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OK but... computationally heavy code in Python isn't generally really running in Python. Almost all of it either calls out to optimized C/C++/Fortran (eg; Numpy

OK but... computationally heavy code in Python isn't generally really running in Python. Almost all of it either calls out to optimized C/C++/Fortran (eg; Numpy which in turn calls BLAS+LAPACK) or do that or some other separate compilation (eg; what ML libraries do with XLA, etc. or cython). Sure, python web servers are a thing. Heck, we use them a fair bit. But it's a deliberate decision from the start where it makes sense to use it for that purpose.

xupybd 4y ago
>But it's a deliberate decision from the start where it makes sense to use it for that purpose. Exactly that! There is a time and a place for performant code but it's not the only metric we need to take into consideration. Sometimes you're better off using the slower tool or algorithm as it improves things on a dimension other than performance.
- mtrower 4y ago
  > There is a time and a place for performant code but it's not the only metric we need to take into consideration. Of course not. But then, nobody is really complaining about apps they think are fast enough. The problem is, when something is noticeably slow, you complain about it, file reports etc, and are met with stiff resistance. What's important is not that you make the machine go as fast as it can possibly go at all times. What's important is to know how fast it can go, so that you're aware of just exactly how much you're leaving on the table. The actual amount in most cases, would, I expect, surprise most people...