On Thu, Jul 14, 2011 at 7:03 PM, Tres Seaver <tsea...@palladion.com> wrote:
> A further micro-optimization is to pre-allocate a big list in a thread
> local. Running the attached script produces::
>
> $ python perftest.py
> Via insert_0: 24.8401839733
> Via append: 15.6596238613
> speedup: 37.0%
> Via tuple_add: 21.9555268288
> speedup: 11.6%
> Via prealloc: 10.5278339386
> speedup: 57.6%
Thanks for that test!
I've run it with a much more reasonable test data set from one of our
live databases, for example:
letters = ['', 'nordic', 'en', 'nordic-council', 'organisation-and-structure',
'committees', 'welfare-committee', 'meetings-and-meeting-documents',
'meeting-of-the-welfare-committee-31-march-2011-stockholm']
which produces quite a different result:
Via insert_0: 4.35216093063
Via append: 4.80827713013
speedup: -10.5%
Via tuple_add: 1.73557782173
speedup: 60.1%
Via prealloc: 2.17882084846
speedup: 49.9%
If I run it with a smaller segment, like:
letters = ['', 'nordic', 'en', 'nordic-council']
the effect is even clearer:
Via insert_0: 1.88715004921
Via append: 2.91810202599
speedup: -54.6%
Via tuple_add: 0.809303998947
speedup: 57.1%
Via prealloc: 1.56584882736
speedup: 17.0%
Looks like the tuple add is faster until you hit very long sequences.
But even if you had such a nested ZODB structure, the majority of
lookups would still happen for the much shorter sequences.
So I think the tuple add is the winner for this case.
Hanno
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