773 lines
22 KiB
Go
773 lines
22 KiB
Go
// Copyright 2013 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 ast
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import (
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"container/heap"
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"fmt"
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"math"
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"sort"
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"strconv"
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"time"
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clientmodel "github.com/prometheus/client_golang/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 []ExprType
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optionalArgs int
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returnType ExprType
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callFn func(timestamp clientmodel.Timestamp, args []Node) interface{}
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}
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// CheckArgTypes returns a non-nil error if the number or types of
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// passed in arg nodes do not match the function's expectations.
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func (function *Function) CheckArgTypes(args []Node) error {
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if len(function.argTypes) < len(args) {
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return fmt.Errorf(
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"too many arguments to function %v(): %v expected at most, %v given",
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function.name, len(function.argTypes), len(args),
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)
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}
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if len(function.argTypes)-function.optionalArgs > len(args) {
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return fmt.Errorf(
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"too few arguments to function %v(): %v expected at least, %v given",
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function.name, len(function.argTypes)-function.optionalArgs, len(args),
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)
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}
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for idx, arg := range args {
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invalidType := false
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var expectedType string
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if _, ok := arg.(ScalarNode); function.argTypes[idx] == ScalarType && !ok {
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invalidType = true
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expectedType = "scalar"
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}
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if _, ok := arg.(VectorNode); function.argTypes[idx] == VectorType && !ok {
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invalidType = true
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expectedType = "vector"
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}
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if _, ok := arg.(MatrixNode); function.argTypes[idx] == MatrixType && !ok {
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invalidType = true
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expectedType = "matrix"
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}
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if _, ok := arg.(StringNode); function.argTypes[idx] == StringType && !ok {
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invalidType = true
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expectedType = "string"
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}
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if invalidType {
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return fmt.Errorf(
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"wrong type for argument %v in function %v(), expected %v",
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idx, function.name, expectedType,
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)
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}
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}
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return nil
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}
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// === time() clientmodel.SampleValue ===
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func timeImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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return clientmodel.SampleValue(timestamp.Unix())
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}
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// === delta(matrix MatrixNode, isCounter=0 ScalarNode) Vector ===
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func deltaImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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matrixNode := args[0].(MatrixNode)
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isCounter := len(args) >= 2 && args[1].(ScalarNode).Eval(timestamp) > 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 = matrixNode.Eval(timestamp)
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} else {
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matrixValue = matrixNode.EvalBoundaries(timestamp)
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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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counterCorrection := clientmodel.SampleValue(0)
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lastValue := clientmodel.SampleValue(0)
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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).interval
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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 := clientmodel.SampleValue(targetInterval) / clientmodel.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: timestamp,
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}
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resultSample.Metric.Delete(clientmodel.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 MatrixNode) Vector ===
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func rateImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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args = append(args, &ScalarLiteral{value: 1})
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vector := deltaImpl(timestamp, args).(Vector)
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// TODO: could be other type of MatrixNode 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).interval
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for i := range vector {
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vector[i].Value /= clientmodel.SampleValue(interval / time.Second)
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}
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return vector
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}
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type vectorByValueHeap Vector
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func (s vectorByValueHeap) Len() int {
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return len(s)
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}
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func (s vectorByValueHeap) Less(i, j int) bool {
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if math.IsNaN(float64(s[i].Value)) {
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return true
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}
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return s[i].Value < s[j].Value
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}
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func (s vectorByValueHeap) Swap(i, j int) {
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s[i], s[j] = s[j], s[i]
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}
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func (s *vectorByValueHeap) Push(x interface{}) {
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*s = append(*s, x.(*Sample))
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}
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func (s *vectorByValueHeap) Pop() interface{} {
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old := *s
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n := len(old)
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el := old[n-1]
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*s = old[0 : n-1]
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return el
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}
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type reverseHeap struct {
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heap.Interface
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}
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func (s reverseHeap) Less(i, j int) bool {
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return s.Interface.Less(j, i)
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}
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// === sort(node VectorNode) Vector ===
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func sortImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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byValueSorter := vectorByValueHeap(args[0].(VectorNode).Eval(timestamp))
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sort.Sort(byValueSorter)
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return Vector(byValueSorter)
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}
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// === sortDesc(node VectorNode) Vector ===
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func sortDescImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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byValueSorter := vectorByValueHeap(args[0].(VectorNode).Eval(timestamp))
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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 ScalarNode, node VectorNode) Vector ===
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func topkImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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k := int(args[0].(ScalarNode).Eval(timestamp))
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if k < 1 {
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return Vector{}
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}
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topk := make(vectorByValueHeap, 0, k)
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vector := args[1].(VectorNode).Eval(timestamp)
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for _, el := range vector {
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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 ScalarNode, node VectorNode) Vector ===
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func bottomkImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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k := int(args[0].(ScalarNode).Eval(timestamp))
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if k < 1 {
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return Vector{}
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}
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bottomk := make(vectorByValueHeap, 0, k)
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bkHeap := reverseHeap{Interface: &bottomk}
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vector := args[1].(VectorNode).Eval(timestamp)
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for _, el := range vector {
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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 VectorNode) Vector ===
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func dropCommonLabelsImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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vector := args[0].(VectorNode).Eval(timestamp)
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if len(vector) < 1 {
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return Vector{}
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}
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common := clientmodel.LabelSet{}
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for k, v := range vector[0].Metric.Metric {
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// TODO(julius): Should we also drop common metric names?
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if k == clientmodel.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 vector[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 vector {
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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.Delete(k)
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}
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}
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}
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return vector
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}
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// === round(vector VectorNode, toNearest=1 Scalar) Vector ===
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func roundImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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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 = float64(args[1].(ScalarNode).Eval(timestamp))
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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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n := args[0].(VectorNode)
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vector := n.Eval(timestamp)
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for _, el := range vector {
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el.Metric.Delete(clientmodel.MetricNameLabel)
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el.Value = clientmodel.SampleValue(math.Floor(float64(el.Value)*toNearestInverse+0.5) / toNearestInverse)
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}
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return vector
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}
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// === scalar(node VectorNode) Scalar ===
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func scalarImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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v := args[0].(VectorNode).Eval(timestamp)
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if len(v) != 1 {
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return clientmodel.SampleValue(math.NaN())
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}
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return clientmodel.SampleValue(v[0].Value)
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}
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// === count_scalar(vector VectorNode) model.SampleValue ===
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func countScalarImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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return clientmodel.SampleValue(len(args[0].(VectorNode).Eval(timestamp)))
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}
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func aggrOverTime(timestamp clientmodel.Timestamp, args []Node, aggrFn func(metric.Values) clientmodel.SampleValue) interface{} {
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n := args[0].(MatrixNode)
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matrixVal := n.Eval(timestamp)
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resultVector := Vector{}
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for _, el := range matrixVal {
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if len(el.Values) == 0 {
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continue
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}
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el.Metric.Delete(clientmodel.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: 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 MatrixNode) Vector ===
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func avgOverTimeImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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return aggrOverTime(timestamp, args, func(values metric.Values) clientmodel.SampleValue {
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var sum clientmodel.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 / clientmodel.SampleValue(len(values))
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})
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}
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// === count_over_time(matrix MatrixNode) Vector ===
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func countOverTimeImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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return aggrOverTime(timestamp, args, func(values metric.Values) clientmodel.SampleValue {
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return clientmodel.SampleValue(len(values))
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})
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}
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// === floor(vector VectorNode) Vector ===
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func floorImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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n := args[0].(VectorNode)
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vector := n.Eval(timestamp)
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for _, el := range vector {
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el.Metric.Delete(clientmodel.MetricNameLabel)
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el.Value = clientmodel.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 MatrixNode) Vector ===
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func maxOverTimeImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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return aggrOverTime(timestamp, args, func(values metric.Values) clientmodel.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 clientmodel.SampleValue(max)
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})
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}
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// === min_over_time(matrix MatrixNode) Vector ===
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func minOverTimeImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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return aggrOverTime(timestamp, args, func(values metric.Values) clientmodel.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 clientmodel.SampleValue(min)
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})
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}
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// === sum_over_time(matrix MatrixNode) Vector ===
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func sumOverTimeImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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return aggrOverTime(timestamp, args, func(values metric.Values) clientmodel.SampleValue {
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var sum clientmodel.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 VectorNode) Vector ===
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func absImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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n := args[0].(VectorNode)
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vector := n.Eval(timestamp)
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for _, el := range vector {
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el.Metric.Delete(clientmodel.MetricNameLabel)
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el.Value = clientmodel.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 VectorNode) Vector ===
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func absentImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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n := args[0].(VectorNode)
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if len(n.Eval(timestamp)) > 0 {
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return Vector{}
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}
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m := clientmodel.Metric{}
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if vs, ok := n.(*VectorSelector); ok {
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for _, matcher := range vs.labelMatchers {
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if matcher.Type == metric.Equal && matcher.Name != clientmodel.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: clientmodel.COWMetric{
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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: timestamp,
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},
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}
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}
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// === ceil(vector VectorNode) Vector ===
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func ceilImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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n := args[0].(VectorNode)
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vector := n.Eval(timestamp)
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for _, el := range vector {
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el.Metric.Delete(clientmodel.MetricNameLabel)
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el.Value = clientmodel.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 VectorNode) Vector ===
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func expImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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n := args[0].(VectorNode)
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vector := n.Eval(timestamp)
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for _, el := range vector {
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el.Metric.Delete(clientmodel.MetricNameLabel)
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el.Value = clientmodel.SampleValue(math.Exp(float64(el.Value)))
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}
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return vector
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}
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// === ln(vector VectorNode) Vector ===
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func lnImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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n := args[0].(VectorNode)
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vector := n.Eval(timestamp)
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for _, el := range vector {
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el.Metric.Delete(clientmodel.MetricNameLabel)
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el.Value = clientmodel.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 VectorNode) Vector ===
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func log2Impl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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n := args[0].(VectorNode)
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vector := n.Eval(timestamp)
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for _, el := range vector {
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el.Metric.Delete(clientmodel.MetricNameLabel)
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el.Value = clientmodel.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 VectorNode) Vector ===
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func log10Impl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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n := args[0].(VectorNode)
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vector := n.Eval(timestamp)
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for _, el := range vector {
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el.Metric.Delete(clientmodel.MetricNameLabel)
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el.Value = clientmodel.SampleValue(math.Log10(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 sqrtImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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n := args[0].(VectorNode)
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vector := n.Eval(timestamp)
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for _, el := range vector {
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el.Metric.Delete(clientmodel.MetricNameLabel)
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el.Value = clientmodel.SampleValue(math.Sqrt(float64(el.Value)))
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}
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return vector
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}
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// === deriv(node MatrixNode) Vector ===
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func derivImpl(timestamp clientmodel.Timestamp, args []Node) interface{} {
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matrixNode := args[0].(MatrixNode)
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resultVector := Vector{}
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matrixValue := matrixNode.Eval(timestamp)
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for _, samples := range matrixValue {
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// 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,
|
|
},
|
|
"exp": {
|
|
name: "exp",
|
|
argTypes: []ExprType{VectorType},
|
|
returnType: VectorType,
|
|
callFn: expImpl,
|
|
},
|
|
"floor": {
|
|
name: "floor",
|
|
argTypes: []ExprType{VectorType},
|
|
returnType: VectorType,
|
|
callFn: floorImpl,
|
|
},
|
|
"histogram_quantile": {
|
|
name: "histogram_quantile",
|
|
argTypes: []ExprType{ScalarType, VectorType},
|
|
returnType: VectorType,
|
|
callFn: histogramQuantileImpl,
|
|
},
|
|
"ln": {
|
|
name: "ln",
|
|
argTypes: []ExprType{VectorType},
|
|
returnType: VectorType,
|
|
callFn: lnImpl,
|
|
},
|
|
"log10": {
|
|
name: "log10",
|
|
argTypes: []ExprType{VectorType},
|
|
returnType: VectorType,
|
|
callFn: log10Impl,
|
|
},
|
|
"log2": {
|
|
name: "log2",
|
|
argTypes: []ExprType{VectorType},
|
|
returnType: VectorType,
|
|
callFn: log2Impl,
|
|
},
|
|
"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,
|
|
},
|
|
"sqrt": {
|
|
name: "sqrt",
|
|
argTypes: []ExprType{VectorType},
|
|
returnType: VectorType,
|
|
callFn: sqrtImpl,
|
|
},
|
|
"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
|
|
}
|