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promql: histogram_stddev and histogram_stdvar should use arithmetic mean for custom buckets

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Signed-off-by: amanycodes <amanycodes@gmail.com>
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Aman 2025-04-24 18:18:58 +05:30 committed by GitHub
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commit 26bddcf068
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5 changed files with 22 additions and 12 deletions
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8
docs/querying/functions.md
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@ -428,9 +428,11 @@ annotation, you should find and remove the source of the invalid data.
## `histogram_stddev()` and `histogram_stdvar()`
`histogram_stddev(v instant-vector)` returns the estimated standard deviation
of observations for each histogram sample in `v`, based on the geometric mean
of the buckets where the observations lie. Float samples are ignored and do not
show up in the returned vector.
of observations for each histogram sample in `v`. For this estimation, all observations
in a bucket are assumed to have the value of the mean of the bucket boundaries. For
the zero bucket and for buckets with custom boundaries, the arithmetic mean is used.
For the usual exponential buckets, the geometric mean is used. Float samples are ignored
and do not show up in the returned vector.
Similarly, `histogram_stdvar(v instant-vector)` returns the estimated standard
variance of observations for each histogram sample in `v`.

10
promql/functions.go
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@ -1350,9 +1350,15 @@ func histogramVariance(vals []parser.Value, enh *EvalNodeHelper, varianceToResul
continue
}
var val float64
if bucket.Lower <= 0 && 0 <= bucket.Upper {
switch {
case sample.H.UsesCustomBuckets():
// Use arithmetic mean in case of custom buckets.
val = (bucket.Upper + bucket.Lower) / 2.0
case bucket.Lower <= 0 && bucket.Upper >= 0:
// Use zero (effectively the arithmetic mean) in the zero bucket of a standard exponential histogram.
val = 0
} else {
default:
// Use geometric mean in case of standard exponential buckets.
val = math.Sqrt(bucket.Upper * bucket.Lower)
if bucket.Upper < 0 {
val = -val

8
promql/promqltest/testdata/histograms.test vendored
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@ -95,13 +95,13 @@ eval instant at 50m histogram_avg(testhistogram3)
# Test histogram_stddev. This has no classic equivalent.
eval instant at 50m histogram_stddev(testhistogram3)
{start="positive"} 2.8189265757336734
{start="negative"} 4.182715937754936
{start="positive"} 2.7435461458749795
{start="negative"} 4.187667907081458
# Test histogram_stdvar. This has no classic equivalent.
eval instant at 50m histogram_stdvar(testhistogram3)
{start="positive"} 7.946347039377573
{start="negative"} 17.495112615949154
{start="positive"} 7.527045454545455
{start="negative"} 17.5365625
# Test histogram_fraction.
#

2
promql/promqltest/testdata/native_histograms.test vendored
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@ -337,7 +337,7 @@ load 10m
histogram_stddev_stdvar_3 {{schema:3 count:7 sum:62 z_bucket:1 buckets:[0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ] n_buckets:[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ]}}x1
eval instant at 10m histogram_stddev(histogram_stddev_stdvar_3)
{} 42.947236400258
{} 42.94723640026
eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_3)
{} 1844.4651144196398

6
web/ui/mantine-ui/src/promql/functionDocs.tsx
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@ -1266,9 +1266,11 @@ const funcDocs: Record<string, React.ReactNode> = {
</p>
<p>
<code>histogram_stddev(v instant-vector)</code> returns the estimated standard deviation of observations in a native
histogram, based on the geometric mean of the buckets where the observations lie. Samples that are not native
histograms are ignored and do not show up in the returned vector.
histogram. For this estimation, all observations in a bucket are assumed to have the value of the mean of the bucket boundaries.
For the zero bucket and for buckets with custom boundaries, the arithmetic mean is used. For the usual exponential buckets,
the geometric mean is used. Samples that are not native histograms are ignored and do not show up in the returned vector.
</p>
<p>