prometheus/promql/functions.go
Vijay Samuel 00ee73ef91
Export members of EvalNodeHelper to facilitate usage in external functions (#7860)
Signed-off-by: Vijay Samuel <vjsamuel@ebay.com>
2020-08-27 19:30:10 +01:00

1031 lines
31 KiB
Go

// Copyright 2015 The Prometheus Authors
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package promql
import (
"math"
"regexp"
"sort"
"strconv"
"strings"
"time"
"github.com/pkg/errors"
"github.com/prometheus/common/model"
"github.com/prometheus/prometheus/pkg/labels"
"github.com/prometheus/prometheus/promql/parser"
)
// FunctionCall is the type of a PromQL function implementation
//
// vals is a list of the evaluated arguments for the function call.
// For range vectors it will be a Matrix with one series, instant vectors a
// Vector, scalars a Vector with one series whose value is the scalar
// value,and nil for strings.
// args are the original arguments to the function, where you can access
// matrixSelectors, vectorSelectors, and StringLiterals.
// enh.Out is a pre-allocated empty vector that you may use to accumulate
// output before returning it. The vectors in vals should not be returned.a
// Range vector functions need only return a vector with the right value,
// the metric and timestamp are not needed.
// Instant vector functions need only return a vector with the right values and
// metrics, the timestamp are not needed.
// Scalar results should be returned as the value of a sample in a Vector.
type FunctionCall func(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector
// === time() float64 ===
func funcTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return Vector{Sample{Point: Point{
V: float64(enh.Ts) / 1000,
}}}
}
// extrapolatedRate is a utility function for rate/increase/delta.
// It calculates the rate (allowing for counter resets if isCounter is true),
// extrapolates if the first/last sample is close to the boundary, and returns
// the result as either per-second (if isRate is true) or overall.
func extrapolatedRate(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper, isCounter bool, isRate bool) Vector {
ms := args[0].(*parser.MatrixSelector)
vs := ms.VectorSelector.(*parser.VectorSelector)
var (
samples = vals[0].(Matrix)[0]
rangeStart = enh.Ts - durationMilliseconds(ms.Range+vs.Offset)
rangeEnd = enh.Ts - durationMilliseconds(vs.Offset)
)
// No sense in trying to compute a rate without at least two points. Drop
// this Vector element.
if len(samples.Points) < 2 {
return enh.Out
}
var (
counterCorrection float64
lastValue float64
)
for _, sample := range samples.Points {
if isCounter && sample.V < lastValue {
counterCorrection += lastValue
}
lastValue = sample.V
}
resultValue := lastValue - samples.Points[0].V + counterCorrection
// Duration between first/last samples and boundary of range.
durationToStart := float64(samples.Points[0].T-rangeStart) / 1000
durationToEnd := float64(rangeEnd-samples.Points[len(samples.Points)-1].T) / 1000
sampledInterval := float64(samples.Points[len(samples.Points)-1].T-samples.Points[0].T) / 1000
averageDurationBetweenSamples := sampledInterval / float64(len(samples.Points)-1)
if isCounter && resultValue > 0 && samples.Points[0].V >= 0 {
// Counters cannot be negative. If we have any slope at
// all (i.e. resultValue went up), we can extrapolate
// the zero point of the counter. If the duration to the
// zero point is shorter than the durationToStart, we
// take the zero point as the start of the series,
// thereby avoiding extrapolation to negative counter
// values.
durationToZero := sampledInterval * (samples.Points[0].V / resultValue)
if durationToZero < durationToStart {
durationToStart = durationToZero
}
}
// If the first/last samples are close to the boundaries of the range,
// extrapolate the result. This is as we expect that another sample
// will exist given the spacing between samples we've seen thus far,
// with an allowance for noise.
extrapolationThreshold := averageDurationBetweenSamples * 1.1
extrapolateToInterval := sampledInterval
if durationToStart < extrapolationThreshold {
extrapolateToInterval += durationToStart
} else {
extrapolateToInterval += averageDurationBetweenSamples / 2
}
if durationToEnd < extrapolationThreshold {
extrapolateToInterval += durationToEnd
} else {
extrapolateToInterval += averageDurationBetweenSamples / 2
}
resultValue = resultValue * (extrapolateToInterval / sampledInterval)
if isRate {
resultValue = resultValue / ms.Range.Seconds()
}
return append(enh.Out, Sample{
Point: Point{V: resultValue},
})
}
// === delta(Matrix parser.ValueTypeMatrix) Vector ===
func funcDelta(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return extrapolatedRate(vals, args, enh, false, false)
}
// === rate(node parser.ValueTypeMatrix) Vector ===
func funcRate(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return extrapolatedRate(vals, args, enh, true, true)
}
// === increase(node parser.ValueTypeMatrix) Vector ===
func funcIncrease(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return extrapolatedRate(vals, args, enh, true, false)
}
// === irate(node parser.ValueTypeMatrix) Vector ===
func funcIrate(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return instantValue(vals, enh.Out, true)
}
// === idelta(node model.ValMatrix) Vector ===
func funcIdelta(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return instantValue(vals, enh.Out, false)
}
func instantValue(vals []parser.Value, out Vector, isRate bool) Vector {
samples := vals[0].(Matrix)[0]
// No sense in trying to compute a rate without at least two points. Drop
// this Vector element.
if len(samples.Points) < 2 {
return out
}
lastSample := samples.Points[len(samples.Points)-1]
previousSample := samples.Points[len(samples.Points)-2]
var resultValue float64
if isRate && lastSample.V < previousSample.V {
// Counter reset.
resultValue = lastSample.V
} else {
resultValue = lastSample.V - previousSample.V
}
sampledInterval := lastSample.T - previousSample.T
if sampledInterval == 0 {
// Avoid dividing by 0.
return out
}
if isRate {
// Convert to per-second.
resultValue /= float64(sampledInterval) / 1000
}
return append(out, Sample{
Point: Point{V: resultValue},
})
}
// Calculate the trend value at the given index i in raw data d.
// This is somewhat analogous to the slope of the trend at the given index.
// The argument "tf" is the trend factor.
// The argument "s0" is the computed smoothed value.
// The argument "s1" is the computed trend factor.
// The argument "b" is the raw input value.
func calcTrendValue(i int, tf, s0, s1, b float64) float64 {
if i == 0 {
return b
}
x := tf * (s1 - s0)
y := (1 - tf) * b
return x + y
}
// Holt-Winters is similar to a weighted moving average, where historical data has exponentially less influence on the current data.
// Holt-Winter also accounts for trends in data. The smoothing factor (0 < sf < 1) affects how historical data will affect the current
// data. A lower smoothing factor increases the influence of historical data. The trend factor (0 < tf < 1) affects
// how trends in historical data will affect the current data. A higher trend factor increases the influence.
// of trends. Algorithm taken from https://en.wikipedia.org/wiki/Exponential_smoothing titled: "Double exponential smoothing".
func funcHoltWinters(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
samples := vals[0].(Matrix)[0]
// The smoothing factor argument.
sf := vals[1].(Vector)[0].V
// The trend factor argument.
tf := vals[2].(Vector)[0].V
// Sanity check the input.
if sf <= 0 || sf >= 1 {
panic(errors.Errorf("invalid smoothing factor. Expected: 0 < sf < 1, got: %f", sf))
}
if tf <= 0 || tf >= 1 {
panic(errors.Errorf("invalid trend factor. Expected: 0 < tf < 1, got: %f", tf))
}
l := len(samples.Points)
// Can't do the smoothing operation with less than two points.
if l < 2 {
return enh.Out
}
var s0, s1, b float64
// Set initial values.
s1 = samples.Points[0].V
b = samples.Points[1].V - samples.Points[0].V
// Run the smoothing operation.
var x, y float64
for i := 1; i < l; i++ {
// Scale the raw value against the smoothing factor.
x = sf * samples.Points[i].V
// Scale the last smoothed value with the trend at this point.
b = calcTrendValue(i-1, tf, s0, s1, b)
y = (1 - sf) * (s1 + b)
s0, s1 = s1, x+y
}
return append(enh.Out, Sample{
Point: Point{V: s1},
})
}
// === sort(node parser.ValueTypeVector) Vector ===
func funcSort(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
// NaN should sort to the bottom, so take descending sort with NaN first and
// reverse it.
byValueSorter := vectorByReverseValueHeap(vals[0].(Vector))
sort.Sort(sort.Reverse(byValueSorter))
return Vector(byValueSorter)
}
// === sortDesc(node parser.ValueTypeVector) Vector ===
func funcSortDesc(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
// NaN should sort to the bottom, so take ascending sort with NaN first and
// reverse it.
byValueSorter := vectorByValueHeap(vals[0].(Vector))
sort.Sort(sort.Reverse(byValueSorter))
return Vector(byValueSorter)
}
// === clamp_max(Vector parser.ValueTypeVector, max Scalar) Vector ===
func funcClampMax(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
vec := vals[0].(Vector)
max := vals[1].(Vector)[0].Point.V
for _, el := range vec {
enh.Out = append(enh.Out, Sample{
Metric: enh.DropMetricName(el.Metric),
Point: Point{V: math.Min(max, el.V)},
})
}
return enh.Out
}
// === clamp_min(Vector parser.ValueTypeVector, min Scalar) Vector ===
func funcClampMin(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
vec := vals[0].(Vector)
min := vals[1].(Vector)[0].Point.V
for _, el := range vec {
enh.Out = append(enh.Out, Sample{
Metric: enh.DropMetricName(el.Metric),
Point: Point{V: math.Max(min, el.V)},
})
}
return enh.Out
}
// === round(Vector parser.ValueTypeVector, toNearest=1 Scalar) Vector ===
func funcRound(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
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].Point.V
}
// Invert as it seems to cause fewer floating point accuracy issues.
toNearestInverse := 1.0 / toNearest
for _, el := range vec {
v := math.Floor(el.V*toNearestInverse+0.5) / toNearestInverse
enh.Out = append(enh.Out, Sample{
Metric: enh.DropMetricName(el.Metric),
Point: Point{V: v},
})
}
return enh.Out
}
// === Scalar(node parser.ValueTypeVector) Scalar ===
func funcScalar(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
v := vals[0].(Vector)
if len(v) != 1 {
return append(enh.Out, Sample{
Point: Point{V: math.NaN()},
})
}
return append(enh.Out, Sample{
Point: Point{V: v[0].V},
})
}
func aggrOverTime(vals []parser.Value, enh *EvalNodeHelper, aggrFn func([]Point) float64) Vector {
el := vals[0].(Matrix)[0]
return append(enh.Out, Sample{
Point: Point{V: aggrFn(el.Points)},
})
}
// === avg_over_time(Matrix parser.ValueTypeMatrix) Vector ===
func funcAvgOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return aggrOverTime(vals, enh, func(values []Point) float64 {
var mean, count float64
for _, v := range values {
count++
if math.IsInf(mean, 0) {
if math.IsInf(v.V, 0) && (mean > 0) == (v.V > 0) {
// The `mean` and `v.V` values are `Inf` of the same sign. They
// can't be subtracted, but the value of `mean` is correct
// already.
continue
}
if !math.IsInf(v.V, 0) && !math.IsNaN(v.V) {
// 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 += v.V/count - mean/count
}
return mean
})
}
// === count_over_time(Matrix parser.ValueTypeMatrix) Vector ===
func funcCountOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return aggrOverTime(vals, enh, func(values []Point) float64 {
return float64(len(values))
})
}
// === floor(Vector parser.ValueTypeVector) Vector ===
// === max_over_time(Matrix parser.ValueTypeMatrix) Vector ===
func funcMaxOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return aggrOverTime(vals, enh, func(values []Point) float64 {
max := values[0].V
for _, v := range values {
if v.V > max || math.IsNaN(max) {
max = v.V
}
}
return max
})
}
// === min_over_time(Matrix parser.ValueTypeMatrix) Vector ===
func funcMinOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return aggrOverTime(vals, enh, func(values []Point) float64 {
min := values[0].V
for _, v := range values {
if v.V < min || math.IsNaN(min) {
min = v.V
}
}
return min
})
}
// === sum_over_time(Matrix parser.ValueTypeMatrix) Vector ===
func funcSumOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return aggrOverTime(vals, enh, func(values []Point) float64 {
var sum float64
for _, v := range values {
sum += v.V
}
return sum
})
}
// === quantile_over_time(Matrix parser.ValueTypeMatrix) Vector ===
func funcQuantileOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
q := vals[0].(Vector)[0].V
el := vals[1].(Matrix)[0]
values := make(vectorByValueHeap, 0, len(el.Points))
for _, v := range el.Points {
values = append(values, Sample{Point: Point{V: v.V}})
}
return append(enh.Out, Sample{
Point: Point{V: quantile(q, values)},
})
}
// === stddev_over_time(Matrix parser.ValueTypeMatrix) Vector ===
func funcStddevOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return aggrOverTime(vals, enh, func(values []Point) float64 {
var aux, count, mean float64
for _, v := range values {
count++
delta := v.V - mean
mean += delta / count
aux += delta * (v.V - mean)
}
return math.Sqrt(aux / count)
})
}
// === stdvar_over_time(Matrix parser.ValueTypeMatrix) Vector ===
func funcStdvarOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return aggrOverTime(vals, enh, func(values []Point) float64 {
var aux, count, mean float64
for _, v := range values {
count++
delta := v.V - mean
mean += delta / count
aux += delta * (v.V - mean)
}
return aux / count
})
}
// === absent(Vector parser.ValueTypeVector) Vector ===
func funcAbsent(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
if len(vals[0].(Vector)) > 0 {
return enh.Out
}
return append(enh.Out,
Sample{
Metric: createLabelsForAbsentFunction(args[0]),
Point: Point{V: 1},
})
}
// === absent_over_time(Vector parser.ValueTypeMatrix) Vector ===
// 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 {
return append(enh.Out,
Sample{
Point: Point{V: 1},
})
}
func simpleFunc(vals []parser.Value, enh *EvalNodeHelper, f func(float64) float64) Vector {
for _, el := range vals[0].(Vector) {
enh.Out = append(enh.Out, Sample{
Metric: enh.DropMetricName(el.Metric),
Point: Point{V: f(el.V)},
})
}
return enh.Out
}
// === abs(Vector parser.ValueTypeVector) Vector ===
func funcAbs(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Abs)
}
// === ceil(Vector parser.ValueTypeVector) Vector ===
func funcCeil(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Ceil)
}
// === floor(Vector parser.ValueTypeVector) Vector ===
func funcFloor(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Floor)
}
// === exp(Vector parser.ValueTypeVector) Vector ===
func funcExp(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Exp)
}
// === sqrt(Vector VectorNode) Vector ===
func funcSqrt(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Sqrt)
}
// === ln(Vector parser.ValueTypeVector) Vector ===
func funcLn(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Log)
}
// === log2(Vector parser.ValueTypeVector) Vector ===
func funcLog2(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Log2)
}
// === log10(Vector parser.ValueTypeVector) Vector ===
func funcLog10(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Log10)
}
// === timestamp(Vector parser.ValueTypeVector) Vector ===
func funcTimestamp(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
vec := vals[0].(Vector)
for _, el := range vec {
enh.Out = append(enh.Out, Sample{
Metric: enh.DropMetricName(el.Metric),
Point: Point{V: float64(el.T) / 1000},
})
}
return enh.Out
}
// 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 []Point, interceptTime int64) (slope, intercept float64) {
var (
n float64
sumX, sumY float64
sumXY, sumX2 float64
)
for _, sample := range samples {
x := float64(sample.T-interceptTime) / 1e3
n += 1.0
sumY += sample.V
sumX += x
sumXY += x * sample.V
sumX2 += x * x
}
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 ===
func funcDeriv(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
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.Points) < 2 {
return enh.Out
}
// 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.Points, samples.Points[0].T)
return append(enh.Out, Sample{
Point: Point{V: slope},
})
}
// === predict_linear(node parser.ValueTypeMatrix, k parser.ValueTypeScalar) Vector ===
func funcPredictLinear(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
samples := vals[0].(Matrix)[0]
duration := vals[1].(Vector)[0].V
// No sense in trying to predict anything without at least two points.
// Drop this Vector element.
if len(samples.Points) < 2 {
return enh.Out
}
slope, intercept := linearRegression(samples.Points, enh.Ts)
return append(enh.Out, Sample{
Point: Point{V: slope*duration + intercept},
})
}
// === histogram_quantile(k parser.ValueTypeScalar, Vector parser.ValueTypeVector) Vector ===
func funcHistogramQuantile(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
q := vals[0].(Vector)[0].V
inVec := vals[1].(Vector)
sigf := signatureFunc(false, enh.lblBuf, excludedLabels...)
if enh.signatureToMetricWithBuckets == nil {
enh.signatureToMetricWithBuckets = map[string]*metricWithBuckets{}
} else {
for _, v := range enh.signatureToMetricWithBuckets {
v.buckets = v.buckets[:0]
}
}
for _, el := range inVec {
upperBound, err := strconv.ParseFloat(
el.Metric.Get(model.BucketLabel), 64,
)
if err != nil {
// Oops, no bucket label or malformed label value. Skip.
// TODO(beorn7): Issue a warning somehow.
continue
}
l := sigf(el.Metric)
mb, ok := enh.signatureToMetricWithBuckets[l]
if !ok {
el.Metric = labels.NewBuilder(el.Metric).
Del(labels.BucketLabel, labels.MetricName).
Labels()
mb = &metricWithBuckets{el.Metric, nil}
enh.signatureToMetricWithBuckets[l] = mb
}
mb.buckets = append(mb.buckets, bucket{upperBound, el.V})
}
for _, mb := range enh.signatureToMetricWithBuckets {
if len(mb.buckets) > 0 {
enh.Out = append(enh.Out, Sample{
Metric: mb.metric,
Point: Point{V: bucketQuantile(q, mb.buckets)},
})
}
}
return enh.Out
}
// === resets(Matrix parser.ValueTypeMatrix) Vector ===
func funcResets(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
samples := vals[0].(Matrix)[0]
resets := 0
prev := samples.Points[0].V
for _, sample := range samples.Points[1:] {
current := sample.V
if current < prev {
resets++
}
prev = current
}
return append(enh.Out, Sample{
Point: Point{V: float64(resets)},
})
}
// === changes(Matrix parser.ValueTypeMatrix) Vector ===
func funcChanges(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
samples := vals[0].(Matrix)[0]
changes := 0
prev := samples.Points[0].V
for _, sample := range samples.Points[1:] {
current := sample.V
if current != prev && !(math.IsNaN(current) && math.IsNaN(prev)) {
changes++
}
prev = current
}
return append(enh.Out, Sample{
Point: Point{V: float64(changes)},
})
}
// === label_replace(Vector parser.ValueTypeVector, dst_label, replacement, src_labelname, regex parser.ValueTypeString) Vector ===
func funcLabelReplace(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
var (
vector = vals[0].(Vector)
dst = args[1].(*parser.StringLiteral).Val
repl = args[2].(*parser.StringLiteral).Val
src = args[3].(*parser.StringLiteral).Val
regexStr = args[4].(*parser.StringLiteral).Val
)
if enh.regex == nil {
var err error
enh.regex, err = regexp.Compile("^(?:" + regexStr + ")$")
if err != nil {
panic(errors.Errorf("invalid regular expression in label_replace(): %s", regexStr))
}
if !model.LabelNameRE.MatchString(dst) {
panic(errors.Errorf("invalid destination label name in label_replace(): %s", dst))
}
enh.Dmn = make(map[uint64]labels.Labels, len(enh.Out))
}
for _, el := range vector {
h := el.Metric.Hash()
var outMetric labels.Labels
if l, ok := enh.Dmn[h]; ok {
outMetric = l
} else {
srcVal := el.Metric.Get(src)
indexes := enh.regex.FindStringSubmatchIndex(srcVal)
if indexes == nil {
// If there is no match, no replacement should take place.
outMetric = el.Metric
enh.Dmn[h] = outMetric
} else {
res := enh.regex.ExpandString([]byte{}, repl, srcVal, indexes)
lb := labels.NewBuilder(el.Metric).Del(dst)
if len(res) > 0 {
lb.Set(dst, string(res))
}
outMetric = lb.Labels()
enh.Dmn[h] = outMetric
}
}
enh.Out = append(enh.Out, Sample{
Metric: outMetric,
Point: Point{V: el.Point.V},
})
}
return enh.Out
}
// === Vector(s Scalar) Vector ===
func funcVector(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return append(enh.Out,
Sample{
Metric: labels.Labels{},
Point: Point{V: vals[0].(Vector)[0].V},
})
}
// === label_join(vector model.ValVector, dest_labelname, separator, src_labelname...) Vector ===
func funcLabelJoin(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
var (
vector = vals[0].(Vector)
dst = args[1].(*parser.StringLiteral).Val
sep = args[2].(*parser.StringLiteral).Val
srcLabels = make([]string, len(args)-3)
)
if enh.Dmn == nil {
enh.Dmn = make(map[uint64]labels.Labels, len(enh.Out))
}
for i := 3; i < len(args); i++ {
src := args[i].(*parser.StringLiteral).Val
if !model.LabelName(src).IsValid() {
panic(errors.Errorf("invalid source label name in label_join(): %s", src))
}
srcLabels[i-3] = src
}
if !model.LabelName(dst).IsValid() {
panic(errors.Errorf("invalid destination label name in label_join(): %s", dst))
}
srcVals := make([]string, len(srcLabels))
for _, el := range vector {
h := el.Metric.Hash()
var outMetric labels.Labels
if l, ok := enh.Dmn[h]; ok {
outMetric = l
} else {
for i, src := range srcLabels {
srcVals[i] = el.Metric.Get(src)
}
lb := labels.NewBuilder(el.Metric)
strval := strings.Join(srcVals, sep)
if strval == "" {
lb.Del(dst)
} else {
lb.Set(dst, strval)
}
outMetric = lb.Labels()
enh.Dmn[h] = outMetric
}
enh.Out = append(enh.Out, Sample{
Metric: outMetric,
Point: Point{V: el.Point.V},
})
}
return enh.Out
}
// 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{},
Point: Point{V: f(time.Unix(enh.Ts/1000, 0).UTC())},
})
}
for _, el := range vals[0].(Vector) {
t := time.Unix(int64(el.V), 0).UTC()
enh.Out = append(enh.Out, Sample{
Metric: enh.DropMetricName(el.Metric),
Point: Point{V: f(t)},
})
}
return enh.Out
}
// === days_in_month(v Vector) Scalar ===
func funcDaysInMonth(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
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())
})
}
// === day_of_month(v Vector) Scalar ===
func funcDayOfMonth(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return dateWrapper(vals, enh, func(t time.Time) float64 {
return float64(t.Day())
})
}
// === day_of_week(v Vector) Scalar ===
func funcDayOfWeek(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return dateWrapper(vals, enh, func(t time.Time) float64 {
return float64(t.Weekday())
})
}
// === hour(v Vector) Scalar ===
func funcHour(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return dateWrapper(vals, enh, func(t time.Time) float64 {
return float64(t.Hour())
})
}
// === minute(v Vector) Scalar ===
func funcMinute(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return dateWrapper(vals, enh, func(t time.Time) float64 {
return float64(t.Minute())
})
}
// === month(v Vector) Scalar ===
func funcMonth(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return dateWrapper(vals, enh, func(t time.Time) float64 {
return float64(t.Month())
})
}
// === year(v Vector) Scalar ===
func funcYear(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return dateWrapper(vals, enh, func(t time.Time) float64 {
return float64(t.Year())
})
}
// 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,
"avg_over_time": funcAvgOverTime,
"ceil": funcCeil,
"changes": funcChanges,
"clamp_max": funcClampMax,
"clamp_min": funcClampMin,
"count_over_time": funcCountOverTime,
"days_in_month": funcDaysInMonth,
"day_of_month": funcDayOfMonth,
"day_of_week": funcDayOfWeek,
"delta": funcDelta,
"deriv": funcDeriv,
"exp": funcExp,
"floor": funcFloor,
"histogram_quantile": funcHistogramQuantile,
"holt_winters": funcHoltWinters,
"hour": funcHour,
"idelta": funcIdelta,
"increase": funcIncrease,
"irate": funcIrate,
"label_replace": funcLabelReplace,
"label_join": funcLabelJoin,
"ln": funcLn,
"log10": funcLog10,
"log2": funcLog2,
"max_over_time": funcMaxOverTime,
"min_over_time": funcMinOverTime,
"minute": funcMinute,
"month": funcMonth,
"predict_linear": funcPredictLinear,
"quantile_over_time": funcQuantileOverTime,
"rate": funcRate,
"resets": funcResets,
"round": funcRound,
"scalar": funcScalar,
"sort": funcSort,
"sort_desc": funcSortDesc,
"sqrt": funcSqrt,
"stddev_over_time": funcStddevOverTime,
"stdvar_over_time": funcStdvarOverTime,
"sum_over_time": funcSumOverTime,
"time": funcTime,
"timestamp": funcTimestamp,
"vector": funcVector,
"year": funcYear,
}
type vectorByValueHeap Vector
func (s vectorByValueHeap) Len() int {
return len(s)
}
func (s vectorByValueHeap) Less(i, j int) bool {
if math.IsNaN(s[i].V) {
return true
}
return s[i].V < s[j].V
}
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 {
if math.IsNaN(s[i].V) {
return true
}
return s[i].V > s[j].V
}
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 {
m := labels.Labels{}
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 m
}
empty := []string{}
for _, ma := range lm {
if ma.Name == labels.MetricName {
continue
}
if ma.Type == labels.MatchEqual && !m.Has(ma.Name) {
m = labels.NewBuilder(m).Set(ma.Name, ma.Value).Labels()
} else {
empty = append(empty, ma.Name)
}
}
for _, v := range empty {
m = labels.NewBuilder(m).Del(v).Labels()
}
return m
}