prometheus/rules/ast/functions.go
beorn7 9e85ab0eef Apply the new signature/fingerprinting functions from client_golang.
This requires the new version of client_golang (vendoring will follow
in the next commit), which changes the fingerprinting for
clientmodel.Metric.
2015-03-03 18:34:01 +01:00

685 lines
19 KiB
Go

// Copyright 2013 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 ast
import (
"container/heap"
"fmt"
"math"
"sort"
"strconv"
"time"
clientmodel "github.com/prometheus/client_golang/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 []ExprType
optionalArgs int
returnType ExprType
callFn func(timestamp clientmodel.Timestamp, args []Node) interface{}
}
// CheckArgTypes returns a non-nil error if the number or types of
// passed in arg nodes do not match the function's expectations.
func (function *Function) CheckArgTypes(args []Node) error {
if len(function.argTypes) < len(args) {
return fmt.Errorf(
"too many arguments to function %v(): %v expected at most, %v given",
function.name, len(function.argTypes), len(args),
)
}
if len(function.argTypes)-function.optionalArgs > len(args) {
return fmt.Errorf(
"too few arguments to function %v(): %v expected at least, %v given",
function.name, len(function.argTypes)-function.optionalArgs, len(args),
)
}
for idx, arg := range args {
invalidType := false
var expectedType string
if _, ok := arg.(ScalarNode); function.argTypes[idx] == ScalarType && !ok {
invalidType = true
expectedType = "scalar"
}
if _, ok := arg.(VectorNode); function.argTypes[idx] == VectorType && !ok {
invalidType = true
expectedType = "vector"
}
if _, ok := arg.(MatrixNode); function.argTypes[idx] == MatrixType && !ok {
invalidType = true
expectedType = "matrix"
}
if _, ok := arg.(StringNode); function.argTypes[idx] == StringType && !ok {
invalidType = true
expectedType = "string"
}
if invalidType {
return fmt.Errorf(
"wrong type for argument %v in function %v(), expected %v",
idx, function.name, expectedType,
)
}
}
return nil
}
// === time() clientmodel.SampleValue ===
func timeImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
return clientmodel.SampleValue(timestamp.Unix())
}
// === delta(matrix MatrixNode, isCounter=0 ScalarNode) Vector ===
func deltaImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
matrixNode := args[0].(MatrixNode)
isCounter := len(args) >= 2 && args[1].(ScalarNode).Eval(timestamp) > 0
resultVector := Vector{}
// If we treat these metrics as counters, we need to fetch all values
// in the interval to find breaks in the timeseries' monotonicity.
// I.e. if a counter resets, we want to ignore that reset.
var matrixValue Matrix
if isCounter {
matrixValue = matrixNode.Eval(timestamp)
} else {
matrixValue = matrixNode.EvalBoundaries(timestamp)
}
for _, samples := range matrixValue {
// No sense in trying to compute a delta without at least two points. Drop
// this vector element.
if len(samples.Values) < 2 {
continue
}
counterCorrection := clientmodel.SampleValue(0)
lastValue := clientmodel.SampleValue(0)
for _, sample := range samples.Values {
currentValue := sample.Value
if isCounter && currentValue < lastValue {
counterCorrection += lastValue - currentValue
}
lastValue = currentValue
}
resultValue := lastValue - samples.Values[0].Value + counterCorrection
targetInterval := args[0].(*MatrixSelector).interval
sampledInterval := samples.Values[len(samples.Values)-1].Timestamp.Sub(samples.Values[0].Timestamp)
if sampledInterval == 0 {
// Only found one sample. Cannot compute a rate from this.
continue
}
// Correct for differences in target vs. actual delta interval.
//
// Above, we didn't actually calculate the delta for the specified target
// interval, but for an interval between the first and last found samples
// under the target interval, which will usually have less time between
// them. Depending on how many samples are found under a target interval,
// the delta results are distorted and temporal aliasing occurs (ugly
// bumps). This effect is corrected for below.
intervalCorrection := clientmodel.SampleValue(targetInterval) / clientmodel.SampleValue(sampledInterval)
resultValue *= intervalCorrection
resultSample := &Sample{
Metric: samples.Metric,
Value: resultValue,
Timestamp: timestamp,
}
resultSample.Metric.Delete(clientmodel.MetricNameLabel)
resultVector = append(resultVector, resultSample)
}
return resultVector
}
// === rate(node MatrixNode) Vector ===
func rateImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
args = append(args, &ScalarLiteral{value: 1})
vector := deltaImpl(timestamp, args).(Vector)
// TODO: could be other type of MatrixNode in the future (right now, only
// MatrixSelector exists). Find a better way of getting the duration of a
// matrix, such as looking at the samples themselves.
interval := args[0].(*MatrixSelector).interval
for i := range vector {
vector[i].Value /= clientmodel.SampleValue(interval / time.Second)
}
return vector
}
type vectorByValueHeap Vector
func (s vectorByValueHeap) Len() int {
return len(s)
}
func (s vectorByValueHeap) Less(i, j int) bool {
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)
}
// === sort(node VectorNode) Vector ===
func sortImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
byValueSorter := vectorByValueHeap(args[0].(VectorNode).Eval(timestamp))
sort.Sort(byValueSorter)
return Vector(byValueSorter)
}
// === sortDesc(node VectorNode) Vector ===
func sortDescImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
byValueSorter := vectorByValueHeap(args[0].(VectorNode).Eval(timestamp))
sort.Sort(sort.Reverse(byValueSorter))
return Vector(byValueSorter)
}
// === topk(k ScalarNode, node VectorNode) Vector ===
func topkImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
k := int(args[0].(ScalarNode).Eval(timestamp))
if k < 1 {
return Vector{}
}
topk := make(vectorByValueHeap, 0, k)
vector := args[1].(VectorNode).Eval(timestamp)
for _, el := range vector {
if len(topk) < k || topk[0].Value < el.Value {
if len(topk) == k {
heap.Pop(&topk)
}
heap.Push(&topk, el)
}
}
sort.Sort(sort.Reverse(topk))
return Vector(topk)
}
// === bottomk(k ScalarNode, node VectorNode) Vector ===
func bottomkImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
k := int(args[0].(ScalarNode).Eval(timestamp))
if k < 1 {
return Vector{}
}
bottomk := make(vectorByValueHeap, 0, k)
bkHeap := reverseHeap{Interface: &bottomk}
vector := args[1].(VectorNode).Eval(timestamp)
for _, el := range vector {
if len(bottomk) < k || bottomk[0].Value > el.Value {
if len(bottomk) == k {
heap.Pop(&bkHeap)
}
heap.Push(&bkHeap, el)
}
}
sort.Sort(bottomk)
return Vector(bottomk)
}
// === drop_common_labels(node VectorNode) Vector ===
func dropCommonLabelsImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
vector := args[0].(VectorNode).Eval(timestamp)
if len(vector) < 1 {
return Vector{}
}
common := clientmodel.LabelSet{}
for k, v := range vector[0].Metric.Metric {
// TODO(julius): Should we also drop common metric names?
if k == clientmodel.MetricNameLabel {
continue
}
common[k] = v
}
for _, el := range vector[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 vector {
for k := range el.Metric.Metric {
if _, ok := common[k]; ok {
el.Metric.Delete(k)
}
}
}
return vector
}
// === round(vector VectorNode, toNearest=1 Scalar) Vector ===
func roundImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
// round returns a number rounded to toNearest.
// Ties are solved by rounding up.
toNearest := float64(1)
if len(args) >= 2 {
toNearest = float64(args[1].(ScalarNode).Eval(timestamp))
}
// Invert as it seems to cause fewer floating point accuracy issues.
toNearestInverse := 1.0 / toNearest
n := args[0].(VectorNode)
vector := n.Eval(timestamp)
for _, el := range vector {
el.Metric.Delete(clientmodel.MetricNameLabel)
el.Value = clientmodel.SampleValue(math.Floor(float64(el.Value)*toNearestInverse+0.5) / toNearestInverse)
}
return vector
}
// === scalar(node VectorNode) Scalar ===
func scalarImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
v := args[0].(VectorNode).Eval(timestamp)
if len(v) != 1 {
return clientmodel.SampleValue(math.NaN())
}
return clientmodel.SampleValue(v[0].Value)
}
// === count_scalar(vector VectorNode) model.SampleValue ===
func countScalarImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
return clientmodel.SampleValue(len(args[0].(VectorNode).Eval(timestamp)))
}
func aggrOverTime(timestamp clientmodel.Timestamp, args []Node, aggrFn func(metric.Values) clientmodel.SampleValue) interface{} {
n := args[0].(MatrixNode)
matrixVal := n.Eval(timestamp)
resultVector := Vector{}
for _, el := range matrixVal {
if len(el.Values) == 0 {
continue
}
el.Metric.Delete(clientmodel.MetricNameLabel)
resultVector = append(resultVector, &Sample{
Metric: el.Metric,
Value: aggrFn(el.Values),
Timestamp: timestamp,
})
}
return resultVector
}
// === avg_over_time(matrix MatrixNode) Vector ===
func avgOverTimeImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
return aggrOverTime(timestamp, args, func(values metric.Values) clientmodel.SampleValue {
var sum clientmodel.SampleValue
for _, v := range values {
sum += v.Value
}
return sum / clientmodel.SampleValue(len(values))
})
}
// === count_over_time(matrix MatrixNode) Vector ===
func countOverTimeImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
return aggrOverTime(timestamp, args, func(values metric.Values) clientmodel.SampleValue {
return clientmodel.SampleValue(len(values))
})
}
// === floor(vector VectorNode) Vector ===
func floorImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
n := args[0].(VectorNode)
vector := n.Eval(timestamp)
for _, el := range vector {
el.Metric.Delete(clientmodel.MetricNameLabel)
el.Value = clientmodel.SampleValue(math.Floor(float64(el.Value)))
}
return vector
}
// === max_over_time(matrix MatrixNode) Vector ===
func maxOverTimeImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
return aggrOverTime(timestamp, args, func(values metric.Values) clientmodel.SampleValue {
max := math.Inf(-1)
for _, v := range values {
max = math.Max(max, float64(v.Value))
}
return clientmodel.SampleValue(max)
})
}
// === min_over_time(matrix MatrixNode) Vector ===
func minOverTimeImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
return aggrOverTime(timestamp, args, func(values metric.Values) clientmodel.SampleValue {
min := math.Inf(1)
for _, v := range values {
min = math.Min(min, float64(v.Value))
}
return clientmodel.SampleValue(min)
})
}
// === sum_over_time(matrix MatrixNode) Vector ===
func sumOverTimeImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
return aggrOverTime(timestamp, args, func(values metric.Values) clientmodel.SampleValue {
var sum clientmodel.SampleValue
for _, v := range values {
sum += v.Value
}
return sum
})
}
// === abs(vector VectorNode) Vector ===
func absImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
n := args[0].(VectorNode)
vector := n.Eval(timestamp)
for _, el := range vector {
el.Metric.Delete(clientmodel.MetricNameLabel)
el.Value = clientmodel.SampleValue(math.Abs(float64(el.Value)))
}
return vector
}
// === absent(vector VectorNode) Vector ===
func absentImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
n := args[0].(VectorNode)
if len(n.Eval(timestamp)) > 0 {
return Vector{}
}
m := clientmodel.Metric{}
if vs, ok := n.(*VectorSelector); ok {
for _, matcher := range vs.labelMatchers {
if matcher.Type == metric.Equal && matcher.Name != clientmodel.MetricNameLabel {
m[matcher.Name] = matcher.Value
}
}
}
return Vector{
&Sample{
Metric: clientmodel.COWMetric{
Metric: m,
Copied: true,
},
Value: 1,
Timestamp: timestamp,
},
}
}
// === ceil(vector VectorNode) Vector ===
func ceilImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
n := args[0].(VectorNode)
vector := n.Eval(timestamp)
for _, el := range vector {
el.Metric.Delete(clientmodel.MetricNameLabel)
el.Value = clientmodel.SampleValue(math.Ceil(float64(el.Value)))
}
return vector
}
// === deriv(node MatrixNode) Vector ===
func derivImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
matrixNode := args[0].(MatrixNode)
resultVector := Vector{}
matrixValue := matrixNode.Eval(timestamp)
for _, samples := range matrixValue {
// No sense in trying to compute a derivative without at least two points.
// Drop this vector element.
if len(samples.Values) < 2 {
continue
}
// Least squares.
n := clientmodel.SampleValue(0)
sumY := clientmodel.SampleValue(0)
sumX := clientmodel.SampleValue(0)
sumXY := clientmodel.SampleValue(0)
sumX2 := clientmodel.SampleValue(0)
for _, sample := range samples.Values {
x := clientmodel.SampleValue(sample.Timestamp.UnixNano() / 1e9)
n += 1.0
sumY += sample.Value
sumX += x
sumXY += x * sample.Value
sumX2 += x * x
}
numerator := sumXY - sumX*sumY/n
denominator := sumX2 - (sumX*sumX)/n
resultValue := numerator / denominator
resultSample := &Sample{
Metric: samples.Metric,
Value: resultValue,
Timestamp: timestamp,
}
resultSample.Metric.Delete(clientmodel.MetricNameLabel)
resultVector = append(resultVector, resultSample)
}
return resultVector
}
// === histogram_quantile(k ScalarNode, vector VectorNode) Vector ===
func histogramQuantileImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
q := args[0].(ScalarNode).Eval(timestamp)
inVec := args[1].(VectorNode).Eval(timestamp)
outVec := Vector{}
signatureToMetricWithBuckets := map[uint64]*metricWithBuckets{}
for _, el := range inVec {
upperBound, err := strconv.ParseFloat(
string(el.Metric.Metric[clientmodel.BucketLabel]), 64,
)
if err != nil {
// Oops, no bucket label or malformed label value. Skip.
// TODO(beorn7): Issue a warning somehow.
continue
}
signature := clientmodel.SignatureWithoutLabels(el.Metric.Metric, excludedLabels)
mb, ok := signatureToMetricWithBuckets[signature]
if !ok {
el.Metric.Delete(clientmodel.BucketLabel)
el.Metric.Delete(clientmodel.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: clientmodel.SampleValue(quantile(q, mb.buckets)),
Timestamp: timestamp,
})
}
return outVec
}
var functions = map[string]*Function{
"abs": {
name: "abs",
argTypes: []ExprType{VectorType},
returnType: VectorType,
callFn: absImpl,
},
"absent": {
name: "absent",
argTypes: []ExprType{VectorType},
returnType: VectorType,
callFn: absentImpl,
},
"avg_over_time": {
name: "avg_over_time",
argTypes: []ExprType{MatrixType},
returnType: VectorType,
callFn: avgOverTimeImpl,
},
"bottomk": {
name: "bottomk",
argTypes: []ExprType{ScalarType, VectorType},
returnType: VectorType,
callFn: bottomkImpl,
},
"ceil": {
name: "ceil",
argTypes: []ExprType{VectorType},
returnType: VectorType,
callFn: ceilImpl,
},
"count_over_time": {
name: "count_over_time",
argTypes: []ExprType{MatrixType},
returnType: VectorType,
callFn: countOverTimeImpl,
},
"count_scalar": {
name: "count_scalar",
argTypes: []ExprType{VectorType},
returnType: ScalarType,
callFn: countScalarImpl,
},
"delta": {
name: "delta",
argTypes: []ExprType{MatrixType, ScalarType},
optionalArgs: 1, // The 2nd argument is deprecated.
returnType: VectorType,
callFn: deltaImpl,
},
"deriv": {
name: "deriv",
argTypes: []ExprType{MatrixType},
returnType: VectorType,
callFn: derivImpl,
},
"drop_common_labels": {
name: "drop_common_labels",
argTypes: []ExprType{VectorType},
returnType: VectorType,
callFn: dropCommonLabelsImpl,
},
"floor": {
name: "floor",
argTypes: []ExprType{VectorType},
returnType: VectorType,
callFn: floorImpl,
},
"histogram_quantile": {
name: "histogram_quantile",
argTypes: []ExprType{ScalarType, VectorType},
returnType: VectorType,
callFn: histogramQuantileImpl,
},
"max_over_time": {
name: "max_over_time",
argTypes: []ExprType{MatrixType},
returnType: VectorType,
callFn: maxOverTimeImpl,
},
"min_over_time": {
name: "min_over_time",
argTypes: []ExprType{MatrixType},
returnType: VectorType,
callFn: minOverTimeImpl,
},
"rate": {
name: "rate",
argTypes: []ExprType{MatrixType},
returnType: VectorType,
callFn: rateImpl,
},
"round": {
name: "round",
argTypes: []ExprType{VectorType, ScalarType},
optionalArgs: 1,
returnType: VectorType,
callFn: roundImpl,
},
"scalar": {
name: "scalar",
argTypes: []ExprType{VectorType},
returnType: ScalarType,
callFn: scalarImpl,
},
"sort": {
name: "sort",
argTypes: []ExprType{VectorType},
returnType: VectorType,
callFn: sortImpl,
},
"sort_desc": {
name: "sort_desc",
argTypes: []ExprType{VectorType},
returnType: VectorType,
callFn: sortDescImpl,
},
"sum_over_time": {
name: "sum_over_time",
argTypes: []ExprType{MatrixType},
returnType: VectorType,
callFn: sumOverTimeImpl,
},
"time": {
name: "time",
argTypes: []ExprType{},
returnType: ScalarType,
callFn: timeImpl,
},
"topk": {
name: "topk",
argTypes: []ExprType{ScalarType, VectorType},
returnType: VectorType,
callFn: topkImpl,
},
}
// GetFunction returns a predefined Function object for the given
// name.
func GetFunction(name string) (*Function, error) {
function, ok := functions[name]
if !ok {
return nil, fmt.Errorf("couldn't find function %v()", name)
}
return function, nil
}