One aspect of scripting is being able to throw something together to create
a correct solution to an immediate problem. If the proprietary software
that you script around takes over 300 Gigs to lay out a CPU and delays are
hugely expensive, then I don't want to waste time on optimisations to get
around arbitrary limits.
It sounds analogous to wanting to move to X bit integers to save a little
here and there.
If you are thinking of making limits then you might think of what limit
say, 4 terabytes of Ram would impose on the smallest object and propose
that.
Remember, 2020 means trials of 2 chip, 256 thread AMD servers with 500Gigs
of ram. Now if I can get Python sub interpreters working on that! Bliss. 😊
On Fri, Dec 6, 2019, 9:37 AM Steven D'Aprano <st...@pearwood.info> wrote:
> Although I am cautiously and tentatively in favour of setting limits
> if the benefits Mark suggests are correct, I have thought of at least
> one case where a million classes may not be enough.
>
> I've seen people write code like this:
>
> for attributes in list_of_attributes:
> obj = namedtuple("Spam", "fe fi fo fum")(*attributes)
> values.append(obj)
>
>
> not realising that every obj is a singleton instance of a unique class.
> They might end up with a million dynamically created classes, each with
> a single instance, when what they wanted was a single class with a
> million instances.
>
> Could there be people doing this deliberately? If so, it must be nice
> to have so much RAM that we can afford to waste it so prodigiously: a
> namedtuple with ten items uses 64 bytes, but the associated class uses
> 444 bytes, plus the sizes of the methods etc. But I suppose there could
> be a justification for such a design.
>
> (Quoted sizes on my system running 3.5; YMMV.)
>
>
>
> --
> Steven
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