libmc: 高效轻便的 C++/Python Memcached 客户端库

[mckelvin]

https://github.com/douban/libmc

Benchmark 可见：

	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-0.5.0-py2.7-linux-x86_64.egg/libmc/__init__.pyc
	INFO:libmc.bench:cmemcached: /home/travis/virtualenv/python2.7.9/lib/python2.7/site-packages/cmemcached.pyc
	INFO:libmc.bench:5 participants in 16 benchmarks
	INFO:libmc.bench:
	INFO:libmc.bench:Multi set 10 keys with value size 100
	INFO:libmc.bench: pylibmc (md5 / ketama): 0.0003s, σ=0.0017, n=3371, snr=1:5.69
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.000297s, σ=0.0017, n=3372, snr=1:5.72
	INFO:libmc.bench: python-memcached: 0.000549s, σ=0.00228, n=1749, snr=1:4.15
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.000162s, σ=0.00126, n=5795, snr=1:7.79
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 0.000332s, σ=0.00179, n=3011, snr=1:5.4
	INFO:libmc.bench:
	INFO:libmc.bench:Multi get 10 keys with value size 100
	INFO:libmc.bench: pylibmc (md5 / ketama): 0.000181s, σ=0.00133, n=5198, snr=1:7.37
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.000123s, σ=0.0011, n=7307, snr=1:8.95
	INFO:libmc.bench: python-memcached: 0.000567s, σ=0.00231, n=1676, snr=1:4.08
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.000154s, σ=0.00123, n=6296, snr=1:7.99
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 0.000204s, σ=0.00141, n=4600, snr=1:6.92
	INFO:libmc.bench:
	INFO:libmc.bench:Multi set 100 keys with value size 100
	INFO:libmc.bench: pylibmc (md5 / ketama): 0.00275s, σ=0.00459, n=364, snr=1:1.67
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.00282s, σ=0.00462, n=355, snr=1:1.64
	INFO:libmc.bench: python-memcached: 0.00316s, σ=0.00485, n=307, snr=1:1.54
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.000801s, σ=0.00271, n=1248, snr=1:3.39
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 0.00303s, σ=0.00479, n=330, snr=1:1.58
	INFO:libmc.bench:
	INFO:libmc.bench:Multi get 100 keys with value size 100
	INFO:libmc.bench: pylibmc (md5 / ketama): 0.000645s, σ=0.00246, n=1504, snr=1:3.81
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.000661s, σ=0.00248, n=1498, snr=1:3.76
	INFO:libmc.bench: python-memcached: 0.00315s, σ=0.00485, n=314, snr=1:1.54
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.000616s, σ=0.0024, n=1607, snr=1:3.9
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 0.000907s, σ=0.00287, n=1080, snr=1:3.17
	INFO:libmc.bench:
	INFO:libmc.bench:Multi set 10 keys with value size 1000
	INFO:libmc.bench: pylibmc (md5 / ketama): 0.000306s, σ=0.00172, n=3265, snr=1:5.63
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.00028s, σ=0.00165, n=3568, snr=1:5.89
	INFO:libmc.bench: python-memcached: 0.000582s, σ=0.00237, n=1666, snr=1:4.07
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.000182s, σ=0.00134, n=5214, snr=1:7.34
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 0.000342s, σ=0.00182, n=2923, snr=1:5.31
	INFO:libmc.bench:
	INFO:libmc.bench:Multi get 10 keys with value size 1000
	INFO:libmc.bench: pylibmc (md5 / ketama): 0.000197s, σ=0.00139, n=4876, snr=1:7.06
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.000167s, σ=0.0013, n=5158, snr=1:7.77
	INFO:libmc.bench: python-memcached: 0.0006s, σ=0.00238, n=1633, snr=1:3.96
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.000159s, σ=0.00125, n=5929, snr=1:7.88
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 0.000222s, σ=0.00147, n=4271, snr=1:6.63
	INFO:libmc.bench:
	INFO:libmc.bench:Small set
	INFO:libmc.bench: pylibmc (md5 / ketama): 4.88e-05s, σ=0.000697, n=20510, snr=1:14.3
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 4.77e-05s, σ=0.000689, n=20756, snr=1:14.4
	INFO:libmc.bench: python-memcached: 8.29e-05s, σ=0.000907, n=11941, snr=1:10.9
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 5.02e-05s, σ=0.000707, n=19906, snr=1:14.1
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 5.51e-05s, σ=0.00074, n=18139, snr=1:13.4
	INFO:libmc.bench:
	INFO:libmc.bench:Small get
	INFO:libmc.bench: pylibmc (md5 / ketama): 4.56e-05s, σ=0.000674, n=21917, snr=1:14.8
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 4.94e-05s, σ=0.000701, n=20039, snr=1:14.2
	INFO:libmc.bench: python-memcached: 8.81e-05s, σ=0.000934, n=11243, snr=1:10.6
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 4.48e-05s, σ=0.000668, n=21863, snr=1:14.9
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 5e-05s, σ=0.000705, n=20010, snr=1:14.1
	INFO:libmc.bench:
	INFO:libmc.bench:4k uncompressed set
	INFO:libmc.bench: pylibmc (md5 / ketama): 4.91e-05s, σ=0.000699, n=20173, snr=1:14.2
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 5.11e-05s, σ=0.000713, n=19570, snr=1:14
	INFO:libmc.bench: python-memcached: 9e-05s, σ=0.000945, n=11108, snr=1:10.5
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 5.21e-05s, σ=0.00072, n=19015, snr=1:13.8
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 6.13e-05s, σ=0.00078, n=16161, snr=1:12.7
	INFO:libmc.bench:
	INFO:libmc.bench:4k uncompressed get
	INFO:libmc.bench: pylibmc (md5 / ketama): 5.59e-05s, σ=0.000746, n=17878, snr=1:13.3
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 5.94e-05s, σ=0.000768, n=16833, snr=1:12.9
	INFO:libmc.bench: python-memcached: 0.000104s, σ=0.00101, n=9606, snr=1:9.75
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 6.12e-05s, σ=0.00078, n=16166, snr=1:12.7
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 6.36e-05s, σ=0.000795, n=15712, snr=1:12.5
	INFO:libmc.bench:
	INFO:libmc.bench:4k compressed set
	INFO:libmc.bench: pylibmc (md5 / ketama): 8.57e-05s, σ=0.000922, n=11665, snr=1:10.8
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 8.34e-05s, σ=0.000909, n=11875, snr=1:10.9
	INFO:libmc.bench: python-memcached: 9.34e-05s, σ=0.000962, n=10701, snr=1:10.3
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 8.84e-05s, σ=0.000936, n=11307, snr=1:10.6
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 9.11e-05s, σ=0.00095, n=10973, snr=1:10.4
	INFO:libmc.bench:
	INFO:libmc.bench:4k compressed get
	INFO:libmc.bench: pylibmc (md5 / ketama): 5.56e-05s, σ=0.000744, n=17802, snr=1:13.4
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 5.52e-05s, σ=0.000741, n=18116, snr=1:13.4
	INFO:libmc.bench: python-memcached: 0.000104s, σ=0.00101, n=9527, snr=1:9.76
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 5.75e-05s, σ=0.000756, n=17382, snr=1:13.1
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 6.26e-05s, σ=0.000789, n=15814, snr=1:12.6
	INFO:libmc.bench:
	INFO:libmc.bench:1M compressed set
	INFO:libmc.bench: pylibmc (md5 / ketama): 0.0084s, σ=0.00366, n=119, snr=2.29:1
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.00806s, σ=0.00395, n=124, snr=2.04:1
	INFO:libmc.bench: python-memcached: 0.0082s, σ=0.00384, n=122, snr=2.13:1
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.00826s, σ=0.004, n=121, snr=2.07:1
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 0.00847s, σ=0.0036, n=118, snr=2.36:1
	INFO:libmc.bench:
	INFO:libmc.bench:1M compressed get
	INFO:libmc.bench: pylibmc (md5 / ketama): 0.00389s, σ=0.00488, n=257, snr=1:1.25
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 0.00351s, σ=0.00477, n=282, snr=1:1.36
	INFO:libmc.bench: python-memcached: 0.0033s, σ=0.0047, n=303, snr=1:1.42
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 0.00341s, σ=0.00474, n=293, snr=1:1.39
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 0.00358s, σ=0.0048, n=279, snr=1:1.34
	INFO:libmc.bench:
	INFO:libmc.bench:Complex data set
	INFO:libmc.bench: pylibmc (md5 / ketama): 6.9e-05s, σ=0.000828, n=14341, snr=1:12
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 7.21e-05s, σ=0.000846, n=13877, snr=1:11.7
	INFO:libmc.bench: python-memcached: 0.000183s, σ=0.00134, n=5475, snr=1:7.33
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 8.06e-05s, σ=0.000894, n=12410, snr=1:11.1
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 7.81e-05s, σ=0.00088, n=12809, snr=1:11.3
	INFO:libmc.bench:
	INFO:libmc.bench:Complex data get
	INFO:libmc.bench: pylibmc (md5 / ketama): 9.14e-05s, σ=0.000951, n=10507, snr=1:10.4
	INFO:libmc.bench: pylibmc (md5 / ketama / nodelay / nonblocking): 8.11e-05s, σ=0.000897, n=12334, snr=1:11.1
	INFO:libmc.bench: python-memcached: 0.000174s, σ=0.00131, n=5733, snr=1:7.51
	INFO:libmc.bench: libmc(md5 / ketama / nodelay / nonblocking, from douban): 7.91e-05s, σ=0.000886, n=12638, snr=1:11.2
	INFO:libmc.bench: python-libmemcached(md5 / ketama / nodelay / nonblocking, from douban): 8.3e-05s, σ=0.000907, n=12048, snr=1:10.9
	labels = ['pylibmc (md5 / ketama)', 'pylibmc (md5 / ketama / nodelay / nonblocking)', 'python-memcached', 'libmc(md5 / ketama / nodelay / nonblocking, from douban)', 'python-libmemcached(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 = [[0.00029961435775734195, 0.00018083878414774937, 0.0027472527472527475, 0.0006449468085106411, 0.00030627871362940275, 0.0001968826907301061, 4.8756704046806435e-05, 4.5626682483916596e-05, 4.907549695137074e-05, 5.593466830741694e-05, 8.572653236176597e-05, 5.561172901921104e-05, 0.008403361344537815, 0.0038910505836575876, 6.903284289798543e-05, 9.136765965546989e-05], [0.00029655990510083044, 0.00012316956343232522, 0.0028169014084507044, 0.0006608811748998678, 0.0002802690582959641, 0.0001667312911981373, 4.769705145500106e-05, 4.9403662857428114e-05, 5.109862033725089e-05, 5.940711697261332e-05, 8.336842105263174e-05, 5.5199823360565245e-05, 0.008064516129032258, 0.0035106382978723726, 7.206168480219068e-05, 8.107669855683477e-05], [0.000548885077186964, 0.0005668257756563253, 0.003159609120521175, 0.003152866242038212, 0.0005822328931572623, 0.0006001224739742786, 8.290762917678543e-05, 8.805478964689095e-05, 9.002520705797624e-05, 0.00010410160316468874, 9.344921035417251e-05, 0.00010391518841188223, 0.00819672131147541, 0.0033003300330033004, 0.00018264840182648402, 0.00017442874585731728], [0.00016220880069025051, 0.00015406607369758586, 0.0008012820512820513, 0.0006160547604231477, 0.0001822017644802467, 0.00015854275594535415, 5.023610971566362e-05, 4.482458948909134e-05, 5.2064159873783586e-05, 6.123963874798974e-05, 8.844078889183692e-05, 5.753077896674721e-05, 0.008264462809917356, 0.0034129692832764505, 8.058017727639001e-05, 7.912644405760406e-05], [0.00033211557622052476, 0.00020434782608695602, 0.0030303030303030303, 0.0009074074074074078, 0.00034211426616489907, 0.00022243034418169115, 5.5129830751419594e-05, 4.9975012493753125e-05, 6.125858548357169e-05, 6.364562118126272e-05, 9.113278046113187e-05, 6.260275705071424e-05, 0.00847457627118644, 0.0035842293906810036, 7.807010695604653e-05, 8.300132802124833e-05]]
	stddevs = [[0.0017048093190145661, 0.0013325483764672367, 0.004585196932972538, 0.0024563207647411225, 0.001723072977523461, 0.001389267473667937, 0.0006965556864160754, 0.0006739325119658266, 0.0006988179770961712, 0.0007458002386399377, 0.0009219090439229022, 0.0007436562551255493, 0.003662940288689638, 0.004875472407059008, 0.0008279872556876655, 0.000951487544492257], [0.001696364133579533, 0.001102961872852839, 0.004621778997632389, 0.002484360646456474, 0.0016504968457775264, 0.001295487671651156, 0.0006889814989045137, 0.0007011390066675464, 0.0007130043017907985, 0.0007684672824390015, 0.0009092490950765486, 0.0007409124193226027, 0.003950789907714805, 0.004773028568973356, 0.0008458273828656763, 0.0008967683951386342], [0.0022776250665794465, 0.0023123508160776416, 0.004854622416302638, 0.0048475689686352606, 0.002367146342942477, 0.00237509531513449, 0.000906753889868792, 0.0009342345800114683, 0.0009445356174724082, 0.0010149772844085362, 0.0009621638886519218, 0.001014077668492992, 0.0038446030818224813, 0.0047022464847442215, 0.0013390756437091664, 0.0013091455500408831], [0.0012632404014601235, 0.0012316348411404918, 0.0027149157605925464, 0.0024043760388906582, 0.0013374678171166133, 0.00124911638928923, 0.000706991817800814, 0.000668009469295211, 0.0007196741776626529, 0.0007801577366954367, 0.0009362617773778323, 0.0007563054932624073, 0.003999521862004169, 0.0047414484605630795, 0.0008940406074636653, 0.0008860042022624746], [0.001791885879802841, 0.0014148216236837387, 0.004789412872758877, 0.0028724007156133756, 0.0018177184849517906, 0.0014747298680789162, 0.0007404451426509585, 0.0007051614163180971, 0.0007802776688720394, 0.0007952392386678663, 0.0009502750238413407, 0.0007887385278511294, 0.003595458209423123, 0.004795372100451486, 0.0008801171103667114, 0.0009072618474063649]]
	travis_time:end:10962820:start=1428165897162349927,finish=1428165992077718138,duration=94915368211
	

view raw libmc-benchmark.txt hosted with ❤ by GitHub

[tomliu]

Benchmark 屌爆

[zzl0]

推荐！mc 的 mc