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Add mad_over_time function
Signed-off-by: Jeanette Tan <jeanette.tan@grafana.com>
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@ -640,6 +640,7 @@ over time and return an instant vector with per-series aggregation results:
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* `quantile_over_time(scalar, range-vector)`: the φ-quantile (0 ≤ φ ≤ 1) of the values in the specified interval.
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* `stddev_over_time(range-vector)`: the population standard deviation of the values in the specified interval.
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* `stdvar_over_time(range-vector)`: the population standard variance of the values in the specified interval.
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* `mad_over_time(range-vector)`: the median absolute deviation of all points in the specified interval.
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* `last_over_time(range-vector)`: the most recent point value in the specified interval.
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* `present_over_time(range-vector)`: the value 1 for any series in the specified interval.
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@ -609,6 +609,25 @@ func funcLastOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNod
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}), nil
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}
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// === mad_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
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func funcMadOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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if len(vals[0].(Matrix)[0].Floats) == 0 {
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return enh.Out, nil
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}
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return aggrOverTime(vals, enh, func(s Series) float64 {
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values := make(vectorByValueHeap, 0, len(s.Floats))
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for _, f := range s.Floats {
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values = append(values, Sample{F: f.F})
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}
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median := quantile(0.5, values)
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values = make(vectorByValueHeap, 0, len(s.Floats))
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for _, f := range s.Floats {
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values = append(values, Sample{F: math.Abs(f.F - median)})
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}
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return quantile(0.5, values)
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}), nil
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}
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// === max_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
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func funcMaxOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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if len(vals[0].(Matrix)[0].Floats) == 0 {
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@ -1538,6 +1557,7 @@ var FunctionCalls = map[string]FunctionCall{
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"log10": funcLog10,
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"log2": funcLog2,
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"last_over_time": funcLastOverTime,
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"mad_over_time": funcMadOverTime,
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"max_over_time": funcMaxOverTime,
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"min_over_time": funcMinOverTime,
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"minute": funcMinute,
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@ -15,6 +15,7 @@ package promql
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import (
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"context"
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"fmt"
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"math"
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"testing"
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"time"
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@ -86,3 +87,52 @@ func TestKahanSum(t *testing.T) {
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expected := 2.0
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require.Equal(t, expected, kahanSum(vals))
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}
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func TestMadOverTime(t *testing.T) {
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cases := []struct {
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series []int
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expectedRes float64
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}{
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{
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series: []int{4, 6, 2, 1, 999, 1, 2},
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expectedRes: 1,
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},
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}
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for i, c := range cases {
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t.Run(fmt.Sprintf("case %d", i), func(t *testing.T) {
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engine := newTestEngine()
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storage := teststorage.New(t)
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t.Cleanup(func() { storage.Close() })
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seriesName := "float_series"
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ts := int64(0)
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app := storage.Appender(context.Background())
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lbls := labels.FromStrings("__name__", seriesName)
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var err error
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for _, num := range c.series {
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_, err = app.Append(0, lbls, ts, float64(num))
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require.NoError(t, err)
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ts += int64(1 * time.Minute / time.Millisecond)
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}
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require.NoError(t, app.Commit())
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queryAndCheck := func(queryString string, exp Vector) {
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qry, err := engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(ts))
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require.NoError(t, err)
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res := qry.Exec(context.Background())
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require.NoError(t, res.Err)
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vector, err := res.Vector()
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require.NoError(t, err)
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require.Equal(t, exp, vector)
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}
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queryString := fmt.Sprintf(`mad_over_time(%s[%dm])`, seriesName, len(c.series))
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queryAndCheck(queryString, []Sample{{T: ts, F: c.expectedRes, Metric: labels.EmptyLabels()}})
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})
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}
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}
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@ -254,6 +254,11 @@ var Functions = map[string]*Function{
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ArgTypes: []ValueType{ValueTypeVector},
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ReturnType: ValueTypeVector,
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},
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"mad_over_time": {
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Name: "mad_over_time",
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ArgTypes: []ValueType{ValueTypeMatrix},
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ReturnType: ValueTypeVector,
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},
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"max_over_time": {
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Name: "max_over_time",
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ArgTypes: []ValueType{ValueTypeMatrix},
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@ -317,6 +317,12 @@ export const functionIdentifierTerms = [
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info: 'Calculate base-2 logarithm of input series',
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type: 'function',
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},
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{
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label: 'mad_over_time',
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detail: 'function',
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info: 'Return the median absolute deviation over time for input series',
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type: 'function',
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},
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{
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label: 'max_over_time',
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detail: 'function',
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@ -95,6 +95,11 @@ describe('promql operations', () => {
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expectedValueType: ValueType.vector,
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expectedDiag: [] as Diagnostic[],
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},
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{
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expr: 'mad_over_time(rate(metric_name[5m])[1h:] offset 1m)',
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expectedValueType: ValueType.vector,
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expectedDiag: [] as Diagnostic[],
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},
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{
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expr: 'max_over_time(rate(metric_name[5m])[1h:] offset 1m)',
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expectedValueType: ValueType.vector,
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@ -56,6 +56,7 @@ import {
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Ln,
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Log10,
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Log2,
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MadOverTime,
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MaxOverTime,
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MinOverTime,
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Minute,
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@ -370,6 +371,12 @@ const promqlFunctions: { [key: number]: PromQLFunction } = {
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variadic: 0,
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returnType: ValueType.vector,
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},
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[MadOverTime]: {
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name: 'mad_over_time',
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argTypes: [ValueType.matrix],
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variadic: 0,
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returnType: ValueType.vector,
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},
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[MaxOverTime]: {
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name: 'max_over_time',
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argTypes: [ValueType.matrix],
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@ -149,6 +149,7 @@ FunctionIdentifier {
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Ln |
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Log10 |
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Log2 |
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MadOverTime |
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MaxOverTime |
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MinOverTime |
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Minute |
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@ -380,6 +381,7 @@ NumberLiteral {
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Ln { condFn<"ln"> }
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Log10 { condFn<"log10"> }
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Log2 { condFn<"log2"> }
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MadOverTime { condFn<"mad_over_time"> }
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MaxOverTime { condFn<"max_over_time"> }
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MinOverTime { condFn<"min_over_time"> }
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Minute { condFn<"minute"> }
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