Merge pull request #886 from prometheus/beorn7/histogram
Switch sparse histograms to base-2 buckets
This commit is contained in:
Björn Rabenstein
GitHub
commit
43f31c25a8
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@ -54,10 +54,10 @@ var (
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// normal distribution, with 20 buckets centered on the mean, each
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// half-sigma wide.
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rpcDurationsHistogram = prometheus.NewHistogram(prometheus.HistogramOpts{
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Name: "rpc_durations_histogram_seconds",
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Help: "RPC latency distributions.",
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Buckets: prometheus.LinearBuckets(*normMean-5**normDomain, .5**normDomain, 20),
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SparseBucketsResolution: 20,
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Name: "rpc_durations_histogram_seconds",
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Help: "RPC latency distributions.",
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Buckets: prometheus.LinearBuckets(*normMean-5**normDomain, .5**normDomain, 20),
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SparseBucketsFactor: 1.1,
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})
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)
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2
go.mod
2
go.mod
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@ -5,7 +5,7 @@ require (
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github.com/cespare/xxhash/v2 v2.1.1
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github.com/golang/protobuf v1.4.3
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github.com/json-iterator/go v1.1.11
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github.com/prometheus/client_model v0.2.1-0.20210403151606-24db95a3d5d6
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github.com/prometheus/client_model v0.2.1-0.20210624201024-61b6c1aac064
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github.com/prometheus/common v0.26.0
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github.com/prometheus/procfs v0.6.0
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golang.org/x/sys v0.0.0-20210603081109-ebe580a85c40
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5
go.sum
5
go.sum
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@ -24,6 +24,7 @@ github.com/gogo/protobuf v1.1.1/go.mod h1:r8qH/GZQm5c6nD/R0oafs1akxWv10x8SbQlK7a
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github.com/golang/protobuf v1.2.0/go.mod h1:6lQm79b+lXiMfvg/cZm0SGofjICqVBUtrP5yJMmIC1U=
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github.com/golang/protobuf v1.3.1/go.mod h1:6lQm79b+lXiMfvg/cZm0SGofjICqVBUtrP5yJMmIC1U=
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github.com/golang/protobuf v1.3.2/go.mod h1:6lQm79b+lXiMfvg/cZm0SGofjICqVBUtrP5yJMmIC1U=
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github.com/golang/protobuf v1.3.5/go.mod h1:6O5/vntMXwX2lRkT1hjjk0nAC1IDOTvTlVgjlRvqsdk=
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github.com/golang/protobuf v1.4.0-rc.1/go.mod h1:ceaxUfeHdC40wWswd/P6IGgMaK3YpKi5j83Wpe3EHw8=
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github.com/golang/protobuf v1.4.0-rc.1.0.20200221234624-67d41d38c208/go.mod h1:xKAWHe0F5eneWXFV3EuXVDTCmh+JuBKY0li0aMyXATA=
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github.com/golang/protobuf v1.4.0-rc.2/go.mod h1:LlEzMj4AhA7rCAGe4KMBDvJI+AwstrUpVNzEA03Pprs=
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@ -77,8 +78,8 @@ github.com/prometheus/client_golang v1.7.1/go.mod h1:PY5Wy2awLA44sXw4AOSfFBetzPP
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github.com/prometheus/client_model v0.0.0-20180712105110-5c3871d89910/go.mod h1:MbSGuTsp3dbXC40dX6PRTWyKYBIrTGTE9sqQNg2J8bo=
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github.com/prometheus/client_model v0.0.0-20190129233127-fd36f4220a90/go.mod h1:xMI15A0UPsDsEKsMN9yxemIoYk6Tm2C1GtYGdfGttqA=
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github.com/prometheus/client_model v0.2.0/go.mod h1:xMI15A0UPsDsEKsMN9yxemIoYk6Tm2C1GtYGdfGttqA=
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github.com/prometheus/client_model v0.2.1-0.20210403151606-24db95a3d5d6 h1:wlZYx9ITBsvMO/wVoi30A36fAdRlBC130JksGGfaYl8=
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github.com/prometheus/client_model v0.2.1-0.20210403151606-24db95a3d5d6/go.mod h1:xMI15A0UPsDsEKsMN9yxemIoYk6Tm2C1GtYGdfGttqA=
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github.com/prometheus/client_model v0.2.1-0.20210624201024-61b6c1aac064 h1:Kyx21CLOfWDA4e2TcOcupRl2g/Bmddu0AL0hR1BldEw=
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github.com/prometheus/client_model v0.2.1-0.20210624201024-61b6c1aac064/go.mod h1:LDGWKZIo7rky3hgvBe+caln+Dr3dPggB5dvjtD7w9+w=
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github.com/prometheus/common v0.4.1/go.mod h1:TNfzLD0ON7rHzMJeJkieUDPYmFC7Snx/y86RQel1bk4=
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github.com/prometheus/common v0.10.0/go.mod h1:Tlit/dnDKsSWFlCLTWaA1cyBgKHSMdTB80sz/V91rCo=
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github.com/prometheus/common v0.26.0 h1:iMAkS2TDoNWnKM+Kopnx/8tnEStIfpYA0ur0xQzzhMQ=
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@ -538,8 +538,8 @@ func ExampleHistogram() {
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// cumulative_count: 816
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// upper_bound: 40
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// >
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// sb_resolution: 0
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// sb_zero_threshold: 1e-128
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// sb_schema: 0
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// sb_zero_threshold: 0
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// >
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}
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@ -28,6 +28,176 @@ import (
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dto "github.com/prometheus/client_model/go"
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)
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// sparseBounds for the frac of observed values. Only relevant for schema > 0.
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// Position in the slice is the schema. (0 is never used, just here for
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// convenience of using the schema directly as the index.)
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var sparseBounds = [][]float64{
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// Schema "0":
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[]float64{0.5},
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// Schema 1:
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[]float64{0.5, 0.7071067811865475},
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// Schema 2:
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[]float64{0.5, 0.5946035575013605, 0.7071067811865475, 0.8408964152537144},
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// Schema 3:
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[]float64{0.5, 0.5452538663326288, 0.5946035575013605, 0.6484197773255048,
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0.7071067811865475, 0.7711054127039704, 0.8408964152537144, 0.9170040432046711},
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// Schema 4:
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[]float64{0.5, 0.5221368912137069, 0.5452538663326288, 0.5693943173783458,
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0.5946035575013605, 0.620928906036742, 0.6484197773255048, 0.6771277734684463,
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0.7071067811865475, 0.7384130729697496, 0.7711054127039704, 0.805245165974627,
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0.8408964152537144, 0.8781260801866495, 0.9170040432046711, 0.9576032806985735},
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// Schema 5:
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[]float64{0.5, 0.5109485743270583, 0.5221368912137069, 0.5335702003384117,
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0.5452538663326288, 0.5571933712979462, 0.5693943173783458, 0.5818624293887887,
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0.5946035575013605, 0.6076236799902344, 0.620928906036742, 0.6345254785958666,
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0.6484197773255048, 0.6626183215798706, 0.6771277734684463, 0.6919549409819159,
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0.7071067811865475, 0.7225904034885232, 0.7384130729697496, 0.7545822137967112,
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0.7711054127039704, 0.7879904225539431, 0.805245165974627, 0.8228777390769823,
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0.8408964152537144, 0.8593096490612387, 0.8781260801866495, 0.8973545375015533,
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0.9170040432046711, 0.9370838170551498, 0.9576032806985735, 0.9785720620876999},
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// Schema 6:
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[]float64{0.5, 0.5054446430258502, 0.5109485743270583, 0.5165124395106142,
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0.5221368912137069, 0.5278225891802786, 0.5335702003384117, 0.5393803988785598,
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0.5452538663326288, 0.5511912916539204, 0.5571933712979462, 0.5632608093041209,
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0.5693943173783458, 0.5755946149764913, 0.5818624293887887, 0.5881984958251406,
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0.5946035575013605, 0.6010783657263515, 0.6076236799902344, 0.6142402680534349,
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0.620928906036742, 0.6276903785123455, 0.6345254785958666, 0.6414350080393891,
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0.6484197773255048, 0.6554806057623822, 0.6626183215798706, 0.6698337620266515,
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0.6771277734684463, 0.6845012114872953, 0.6919549409819159, 0.6994898362691555,
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0.7071067811865475, 0.7148066691959849, 0.7225904034885232, 0.7304588970903234,
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0.7384130729697496, 0.7464538641456323, 0.7545822137967112, 0.762799075372269,
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0.7711054127039704, 0.7795022001189185, 0.7879904225539431, 0.7965710756711334,
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0.805245165974627, 0.8140137109286738, 0.8228777390769823, 0.8318382901633681,
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0.8408964152537144, 0.8500531768592616, 0.8593096490612387, 0.8686669176368529,
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0.8781260801866495, 0.8876882462632604, 0.8973545375015533, 0.9071260877501991,
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0.9170040432046711, 0.9269895625416926, 0.9370838170551498, 0.9472879907934827,
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0.9576032806985735, 0.9680308967461471, 0.9785720620876999, 0.9892280131939752},
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// Schema 7:
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[]float64{0.5, 0.5027149505564014, 0.5054446430258502, 0.5081891574554764,
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0.5109485743270583, 0.5137229745593818, 0.5165124395106142, 0.5193170509806894,
|
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0.5221368912137069, 0.5249720429003435, 0.5278225891802786, 0.5306886136446309,
|
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0.5335702003384117, 0.5364674337629877, 0.5393803988785598, 0.5423091811066545,
|
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0.5452538663326288, 0.5482145409081883, 0.5511912916539204, 0.5541842058618393,
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0.5571933712979462, 0.5602188762048033, 0.5632608093041209, 0.5663192597993595,
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0.5693943173783458, 0.572486072215902, 0.5755946149764913, 0.5787200368168754,
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0.5818624293887887, 0.585021884841625, 0.5881984958251406, 0.5913923554921704,
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0.5946035575013605, 0.5978321960199137, 0.6010783657263515, 0.6043421618132907,
|
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0.6076236799902344, 0.6109230164863786, 0.6142402680534349, 0.6175755319684665,
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0.620928906036742, 0.6243004885946023, 0.6276903785123455, 0.6310986751971253,
|
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0.6345254785958666, 0.637970889198196, 0.6414350080393891, 0.6449179367033329,
|
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0.6484197773255048, 0.6519406325959679, 0.6554806057623822, 0.659039800633032,
|
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0.6626183215798706, 0.6662162735415805, 0.6698337620266515, 0.6734708931164728,
|
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0.6771277734684463, 0.6808045103191123, 0.6845012114872953, 0.688217985377265,
|
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0.6919549409819159, 0.6957121878859629, 0.6994898362691555, 0.7032879969095076,
|
||||
0.7071067811865475, 0.7109463010845827, 0.7148066691959849, 0.718687998724491,
|
||||
0.7225904034885232, 0.7265139979245261, 0.7304588970903234, 0.7344252166684908,
|
||||
0.7384130729697496, 0.7424225829363761, 0.7464538641456323, 0.7505070348132126,
|
||||
0.7545822137967112, 0.7586795205991071, 0.762799075372269, 0.7669409989204777,
|
||||
0.7711054127039704, 0.7752924388424999, 0.7795022001189185, 0.7837348199827764,
|
||||
0.7879904225539431, 0.7922691326262467, 0.7965710756711334, 0.8008963778413465,
|
||||
0.805245165974627, 0.8096175675974316, 0.8140137109286738, 0.8184337248834821,
|
||||
0.8228777390769823, 0.8273458838280969, 0.8318382901633681, 0.8363550898207981,
|
||||
0.8408964152537144, 0.8454623996346523, 0.8500531768592616, 0.8546688815502312,
|
||||
0.8593096490612387, 0.8639756154809185, 0.8686669176368529, 0.8733836930995842,
|
||||
0.8781260801866495, 0.8828942179666361, 0.8876882462632604, 0.8925083056594671,
|
||||
0.8973545375015533, 0.9022270839033115, 0.9071260877501991, 0.9120516927035263,
|
||||
0.9170040432046711, 0.9219832844793128, 0.9269895625416926, 0.9320230241988943,
|
||||
0.9370838170551498, 0.9421720895161669, 0.9472879907934827, 0.9524316709088368,
|
||||
0.9576032806985735, 0.9628029718180622, 0.9680308967461471, 0.9732872087896164,
|
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0.9785720620876999, 0.9838856116165875, 0.9892280131939752, 0.9945994234836328},
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// Schema 8:
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[]float64{0.5, 0.5013556375251013, 0.5027149505564014, 0.5040779490592088,
|
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0.5054446430258502, 0.5068150424757447, 0.5081891574554764, 0.509566998038869,
|
||||
0.5109485743270583, 0.5123338964485679, 0.5137229745593818, 0.5151158188430205,
|
||||
0.5165124395106142, 0.5179128468009786, 0.5193170509806894, 0.520725062344158,
|
||||
0.5221368912137069, 0.5235525479396449, 0.5249720429003435, 0.526395386502313,
|
||||
0.5278225891802786, 0.5292536613972564, 0.5306886136446309, 0.5321274564422321,
|
||||
0.5335702003384117, 0.5350168559101208, 0.5364674337629877, 0.5379219445313954,
|
||||
0.5393803988785598, 0.5408428074966075, 0.5423091811066545, 0.5437795304588847,
|
||||
0.5452538663326288, 0.5467321995364429, 0.5482145409081883, 0.549700901315111,
|
||||
0.5511912916539204, 0.5526857228508706, 0.5541842058618393, 0.5556867516724088,
|
||||
0.5571933712979462, 0.5587040757836845, 0.5602188762048033, 0.5617377836665098,
|
||||
0.5632608093041209, 0.564787964283144, 0.5663192597993595, 0.5678547070789026,
|
||||
0.5693943173783458, 0.5709381019847808, 0.572486072215902, 0.5740382394200894,
|
||||
0.5755946149764913, 0.5771552102951081, 0.5787200368168754, 0.5802891060137493,
|
||||
0.5818624293887887, 0.5834400184762408, 0.585021884841625, 0.5866080400818185,
|
||||
0.5881984958251406, 0.5897932637314379, 0.5913923554921704, 0.5929957828304968,
|
||||
0.5946035575013605, 0.5962156912915756, 0.5978321960199137, 0.5994530835371903,
|
||||
0.6010783657263515, 0.6027080545025619, 0.6043421618132907, 0.6059806996384005,
|
||||
0.6076236799902344, 0.6092711149137041, 0.6109230164863786, 0.6125793968185725,
|
||||
0.6142402680534349, 0.6159056423670379, 0.6175755319684665, 0.6192499490999082,
|
||||
0.620928906036742, 0.622612415087629, 0.6243004885946023, 0.6259931389331581,
|
||||
0.6276903785123455, 0.6293922197748583, 0.6310986751971253, 0.6328097572894031,
|
||||
0.6345254785958666, 0.6362458516947014, 0.637970889198196, 0.6397006037528346,
|
||||
0.6414350080393891, 0.6431741147730128, 0.6449179367033329, 0.6466664866145447,
|
||||
0.6484197773255048, 0.6501778216898253, 0.6519406325959679, 0.6537082229673385,
|
||||
0.6554806057623822, 0.6572577939746774, 0.659039800633032, 0.6608266388015788,
|
||||
0.6626183215798706, 0.6644148621029772, 0.6662162735415805, 0.6680225691020727,
|
||||
0.6698337620266515, 0.6716498655934177, 0.6734708931164728, 0.6752968579460171,
|
||||
0.6771277734684463, 0.6789636531064505, 0.6808045103191123, 0.6826503586020058,
|
||||
0.6845012114872953, 0.6863570825438342, 0.688217985377265, 0.690083933630119,
|
||||
0.6919549409819159, 0.6938310211492645, 0.6957121878859629, 0.6975984549830999,
|
||||
0.6994898362691555, 0.7013863456101023, 0.7032879969095076, 0.7051948041086352,
|
||||
0.7071067811865475, 0.7090239421602076, 0.7109463010845827, 0.7128738720527471,
|
||||
0.7148066691959849, 0.7167447066838943, 0.718687998724491, 0.7206365595643126,
|
||||
0.7225904034885232, 0.7245495448210174, 0.7265139979245261, 0.7284837772007218,
|
||||
0.7304588970903234, 0.7324393720732029, 0.7344252166684908, 0.7364164454346837,
|
||||
0.7384130729697496, 0.7404151139112358, 0.7424225829363761, 0.7444354947621984,
|
||||
0.7464538641456323, 0.7484777058836176, 0.7505070348132126, 0.7525418658117031,
|
||||
0.7545822137967112, 0.7566280937263048, 0.7586795205991071, 0.7607365094544071,
|
||||
0.762799075372269, 0.7648672334736434, 0.7669409989204777, 0.7690203869158282,
|
||||
0.7711054127039704, 0.7731960915705107, 0.7752924388424999, 0.7773944698885442,
|
||||
0.7795022001189185, 0.7816156449856788, 0.7837348199827764, 0.7858597406461707,
|
||||
0.7879904225539431, 0.7901268813264122, 0.7922691326262467, 0.7944171921585818,
|
||||
0.7965710756711334, 0.7987307989543135, 0.8008963778413465, 0.8030678282083853,
|
||||
0.805245165974627, 0.8074284071024302, 0.8096175675974316, 0.8118126635086642,
|
||||
0.8140137109286738, 0.8162207259936375, 0.8184337248834821, 0.820652723822003,
|
||||
0.8228777390769823, 0.8251087869603088, 0.8273458838280969, 0.8295890460808079,
|
||||
0.8318382901633681, 0.8340936325652911, 0.8363550898207981, 0.8386226785089391,
|
||||
0.8408964152537144, 0.8431763167241966, 0.8454623996346523, 0.8477546807446661,
|
||||
0.8500531768592616, 0.8523579048290255, 0.8546688815502312, 0.8569861239649629,
|
||||
0.8593096490612387, 0.8616394738731368, 0.8639756154809185, 0.8663180910111553,
|
||||
0.8686669176368529, 0.871022112577578, 0.8733836930995842, 0.8757516765159389,
|
||||
0.8781260801866495, 0.8805069215187917, 0.8828942179666361, 0.8852879870317771,
|
||||
0.8876882462632604, 0.890095013257712, 0.8925083056594671, 0.8949281411607002,
|
||||
0.8973545375015533, 0.8997875124702672, 0.9022270839033115, 0.9046732696855155,
|
||||
0.9071260877501991, 0.909585556079304, 0.9120516927035263, 0.9145245157024483,
|
||||
0.9170040432046711, 0.9194902933879467, 0.9219832844793128, 0.9244830347552253,
|
||||
0.9269895625416926, 0.92950288621441, 0.9320230241988943, 0.9345499949706191,
|
||||
0.9370838170551498, 0.93962450902828, 0.9421720895161669, 0.9447265771954693,
|
||||
0.9472879907934827, 0.9498563490882775, 0.9524316709088368, 0.9550139751351947,
|
||||
0.9576032806985735, 0.9601996065815236, 0.9628029718180622, 0.9654133954938133,
|
||||
0.9680308967461471, 0.9706554947643201, 0.9732872087896164, 0.9759260581154889,
|
||||
0.9785720620876999, 0.9812252401044634, 0.9838856116165875, 0.9865531961276168,
|
||||
0.9892280131939752, 0.9919100824251095, 0.9945994234836328, 0.9972960560854698},
|
||||
}
|
||||
|
||||
// The sparseBounds above can be generated with the code below.
|
||||
// TODO(beorn7): Actually do it via go generate.
|
||||
//
|
||||
// var sparseBounds [][]float64 = make([][]float64, 9)
|
||||
//
|
||||
// func init() {
|
||||
// // Populate sparseBounds.
|
||||
// numBuckets := 1
|
||||
// for i := range sparseBounds {
|
||||
// bounds := []float64{0.5}
|
||||
// factor := math.Exp2(math.Exp2(float64(-i)))
|
||||
// for j := 0; j < numBuckets-1; j++ {
|
||||
// var bound float64
|
||||
// if (j+1)%2 == 0 {
|
||||
// // Use previously calculated value for increased precision.
|
||||
// bound = sparseBounds[i-1][j/2+1]
|
||||
// } else {
|
||||
// bound = bounds[j] * factor
|
||||
// }
|
||||
// bounds = append(bounds, bound)
|
||||
// }
|
||||
// numBuckets *= 2
|
||||
// sparseBounds[i] = bounds
|
||||
// }
|
||||
// }
|
||||
|
||||
// A Histogram counts individual observations from an event or sample stream in
|
||||
// configurable buckets. Similar to a summary, it also provides a sum of
|
||||
// observations and an observation count.
|
||||
|
|
@ -68,7 +238,10 @@ var DefBuckets = []float64{.005, .01, .025, .05, .1, .25, .5, 1, 2.5, 5, 10}
|
|||
|
||||
// DefSparseBucketsZeroThreshold is the default value for
|
||||
// SparseBucketsZeroThreshold in the HistogramOpts.
|
||||
var DefSparseBucketsZeroThreshold = 1e-128
|
||||
//
|
||||
// The value is 2^-128 (or 0.5*2^-127 in the actual IEEE 754 representation),
|
||||
// which is a bucket boundary at all possible resolutions.
|
||||
const DefSparseBucketsZeroThreshold = 2.938735877055719e-39
|
||||
|
||||
var errBucketLabelNotAllowed = fmt.Errorf(
|
||||
"%q is not allowed as label name in histograms", bucketLabel,
|
||||
|
|
@ -162,24 +335,41 @@ type HistogramOpts struct {
|
|||
// buckets here explicitly.)
|
||||
Buckets []float64
|
||||
|
||||
// If SparseBucketsResolution is not zero, sparse buckets are used (in
|
||||
// addition to the regular buckets, if defined above). Every power of
|
||||
// ten is divided into the given number of exponential buckets. For
|
||||
// example, if set to 3, the bucket boundaries are approximately […,
|
||||
// 0.1, 0.215, 0.464, 1, 2.15, 4,64, 10, 21.5, 46.4, 100, …] Histograms
|
||||
// can only be properly aggregated if they use the same
|
||||
// resolution. Therefore, it is recommended to use 20 as a resolution,
|
||||
// which is generally expected to be a good tradeoff between resource
|
||||
// usage and accuracy (resulting in a maximum error of quantile values
|
||||
// of about 6%).
|
||||
SparseBucketsResolution uint8
|
||||
// If SparseBucketsFactor is greater than one, sparse buckets are used
|
||||
// (in addition to the regular buckets, if defined above). Sparse
|
||||
// buckets are exponential buckets covering the whole float64 range
|
||||
// (with the exception of the “zero” bucket, see
|
||||
// SparseBucketsZeroThreshold below). From any one bucket to the next,
|
||||
// the width of the bucket grows by a constant factor.
|
||||
// SparseBucketsFactor provides an upper bound for this factor
|
||||
// (exception see below). The smaller SparseBucketsFactor, the more
|
||||
// buckets will be used and thus the more costly the histogram will
|
||||
// become. A generally good trade-off between cost and accuracy is a
|
||||
// value of 1.1 (each bucket is at most 10% wider than the previous
|
||||
// one), which will result in each power of two divided into 8 buckets
|
||||
// (e.g. there will be 8 buckets between 1 and 2, same as between 2 and
|
||||
// 4, and 4 and 8, etc.).
|
||||
//
|
||||
// Details about the actually used factor: The factor is calculated as
|
||||
// 2^(2^n), where n is an integer number between (and including) -8 and
|
||||
// 4. n is chosen so that the resulting factor is the largest that is
|
||||
// still smaller or equal to SparseBucketsFactor. Note that the smallest
|
||||
// possible factor is therefore approx. 1.00271 (i.e. 2^(2^-8) ). If
|
||||
// SparseBucketsFactor is greater than 1 but smaller than 2^(2^-8), then
|
||||
// the actually used factor is still 2^(2^-8) even though it is larger
|
||||
// than the provided SparseBucketsFactor.
|
||||
SparseBucketsFactor float64
|
||||
// All observations with an absolute value of less or equal
|
||||
// SparseBucketsZeroThreshold are accumulated into a “zero” bucket. For
|
||||
// best results, this should be close to a bucket boundary. This is
|
||||
// most easily accomplished by picking a power of ten. If
|
||||
// usually the case if picking a power of two. If
|
||||
// SparseBucketsZeroThreshold is left at zero (or set to a negative
|
||||
// value), DefSparseBucketsZeroThreshold is used as the threshold.
|
||||
SparseBucketsZeroThreshold float64
|
||||
// TODO(beorn7): Need a setting to limit total bucket count and to
|
||||
// configure a strategy to enforce the limit, e.g. if minimum duration
|
||||
// after last reset, reset. If not, half the resolution and/or expand
|
||||
// the zero bucket.
|
||||
}
|
||||
|
||||
// NewHistogram creates a new Histogram based on the provided HistogramOpts. It
|
||||
|
|
@ -217,20 +407,24 @@ func newHistogram(desc *Desc, opts HistogramOpts, labelValues ...string) Histogr
|
|||
}
|
||||
|
||||
h := &histogram{
|
||||
desc: desc,
|
||||
upperBounds: opts.Buckets,
|
||||
sparseResolution: uint32(opts.SparseBucketsResolution),
|
||||
sparseThreshold: opts.SparseBucketsZeroThreshold,
|
||||
labelPairs: MakeLabelPairs(desc, labelValues),
|
||||
counts: [2]*histogramCounts{{}, {}},
|
||||
now: time.Now,
|
||||
desc: desc,
|
||||
upperBounds: opts.Buckets,
|
||||
sparseThreshold: opts.SparseBucketsZeroThreshold,
|
||||
labelPairs: MakeLabelPairs(desc, labelValues),
|
||||
counts: [2]*histogramCounts{{}, {}},
|
||||
now: time.Now,
|
||||
}
|
||||
if len(h.upperBounds) == 0 && opts.SparseBucketsResolution == 0 {
|
||||
if len(h.upperBounds) == 0 && opts.SparseBucketsFactor <= 1 {
|
||||
h.upperBounds = DefBuckets
|
||||
}
|
||||
if h.sparseThreshold <= 0 {
|
||||
h.sparseThreshold = DefSparseBucketsZeroThreshold
|
||||
}
|
||||
if opts.SparseBucketsFactor <= 1 {
|
||||
h.sparseThreshold = 0 // To mark that there are no sparse buckets.
|
||||
} else {
|
||||
h.sparseSchema = pickSparseSchema(opts.SparseBucketsFactor)
|
||||
}
|
||||
for i, upperBound := range h.upperBounds {
|
||||
if i < len(h.upperBounds)-1 {
|
||||
if upperBound >= h.upperBounds[i+1] {
|
||||
|
|
@ -264,14 +458,14 @@ type histogramCounts struct {
|
|||
sumBits uint64
|
||||
count uint64
|
||||
buckets []uint64
|
||||
// sparse buckets are implemented with a sync.Map for this PoC. A
|
||||
// dedicated data structure will likely be more efficient.
|
||||
// There are separate maps for negative and positive observations.
|
||||
// The map's value is a *uint64, counting observations in that bucket.
|
||||
// The map's key is the logarithmic index of the bucket. Index 0 is for an
|
||||
// upper bound of 1. Each increment/decrement by SparseBucketsResolution
|
||||
// multiplies/divides the upper bound by 10. Indices in between are
|
||||
// spaced exponentially as defined in spareBounds.
|
||||
// sparse buckets are implemented with a sync.Map for now. A dedicated
|
||||
// data structure will likely be more efficient. There are separate maps
|
||||
// for negative and positive observations. The map's value is an *int64,
|
||||
// counting observations in that bucket. (Note that we don't use uint64
|
||||
// as an int64 won't overflow in practice, and working with signed
|
||||
// numbers from the beginning simplifies the handling of deltas.) The
|
||||
// map's key is the index of the bucket according to the used
|
||||
// sparseSchema. Index 0 is for an upper bound of 1.
|
||||
sparseBucketsPositive, sparseBucketsNegative sync.Map
|
||||
// sparseZeroBucket counts all (positive and negative) observations in
|
||||
// the zero bucket (with an absolute value less or equal
|
||||
|
|
@ -312,10 +506,10 @@ func (hc *histogramCounts) observe(v float64, bucket int, doSparse bool, whichSp
|
|||
atomic.AddUint64(&hc.count, 1)
|
||||
}
|
||||
|
||||
func addToSparseBucket(buckets *sync.Map, key int, increment uint64) {
|
||||
func addToSparseBucket(buckets *sync.Map, key int, increment int64) {
|
||||
if existingBucket, ok := buckets.Load(key); ok {
|
||||
// Fast path without allocation.
|
||||
atomic.AddUint64(existingBucket.(*uint64), increment)
|
||||
atomic.AddInt64(existingBucket.(*int64), increment)
|
||||
return
|
||||
}
|
||||
// Bucket doesn't exist yet. Slow path allocating new counter.
|
||||
|
|
@ -323,7 +517,7 @@ func addToSparseBucket(buckets *sync.Map, key int, increment uint64) {
|
|||
if actualBucket, loaded := buckets.LoadOrStore(key, &newBucket); loaded {
|
||||
// The bucket was created concurrently in another goroutine.
|
||||
// Have to increment after all.
|
||||
atomic.AddUint64(actualBucket.(*uint64), increment)
|
||||
atomic.AddInt64(actualBucket.(*int64), increment)
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -339,7 +533,7 @@ type histogram struct {
|
|||
// perspective of the histogram) swap the hot–cold under the writeMtx
|
||||
// lock. A cooldown is awaited (while locked) by comparing the number of
|
||||
// observations with the initiation count. Once they match, then the
|
||||
// last observation on the now cool one has completed. All cool fields must
|
||||
// last observation on the now cool one has completed. All cold fields must
|
||||
// be merged into the new hot before releasing writeMtx.
|
||||
//
|
||||
// Fields with atomic access first! See alignment constraint:
|
||||
|
|
@ -356,11 +550,11 @@ type histogram struct {
|
|||
// http://golang.org/pkg/sync/atomic/#pkg-note-BUG.
|
||||
counts [2]*histogramCounts
|
||||
|
||||
upperBounds []float64
|
||||
labelPairs []*dto.LabelPair
|
||||
exemplars []atomic.Value // One more than buckets (to include +Inf), each a *dto.Exemplar.
|
||||
sparseResolution uint32 // Instead of uint8 to be ready for protobuf encoding.
|
||||
sparseThreshold float64
|
||||
upperBounds []float64
|
||||
labelPairs []*dto.LabelPair
|
||||
exemplars []atomic.Value // One more than buckets (to include +Inf), each a *dto.Exemplar.
|
||||
sparseSchema int32
|
||||
sparseThreshold float64 // This is zero iff no sparse buckets are used.
|
||||
|
||||
now func() time.Time // To mock out time.Now() for testing.
|
||||
}
|
||||
|
|
@ -407,7 +601,7 @@ func (h *histogram) Write(out *dto.Metric) error {
|
|||
Bucket: make([]*dto.Bucket, len(h.upperBounds)),
|
||||
SampleCount: proto.Uint64(count),
|
||||
SampleSum: proto.Float64(math.Float64frombits(atomic.LoadUint64(&coldCounts.sumBits))),
|
||||
SbResolution: &h.sparseResolution,
|
||||
SbSchema: &h.sparseSchema,
|
||||
SbZeroThreshold: &h.sparseThreshold,
|
||||
}
|
||||
out.Histogram = his
|
||||
|
|
@ -448,7 +642,7 @@ func (h *histogram) Write(out *dto.Metric) error {
|
|||
atomic.AddUint64(&hotCounts.buckets[i], atomic.LoadUint64(&coldCounts.buckets[i]))
|
||||
atomic.StoreUint64(&coldCounts.buckets[i], 0)
|
||||
}
|
||||
if h.sparseResolution != 0 {
|
||||
if h.sparseThreshold != 0 {
|
||||
zeroBucket := atomic.LoadUint64(&coldCounts.sparseZeroBucket)
|
||||
|
||||
defer func() {
|
||||
|
|
@ -478,21 +672,41 @@ func makeSparseBuckets(buckets *sync.Map) *dto.SparseBuckets {
|
|||
}
|
||||
|
||||
sbs := dto.SparseBuckets{}
|
||||
var prevCount uint64
|
||||
var prevCount int64
|
||||
var nextI int
|
||||
|
||||
appendDelta := func(count int64) {
|
||||
*sbs.Span[len(sbs.Span)-1].Length++
|
||||
sbs.Delta = append(sbs.Delta, count-prevCount)
|
||||
prevCount = count
|
||||
}
|
||||
|
||||
for n, i := range ii {
|
||||
v, _ := buckets.Load(i)
|
||||
count := atomic.LoadUint64(v.(*uint64))
|
||||
if n == 0 || i-nextI != 0 {
|
||||
count := atomic.LoadInt64(v.(*int64))
|
||||
// Multiple spans with only small gaps in between are probably
|
||||
// encoded more efficiently as one larger span with a few empty
|
||||
// buckets. Needs some research to find the sweet spot. For now,
|
||||
// we assume that gaps of one ore two buckets should not create
|
||||
// a new span.
|
||||
iDelta := int32(i - nextI)
|
||||
if n == 0 || iDelta > 2 {
|
||||
// We have to create a new span, either because we are
|
||||
// at the very beginning, or because we have found a gap
|
||||
// of more than two buckets.
|
||||
sbs.Span = append(sbs.Span, &dto.SparseBuckets_Span{
|
||||
Offset: proto.Int32(int32(i - nextI)),
|
||||
Length: proto.Uint32(1),
|
||||
Offset: proto.Int32(iDelta),
|
||||
Length: proto.Uint32(0),
|
||||
})
|
||||
} else {
|
||||
*sbs.Span[len(sbs.Span)-1].Length++
|
||||
// We have found a small gap (or no gap at all).
|
||||
// Insert empty buckets as needed.
|
||||
for j := int32(0); j < iDelta; j++ {
|
||||
appendDelta(0)
|
||||
}
|
||||
}
|
||||
sbs.Delta = append(sbs.Delta, int64(count)-int64(prevCount)) // TODO(beorn7): Do proper overflow handling.
|
||||
nextI, prevCount = i+1, count
|
||||
appendDelta(count)
|
||||
nextI = i + 1
|
||||
}
|
||||
return &sbs
|
||||
}
|
||||
|
|
@ -504,9 +718,9 @@ func makeSparseBuckets(buckets *sync.Map) *dto.SparseBuckets {
|
|||
// recreated on the next scrape).
|
||||
func addAndReset(hotBuckets *sync.Map) func(k, v interface{}) bool {
|
||||
return func(k, v interface{}) bool {
|
||||
bucket := v.(*uint64)
|
||||
addToSparseBucket(hotBuckets, k.(int), atomic.LoadUint64(bucket))
|
||||
atomic.StoreUint64(bucket, 0)
|
||||
bucket := v.(*int64)
|
||||
addToSparseBucket(hotBuckets, k.(int), atomic.LoadInt64(bucket))
|
||||
atomic.StoreInt64(bucket, 0)
|
||||
return true
|
||||
}
|
||||
}
|
||||
|
|
@ -528,7 +742,8 @@ func (h *histogram) findBucket(v float64) int {
|
|||
|
||||
// observe is the implementation for Observe without the findBucket part.
|
||||
func (h *histogram) observe(v float64, bucket int) {
|
||||
doSparse := h.sparseResolution != 0
|
||||
// Do not add to sparse buckets for NaN observations.
|
||||
doSparse := h.sparseThreshold != 0 && !math.IsNaN(v)
|
||||
var whichSparse, sparseKey int
|
||||
if doSparse {
|
||||
switch {
|
||||
|
|
@ -537,13 +752,20 @@ func (h *histogram) observe(v float64, bucket int) {
|
|||
case v < -h.sparseThreshold:
|
||||
whichSparse = -1
|
||||
}
|
||||
// TODO(beorn7): This sometimes gives inaccurate results for
|
||||
// floats that are actual powers of 10, e.g. math.Log10(0.1) is
|
||||
// calculated as -0.9999999999999999 rather than -1 and thus
|
||||
// yields a key unexpectedly one off. Maybe special-case precise
|
||||
// powers of 10.
|
||||
// TODO(beorn7): This needs special-casing for ±Inf and NaN.
|
||||
sparseKey = int(math.Ceil(math.Log10(math.Abs(v)) * float64(h.sparseResolution)))
|
||||
frac, exp := math.Frexp(math.Abs(v))
|
||||
switch {
|
||||
case math.IsInf(v, 0):
|
||||
sparseKey = math.MaxInt32 // Largest possible sparseKey.
|
||||
case h.sparseSchema > 0:
|
||||
bounds := sparseBounds[h.sparseSchema]
|
||||
sparseKey = sort.SearchFloat64s(bounds, frac) + (exp-1)*len(bounds)
|
||||
default:
|
||||
sparseKey = exp
|
||||
if frac == 0.5 {
|
||||
sparseKey--
|
||||
}
|
||||
sparseKey /= 1 << -h.sparseSchema
|
||||
}
|
||||
}
|
||||
// We increment h.countAndHotIdx so that the counter in the lower
|
||||
// 63 bits gets incremented. At the same time, we get the new value
|
||||
|
|
@ -797,3 +1019,24 @@ func (s buckSort) Swap(i, j int) {
|
|||
func (s buckSort) Less(i, j int) bool {
|
||||
return s[i].GetUpperBound() < s[j].GetUpperBound()
|
||||
}
|
||||
|
||||
// pickSparseschema returns the largest number n between -4 and 8 such that
|
||||
// 2^(2^-n) is less or equal the provided bucketFactor.
|
||||
//
|
||||
// Special cases:
|
||||
// - bucketFactor <= 1: panics.
|
||||
// - bucketFactor < 2^(2^-8) (but > 1): still returns 8.
|
||||
func pickSparseSchema(bucketFactor float64) int32 {
|
||||
if bucketFactor <= 1 {
|
||||
panic(fmt.Errorf("bucketFactor %f is <=1", bucketFactor))
|
||||
}
|
||||
floor := math.Floor(math.Log2(math.Log2(bucketFactor)))
|
||||
switch {
|
||||
case floor <= -8:
|
||||
return 8
|
||||
case floor >= 4:
|
||||
return -4
|
||||
default:
|
||||
return -int32(floor)
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -456,3 +456,93 @@ func TestHistogramExemplar(t *testing.T) {
|
|||
}
|
||||
}
|
||||
}
|
||||
|
||||
func TestSparseHistogram(t *testing.T) {
|
||||
|
||||
scenarios := []struct {
|
||||
name string
|
||||
observations []float64
|
||||
factor float64
|
||||
zeroThreshold float64
|
||||
want string // String representation of protobuf.
|
||||
}{
|
||||
{
|
||||
name: "no sparse buckets",
|
||||
observations: []float64{1, 2, 3},
|
||||
factor: 1,
|
||||
want: `sample_count:3 sample_sum:6 bucket:<cumulative_count:0 upper_bound:0.005 > bucket:<cumulative_count:0 upper_bound:0.01 > bucket:<cumulative_count:0 upper_bound:0.025 > bucket:<cumulative_count:0 upper_bound:0.05 > bucket:<cumulative_count:0 upper_bound:0.1 > bucket:<cumulative_count:0 upper_bound:0.25 > bucket:<cumulative_count:0 upper_bound:0.5 > bucket:<cumulative_count:1 upper_bound:1 > bucket:<cumulative_count:2 upper_bound:2.5 > bucket:<cumulative_count:3 upper_bound:5 > bucket:<cumulative_count:3 upper_bound:10 > sb_schema:0 sb_zero_threshold:0 `, // Has conventional buckets because there are no sparse buckets.
|
||||
},
|
||||
{
|
||||
name: "factor 1.1 results in schema 3",
|
||||
observations: []float64{0, 1, 2, 3},
|
||||
factor: 1.1,
|
||||
want: `sample_count:4 sample_sum:6 sb_schema:3 sb_zero_threshold:2.938735877055719e-39 sb_zero_count:1 sb_positive:<span:<offset:0 length:1 > span:<offset:7 length:1 > span:<offset:4 length:1 > delta:1 delta:0 delta:0 > `,
|
||||
},
|
||||
{
|
||||
name: "factor 1.2 results in schema 2",
|
||||
observations: []float64{0, 1, 1.2, 1.4, 1.8, 2},
|
||||
factor: 1.2,
|
||||
want: `sample_count:6 sample_sum:7.4 sb_schema:2 sb_zero_threshold:2.938735877055719e-39 sb_zero_count:1 sb_positive:<span:<offset:0 length:5 > delta:1 delta:-1 delta:2 delta:-2 delta:2 > `,
|
||||
},
|
||||
{
|
||||
name: "negative buckets",
|
||||
observations: []float64{0, -1, -1.2, -1.4, -1.8, -2},
|
||||
factor: 1.2,
|
||||
want: `sample_count:6 sample_sum:-7.4 sb_schema:2 sb_zero_threshold:2.938735877055719e-39 sb_zero_count:1 sb_negative:<span:<offset:0 length:5 > delta:1 delta:-1 delta:2 delta:-2 delta:2 > `,
|
||||
},
|
||||
{
|
||||
name: "negative and positive buckets",
|
||||
observations: []float64{0, -1, -1.2, -1.4, -1.8, -2, 1, 1.2, 1.4, 1.8, 2},
|
||||
factor: 1.2,
|
||||
want: `sample_count:11 sample_sum:0 sb_schema:2 sb_zero_threshold:2.938735877055719e-39 sb_zero_count:1 sb_negative:<span:<offset:0 length:5 > delta:1 delta:-1 delta:2 delta:-2 delta:2 > sb_positive:<span:<offset:0 length:5 > delta:1 delta:-1 delta:2 delta:-2 delta:2 > `,
|
||||
},
|
||||
{
|
||||
name: "wide zero bucket",
|
||||
observations: []float64{0, -1, -1.2, -1.4, -1.8, -2, 1, 1.2, 1.4, 1.8, 2},
|
||||
factor: 1.2,
|
||||
zeroThreshold: 1.4,
|
||||
want: `sample_count:11 sample_sum:0 sb_schema:2 sb_zero_threshold:1.4 sb_zero_count:7 sb_negative:<span:<offset:4 length:1 > delta:2 > sb_positive:<span:<offset:4 length:1 > delta:2 > `,
|
||||
},
|
||||
{
|
||||
name: "NaN observation",
|
||||
observations: []float64{0, 1, 1.2, 1.4, 1.8, 2, math.NaN()},
|
||||
factor: 1.2,
|
||||
want: `sample_count:7 sample_sum:nan sb_schema:2 sb_zero_threshold:2.938735877055719e-39 sb_zero_count:1 sb_positive:<span:<offset:0 length:5 > delta:1 delta:-1 delta:2 delta:-2 delta:2 > `,
|
||||
},
|
||||
{
|
||||
name: "+Inf observation",
|
||||
observations: []float64{0, 1, 1.2, 1.4, 1.8, 2, math.Inf(+1)},
|
||||
factor: 1.2,
|
||||
want: `sample_count:7 sample_sum:inf sb_schema:2 sb_zero_threshold:2.938735877055719e-39 sb_zero_count:1 sb_positive:<span:<offset:0 length:5 > span:<offset:2147483642 length:1 > delta:1 delta:-1 delta:2 delta:-2 delta:2 delta:-1 > `,
|
||||
},
|
||||
{
|
||||
name: "-Inf observation",
|
||||
observations: []float64{0, 1, 1.2, 1.4, 1.8, 2, math.Inf(-1)},
|
||||
factor: 1.2,
|
||||
want: `sample_count:7 sample_sum:-inf sb_schema:2 sb_zero_threshold:2.938735877055719e-39 sb_zero_count:1 sb_negative:<span:<offset:2147483647 length:1 > delta:1 > sb_positive:<span:<offset:0 length:5 > delta:1 delta:-1 delta:2 delta:-2 delta:2 > `,
|
||||
},
|
||||
}
|
||||
|
||||
for _, s := range scenarios {
|
||||
t.Run(s.name, func(t *testing.T) {
|
||||
his := NewHistogram(HistogramOpts{
|
||||
Name: "name",
|
||||
Help: "help",
|
||||
SparseBucketsFactor: s.factor,
|
||||
SparseBucketsZeroThreshold: s.zeroThreshold,
|
||||
})
|
||||
for _, o := range s.observations {
|
||||
his.Observe(o)
|
||||
}
|
||||
m := &dto.Metric{}
|
||||
if err := his.Write(m); err != nil {
|
||||
t.Fatal("unexpected error writing metric", err)
|
||||
}
|
||||
got := m.Histogram.String()
|
||||
if s.want != got {
|
||||
t.Errorf("want histogram %q, got %q", s.want, got)
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue