Seth Bromberger added the comment:
As a test, I tried the following (taken mostly from
http://codesnipers.com/?q=python-flyweights):
class Foo(object):
_Foo = weakref.WeakValueDictionary()
def __new__(cls, addr):
obj = Foo._Foo.get(addr, None)
if obj is None:
obj = object.__new__(cls)
Foo._Foo[addr] = obj
obj.addr = addr
return obj
I created 10 million instances of Foo(34) in an array. Total space taken: ~80
MB. Times: CPU times: user 6.93 s, sys: 48.7 ms, total: 6.98 s
Wall time: 6.98 s
I then created 10 million instances of a non-flyweight object, assigning an int
to an instance variable:
class Bar(object):
pass
Total space taken: ~1.4 GB. Times:
CPU times: user 7.64 s, sys: 794 ms, total: 8.44 s
Wall time: 8.44 s
This corresponds (roughly) to the space taken by 10 million IPAddr objects.
So it appears, informally, that caching / flyweight results in modest time and
significant memory savings.
I understand that the ship has sailed for a stdlib implementation, but these
results are compelling enough for me to create a separate package.
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Python tracker <rep...@bugs.python.org>
<http://bugs.python.org/issue23103>
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