https://news.ycombinator.com/item?id=45023730

Do library authors have to test against every type checker to ensure maximum compatibility? Do application developers need to limit their use of libraries to ones that support their particular choice of type checker?

So only the outer API surface of the library matters. That’s mostly explicitly typed functions and classes so the room for different interpretations is lower (but not gone).

This is obviously out the window for libraries like Pydantic, Django etc with type semantics that aren’t really covered by the spec.

  x = []
  x.append(1)
  x[0] = "new"
  x[0] + "oops"

    const x = []
    x.push(1)
    type t = typeof x
    //   ^? type t = number[]
    x[0] = "new"
    type t2 = typeof x
    //   ^? type t2 = (number | string)[]
    const y = x[0] + "oops"
    //    ^? const y: string

There are several literals in that code snippet; I could annotate them with their types, and this code would still be exactly as it is. You asked why there are competing type checkers, and the fact that the language is only optionally typed means ambiguity like that example exists, and should be a warning/bug/allowed; choose the type checker that most closely matches the semantics you want to impose.

Well, no, there is one literal that has an ambiguous type, and if you annotated its type, it would resolve entirely the question of what a typechecker should say; literally the entire reason it is an open question is because that one literal is not annotated.

Optimally, this will result in a democratic consensus of semantics.

Pessimistically, this will result in dialects of semantics that result in dialects of runtime languages as folks adopt type checkers.

That hasn't been a fact for quite a while. Npw, it does specify the semantics of its type annotations. It didn't when it first created annotations for Python 3.0 (PEP 3107), but it has progressively since, starting with Python 3.5 (PEP 484) through several subsequent PEPs including creation of the Python Typing Council (PEP 729).

Well, no, you didn't. Because it's not clear whether the list is a list of value or a list of values of a distinct type. And there are many other ways you could quibble with this statement.

I think everyone basically agrees that at the package boundary, you want explicit types, but inside application code things are much more murky.

(plus of course, performance, particularly around incremental processing, which Astral is specifically calling out as a design goal here)

Yes, but in practice, the ecosystem mostly tests against mypy. pyright has been making some inroads, mostly because it backs the diagnostics of the default VS Code Python extension.

> Do application developers need to limit their use of libraries to ones that support their particular choice of type checker?

You can provide your own type stubs instead of using the library's built-in types or existing stubs.

IIRC it used to be common to do builds on an old version of RHEL or CentOS and dynamically link an old version of glibc. Binaries would then work on newer systems because glibc is backwards compatible.

Does anyone still use that approach?

https://peps.python.org/pep-0416/

Also one in 2019 for a "frozenmap":

https://peps.python.org/pep-0603/

The entries of a tuple cannot be assigned to, but the values can be mutated. The same is true for a `frozendict` (according to the PEP they don't support `__setitem__`, but "values can be mutable").

  >>> hash([1, 2])
  TypeError: unhashable type: 'list'

  >>> t = ([1, 2], [3, 4])
  >>> print(t)
  ([1, 2], [3, 4])

https://mail.python.org/pipermail/python-dev/2006-February/0...

Kotlin _kinda_ does this as well, but if you have a reference to an immutable map in Kotlin, you are still free to mutate the values (and even keys!) as much as you like.

If you in fact return e.g. an Rc::new(thing) or Arc::new(thing), that's forever (though of course you can unwrap the last reference!)

Might be worth noting that "dropped" in this context doesn't necessarily correspond to the reference going out of scope:

    fn get_first(v: &Vec<i32>) -> &i32 {
        &v[0]
    }

    fn main() {
        let mut v = vec![0, 1, 2];
        let first = get_first(&v);
        print!("{}", first});
        v.push(3); // Works!
        // print!("{}", first); // Doesn't work
    }

It explains a lot about the design of Python container classes, and the boundaries of polymorphism / duck typing with them, and mutation between them.

I don't always agree with the choices made in Python's container APIs...but I always want to understand them as well as possible.

Also worth noting that understanding changes over time. Remember when GvR and the rest of the core developers argued adamantly against ordered dictionaries? Haha! Good times! Thank goodness their first wave of understanding wasn't their last. Concurrency and parallelism in Python was a TINY issue in 2006, but at the forefront of Python evolution these days. And immutability has come a long way as a design theme, even for languages that fully embrace stateful change.

The new implementation has saved space, but there are opportunities to save more space (specifically after deleting keys) that they've now denied themselves by offering the ordering guarantee.

This is optimizing for the common case, where memory is generally plentiful and dicts grow more than they shrink. Python has so many memory inefficiencies that occasional tombstones in the dict internal structure is unlikely to be a major effect. If you're really concerned, do `d = dict(d)` after aggressive deletion.

I can't say I've noticed any good reasons to rely on it. Didn't reach for `OrderedDict` often back in the day either. I've had more use for actual sorting than for preserving the insertion order.

This morning for example, I tested an object serialized through a JSON API. My test data seems to never match the next run.

After a while, I realized one of the objects was using a set of objects, which in the API was turned into a JSON array, but the order of said array would change depending of the initial Python VM state.

3 days ago, I used itertools.group by to group a bunch of things. But itertools.group by only works on iterable that are sorted by the grouping key.

Now granted, none of those recent example are related to dicts, but dict is not a special case. And it's iterated over regularly.

I don't often care about a specific order, only that I get the same order every time.

Granted, I live and work in TypeScript, where I can't `===` two objects but I could see this deterministic behavior making it easier for a language to compare two objects, especially if equality comparison is dependent on a generated hash.

The other is guaranteed iteration order, if you are reliant on the index-contents relationship of an iterable, but we're talking about Dicts which are keyed, but extending this idea to List, I see this usefulness in some scenarios.

Beyond that, I'm not sure it matters, but I also realize I could simply not have enough imagination at the moment to think of other benefits

But maybe it does all just come down to equality comparisons. Just not always within your own code.

For me, it creates more reproducible programs and scripts, even simple ones.

I would expect to use a different data structure if I needed an ordered set.

Things that were not useful in 2006 might be totally useful in 2026 ;P

Still, like you, I'm curious wether he has anything to say about it.

“What has been is what will be, and what has been done is what will be done; there is nothing new under the sun.”

Still well before the talk.

He doesn't address the reason that most of us in 2025 immediately think of, which is that it's easier to reason about code if you know that certain values can't change after they're created.

What a change in culture over the last 20 years!

Not that it's entirely unwarranted, of course.

... Well, yes; it doesn't support the methods for mutation. Thinking of ImmutableFoo as a subclass of Foo is never going to work. And, indeed, `set` and `frozenset` don't have an inheritance relationship.

I normally find Hettinger very insightful so this one is disappointing. But nobody's perfect, and we change over time (and so do the underlying conditions). I've felt like frozendict was missing for a long time, though. And really I think the language would have been better with a more formal concept of immutability (e.g. linking it more explicitly to hashability; having explicit recognition of "cache" attributes, ...), even if it didn't go the immutable-by-default route.

Given how dynamic Python is, such a subclass relationship need not be evident at the C level. You can totally make one class whose implementation is independent of another class a subclass of the other, using PEP 3119. This gives implementations complete flexibility in how to implement the class while retaining the ontological subclass relationship.

Theoretically, could `set` be a subclass of `frozenset` (and `dict` of `frozendict`)? Do other languages take that approach?

> linking [immutability] more explicitly to hashability

AFAIK immutability and hashability are equivalent for the language's "core" types. Would it be possible to enforce that equivalence for user-defined types, given that mutability and the implementation of `__hash__` are entirely controlled by the programmer?

  class frozenset:
    pass
  
  class set(frozenset):
    def pop(self, key):
      pass

I never said it's a problem (and I never said it's not!). I was specifically addressing two things:

- The "theoretical" nature of the question I quoted (i.e. ignoring other aspects like subjectivity, practicality, convention, etc.)

- The reasoning about "Liskov violation", which was quoted further up this thread.

For context, here's Liskov's definition of their principle (from https://en.wikipedia.org/wiki/Liskov_substitution_principle ):

> Barbara Liskov and Jeannette Wing described the principle succinctly in a 1994 paper as follows:[1]

> > Subtype Requirement: Let ϕ(x) be a property provable about objects x of type T. Then ϕ(y) should be true for objects y of type S where S is a subtype of T.

My expression `"pop" in dir(mySet)` gives an explicit example of how `set` and `frozenset` are not subtypes of each other (regardless of how they're encoded in the language, with "subclasses" or whatever). In this case `ϕ(x)` would be a property like `'"pop" in dir(x)' = 'False'`, which holds for objects x of type frozenset. Yet it does not hold for objects y of type set.

Your example of `hasattr(mySet, 'pop')` gives another property that would be violated.

My point is that avoiding "Liskov violations" is ("theoretically") impossible, especially in Python (which allows programs to introspect/reflect on values, using facilities like 'dir', 'hasattr', etc.).

(FYI I became rather jaded on the Liskov substitution principle after reading https://okmij.org/ftp/Computation/Subtyping )

The root of the issue here is that Liskov substitution principle simply references ϕ(x) to be some property satisfied by objects of a class. It does not distinguish between properties that are designed by the author of the class to be satisfied or properties that happen to be satisfied in this particular implementation. But the Hyrum’s Law also states that properties that are accidentally true can become relied upon and as time passes become an intrinsic property. This to me suggests that the crux of the problem is that people don’t communicate sufficiently about invariants and non-invariants of their code.

This says "if hasattr(parent, 'pop') == True then hasattr(child, 'pop') must be True". This is not violated in this case, since hasattr(parent, 'pop') is False. If you want to extend the above definition so that negative proofs concerning the parent should also hold true for the child, then subtyping becomes impossible since all parent and child types must be identical, by definition.

> If you want to extend the above definition so that negative proofs concerning the parent should also hold true for the child, then subtyping becomes impossible since all parent and child types must be identical, by definition.

The distinction isn’t “negative proofs”, but yes, that’s their point. In Python, you have to draw a line as to which observable properties are eligible.

When you interpret Liskov substitution properly, it's very rare that anything Liskov-substitutes anything, making the entire property meaningless. So just do things based on what works best in the real world and aim for as much Liskov-substitution as is reasonable. Python is duck-typed anyway.

It's a decent guiding principle - Set and ImmutableSet are more substitutable than Set and Map, so Set deriving from ImmutableSet makes more sense than Set deriving from Map. It's just not something you can ever actually achieve.

Type the dict as a mapping when you want immutability:

  x: Mapping[int, int] = {1: 1}

  x[1] = 2  # Unsupported target for indexed assignment ("Mapping[int, int]").

  y = {}
  y[x] = 0  # Mypy thinks this is fine. Mapping is hashable, after all!

  x: dict[list, int] = {}

  x[[1, 2, 3]] = 0

  str
  bytes
  int, float
  complex
  tuple
  frozenset

I have crossed that bridge, and I'm telling you (again) that a sorted tuple is not a generic solution.

Is "syntax-directed translation" just another term for a single-pass compiler, e.g. as used by Lua (albeit to generate bytecode instead of assembly / machine code)? Or is it something more specific?

> in the latter parts of the tutorial it starts showing its limitations. Especially once we get to types [...] it's easy to generate working code; it's just not easy to generate optimal code

So, using a single-pass compiler for a statically-typed language makes it difficult to apply type-based optimizations. (Of course, Lua sidesteps this problem because the language is dynamically typed.)

Are there any other downsides? Does single-pass compilation also restrict the level of type checking that can be performed?

I'm actually a big fan a function-by-function dual-pass compilation. You generate IR from the parser in one pass, and do codegen right after. Most intermediate state is thrown out (including the AST, for non-polymorphic functions) and you move on to the next function. This give you an extremely fast data-oriented baseline compiler with reasonable codegen (much better than something like tcc).

A sigle pass compiler can still split the various phases, and only do the code generation on the last phase.

Where did they go?

> In Pursuit of Honor is a 1995 American made-for-cable Western film directed by Ken Olin. Don Johnson stars as a member of a United States Cavalry detachment refusing to slaughter its horses after being ordered to do so by General Douglas MacArthur. The movie follows the plight of the officers as they attempt to save the animals that the Army no longer needs as it modernizes toward a mechanized military.