Shamelessly cross-posting this …

  • xurxia
    link
    fedilink
    English
    arrow-up
    13
    arrow-down
    3
    ·
    edit-2
    1 year ago

    deleted by creator

    • Welmo@programming.dev
      link
      fedilink
      English
      arrow-up
      5
      ·
      1 year ago

      There is also lots of fields where Python performance isn’t the bottleneck. In my backend web application, Python isnt holding us back and actually help us deliver features faster. And we can scale to much more clients before performance starts being an issue.

      My last project was a legacy Django web app, that actually worked fairly well, the problem was the shitty codebase but it was in Production for almost 10 years, thousands of users and everything worked

      • xurxia
        link
        fedilink
        English
        arrow-up
        2
        arrow-down
        2
        ·
        edit-2
        1 year ago

        deleted by creator

    • cd_slash_rmrf@programming.dev
      link
      fedilink
      English
      arrow-up
      1
      ·
      1 year ago

      there can also ne inertia for existing projects though. for example it can be tough to get more research-y work (eg grad students) to switch over from r/python/Matlab for data processing in favor of c/rust

  • flatbield@beehaw.org
    link
    fedilink
    arrow-up
    8
    ·
    1 year ago

    They could probably have gotten similar results by using a combination of numpy and numba. They could also have just written a C extension which they basically did. The key is to get the final code to run both in parallel and vectorize on your exact hardware. So there are compiler flag choices too if your using C. Nice though.