Skip to content

Instantly share code, notes, and snippets.

@mckelvin

mckelvin/libmc_benchmark.txt

Created February 1, 2016 10:48
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save mckelvin/8ded053f6523931765d4 to your computer and use it in GitHub Desktop.
Save mckelvin/8ded053f6523931765d4 to your computer and use it in GitHub Desktop.
Download ZIP
+python ./misc/runbench.py https://github.com/douban/libmc
Raw
libmc_benchmark.txt
INFO:libmc.bench:pylibmc: /home/travis/virtualenv/python2.7.9/lib/python2.7/site-packages/pylibmc/__init__.pyc
INFO:libmc.bench:libmc: /home/travis/virtualenv/python2.7.9/lib/python2.7/site-packages/libmc-0.5.6-py2.7-linux-x86_64.egg/libmc/__init__.pyc
INFO:libmc.bench:4 participants in 16 benchmarks
INFO:libmc.bench:
INFO:libmc.bench:Multi set 10 keys with value size 100
INFO:libmc.bench: pylibmc (md5 / ketama): 0.000346s, σ=0.00226, n=2833, snr=1:6.53
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.000327s, σ=0.00352, n=2571, snr=1:10.8
INFO:libmc.bench: python-memcached: 0.000497s, σ=0.00592, n=2011, snr=1:11.9
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.000137s, σ=0.00176, n=6350, snr=1:12.9
INFO:libmc.bench:
INFO:libmc.bench:Multi get 10 keys with value size 100
INFO:libmc.bench: pylibmc (md5 / ketama): 0.000156s, σ=0.00194, n=6041, snr=1:12.4
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.000113s, σ=0.00107, n=8140, snr=1:9.46
INFO:libmc.bench: python-memcached: 0.000455s, σ=0.0026, n=2155, snr=1:5.71
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.000157s, σ=0.00146, n=6120, snr=1:9.31
INFO:libmc.bench:
INFO:libmc.bench:Multi set 100 keys with value size 100
INFO:libmc.bench: pylibmc (md5 / ketama): 0.00344s, σ=0.0053, n=291, snr=1:1.54
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.0029s, σ=0.00508, n=345, snr=1:1.75
INFO:libmc.bench: python-memcached: 0.00358s, σ=0.0189, n=324, snr=1:5.27
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.00042s, σ=0.00203, n=2283, snr=1:4.82
INFO:libmc.bench:
INFO:libmc.bench:Multi get 100 keys with value size 100
INFO:libmc.bench: pylibmc (md5 / ketama): 0.000402s, σ=0.00207, n=2412, snr=1:5.14
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.000504s, σ=0.00219, n=1925, snr=1:4.34
INFO:libmc.bench: python-memcached: 0.00283s, σ=0.0114, n=360, snr=1:4.02
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.000455s, σ=0.00221, n=2153, snr=1:4.86
INFO:libmc.bench:
INFO:libmc.bench:Multi set 10 keys with value size 1000
INFO:libmc.bench: pylibmc (md5 / ketama): 0.000351s, σ=0.00186, n=2732, snr=1:5.3
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.000515s, σ=0.00223, n=1807, snr=1:4.34
INFO:libmc.bench: python-memcached: 0.000584s, σ=0.00235, n=1695, snr=1:4.02
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.000179s, σ=0.00133, n=5087, snr=1:7.41
INFO:libmc.bench:
INFO:libmc.bench:Multi get 10 keys with value size 1000
INFO:libmc.bench: pylibmc (md5 / ketama): 0.000168s, σ=0.0013, n=5649, snr=1:7.73
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.00014s, σ=0.00119, n=6715, snr=1:8.48
INFO:libmc.bench: python-memcached: 0.000765s, σ=0.00269, n=1268, snr=1:3.51
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.000151s, σ=0.00123, n=6086, snr=1:8.16
INFO:libmc.bench:
INFO:libmc.bench:Small set
INFO:libmc.bench: pylibmc (md5 / ketama): 5.25e-05s, σ=0.000723, n=15420, snr=1:13.8
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 6.83e-05s, σ=0.000834, n=11717, snr=1:12.2
INFO:libmc.bench: python-memcached: 0.00012s, σ=0.0011, n=7419, snr=1:9.18
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 6.67e-05s, σ=0.000814, n=11849, snr=1:12.2
INFO:libmc.bench:
INFO:libmc.bench:Small get
INFO:libmc.bench: pylibmc (md5 / ketama): 6.69e-05s, σ=0.000825, n=12102, snr=1:12.3
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 6.11e-05s, σ=0.000779, n=12442, snr=1:12.8
INFO:libmc.bench: python-memcached: 0.000105s, σ=0.00103, n=8107, snr=1:9.83
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 6.54e-05s, σ=0.000806, n=13142, snr=1:12.3
INFO:libmc.bench:
INFO:libmc.bench:4k uncompressed set
INFO:libmc.bench: pylibmc (md5 / ketama): 5.73e-05s, σ=0.000763, n=15359, snr=1:13.3
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 4.99e-05s, σ=0.000713, n=16029, snr=1:14.3
INFO:libmc.bench: python-memcached: 8.81e-05s, σ=0.000934, n=10444, snr=1:10.6
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 5.51e-05s, σ=0.000741, n=14146, snr=1:13.4
INFO:libmc.bench:
INFO:libmc.bench:4k uncompressed get
INFO:libmc.bench: pylibmc (md5 / ketama): 5.97e-05s, σ=0.000771, n=14397, snr=1:12.9
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 7.24e-05s, σ=0.000848, n=11192, snr=1:11.7
INFO:libmc.bench: python-memcached: 0.000106s, σ=0.00103, n=8365, snr=1:9.64
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 6.48e-05s, σ=0.000802, n=12342, snr=1:12.4
INFO:libmc.bench:
INFO:libmc.bench:4k compressed set
INFO:libmc.bench: pylibmc (md5 / ketama): 0.00011s, σ=0.00107, n=8012, snr=1:9.7
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.000116s, σ=0.00107, n=7775, snr=1:9.24
INFO:libmc.bench: python-memcached: 0.000132s, σ=0.00114, n=6732, snr=1:8.64
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.00012s, σ=0.00109, n=7310, snr=1:9.06
INFO:libmc.bench:
INFO:libmc.bench:4k compressed get
INFO:libmc.bench: pylibmc (md5 / ketama): 7.51e-05s, σ=0.000863, n=10653, snr=1:11.5
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 8.32e-05s, σ=0.000919, n=10695, snr=1:11
INFO:libmc.bench: python-memcached: 0.000118s, σ=0.00111, n=7178, snr=1:9.35
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 7.27e-05s, σ=0.000849, n=12523, snr=1:11.7
INFO:libmc.bench:
INFO:libmc.bench:1M compressed set
INFO:libmc.bench: pylibmc (md5 / ketama): 0.00968s, σ=0.0547, n=126, snr=1:5.64
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.00964s, σ=0.104, n=224, snr=1:10.8
INFO:libmc.bench: python-memcached: 0.00995s, σ=0.143, n=207, snr=1:14.4
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.00902s, σ=0.165, n=337, snr=1:18.3
INFO:libmc.bench:
INFO:libmc.bench:1M compressed get
INFO:libmc.bench: pylibmc (md5 / ketama): 0.00259s, σ=0.0791, n=936, snr=1:30.6
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.00266s, σ=0.0731, n=756, snr=1:27.5
INFO:libmc.bench: python-memcached: 0.00286s, σ=0.0861, n=910, snr=1:30.1
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.00272s, σ=0.0941, n=1198, snr=1:34.6
INFO:libmc.bench:
INFO:libmc.bench:Complex data set
INFO:libmc.bench: pylibmc (md5 / ketama): 0s, σ=0, n=41537, snr=1:1
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 5.49e-05s, σ=0.00922, n=28232, snr=1:168
INFO:libmc.bench: python-memcached: 9.78e-05s, σ=0.0101, n=10631, snr=1:103
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 5.97e-05s, σ=0.0061, n=20261, snr=1:102
INFO:libmc.bench:
INFO:libmc.bench:Complex data get
INFO:libmc.bench: pylibmc (md5 / ketama): 6.6e-05s, σ=0.00411, n=16053, snr=1:62.3
INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 8.16e-05s, σ=0.00124, n=11148, snr=1:15.2
INFO:libmc.bench: python-memcached: 0.000152s, σ=0.00124, n=6046, snr=1:8.13
INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 7.62e-05s, σ=0.000869, n=11291, snr=1:11.4
labels = ['pylibmc (md5 / ketama)', 'pylibmc (md5 / ketama / nodelay / nonblocking)', 'python-memcached', 'libmc(md5 / ketama / nodelay / nonblocking, from douban)']
benchmarks = ['Multi set 10 keys with value size 100', 'Multi get 10 keys with value size 100', 'Multi set 100 keys with value size 100', 'Multi get 100 keys with value size 100', 'Multi set 10 keys with value size 1000', 'Multi get 10 keys with value size 1000', 'Small set', 'Small get', '4k uncompressed set', '4k uncompressed get', '4k compressed set', '4k compressed get', '1M compressed set', '1M compressed get', 'Complex data set', 'Complex data get']
means = [[0.00034592304977056125, 0.00015560337692435033, 0.003436426116838488, 0.00040215588723051363, 0.0003513909224011729, 0.0001681713577624357, 5.252918287937712e-05, 6.693108577094655e-05, 5.729539683573166e-05, 5.9734666944502285e-05, 0.0001098352471293037, 7.509621702806695e-05, 0.009682539682539673, 0.0025854700854700797, 0.0, 6.603127141344312e-05], [0.0003267211201866977, 0.00011302211302211301, 0.002898550724637681, 0.0005038961038961033, 0.0005146651909241816, 0.0001399851079672372, 6.82768626781597e-05, 6.10834271017514e-05, 4.990953896063234e-05, 7.237312365975527e-05, 0.0001157556270096443, 8.321645628798576e-05, 0.00964285714285716, 0.0026587301587301655, 5.490223859450259e-05, 8.162899174739705e-05], [0.0004972650422675286, 0.0004547563805104406, 0.0035802469135802475, 0.0028333333333333322, 0.000584070796460176, 0.0007649842271293394, 0.00011996225906456356, 0.00010484766251387664, 8.808885484488582e-05, 0.00010639569635385626, 0.0001322043969102804, 0.00011841738645862377, 0.00995169082125605, 0.002857142857142851, 9.782710939704562e-05, 0.00015216672179953482], [0.00013700787401574804, 0.00015686274509803922, 0.0004204993429697762, 0.0004551788202508122, 0.00017888735993709597, 0.0001511666118961548, 6.667229302050708e-05, 6.543905037284982e-05, 5.513926198218736e-05, 6.481931615621375e-05, 0.00012038303693570293, 7.26662940190042e-05, 0.009020771513353114, 0.0027212020033389023, 5.972064557524347e-05, 7.616685855991744e-05]]
stddevs = [[0.002259309908042795, 0.0019364909390548793, 0.005296557230771414, 0.002067467826314768, 0.0018610857276957333, 0.0012995524108028705, 0.0007228641046143324, 0.0008254436876519501, 0.0007633432301119934, 0.0007705702038167098, 0.0010659333009679977, 0.00086332075642184, 0.054655290743425844, 0.07905790884708953, 0.0, 0.004111220876957816], [0.003523856256543473, 0.0010686520279113816, 0.0050794982279185105, 0.0021874756582506635, 0.002234379631287666, 0.001187450763082253, 0.0008337722185788244, 0.0007791682013218349, 0.0007134996932494605, 0.0008476398808275112, 0.001069652702942416, 0.0009186620224754255, 0.10430405905185994, 0.07305462868970525, 0.009224719809602683, 0.0012430545333144028], [0.005921025711710369, 0.0025987984683180716, 0.0188600198980891, 0.01139078574989454, 0.002345116046025054, 0.0026874467641539194, 0.0011009947584214902, 0.0010306083790861589, 0.0009344136675482618, 0.001025980954665256, 0.001142175978796853, 0.0011071933056572586, 0.1428336337897542, 0.08614179103817889, 0.01008616000416381, 0.0012375751407095778], [0.0017649609372939958, 0.0014602249225028146, 0.002028737972741915, 0.002214037548195218, 0.0013254708264710002, 0.001233562759385715, 0.0008138044823841819, 0.0008062928961703135, 0.0007405081239325128, 0.0008024908833220606, 0.0010905678767390687, 0.0008493424220557922, 0.16535347314736792, 0.09414729730874838, 0.006100468976425601, 0.0008694062314340169]]
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment