mirror of https://github.com/prometheus/prometheus
961 lines
26 KiB
Go
961 lines
26 KiB
Go
// Copyright 2015 The Prometheus Authors
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package promql
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import (
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"container/heap"
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"math"
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"regexp"
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"sort"
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"strconv"
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"time"
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"github.com/prometheus/common/model"
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"github.com/prometheus/prometheus/storage/metric"
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)
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// Function represents a function of the expression language and is
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// used by function nodes.
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type Function struct {
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Name string
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ArgTypes []model.ValueType
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OptionalArgs int
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ReturnType model.ValueType
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Call func(ev *evaluator, args Expressions) model.Value
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}
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// === time() model.SampleValue ===
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func funcTime(ev *evaluator, args Expressions) model.Value {
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return &model.Scalar{
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Value: model.SampleValue(ev.Timestamp.Unix()),
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Timestamp: ev.Timestamp,
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}
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}
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// === delta(matrix model.ValMatrix) Vector ===
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func funcDelta(ev *evaluator, args Expressions) model.Value {
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// This function still takes a 2nd argument for use by rate() and increase().
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isCounter := len(args) >= 2 && ev.evalInt(args[1]) > 0
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resultVector := vector{}
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// If we treat these metrics as counters, we need to fetch all values
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// in the interval to find breaks in the timeseries' monotonicity.
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// I.e. if a counter resets, we want to ignore that reset.
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var matrixValue matrix
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if isCounter {
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matrixValue = ev.evalMatrix(args[0])
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} else {
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matrixValue = ev.evalMatrixBounds(args[0])
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}
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for _, samples := range matrixValue {
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// No sense in trying to compute a delta without at least two points. Drop
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// this vector element.
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if len(samples.Values) < 2 {
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continue
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}
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var (
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counterCorrection model.SampleValue
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lastValue model.SampleValue
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)
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for _, sample := range samples.Values {
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currentValue := sample.Value
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if isCounter && currentValue < lastValue {
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counterCorrection += lastValue - currentValue
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}
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lastValue = currentValue
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}
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resultValue := lastValue - samples.Values[0].Value + counterCorrection
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targetInterval := args[0].(*MatrixSelector).Range
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sampledInterval := samples.Values[len(samples.Values)-1].Timestamp.Sub(samples.Values[0].Timestamp)
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if sampledInterval == 0 {
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// Only found one sample. Cannot compute a rate from this.
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continue
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}
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// Correct for differences in target vs. actual delta interval.
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//
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// Above, we didn't actually calculate the delta for the specified target
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// interval, but for an interval between the first and last found samples
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// under the target interval, which will usually have less time between
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// them. Depending on how many samples are found under a target interval,
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// the delta results are distorted and temporal aliasing occurs (ugly
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// bumps). This effect is corrected for below.
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intervalCorrection := model.SampleValue(targetInterval) / model.SampleValue(sampledInterval)
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resultValue *= intervalCorrection
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resultSample := &sample{
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Metric: samples.Metric,
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Value: resultValue,
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Timestamp: ev.Timestamp,
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}
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resultSample.Metric.Del(model.MetricNameLabel)
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resultVector = append(resultVector, resultSample)
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}
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return resultVector
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}
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// === rate(node model.ValMatrix) Vector ===
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func funcRate(ev *evaluator, args Expressions) model.Value {
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args = append(args, &NumberLiteral{1})
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vector := funcDelta(ev, args).(vector)
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// TODO: could be other type of model.ValMatrix in the future (right now, only
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// MatrixSelector exists). Find a better way of getting the duration of a
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// matrix, such as looking at the samples themselves.
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interval := args[0].(*MatrixSelector).Range
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for i := range vector {
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vector[i].Value /= model.SampleValue(interval / time.Second)
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}
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return vector
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}
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// === increase(node model.ValMatrix) Vector ===
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func funcIncrease(ev *evaluator, args Expressions) model.Value {
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args = append(args, &NumberLiteral{1})
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return funcDelta(ev, args).(vector)
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}
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// === irate(node model.ValMatrix) Vector ===
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func funcIrate(ev *evaluator, args Expressions) model.Value {
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resultVector := vector{}
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for _, samples := range ev.evalMatrix(args[0]) {
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// No sense in trying to compute a rate without at least two points. Drop
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// this vector element.
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if len(samples.Values) < 2 {
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continue
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}
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lastSample := samples.Values[len(samples.Values)-1]
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previousSample := samples.Values[len(samples.Values)-2]
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var resultValue model.SampleValue
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if lastSample.Value < previousSample.Value {
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// Counter reset.
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resultValue = lastSample.Value
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} else {
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resultValue = lastSample.Value - previousSample.Value
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}
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sampledInterval := lastSample.Timestamp.Sub(previousSample.Timestamp)
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if sampledInterval == 0 {
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// Avoid dividing by 0.
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continue
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}
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// Convert to per-second.
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resultValue /= model.SampleValue(sampledInterval.Seconds())
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resultSample := &sample{
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Metric: samples.Metric,
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Value: resultValue,
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Timestamp: ev.Timestamp,
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}
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resultSample.Metric.Del(model.MetricNameLabel)
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resultVector = append(resultVector, resultSample)
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}
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return resultVector
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}
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// === sort(node model.ValVector) Vector ===
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func funcSort(ev *evaluator, args Expressions) model.Value {
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byValueSorter := vectorByValueHeap(ev.evalVector(args[0]))
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sort.Sort(byValueSorter)
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return vector(byValueSorter)
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}
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// === sortDesc(node model.ValVector) Vector ===
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func funcSortDesc(ev *evaluator, args Expressions) model.Value {
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byValueSorter := vectorByValueHeap(ev.evalVector(args[0]))
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sort.Sort(sort.Reverse(byValueSorter))
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return vector(byValueSorter)
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}
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// === topk(k model.ValScalar, node model.ValVector) Vector ===
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func funcTopk(ev *evaluator, args Expressions) model.Value {
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k := ev.evalInt(args[0])
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if k < 1 {
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return vector{}
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}
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vec := ev.evalVector(args[1])
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topk := make(vectorByValueHeap, 0, k)
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for _, el := range vec {
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if len(topk) < k || topk[0].Value < el.Value {
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if len(topk) == k {
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heap.Pop(&topk)
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}
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heap.Push(&topk, el)
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}
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}
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sort.Sort(sort.Reverse(topk))
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return vector(topk)
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}
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// === bottomk(k model.ValScalar, node model.ValVector) Vector ===
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func funcBottomk(ev *evaluator, args Expressions) model.Value {
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k := ev.evalInt(args[0])
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if k < 1 {
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return vector{}
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}
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vec := ev.evalVector(args[1])
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bottomk := make(vectorByValueHeap, 0, k)
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bkHeap := reverseHeap{Interface: &bottomk}
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for _, el := range vec {
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if len(bottomk) < k || bottomk[0].Value > el.Value {
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if len(bottomk) == k {
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heap.Pop(&bkHeap)
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}
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heap.Push(&bkHeap, el)
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}
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}
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sort.Sort(bottomk)
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return vector(bottomk)
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}
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// === drop_common_labels(node model.ValVector) Vector ===
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func funcDropCommonLabels(ev *evaluator, args Expressions) model.Value {
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vec := ev.evalVector(args[0])
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if len(vec) < 1 {
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return vector{}
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}
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common := model.LabelSet{}
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for k, v := range vec[0].Metric.Metric {
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// TODO(julius): Should we also drop common metric names?
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if k == model.MetricNameLabel {
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continue
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}
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common[k] = v
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}
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for _, el := range vec[1:] {
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for k, v := range common {
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if el.Metric.Metric[k] != v {
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// Deletion of map entries while iterating over them is safe.
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// From http://golang.org/ref/spec#For_statements:
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// "If map entries that have not yet been reached are deleted during
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// iteration, the corresponding iteration values will not be produced."
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delete(common, k)
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}
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}
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}
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for _, el := range vec {
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for k := range el.Metric.Metric {
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if _, ok := common[k]; ok {
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el.Metric.Del(k)
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}
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}
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}
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return vec
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}
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// === round(vector model.ValVector, toNearest=1 Scalar) Vector ===
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func funcRound(ev *evaluator, args Expressions) model.Value {
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// round returns a number rounded to toNearest.
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// Ties are solved by rounding up.
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toNearest := float64(1)
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if len(args) >= 2 {
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toNearest = ev.evalFloat(args[1])
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}
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// Invert as it seems to cause fewer floating point accuracy issues.
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toNearestInverse := 1.0 / toNearest
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vec := ev.evalVector(args[0])
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for _, el := range vec {
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el.Metric.Del(model.MetricNameLabel)
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el.Value = model.SampleValue(math.Floor(float64(el.Value)*toNearestInverse+0.5) / toNearestInverse)
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}
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return vec
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}
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// === scalar(node model.ValVector) Scalar ===
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func funcScalar(ev *evaluator, args Expressions) model.Value {
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v := ev.evalVector(args[0])
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if len(v) != 1 {
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return &model.Scalar{
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Value: model.SampleValue(math.NaN()),
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Timestamp: ev.Timestamp,
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}
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}
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return &model.Scalar{
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Value: model.SampleValue(v[0].Value),
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Timestamp: ev.Timestamp,
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}
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}
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// === count_scalar(vector model.ValVector) model.SampleValue ===
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func funcCountScalar(ev *evaluator, args Expressions) model.Value {
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return &model.Scalar{
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Value: model.SampleValue(len(ev.evalVector(args[0]))),
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Timestamp: ev.Timestamp,
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}
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}
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func aggrOverTime(ev *evaluator, args Expressions, aggrFn func([]model.SamplePair) model.SampleValue) model.Value {
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mat := ev.evalMatrix(args[0])
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resultVector := vector{}
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for _, el := range mat {
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if len(el.Values) == 0 {
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continue
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}
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el.Metric.Del(model.MetricNameLabel)
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resultVector = append(resultVector, &sample{
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Metric: el.Metric,
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Value: aggrFn(el.Values),
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Timestamp: ev.Timestamp,
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})
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}
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return resultVector
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}
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// === avg_over_time(matrix model.ValMatrix) Vector ===
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func funcAvgOverTime(ev *evaluator, args Expressions) model.Value {
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return aggrOverTime(ev, args, func(values []model.SamplePair) model.SampleValue {
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var sum model.SampleValue
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for _, v := range values {
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sum += v.Value
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}
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return sum / model.SampleValue(len(values))
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})
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}
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// === count_over_time(matrix model.ValMatrix) Vector ===
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func funcCountOverTime(ev *evaluator, args Expressions) model.Value {
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return aggrOverTime(ev, args, func(values []model.SamplePair) model.SampleValue {
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return model.SampleValue(len(values))
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})
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}
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// === floor(vector model.ValVector) Vector ===
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func funcFloor(ev *evaluator, args Expressions) model.Value {
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vector := ev.evalVector(args[0])
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for _, el := range vector {
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el.Metric.Del(model.MetricNameLabel)
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el.Value = model.SampleValue(math.Floor(float64(el.Value)))
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}
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return vector
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}
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// === max_over_time(matrix model.ValMatrix) Vector ===
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func funcMaxOverTime(ev *evaluator, args Expressions) model.Value {
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return aggrOverTime(ev, args, func(values []model.SamplePair) model.SampleValue {
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max := math.Inf(-1)
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for _, v := range values {
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max = math.Max(max, float64(v.Value))
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}
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return model.SampleValue(max)
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})
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}
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// === min_over_time(matrix model.ValMatrix) Vector ===
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func funcMinOverTime(ev *evaluator, args Expressions) model.Value {
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return aggrOverTime(ev, args, func(values []model.SamplePair) model.SampleValue {
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min := math.Inf(1)
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for _, v := range values {
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min = math.Min(min, float64(v.Value))
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}
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return model.SampleValue(min)
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})
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}
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// === sum_over_time(matrix model.ValMatrix) Vector ===
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func funcSumOverTime(ev *evaluator, args Expressions) model.Value {
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return aggrOverTime(ev, args, func(values []model.SamplePair) model.SampleValue {
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var sum model.SampleValue
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for _, v := range values {
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sum += v.Value
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}
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return sum
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})
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}
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// === abs(vector model.ValVector) Vector ===
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func funcAbs(ev *evaluator, args Expressions) model.Value {
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vector := ev.evalVector(args[0])
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for _, el := range vector {
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el.Metric.Del(model.MetricNameLabel)
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el.Value = model.SampleValue(math.Abs(float64(el.Value)))
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}
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return vector
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}
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// === absent(vector model.ValVector) Vector ===
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func funcAbsent(ev *evaluator, args Expressions) model.Value {
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if len(ev.evalVector(args[0])) > 0 {
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return vector{}
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}
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m := model.Metric{}
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if vs, ok := args[0].(*VectorSelector); ok {
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for _, matcher := range vs.LabelMatchers {
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if matcher.Type == metric.Equal && matcher.Name != model.MetricNameLabel {
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m[matcher.Name] = matcher.Value
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}
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}
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}
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return vector{
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&sample{
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Metric: metric.Metric{
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Metric: m,
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Copied: true,
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},
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Value: 1,
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Timestamp: ev.Timestamp,
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},
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}
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}
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// === ceil(vector model.ValVector) Vector ===
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func funcCeil(ev *evaluator, args Expressions) model.Value {
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vector := ev.evalVector(args[0])
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for _, el := range vector {
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el.Metric.Del(model.MetricNameLabel)
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el.Value = model.SampleValue(math.Ceil(float64(el.Value)))
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}
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return vector
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}
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// === exp(vector model.ValVector) Vector ===
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func funcExp(ev *evaluator, args Expressions) model.Value {
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vector := ev.evalVector(args[0])
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for _, el := range vector {
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el.Metric.Del(model.MetricNameLabel)
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el.Value = model.SampleValue(math.Exp(float64(el.Value)))
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}
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return vector
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}
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// === sqrt(vector VectorNode) Vector ===
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func funcSqrt(ev *evaluator, args Expressions) model.Value {
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vector := ev.evalVector(args[0])
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for _, el := range vector {
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el.Metric.Del(model.MetricNameLabel)
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el.Value = model.SampleValue(math.Sqrt(float64(el.Value)))
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}
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return vector
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}
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// === ln(vector model.ValVector) Vector ===
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func funcLn(ev *evaluator, args Expressions) model.Value {
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vector := ev.evalVector(args[0])
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for _, el := range vector {
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el.Metric.Del(model.MetricNameLabel)
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el.Value = model.SampleValue(math.Log(float64(el.Value)))
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}
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return vector
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}
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// === log2(vector model.ValVector) Vector ===
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func funcLog2(ev *evaluator, args Expressions) model.Value {
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vector := ev.evalVector(args[0])
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for _, el := range vector {
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el.Metric.Del(model.MetricNameLabel)
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el.Value = model.SampleValue(math.Log2(float64(el.Value)))
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}
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return vector
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}
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// === log10(vector model.ValVector) Vector ===
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func funcLog10(ev *evaluator, args Expressions) model.Value {
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vector := ev.evalVector(args[0])
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for _, el := range vector {
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el.Metric.Del(model.MetricNameLabel)
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el.Value = model.SampleValue(math.Log10(float64(el.Value)))
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}
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return vector
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}
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// === deriv(node model.ValMatrix) Vector ===
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func funcDeriv(ev *evaluator, args Expressions) model.Value {
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resultVector := vector{}
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mat := ev.evalMatrix(args[0])
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for _, samples := range mat {
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// No sense in trying to compute a derivative without at least two points.
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// Drop this vector element.
|
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if len(samples.Values) < 2 {
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continue
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}
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|
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// Least squares.
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var (
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n model.SampleValue
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sumX, sumY model.SampleValue
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sumXY, sumX2 model.SampleValue
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)
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for _, sample := range samples.Values {
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x := model.SampleValue(sample.Timestamp.UnixNano() / 1e9)
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n += 1.0
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sumY += sample.Value
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sumX += x
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sumXY += x * sample.Value
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sumX2 += x * x
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}
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numerator := sumXY - sumX*sumY/n
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denominator := sumX2 - (sumX*sumX)/n
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resultValue := numerator / denominator
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resultSample := &sample{
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Metric: samples.Metric,
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Value: resultValue,
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Timestamp: ev.Timestamp,
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}
|
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resultSample.Metric.Del(model.MetricNameLabel)
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resultVector = append(resultVector, resultSample)
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}
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return resultVector
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}
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// === predict_linear(node model.ValMatrix, k model.ValScalar) Vector ===
|
|
func funcPredictLinear(ev *evaluator, args Expressions) model.Value {
|
|
vec := funcDeriv(ev, args[0:1]).(vector)
|
|
duration := model.SampleValue(model.SampleValue(ev.evalFloat(args[1])))
|
|
|
|
excludedLabels := map[model.LabelName]struct{}{
|
|
model.MetricNameLabel: {},
|
|
}
|
|
|
|
// Calculate predicted delta over the duration.
|
|
signatureToDelta := map[uint64]model.SampleValue{}
|
|
for _, el := range vec {
|
|
signature := model.SignatureWithoutLabels(el.Metric.Metric, excludedLabels)
|
|
signatureToDelta[signature] = el.Value * duration
|
|
}
|
|
|
|
// add predicted delta to last value.
|
|
matrixBounds := ev.evalMatrixBounds(args[0])
|
|
outVec := make(vector, 0, len(signatureToDelta))
|
|
for _, samples := range matrixBounds {
|
|
if len(samples.Values) < 2 {
|
|
continue
|
|
}
|
|
signature := model.SignatureWithoutLabels(samples.Metric.Metric, excludedLabels)
|
|
delta, ok := signatureToDelta[signature]
|
|
if ok {
|
|
samples.Metric.Del(model.MetricNameLabel)
|
|
outVec = append(outVec, &sample{
|
|
Metric: samples.Metric,
|
|
Value: delta + samples.Values[1].Value,
|
|
Timestamp: ev.Timestamp,
|
|
})
|
|
}
|
|
}
|
|
return outVec
|
|
}
|
|
|
|
// === 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(quantile(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,
|
|
},
|
|
"bottomk": {
|
|
Name: "bottomk",
|
|
ArgTypes: []model.ValueType{model.ValScalar, model.ValVector},
|
|
ReturnType: model.ValVector,
|
|
Call: funcBottomk,
|
|
},
|
|
"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,
|
|
},
|
|
"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,
|
|
},
|
|
"irate": {
|
|
Name: "irate",
|
|
ArgTypes: []model.ValueType{model.ValMatrix},
|
|
ReturnType: model.ValVector,
|
|
Call: funcIrate,
|
|
},
|
|
"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,
|
|
},
|
|
"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,
|
|
},
|
|
"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,
|
|
},
|
|
"topk": {
|
|
Name: "topk",
|
|
ArgTypes: []model.ValueType{model.ValScalar, model.ValVector},
|
|
ReturnType: model.ValVector,
|
|
Call: funcTopk,
|
|
},
|
|
"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 reverseHeap struct {
|
|
heap.Interface
|
|
}
|
|
|
|
func (s reverseHeap) Less(i, j int) bool {
|
|
return s.Interface.Less(j, i)
|
|
}
|