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In histogram_quantile merge buckets with equivalent le values (#5158)

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This makes things generally more resilient, and will
help with OpenMetrics transitions (and inconsistencies).

Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
This commit is contained in:
Brian Brazil 2019-02-01 10:22:44 +00:00 committed by GitHub
parent a60431f3cd
commit c66aeb3fff
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GPG key ID: 4AEE18F83AFDEB23
2 changed files with 46 additions and 3 deletions
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27
promql/quantile.go
View file

@ -75,16 +75,18 @@ func bucketQuantile(q float64, buckets buckets) float64 {
if q > 1 { if q > 1 {
return math.Inf(+1) return math.Inf(+1)
} }
if len(buckets) < 2 {
return math.NaN()
}
sort.Sort(buckets) sort.Sort(buckets)
if !math.IsInf(buckets[len(buckets)-1].upperBound, +1) { if !math.IsInf(buckets[len(buckets)-1].upperBound, +1) {
return math.NaN() return math.NaN()
} }
buckets = coalesceBuckets(buckets)
ensureMonotonic(buckets) ensureMonotonic(buckets)
if len(buckets) < 2 {
return math.NaN()
}
rank := q * buckets[len(buckets)-1].count rank := q * buckets[len(buckets)-1].count
b := sort.Search(len(buckets)-1, func(i int) bool { return buckets[i].count >= rank }) b := sort.Search(len(buckets)-1, func(i int) bool { return buckets[i].count >= rank })
@ -107,6 +109,25 @@ func bucketQuantile(q float64, buckets buckets) float64 {
return bucketStart + (bucketEnd-bucketStart)*(rank/count) return bucketStart + (bucketEnd-bucketStart)*(rank/count)
} }
// coalesceBuckets merges buckets with the same upper bound.
//
// The input buckets must be sorted.
func coalesceBuckets(buckets buckets) buckets {
last := buckets[0]
i := 0
for _, b := range buckets[1:] {
if b.upperBound == last.upperBound {
last.count += b.count
} else {
buckets[i] = last
last = b
i++
}
}
buckets[i] = last
return buckets[:i+1]
}
// The assumption that bucket counts increase monotonically with increasing // The assumption that bucket counts increase monotonically with increasing
// upperBound may be violated during: // upperBound may be violated during:
// //

22
promql/testdata/histograms.test vendored
View file

@ -31,6 +31,15 @@ load 5m
request_duration_seconds_bucket{job="job2", instance="ins2", le="0.2"} 0+7x10 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 request_duration_seconds_bucket{job="job2", instance="ins2", le="+Inf"} 0+9x10
# Different le representations in one histogram.
load 5m
mixed_bucket{job="job1", instance="ins1", le="0.1"} 0+1x10
mixed_bucket{job="job1", instance="ins1", le="0.2"} 0+1x10
mixed_bucket{job="job1", instance="ins1", le="2e-1"} 0+1x10
mixed_bucket{job="job1", instance="ins1", le="2.0e-1"} 0+1x10
mixed_bucket{job="job1", instance="ins1", le="+Inf"} 0+4x10
mixed_bucket{job="job1", instance="ins2", le="+inf"} 0+0x10
mixed_bucket{job="job1", instance="ins2", le="+Inf"} 0+0x10
# Quantile too low. # Quantile too low.
eval instant at 50m histogram_quantile(-0.1, testhistogram_bucket) eval instant at 50m histogram_quantile(-0.1, testhistogram_bucket)
@ -157,3 +166,16 @@ load 5m
# Nonmonotonic buckets # Nonmonotonic buckets
eval instant at 50m histogram_quantile(0.99, nonmonotonic_bucket) eval instant at 50m histogram_quantile(0.99, nonmonotonic_bucket)
{} 0.989875 {} 0.989875
# Buckets with different representations of the same upper bound.
eval instant at 50m histogram_quantile(0.5, rate(mixed_bucket[5m]))
{instance="ins1", job="job1"} 0.15
{instance="ins2", job="job1"} NaN
eval instant at 50m histogram_quantile(0.75, rate(mixed_bucket[5m]))
{instance="ins1", job="job1"} 0.2
{instance="ins2", job="job1"} NaN
eval instant at 50m histogram_quantile(1, rate(mixed_bucket[5m]))
{instance="ins1", job="job1"} 0.2
{instance="ins2", job="job1"} NaN