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Migrate histogram tests to test language.

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pull/689/head
Fabian Reinartz 10 years ago
parent 03094eff04
commit eba07a7d3d
3 changed files with 141 additions and 269 deletions
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184
promql/promql_test.go
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@ -627,14 +627,6 @@ func TestExpressions(t *testing.T) {
`x{y="testvalue"} => 100 @[%v]`,
`label_grouping_test{a="a", b="abb"} => 200 @[%v]`,
`label_grouping_test{a="aa", b="bb"} => 100 @[%v]`,
`testhistogram_bucket{le="0.1", start="positive"} => 50 @[%v]`,
`testhistogram_bucket{le=".2", start="positive"} => 70 @[%v]`,
`testhistogram_bucket{le="1e0", start="positive"} => 110 @[%v]`,
`testhistogram_bucket{le="+Inf", start="positive"} => 120 @[%v]`,
`testhistogram_bucket{le="-.2", start="negative"} => 10 @[%v]`,
`testhistogram_bucket{le="-0.1", start="negative"} => 20 @[%v]`,
`testhistogram_bucket{le="0.3", start="negative"} => 20 @[%v]`,
`testhistogram_bucket{le="+Inf", start="negative"} => 30 @[%v]`,
`request_duration_seconds_bucket{instance="ins1", job="job1", le="0.1"} => 10 @[%v]`,
`request_duration_seconds_bucket{instance="ins1", job="job1", le="0.2"} => 30 @[%v]`,
`request_duration_seconds_bucket{instance="ins1", job="job1", le="+Inf"} => 40 @[%v]`,
@ -934,182 +926,6 @@ func TestExpressions(t *testing.T) {
`{a="aa", b="bb"} => 100 @[%v]`,
},
},
// Quantile too low.
{
expr: `histogram_quantile(-0.1, testhistogram_bucket)`,
output: []string{
`{start="positive"} => -Inf @[%v]`,
`{start="negative"} => -Inf @[%v]`,
},
},
// Quantile too high.
{
expr: `histogram_quantile(1.01, testhistogram_bucket)`,
output: []string{
`{start="positive"} => +Inf @[%v]`,
`{start="negative"} => +Inf @[%v]`,
},
},
// Quantile value in lowest bucket, which is positive.
{
expr: `histogram_quantile(0, testhistogram_bucket{start="positive"})`,
output: []string{
`{start="positive"} => 0 @[%v]`,
},
},
// Quantile value in lowest bucket, which is negative.
{
expr: `histogram_quantile(0, testhistogram_bucket{start="negative"})`,
output: []string{
`{start="negative"} => -0.2 @[%v]`,
},
},
// Quantile value in highest bucket.
{
expr: `histogram_quantile(1, testhistogram_bucket)`,
output: []string{
`{start="positive"} => 1 @[%v]`,
`{start="negative"} => 0.3 @[%v]`,
},
},
// Finally some useful quantiles.
{
expr: `histogram_quantile(0.2, testhistogram_bucket)`,
output: []string{
`{start="positive"} => 0.048 @[%v]`,
`{start="negative"} => -0.2 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, testhistogram_bucket)`,
output: []string{
`{start="positive"} => 0.15 @[%v]`,
`{start="negative"} => -0.15 @[%v]`,
},
},
{
expr: `histogram_quantile(0.8, testhistogram_bucket)`,
output: []string{
`{start="positive"} => 0.72 @[%v]`,
`{start="negative"} => 0.3 @[%v]`,
},
},
// More realistic with rates.
{
expr: `histogram_quantile(0.2, rate(testhistogram_bucket[5m]))`,
output: []string{
`{start="positive"} => 0.048 @[%v]`,
`{start="negative"} => -0.2 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, rate(testhistogram_bucket[5m]))`,
output: []string{
`{start="positive"} => 0.15 @[%v]`,
`{start="negative"} => -0.15 @[%v]`,
},
},
{
expr: `histogram_quantile(0.8, rate(testhistogram_bucket[5m]))`,
output: []string{
`{start="positive"} => 0.72 @[%v]`,
`{start="negative"} => 0.3 @[%v]`,
},
},
// Aggregated histogram: Everything in one.
{
expr: `histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le))`,
output: []string{
`{} => 0.075 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le))`,
output: []string{
`{} => 0.1277777777777778 @[%v]`,
},
},
// Aggregated histogram: Everything in one. Now with avg, which does not change anything.
{
expr: `histogram_quantile(0.3, avg(rate(request_duration_seconds_bucket[5m])) by (le))`,
output: []string{
`{} => 0.075 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, avg(rate(request_duration_seconds_bucket[5m])) by (le))`,
output: []string{
`{} => 0.12777777777777778 @[%v]`,
},
},
// Aggregated histogram: By job.
{
expr: `histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))`,
output: []string{
`{instance="ins1"} => 0.075 @[%v]`,
`{instance="ins2"} => 0.075 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))`,
output: []string{
`{instance="ins1"} => 0.1333333333 @[%v]`,
`{instance="ins2"} => 0.125 @[%v]`,
},
},
// Aggregated histogram: By instance.
{
expr: `histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))`,
output: []string{
`{job="job1"} => 0.1 @[%v]`,
`{job="job2"} => 0.0642857142857143 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))`,
output: []string{
`{job="job1"} => 0.14 @[%v]`,
`{job="job2"} => 0.1125 @[%v]`,
},
},
// Aggregated histogram: By job and instance.
{
expr: `histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))`,
output: []string{
`{instance="ins1", job="job1"} => 0.11 @[%v]`,
`{instance="ins2", job="job1"} => 0.09 @[%v]`,
`{instance="ins1", job="job2"} => 0.06 @[%v]`,
`{instance="ins2", job="job2"} => 0.0675 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))`,
output: []string{
`{instance="ins1", job="job1"} => 0.15 @[%v]`,
`{instance="ins2", job="job1"} => 0.1333333333333333 @[%v]`,
`{instance="ins1", job="job2"} => 0.1 @[%v]`,
`{instance="ins2", job="job2"} => 0.1166666666666667 @[%v]`,
},
},
// The unaggregated histogram for comparison. Same result as the previous one.
{
expr: `histogram_quantile(0.3, rate(request_duration_seconds_bucket[5m]))`,
output: []string{
`{instance="ins1", job="job1"} => 0.11 @[%v]`,
`{instance="ins2", job="job1"} => 0.09 @[%v]`,
`{instance="ins1", job="job2"} => 0.06 @[%v]`,
`{instance="ins2", job="job2"} => 0.0675 @[%v]`,
},
},
{
expr: `histogram_quantile(0.5, rate(request_duration_seconds_bucket[5m]))`,
output: []string{
`{instance="ins1", job="job1"} => 0.15 @[%v]`,
`{instance="ins2", job="job1"} => 0.13333333333333333 @[%v]`,
`{instance="ins1", job="job2"} => 0.1 @[%v]`,
`{instance="ins2", job="job2"} => 0.11666666666666667 @[%v]`,
},
},
{
expr: `12.34e6`,
output: []string{`scalar: 12340000 @[%v]`},

85
promql/setup_test.go
Unescape View File

@ -209,91 +209,6 @@ var testMatrix = Matrix{
},
Values: getTestValueStream(0, 200, 20, testStartTime),
},
// Two histograms with 4 buckets each (*_sum and *_count not included,
// only buckets). Lowest bucket for one histogram < 0, for the other >
// 0. They have the same name, just separated by label. Not useful in
// practice, but can happen (if clients change bucketing), and the
// server has to cope with it.
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "0.1",
"start": "positive",
},
},
Values: getTestValueStream(0, 50, 5, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": ".2",
"start": "positive",
},
},
Values: getTestValueStream(0, 70, 7, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "1e0",
"start": "positive",
},
},
Values: getTestValueStream(0, 110, 11, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "+Inf",
"start": "positive",
},
},
Values: getTestValueStream(0, 120, 12, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "-.2",
"start": "negative",
},
},
Values: getTestValueStream(0, 10, 1, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "-0.1",
"start": "negative",
},
},
Values: getTestValueStream(0, 20, 2, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "0.3",
"start": "negative",
},
},
Values: getTestValueStream(0, 20, 2, testStartTime),
},
{
Metric: clientmodel.COWMetric{
Metric: clientmodel.Metric{
clientmodel.MetricNameLabel: "testhistogram_bucket",
"le": "+Inf",
"start": "negative",
},
},
Values: getTestValueStream(0, 30, 3, testStartTime),
},
// Now a more realistic histogram per job and instance to test aggregation.
{
Metric: clientmodel.COWMetric{

141
promql/testdata/histograms.test vendored
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@ -0,0 +1,141 @@
# Two histograms with 4 buckets each (x_sum and x_count not included,
# only buckets). Lowest bucket for one histogram < 0, for the other >
# 0. They have the same name, just separated by label. Not useful in
# practice, but can happen (if clients change bucketing), and the
# server has to cope with it.
# Test histogram.
load 5m
testhistogram_bucket{le="0.1", start="positive"} 0+5x10
testhistogram_bucket{le=".2", start="positive"} 0+7x10
testhistogram_bucket{le="1e0", start="positive"} 0+11x10
testhistogram_bucket{le="+Inf", start="positive"} 0+12x10
testhistogram_bucket{le="-.2", start="negative"} 0+1x10
testhistogram_bucket{le="-0.1", start="negative"} 0+2x10
testhistogram_bucket{le="0.3", start="negative"} 0+2x10
testhistogram_bucket{le="+Inf", start="negative"} 0+3x10
# Now a more realistic histogram per job and instance to test aggregation.
load 5m
request_duration_seconds_bucket{job="job1", instance="ins1", le="0.1"} 0+1x10
request_duration_seconds_bucket{job="job1", instance="ins1", le="0.2"} 0+3x10
request_duration_seconds_bucket{job="job1", instance="ins1", le="+Inf"} 0+4x10
request_duration_seconds_bucket{job="job1", instance="ins2", le="0.1"} 0+2x10
request_duration_seconds_bucket{job="job1", instance="ins2", le="0.2"} 0+5x10
request_duration_seconds_bucket{job="job1", instance="ins2", le="+Inf"} 0+6x10
request_duration_seconds_bucket{job="job2", instance="ins1", le="0.1"} 0+3x10
request_duration_seconds_bucket{job="job2", instance="ins1", le="0.2"} 0+4x10
request_duration_seconds_bucket{job="job2", instance="ins1", le="+Inf"} 0+6x10
request_duration_seconds_bucket{job="job2", instance="ins2", le="0.1"} 0+4x10
request_duration_seconds_bucket{job="job2", instance="ins2", le="0.2"} 0+7x10
request_duration_seconds_bucket{job="job2", instance="ins2", le="+Inf"} 0+9x10
# Quantile too low.
eval instant at 50m histogram_quantile(-0.1, testhistogram_bucket)
{start="positive"} -Inf
{start="negative"} -Inf
# Quantile too high.
eval instant at 50m histogram_quantile(1.01, testhistogram_bucket)
{start="positive"} +Inf
{start="negative"} +Inf
# Quantile value in lowest bucket, which is positive.
eval instant at 50m histogram_quantile(0, testhistogram_bucket{start="positive"})
{start="positive"} 0
# Quantile value in lowest bucket, which is negative.
eval instant at 50m histogram_quantile(0, testhistogram_bucket{start="negative"})
{start="negative"} -0.2
# Quantile value in highest bucket.
eval instant at 50m histogram_quantile(1, testhistogram_bucket)
{start="positive"} 1
{start="negative"} 0.3
# Finally some useful quantiles.
eval instant at 50m histogram_quantile(0.2, testhistogram_bucket)
{start="positive"} 0.048
{start="negative"} -0.2
eval instant at 50m histogram_quantile(0.5, testhistogram_bucket)
{start="positive"} 0.15
{start="negative"} -0.15
eval instant at 50m histogram_quantile(0.8, testhistogram_bucket)
{start="positive"} 0.72
{start="negative"} 0.3
# More realistic with rates.
eval instant at 50m histogram_quantile(0.2, rate(testhistogram_bucket[5m]))
{start="positive"} 0.048
{start="negative"} -0.2
eval instant at 50m histogram_quantile(0.5, rate(testhistogram_bucket[5m]))
{start="positive"} 0.15
{start="negative"} -0.15
eval instant at 50m histogram_quantile(0.8, rate(testhistogram_bucket[5m]))
{start="positive"} 0.72
{start="negative"} 0.3
# Aggregated histogram: Everything in one.
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le))
{} 0.075
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le))
{} 0.1277777777777778
# Aggregated histogram: Everything in one. Now with avg, which does not change anything.
eval instant at 50m histogram_quantile(0.3, avg(rate(request_duration_seconds_bucket[5m])) by (le))
{} 0.075
eval instant at 50m histogram_quantile(0.5, avg(rate(request_duration_seconds_bucket[5m])) by (le))
{} 0.12777777777777778
# Aggregated histogram: By job.
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))
{instance="ins1"} 0.075
{instance="ins2"} 0.075
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))
{instance="ins1"} 0.1333333333
{instance="ins2"} 0.125
# Aggregated histogram: By instance.
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))
{job="job1"} 0.1
{job="job2"} 0.0642857142857143
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))
{job="job1"} 0.14
{job="job2"} 0.1125
# Aggregated histogram: By job and instance.
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))
{instance="ins1", job="job1"} 0.11
{instance="ins2", job="job1"} 0.09
{instance="ins1", job="job2"} 0.06
{instance="ins2", job="job2"} 0.0675
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))
{instance="ins1", job="job1"} 0.15
{instance="ins2", job="job1"} 0.1333333333333333
{instance="ins1", job="job2"} 0.1
{instance="ins2", job="job2"} 0.1166666666666667
# The unaggregated histogram for comparison. Same result as the previous one.
eval instant at 50m histogram_quantile(0.3, rate(request_duration_seconds_bucket[5m]))
{instance="ins1", job="job1"} 0.11
{instance="ins2", job="job1"} 0.09
{instance="ins1", job="job2"} 0.06
{instance="ins2", job="job2"} 0.0675
eval instant at 50m histogram_quantile(0.5, rate(request_duration_seconds_bucket[5m]))
{instance="ins1", job="job1"} 0.15
{instance="ins2", job="job1"} 0.13333333333333333
{instance="ins1", job="job2"} 0.1
{instance="ins2", job="job2"} 0.11666666666666667
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