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Merge pull request #545 from prometheus/beorn7/quantile

Browse source 
Add the histogram_quantile function.
This commit is contained in:
Björn Rabenstein 2015-02-23 15:30:24 +01:00
parent 03191cfc8e 1a61bcae07
commit 136cf661b7
17 changed files with 953 additions and 99 deletions
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24
Godeps/Godeps.json generated
View file

@ -20,33 +20,33 @@
},
{
"ImportPath": "github.com/prometheus/client_golang/_vendor/goautoneg",
"Comment": "0.1.0-24-g4627d59",
"Rev": "4627d59e8a09c330c5ccfe7414baca28d8df847d"
"Comment": "0.1.0-37-g54238de",
"Rev": "54238dea8fa4dfaa411b8c9ef0680ba441fb7b1c"
},
{
"ImportPath": "github.com/prometheus/client_golang/_vendor/perks/quantile",
"Comment": "0.1.0-24-g4627d59",
"Rev": "4627d59e8a09c330c5ccfe7414baca28d8df847d"
"Comment": "0.1.0-37-g54238de",
"Rev": "54238dea8fa4dfaa411b8c9ef0680ba441fb7b1c"
},
{
"ImportPath": "github.com/prometheus/client_golang/extraction",
"Comment": "0.1.0-24-g4627d59",
"Rev": "4627d59e8a09c330c5ccfe7414baca28d8df847d"
"Comment": "0.1.0-37-g54238de",
"Rev": "54238dea8fa4dfaa411b8c9ef0680ba441fb7b1c"
},
{
"ImportPath": "github.com/prometheus/client_golang/model",
"Comment": "0.1.0-24-g4627d59",
"Rev": "4627d59e8a09c330c5ccfe7414baca28d8df847d"
"Comment": "0.1.0-37-g54238de",
"Rev": "54238dea8fa4dfaa411b8c9ef0680ba441fb7b1c"
},
{
"ImportPath": "github.com/prometheus/client_golang/prometheus",
"Comment": "0.1.0-24-g4627d59",
"Rev": "4627d59e8a09c330c5ccfe7414baca28d8df847d"
"Comment": "0.1.0-37-g54238de",
"Rev": "54238dea8fa4dfaa411b8c9ef0680ba441fb7b1c"
},
{
"ImportPath": "github.com/prometheus/client_golang/text",
"Comment": "0.1.0-24-g4627d59",
"Rev": "4627d59e8a09c330c5ccfe7414baca28d8df847d"
"Comment": "0.1.0-37-g54238de",
"Rev": "54238dea8fa4dfaa411b8c9ef0680ba441fb7b1c"
},
{
"ImportPath": "github.com/prometheus/client_model/go",

139
Godeps/_workspace/src/github.com/prometheus/client_golang/extraction/metricfamilyprocessor.go generated vendored
View file

@ -16,6 +16,7 @@ package extraction
import (
"fmt"
"io"
"math"
dto "github.com/prometheus/client_model/go"
@ -85,7 +86,10 @@ func extractCounter(out Ingester, o *ProcessOptions, f *dto.MetricFamily) error
continue
}
sample := new(model.Sample)
sample := &model.Sample{
Metric: model.Metric{},
Value: model.SampleValue(m.Counter.GetValue()),
}
samples = append(samples, sample)
if m.TimestampMs != nil {
@ -93,16 +97,12 @@ func extractCounter(out Ingester, o *ProcessOptions, f *dto.MetricFamily) error
} else {
sample.Timestamp = o.Timestamp
}
sample.Metric = model.Metric{}
metric := sample.Metric
metric := sample.Metric
for _, p := range m.Label {
metric[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
}
metric[model.MetricNameLabel] = model.LabelValue(f.GetName())
sample.Value = model.SampleValue(m.Counter.GetValue())
}
return out.Ingest(samples)
@ -116,7 +116,10 @@ func extractGauge(out Ingester, o *ProcessOptions, f *dto.MetricFamily) error {
continue
}
sample := new(model.Sample)
sample := &model.Sample{
Metric: model.Metric{},
Value: model.SampleValue(m.Gauge.GetValue()),
}
samples = append(samples, sample)
if m.TimestampMs != nil {
@ -124,16 +127,12 @@ func extractGauge(out Ingester, o *ProcessOptions, f *dto.MetricFamily) error {
} else {
sample.Timestamp = o.Timestamp
}
sample.Metric = model.Metric{}
metric := sample.Metric
metric := sample.Metric
for _, p := range m.Label {
metric[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
}
metric[model.MetricNameLabel] = model.LabelValue(f.GetName())
sample.Value = model.SampleValue(m.Gauge.GetValue())
}
return out.Ingest(samples)
@ -153,48 +152,50 @@ func extractSummary(out Ingester, o *ProcessOptions, f *dto.MetricFamily) error
}
for _, q := range m.Summary.Quantile {
sample := new(model.Sample)
sample := &model.Sample{
Metric: model.Metric{},
Value: model.SampleValue(q.GetValue()),
Timestamp: timestamp,
}
samples = append(samples, sample)
sample.Timestamp = timestamp
sample.Metric = model.Metric{}
metric := sample.Metric
for _, p := range m.Label {
metric[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
}
// BUG(matt): Update other names to "quantile".
metric[model.LabelName("quantile")] = model.LabelValue(fmt.Sprint(q.GetQuantile()))
metric[model.LabelName(model.QuantileLabel)] = model.LabelValue(fmt.Sprint(q.GetQuantile()))
metric[model.MetricNameLabel] = model.LabelValue(f.GetName())
sample.Value = model.SampleValue(q.GetValue())
}
if m.Summary.SampleSum != nil {
sum := new(model.Sample)
sum.Timestamp = timestamp
metric := model.Metric{}
sum := &model.Sample{
Metric: model.Metric{},
Value: model.SampleValue(m.Summary.GetSampleSum()),
Timestamp: timestamp,
}
samples = append(samples, sum)
metric := sum.Metric
for _, p := range m.Label {
metric[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
}
metric[model.MetricNameLabel] = model.LabelValue(f.GetName() + "_sum")
sum.Metric = metric
sum.Value = model.SampleValue(m.Summary.GetSampleSum())
samples = append(samples, sum)
}
if m.Summary.SampleCount != nil {
count := new(model.Sample)
count.Timestamp = timestamp
metric := model.Metric{}
count := &model.Sample{
Metric: model.Metric{},
Value: model.SampleValue(m.Summary.GetSampleCount()),
Timestamp: timestamp,
}
samples = append(samples, count)
metric := count.Metric
for _, p := range m.Label {
metric[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
}
metric[model.MetricNameLabel] = model.LabelValue(f.GetName() + "_count")
count.Metric = metric
count.Value = model.SampleValue(m.Summary.GetSampleCount())
samples = append(samples, count)
}
}
@ -209,7 +210,10 @@ func extractUntyped(out Ingester, o *ProcessOptions, f *dto.MetricFamily) error
continue
}
sample := new(model.Sample)
sample := &model.Sample{
Metric: model.Metric{},
Value: model.SampleValue(m.Untyped.GetValue()),
}
samples = append(samples, sample)
if m.TimestampMs != nil {
@ -217,16 +221,12 @@ func extractUntyped(out Ingester, o *ProcessOptions, f *dto.MetricFamily) error
} else {
sample.Timestamp = o.Timestamp
}
sample.Metric = model.Metric{}
metric := sample.Metric
metric := sample.Metric
for _, p := range m.Label {
metric[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
}
metric[model.MetricNameLabel] = model.LabelValue(f.GetName())
sample.Value = model.SampleValue(m.Untyped.GetValue())
}
return out.Ingest(samples)
@ -245,49 +245,72 @@ func extractHistogram(out Ingester, o *ProcessOptions, f *dto.MetricFamily) erro
timestamp = model.TimestampFromUnixNano(*m.TimestampMs * 1000000)
}
infSeen := false
for _, q := range m.Histogram.Bucket {
sample := new(model.Sample)
sample := &model.Sample{
Metric: model.Metric{},
Value: model.SampleValue(q.GetCumulativeCount()),
Timestamp: timestamp,
}
samples = append(samples, sample)
sample.Timestamp = timestamp
sample.Metric = model.Metric{}
metric := sample.Metric
for _, p := range m.Label {
metric[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
}
metric[model.LabelName("le")] = model.LabelValue(fmt.Sprint(q.GetUpperBound()))
metric[model.LabelName(model.BucketLabel)] = model.LabelValue(fmt.Sprint(q.GetUpperBound()))
metric[model.MetricNameLabel] = model.LabelValue(f.GetName() + "_bucket")
sample.Value = model.SampleValue(q.GetCumulativeCount())
if math.IsInf(q.GetUpperBound(), +1) {
infSeen = true
}
}
// TODO: If +Inf bucket is missing, add it.
if m.Histogram.SampleSum != nil {
sum := new(model.Sample)
sum.Timestamp = timestamp
metric := model.Metric{}
sum := &model.Sample{
Metric: model.Metric{},
Value: model.SampleValue(m.Histogram.GetSampleSum()),
Timestamp: timestamp,
}
samples = append(samples, sum)
metric := sum.Metric
for _, p := range m.Label {
metric[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
}
metric[model.MetricNameLabel] = model.LabelValue(f.GetName() + "_sum")
sum.Metric = metric
sum.Value = model.SampleValue(m.Histogram.GetSampleSum())
samples = append(samples, sum)
}
if m.Histogram.SampleCount != nil {
count := new(model.Sample)
count.Timestamp = timestamp
metric := model.Metric{}
count := &model.Sample{
Metric: model.Metric{},
Value: model.SampleValue(m.Histogram.GetSampleCount()),
Timestamp: timestamp,
}
samples = append(samples, count)
metric := count.Metric
for _, p := range m.Label {
metric[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
}
metric[model.MetricNameLabel] = model.LabelValue(f.GetName() + "_count")
count.Metric = metric
count.Value = model.SampleValue(m.Histogram.GetSampleCount())
samples = append(samples, count)
if !infSeen {
infBucket := &model.Sample{
Metric: model.Metric{},
Value: count.Value,
Timestamp: timestamp,
}
samples = append(samples, infBucket)
metric := infBucket.Metric
for _, p := range m.Label {
metric[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
}
metric[model.LabelName(model.BucketLabel)] = model.LabelValue("+Inf")
metric[model.MetricNameLabel] = model.LabelValue(f.GetName() + "_bucket")
}
}
}

8
Godeps/_workspace/src/github.com/prometheus/client_golang/model/labelname.go generated vendored
View file

@ -33,6 +33,14 @@ const (
// JobLabel is the label name indicating the job from which a timeseries
// was scraped.
JobLabel LabelName = "job"
// BucketLabel is used for the label that defines the upper bound of a
// bucket of a histogram ("le" -> "less or equal").
BucketLabel = "le"
// QuantileLabel is used for the label that defines the quantile in a
// summary.
QuantileLabel = "quantile"
)
// A LabelName is a key for a LabelSet or Metric. It has a value associated

28
Godeps/_workspace/src/github.com/prometheus/client_golang/prometheus/benchmark_test.go generated vendored
View file

@ -129,3 +129,31 @@ func BenchmarkSummaryNoLabels(b *testing.B) {
m.Observe(3.1415)
}
}
func BenchmarkHistogramWithLabelValues(b *testing.B) {
m := NewHistogramVec(
HistogramOpts{
Name: "benchmark_histogram",
Help: "A histogram to benchmark it.",
},
[]string{"one", "two", "three"},
)
b.ReportAllocs()
b.ResetTimer()
for i := 0; i < b.N; i++ {
m.WithLabelValues("eins", "zwei", "drei").Observe(3.1415)
}
}
func BenchmarkHistogramNoLabels(b *testing.B) {
m := NewHistogram(HistogramOpts{
Name: "benchmark_histogram",
Help: "A histogram to benchmark it.",
},
)
b.ReportAllocs()
b.ResetTimer()
for i := 0; i < b.N; i++ {
m.Observe(3.1415)
}
}

2
Godeps/_workspace/src/github.com/prometheus/client_golang/prometheus/counter.go generated vendored
View file

@ -74,7 +74,7 @@ func (c *counter) Add(v float64) {
// CounterVec is a Collector that bundles a set of Counters that all share the
// same Desc, but have different values for their variable labels. This is used
// if you want to count the same thing partitioned by various dimensions
// (e.g. number of http requests, partitioned by response code and
// (e.g. number of HTTP requests, partitioned by response code and
// method). Create instances with NewCounterVec.
//
// CounterVec embeds MetricVec. See there for a full list of methods with

52
Godeps/_workspace/src/github.com/prometheus/client_golang/prometheus/examples_test.go generated vendored
View file

@ -129,7 +129,7 @@ func ExampleCounterVec() {
httpReqs := prometheus.NewCounterVec(
prometheus.CounterOpts{
Name: "http_requests_total",
Help: "How many HTTP requests processed, partitioned by status code and http method.",
Help: "How many HTTP requests processed, partitioned by status code and HTTP method.",
ConstLabels: prometheus.Labels{"env": *binaryVersion},
},
[]string{"code", "method"},
@ -200,7 +200,7 @@ func ExampleRegister() {
fmt.Println("taskCounter registered.")
}
// Don't forget to tell the HTTP server about the Prometheus handler.
// (In a real program, you still need to start the http server...)
// (In a real program, you still need to start the HTTP server...)
http.Handle("/metrics", prometheus.Handler())
// Now you can start workers and give every one of them a pointer to
@ -240,7 +240,7 @@ func ExampleRegister() {
// Prometheus will not allow you to ever export metrics with
// inconsistent help strings or label names. After unregistering, the
// unregistered metrics will cease to show up in the /metrics http
// unregistered metrics will cease to show up in the /metrics HTTP
// response, but the registry still remembers that those metrics had
// been exported before. For this example, we will now choose a
// different name. (In a real program, you would obviously not export
@ -452,3 +452,49 @@ func ExampleSummaryVec() {
// >
// ]
}
func ExampleHistogram() {
temps := prometheus.NewHistogram(prometheus.HistogramOpts{
Name: "pond_temperature_celsius",
Help: "The temperature of the frog pond.", // Sorry, we can't measure how badly it smells.
Buckets: prometheus.LinearBuckets(20, 5, 5), // 5 buckets, each 5 centigrade wide.
})
// Simulate some observations.
for i := 0; i < 1000; i++ {
temps.Observe(30 + math.Floor(120*math.Sin(float64(i)*0.1))/10)
}
// Just for demonstration, let's check the state of the histogram by
// (ab)using its Write method (which is usually only used by Prometheus
// internally).
metric := &dto.Metric{}
temps.Write(metric)
fmt.Println(proto.MarshalTextString(metric))
// Output:
// histogram: <
// sample_count: 1000
// sample_sum: 29969.50000000001
// bucket: <
// cumulative_count: 192
// upper_bound: 20
// >
// bucket: <
// cumulative_count: 366
// upper_bound: 25
// >
// bucket: <
// cumulative_count: 501
// upper_bound: 30
// >
// bucket: <
// cumulative_count: 638
// upper_bound: 35
// >
// bucket: <
// cumulative_count: 816
// upper_bound: 40
// >
// >
}

2
Godeps/_workspace/src/github.com/prometheus/client_golang/prometheus/http.go generated vendored
View file

@ -47,7 +47,7 @@ func nowSeries(t ...time.Time) nower {
}
// InstrumentHandler wraps the given HTTP handler for instrumentation. It
// registers four metric collectors (if not already done) and reports http
// registers four metric collectors (if not already done) and reports HTTP
// metrics to the (newly or already) registered collectors: http_requests_total
// (CounterVec), http_request_duration_microseconds (Summary),
// http_request_size_bytes (Summary), http_response_size_bytes (Summary). Each

4
Godeps/_workspace/src/github.com/prometheus/client_golang/prometheus/registry.go generated vendored
View file

@ -171,7 +171,7 @@ func SetMetricFamilyInjectionHook(hook func() []*dto.MetricFamily) {
}
// PanicOnCollectError sets the behavior whether a panic is caused upon an error
// while metrics are collected and served to the http endpoint. By default, an
// while metrics are collected and served to the HTTP endpoint. By default, an
// internal server error (status code 500) is served with an error message.
func PanicOnCollectError(b bool) {
defRegistry.panicOnCollectError = b
@ -464,6 +464,8 @@ func (r *registry) writePB(w io.Writer, writeEncoded encoder) (int, error) {
metricFamily.Type = dto.MetricType_SUMMARY.Enum()
case dtoMetric.Untyped != nil:
metricFamily.Type = dto.MetricType_UNTYPED.Enum()
case dtoMetric.Histogram != nil:
metricFamily.Type = dto.MetricType_HISTOGRAM.Enum()
default:
return 0, fmt.Errorf("empty metric collected: %s", dtoMetric)
}

39
Godeps/_workspace/src/github.com/prometheus/client_golang/prometheus/summary.go generated vendored
View file

@ -25,6 +25,7 @@ import (
dto "github.com/prometheus/client_model/go"
"github.com/prometheus/client_golang/_vendor/perks/quantile"
"github.com/prometheus/client_golang/model"
)
// A Summary captures individual observations from an event or sample stream and
@ -35,6 +36,12 @@ import (
// Summary provides the median, the 90th and the 99th percentile of the latency
// as rank estimations.
//
// Note that the rank estimations cannot be aggregated in a meaningful way with
// the Prometheus query language (i.e. you cannot average or add them). If you
// need aggregatable quantiles (e.g. you want the 99th percentile latency of all
// queries served across all instances of a service), consider the Histogram
// metric type. See the Prometheus documentation for more details.
//
// To create Summary instances, use NewSummary.
type Summary interface {
Metric
@ -44,9 +51,13 @@ type Summary interface {
Observe(float64)
}
// DefObjectives are the default Summary quantile values.
var (
// DefObjectives are the default Summary quantile values.
DefObjectives = map[float64]float64{0.5: 0.05, 0.9: 0.01, 0.99: 0.001}
errQuantileLabelNotAllowed = fmt.Errorf(
"%q is not allowed as label name in summaries", model.QuantileLabel,
)
)
// Default values for SummaryOpts.
@ -110,7 +121,10 @@ type SummaryOpts struct {
// AgeBuckets is the number of buckets used to exclude observations that
// are older than MaxAge from the summary. A higher number has a
// resource penalty, so only increase it if the higher resolution is
// really required. The default value is DefAgeBuckets.
// really required. For very high observation rates, you might want to
// reduce the number of age buckets. With only one age bucket, you will
// effectively see a complete reset of the summary each time MaxAge has
// passed. The default value is DefAgeBuckets.
AgeBuckets uint32
// BufCap defines the default sample stream buffer size. The default
@ -119,10 +133,6 @@ type SummaryOpts struct {
// is the internal buffer size of the underlying package
// "github.com/bmizerany/perks/quantile").
BufCap uint32
// Epsilon is the error epsilon for the quantile rank estimate. Must be
// positive. The default is DefEpsilon.
Epsilon float64
}
// TODO: Great fuck-up with the sliding-window decay algorithm... The Merge
@ -158,6 +168,17 @@ func newSummary(desc *Desc, opts SummaryOpts, labelValues ...string) Summary {
panic(errInconsistentCardinality)
}
for _, n := range desc.variableLabels {
if n == model.QuantileLabel {
panic(errQuantileLabelNotAllowed)
}
}
for _, lp := range desc.constLabelPairs {
if lp.GetName() == model.QuantileLabel {
panic(errQuantileLabelNotAllowed)
}
}
if len(opts.Objectives) == 0 {
opts.Objectives = DefObjectives
}
@ -358,7 +379,7 @@ func (s quantSort) Less(i, j int) bool {
// SummaryVec is a Collector that bundles a set of Summaries that all share the
// same Desc, but have different values for their variable labels. This is used
// if you want to count the same thing partitioned by various dimensions
// (e.g. http request latencies, partitioned by status code and method). Create
// (e.g. HTTP request latencies, partitioned by status code and method). Create
// instances with NewSummaryVec.
type SummaryVec struct {
MetricVec
@ -411,14 +432,14 @@ func (m *SummaryVec) GetMetricWith(labels Labels) (Summary, error) {
// WithLabelValues works as GetMetricWithLabelValues, but panics where
// GetMetricWithLabelValues would have returned an error. By not returning an
// error, WithLabelValues allows shortcuts like
// myVec.WithLabelValues("404", "GET").Add(42)
// myVec.WithLabelValues("404", "GET").Observe(42.21)
func (m *SummaryVec) WithLabelValues(lvs ...string) Summary {
return m.MetricVec.WithLabelValues(lvs...).(Summary)
}
// With works as GetMetricWith, but panics where GetMetricWithLabels would have
// returned an error. By not returning an error, With allows shortcuts like
// myVec.With(Labels{"code": "404", "method": "GET"}).Add(42)
// myVec.With(Labels{"code": "404", "method": "GET"}).Observe(42.21)
func (m *SummaryVec) With(labels Labels) Summary {
return m.MetricVec.With(labels).(Summary)
}

8
Godeps/_workspace/src/github.com/prometheus/client_golang/prometheus/summary_test.go generated vendored
View file

@ -120,6 +120,10 @@ func BenchmarkSummaryWrite8(b *testing.B) {
}
func TestSummaryConcurrency(t *testing.T) {
if testing.Short() {
t.Skip("Skipping test in short mode.")
}
rand.Seed(42)
it := func(n uint32) bool {
@ -195,6 +199,10 @@ func TestSummaryConcurrency(t *testing.T) {
}
func TestSummaryVecConcurrency(t *testing.T) {
if testing.Short() {
t.Skip("Skipping test in short mode.")
}
rand.Seed(42)
objectives := make([]float64, 0, len(DefObjectives))

23
Godeps/_workspace/src/github.com/prometheus/client_golang/text/create.go generated vendored
View file

@ -24,8 +24,10 @@ import (
"bytes"
"fmt"
"io"
"math"
"strings"
"github.com/prometheus/client_golang/model"
dto "github.com/prometheus/client_model/go"
)
@ -116,7 +118,7 @@ func MetricFamilyToText(out io.Writer, in *dto.MetricFamily) (int, error) {
for _, q := range metric.Summary.Quantile {
n, err = writeSample(
name, metric,
"quantile", fmt.Sprint(q.GetQuantile()),
model.QuantileLabel, fmt.Sprint(q.GetQuantile()),
q.GetValue(),
out,
)
@ -145,10 +147,11 @@ func MetricFamilyToText(out io.Writer, in *dto.MetricFamily) (int, error) {
"expected summary in metric %s", metric,
)
}
infSeen := false
for _, q := range metric.Histogram.Bucket {
n, err = writeSample(
name+"_bucket", metric,
"le", fmt.Sprint(q.GetUpperBound()),
model.BucketLabel, fmt.Sprint(q.GetUpperBound()),
float64(q.GetCumulativeCount()),
out,
)
@ -156,7 +159,21 @@ func MetricFamilyToText(out io.Writer, in *dto.MetricFamily) (int, error) {
if err != nil {
return written, err
}
// TODO: Add +inf bucket if it's missing.
if math.IsInf(q.GetUpperBound(), +1) {
infSeen = true
}
}
if !infSeen {
n, err = writeSample(
name+"_bucket", metric,
model.BucketLabel, "+Inf",
float64(metric.Histogram.GetSampleCount()),
out,
)
if err != nil {
return written, err
}
written += n
}
n, err = writeSample(
name+"_sum", metric, "", "",

44
Godeps/_workspace/src/github.com/prometheus/client_golang/text/create_test.go generated vendored
View file

@ -267,6 +267,50 @@ request_duration_microseconds_bucket{le="172.8"} 1524
request_duration_microseconds_bucket{le="+Inf"} 2693
request_duration_microseconds_sum 1.7560473e+06
request_duration_microseconds_count 2693
`,
},
// 5: Histogram with missing +Inf bucket.
{
in: &dto.MetricFamily{
Name: proto.String("request_duration_microseconds"),
Help: proto.String("The response latency."),
Type: dto.MetricType_HISTOGRAM.Enum(),
Metric: []*dto.Metric{
&dto.Metric{
Histogram: &dto.Histogram{
SampleCount: proto.Uint64(2693),
SampleSum: proto.Float64(1756047.3),
Bucket: []*dto.Bucket{
&dto.Bucket{
UpperBound: proto.Float64(100),
CumulativeCount: proto.Uint64(123),
},
&dto.Bucket{
UpperBound: proto.Float64(120),
CumulativeCount: proto.Uint64(412),
},
&dto.Bucket{
UpperBound: proto.Float64(144),
CumulativeCount: proto.Uint64(592),
},
&dto.Bucket{
UpperBound: proto.Float64(172.8),
CumulativeCount: proto.Uint64(1524),
},
},
},
},
},
},
out: `# HELP request_duration_microseconds The response latency.
# TYPE request_duration_microseconds histogram
request_duration_microseconds_bucket{le="100"} 123
request_duration_microseconds_bucket{le="120"} 412
request_duration_microseconds_bucket{le="144"} 592
request_duration_microseconds_bucket{le="172.8"} 1524
request_duration_microseconds_bucket{le="+Inf"} 2693
request_duration_microseconds_sum 1.7560473e+06
request_duration_microseconds_count 2693
`,
},
}

8
Godeps/_workspace/src/github.com/prometheus/client_golang/text/parse.go generated vendored
View file

@ -274,8 +274,8 @@ func (p *Parser) startLabelName() stateFn {
}
// Special summary/histogram treatment. Don't add 'quantile' and 'le'
// labels to 'real' labels.
if !(p.currentMF.GetType() == dto.MetricType_SUMMARY && p.currentLabelPair.GetName() == "quantile") &&
!(p.currentMF.GetType() == dto.MetricType_HISTOGRAM && p.currentLabelPair.GetName() == "le") {
if !(p.currentMF.GetType() == dto.MetricType_SUMMARY && p.currentLabelPair.GetName() == model.QuantileLabel) &&
!(p.currentMF.GetType() == dto.MetricType_HISTOGRAM && p.currentLabelPair.GetName() == model.BucketLabel) {
p.currentMetric.Label = append(p.currentMetric.Label, p.currentLabelPair)
}
if p.skipBlankTabIfCurrentBlankTab(); p.err != nil {
@ -306,7 +306,7 @@ func (p *Parser) startLabelValue() stateFn {
// - Quantile labels are special, will result in dto.Quantile later.
// - Other labels have to be added to currentLabels for signature calculation.
if p.currentMF.GetType() == dto.MetricType_SUMMARY {
if p.currentLabelPair.GetName() == "quantile" {
if p.currentLabelPair.GetName() == model.QuantileLabel {
if p.currentQuantile, p.err = strconv.ParseFloat(p.currentLabelPair.GetValue(), 64); p.err != nil {
// Create a more helpful error message.
p.parseError(fmt.Sprintf("expected float as value for 'quantile' label, got %q", p.currentLabelPair.GetValue()))
@ -318,7 +318,7 @@ func (p *Parser) startLabelValue() stateFn {
}
// Similar special treatment of histograms.
if p.currentMF.GetType() == dto.MetricType_HISTOGRAM {
if p.currentLabelPair.GetName() == "le" {
if p.currentLabelPair.GetName() == model.BucketLabel {
if p.currentBucket, p.err = strconv.ParseFloat(p.currentLabelPair.GetValue(), 64); p.err != nil {
// Create a more helpful error message.
p.parseError(fmt.Sprintf("expected float as value for 'le' label, got %q", p.currentLabelPair.GetValue()))

56
rules/ast/functions.go
View file

@ -18,6 +18,7 @@ import (
"fmt"
"math"
"sort"
"strconv"
"time"
clientmodel "github.com/prometheus/client_golang/model"
@ -498,6 +499,43 @@ func derivImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
return resultVector
}
// === histogram_quantile(k ScalarNode, vector VectorNode) Vector ===
func histogramQuantileImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
q := args[0].(ScalarNode).Eval(timestamp)
inVec := args[1].(VectorNode).Eval(timestamp)
outVec := Vector{}
fpToMetricWithBuckets := map[clientmodel.Fingerprint]*metricWithBuckets{}
for _, el := range inVec {
upperBound, err := strconv.ParseFloat(
string(el.Metric.Metric[clientmodel.BucketLabel]), 64,
)
if err != nil {
// Oops, no bucket label or malformed label value. Skip.
// TODO(beorn7): Issue a warning somehow.
continue
}
fp := bucketFingerprint(el.Metric.Metric)
mb, ok := fpToMetricWithBuckets[fp]
if !ok {
el.Metric.Delete(clientmodel.BucketLabel)
el.Metric.Delete(clientmodel.MetricNameLabel)
mb = &metricWithBuckets{el.Metric, nil}
fpToMetricWithBuckets[fp] = mb
}
mb.buckets = append(mb.buckets, bucket{upperBound, el.Value})
}
for _, mb := range fpToMetricWithBuckets {
outVec = append(outVec, &Sample{
Metric: mb.metric,
Value: clientmodel.SampleValue(quantile(q, mb.buckets)),
Timestamp: timestamp,
})
}
return outVec
}
var functions = map[string]*Function{
"abs": {
name: "abs",
@ -548,6 +586,12 @@ var functions = map[string]*Function{
returnType: VectorType,
callFn: deltaImpl,
},
"deriv": {
name: "deriv",
argTypes: []ExprType{MatrixType},
returnType: VectorType,
callFn: derivImpl,
},
"drop_common_labels": {
name: "drop_common_labels",
argTypes: []ExprType{VectorType},
@ -560,6 +604,12 @@ var functions = map[string]*Function{
returnType: VectorType,
callFn: floorImpl,
},
"histogram_quantile": {
name: "histogram_quantile",
argTypes: []ExprType{ScalarType, VectorType},
returnType: VectorType,
callFn: histogramQuantileImpl,
},
"max_over_time": {
name: "max_over_time",
argTypes: []ExprType{MatrixType},
@ -621,12 +671,6 @@ var functions = map[string]*Function{
returnType: VectorType,
callFn: topkImpl,
},
"deriv": {
name: "deriv",
argTypes: []ExprType{MatrixType},
returnType: VectorType,
callFn: derivImpl,
},
}
// GetFunction returns a predefined Function object for the given

144
rules/ast/quantile.go Normal file
View file

@ -0,0 +1,144 @@
// Copyright 2015 The Prometheus Authors
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package ast
import (
"encoding/binary"
"hash/fnv"
"math"
"sort"
clientmodel "github.com/prometheus/client_golang/model"
)
// Helpers to calculate quantiles.
type bucket struct {
upperBound float64
count clientmodel.SampleValue
}
// buckets implements sort.Interface.
type buckets []bucket
func (b buckets) Len() int { return len(b) }
func (b buckets) Swap(i, j int) { b[i], b[j] = b[j], b[i] }
func (b buckets) Less(i, j int) bool { return b[i].upperBound < b[j].upperBound }
type metricWithBuckets struct {
metric clientmodel.COWMetric
buckets buckets
}
// quantile calculates the quantile 'q' based on the given buckets. The buckets
// will be sorted by upperBound by this function (i.e. no sorting needed before
// calling this function). The quantile value is interpolated assuming a linear
// distribution within a bucket. However, if the quantile falls into the highest
// bucket, the upper bound of the 2nd highest bucket is returned. A natural
// lower bound of 0 is assumed if the upper bound of the lowest bucket is
// greater 0. In that case, interpolation in the lowest bucket happens linearly
// between 0 and the upper bound of the lowest bucket. However, if the lowest
// bucket has an upper bound less or equal 0, this upper bound is returned if
// the quantile falls into the lowest bucket.
//
// There are a number of special cases (once we have a way to report errors
// happening during evaluations of AST functions, we should report those
// explicitly):
//
// If 'buckets' has fewer than 2 elements, NaN is returned.
//
// If the highest bucket is not +Inf, NaN is returned.
//
// If q<0, -Inf is returned.
//
// If q>1, +Inf is returned.
func quantile(q clientmodel.SampleValue, buckets buckets) float64 {
if q < 0 {
return math.Inf(-1)
}
if q > 1 {
return math.Inf(+1)
}
if len(buckets) < 2 {
return math.NaN()
}
sort.Sort(buckets)
if !math.IsInf(buckets[len(buckets)-1].upperBound, +1) {
return math.NaN()
}
rank := q * buckets[len(buckets)-1].count
b := sort.Search(len(buckets)-1, func(i int) bool { return buckets[i].count >= rank })
if b == len(buckets)-1 {
return buckets[len(buckets)-2].upperBound
}
if b == 0 && buckets[0].upperBound <= 0 {
return buckets[0].upperBound
}
var (
bucketStart float64
bucketEnd = buckets[b].upperBound
count = buckets[b].count
)
if b > 0 {
bucketStart = buckets[b-1].upperBound
count -= buckets[b-1].count
rank -= buckets[b-1].count
}
return bucketStart + (bucketEnd-bucketStart)*float64(rank/count)
}
// bucketFingerprint works like the Fingerprint method of Metric, but ignores
// the name and the bucket label.
func bucketFingerprint(m clientmodel.Metric) clientmodel.Fingerprint {
numLabels := 0
if len(m) > 2 {
numLabels = len(m) - 2
}
labelNames := make([]string, 0, numLabels)
maxLength := 0
for labelName, labelValue := range m {
if labelName == clientmodel.MetricNameLabel || labelName == clientmodel.BucketLabel {
continue
}
labelNames = append(labelNames, string(labelName))
if len(labelName) > maxLength {
maxLength = len(labelName)
}
if len(labelValue) > maxLength {
maxLength = len(labelValue)
}
}
sort.Strings(labelNames)
summer := fnv.New64a()
buf := make([]byte, maxLength)
for _, labelName := range labelNames {
labelValue := m[clientmodel.LabelName(labelName)]
copy(buf, labelName)
summer.Write(buf[:len(labelName)])
summer.Write([]byte{clientmodel.SeparatorByte})
copy(buf, labelValue)
summer.Write(buf[:len(labelValue)])
summer.Write([]byte{clientmodel.SeparatorByte})
}
return clientmodel.Fingerprint(binary.LittleEndian.Uint64(summer.Sum(nil)))
}

218
rules/helpers_test.go
View file

@ -205,6 +205,224 @@ var testMatrix = ast.Matrix{
},
Values: getTestValueStream(0, 200, 20, testStartTime),
},
// Two histograms with 4 buckets each (*_sum and *_count not included,
// only buckets). Lowest bucket for one histogram < 0, for the other >
// 0. They have the same name, just separated by label. Not useful in
// practice, but can happen (if clients change bucketing), and the
// server has to cope with it.
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "0.1",
"start": "positive",
},
},
Values: getTestValueStream(0, 50, 5, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": ".2",
"start": "positive",
},
},
Values: getTestValueStream(0, 70, 7, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "1e0",
"start": "positive",
},
},
Values: getTestValueStream(0, 110, 11, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "+Inf",
"start": "positive",
},
},
Values: getTestValueStream(0, 120, 12, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "-.2",
"start": "negative",
},
},
Values: getTestValueStream(0, 10, 1, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "-0.1",
"start": "negative",
},
},
Values: getTestValueStream(0, 20, 2, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "0.3",
"start": "negative",
},
},
Values: getTestValueStream(0, 20, 2, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "+Inf",
"start": "negative",
},
},
Values: getTestValueStream(0, 30, 3, testStartTime),
},
// Now a more realistic histogram per job and instance to test aggregation.
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "request_duration_seconds_bucket",
clientmodel.JobLabel: "job1",
"instance": "ins1",
"le": "0.1",
},
},
Values: getTestValueStream(0, 10, 1, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "request_duration_seconds_bucket",
clientmodel.JobLabel: "job1",
"instance": "ins1",
"le": "0.2",
},
},
Values: getTestValueStream(0, 30, 3, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "request_duration_seconds_bucket",
clientmodel.JobLabel: "job1",
"instance": "ins1",
"le": "+Inf",
},
},
Values: getTestValueStream(0, 40, 4, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "request_duration_seconds_bucket",
clientmodel.JobLabel: "job1",
"instance": "ins2",
"le": "0.1",
},
},
Values: getTestValueStream(0, 20, 2, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "request_duration_seconds_bucket",
clientmodel.JobLabel: "job1",
"instance": "ins2",
"le": "0.2",
},
},
Values: getTestValueStream(0, 50, 5, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "request_duration_seconds_bucket",
clientmodel.JobLabel: "job1",
"instance": "ins2",
"le": "+Inf",
},
},
Values: getTestValueStream(0, 60, 6, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "request_duration_seconds_bucket",
clientmodel.JobLabel: "job2",
"instance": "ins1",
"le": "0.1",
},
},
Values: getTestValueStream(0, 30, 3, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "request_duration_seconds_bucket",
clientmodel.JobLabel: "job2",
"instance": "ins1",
"le": "0.2",
},
},
Values: getTestValueStream(0, 40, 4, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "request_duration_seconds_bucket",
clientmodel.JobLabel: "job2",
"instance": "ins1",
"le": "+Inf",
},
},
Values: getTestValueStream(0, 60, 6, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "request_duration_seconds_bucket",
clientmodel.JobLabel: "job2",
"instance": "ins2",
"le": "0.1",
},
},
Values: getTestValueStream(0, 40, 4, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "request_duration_seconds_bucket",
clientmodel.JobLabel: "job2",
"instance": "ins2",
"le": "0.2",
},
},
Values: getTestValueStream(0, 70, 7, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "request_duration_seconds_bucket",
clientmodel.JobLabel: "job2",
"instance": "ins2",
"le": "+Inf",
},
},
Values: getTestValueStream(0, 90, 9, testStartTime),
},
}
var testVector = getTestVectorFromTestMatrix(testMatrix)

253
rules/rules_test.go
View file

@ -15,7 +15,10 @@ package rules
import (
"fmt"
"math"
"path"
"regexp"
"strconv"
"strings"
"testing"
"time"
@ -32,6 +35,13 @@ import (
var (
testEvalTime = testStartTime.Add(testSampleInterval * 10)
fixturesPath = "fixtures"
reSample = regexp.MustCompile(`^(.*) \=\> (\-?\d+\.?\d*e?\d*|[+-]Inf|NaN) \@\[(\d+)\]$`)
minNormal = math.Float64frombits(0x0010000000000000) // The smallest positive normal value of type float64.
)
const (
epsilon = 0.000001 // Relative error allowed for sample values.
)
func annotateWithTime(lines []string, timestamp clientmodel.Timestamp) []string {
@ -53,6 +63,51 @@ func vectorComparisonString(expected []string, actual []string) string {
separator)
}
// samplesAlmostEqual returns true if the two sample lines only differ by a
// small relative error in their sample value.
func samplesAlmostEqual(a, b string) bool {
if a == b {
// Fast path if strings are equal.
return true
}
aMatches := reSample.FindStringSubmatch(a)
if aMatches == nil {
panic(fmt.Errorf("sample %q did not match regular expression", a))
}
bMatches := reSample.FindStringSubmatch(b)
if bMatches == nil {
panic(fmt.Errorf("sample %q did not match regular expression", b))
}
if aMatches[1] != bMatches[1] {
return false // Labels don't match.
}
if aMatches[3] != bMatches[3] {
return false // Timestamps don't match.
}
// If we are here, we have the diff in the floats.
// We have to check if they are almost equal.
aVal, err := strconv.ParseFloat(aMatches[2], 64)
if err != nil {
panic(err)
}
bVal, err := strconv.ParseFloat(bMatches[2], 64)
if err != nil {
panic(err)
}
// Cf. http://floating-point-gui.de/errors/comparison/
if aVal == bVal {
return true
}
diff := math.Abs(aVal - bVal)
if aVal == 0 || bVal == 0 || diff < minNormal {
return diff < epsilon*minNormal
}
return diff/(math.Abs(aVal)+math.Abs(bVal)) < epsilon
}
func newTestStorage(t testing.TB) (storage local.Storage, closer test.Closer) {
storage, closer = local.NewTestStorage(t)
storeMatrix(storage, testMatrix)
@ -555,6 +610,26 @@ func TestExpressions(t *testing.T) {
`x{y="testvalue"} => 100 @[%v]`,
`label_grouping_test{a="a", b="abb"} => 200 @[%v]`,
`label_grouping_test{a="aa", b="bb"} => 100 @[%v]`,
`testhistogram_bucket{le="0.1", start="positive"} => 50 @[%v]`,
`testhistogram_bucket{le=".2", start="positive"} => 70 @[%v]`,
`testhistogram_bucket{le="1e0", start="positive"} => 110 @[%v]`,
`testhistogram_bucket{le="+Inf", start="positive"} => 120 @[%v]`,
`testhistogram_bucket{le="-.2", start="negative"} => 10 @[%v]`,
`testhistogram_bucket{le="-0.1", start="negative"} => 20 @[%v]`,
`testhistogram_bucket{le="0.3", start="negative"} => 20 @[%v]`,
`testhistogram_bucket{le="+Inf", start="negative"} => 30 @[%v]`,
`request_duration_seconds_bucket{instance="ins1", job="job1", le="0.1"} => 10 @[%v]`,
`request_duration_seconds_bucket{instance="ins1", job="job1", le="0.2"} => 30 @[%v]`,
`request_duration_seconds_bucket{instance="ins1", job="job1", le="+Inf"} => 40 @[%v]`,
`request_duration_seconds_bucket{instance="ins2", job="job1", le="0.1"} => 20 @[%v]`,
`request_duration_seconds_bucket{instance="ins2", job="job1", le="0.2"} => 50 @[%v]`,
`request_duration_seconds_bucket{instance="ins2", job="job1", le="+Inf"} => 60 @[%v]`,
`request_duration_seconds_bucket{instance="ins1", job="job2", le="0.1"} => 30 @[%v]`,
`request_duration_seconds_bucket{instance="ins1", job="job2", le="0.2"} => 40 @[%v]`,
`request_duration_seconds_bucket{instance="ins1", job="job2", le="+Inf"} => 60 @[%v]`,
`request_duration_seconds_bucket{instance="ins2", job="job2", le="0.1"} => 40 @[%v]`,
`request_duration_seconds_bucket{instance="ins2", job="job2", le="0.2"} => 70 @[%v]`,
`request_duration_seconds_bucket{instance="ins2", job="job2", le="+Inf"} => 90 @[%v]`,
},
},
{
@ -651,6 +726,182 @@ func TestExpressions(t *testing.T) {
`{a="aa", b="bb"} => 100 @[%v]`,
},
},
// Quantile too low.
{
expr: `histogram_quantile(-0.1, testhistogram_bucket)`,
output: []string{
`{start="positive"} => -Inf @[%v]`,
`{start="negative"} => -Inf @[%v]`,
},
},
// Quantile too high.
{
expr: `histogram_quantile(1.01, testhistogram_bucket)`,
output: []string{
`{start="positive"} => +Inf @[%v]`,
`{start="negative"} => +Inf @[%v]`,
},
},
// Quantile value in lowest bucket, which is positive.
{
expr: `histogram_quantile(0, testhistogram_bucket{start="positive"})`,
output: []string{
`{start="positive"} => 0 @[%v]`,
},
},
// Quantile value in lowest bucket, which is negative.
{
expr: `histogram_quantile(0, testhistogram_bucket{start="negative"})`,
output: []string{
`{start="negative"} => -0.2 @[%v]`,
},
},
// Quantile value in highest bucket.
{
expr: `histogram_quantile(1, testhistogram_bucket)`,
output: []string{
`{start="positive"} => 1 @[%v]`,
`{start="negative"} => 0.3 @[%v]`,
},
},
// Finally some useful quantiles.
{
expr: `histogram_quantile(0.2, testhistogram_bucket)`,
output: []string{
`{start="positive"} => 0.048 @[%v]`,
`{start="negative"} => -0.2 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, testhistogram_bucket)`,
output: []string{
`{start="positive"} => 0.15 @[%v]`,
`{start="negative"} => -0.15 @[%v]`,
},
},
{
expr: `histogram_quantile(0.8, testhistogram_bucket)`,
output: []string{
`{start="positive"} => 0.72 @[%v]`,
`{start="negative"} => 0.3 @[%v]`,
},
},
// More realistic with rates.
{
expr: `histogram_quantile(0.2, rate(testhistogram_bucket[5m]))`,
output: []string{
`{start="positive"} => 0.048 @[%v]`,
`{start="negative"} => -0.2 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, rate(testhistogram_bucket[5m]))`,
output: []string{
`{start="positive"} => 0.15 @[%v]`,
`{start="negative"} => -0.15 @[%v]`,
},
},
{
expr: `histogram_quantile(0.8, rate(testhistogram_bucket[5m]))`,
output: []string{
`{start="positive"} => 0.72 @[%v]`,
`{start="negative"} => 0.3 @[%v]`,
},
},
// Aggregated histogram: Everything in one.
{
expr: `histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le))`,
output: []string{
`{} => 0.075 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le))`,
output: []string{
`{} => 0.1277777777777778 @[%v]`,
},
},
// Aggregated histogram: Everything in one. Now with avg, which does not change anything.
{
expr: `histogram_quantile(0.3, avg(rate(request_duration_seconds_bucket[5m])) by (le))`,
output: []string{
`{} => 0.075 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, avg(rate(request_duration_seconds_bucket[5m])) by (le))`,
output: []string{
`{} => 0.12777777777777778 @[%v]`,
},
},
// Aggregated histogram: By job.
{
expr: `histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))`,
output: []string{
`{instance="ins1"} => 0.075 @[%v]`,
`{instance="ins2"} => 0.075 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))`,
output: []string{
`{instance="ins1"} => 0.1333333333 @[%v]`,
`{instance="ins2"} => 0.125 @[%v]`,
},
},
// Aggregated histogram: By instance.
{
expr: `histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))`,
output: []string{
`{job="job1"} => 0.1 @[%v]`,
`{job="job2"} => 0.0642857142857143 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))`,
output: []string{
`{job="job1"} => 0.14 @[%v]`,
`{job="job2"} => 0.1125 @[%v]`,
},
},
// Aggregated histogram: By job and instance.
{
expr: `histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))`,
output: []string{
`{instance="ins1", job="job1"} => 0.11 @[%v]`,
`{instance="ins2", job="job1"} => 0.09 @[%v]`,
`{instance="ins1", job="job2"} => 0.06 @[%v]`,
`{instance="ins2", job="job2"} => 0.0675 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))`,
output: []string{
`{instance="ins1", job="job1"} => 0.15 @[%v]`,
`{instance="ins2", job="job1"} => 0.1333333333333333 @[%v]`,
`{instance="ins1", job="job2"} => 0.1 @[%v]`,
`{instance="ins2", job="job2"} => 0.1166666666666667 @[%v]`,
},
},
// The unaggregated histogram for comparison. Same result as the previous one.
{
expr: `histogram_quantile(0.3, rate(request_duration_seconds_bucket[5m]))`,
output: []string{
`{instance="ins1", job="job1"} => 0.11 @[%v]`,
`{instance="ins2", job="job1"} => 0.09 @[%v]`,
`{instance="ins1", job="job2"} => 0.06 @[%v]`,
`{instance="ins2", job="job2"} => 0.0675 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, rate(request_duration_seconds_bucket[5m]))`,
output: []string{
`{instance="ins1", job="job1"} => 0.15 @[%v]`,
`{instance="ins2", job="job1"} => 0.13333333333333333 @[%v]`,
`{instance="ins1", job="job2"} => 0.1 @[%v]`,
`{instance="ins2", job="job2"} => 0.11666666666666667 @[%v]`,
},
},
}
storage, closer := newTestStorage(t)
@ -691,7 +942,7 @@ func TestExpressions(t *testing.T) {
for j, expectedSample := range expectedLines {
found := false
for _, actualSample := range resultLines {
if actualSample == expectedSample {
if samplesAlmostEqual(actualSample, expectedSample) {
found = true
}
}