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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