Add mad_over_time function

Signed-off-by: Jeanette Tan <jeanette.tan@grafana.com>
This commit is contained in:
Jeanette Tan 2023-12-01 01:22:58 +08:00
parent 5dbbadf598
commit 9bf4cc993e
8 changed files with 96 additions and 0 deletions

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@ -640,6 +640,7 @@ over time and return an instant vector with per-series aggregation results:
* `quantile_over_time(scalar, range-vector)`: the φ-quantile (0 ≤ φ ≤ 1) of the values in the specified interval.
* `stddev_over_time(range-vector)`: the population standard deviation of the values in the specified interval.
* `stdvar_over_time(range-vector)`: the population standard variance of the values in the specified interval.
* `mad_over_time(range-vector)`: the median absolute deviation of all points in the specified interval.
* `last_over_time(range-vector)`: the most recent point value in the specified interval.
* `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
}), nil
}
// === mad_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
func funcMadOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
if len(vals[0].(Matrix)[0].Floats) == 0 {
return enh.Out, nil
}
return aggrOverTime(vals, enh, func(s Series) float64 {
values := make(vectorByValueHeap, 0, len(s.Floats))
for _, f := range s.Floats {
values = append(values, Sample{F: f.F})
}
median := quantile(0.5, values)
values = make(vectorByValueHeap, 0, len(s.Floats))
for _, f := range s.Floats {
values = append(values, Sample{F: math.Abs(f.F - median)})
}
return quantile(0.5, values)
}), nil
}
// === max_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
func funcMaxOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
if len(vals[0].(Matrix)[0].Floats) == 0 {
@ -1538,6 +1557,7 @@ var FunctionCalls = map[string]FunctionCall{
"log10": funcLog10,
"log2": funcLog2,
"last_over_time": funcLastOverTime,
"mad_over_time": funcMadOverTime,
"max_over_time": funcMaxOverTime,
"min_over_time": funcMinOverTime,
"minute": funcMinute,

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@ -15,6 +15,7 @@ package promql
import (
"context"
"fmt"
"math"
"testing"
"time"
@ -86,3 +87,52 @@ func TestKahanSum(t *testing.T) {
expected := 2.0
require.Equal(t, expected, kahanSum(vals))
}
func TestMadOverTime(t *testing.T) {
cases := []struct {
series []int
expectedRes float64
}{
{
series: []int{4, 6, 2, 1, 999, 1, 2},
expectedRes: 1,
},
}
for i, c := range cases {
t.Run(fmt.Sprintf("case %d", i), func(t *testing.T) {
engine := newTestEngine()
storage := teststorage.New(t)
t.Cleanup(func() { storage.Close() })
seriesName := "float_series"
ts := int64(0)
app := storage.Appender(context.Background())
lbls := labels.FromStrings("__name__", seriesName)
var err error
for _, num := range c.series {
_, err = app.Append(0, lbls, ts, float64(num))
require.NoError(t, err)
ts += int64(1 * time.Minute / time.Millisecond)
}
require.NoError(t, app.Commit())
queryAndCheck := func(queryString string, exp Vector) {
qry, err := engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(ts))
require.NoError(t, err)
res := qry.Exec(context.Background())
require.NoError(t, res.Err)
vector, err := res.Vector()
require.NoError(t, err)
require.Equal(t, exp, vector)
}
queryString := fmt.Sprintf(`mad_over_time(%s[%dm])`, seriesName, len(c.series))
queryAndCheck(queryString, []Sample{{T: ts, F: c.expectedRes, Metric: labels.EmptyLabels()}})
})
}
}

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@ -254,6 +254,11 @@ var Functions = map[string]*Function{
ArgTypes: []ValueType{ValueTypeVector},
ReturnType: ValueTypeVector,
},
"mad_over_time": {
Name: "mad_over_time",
ArgTypes: []ValueType{ValueTypeMatrix},
ReturnType: ValueTypeVector,
},
"max_over_time": {
Name: "max_over_time",
ArgTypes: []ValueType{ValueTypeMatrix},

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@ -317,6 +317,12 @@ export const functionIdentifierTerms = [
info: 'Calculate base-2 logarithm of input series',
type: 'function',
},
{
label: 'mad_over_time',
detail: 'function',
info: 'Return the median absolute deviation over time for input series',
type: 'function',
},
{
label: 'max_over_time',
detail: 'function',

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@ -95,6 +95,11 @@ describe('promql operations', () => {
expectedValueType: ValueType.vector,
expectedDiag: [] as Diagnostic[],
},
{
expr: 'mad_over_time(rate(metric_name[5m])[1h:] offset 1m)',
expectedValueType: ValueType.vector,
expectedDiag: [] as Diagnostic[],
},
{
expr: 'max_over_time(rate(metric_name[5m])[1h:] offset 1m)',
expectedValueType: ValueType.vector,

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@ -56,6 +56,7 @@ import {
Ln,
Log10,
Log2,
MadOverTime,
MaxOverTime,
MinOverTime,
Minute,
@ -370,6 +371,12 @@ const promqlFunctions: { [key: number]: PromQLFunction } = {
variadic: 0,
returnType: ValueType.vector,
},
[MadOverTime]: {
name: 'mad_over_time',
argTypes: [ValueType.matrix],
variadic: 0,
returnType: ValueType.vector,
},
[MaxOverTime]: {
name: 'max_over_time',
argTypes: [ValueType.matrix],

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@ -149,6 +149,7 @@ FunctionIdentifier {
Ln |
Log10 |
Log2 |
MadOverTime |
MaxOverTime |
MinOverTime |
Minute |
@ -380,6 +381,7 @@ NumberLiteral {
Ln { condFn<"ln"> }
Log10 { condFn<"log10"> }
Log2 { condFn<"log2"> }
MadOverTime { condFn<"mad_over_time"> }
MaxOverTime { condFn<"max_over_time"> }
MinOverTime { condFn<"min_over_time"> }
Minute { condFn<"minute"> }