[Python-Dev] Optimizing list.sort() by checking type in advance (original) (raw)

Elliot Gorokhovsky elliot.gorokhovsky at gmail.com
Mon Oct 10 02🔞35 EDT 2016

Hi,

I posted here a while back asking what you guys thought about implementing radix sort for strings in list.sort(). You gave me a lot of reasons why that would be a bad idea. However, it got me thinking, and I came up with something that you may find interesting.

First, some simple benchmark results (numbers are seconds, check out the extension module at https://github.com/embg/python-fast-listsort.git):

*** 1e3 ints *** F.fastsort(): 0.00018930435180664062 F.sort(): 0.0002830028533935547 *** 1e3 strings *** F.fastsort(): 0.0003533363342285156 F.sort(): 0.00044846534729003906 *** 1e7 ints *** F.fastsort(): 5.479267358779907 F.sort(): 8.063318014144897 *** 1e7 strings *** F.fastsort(): 9.992833137512207 F.sort(): 13.730914115905762

The optimization uses the fact that, in practice, we are almost always sorting keys of the same type (note: not objects of the same type, keys of the same type; we could have a complex key function like str that takes many different types, but the keys are still all the same type). So we can just do one typecheck up front and then do unsafe comparisons during the sort (if our initial check passes). Specifically, we check that for all the PyObject* in saved_ob_item, the ob->ob_type are the same. Then we replace PyObject_RichCompare with ob_type->tp_richcompare. Additionally, we can check for the very common cases of ints and strings and give optimized comparison functions for those cases. It might not seem like this would save a lot of time, but it turns out that PyObject_RichCompare is a massive clusterf**k that has to test tons of type stuff before it can actually compare. Cutting that out ends up saving a lot of time, as the benchmark demonstrates.

What do you think? I'm planning on writing this up into a patch, but wanted to get some feedback on the implementation and ideas for improvement first.

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