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Comment by miohtama | Hacker News Reader

I wonder why much speedup could be gained by having a control over Python’s dynamism. Dynamic attributes, etc. are not needed in most of normal application code that is not meta heavy. Could there be any gains via making backwards incompatible changes e.g. by sealing some modules or functions to be “static” only?

It would not matter if a subset of an application is partially incompatible, as often one wants to optimise smaller hot code paths only.

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One thing you can do as a user already today to reduce Python dynamism of classes is using __slots__: https://wiki.python.org/moin/UsingSlots. I enjoy this a lot, not only for the memory and access performance boost, but because it also makes the code more rigid (and thus more resilient to monkey code attacks).

Yes, already using __slots__ heavily.

Since Python 3.10 you can also do

     @dataclass(slots=True)
     Class MyClass:
         pass

Isn't that the whole idea behind mypyc? I don't know what performance gains are realized though, as I haven't tested it. We're in the process of typing a 300 KLOC codebase mostly for the correctness benefits, not so much performance.

Mojo is taking this approach. I think it's a good idea.

Mojo focuses on numeric highly parallel workloads only as far as I know. Does it offer anything for normal SaaS applications?

I assume one could just use 'fn' and 'var' and 'let' and get all the nice benefits of the static-ness of those features in any kind of code.

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