prometheus/promql/functions.go

1163 lines
32 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"
"github.com/prometheus/common/model"
"github.com/prometheus/prometheus/storage/metric"
)
// Function represents a function of the expression language and is
// used by function nodes.
type Function struct {
Name string
ArgTypes []model.ValueType
OptionalArgs int
ReturnType model.ValueType
Call func(ev *evaluator, args Expressions) model.Value
}
// === time() model.SampleValue ===
func funcTime(ev *evaluator, args Expressions) model.Value {
return &model.Scalar{
Value: model.SampleValue(ev.Timestamp.Unix()),
Timestamp: ev.Timestamp,
}
}
// 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(ev *evaluator, arg Expr, isCounter bool, isRate bool) model.Value {
ms := arg.(*MatrixSelector)
rangeStart := ev.Timestamp.Add(-ms.Range - ms.Offset)
rangeEnd := ev.Timestamp.Add(-ms.Offset)
resultVector := vector{}
matrixValue := ev.evalMatrix(ms)
for _, samples := range matrixValue {
// No sense in trying to compute a rate without at least two points. Drop
// this vector element.
if len(samples.Values) < 2 {
continue
}
var (
counterCorrection model.SampleValue
lastValue model.SampleValue
)
for _, sample := range samples.Values {
currentValue := sample.Value
if isCounter && currentValue < lastValue {
counterCorrection += lastValue - currentValue
}
lastValue = currentValue
}
resultValue := lastValue - samples.Values[0].Value + counterCorrection
// Duration between first/last samples and boundary of range.
durationToStart := samples.Values[0].Timestamp.Sub(rangeStart).Seconds()
durationToEnd := rangeEnd.Sub(samples.Values[len(samples.Values)-1].Timestamp).Seconds()
sampledInterval := samples.Values[len(samples.Values)-1].Timestamp.Sub(samples.Values[0].Timestamp).Seconds()
averageDurationBetweenSamples := sampledInterval / float64(len(samples.Values)-1)
if isCounter && resultValue > 0 && samples.Values[0].Value >= 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 * float64(samples.Values[0].Value/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 * model.SampleValue(extrapolateToInterval/sampledInterval)
if isRate {
resultValue = resultValue / model.SampleValue(ms.Range.Seconds())
}
resultSample := &sample{
Metric: samples.Metric,
Value: resultValue,
Timestamp: ev.Timestamp,
}
resultSample.Metric.Del(model.MetricNameLabel)
resultVector = append(resultVector, resultSample)
}
return resultVector
}
// === delta(matrix model.ValMatrix) Vector ===
func funcDelta(ev *evaluator, args Expressions) model.Value {
return extrapolatedRate(ev, args[0], false, false)
}
// === rate(node model.ValMatrix) Vector ===
func funcRate(ev *evaluator, args Expressions) model.Value {
return extrapolatedRate(ev, args[0], true, true)
}
// === increase(node model.ValMatrix) Vector ===
func funcIncrease(ev *evaluator, args Expressions) model.Value {
return extrapolatedRate(ev, args[0], true, false)
}
// === irate(node model.ValMatrix) Vector ===
func funcIrate(ev *evaluator, args Expressions) model.Value {
return instantValue(ev, args[0], true)
}
// === idelta(node model.ValMatric) Vector ===
func funcIdelta(ev *evaluator, args Expressions) model.Value {
return instantValue(ev, args[0], false)
}
func instantValue(ev *evaluator, arg Expr, isRate bool) model.Value {
resultVector := vector{}
for _, samples := range ev.evalMatrix(arg) {
// No sense in trying to compute a rate without at least two points. Drop
// this vector element.
if len(samples.Values) < 2 {
continue
}
lastSample := samples.Values[len(samples.Values)-1]
previousSample := samples.Values[len(samples.Values)-2]
var resultValue model.SampleValue
if isRate && lastSample.Value < previousSample.Value {
// Counter reset.
resultValue = lastSample.Value
} else {
resultValue = lastSample.Value - previousSample.Value
}
sampledInterval := lastSample.Timestamp.Sub(previousSample.Timestamp)
if sampledInterval == 0 {
// Avoid dividing by 0.
continue
}
if isRate {
// Convert to per-second.
resultValue /= model.SampleValue(sampledInterval.Seconds())
}
resultSample := &sample{
Metric: samples.Metric,
Value: resultValue,
Timestamp: ev.Timestamp,
}
resultSample.Metric.Del(model.MetricNameLabel)
resultVector = append(resultVector, resultSample)
}
return resultVector
}
// 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 float64, s, b, d []float64) float64 {
if i == 0 {
return b[0]
}
x := tf * (s[i] - s[i-1])
y := (1 - tf) * b[i-1]
// Cache the computed value.
b[i] = x + y
return b[i]
}
// 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(ev *evaluator, args Expressions) model.Value {
mat := ev.evalMatrix(args[0])
// The smoothing factor argument.
sf := ev.evalFloat(args[1])
// The trend factor argument.
tf := ev.evalFloat(args[2])
// Sanity check the input.
if sf <= 0 || sf >= 1 {
ev.errorf("invalid smoothing factor. Expected: 0 < sf < 1 got: %f", sf)
}
if tf <= 0 || tf >= 1 {
ev.errorf("invalid trend factor. Expected: 0 < tf < 1 got: %f", sf)
}
// Make an output vector large enough to hold the entire result.
resultVector := make(vector, 0, len(mat))
// Create scratch values.
var s, b, d []float64
var l int
for _, samples := range mat {
l = len(samples.Values)
// Can't do the smoothing operation with less than two points.
if l < 2 {
continue
}
// Resize scratch values.
if l != len(s) {
s = make([]float64, l)
b = make([]float64, l)
d = make([]float64, l)
}
// Fill in the d values with the raw values from the input.
for i, v := range samples.Values {
d[i] = float64(v.Value)
}
// Set initial values.
s[0] = d[0]
b[0] = d[1] - d[0]
// Run the smoothing operation.
var x, y float64
for i := 1; i < len(d); i++ {
// Scale the raw value against the smoothing factor.
x = sf * d[i]
// Scale the last smoothed value with the trend at this point.
y = (1 - sf) * (s[i-1] + calcTrendValue(i-1, sf, tf, s, b, d))
s[i] = x + y
}
samples.Metric.Del(model.MetricNameLabel)
resultVector = append(resultVector, &sample{
Metric: samples.Metric,
Value: model.SampleValue(s[len(s)-1]), // The last value in the vector is the smoothed result.
Timestamp: ev.Timestamp,
})
}
return resultVector
}
// === sort(node model.ValVector) Vector ===
func funcSort(ev *evaluator, args Expressions) model.Value {
// NaN should sort to the bottom, so take descending sort with NaN first and
// reverse it.
byValueSorter := vectorByReverseValueHeap(ev.evalVector(args[0]))
sort.Sort(sort.Reverse(byValueSorter))
return vector(byValueSorter)
}
// === sortDesc(node model.ValVector) Vector ===
func funcSortDesc(ev *evaluator, args Expressions) model.Value {
// NaN should sort to the bottom, so take ascending sort with NaN first and
// reverse it.
byValueSorter := vectorByValueHeap(ev.evalVector(args[0]))
sort.Sort(sort.Reverse(byValueSorter))
return vector(byValueSorter)
}
// === clamp_max(vector model.ValVector, max Scalar) Vector ===
func funcClampMax(ev *evaluator, args Expressions) model.Value {
vec := ev.evalVector(args[0])
max := ev.evalFloat(args[1])
for _, el := range vec {
el.Metric.Del(model.MetricNameLabel)
el.Value = model.SampleValue(math.Min(max, float64(el.Value)))
}
return vec
}
// === clamp_min(vector model.ValVector, min Scalar) Vector ===
func funcClampMin(ev *evaluator, args Expressions) model.Value {
vec := ev.evalVector(args[0])
min := ev.evalFloat(args[1])
for _, el := range vec {
el.Metric.Del(model.MetricNameLabel)
el.Value = model.SampleValue(math.Max(min, float64(el.Value)))
}
return vec
}
// === drop_common_labels(node model.ValVector) Vector ===
func funcDropCommonLabels(ev *evaluator, args Expressions) model.Value {
vec := ev.evalVector(args[0])
if len(vec) < 1 {
return vector{}
}
common := model.LabelSet{}
for k, v := range vec[0].Metric.Metric {
// TODO(julius): Should we also drop common metric names?
if k == model.MetricNameLabel {
continue
}
common[k] = v
}
for _, el := range vec[1:] {
for k, v := range common {
if el.Metric.Metric[k] != v {
// Deletion of map entries while iterating over them is safe.
// From http://golang.org/ref/spec#For_statements:
// "If map entries that have not yet been reached are deleted during
// iteration, the corresponding iteration values will not be produced."
delete(common, k)
}
}
}
for _, el := range vec {
for k := range el.Metric.Metric {
if _, ok := common[k]; ok {
el.Metric.Del(k)
}
}
}
return vec
}
// === round(vector model.ValVector, toNearest=1 Scalar) Vector ===
func funcRound(ev *evaluator, args Expressions) model.Value {
// round returns a number rounded to toNearest.
// Ties are solved by rounding up.
toNearest := float64(1)
if len(args) >= 2 {
toNearest = ev.evalFloat(args[1])
}
// Invert as it seems to cause fewer floating point accuracy issues.
toNearestInverse := 1.0 / toNearest
vec := ev.evalVector(args[0])
for _, el := range vec {
el.Metric.Del(model.MetricNameLabel)
el.Value = model.SampleValue(math.Floor(float64(el.Value)*toNearestInverse+0.5) / toNearestInverse)
}
return vec
}
// === scalar(node model.ValVector) Scalar ===
func funcScalar(ev *evaluator, args Expressions) model.Value {
v := ev.evalVector(args[0])
if len(v) != 1 {
return &model.Scalar{
Value: model.SampleValue(math.NaN()),
Timestamp: ev.Timestamp,
}
}
return &model.Scalar{
Value: model.SampleValue(v[0].Value),
Timestamp: ev.Timestamp,
}
}
// === count_scalar(vector model.ValVector) model.SampleValue ===
func funcCountScalar(ev *evaluator, args Expressions) model.Value {
return &model.Scalar{
Value: model.SampleValue(len(ev.evalVector(args[0]))),
Timestamp: ev.Timestamp,
}
}
func aggrOverTime(ev *evaluator, args Expressions, aggrFn func([]model.SamplePair) model.SampleValue) model.Value {
mat := ev.evalMatrix(args[0])
resultVector := vector{}
for _, el := range mat {
if len(el.Values) == 0 {
continue
}
el.Metric.Del(model.MetricNameLabel)
resultVector = append(resultVector, &sample{
Metric: el.Metric,
Value: aggrFn(el.Values),
Timestamp: ev.Timestamp,
})
}
return resultVector
}
// === avg_over_time(matrix model.ValMatrix) Vector ===
func funcAvgOverTime(ev *evaluator, args Expressions) model.Value {
return aggrOverTime(ev, args, func(values []model.SamplePair) model.SampleValue {
var sum model.SampleValue
for _, v := range values {
sum += v.Value
}
return sum / model.SampleValue(len(values))
})
}
// === count_over_time(matrix model.ValMatrix) Vector ===
func funcCountOverTime(ev *evaluator, args Expressions) model.Value {
return aggrOverTime(ev, args, func(values []model.SamplePair) model.SampleValue {
return model.SampleValue(len(values))
})
}
// === floor(vector model.ValVector) Vector ===
func funcFloor(ev *evaluator, args Expressions) model.Value {
vector := ev.evalVector(args[0])
for _, el := range vector {
el.Metric.Del(model.MetricNameLabel)
el.Value = model.SampleValue(math.Floor(float64(el.Value)))
}
return vector
}
// === max_over_time(matrix model.ValMatrix) Vector ===
func funcMaxOverTime(ev *evaluator, args Expressions) model.Value {
return aggrOverTime(ev, args, func(values []model.SamplePair) model.SampleValue {
max := math.Inf(-1)
for _, v := range values {
max = math.Max(max, float64(v.Value))
}
return model.SampleValue(max)
})
}
// === min_over_time(matrix model.ValMatrix) Vector ===
func funcMinOverTime(ev *evaluator, args Expressions) model.Value {
return aggrOverTime(ev, args, func(values []model.SamplePair) model.SampleValue {
min := math.Inf(1)
for _, v := range values {
min = math.Min(min, float64(v.Value))
}
return model.SampleValue(min)
})
}
// === sum_over_time(matrix model.ValMatrix) Vector ===
func funcSumOverTime(ev *evaluator, args Expressions) model.Value {
return aggrOverTime(ev, args, func(values []model.SamplePair) model.SampleValue {
var sum model.SampleValue
for _, v := range values {
sum += v.Value
}
return sum
})
}
// === quantile_over_time(matrix model.ValMatrix) Vector ===
func funcQuantileOverTime(ev *evaluator, args Expressions) model.Value {
q := ev.evalFloat(args[0])
mat := ev.evalMatrix(args[1])
resultVector := vector{}
for _, el := range mat {
if len(el.Values) == 0 {
continue
}
el.Metric.Del(model.MetricNameLabel)
values := make(vectorByValueHeap, 0, len(el.Values))
for _, v := range el.Values {
values = append(values, &sample{Value: v.Value})
}
resultVector = append(resultVector, &sample{
Metric: el.Metric,
Value: model.SampleValue(quantile(q, values)),
Timestamp: ev.Timestamp,
})
}
return resultVector
}
// === stddev_over_time(matrix model.ValMatrix) Vector ===
func funcStddevOverTime(ev *evaluator, args Expressions) model.Value {
return aggrOverTime(ev, args, func(values []model.SamplePair) model.SampleValue {
var sum, squaredSum, count model.SampleValue
for _, v := range values {
sum += v.Value
squaredSum += v.Value * v.Value
count += 1
}
avg := sum / count
return model.SampleValue(math.Sqrt(float64(squaredSum/count - avg*avg)))
})
}
// === stdvar_over_time(matrix model.ValMatrix) Vector ===
func funcStdvarOverTime(ev *evaluator, args Expressions) model.Value {
return aggrOverTime(ev, args, func(values []model.SamplePair) model.SampleValue {
var sum, squaredSum, count model.SampleValue
for _, v := range values {
sum += v.Value
squaredSum += v.Value * v.Value
count += 1
}
avg := sum / count
return squaredSum/count - avg*avg
})
}
// === abs(vector model.ValVector) Vector ===
func funcAbs(ev *evaluator, args Expressions) model.Value {
vector := ev.evalVector(args[0])
for _, el := range vector {
el.Metric.Del(model.MetricNameLabel)
el.Value = model.SampleValue(math.Abs(float64(el.Value)))
}
return vector
}
// === absent(vector model.ValVector) Vector ===
func funcAbsent(ev *evaluator, args Expressions) model.Value {
if len(ev.evalVector(args[0])) > 0 {
return vector{}
}
m := model.Metric{}
if vs, ok := args[0].(*VectorSelector); ok {
for _, matcher := range vs.LabelMatchers {
if matcher.Type == metric.Equal && matcher.Name != model.MetricNameLabel {
m[matcher.Name] = matcher.Value
}
}
}
return vector{
&sample{
Metric: metric.Metric{
Metric: m,
Copied: true,
},
Value: 1,
Timestamp: ev.Timestamp,
},
}
}
// === ceil(vector model.ValVector) Vector ===
func funcCeil(ev *evaluator, args Expressions) model.Value {
vector := ev.evalVector(args[0])
for _, el := range vector {
el.Metric.Del(model.MetricNameLabel)
el.Value = model.SampleValue(math.Ceil(float64(el.Value)))
}
return vector
}
// === exp(vector model.ValVector) Vector ===
func funcExp(ev *evaluator, args Expressions) model.Value {
vector := ev.evalVector(args[0])
for _, el := range vector {
el.Metric.Del(model.MetricNameLabel)
el.Value = model.SampleValue(math.Exp(float64(el.Value)))
}
return vector
}
// === sqrt(vector VectorNode) Vector ===
func funcSqrt(ev *evaluator, args Expressions) model.Value {
vector := ev.evalVector(args[0])
for _, el := range vector {
el.Metric.Del(model.MetricNameLabel)
el.Value = model.SampleValue(math.Sqrt(float64(el.Value)))
}
return vector
}
// === ln(vector model.ValVector) Vector ===
func funcLn(ev *evaluator, args Expressions) model.Value {
vector := ev.evalVector(args[0])
for _, el := range vector {
el.Metric.Del(model.MetricNameLabel)
el.Value = model.SampleValue(math.Log(float64(el.Value)))
}
return vector
}
// === log2(vector model.ValVector) Vector ===
func funcLog2(ev *evaluator, args Expressions) model.Value {
vector := ev.evalVector(args[0])
for _, el := range vector {
el.Metric.Del(model.MetricNameLabel)
el.Value = model.SampleValue(math.Log2(float64(el.Value)))
}
return vector
}
// === log10(vector model.ValVector) Vector ===
func funcLog10(ev *evaluator, args Expressions) model.Value {
vector := ev.evalVector(args[0])
for _, el := range vector {
el.Metric.Del(model.MetricNameLabel)
el.Value = model.SampleValue(math.Log10(float64(el.Value)))
}
return vector
}
// 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 []model.SamplePair, interceptTime model.Time) (slope, intercept model.SampleValue) {
var (
n model.SampleValue
sumX, sumY model.SampleValue
sumXY, sumX2 model.SampleValue
)
for _, sample := range samples {
x := model.SampleValue(
model.Time(sample.Timestamp-interceptTime).UnixNano(),
) / 1e9
n += 1.0
sumY += sample.Value
sumX += x
sumXY += x * sample.Value
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 model.ValMatrix) Vector ===
func funcDeriv(ev *evaluator, args Expressions) model.Value {
mat := ev.evalMatrix(args[0])
resultVector := make(vector, 0, len(mat))
for _, samples := range mat {
// No sense in trying to compute a derivative without at least two points.
// Drop this vector element.
if len(samples.Values) < 2 {
continue
}
slope, _ := linearRegression(samples.Values, 0)
resultSample := &sample{
Metric: samples.Metric,
Value: slope,
Timestamp: ev.Timestamp,
}
resultSample.Metric.Del(model.MetricNameLabel)
resultVector = append(resultVector, resultSample)
}
return resultVector
}
// === predict_linear(node model.ValMatrix, k model.ValScalar) Vector ===
func funcPredictLinear(ev *evaluator, args Expressions) model.Value {
mat := ev.evalMatrix(args[0])
resultVector := make(vector, 0, len(mat))
duration := model.SampleValue(ev.evalFloat(args[1]))
for _, samples := range mat {
// No sense in trying to predict anything without at least two points.
// Drop this vector element.
if len(samples.Values) < 2 {
continue
}
slope, intercept := linearRegression(samples.Values, ev.Timestamp)
resultSample := &sample{
Metric: samples.Metric,
Value: slope*duration + intercept,
Timestamp: ev.Timestamp,
}
resultSample.Metric.Del(model.MetricNameLabel)
resultVector = append(resultVector, resultSample)
}
return resultVector
}
// === histogram_quantile(k model.ValScalar, vector model.ValVector) Vector ===
func funcHistogramQuantile(ev *evaluator, args Expressions) model.Value {
q := model.SampleValue(ev.evalFloat(args[0]))
inVec := ev.evalVector(args[1])
outVec := vector{}
signatureToMetricWithBuckets := map[uint64]*metricWithBuckets{}
for _, el := range inVec {
upperBound, err := strconv.ParseFloat(
string(el.Metric.Metric[model.BucketLabel]), 64,
)
if err != nil {
// Oops, no bucket label or malformed label value. Skip.
// TODO(beorn7): Issue a warning somehow.
continue
}
signature := model.SignatureWithoutLabels(el.Metric.Metric, excludedLabels)
mb, ok := signatureToMetricWithBuckets[signature]
if !ok {
el.Metric.Del(model.BucketLabel)
el.Metric.Del(model.MetricNameLabel)
mb = &metricWithBuckets{el.Metric, nil}
signatureToMetricWithBuckets[signature] = mb
}
mb.buckets = append(mb.buckets, bucket{upperBound, el.Value})
}
for _, mb := range signatureToMetricWithBuckets {
outVec = append(outVec, &sample{
Metric: mb.metric,
Value: model.SampleValue(bucketQuantile(q, mb.buckets)),
Timestamp: ev.Timestamp,
})
}
return outVec
}
// === resets(matrix model.ValMatrix) Vector ===
func funcResets(ev *evaluator, args Expressions) model.Value {
in := ev.evalMatrix(args[0])
out := make(vector, 0, len(in))
for _, samples := range in {
resets := 0
prev := model.SampleValue(samples.Values[0].Value)
for _, sample := range samples.Values[1:] {
current := sample.Value
if current < prev {
resets++
}
prev = current
}
rs := &sample{
Metric: samples.Metric,
Value: model.SampleValue(resets),
Timestamp: ev.Timestamp,
}
rs.Metric.Del(model.MetricNameLabel)
out = append(out, rs)
}
return out
}
// === changes(matrix model.ValMatrix) Vector ===
func funcChanges(ev *evaluator, args Expressions) model.Value {
in := ev.evalMatrix(args[0])
out := make(vector, 0, len(in))
for _, samples := range in {
changes := 0
prev := model.SampleValue(samples.Values[0].Value)
for _, sample := range samples.Values[1:] {
current := sample.Value
if current != prev {
changes++
}
prev = current
}
rs := &sample{
Metric: samples.Metric,
Value: model.SampleValue(changes),
Timestamp: ev.Timestamp,
}
rs.Metric.Del(model.MetricNameLabel)
out = append(out, rs)
}
return out
}
// === label_replace(vector model.ValVector, dst_label, replacement, src_labelname, regex model.ValString) Vector ===
func funcLabelReplace(ev *evaluator, args Expressions) model.Value {
var (
vector = ev.evalVector(args[0])
dst = model.LabelName(ev.evalString(args[1]).Value)
repl = ev.evalString(args[2]).Value
src = model.LabelName(ev.evalString(args[3]).Value)
regexStr = ev.evalString(args[4]).Value
)
regex, err := regexp.Compile("^(?:" + regexStr + ")$")
if err != nil {
ev.errorf("invalid regular expression in label_replace(): %s", regexStr)
}
if !model.LabelNameRE.MatchString(string(dst)) {
ev.errorf("invalid destination label name in label_replace(): %s", dst)
}
outSet := make(map[model.Fingerprint]struct{}, len(vector))
for _, el := range vector {
srcVal := string(el.Metric.Metric[src])
indexes := regex.FindStringSubmatchIndex(srcVal)
// If there is no match, no replacement should take place.
if indexes == nil {
continue
}
res := regex.ExpandString([]byte{}, repl, srcVal, indexes)
if len(res) == 0 {
el.Metric.Del(dst)
} else {
el.Metric.Set(dst, model.LabelValue(res))
}
fp := el.Metric.Metric.Fingerprint()
if _, exists := outSet[fp]; exists {
ev.errorf("duplicated label set in output of label_replace(): %s", el.Metric.Metric)
} else {
outSet[fp] = struct{}{}
}
}
return vector
}
// === vector(s scalar) Vector ===
func funcVector(ev *evaluator, args Expressions) model.Value {
return vector{
&sample{
Metric: metric.Metric{},
Value: model.SampleValue(ev.evalFloat(args[0])),
Timestamp: ev.Timestamp,
},
}
}
var functions = map[string]*Function{
"abs": {
Name: "abs",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValVector,
Call: funcAbs,
},
"absent": {
Name: "absent",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValVector,
Call: funcAbsent,
},
"increase": {
Name: "increase",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcIncrease,
},
"avg_over_time": {
Name: "avg_over_time",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcAvgOverTime,
},
"ceil": {
Name: "ceil",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValVector,
Call: funcCeil,
},
"changes": {
Name: "changes",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcChanges,
},
"clamp_max": {
Name: "clamp_max",
ArgTypes: []model.ValueType{model.ValVector, model.ValScalar},
ReturnType: model.ValVector,
Call: funcClampMax,
},
"clamp_min": {
Name: "clamp_min",
ArgTypes: []model.ValueType{model.ValVector, model.ValScalar},
ReturnType: model.ValVector,
Call: funcClampMin,
},
"count_over_time": {
Name: "count_over_time",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcCountOverTime,
},
"count_scalar": {
Name: "count_scalar",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValScalar,
Call: funcCountScalar,
},
"delta": {
Name: "delta",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcDelta,
},
"deriv": {
Name: "deriv",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcDeriv,
},
"drop_common_labels": {
Name: "drop_common_labels",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValVector,
Call: funcDropCommonLabels,
},
"exp": {
Name: "exp",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValVector,
Call: funcExp,
},
"floor": {
Name: "floor",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValVector,
Call: funcFloor,
},
"histogram_quantile": {
Name: "histogram_quantile",
ArgTypes: []model.ValueType{model.ValScalar, model.ValVector},
ReturnType: model.ValVector,
Call: funcHistogramQuantile,
},
"holt_winters": {
Name: "holt_winters",
ArgTypes: []model.ValueType{model.ValMatrix, model.ValScalar, model.ValScalar},
ReturnType: model.ValVector,
Call: funcHoltWinters,
},
"irate": {
Name: "irate",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcIrate,
},
"idelta": {
Name: "idelta",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcIdelta,
},
"label_replace": {
Name: "label_replace",
ArgTypes: []model.ValueType{model.ValVector, model.ValString, model.ValString, model.ValString, model.ValString},
ReturnType: model.ValVector,
Call: funcLabelReplace,
},
"ln": {
Name: "ln",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValVector,
Call: funcLn,
},
"log10": {
Name: "log10",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValVector,
Call: funcLog10,
},
"log2": {
Name: "log2",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValVector,
Call: funcLog2,
},
"max_over_time": {
Name: "max_over_time",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcMaxOverTime,
},
"min_over_time": {
Name: "min_over_time",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcMinOverTime,
},
"predict_linear": {
Name: "predict_linear",
ArgTypes: []model.ValueType{model.ValMatrix, model.ValScalar},
ReturnType: model.ValVector,
Call: funcPredictLinear,
},
"quantile_over_time": {
Name: "quantile_over_time",
ArgTypes: []model.ValueType{model.ValScalar, model.ValMatrix},
ReturnType: model.ValVector,
Call: funcQuantileOverTime,
},
"rate": {
Name: "rate",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcRate,
},
"resets": {
Name: "resets",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcResets,
},
"round": {
Name: "round",
ArgTypes: []model.ValueType{model.ValVector, model.ValScalar},
OptionalArgs: 1,
ReturnType: model.ValVector,
Call: funcRound,
},
"scalar": {
Name: "scalar",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValScalar,
Call: funcScalar,
},
"sort": {
Name: "sort",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValVector,
Call: funcSort,
},
"sort_desc": {
Name: "sort_desc",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValVector,
Call: funcSortDesc,
},
"sqrt": {
Name: "sqrt",
ArgTypes: []model.ValueType{model.ValVector},
ReturnType: model.ValVector,
Call: funcSqrt,
},
"stddev_over_time": {
Name: "stddev_over_time",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcStddevOverTime,
},
"stdvar_over_time": {
Name: "stdvar_over_time",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcStdvarOverTime,
},
"sum_over_time": {
Name: "sum_over_time",
ArgTypes: []model.ValueType{model.ValMatrix},
ReturnType: model.ValVector,
Call: funcSumOverTime,
},
"time": {
Name: "time",
ArgTypes: []model.ValueType{},
ReturnType: model.ValScalar,
Call: funcTime,
},
"vector": {
Name: "vector",
ArgTypes: []model.ValueType{model.ValScalar},
ReturnType: model.ValVector,
Call: funcVector,
},
}
// 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(float64(s[i].Value)) {
return true
}
return s[i].Value < s[j].Value
}
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(float64(s[i].Value)) {
return true
}
return s[i].Value > s[j].Value
}
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
}