mirror of
https://github.com/prometheus/prometheus.git
synced 2025-02-02 08:31:11 -08:00
7f99d2930d
Go's sorting functions can re-order equal elements, so the strategy of sorting by the fallback ordering first does not always work. Pulling the fallback into the main comparison function is more reliable and more efficient. Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
1860 lines
61 KiB
Go
1860 lines
61 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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"context"
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"errors"
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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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annos.Merge(newAnnos)
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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
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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 samples are close enough to the (lower or upper) boundary of the
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// range, we extrapolate the rate all the way to the boundary in
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// question. "Close enough" is defined as "up to 10% more than the
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// average duration between samples within the range", see
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// extrapolationThreshold below. Essentially, we are assuming a more or
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// less regular spacing between samples, and if we don't see a sample
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// where we would expect one, we assume the series does not cover the
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// whole range, but starts and/or ends within the range. We still
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// extrapolate the rate in this case, but not all the way to the
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// boundary, but only by half of the average duration between samples
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// (which is our guess for where the series actually starts or ends).
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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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usingCustomBuckets := prev.UsesCustomBuckets()
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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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if last.UsesCustomBuckets() != usingCustomBuckets {
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return nil, annotations.New().Add(annotations.NewMixedExponentialCustomHistogramsWarning(metricName, pos))
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}
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var annos annotations.Annotations
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// We check for gauge type histograms in the loop below, but the loop below does not run on the first and last point,
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// so check the first and last point now.
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if isCounter && (prev.CounterResetHint == histogram.GaugeType || last.CounterResetHint == histogram.GaugeType) {
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annos.Add(annotations.NewNativeHistogramNotCounterWarning(metricName, pos))
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}
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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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if curr.UsesCustomBuckets() != usingCustomBuckets {
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return nil, annotations.New().Add(annotations.NewMixedExponentialCustomHistogramsWarning(metricName, pos))
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}
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}
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h := last.CopyToSchema(minSchema)
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_, err := h.Sub(prev)
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if err != nil {
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if errors.Is(err, histogram.ErrHistogramsIncompatibleSchema) {
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return nil, annotations.New().Add(annotations.NewMixedExponentialCustomHistogramsWarning(metricName, pos))
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} else if errors.Is(err, histogram.ErrHistogramsIncompatibleBounds) {
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return nil, annotations.New().Add(annotations.NewIncompatibleCustomBucketsHistogramsWarning(metricName, pos))
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}
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}
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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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_, err := h.Add(prev)
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if err != nil {
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if errors.Is(err, histogram.ErrHistogramsIncompatibleSchema) {
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return nil, annotations.New().Add(annotations.NewMixedExponentialCustomHistogramsWarning(metricName, pos))
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} else if errors.Is(err, histogram.ErrHistogramsIncompatibleBounds) {
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return nil, annotations.New().Add(annotations.NewIncompatibleCustomBucketsHistogramsWarning(metricName, pos))
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}
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}
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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), annos
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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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lbls := stringSliceFromArgs(args[1:])
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slices.SortFunc(vals[0].(Vector), func(a, b Sample) int {
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for _, label := range lbls {
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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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// If all labels provided as arguments were equal, sort by the full label set. This ensures a consistent ordering.
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return labels.Compare(a.Metric, b.Metric)
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})
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return vals[0].(Vector), nil
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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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lbls := stringSliceFromArgs(args[1:])
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slices.SortFunc(vals[0].(Vector), func(a, b Sample) int {
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for _, label := range lbls {
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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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// If all labels provided as arguments were equal, sort by the full label set. This ensures a consistent ordering.
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return -labels.Compare(a.Metric, b.Metric)
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})
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|
|
return vals[0].(Vector), nil
|
|
}
|
|
|
|
// === clamp(Vector parser.ValueTypeVector, min, max Scalar) (Vector, Annotations) ===
|
|
func funcClamp(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
vec := vals[0].(Vector)
|
|
minVal := vals[1].(Vector)[0].F
|
|
maxVal := vals[2].(Vector)[0].F
|
|
if maxVal < minVal {
|
|
return enh.Out, nil
|
|
}
|
|
for _, el := range vec {
|
|
if !enh.enableDelayedNameRemoval {
|
|
el.Metric = el.Metric.DropMetricName()
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: el.Metric,
|
|
F: math.Max(minVal, math.Min(maxVal, el.F)),
|
|
DropName: true,
|
|
})
|
|
}
|
|
return enh.Out, nil
|
|
}
|
|
|
|
// === clamp_max(Vector parser.ValueTypeVector, max Scalar) (Vector, Annotations) ===
|
|
func funcClampMax(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
vec := vals[0].(Vector)
|
|
maxVal := vals[1].(Vector)[0].F
|
|
for _, el := range vec {
|
|
if !enh.enableDelayedNameRemoval {
|
|
el.Metric = el.Metric.DropMetricName()
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: el.Metric,
|
|
F: math.Min(maxVal, el.F),
|
|
DropName: true,
|
|
})
|
|
}
|
|
return enh.Out, nil
|
|
}
|
|
|
|
// === clamp_min(Vector parser.ValueTypeVector, min Scalar) (Vector, Annotations) ===
|
|
func funcClampMin(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
|
|
vec := vals[0].(Vector)
|
|
minVal := vals[1].(Vector)[0].F
|
|
for _, el := range vec {
|
|
if !enh.enableDelayedNameRemoval {
|
|
el.Metric = el.Metric.DropMetricName()
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: el.Metric,
|
|
F: math.Max(minVal, el.F),
|
|
DropName: true,
|
|
})
|
|
}
|
|
return enh.Out, nil
|
|
}
|
|
|
|
// === 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,
|
|
F: f,
|
|
DropName: true,
|
|
})
|
|
}
|
|
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, error)) (Vector, error) {
|
|
el := vals[0].(Matrix)[0]
|
|
res, err := aggrFn(el)
|
|
|
|
return append(enh.Out, Sample{H: res}), err
|
|
}
|
|
|
|
// === 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.
|
|
vec, err := aggrHistOverTime(vals, enh, func(s Series) (*histogram.FloatHistogram, error) {
|
|
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, err := left.Sub(right)
|
|
if err != nil {
|
|
return mean, err
|
|
}
|
|
_, err = mean.Add(toAdd)
|
|
if err != nil {
|
|
return mean, err
|
|
}
|
|
}
|
|
return mean, nil
|
|
})
|
|
if err != nil {
|
|
metricName := firstSeries.Metric.Get(labels.MetricName)
|
|
if errors.Is(err, histogram.ErrHistogramsIncompatibleSchema) {
|
|
return enh.Out, annotations.New().Add(annotations.NewMixedExponentialCustomHistogramsWarning(metricName, args[0].PositionRange()))
|
|
} else if errors.Is(err, histogram.ErrHistogramsIncompatibleBounds) {
|
|
return enh.Out, annotations.New().Add(annotations.NewIncompatibleCustomBucketsHistogramsWarning(metricName, args[0].PositionRange()))
|
|
}
|
|
}
|
|
return vec, nil
|
|
}
|
|
return aggrOverTime(vals, enh, func(s Series) float64 {
|
|
var (
|
|
sum, mean, count, kahanC float64
|
|
incrementalMean bool
|
|
)
|
|
for _, f := range s.Floats {
|
|
count++
|
|
if !incrementalMean {
|
|
newSum, newC := kahanSumInc(f.F, sum, kahanC)
|
|
// Perform regular mean calculation as long as
|
|
// the sum doesn't overflow and (in any case)
|
|
// for the first iteration (even if we start
|
|
// with ±Inf) to not run into division-by-zero
|
|
// problems below.
|
|
if count == 1 || !math.IsInf(newSum, 0) {
|
|
sum, kahanC = newSum, newC
|
|
continue
|
|
}
|
|
// Handle overflow by reverting to incremental calculation of the mean value.
|
|
incrementalMean = true
|
|
mean = sum / (count - 1)
|
|
kahanC /= count - 1
|
|
}
|
|
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
|
|
}
|
|
}
|
|
correctedMean := mean + kahanC
|
|
mean, kahanC = kahanSumInc(f.F/count-correctedMean/count, mean, kahanC)
|
|
}
|
|
if incrementalMean {
|
|
return mean + kahanC
|
|
}
|
|
return (sum + kahanC) / count
|
|
}), 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 {
|
|
maxVal := s.Floats[0].F
|
|
for _, f := range s.Floats {
|
|
if f.F > maxVal || math.IsNaN(maxVal) {
|
|
maxVal = f.F
|
|
}
|
|
}
|
|
return maxVal
|
|
}), 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 {
|
|
minVal := s.Floats[0].F
|
|
for _, f := range s.Floats {
|
|
if f.F < minVal || math.IsNaN(minVal) {
|
|
minVal = f.F
|
|
}
|
|
}
|
|
return minVal
|
|
}), 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.
|
|
vec, err := aggrHistOverTime(vals, enh, func(s Series) (*histogram.FloatHistogram, error) {
|
|
sum := s.Histograms[0].H.Copy()
|
|
for _, h := range s.Histograms[1:] {
|
|
_, err := sum.Add(h.H)
|
|
if err != nil {
|
|
return sum, err
|
|
}
|
|
}
|
|
return sum, nil
|
|
})
|
|
if err != nil {
|
|
metricName := firstSeries.Metric.Get(labels.MetricName)
|
|
if errors.Is(err, histogram.ErrHistogramsIncompatibleSchema) {
|
|
return enh.Out, annotations.New().Add(annotations.NewMixedExponentialCustomHistogramsWarning(metricName, args[0].PositionRange()))
|
|
} else if errors.Is(err, histogram.ErrHistogramsIncompatibleBounds) {
|
|
return enh.Out, annotations.New().Add(annotations.NewIncompatibleCustomBucketsHistogramsWarning(metricName, args[0].PositionRange()))
|
|
}
|
|
}
|
|
return vec, 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.
|
|
if !enh.enableDelayedNameRemoval {
|
|
el.Metric = el.Metric.DropMetricName()
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: el.Metric,
|
|
F: f(el.F),
|
|
DropName: true,
|
|
})
|
|
}
|
|
}
|
|
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 {
|
|
if !enh.enableDelayedNameRemoval {
|
|
el.Metric = el.Metric.DropMetricName()
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: el.Metric,
|
|
F: float64(el.T) / 1000,
|
|
DropName: true,
|
|
})
|
|
}
|
|
return enh.Out, nil
|
|
}
|
|
|
|
func kahanSumInc(inc, sum, c float64) (newSum, newC float64) {
|
|
t := sum + inc
|
|
switch {
|
|
case math.IsInf(t, 0):
|
|
c = 0
|
|
|
|
// Using Neumaier improvement, swap if next term larger than sum.
|
|
case math.Abs(sum) >= math.Abs(inc):
|
|
c += (sum - t) + inc
|
|
default:
|
|
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
|
|
}
|
|
if !enh.enableDelayedNameRemoval {
|
|
sample.Metric = sample.Metric.DropMetricName()
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric,
|
|
F: sample.H.Count,
|
|
DropName: true,
|
|
})
|
|
}
|
|
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
|
|
}
|
|
if !enh.enableDelayedNameRemoval {
|
|
sample.Metric = sample.Metric.DropMetricName()
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric,
|
|
F: sample.H.Sum,
|
|
DropName: true,
|
|
})
|
|
}
|
|
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
|
|
}
|
|
if !enh.enableDelayedNameRemoval {
|
|
sample.Metric = sample.Metric.DropMetricName()
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric,
|
|
F: sample.H.Sum / sample.H.Count,
|
|
DropName: true,
|
|
})
|
|
}
|
|
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()
|
|
if bucket.Count == 0 {
|
|
continue
|
|
}
|
|
var val float64
|
|
if bucket.Lower <= 0 && 0 <= bucket.Upper {
|
|
val = 0
|
|
} else {
|
|
val = math.Sqrt(bucket.Upper * bucket.Lower)
|
|
if bucket.Upper < 0 {
|
|
val = -val
|
|
}
|
|
}
|
|
delta := val - mean
|
|
variance, cVariance = kahanSumInc(bucket.Count*delta*delta, variance, cVariance)
|
|
}
|
|
variance += cVariance
|
|
variance /= sample.H.Count
|
|
if !enh.enableDelayedNameRemoval {
|
|
sample.Metric = sample.Metric.DropMetricName()
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric,
|
|
F: math.Sqrt(variance),
|
|
DropName: true,
|
|
})
|
|
}
|
|
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()
|
|
if bucket.Count == 0 {
|
|
continue
|
|
}
|
|
var val float64
|
|
if bucket.Lower <= 0 && 0 <= bucket.Upper {
|
|
val = 0
|
|
} else {
|
|
val = math.Sqrt(bucket.Upper * bucket.Lower)
|
|
if bucket.Upper < 0 {
|
|
val = -val
|
|
}
|
|
}
|
|
delta := val - mean
|
|
variance, cVariance = kahanSumInc(bucket.Count*delta*delta, variance, cVariance)
|
|
}
|
|
variance += cVariance
|
|
variance /= sample.H.Count
|
|
if !enh.enableDelayedNameRemoval {
|
|
sample.Metric = sample.Metric.DropMetricName()
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric,
|
|
F: variance,
|
|
DropName: true,
|
|
})
|
|
}
|
|
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
|
|
}
|
|
if !enh.enableDelayedNameRemoval {
|
|
sample.Metric = sample.Metric.DropMetricName()
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric,
|
|
F: histogramFraction(lower, upper, sample.H),
|
|
DropName: true,
|
|
})
|
|
}
|
|
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
|
|
}
|
|
|
|
if !enh.enableDelayedNameRemoval {
|
|
sample.Metric = sample.Metric.DropMetricName()
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: sample.Metric,
|
|
F: histogramQuantile(q, sample.H),
|
|
DropName: true,
|
|
})
|
|
}
|
|
|
|
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(ctx context.Context, 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(ctx, 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 dst == model.MetricNameLabel {
|
|
matrix[i].DropName = false
|
|
} else {
|
|
matrix[i].DropName = el.DropName
|
|
}
|
|
}
|
|
}
|
|
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(ctx context.Context, 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(ctx, 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()
|
|
|
|
if dst == model.MetricNameLabel {
|
|
matrix[i].DropName = false
|
|
} else {
|
|
matrix[i].DropName = el.DropName
|
|
}
|
|
}
|
|
|
|
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()
|
|
if !enh.enableDelayedNameRemoval {
|
|
el.Metric = el.Metric.DropMetricName()
|
|
}
|
|
enh.Out = append(enh.Out, Sample{
|
|
Metric: el.Metric,
|
|
F: f(t),
|
|
DropName: true,
|
|
})
|
|
}
|
|
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
|
|
}
|