prometheus/promql/functions.go
Julien Pivotto 577e738986 Cleanup PromQL functions (#6551)
* Cleanup PromQL functions

The engine ensures, for Matrix functions, that functions are called with exactly one series at the time.
Therefore a lot of code can be inlined and we can directly assume the first element of the arguments exists and contains all the samples needed.

Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
2020-01-06 10:33:36 +00:00

1268 lines
35 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"
)
// Function represents a function of the expression language and is
// used by function nodes.
type Function struct {
Name string
ArgTypes []ValueType
Variadic int
ReturnType ValueType
// 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.
Call func(vals []Value, args Expressions, enh *EvalNodeHelper) Vector
}
// === time() float64 ===
func funcTime(vals []Value, args 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 []Value, args Expressions, enh *EvalNodeHelper, isCounter bool, isRate bool) Vector {
ms := args[0].(*MatrixSelector)
var (
samples = vals[0].(Matrix)[0]
rangeStart = enh.ts - durationMilliseconds(ms.Range+ms.Offset)
rangeEnd = enh.ts - durationMilliseconds(ms.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 ValueTypeMatrix) Vector ===
func funcDelta(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return extrapolatedRate(vals, args, enh, false, false)
}
// === rate(node ValueTypeMatrix) Vector ===
func funcRate(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return extrapolatedRate(vals, args, enh, true, true)
}
// === increase(node ValueTypeMatrix) Vector ===
func funcIncrease(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return extrapolatedRate(vals, args, enh, true, false)
}
// === irate(node ValueTypeMatrix) Vector ===
func funcIrate(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return instantValue(vals, enh.out, true)
}
// === idelta(node model.ValMatrix) Vector ===
func funcIdelta(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return instantValue(vals, enh.out, false)
}
func instantValue(vals []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 "s" is the set of computed smoothed values.
// The argument "b" is the set of computed trend factors.
// The argument "d" is the set of raw input values.
func calcTrendValue(i int, sf, 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 []Value, args 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, sf, 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 ValueTypeVector) Vector ===
func funcSort(vals []Value, args 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 ValueTypeVector) Vector ===
func funcSortDesc(vals []Value, args 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 ValueTypeVector, max Scalar) Vector ===
func funcClampMax(vals []Value, args 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 ValueTypeVector, min Scalar) Vector ===
func funcClampMin(vals []Value, args 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 ValueTypeVector, toNearest=1 Scalar) Vector ===
func funcRound(vals []Value, args 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 ValueTypeVector) Scalar ===
func funcScalar(vals []Value, args 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 []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 ValueTypeMatrix) Vector ===
func funcAvgOverTime(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return aggrOverTime(vals, enh, func(values []Point) float64 {
var mean, count float64
for _, v := range values {
count++
mean += (v.V - mean) / count
}
return mean
})
}
// === count_over_time(Matrix ValueTypeMatrix) Vector ===
func funcCountOverTime(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return aggrOverTime(vals, enh, func(values []Point) float64 {
return float64(len(values))
})
}
// === floor(Vector ValueTypeVector) Vector ===
// === max_over_time(Matrix ValueTypeMatrix) Vector ===
func funcMaxOverTime(vals []Value, args 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 ValueTypeMatrix) Vector ===
func funcMinOverTime(vals []Value, args 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 ValueTypeMatrix) Vector ===
func funcSumOverTime(vals []Value, args 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 ValueTypeMatrix) Vector ===
func funcQuantileOverTime(vals []Value, args 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 ValueTypeMatrix) Vector ===
func funcStddevOverTime(vals []Value, args 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 ValueTypeMatrix) Vector ===
func funcStdvarOverTime(vals []Value, args 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 ValueTypeVector) Vector ===
func funcAbsent(vals []Value, args 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 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 []Value, args Expressions, enh *EvalNodeHelper) Vector {
return append(enh.out,
Sample{
Point: Point{V: 1},
})
}
func simpleFunc(vals []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 ValueTypeVector) Vector ===
func funcAbs(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Abs)
}
// === ceil(Vector ValueTypeVector) Vector ===
func funcCeil(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Ceil)
}
// === floor(Vector ValueTypeVector) Vector ===
func funcFloor(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Floor)
}
// === exp(Vector ValueTypeVector) Vector ===
func funcExp(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Exp)
}
// === sqrt(Vector VectorNode) Vector ===
func funcSqrt(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Sqrt)
}
// === ln(Vector ValueTypeVector) Vector ===
func funcLn(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Log)
}
// === log2(Vector ValueTypeVector) Vector ===
func funcLog2(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Log2)
}
// === log10(Vector ValueTypeVector) Vector ===
func funcLog10(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return simpleFunc(vals, enh, math.Log10)
}
// === timestamp(Vector ValueTypeVector) Vector ===
func funcTimestamp(vals []Value, args 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 ValueTypeMatrix) Vector ===
func funcDeriv(vals []Value, args 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 ValueTypeMatrix, k ValueTypeScalar) Vector ===
func funcPredictLinear(vals []Value, args 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 ValueTypeScalar, Vector ValueTypeVector) Vector ===
func funcHistogramQuantile(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
q := vals[0].(Vector)[0].V
inVec := vals[1].(Vector)
sigf := enh.signatureFunc(false, excludedLabels...)
if enh.signatureToMetricWithBuckets == nil {
enh.signatureToMetricWithBuckets = map[uint64]*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
}
hash := sigf(el.Metric)
mb, ok := enh.signatureToMetricWithBuckets[hash]
if !ok {
el.Metric = labels.NewBuilder(el.Metric).
Del(labels.BucketLabel, labels.MetricName).
Labels()
mb = &metricWithBuckets{el.Metric, nil}
enh.signatureToMetricWithBuckets[hash] = 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 ValueTypeMatrix) Vector ===
func funcResets(vals []Value, args 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 ValueTypeMatrix) Vector ===
func funcChanges(vals []Value, args 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 ValueTypeVector, dst_label, replacement, src_labelname, regex ValueTypeString) Vector ===
func funcLabelReplace(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
var (
vector = vals[0].(Vector)
dst = args[1].(*StringLiteral).Val
repl = args[2].(*StringLiteral).Val
src = args[3].(*StringLiteral).Val
regexStr = args[4].(*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 []Value, args 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 []Value, args Expressions, enh *EvalNodeHelper) Vector {
var (
vector = vals[0].(Vector)
dst = args[1].(*StringLiteral).Val
sep = args[2].(*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].(*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 []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 []Value, args 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 []Value, args 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 []Value, args Expressions, enh *EvalNodeHelper) Vector {
return dateWrapper(vals, enh, func(t time.Time) float64 {
return float64(t.Weekday())
})
}
// === hour(v Vector) Scalar ===
func funcHour(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return dateWrapper(vals, enh, func(t time.Time) float64 {
return float64(t.Hour())
})
}
// === minute(v Vector) Scalar ===
func funcMinute(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return dateWrapper(vals, enh, func(t time.Time) float64 {
return float64(t.Minute())
})
}
// === month(v Vector) Scalar ===
func funcMonth(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return dateWrapper(vals, enh, func(t time.Time) float64 {
return float64(t.Month())
})
}
// === year(v Vector) Scalar ===
func funcYear(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
return dateWrapper(vals, enh, func(t time.Time) float64 {
return float64(t.Year())
})
}
// Functions is a list of all functions supported by PromQL, including their types.
var Functions = map[string]*Function{
"abs": {
Name: "abs",
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeVector,
Call: funcAbs,
},
"absent": {
Name: "absent",
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeVector,
Call: funcAbsent,
},
"absent_over_time": {
Name: "absent_over_time",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcAbsentOverTime,
},
"avg_over_time": {
Name: "avg_over_time",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcAvgOverTime,
},
"ceil": {
Name: "ceil",
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeVector,
Call: funcCeil,
},
"changes": {
Name: "changes",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcChanges,
},
"clamp_max": {
Name: "clamp_max",
ArgTypes: []ValueType{ValueTypeVector, ValueTypeScalar},
ReturnType: ValueTypeVector,
Call: funcClampMax,
},
"clamp_min": {
Name: "clamp_min",
ArgTypes: []ValueType{ValueTypeVector, ValueTypeScalar},
ReturnType: ValueTypeVector,
Call: funcClampMin,
},
"count_over_time": {
Name: "count_over_time",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcCountOverTime,
},
"days_in_month": {
Name: "days_in_month",
ArgTypes: []ValueType{ValueTypeVector},
Variadic: 1,
ReturnType: ValueTypeVector,
Call: funcDaysInMonth,
},
"day_of_month": {
Name: "day_of_month",
ArgTypes: []ValueType{ValueTypeVector},
Variadic: 1,
ReturnType: ValueTypeVector,
Call: funcDayOfMonth,
},
"day_of_week": {
Name: "day_of_week",
ArgTypes: []ValueType{ValueTypeVector},
Variadic: 1,
ReturnType: ValueTypeVector,
Call: funcDayOfWeek,
},
"delta": {
Name: "delta",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcDelta,
},
"deriv": {
Name: "deriv",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcDeriv,
},
"exp": {
Name: "exp",
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeVector,
Call: funcExp,
},
"floor": {
Name: "floor",
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeVector,
Call: funcFloor,
},
"histogram_quantile": {
Name: "histogram_quantile",
ArgTypes: []ValueType{ValueTypeScalar, ValueTypeVector},
ReturnType: ValueTypeVector,
Call: funcHistogramQuantile,
},
"holt_winters": {
Name: "holt_winters",
ArgTypes: []ValueType{ValueTypeMatrix, ValueTypeScalar, ValueTypeScalar},
ReturnType: ValueTypeVector,
Call: funcHoltWinters,
},
"hour": {
Name: "hour",
ArgTypes: []ValueType{ValueTypeVector},
Variadic: 1,
ReturnType: ValueTypeVector,
Call: funcHour,
},
"idelta": {
Name: "idelta",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcIdelta,
},
"increase": {
Name: "increase",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcIncrease,
},
"irate": {
Name: "irate",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcIrate,
},
"label_replace": {
Name: "label_replace",
ArgTypes: []ValueType{ValueTypeVector, ValueTypeString, ValueTypeString, ValueTypeString, ValueTypeString},
ReturnType: ValueTypeVector,
Call: funcLabelReplace,
},
"label_join": {
Name: "label_join",
ArgTypes: []ValueType{ValueTypeVector, ValueTypeString, ValueTypeString, ValueTypeString},
Variadic: -1,
ReturnType: ValueTypeVector,
Call: funcLabelJoin,
},
"ln": {
Name: "ln",
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeVector,
Call: funcLn,
},
"log10": {
Name: "log10",
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeVector,
Call: funcLog10,
},
"log2": {
Name: "log2",
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeVector,
Call: funcLog2,
},
"max_over_time": {
Name: "max_over_time",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcMaxOverTime,
},
"min_over_time": {
Name: "min_over_time",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcMinOverTime,
},
"minute": {
Name: "minute",
ArgTypes: []ValueType{ValueTypeVector},
Variadic: 1,
ReturnType: ValueTypeVector,
Call: funcMinute,
},
"month": {
Name: "month",
ArgTypes: []ValueType{ValueTypeVector},
Variadic: 1,
ReturnType: ValueTypeVector,
Call: funcMonth,
},
"predict_linear": {
Name: "predict_linear",
ArgTypes: []ValueType{ValueTypeMatrix, ValueTypeScalar},
ReturnType: ValueTypeVector,
Call: funcPredictLinear,
},
"quantile_over_time": {
Name: "quantile_over_time",
ArgTypes: []ValueType{ValueTypeScalar, ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcQuantileOverTime,
},
"rate": {
Name: "rate",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcRate,
},
"resets": {
Name: "resets",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcResets,
},
"round": {
Name: "round",
ArgTypes: []ValueType{ValueTypeVector, ValueTypeScalar},
Variadic: 1,
ReturnType: ValueTypeVector,
Call: funcRound,
},
"scalar": {
Name: "scalar",
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeScalar,
Call: funcScalar,
},
"sort": {
Name: "sort",
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeVector,
Call: funcSort,
},
"sort_desc": {
Name: "sort_desc",
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeVector,
Call: funcSortDesc,
},
"sqrt": {
Name: "sqrt",
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeVector,
Call: funcSqrt,
},
"stddev_over_time": {
Name: "stddev_over_time",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcStddevOverTime,
},
"stdvar_over_time": {
Name: "stdvar_over_time",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcStdvarOverTime,
},
"sum_over_time": {
Name: "sum_over_time",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
Call: funcSumOverTime,
},
"time": {
Name: "time",
ArgTypes: []ValueType{},
ReturnType: ValueTypeScalar,
Call: funcTime,
},
"timestamp": {
Name: "timestamp",
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeVector,
Call: funcTimestamp,
},
"vector": {
Name: "vector",
ArgTypes: []ValueType{ValueTypeScalar},
ReturnType: ValueTypeVector,
Call: funcVector,
},
"year": {
Name: "year",
ArgTypes: []ValueType{ValueTypeVector},
Variadic: 1,
ReturnType: ValueTypeVector,
Call: funcYear,
},
}
// getFunction returns a predefined Function object for the given name.
func getFunction(name string) (*Function, bool) {
function, ok := Functions[name]
return function, ok
}
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 Expr) labels.Labels {
m := labels.Labels{}
var lm []*labels.Matcher
switch n := expr.(type) {
case *VectorSelector:
lm = n.LabelMatchers
case *MatrixSelector:
lm = n.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
}