8 ms·

Do people actually use languages where it doesn't do any compile time checks on the API in 2026? Why would developers put up with the lack of that. I'm not in t

Do people actually use languages where it doesn't do any compile time checks on the API in 2026? Why would developers put up with the lack of that. I'm not in this ecosystem but what you are describing sounds like the bare minimum to me that should be table stakes.

perrygeo 13d ago
I agree. But the data science/engineering space is enamored with Python. Makes good sense when doing interactive work. Makes no sense in production. But by that point, most developers consider it a sunk cost and just keep their Python apps limping along rather than rewriting. That's why polars is a great option. Start prototyping in python, then a relatively easy port to a Rust app when the need hits.
- maleldil 13d ago
  You can still have extensive "compile-time" checking with linters and static type checkers. This makes Polars even better in typed Python, as it type-checks much better than the dynamic mess that is Pandas.
  - perrygeo 13d ago
    Polars is much better than pandas in this regard. But using it via Python still carries the risk of runtime errors. Python's linters leave many holes unfilled - it catches some stuff but still leaves everything else to the runtime. Literally every Python application that I've worked on in the last 3 years a) is 100% type checked yet b) still hits massive numbers of novel runtime errors on the regular. Python's anemic type system is in no way a substitute for an advanced compiler.
    - paddy_m 13d ago
      When I deal with datascience, I rarely have type problems in my programs that python typecheckers can't catch. I do have type and dirty data problems of the dataframes/csvs/parquet I'm reading.
      - perrygeo 13d ago
        > I rarely have type problems in my programs that python typecheckers can't catch. > I do have type and dirty data problems of the dataframes/csvs/parquet I'm reading. Something about those two statements makes my brain explode. You rarely have type problems, except when dealing with data? Isn't the whole point of a program to deal with data?
    - what 13d ago
      If it’s 100% type checked and you’re still hitting massive numbers of type related runtime errors, it probably isn’t 100% checked.
      - perrygeo 11d ago
        Empirically false. Python's type system is not sufficient to describe runtime variants, period. I have worked on at least three system that used mypy in strict mode yet hit runtime errors constantly. I get where you're coming from. A good type system should provide invariant guarantees about runtime behavior. But Python's does not.

perrygeo 13d ago

I agree. But the data science/engineering space is enamored with Python. Makes good sense when doing interactive work. Makes no sense in production. But by that point, most developers consider it a sunk cost and just keep their Python apps limping along rather than rewriting. That's why polars is a great option. Start prototyping in python, then a relatively easy port to a Rust app when the need hits.

maleldil 13d ago
You can still have extensive "compile-time" checking with linters and static type checkers. This makes Polars even better in typed Python, as it type-checks much better than the dynamic mess that is Pandas.
- perrygeo 13d ago
  Polars is much better than pandas in this regard. But using it via Python still carries the risk of runtime errors. Python's linters leave many holes unfilled - it catches some stuff but still leaves everything else to the runtime. Literally every Python application that I've worked on in the last 3 years a) is 100% type checked yet b) still hits massive numbers of novel runtime errors on the regular. Python's anemic type system is in no way a substitute for an advanced compiler.
  - paddy_m 13d ago
    When I deal with datascience, I rarely have type problems in my programs that python typecheckers can't catch. I do have type and dirty data problems of the dataframes/csvs/parquet I'm reading.
    - perrygeo 13d ago
      > I rarely have type problems in my programs that python typecheckers can't catch. > I do have type and dirty data problems of the dataframes/csvs/parquet I'm reading. Something about those two statements makes my brain explode. You rarely have type problems, except when dealing with data? Isn't the whole point of a program to deal with data?
  - what 13d ago
    If it’s 100% type checked and you’re still hitting massive numbers of type related runtime errors, it probably isn’t 100% checked.
    - perrygeo 11d ago
      Empirically false. Python's type system is not sufficient to describe runtime variants, period. I have worked on at least three system that used mypy in strict mode yet hit runtime errors constantly. I get where you're coming from. A good type system should provide invariant guarantees about runtime behavior. But Python's does not.