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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-archive...
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