs such as readability and a reduction in certain classes of bugs.

That said, I’ve found Python’s approach lets me still build things through experimentation then fixing the types when I’m a bit more confident that I’ve built the right thing.

As a comparison, I found Go really clunky when I tried to learn it because it wouldn’t compile if the code wasn’t totally correct. It makes it hard to find the right solution through experimentation.

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Python’s problem is that there’s multiple 3rd party type checkers and they all disagree in subtle ways about what the spec says. It’s a mess.

Python is probably an all-round better language than JavaScript. But typescript absolutely embarrasses Python’s “gradual” type system with just how good it is in comparison.

I don’t see that as a problem since you’re not going to run multiple type checkers over your code, much like most C++ codebases pick a compiler and stick to it.

Nobody wants to recreate the mess of mutually incompatible c++ compilers.

And it does matter when 3rd party libraries are involved. Types are a wonderful gift to anyone who imports & needs to figure out your library.

> And it does matter when 3rd party libraries are involved.

That's a fair point.

I would love to instead have one official upstream runtime type checker which can be enabled per function, class, or module via decorators. Basically, providing opt-in strong typing as a language feature.

Currently, I’m using the third-party Beartype decorators to provide this feature, but I would prefer something built-in and ideally something with more readable error messages for users when type errors occur, which might be easier to provide if it was built into Python.

(I use Pyright too, but I find the runtime type checking to be more robust since it understands also the types returned by highly dynamic untyped code.)

Two things here:

1. You need to understand how to express concepts in a form the type system can understand. Each type system will have patterns it understands, and patterns it does not.

2. Go isn't a great example. It has a Java-circa-2000 type system. Modern type systems are much more expressive.

> Go isn't a great example.

I picked Go as an example of a language that gets a lot of praise specifically for having strict compiler rules.

I've also tried Scala and found it clunky but I do suspect most of its problems came from the insane operator overloading and opaque build system rather than the type system. I understand it's gotten better since I looked 8 years ago but I've not had a reason to check it out again.

Yeah, the early days of Scala were a bad time for people going crazy with symbolic method names. Thankfully, most of that is in the past. (And Scala 3 is a much cleaner language in every way.)

Those people haven't used Java 11+ or Python with types.

Python with types is a double edged sword. If you can enforce other collaborators and SDKs use strict typing, it's a very pleasant experience to write, coupled with Pydantic for defining POJO-like structs. For CLI tools Click + Poetry build system.

Java17+ type system is pretty good. Way better than Go in terms of dev experience. you may need a POJO generation tool (Lombok, Autovalue) and preferably validation libraries. I'd still write java over Go if efficiency and single binary is not a concern.

> For CLI tools Click

Once you're using types, I would recommend Typer instead. Built on-top of Click but configured via type hints:

https://typer.tiangolo.com/

> then fixing the types

man I wish my coworkers did this step

Having CI hard fail definitely helps. I won’t pretend I’d be so judicious without it.

Sorry this is probably a stupid question - how do you make your CI hard fail if you don't have types? This sounds like the missing piece for me, someone who also prefers just to crack on without types and then add them later.

You can add `mypy` to your CI pipeline https://mypy.readthedocs.io/en/stable/running_mypy.html. Pyright is an alternative, and there are more.

I was referring more specifically to fixing the types. It looks like it's possible to enforce type-hinting with config files to mypy (but I've not tried it): https://stackoverflow.com/questions/55944201/python-type-hin...

Run mypy in strict mode and make that check required.

CI generally has a pipeline, no? Or even at worse a shell script that you built? Just add the equivalent of “exit()” when mypy (or whatever typing linter you’re using fails), and then the dev gets notified he broke the build for everyone. That’ll get them to fixing and checking their code before it goes in to the main branch. Peer pressure is underestimated these days I think.

> because it wouldn’t compile if the code wasn’t totally correct

Go won't even compile if a variable is unused.

Makes it really hard to comment-out lines when debugging.

You can remove "larger programs".

Personally I also find python typing useful in "smaller" programs and I use them pretty much everywhere.

For example I use it in all my automation scripts which are usually not much longer than a few pages or a few modules. Adding type hints is a very minor task, really comes straight out of brain just like the code i type.

Type hints have given me wonderful completion and documentation which has helped me to write more correct and bug free code. Often little things pop up that would only have been caught at runtime. Now I see those things as soon as I type them.

Python + Type Hints == Awesome

If it works for you, great. I’ll also do that for the same reasons. What I really love, though, is I don’t need to be totally correct in my types all the time. If the code will run, it’ll run and, in plenty of simple cases, that’s good enough (even if mypy tells me I passed the wrong type).

> It makes it hard to find the right solution through experimentation.

To me this suggests the tooling was insufficient. One of the things I value about type systems is being in a tight feedback loop with the compiler. Strong typing helps me run more experiments and to run them from within my editor. But without a good LSP and such, it would be miserable.

> To me this suggests the tooling was insufficient.

Perhaps but I find that type systems usually introduce friction to getting things working that, for some types of projects, can be worthwhile and, for others, not worth the overhead.

I think what you perceive as "friction" I perceive as "feedback," and it helps me work faster while writing code that's as good or better. When I interact with unfamiliar untyped code, it takes me longer to find my footing. So I don't find that there's overhead.

I'm not dogmatic, type systems aren't the end all. But I think a lot of the trouble people have with them comes from using the approaches they've developed in untyped systems and saying, "this is the same, I just get more warnings and errors." But the opportunity is in developing new approaches and habits that leverage the type system.

For me the "aha" moment was watching Jon Gjenset's YouTube videos, and seeing him intentionally write code with type errors in order to figure out what the type of something was. This helped me make the switch from thinking of errors as drudgery to be avoided to errors as feedback I may want to elicit.

Now I'm able to answer lots of questions within my editor that I'd previously need to use a REPL or consult the docs to answer. It's like I'm able to query my codebase, but the query language is just the language I'm already working in. The magic of strong typing is good tooling.

If it’s not clear: I like working with typed Python code. It absolutely solves a lot of issues that would require a stupid amount of testing and manual checking otherwise (dict-passing is a personal “favourite” I’ve seen).

We shouldn’t pretend that it’s free. The fact is, sometimes running some code that I know works despite what the type system is telling me is very useful. Comparing to, say, Go, where I need to deal with everything (even if it’s incidental to the actual problem) adds friction. Sometimes I don’t want to deal with edge cases because this code will never leave my machine!

The cost of strong typing is increased compilation time, increased memory use during compilation, and increased complexity in learning the language. Time spent resolving type issues during development is time spent testing and debugging shifted left, and generally faster than chasing down bugs.

I can recall times when I've neglected a special case to write a one-time script faster, but they were always external to the type system. Eg, "this regex is pretty sketchy, but I know it will work with this particular set of logs." So I'm confused on this point, am I missing something?

All programs need to be type checked, even one-off scripts written in dynamic languages, to ensure that they function. The difference is whether the process is manual or automated. The computer is going to do it better and faster than I can, and that will free up mental bandwidth that I can apply to the problem at hand.

Example: Optional types in a type system require you to handle the null case. Plenty of times you know null isn’t possible (just the interface requires it) so you ignore handling assume it’s never null.

In a statically typed language you’ll get an error in the compiler. In Python it’ll run. Reading a file can be similar. These small frictions add up when I’m just trying to make a simple script work by experimentation.

At the risk of repeating myself, that sounds like insufficient tooling to me. Are you only seeing these issues when you explicitly compile? And then you have to fix a bunch of them? Because I see them in my editor in real time, and often clean them up with code completion. When I'm writing a one-file script, it's probably going to compile and run the first time.

My problem is I don’t want to waste my time fixing them at all. I just want to run/debug the code and move on with my life to the next thing.

At the risk of repeating myself: I think typing is a powerful tool but to pretend it has no trade-offs is 100% disingenuous or naive. If it didn’t, dynamic or untyped languages simply would never exist!

That's understandable. Personally after embracing strong typing I find it hard to go back, and I feel like my time and energy is wasted without good feedback from the LSP and reliably understanding what types I'm dealing with or what errors may occur. Dynamic typing isn't free either.

As far as the origins of dynamic typing goes, the amount of compute we can dedicate to tooling today, and the development of language servers in particular, radically changes the tradeoffs we can make. Strongly typed languages are better positioned to leverage tooling, and in my view this has completely upended the landscape of language ergonomics. Strong typing might not be free, but it does buy you a lot.

Does that view really make me naive or disingenuous? Perhaps we've just had different experiences. To be frank, when you say something that I solve in a blink of the eye is a serious friction, I have a similar reaction.

> To be frank, when you say something that I solve in a blink of the eye is a serious friction, I have a similar reaction.

So you're telling me that the following:

data = requests.get(some_link).json()['the_field_i_care_about']

do_something_with(data)

is just as much effort as defining the types and dealing with the error case (depending on the language). You're either vastly more intelligent—and a faster typist—than pretty much every human being I've ever interacted with or, more likely, you're lying to me and/or yourself.

I'm sorry that I made you feel disrespected. That wasn't my intention.

I'm not any more intelligent or a faster a typist than the next guy. Which is why I place such an emphasis on tooling (like code completion).

If you haven't given VSCode or another editor with LSP capabilities a try, you won't regret it.

I use VSCode and it helps but there's three points here:

1. It doesn't remove all friction—it can't magic type definitions out of thin air.

2. The fact that tooling exists means it's there to remove friction. Ergo, that friction exists.

3. Following 2: this tooling isn't appropriate in all environments. There's situations where opening vim and sketching out an idea is the fastest way.

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