[tor-dev] Comparing Stem, metrics-lib, and zoossh

[Karsten Loesing]

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On 14/01/16 17:22, Damian Johnson wrote:
> Oh, forgot to talk about compression. You can run the stem script 
> against compressed tarballs but python didn't add lzma support
> until python 3.3...
> 
> https://stem.torproject.org/faq.html#how-do-i-read-tar-xz-descriptor-archives
>
>  I suppose we could run over bz2 or gz tarballs, or upgrade python.
> But can't say the compressed benchmark is overly important.

I just ran all the Stem measurements using Python 3, which now
includes xz tarballs.  The table below contains all results:

server-descriptors-2015-11.tar.xz:
 - metrics-lib: 0.334261 ms
 - Stem[**]: 0.63 ms (188%)

server-descriptors-2015-11.tar:
 - metrics-lib: 0.28543 ms
 - Stem: 1.02 ms (357%)
 - Stem[**]: 0.63 ms (221%)

server-descriptors-2015-11/:
 - metrics-lib: 0.682293 ms
 - Stem: 1.11 ms (163%)
 - Stem[**]: 1.03 ms (151%)
 - Zoossh: 0.458566 ms (67%)

extra-infos-2015-11.tar.xz:
 - metrics-lib: 0.274610 ms
 - Stem[**]: 0.46 ms (168%)

extra-infos-2015-11.tar:
 - metrics-lib: 0.2155 ms
 - Stem: 0.68 ms (316%)
 - Stem[**]: 0.42 ms (195%)

consensuses-2015-11.tar.xz:
 - metrics-lib: 255.760446 ms
 - Stem[**]: 913.12 ms (357%)

consensuses-2015-11.tar:
 - metrics-lib: 246.713092 ms
 - Stem: 1393.10 ms (565%)
 - Stem[**]: 876.09 ms (355%)

consensuses-2015-11/:
 - metrics-lib: 283.910864 ms
 - Stem: 1303.53 ms (459%)
 - Stem[**]: 873.45 ms (308%)
 - Zoossh: 83 ms (29%)

microdescs-2015-11.tar.xz[*]:
 - metrics-lib: 0.099397 ms
 - Stem[**]: 0.33 ms (332%)

microdescs-2015-11.tar[*]:
 - metrics-lib: 0.066566 ms
 - Stem: 0.66 ms (991%)
 - Stem[**]: 0.34 ms (511%)

[*] The microdescs* tarballs contain microdesc consensuses and
microdescriptors, but I only cared about the latter; what I did is
extract tarballs, delete microdesc consensuses, and re-create and
re-compress tarballs

[**] Run with Python 3.5.1

Is Python 3 really that much faster than Python 2?  Should we just
omit Python 2 results from this comparison?

All the best,
Karsten
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