The Prometheus monitoring system and time series database.
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// Copyright 2023 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 promql
import (
"math"
"testing"
"github.com/stretchr/testify/require"
)
func TestBucketQuantile_ForcedMonotonicity(t *testing.T) {
eps := 1e-12
for name, tc := range map[string]struct {
getInput func() buckets // The buckets can be modified in-place so return a new one each time.
expectedForced bool
expectedFixed bool
expectedValues map[float64]float64
}{
"simple - monotonic": {
getInput: func() buckets {
return buckets{
{
upperBound: 10,
count: 10,
}, {
upperBound: 15,
count: 15,
}, {
upperBound: 20,
count: 15,
}, {
upperBound: 30,
count: 15,
}, {
upperBound: math.Inf(1),
count: 15,
},
}
},
expectedForced: false,
expectedFixed: false,
expectedValues: map[float64]float64{
1: 15.,
0.99: 14.85,
0.9: 13.5,
0.5: 7.5,
},
},
"simple - non-monotonic middle": {
getInput: func() buckets {
return buckets{
{
upperBound: 10,
count: 10,
}, {
upperBound: 15,
count: 15,
}, {
upperBound: 20,
count: 15.00000000001, // Simulate the case there's a small imprecision in float64.
}, {
upperBound: 30,
count: 15,
}, {
upperBound: math.Inf(1),
count: 15,
},
}
},
expectedForced: false,
expectedFixed: true,
expectedValues: map[float64]float64{
1: 15.,
0.99: 14.85,
0.9: 13.5,
0.5: 7.5,
},
},
"real example - monotonic": {
getInput: func() buckets {
return buckets{
{
upperBound: 1,
count: 6454661.3014166197,
}, {
upperBound: 5,
count: 8339611.2001912938,
}, {
upperBound: 10,
count: 14118319.2444762159,
}, {
upperBound: 25,
count: 14130031.5272856522,
}, {
upperBound: 50,
count: 46001270.3030008152,
}, {
upperBound: 64,
count: 46008473.8585563600,
}, {
upperBound: 80,
count: 46008473.8585563600,
}, {
upperBound: 100,
count: 46008473.8585563600,
}, {
upperBound: 250,
count: 46008473.8585563600,
}, {
upperBound: 1000,
count: 46008473.8585563600,
}, {
upperBound: math.Inf(1),
count: 46008473.8585563600,
},
}
},
expectedForced: false,
expectedFixed: false,
expectedValues: map[float64]float64{
1: 64.,
0.99: 49.64475715376406,
0.9: 46.39671690938454,
0.5: 31.96098248992002,
},
},
"real example - non-monotonic": {
getInput: func() buckets {
return buckets{
{
upperBound: 1,
count: 6454661.3014166225,
}, {
upperBound: 5,
count: 8339611.2001912957,
}, {
upperBound: 10,
count: 14118319.2444762159,
}, {
upperBound: 25,
count: 14130031.5272856504,
}, {
upperBound: 50,
count: 46001270.3030008227,
}, {
upperBound: 64,
count: 46008473.8585563824,
}, {
upperBound: 80,
count: 46008473.8585563898,
}, {
upperBound: 100,
count: 46008473.8585563824,
}, {
upperBound: 250,
count: 46008473.8585563824,
}, {
upperBound: 1000,
count: 46008473.8585563898,
}, {
upperBound: math.Inf(1),
count: 46008473.8585563824,
},
}
},
expectedForced: false,
expectedFixed: true,
expectedValues: map[float64]float64{
1: 64.,
0.99: 49.64475715376406,
0.9: 46.39671690938454,
0.5: 31.96098248992002,
},
},
"real example 2 - monotonic": {
getInput: func() buckets {
return buckets{
{
upperBound: 0.005,
count: 9.6,
}, {
upperBound: 0.01,
count: 9.688888889,
}, {
upperBound: 0.025,
count: 9.755555556,
}, {
upperBound: 0.05,
count: 9.844444444,
}, {
upperBound: 0.1,
count: 9.888888889,
}, {
upperBound: 0.25,
count: 9.888888889,
}, {
upperBound: 0.5,
count: 9.888888889,
}, {
upperBound: 1,
count: 9.888888889,
}, {
upperBound: 2.5,
count: 9.888888889,
}, {
upperBound: 5,
count: 9.888888889,
}, {
upperBound: 10,
count: 9.888888889,
}, {
upperBound: 25,
count: 9.888888889,
}, {
upperBound: 50,
count: 9.888888889,
}, {
upperBound: 100,
count: 9.888888889,
}, {
upperBound: math.Inf(1),
count: 9.888888889,
},
}
},
expectedForced: false,
expectedFixed: false,
expectedValues: map[float64]float64{
1: 0.1,
0.99: 0.03468750000281261,
0.9: 0.00463541666671875,
0.5: 0.0025752314815104174,
},
},
"real example 2 - non-monotonic": {
getInput: func() buckets {
return buckets{
{
upperBound: 0.005,
count: 9.6,
}, {
upperBound: 0.01,
count: 9.688888889,
}, {
upperBound: 0.025,
count: 9.755555556,
}, {
upperBound: 0.05,
count: 9.844444444,
}, {
upperBound: 0.1,
count: 9.888888889,
}, {
upperBound: 0.25,
count: 9.888888889,
}, {
upperBound: 0.5,
count: 9.888888889,
}, {
upperBound: 1,
count: 9.888888889,
}, {
upperBound: 2.5,
count: 9.888888889,
}, {
upperBound: 5,
count: 9.888888889,
}, {
upperBound: 10,
count: 9.888888889001, // Simulate the case there's a small imprecision in float64.
}, {
upperBound: 25,
count: 9.888888889,
}, {
upperBound: 50,
count: 9.888888888999, // Simulate the case there's a small imprecision in float64.
}, {
upperBound: 100,
count: 9.888888889,
}, {
upperBound: math.Inf(1),
count: 9.888888889,
},
}
},
expectedForced: false,
expectedFixed: true,
expectedValues: map[float64]float64{
1: 0.1,
0.99: 0.03468750000281261,
0.9: 0.00463541666671875,
0.5: 0.0025752314815104174,
},
},
} {
t.Run(name, func(t *testing.T) {
for q, v := range tc.expectedValues {
res, forced, fixed := bucketQuantile(q, tc.getInput())
require.Equal(t, tc.expectedForced, forced)
require.Equal(t, tc.expectedFixed, fixed)
require.InEpsilon(t, v, res, eps)
}
})
}
}