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Most of these seem to be about packages in the ecosystem (which, after clicking through all links, actually almost all got fixed in a very timely manner, someti

by Sukera 4y ago

Most of these seem to be about packages in the ecosystem (which, after clicking through all links, actually almost all got fixed in a very timely manner, sometimes already in a newer version of the packages than the author was using), not about the language itself. Other than that, the message of this seems to be "newer software has bugs", which yes is a thing..? For example, the majority of issues referenced are specific to a single package, StatsBase.jl - which apparently was written before OffsetArrays.jl was a thing and thus is known to be incompatible: > Yes, lots of JuliaStats packages have been written before offset axes existed. Feel free to make a PR adding checks. https://github.com/JuliaStats/StatsBase.jl/issues/646#issuecomment-766756335 https://github.com/JuliaStats/StatsBase.jl/issues/646#issuec... EDIT: Since this comment seems to gain some traction - title is editorialized, original is "Why I no longer recommend Julia".

snicker7 4y ago
"known to be incompatible" Known to whom? People who regularly participate in the Julia forum/chat? Julia's composability relies on people agreeing on unwritten rules and standards. In other languages, such incompatibilities are caught by the compiler. Even in other dynamic languages like Python or Javascript, it is now considered best practice by many to annotate types whenever you can. Like Julia, Haskell is also composable. Unlike Julia, it does not need to sacrifice correctness.
- DNF2 4y ago
  Agreed, one cannot just expect this to be known. Does type annotations in Python actually catch type errors? I thought they were mainly for documentation.
  - snicker7 4y ago
    Yes, if you use tooling (mypy). It definitely helped me a few times.
  - nickm12 4y ago
    Absolutely yes, but you have to use a typechecker like mypy (and generally make it part of your release builds). I've found typechecking my Python code makes my development iterations much faster than writing tests. My biggest issue is that if you are using a legacy codebase or 3P library without type annotations then the "Any" type become pervasive and removes much of the value you get from type annotations. You can run mypy in a mode that flags when this is happening, but it's not like you're going to go type annotate the world just to push your code change.

by Sukera 4y ago

snicker7 4y ago

"known to be incompatible" Known to whom? People who regularly participate in the Julia forum/chat? Julia's composability relies on people agreeing on unwritten rules and standards. In other languages, such incompatibilities are caught by the compiler. Even in other dynamic languages like Python or Javascript, it is now considered best practice by many to annotate types whenever you can. Like Julia, Haskell is also composable. Unlike Julia, it does not need to sacrifice correctness.

DNF2 4y ago
Agreed, one cannot just expect this to be known. Does type annotations in Python actually catch type errors? I thought they were mainly for documentation.
- snicker7 4y ago
  Yes, if you use tooling (mypy). It definitely helped me a few times.
- nickm12 4y ago
  Absolutely yes, but you have to use a typechecker like mypy (and generally make it part of your release builds). I've found typechecking my Python code makes my development iterations much faster than writing tests. My biggest issue is that if you are using a legacy codebase or 3P library without type annotations then the "Any" type become pervasive and removes much of the value you get from type annotations. You can run mypy in a mode that flags when this is happening, but it's not like you're going to go type annotate the world just to push your code change.