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| Started by | Phil Connell <pconnell@gmail.com> |
|---|---|
| First post | 2014-04-23 14:59 +0100 |
| Last post | 2014-04-23 14:59 +0100 |
| Articles | 1 — 1 participant |
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Re: object().__dict__ Phil Connell <pconnell@gmail.com> - 2014-04-23 14:59 +0100
| From | Phil Connell <pconnell@gmail.com> |
|---|---|
| Date | 2014-04-23 14:59 +0100 |
| Subject | Re: object().__dict__ |
| Message-ID | <mailman.9466.1398261579.18130.python-list@python.org> |
On Wed, Apr 23, 2014 at 03:48:32PM +0200, Amirouche Boubekki wrote: > 2014-04-23 8:11 GMT+02:00 Cameron Simpson <cs@zip.com.au>: > > Look up the "__slots__" dunder var in the Python doco index: > > > > https://docs.python.org/3/glossary.html#term-slots > > > > You'll see it as a (rarely used, mostly discouraged) way to force a fixed > > set of attributes onto a class. As with object, this brings a smaller > > memory footprint and faster attribute access, but the price is flexibility. > > > > True, still can be the only way to save few MB or... GB without falling > back to C or PyPy. > > Have a look at PyPy to how to save memory (and speed things up) without > slots: > http://morepypy.blogspot.fr/2010/11/efficiently-implementing-python-objects.html Is there any analysis of how this balances increased memory usage from the JIT vs the CPython VM (with a reasonable amount of code)? I'd thought that one of the main disadvantages of PyPy was drastically increased memory usage for any decent-sized program. Would be interested to know if this was not the case :) Cheers, Phil
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