mirror of
https://github.com/prometheus/prometheus.git
synced 2024-11-13 17:14:05 -08:00
1712 lines
56 KiB
Go
1712 lines
56 KiB
Go
// Copyright 2015 The Prometheus Authors
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package promql
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import (
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"fmt"
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"math"
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"slices"
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"sort"
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"strconv"
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"strings"
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"time"
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"github.com/facette/natsort"
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"github.com/grafana/regexp"
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"github.com/prometheus/common/model"
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"github.com/prometheus/prometheus/model/histogram"
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"github.com/prometheus/prometheus/model/labels"
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"github.com/prometheus/prometheus/promql/parser"
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"github.com/prometheus/prometheus/promql/parser/posrange"
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"github.com/prometheus/prometheus/util/annotations"
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)
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// FunctionCall is the type of a PromQL function implementation
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//
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// vals is a list of the evaluated arguments for the function call.
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//
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// For range vectors it will be a Matrix with one series, instant vectors a
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// Vector, scalars a Vector with one series whose value is the scalar
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// value,and nil for strings.
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//
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// args are the original arguments to the function, where you can access
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// matrixSelectors, vectorSelectors, and StringLiterals.
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//
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// enh.Out is a pre-allocated empty vector that you may use to accumulate
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// output before returning it. The vectors in vals should not be returned.a
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//
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// Range vector functions need only return a vector with the right value,
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// the metric and timestamp are not needed.
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//
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// Instant vector functions need only return a vector with the right values and
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// metrics, the timestamp are not needed.
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//
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// Scalar results should be returned as the value of a sample in a Vector.
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type FunctionCall func(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations)
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// === time() float64 ===
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func funcTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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return Vector{Sample{
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F: float64(enh.Ts) / 1000,
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}}, nil
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}
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// extrapolatedRate is a utility function for rate/increase/delta.
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// It calculates the rate (allowing for counter resets if isCounter is true),
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// extrapolates if the first/last sample is close to the boundary, and returns
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// the result as either per-second (if isRate is true) or overall.
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func extrapolatedRate(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper, isCounter, isRate bool) (Vector, annotations.Annotations) {
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ms := args[0].(*parser.MatrixSelector)
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vs := ms.VectorSelector.(*parser.VectorSelector)
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var (
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samples = vals[0].(Matrix)[0]
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rangeStart = enh.Ts - durationMilliseconds(ms.Range+vs.Offset)
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rangeEnd = enh.Ts - durationMilliseconds(vs.Offset)
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resultFloat float64
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resultHistogram *histogram.FloatHistogram
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firstT, lastT int64
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numSamplesMinusOne int
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annos annotations.Annotations
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)
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// We need either at least two Histograms and no Floats, or at least two
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// Floats and no Histograms to calculate a rate. Otherwise, drop this
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// Vector element.
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metricName := samples.Metric.Get(labels.MetricName)
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if len(samples.Histograms) > 0 && len(samples.Floats) > 0 {
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return enh.Out, annos.Add(annotations.NewMixedFloatsHistogramsWarning(metricName, args[0].PositionRange()))
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}
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switch {
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case len(samples.Histograms) > 1:
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numSamplesMinusOne = len(samples.Histograms) - 1
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firstT = samples.Histograms[0].T
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lastT = samples.Histograms[numSamplesMinusOne].T
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var newAnnos annotations.Annotations
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resultHistogram, newAnnos = histogramRate(samples.Histograms, isCounter, metricName, args[0].PositionRange())
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if resultHistogram == nil {
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// The histograms are not compatible with each other.
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return enh.Out, annos.Merge(newAnnos)
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}
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case len(samples.Floats) > 1:
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numSamplesMinusOne = len(samples.Floats) - 1
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firstT = samples.Floats[0].T
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lastT = samples.Floats[numSamplesMinusOne].T
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resultFloat = samples.Floats[numSamplesMinusOne].F - samples.Floats[0].F
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if !isCounter {
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break
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}
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// Handle counter resets:
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prevValue := samples.Floats[0].F
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for _, currPoint := range samples.Floats[1:] {
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if currPoint.F < prevValue {
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resultFloat += prevValue
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}
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prevValue = currPoint.F
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}
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default:
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// TODO: add RangeTooShortWarning
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return enh.Out, annos
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}
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// Duration between first/last samples and boundary of range.
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durationToStart := float64(firstT-rangeStart) / 1000
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durationToEnd := float64(rangeEnd-lastT) / 1000
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sampledInterval := float64(lastT-firstT) / 1000
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averageDurationBetweenSamples := sampledInterval / float64(numSamplesMinusOne)
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// If the first/last samples are close to the boundaries of the range,
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// extrapolate the result. This is as we expect that another sample
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// will exist given the spacing between samples we've seen thus far,
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// with an allowance for noise.
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extrapolationThreshold := averageDurationBetweenSamples * 1.1
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extrapolateToInterval := sampledInterval
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if durationToStart >= extrapolationThreshold {
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durationToStart = averageDurationBetweenSamples / 2
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}
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if isCounter && resultFloat > 0 && len(samples.Floats) > 0 && samples.Floats[0].F >= 0 {
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// Counters cannot be negative. If we have any slope at all
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// (i.e. resultFloat went up), we can extrapolate the zero point
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// of the counter. If the duration to the zero point is shorter
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// than the durationToStart, we take the zero point as the start
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// of the series, thereby avoiding extrapolation to negative
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// counter values.
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// TODO(beorn7): Do this for histograms, too.
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durationToZero := sampledInterval * (samples.Floats[0].F / resultFloat)
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if durationToZero < durationToStart {
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durationToStart = durationToZero
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}
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}
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extrapolateToInterval += durationToStart
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if durationToEnd >= extrapolationThreshold {
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durationToEnd = averageDurationBetweenSamples / 2
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}
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extrapolateToInterval += durationToEnd
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factor := extrapolateToInterval / sampledInterval
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if isRate {
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factor /= ms.Range.Seconds()
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}
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if resultHistogram == nil {
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resultFloat *= factor
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} else {
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resultHistogram.Mul(factor)
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}
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return append(enh.Out, Sample{F: resultFloat, H: resultHistogram}), annos
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}
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// histogramRate is a helper function for extrapolatedRate. It requires
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// points[0] to be a histogram. It returns nil if any other Point in points is
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// not a histogram, and a warning wrapped in an annotation in that case.
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// Otherwise, it returns the calculated histogram and an empty annotation.
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func histogramRate(points []HPoint, isCounter bool, metricName string, pos posrange.PositionRange) (*histogram.FloatHistogram, annotations.Annotations) {
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prev := points[0].H
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last := points[len(points)-1].H
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if last == nil {
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return nil, annotations.New().Add(annotations.NewMixedFloatsHistogramsWarning(metricName, pos))
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}
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minSchema := prev.Schema
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if last.Schema < minSchema {
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minSchema = last.Schema
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}
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var annos annotations.Annotations
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// First iteration to find out two things:
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// - What's the smallest relevant schema?
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// - Are all data points histograms?
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// TODO(beorn7): Find a way to check that earlier, e.g. by handing in a
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// []FloatPoint and a []HistogramPoint separately.
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for _, currPoint := range points[1 : len(points)-1] {
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curr := currPoint.H
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if curr == nil {
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return nil, annotations.New().Add(annotations.NewMixedFloatsHistogramsWarning(metricName, pos))
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}
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if !isCounter {
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continue
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}
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if curr.CounterResetHint == histogram.GaugeType {
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annos.Add(annotations.NewNativeHistogramNotCounterWarning(metricName, pos))
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}
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if curr.Schema < minSchema {
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minSchema = curr.Schema
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}
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}
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h := last.CopyToSchema(minSchema)
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h.Sub(prev)
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if isCounter {
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// Second iteration to deal with counter resets.
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for _, currPoint := range points[1:] {
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curr := currPoint.H
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if curr.DetectReset(prev) {
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h.Add(prev)
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}
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prev = curr
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}
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} else if points[0].H.CounterResetHint != histogram.GaugeType || points[len(points)-1].H.CounterResetHint != histogram.GaugeType {
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annos.Add(annotations.NewNativeHistogramNotGaugeWarning(metricName, pos))
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}
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h.CounterResetHint = histogram.GaugeType
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return h.Compact(0), nil
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}
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// === delta(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
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func funcDelta(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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return extrapolatedRate(vals, args, enh, false, false)
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}
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// === rate(node parser.ValueTypeMatrix) (Vector, Annotations) ===
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func funcRate(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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return extrapolatedRate(vals, args, enh, true, true)
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}
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// === increase(node parser.ValueTypeMatrix) (Vector, Annotations) ===
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func funcIncrease(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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return extrapolatedRate(vals, args, enh, true, false)
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}
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// === irate(node parser.ValueTypeMatrix) (Vector, Annotations) ===
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func funcIrate(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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return instantValue(vals, enh.Out, true)
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}
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// === idelta(node model.ValMatrix) (Vector, Annotations) ===
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func funcIdelta(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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return instantValue(vals, enh.Out, false)
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}
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func instantValue(vals []parser.Value, out Vector, isRate bool) (Vector, annotations.Annotations) {
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samples := vals[0].(Matrix)[0]
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// No sense in trying to compute a rate without at least two points. Drop
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// this Vector element.
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// TODO: add RangeTooShortWarning
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if len(samples.Floats) < 2 {
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return out, nil
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}
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lastSample := samples.Floats[len(samples.Floats)-1]
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previousSample := samples.Floats[len(samples.Floats)-2]
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var resultValue float64
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if isRate && lastSample.F < previousSample.F {
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// Counter reset.
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resultValue = lastSample.F
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} else {
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resultValue = lastSample.F - previousSample.F
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}
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sampledInterval := lastSample.T - previousSample.T
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if sampledInterval == 0 {
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// Avoid dividing by 0.
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return out, nil
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}
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if isRate {
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// Convert to per-second.
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resultValue /= float64(sampledInterval) / 1000
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}
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return append(out, Sample{F: resultValue}), nil
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}
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// Calculate the trend value at the given index i in raw data d.
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// This is somewhat analogous to the slope of the trend at the given index.
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// The argument "tf" is the trend factor.
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// The argument "s0" is the computed smoothed value.
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// The argument "s1" is the computed trend factor.
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// The argument "b" is the raw input value.
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func calcTrendValue(i int, tf, s0, s1, b float64) float64 {
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if i == 0 {
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return b
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}
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x := tf * (s1 - s0)
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y := (1 - tf) * b
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return x + y
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}
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// Holt-Winters is similar to a weighted moving average, where historical data has exponentially less influence on the current data.
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// Holt-Winter also accounts for trends in data. The smoothing factor (0 < sf < 1) affects how historical data will affect the current
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// data. A lower smoothing factor increases the influence of historical data. The trend factor (0 < tf < 1) affects
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// how trends in historical data will affect the current data. A higher trend factor increases the influence.
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// of trends. Algorithm taken from https://en.wikipedia.org/wiki/Exponential_smoothing titled: "Double exponential smoothing".
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func funcHoltWinters(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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samples := vals[0].(Matrix)[0]
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// The smoothing factor argument.
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sf := vals[1].(Vector)[0].F
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// The trend factor argument.
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tf := vals[2].(Vector)[0].F
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// Check that the input parameters are valid.
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if sf <= 0 || sf >= 1 {
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panic(fmt.Errorf("invalid smoothing factor. Expected: 0 < sf < 1, got: %f", sf))
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}
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if tf <= 0 || tf >= 1 {
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panic(fmt.Errorf("invalid trend factor. Expected: 0 < tf < 1, got: %f", tf))
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}
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l := len(samples.Floats)
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// Can't do the smoothing operation with less than two points.
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if l < 2 {
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return enh.Out, nil
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}
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var s0, s1, b float64
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// Set initial values.
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s1 = samples.Floats[0].F
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b = samples.Floats[1].F - samples.Floats[0].F
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// Run the smoothing operation.
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var x, y float64
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for i := 1; i < l; i++ {
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// Scale the raw value against the smoothing factor.
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x = sf * samples.Floats[i].F
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// Scale the last smoothed value with the trend at this point.
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b = calcTrendValue(i-1, tf, s0, s1, b)
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y = (1 - sf) * (s1 + b)
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s0, s1 = s1, x+y
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}
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return append(enh.Out, Sample{F: s1}), nil
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}
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// === sort(node parser.ValueTypeVector) (Vector, Annotations) ===
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func funcSort(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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// NaN should sort to the bottom, so take descending sort with NaN first and
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// reverse it.
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byValueSorter := vectorByReverseValueHeap(vals[0].(Vector))
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sort.Sort(sort.Reverse(byValueSorter))
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return Vector(byValueSorter), nil
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}
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// === sortDesc(node parser.ValueTypeVector) (Vector, Annotations) ===
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func funcSortDesc(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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// NaN should sort to the bottom, so take ascending sort with NaN first and
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// reverse it.
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byValueSorter := vectorByValueHeap(vals[0].(Vector))
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sort.Sort(sort.Reverse(byValueSorter))
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return Vector(byValueSorter), nil
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}
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// === sort_by_label(vector parser.ValueTypeVector, label parser.ValueTypeString...) (Vector, Annotations) ===
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func funcSortByLabel(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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// In case the labels are the same, NaN should sort to the bottom, so take
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// ascending sort with NaN first and reverse it.
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var anno annotations.Annotations
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vals[0], anno = funcSort(vals, args, enh)
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labels := stringSliceFromArgs(args[1:])
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slices.SortFunc(vals[0].(Vector), func(a, b Sample) int {
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// Iterate over each given label
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for _, label := range labels {
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lv1 := a.Metric.Get(label)
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lv2 := b.Metric.Get(label)
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if lv1 == lv2 {
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continue
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}
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if natsort.Compare(lv1, lv2) {
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return -1
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}
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return +1
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}
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return 0
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})
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return vals[0].(Vector), anno
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}
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// === sort_by_label_desc(vector parser.ValueTypeVector, label parser.ValueTypeString...) (Vector, Annotations) ===
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func funcSortByLabelDesc(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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// In case the labels are the same, NaN should sort to the bottom, so take
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// ascending sort with NaN first and reverse it.
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var anno annotations.Annotations
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vals[0], anno = funcSortDesc(vals, args, enh)
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labels := stringSliceFromArgs(args[1:])
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slices.SortFunc(vals[0].(Vector), func(a, b Sample) int {
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// Iterate over each given label
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for _, label := range labels {
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lv1 := a.Metric.Get(label)
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lv2 := b.Metric.Get(label)
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if lv1 == lv2 {
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continue
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}
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if natsort.Compare(lv1, lv2) {
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return +1
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}
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return -1
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}
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return 0
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})
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return vals[0].(Vector), anno
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}
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// === clamp(Vector parser.ValueTypeVector, min, max Scalar) (Vector, Annotations) ===
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func funcClamp(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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vec := vals[0].(Vector)
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min := vals[1].(Vector)[0].F
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max := vals[2].(Vector)[0].F
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if max < min {
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return enh.Out, nil
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}
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for _, el := range vec {
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enh.Out = append(enh.Out, Sample{
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Metric: el.Metric.DropMetricName(),
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F: math.Max(min, math.Min(max, el.F)),
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})
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}
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return enh.Out, nil
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}
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// === clamp_max(Vector parser.ValueTypeVector, max Scalar) (Vector, Annotations) ===
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func funcClampMax(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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vec := vals[0].(Vector)
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max := vals[1].(Vector)[0].F
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for _, el := range vec {
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enh.Out = append(enh.Out, Sample{
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Metric: el.Metric.DropMetricName(),
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F: math.Min(max, el.F),
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})
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}
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return enh.Out, nil
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}
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// === clamp_min(Vector parser.ValueTypeVector, min Scalar) (Vector, Annotations) ===
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func funcClampMin(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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vec := vals[0].(Vector)
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min := vals[1].(Vector)[0].F
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for _, el := range vec {
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enh.Out = append(enh.Out, Sample{
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Metric: el.Metric.DropMetricName(),
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F: math.Max(min, el.F),
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})
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}
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return enh.Out, nil
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}
|
|
|
|
// === round(Vector parser.ValueTypeVector, toNearest=1 Scalar) (Vector, Annotations) ===
|
|
func funcRound(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
vec := vals[0].(Vector)
|
|
// round returns a number rounded to toNearest.
|
|
// Ties are solved by rounding up.
|
|
toNearest := float64(1)
|
|
if len(args) >= 2 {
|
|
toNearest = vals[1].(Vector)[0].F
|
|
}
|
|
// Invert as it seems to cause fewer floating point accuracy issues.
|
|
toNearestInverse := 1.0 / toNearest
|
|
|
|
for _, el := range vec {
|
|
f := math.Floor(el.F*toNearestInverse+0.5) / toNearestInverse
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: el.Metric.DropMetricName(),
|
|
F: f,
|
|
})
|
|
}
|
|
return enh.Out, nil
|
|
}
|
|
|
|
// === Scalar(node parser.ValueTypeVector) Scalar ===
|
|
func funcScalar(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
v := vals[0].(Vector)
|
|
if len(v) != 1 {
|
|
return append(enh.Out, Sample{F: math.NaN()}), nil
|
|
}
|
|
return append(enh.Out, Sample{F: v[0].F}), nil
|
|
}
|
|
|
|
func aggrOverTime(vals []parser.Value, enh *EvalNodeHelper, aggrFn func(Series) float64) Vector {
|
|
el := vals[0].(Matrix)[0]
|
|
|
|
return append(enh.Out, Sample{F: aggrFn(el)})
|
|
}
|
|
|
|
func aggrHistOverTime(vals []parser.Value, enh *EvalNodeHelper, aggrFn func(Series) *histogram.FloatHistogram) Vector {
|
|
el := vals[0].(Matrix)[0]
|
|
|
|
return append(enh.Out, Sample{H: aggrFn(el)})
|
|
}
|
|
|
|
// === avg_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
|
|
func funcAvgOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
firstSeries := vals[0].(Matrix)[0]
|
|
if len(firstSeries.Floats) > 0 && len(firstSeries.Histograms) > 0 {
|
|
metricName := firstSeries.Metric.Get(labels.MetricName)
|
|
return enh.Out, annotations.New().Add(annotations.NewMixedFloatsHistogramsWarning(metricName, args[0].PositionRange()))
|
|
}
|
|
if len(firstSeries.Floats) == 0 {
|
|
// The passed values only contain histograms.
|
|
return aggrHistOverTime(vals, enh, func(s Series) *histogram.FloatHistogram {
|
|
count := 1
|
|
mean := s.Histograms[0].H.Copy()
|
|
for _, h := range s.Histograms[1:] {
|
|
count++
|
|
left := h.H.Copy().Div(float64(count))
|
|
right := mean.Copy().Div(float64(count))
|
|
toAdd := left.Sub(right)
|
|
mean.Add(toAdd)
|
|
}
|
|
return mean
|
|
}), nil
|
|
}
|
|
return aggrOverTime(vals, enh, func(s Series) float64 {
|
|
var mean, count, c float64
|
|
for _, f := range s.Floats {
|
|
count++
|
|
if math.IsInf(mean, 0) {
|
|
if math.IsInf(f.F, 0) && (mean > 0) == (f.F > 0) {
|
|
// The `mean` and `f.F` values are `Inf` of the same sign. They
|
|
// can't be subtracted, but the value of `mean` is correct
|
|
// already.
|
|
continue
|
|
}
|
|
if !math.IsInf(f.F, 0) && !math.IsNaN(f.F) {
|
|
// At this stage, the mean is an infinite. If the added
|
|
// value is neither an Inf or a Nan, we can keep that mean
|
|
// value.
|
|
// This is required because our calculation below removes
|
|
// the mean value, which would look like Inf += x - Inf and
|
|
// end up as a NaN.
|
|
continue
|
|
}
|
|
}
|
|
mean, c = kahanSumInc(f.F/count-mean/count, mean, c)
|
|
}
|
|
|
|
if math.IsInf(mean, 0) {
|
|
return mean
|
|
}
|
|
return mean + c
|
|
}), nil
|
|
}
|
|
|
|
// === count_over_time(Matrix parser.ValueTypeMatrix) (Vector, Notes) ===
|
|
func funcCountOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return aggrOverTime(vals, enh, func(s Series) float64 {
|
|
return float64(len(s.Floats) + len(s.Histograms))
|
|
}), nil
|
|
}
|
|
|
|
// === last_over_time(Matrix parser.ValueTypeMatrix) (Vector, Notes) ===
|
|
func funcLastOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
el := vals[0].(Matrix)[0]
|
|
|
|
var f FPoint
|
|
if len(el.Floats) > 0 {
|
|
f = el.Floats[len(el.Floats)-1]
|
|
}
|
|
|
|
var h HPoint
|
|
if len(el.Histograms) > 0 {
|
|
h = el.Histograms[len(el.Histograms)-1]
|
|
}
|
|
|
|
if h.H == nil || h.T < f.T {
|
|
return append(enh.Out, Sample{
|
|
Metric: el.Metric,
|
|
F: f.F,
|
|
}), nil
|
|
}
|
|
return append(enh.Out, Sample{
|
|
Metric: el.Metric,
|
|
H: h.H.Copy(),
|
|
}), nil
|
|
}
|
|
|
|
// === mad_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
|
|
func funcMadOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
if len(vals[0].(Matrix)[0].Floats) == 0 {
|
|
return enh.Out, nil
|
|
}
|
|
return aggrOverTime(vals, enh, func(s Series) float64 {
|
|
values := make(vectorByValueHeap, 0, len(s.Floats))
|
|
for _, f := range s.Floats {
|
|
values = append(values, Sample{F: f.F})
|
|
}
|
|
median := quantile(0.5, values)
|
|
values = make(vectorByValueHeap, 0, len(s.Floats))
|
|
for _, f := range s.Floats {
|
|
values = append(values, Sample{F: math.Abs(f.F - median)})
|
|
}
|
|
return quantile(0.5, values)
|
|
}), nil
|
|
}
|
|
|
|
// === max_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
|
|
func funcMaxOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
if len(vals[0].(Matrix)[0].Floats) == 0 {
|
|
// TODO(beorn7): The passed values only contain
|
|
// histograms. max_over_time ignores histograms for now. If
|
|
// there are only histograms, we have to return without adding
|
|
// anything to enh.Out.
|
|
return enh.Out, nil
|
|
}
|
|
return aggrOverTime(vals, enh, func(s Series) float64 {
|
|
max := s.Floats[0].F
|
|
for _, f := range s.Floats {
|
|
if f.F > max || math.IsNaN(max) {
|
|
max = f.F
|
|
}
|
|
}
|
|
return max
|
|
}), nil
|
|
}
|
|
|
|
// === min_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
|
|
func funcMinOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
if len(vals[0].(Matrix)[0].Floats) == 0 {
|
|
// TODO(beorn7): The passed values only contain
|
|
// histograms. min_over_time ignores histograms for now. If
|
|
// there are only histograms, we have to return without adding
|
|
// anything to enh.Out.
|
|
return enh.Out, nil
|
|
}
|
|
return aggrOverTime(vals, enh, func(s Series) float64 {
|
|
min := s.Floats[0].F
|
|
for _, f := range s.Floats {
|
|
if f.F < min || math.IsNaN(min) {
|
|
min = f.F
|
|
}
|
|
}
|
|
return min
|
|
}), nil
|
|
}
|
|
|
|
// === sum_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
|
|
func funcSumOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
firstSeries := vals[0].(Matrix)[0]
|
|
if len(firstSeries.Floats) > 0 && len(firstSeries.Histograms) > 0 {
|
|
metricName := firstSeries.Metric.Get(labels.MetricName)
|
|
return enh.Out, annotations.New().Add(annotations.NewMixedFloatsHistogramsWarning(metricName, args[0].PositionRange()))
|
|
}
|
|
if len(firstSeries.Floats) == 0 {
|
|
// The passed values only contain histograms.
|
|
return aggrHistOverTime(vals, enh, func(s Series) *histogram.FloatHistogram {
|
|
sum := s.Histograms[0].H.Copy()
|
|
for _, h := range s.Histograms[1:] {
|
|
sum.Add(h.H)
|
|
}
|
|
return sum
|
|
}), nil
|
|
}
|
|
return aggrOverTime(vals, enh, func(s Series) float64 {
|
|
var sum, c float64
|
|
for _, f := range s.Floats {
|
|
sum, c = kahanSumInc(f.F, sum, c)
|
|
}
|
|
if math.IsInf(sum, 0) {
|
|
return sum
|
|
}
|
|
return sum + c
|
|
}), nil
|
|
}
|
|
|
|
// === quantile_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
|
|
func funcQuantileOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
q := vals[0].(Vector)[0].F
|
|
el := vals[1].(Matrix)[0]
|
|
if len(el.Floats) == 0 {
|
|
// TODO(beorn7): The passed values only contain
|
|
// histograms. quantile_over_time ignores histograms for now. If
|
|
// there are only histograms, we have to return without adding
|
|
// anything to enh.Out.
|
|
return enh.Out, nil
|
|
}
|
|
|
|
var annos annotations.Annotations
|
|
if math.IsNaN(q) || q < 0 || q > 1 {
|
|
annos.Add(annotations.NewInvalidQuantileWarning(q, args[0].PositionRange()))
|
|
}
|
|
|
|
values := make(vectorByValueHeap, 0, len(el.Floats))
|
|
for _, f := range el.Floats {
|
|
values = append(values, Sample{F: f.F})
|
|
}
|
|
return append(enh.Out, Sample{F: quantile(q, values)}), annos
|
|
}
|
|
|
|
// === stddev_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
|
|
func funcStddevOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
if len(vals[0].(Matrix)[0].Floats) == 0 {
|
|
// TODO(beorn7): The passed values only contain
|
|
// histograms. stddev_over_time ignores histograms for now. If
|
|
// there are only histograms, we have to return without adding
|
|
// anything to enh.Out.
|
|
return enh.Out, nil
|
|
}
|
|
return aggrOverTime(vals, enh, func(s Series) float64 {
|
|
var count float64
|
|
var mean, cMean float64
|
|
var aux, cAux float64
|
|
for _, f := range s.Floats {
|
|
count++
|
|
delta := f.F - (mean + cMean)
|
|
mean, cMean = kahanSumInc(delta/count, mean, cMean)
|
|
aux, cAux = kahanSumInc(delta*(f.F-(mean+cMean)), aux, cAux)
|
|
}
|
|
return math.Sqrt((aux + cAux) / count)
|
|
}), nil
|
|
}
|
|
|
|
// === stdvar_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
|
|
func funcStdvarOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
if len(vals[0].(Matrix)[0].Floats) == 0 {
|
|
// TODO(beorn7): The passed values only contain
|
|
// histograms. stdvar_over_time ignores histograms for now. If
|
|
// there are only histograms, we have to return without adding
|
|
// anything to enh.Out.
|
|
return enh.Out, nil
|
|
}
|
|
return aggrOverTime(vals, enh, func(s Series) float64 {
|
|
var count float64
|
|
var mean, cMean float64
|
|
var aux, cAux float64
|
|
for _, f := range s.Floats {
|
|
count++
|
|
delta := f.F - (mean + cMean)
|
|
mean, cMean = kahanSumInc(delta/count, mean, cMean)
|
|
aux, cAux = kahanSumInc(delta*(f.F-(mean+cMean)), aux, cAux)
|
|
}
|
|
return (aux + cAux) / count
|
|
}), nil
|
|
}
|
|
|
|
// === absent(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcAbsent(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
if len(vals[0].(Vector)) > 0 {
|
|
return enh.Out, nil
|
|
}
|
|
return append(enh.Out,
|
|
Sample{
|
|
Metric: createLabelsForAbsentFunction(args[0]),
|
|
F: 1,
|
|
}), nil
|
|
}
|
|
|
|
// === absent_over_time(Vector parser.ValueTypeMatrix) (Vector, Annotations) ===
|
|
// As this function has a matrix as argument, it does not get all the Series.
|
|
// This function will return 1 if the matrix has at least one element.
|
|
// Due to engine optimization, this function is only called when this condition is true.
|
|
// Then, the engine post-processes the results to get the expected output.
|
|
func funcAbsentOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return append(enh.Out, Sample{F: 1}), nil
|
|
}
|
|
|
|
// === present_over_time(Vector parser.ValueTypeMatrix) (Vector, Annotations) ===
|
|
func funcPresentOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return aggrOverTime(vals, enh, func(s Series) float64 {
|
|
return 1
|
|
}), nil
|
|
}
|
|
|
|
func simpleFunc(vals []parser.Value, enh *EvalNodeHelper, f func(float64) float64) Vector {
|
|
for _, el := range vals[0].(Vector) {
|
|
if el.H == nil { // Process only float samples.
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: el.Metric.DropMetricName(),
|
|
F: f(el.F),
|
|
})
|
|
}
|
|
}
|
|
return enh.Out
|
|
}
|
|
|
|
// === abs(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcAbs(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Abs), nil
|
|
}
|
|
|
|
// === ceil(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcCeil(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Ceil), nil
|
|
}
|
|
|
|
// === floor(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcFloor(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Floor), nil
|
|
}
|
|
|
|
// === exp(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcExp(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Exp), nil
|
|
}
|
|
|
|
// === sqrt(Vector VectorNode) (Vector, Annotations) ===
|
|
func funcSqrt(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Sqrt), nil
|
|
}
|
|
|
|
// === ln(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcLn(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Log), nil
|
|
}
|
|
|
|
// === log2(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcLog2(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Log2), nil
|
|
}
|
|
|
|
// === log10(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcLog10(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Log10), nil
|
|
}
|
|
|
|
// === sin(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcSin(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Sin), nil
|
|
}
|
|
|
|
// === cos(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcCos(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Cos), nil
|
|
}
|
|
|
|
// === tan(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcTan(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Tan), nil
|
|
}
|
|
|
|
// === asin(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcAsin(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Asin), nil
|
|
}
|
|
|
|
// === acos(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcAcos(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Acos), nil
|
|
}
|
|
|
|
// === atan(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcAtan(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Atan), nil
|
|
}
|
|
|
|
// === sinh(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcSinh(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Sinh), nil
|
|
}
|
|
|
|
// === cosh(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcCosh(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Cosh), nil
|
|
}
|
|
|
|
// === tanh(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcTanh(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Tanh), nil
|
|
}
|
|
|
|
// === asinh(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcAsinh(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Asinh), nil
|
|
}
|
|
|
|
// === acosh(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcAcosh(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Acosh), nil
|
|
}
|
|
|
|
// === atanh(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcAtanh(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, math.Atanh), nil
|
|
}
|
|
|
|
// === rad(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcRad(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, func(v float64) float64 {
|
|
return v * math.Pi / 180
|
|
}), nil
|
|
}
|
|
|
|
// === deg(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcDeg(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, func(v float64) float64 {
|
|
return v * 180 / math.Pi
|
|
}), nil
|
|
}
|
|
|
|
// === pi() Scalar ===
|
|
func funcPi(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return Vector{Sample{F: math.Pi}}, nil
|
|
}
|
|
|
|
// === sgn(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcSgn(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return simpleFunc(vals, enh, func(v float64) float64 {
|
|
switch {
|
|
case v < 0:
|
|
return -1
|
|
case v > 0:
|
|
return 1
|
|
default:
|
|
return v
|
|
}
|
|
}), nil
|
|
}
|
|
|
|
// === timestamp(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcTimestamp(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
vec := vals[0].(Vector)
|
|
for _, el := range vec {
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: el.Metric.DropMetricName(),
|
|
F: float64(el.T) / 1000,
|
|
})
|
|
}
|
|
return enh.Out, nil
|
|
}
|
|
|
|
func kahanSum(samples []float64) float64 {
|
|
var sum, c float64
|
|
|
|
for _, v := range samples {
|
|
sum, c = kahanSumInc(v, sum, c)
|
|
}
|
|
return sum + c
|
|
}
|
|
|
|
func kahanSumInc(inc, sum, c float64) (newSum, newC float64) {
|
|
t := sum + inc
|
|
// Using Neumaier improvement, swap if next term larger than sum.
|
|
if math.Abs(sum) >= math.Abs(inc) {
|
|
c += (sum - t) + inc
|
|
} else {
|
|
c += (inc - t) + sum
|
|
}
|
|
return t, c
|
|
}
|
|
|
|
// linearRegression performs a least-square linear regression analysis on the
|
|
// provided SamplePairs. It returns the slope, and the intercept value at the
|
|
// provided time.
|
|
func linearRegression(samples []FPoint, interceptTime int64) (slope, intercept float64) {
|
|
var (
|
|
n float64
|
|
sumX, cX float64
|
|
sumY, cY float64
|
|
sumXY, cXY float64
|
|
sumX2, cX2 float64
|
|
initY float64
|
|
constY bool
|
|
)
|
|
initY = samples[0].F
|
|
constY = true
|
|
for i, sample := range samples {
|
|
// Set constY to false if any new y values are encountered.
|
|
if constY && i > 0 && sample.F != initY {
|
|
constY = false
|
|
}
|
|
n += 1.0
|
|
x := float64(sample.T-interceptTime) / 1e3
|
|
sumX, cX = kahanSumInc(x, sumX, cX)
|
|
sumY, cY = kahanSumInc(sample.F, sumY, cY)
|
|
sumXY, cXY = kahanSumInc(x*sample.F, sumXY, cXY)
|
|
sumX2, cX2 = kahanSumInc(x*x, sumX2, cX2)
|
|
}
|
|
if constY {
|
|
if math.IsInf(initY, 0) {
|
|
return math.NaN(), math.NaN()
|
|
}
|
|
return 0, initY
|
|
}
|
|
sumX += cX
|
|
sumY += cY
|
|
sumXY += cXY
|
|
sumX2 += cX2
|
|
|
|
covXY := sumXY - sumX*sumY/n
|
|
varX := sumX2 - sumX*sumX/n
|
|
|
|
slope = covXY / varX
|
|
intercept = sumY/n - slope*sumX/n
|
|
return slope, intercept
|
|
}
|
|
|
|
// === deriv(node parser.ValueTypeMatrix) (Vector, Annotations) ===
|
|
func funcDeriv(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
samples := vals[0].(Matrix)[0]
|
|
|
|
// No sense in trying to compute a derivative without at least two points.
|
|
// Drop this Vector element.
|
|
if len(samples.Floats) < 2 {
|
|
return enh.Out, nil
|
|
}
|
|
|
|
// We pass in an arbitrary timestamp that is near the values in use
|
|
// to avoid floating point accuracy issues, see
|
|
// https://github.com/prometheus/prometheus/issues/2674
|
|
slope, _ := linearRegression(samples.Floats, samples.Floats[0].T)
|
|
return append(enh.Out, Sample{F: slope}), nil
|
|
}
|
|
|
|
// === predict_linear(node parser.ValueTypeMatrix, k parser.ValueTypeScalar) (Vector, Annotations) ===
|
|
func funcPredictLinear(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
samples := vals[0].(Matrix)[0]
|
|
duration := vals[1].(Vector)[0].F
|
|
// No sense in trying to predict anything without at least two points.
|
|
// Drop this Vector element.
|
|
if len(samples.Floats) < 2 {
|
|
return enh.Out, nil
|
|
}
|
|
slope, intercept := linearRegression(samples.Floats, enh.Ts)
|
|
|
|
return append(enh.Out, Sample{F: slope*duration + intercept}), nil
|
|
}
|
|
|
|
// === histogram_count(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcHistogramCount(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
inVec := vals[0].(Vector)
|
|
|
|
for _, sample := range inVec {
|
|
// Skip non-histogram samples.
|
|
if sample.H == nil {
|
|
continue
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric.DropMetricName(),
|
|
F: sample.H.Count,
|
|
})
|
|
}
|
|
return enh.Out, nil
|
|
}
|
|
|
|
// === histogram_sum(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcHistogramSum(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
inVec := vals[0].(Vector)
|
|
|
|
for _, sample := range inVec {
|
|
// Skip non-histogram samples.
|
|
if sample.H == nil {
|
|
continue
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric.DropMetricName(),
|
|
F: sample.H.Sum,
|
|
})
|
|
}
|
|
return enh.Out, nil
|
|
}
|
|
|
|
// === histogram_avg(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcHistogramAvg(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
inVec := vals[0].(Vector)
|
|
|
|
for _, sample := range inVec {
|
|
// Skip non-histogram samples.
|
|
if sample.H == nil {
|
|
continue
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric.DropMetricName(),
|
|
F: sample.H.Sum / sample.H.Count,
|
|
})
|
|
}
|
|
return enh.Out, nil
|
|
}
|
|
|
|
// === histogram_stddev(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcHistogramStdDev(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
inVec := vals[0].(Vector)
|
|
|
|
for _, sample := range inVec {
|
|
// Skip non-histogram samples.
|
|
if sample.H == nil {
|
|
continue
|
|
}
|
|
mean := sample.H.Sum / sample.H.Count
|
|
var variance, cVariance float64
|
|
it := sample.H.AllBucketIterator()
|
|
for it.Next() {
|
|
bucket := it.At()
|
|
var val float64
|
|
if bucket.Lower <= 0 && 0 <= bucket.Upper {
|
|
val = 0
|
|
} else {
|
|
val = math.Sqrt(bucket.Upper * bucket.Lower)
|
|
}
|
|
delta := val - mean
|
|
variance, cVariance = kahanSumInc(bucket.Count*delta*delta, variance, cVariance)
|
|
}
|
|
variance += cVariance
|
|
variance /= sample.H.Count
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric.DropMetricName(),
|
|
F: math.Sqrt(variance),
|
|
})
|
|
}
|
|
return enh.Out, nil
|
|
}
|
|
|
|
// === histogram_stdvar(Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcHistogramStdVar(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
inVec := vals[0].(Vector)
|
|
|
|
for _, sample := range inVec {
|
|
// Skip non-histogram samples.
|
|
if sample.H == nil {
|
|
continue
|
|
}
|
|
mean := sample.H.Sum / sample.H.Count
|
|
var variance, cVariance float64
|
|
it := sample.H.AllBucketIterator()
|
|
for it.Next() {
|
|
bucket := it.At()
|
|
var val float64
|
|
if bucket.Lower <= 0 && 0 <= bucket.Upper {
|
|
val = 0
|
|
} else {
|
|
val = math.Sqrt(bucket.Upper * bucket.Lower)
|
|
}
|
|
delta := val - mean
|
|
variance, cVariance = kahanSumInc(bucket.Count*delta*delta, variance, cVariance)
|
|
}
|
|
variance += cVariance
|
|
variance /= sample.H.Count
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric.DropMetricName(),
|
|
F: variance,
|
|
})
|
|
}
|
|
return enh.Out, nil
|
|
}
|
|
|
|
// === histogram_fraction(lower, upper parser.ValueTypeScalar, Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcHistogramFraction(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
lower := vals[0].(Vector)[0].F
|
|
upper := vals[1].(Vector)[0].F
|
|
inVec := vals[2].(Vector)
|
|
|
|
for _, sample := range inVec {
|
|
// Skip non-histogram samples.
|
|
if sample.H == nil {
|
|
continue
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric.DropMetricName(),
|
|
F: histogramFraction(lower, upper, sample.H),
|
|
})
|
|
}
|
|
return enh.Out, nil
|
|
}
|
|
|
|
// === histogram_quantile(k parser.ValueTypeScalar, Vector parser.ValueTypeVector) (Vector, Annotations) ===
|
|
func funcHistogramQuantile(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
q := vals[0].(Vector)[0].F
|
|
inVec := vals[1].(Vector)
|
|
var annos annotations.Annotations
|
|
|
|
if math.IsNaN(q) || q < 0 || q > 1 {
|
|
annos.Add(annotations.NewInvalidQuantileWarning(q, args[0].PositionRange()))
|
|
}
|
|
|
|
if enh.signatureToMetricWithBuckets == nil {
|
|
enh.signatureToMetricWithBuckets = map[string]*metricWithBuckets{}
|
|
} else {
|
|
for _, v := range enh.signatureToMetricWithBuckets {
|
|
v.buckets = v.buckets[:0]
|
|
}
|
|
}
|
|
|
|
var histogramSamples []Sample
|
|
|
|
for _, sample := range inVec {
|
|
// We are only looking for classic buckets here. Remember
|
|
// the histograms for later treatment.
|
|
if sample.H != nil {
|
|
histogramSamples = append(histogramSamples, sample)
|
|
continue
|
|
}
|
|
|
|
upperBound, err := strconv.ParseFloat(
|
|
sample.Metric.Get(model.BucketLabel), 64,
|
|
)
|
|
if err != nil {
|
|
annos.Add(annotations.NewBadBucketLabelWarning(sample.Metric.Get(labels.MetricName), sample.Metric.Get(model.BucketLabel), args[1].PositionRange()))
|
|
continue
|
|
}
|
|
enh.lblBuf = sample.Metric.BytesWithoutLabels(enh.lblBuf, labels.BucketLabel)
|
|
mb, ok := enh.signatureToMetricWithBuckets[string(enh.lblBuf)]
|
|
if !ok {
|
|
sample.Metric = labels.NewBuilder(sample.Metric).
|
|
Del(excludedLabels...).
|
|
Labels()
|
|
|
|
mb = &metricWithBuckets{sample.Metric, nil}
|
|
enh.signatureToMetricWithBuckets[string(enh.lblBuf)] = mb
|
|
}
|
|
mb.buckets = append(mb.buckets, bucket{upperBound, sample.F})
|
|
|
|
}
|
|
|
|
// Now deal with the histograms.
|
|
for _, sample := range histogramSamples {
|
|
// We have to reconstruct the exact same signature as above for
|
|
// a classic histogram, just ignoring any le label.
|
|
enh.lblBuf = sample.Metric.Bytes(enh.lblBuf)
|
|
if mb, ok := enh.signatureToMetricWithBuckets[string(enh.lblBuf)]; ok && len(mb.buckets) > 0 {
|
|
// At this data point, we have classic histogram
|
|
// buckets and a native histogram with the same name and
|
|
// labels. Do not evaluate anything.
|
|
annos.Add(annotations.NewMixedClassicNativeHistogramsWarning(sample.Metric.Get(labels.MetricName), args[1].PositionRange()))
|
|
delete(enh.signatureToMetricWithBuckets, string(enh.lblBuf))
|
|
continue
|
|
}
|
|
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric.DropMetricName(),
|
|
F: histogramQuantile(q, sample.H),
|
|
})
|
|
}
|
|
|
|
for _, mb := range enh.signatureToMetricWithBuckets {
|
|
if len(mb.buckets) > 0 {
|
|
res, forcedMonotonicity, _ := bucketQuantile(q, mb.buckets)
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: mb.metric,
|
|
F: res,
|
|
})
|
|
if forcedMonotonicity {
|
|
annos.Add(annotations.NewHistogramQuantileForcedMonotonicityInfo(mb.metric.Get(labels.MetricName), args[1].PositionRange()))
|
|
}
|
|
}
|
|
}
|
|
|
|
return enh.Out, annos
|
|
}
|
|
|
|
// === resets(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
|
|
func funcResets(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
floats := vals[0].(Matrix)[0].Floats
|
|
histograms := vals[0].(Matrix)[0].Histograms
|
|
resets := 0
|
|
|
|
if len(floats) > 1 {
|
|
prev := floats[0].F
|
|
for _, sample := range floats[1:] {
|
|
current := sample.F
|
|
if current < prev {
|
|
resets++
|
|
}
|
|
prev = current
|
|
}
|
|
}
|
|
|
|
if len(histograms) > 1 {
|
|
prev := histograms[0].H
|
|
for _, sample := range histograms[1:] {
|
|
current := sample.H
|
|
if current.DetectReset(prev) {
|
|
resets++
|
|
}
|
|
prev = current
|
|
}
|
|
}
|
|
|
|
return append(enh.Out, Sample{F: float64(resets)}), nil
|
|
}
|
|
|
|
// === changes(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
|
|
func funcChanges(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
floats := vals[0].(Matrix)[0].Floats
|
|
changes := 0
|
|
|
|
if len(floats) == 0 {
|
|
// TODO(beorn7): Only histogram values, still need to add support.
|
|
return enh.Out, nil
|
|
}
|
|
|
|
prev := floats[0].F
|
|
for _, sample := range floats[1:] {
|
|
current := sample.F
|
|
if current != prev && !(math.IsNaN(current) && math.IsNaN(prev)) {
|
|
changes++
|
|
}
|
|
prev = current
|
|
}
|
|
|
|
return append(enh.Out, Sample{F: float64(changes)}), nil
|
|
}
|
|
|
|
// label_replace function operates only on series; does not look at timestamps or values.
|
|
func (ev *evaluator) evalLabelReplace(args parser.Expressions) (parser.Value, annotations.Annotations) {
|
|
var (
|
|
dst = stringFromArg(args[1])
|
|
repl = stringFromArg(args[2])
|
|
src = stringFromArg(args[3])
|
|
regexStr = stringFromArg(args[4])
|
|
)
|
|
|
|
regex, err := regexp.Compile("^(?:" + regexStr + ")$")
|
|
if err != nil {
|
|
panic(fmt.Errorf("invalid regular expression in label_replace(): %s", regexStr))
|
|
}
|
|
if !model.LabelNameRE.MatchString(dst) {
|
|
panic(fmt.Errorf("invalid destination label name in label_replace(): %s", dst))
|
|
}
|
|
|
|
val, ws := ev.eval(args[0])
|
|
matrix := val.(Matrix)
|
|
lb := labels.NewBuilder(labels.EmptyLabels())
|
|
|
|
for i, el := range matrix {
|
|
srcVal := el.Metric.Get(src)
|
|
indexes := regex.FindStringSubmatchIndex(srcVal)
|
|
if indexes != nil { // Only replace when regexp matches.
|
|
res := regex.ExpandString([]byte{}, repl, srcVal, indexes)
|
|
lb.Reset(el.Metric)
|
|
lb.Set(dst, string(res))
|
|
matrix[i].Metric = lb.Labels()
|
|
}
|
|
}
|
|
if matrix.ContainsSameLabelset() {
|
|
ev.errorf("vector cannot contain metrics with the same labelset")
|
|
}
|
|
|
|
return matrix, ws
|
|
}
|
|
|
|
// === label_replace(Vector parser.ValueTypeVector, dst_label, replacement, src_labelname, regex parser.ValueTypeString) (Vector, Annotations) ===
|
|
func funcLabelReplace(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
panic("funcLabelReplace wrong implementation called")
|
|
}
|
|
|
|
// === Vector(s Scalar) (Vector, Annotations) ===
|
|
func funcVector(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return append(enh.Out,
|
|
Sample{
|
|
Metric: labels.Labels{},
|
|
F: vals[0].(Vector)[0].F,
|
|
}), nil
|
|
}
|
|
|
|
// label_join function operates only on series; does not look at timestamps or values.
|
|
func (ev *evaluator) evalLabelJoin(args parser.Expressions) (parser.Value, annotations.Annotations) {
|
|
var (
|
|
dst = stringFromArg(args[1])
|
|
sep = stringFromArg(args[2])
|
|
srcLabels = make([]string, len(args)-3)
|
|
)
|
|
for i := 3; i < len(args); i++ {
|
|
src := stringFromArg(args[i])
|
|
if !model.LabelName(src).IsValid() {
|
|
panic(fmt.Errorf("invalid source label name in label_join(): %s", src))
|
|
}
|
|
srcLabels[i-3] = src
|
|
}
|
|
if !model.LabelName(dst).IsValid() {
|
|
panic(fmt.Errorf("invalid destination label name in label_join(): %s", dst))
|
|
}
|
|
|
|
val, ws := ev.eval(args[0])
|
|
matrix := val.(Matrix)
|
|
srcVals := make([]string, len(srcLabels))
|
|
lb := labels.NewBuilder(labels.EmptyLabels())
|
|
|
|
for i, el := range matrix {
|
|
for i, src := range srcLabels {
|
|
srcVals[i] = el.Metric.Get(src)
|
|
}
|
|
strval := strings.Join(srcVals, sep)
|
|
lb.Reset(el.Metric)
|
|
lb.Set(dst, strval)
|
|
matrix[i].Metric = lb.Labels()
|
|
}
|
|
|
|
return matrix, ws
|
|
}
|
|
|
|
// === label_join(vector model.ValVector, dest_labelname, separator, src_labelname...) (Vector, Annotations) ===
|
|
func funcLabelJoin(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
panic("funcLabelReplace wrong implementation called")
|
|
}
|
|
|
|
// Common code for date related functions.
|
|
func dateWrapper(vals []parser.Value, enh *EvalNodeHelper, f func(time.Time) float64) Vector {
|
|
if len(vals) == 0 {
|
|
return append(enh.Out,
|
|
Sample{
|
|
Metric: labels.Labels{},
|
|
F: f(time.Unix(enh.Ts/1000, 0).UTC()),
|
|
})
|
|
}
|
|
|
|
for _, el := range vals[0].(Vector) {
|
|
t := time.Unix(int64(el.F), 0).UTC()
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: el.Metric.DropMetricName(),
|
|
F: f(t),
|
|
})
|
|
}
|
|
return enh.Out
|
|
}
|
|
|
|
// === days_in_month(v Vector) Scalar ===
|
|
func funcDaysInMonth(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return dateWrapper(vals, enh, func(t time.Time) float64 {
|
|
return float64(32 - time.Date(t.Year(), t.Month(), 32, 0, 0, 0, 0, time.UTC).Day())
|
|
}), nil
|
|
}
|
|
|
|
// === day_of_month(v Vector) Scalar ===
|
|
func funcDayOfMonth(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return dateWrapper(vals, enh, func(t time.Time) float64 {
|
|
return float64(t.Day())
|
|
}), nil
|
|
}
|
|
|
|
// === day_of_week(v Vector) Scalar ===
|
|
func funcDayOfWeek(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return dateWrapper(vals, enh, func(t time.Time) float64 {
|
|
return float64(t.Weekday())
|
|
}), nil
|
|
}
|
|
|
|
// === day_of_year(v Vector) Scalar ===
|
|
func funcDayOfYear(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return dateWrapper(vals, enh, func(t time.Time) float64 {
|
|
return float64(t.YearDay())
|
|
}), nil
|
|
}
|
|
|
|
// === hour(v Vector) Scalar ===
|
|
func funcHour(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return dateWrapper(vals, enh, func(t time.Time) float64 {
|
|
return float64(t.Hour())
|
|
}), nil
|
|
}
|
|
|
|
// === minute(v Vector) Scalar ===
|
|
func funcMinute(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return dateWrapper(vals, enh, func(t time.Time) float64 {
|
|
return float64(t.Minute())
|
|
}), nil
|
|
}
|
|
|
|
// === month(v Vector) Scalar ===
|
|
func funcMonth(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return dateWrapper(vals, enh, func(t time.Time) float64 {
|
|
return float64(t.Month())
|
|
}), nil
|
|
}
|
|
|
|
// === year(v Vector) Scalar ===
|
|
func funcYear(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
return dateWrapper(vals, enh, func(t time.Time) float64 {
|
|
return float64(t.Year())
|
|
}), nil
|
|
}
|
|
|
|
// FunctionCalls is a list of all functions supported by PromQL, including their types.
|
|
var FunctionCalls = map[string]FunctionCall{
|
|
"abs": funcAbs,
|
|
"absent": funcAbsent,
|
|
"absent_over_time": funcAbsentOverTime,
|
|
"acos": funcAcos,
|
|
"acosh": funcAcosh,
|
|
"asin": funcAsin,
|
|
"asinh": funcAsinh,
|
|
"atan": funcAtan,
|
|
"atanh": funcAtanh,
|
|
"avg_over_time": funcAvgOverTime,
|
|
"ceil": funcCeil,
|
|
"changes": funcChanges,
|
|
"clamp": funcClamp,
|
|
"clamp_max": funcClampMax,
|
|
"clamp_min": funcClampMin,
|
|
"cos": funcCos,
|
|
"cosh": funcCosh,
|
|
"count_over_time": funcCountOverTime,
|
|
"days_in_month": funcDaysInMonth,
|
|
"day_of_month": funcDayOfMonth,
|
|
"day_of_week": funcDayOfWeek,
|
|
"day_of_year": funcDayOfYear,
|
|
"deg": funcDeg,
|
|
"delta": funcDelta,
|
|
"deriv": funcDeriv,
|
|
"exp": funcExp,
|
|
"floor": funcFloor,
|
|
"histogram_avg": funcHistogramAvg,
|
|
"histogram_count": funcHistogramCount,
|
|
"histogram_fraction": funcHistogramFraction,
|
|
"histogram_quantile": funcHistogramQuantile,
|
|
"histogram_sum": funcHistogramSum,
|
|
"histogram_stddev": funcHistogramStdDev,
|
|
"histogram_stdvar": funcHistogramStdVar,
|
|
"holt_winters": funcHoltWinters,
|
|
"hour": funcHour,
|
|
"idelta": funcIdelta,
|
|
"increase": funcIncrease,
|
|
"irate": funcIrate,
|
|
"label_replace": funcLabelReplace,
|
|
"label_join": funcLabelJoin,
|
|
"ln": funcLn,
|
|
"log10": funcLog10,
|
|
"log2": funcLog2,
|
|
"last_over_time": funcLastOverTime,
|
|
"mad_over_time": funcMadOverTime,
|
|
"max_over_time": funcMaxOverTime,
|
|
"min_over_time": funcMinOverTime,
|
|
"minute": funcMinute,
|
|
"month": funcMonth,
|
|
"pi": funcPi,
|
|
"predict_linear": funcPredictLinear,
|
|
"present_over_time": funcPresentOverTime,
|
|
"quantile_over_time": funcQuantileOverTime,
|
|
"rad": funcRad,
|
|
"rate": funcRate,
|
|
"resets": funcResets,
|
|
"round": funcRound,
|
|
"scalar": funcScalar,
|
|
"sgn": funcSgn,
|
|
"sin": funcSin,
|
|
"sinh": funcSinh,
|
|
"sort": funcSort,
|
|
"sort_desc": funcSortDesc,
|
|
"sort_by_label": funcSortByLabel,
|
|
"sort_by_label_desc": funcSortByLabelDesc,
|
|
"sqrt": funcSqrt,
|
|
"stddev_over_time": funcStddevOverTime,
|
|
"stdvar_over_time": funcStdvarOverTime,
|
|
"sum_over_time": funcSumOverTime,
|
|
"tan": funcTan,
|
|
"tanh": funcTanh,
|
|
"time": funcTime,
|
|
"timestamp": funcTimestamp,
|
|
"vector": funcVector,
|
|
"year": funcYear,
|
|
}
|
|
|
|
// AtModifierUnsafeFunctions are the functions whose result
|
|
// can vary if evaluation time is changed when the arguments are
|
|
// step invariant. It also includes functions that use the timestamps
|
|
// of the passed instant vector argument to calculate a result since
|
|
// that can also change with change in eval time.
|
|
var AtModifierUnsafeFunctions = map[string]struct{}{
|
|
// Step invariant functions.
|
|
"days_in_month": {}, "day_of_month": {}, "day_of_week": {}, "day_of_year": {},
|
|
"hour": {}, "minute": {}, "month": {}, "year": {},
|
|
"predict_linear": {}, "time": {},
|
|
// Uses timestamp of the argument for the result,
|
|
// hence unsafe to use with @ modifier.
|
|
"timestamp": {},
|
|
}
|
|
|
|
type vectorByValueHeap Vector
|
|
|
|
func (s vectorByValueHeap) Len() int {
|
|
return len(s)
|
|
}
|
|
|
|
func (s vectorByValueHeap) Less(i, j int) bool {
|
|
// We compare histograms based on their sum of observations.
|
|
// TODO(beorn7): Is that what we want?
|
|
vi, vj := s[i].F, s[j].F
|
|
if s[i].H != nil {
|
|
vi = s[i].H.Sum
|
|
}
|
|
if s[j].H != nil {
|
|
vj = s[j].H.Sum
|
|
}
|
|
|
|
if math.IsNaN(vi) {
|
|
return true
|
|
}
|
|
return vi < vj
|
|
}
|
|
|
|
func (s vectorByValueHeap) Swap(i, j int) {
|
|
s[i], s[j] = s[j], s[i]
|
|
}
|
|
|
|
func (s *vectorByValueHeap) Push(x interface{}) {
|
|
*s = append(*s, *(x.(*Sample)))
|
|
}
|
|
|
|
func (s *vectorByValueHeap) Pop() interface{} {
|
|
old := *s
|
|
n := len(old)
|
|
el := old[n-1]
|
|
*s = old[0 : n-1]
|
|
return el
|
|
}
|
|
|
|
type vectorByReverseValueHeap Vector
|
|
|
|
func (s vectorByReverseValueHeap) Len() int {
|
|
return len(s)
|
|
}
|
|
|
|
func (s vectorByReverseValueHeap) Less(i, j int) bool {
|
|
// We compare histograms based on their sum of observations.
|
|
// TODO(beorn7): Is that what we want?
|
|
vi, vj := s[i].F, s[j].F
|
|
if s[i].H != nil {
|
|
vi = s[i].H.Sum
|
|
}
|
|
if s[j].H != nil {
|
|
vj = s[j].H.Sum
|
|
}
|
|
|
|
if math.IsNaN(vi) {
|
|
return true
|
|
}
|
|
return vi > vj
|
|
}
|
|
|
|
func (s vectorByReverseValueHeap) Swap(i, j int) {
|
|
s[i], s[j] = s[j], s[i]
|
|
}
|
|
|
|
func (s *vectorByReverseValueHeap) Push(x interface{}) {
|
|
*s = append(*s, *(x.(*Sample)))
|
|
}
|
|
|
|
func (s *vectorByReverseValueHeap) Pop() interface{} {
|
|
old := *s
|
|
n := len(old)
|
|
el := old[n-1]
|
|
*s = old[0 : n-1]
|
|
return el
|
|
}
|
|
|
|
// createLabelsForAbsentFunction returns the labels that are uniquely and exactly matched
|
|
// in a given expression. It is used in the absent functions.
|
|
func createLabelsForAbsentFunction(expr parser.Expr) labels.Labels {
|
|
b := labels.NewBuilder(labels.EmptyLabels())
|
|
|
|
var lm []*labels.Matcher
|
|
switch n := expr.(type) {
|
|
case *parser.VectorSelector:
|
|
lm = n.LabelMatchers
|
|
case *parser.MatrixSelector:
|
|
lm = n.VectorSelector.(*parser.VectorSelector).LabelMatchers
|
|
default:
|
|
return labels.EmptyLabels()
|
|
}
|
|
|
|
// The 'has' map implements backwards-compatibility for historic behaviour:
|
|
// e.g. in `absent(x{job="a",job="b",foo="bar"})` then `job` is removed from the output.
|
|
// Note this gives arguably wrong behaviour for `absent(x{job="a",job="a",foo="bar"})`.
|
|
has := make(map[string]bool, len(lm))
|
|
for _, ma := range lm {
|
|
if ma.Name == labels.MetricName {
|
|
continue
|
|
}
|
|
if ma.Type == labels.MatchEqual && !has[ma.Name] {
|
|
b.Set(ma.Name, ma.Value)
|
|
has[ma.Name] = true
|
|
} else {
|
|
b.Del(ma.Name)
|
|
}
|
|
}
|
|
|
|
return b.Labels()
|
|
}
|
|
|
|
func stringFromArg(e parser.Expr) string {
|
|
tmp := unwrapStepInvariantExpr(e) // Unwrap StepInvariant
|
|
unwrapParenExpr(&tmp) // Optionally unwrap ParenExpr
|
|
return tmp.(*parser.StringLiteral).Val
|
|
}
|
|
|
|
func stringSliceFromArgs(args parser.Expressions) []string {
|
|
tmp := make([]string, len(args))
|
|
for i := 0; i < len(args); i++ {
|
|
tmp[i] = stringFromArg(args[i])
|
|
}
|
|
return tmp
|
|
}
|