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[BUGFIX] PromQL: Fix deriv, predict_linear and double_exponential_smoothing with histograms (#15686)

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PromQL: Fix deriv, predict_linear and double_exponential_smoothing with histograms

Signed-off-by: Neeraj Gartia <neerajgartia211002@gmail.com>

---------

Signed-off-by: Neeraj Gartia <neerajgartia211002@gmail.com>
This commit is contained in:
Neeraj Gartia 2024-12-20 04:20:28 +05:30 committed by GitHub
parent 804ab49cfc
commit 0e99ca3e8c
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3 changed files with 75 additions and 10 deletions
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33
promql/functions.go
View file

@ -355,7 +355,7 @@ func calcTrendValue(i int, tf, s0, s1, b float64) float64 {
// https://en.wikipedia.org/wiki/Exponential_smoothing .
func funcDoubleExponentialSmoothing(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
samples := vals[0].(Matrix)[0]
metricName := samples.Metric.Get(labels.MetricName)
// The smoothing factor argument.
sf := vals[1].(Vector)[0].F
@ -374,6 +374,10 @@ func funcDoubleExponentialSmoothing(vals []parser.Value, args parser.Expressions
// Can't do the smoothing operation with less than two points.
if l < 2 {
// Annotate mix of float and histogram.
if l == 1 && len(samples.Histograms) > 0 {
return enh.Out, annotations.New().Add(annotations.NewHistogramIgnoredInMixedRangeInfo(metricName, args[0].PositionRange()))
}
return enh.Out, nil
}
@ -394,7 +398,9 @@ func funcDoubleExponentialSmoothing(vals []parser.Value, args parser.Expressions
s0, s1 = s1, x+y
}
if len(samples.Histograms) > 0 {
return append(enh.Out, Sample{F: s1}), annotations.New().Add(annotations.NewHistogramIgnoredInMixedRangeInfo(metricName, args[0].PositionRange()))
}
return append(enh.Out, Sample{F: s1}), nil
}
@ -1110,10 +1116,15 @@ func linearRegression(samples []FPoint, interceptTime int64) (slope, intercept f
// === deriv(node parser.ValueTypeMatrix) (Vector, Annotations) ===
func funcDeriv(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
samples := vals[0].(Matrix)[0]
metricName := samples.Metric.Get(labels.MetricName)
// No sense in trying to compute a derivative without at least two points.
// No sense in trying to compute a derivative without at least two float points.
// Drop this Vector element.
if len(samples.Floats) < 2 {
// Annotate mix of float and histogram.
if len(samples.Floats) == 1 && len(samples.Histograms) > 0 {
return enh.Out, annotations.New().Add(annotations.NewHistogramIgnoredInMixedRangeInfo(metricName, args[0].PositionRange()))
}
return enh.Out, nil
}
@ -1121,6 +1132,9 @@ func funcDeriv(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper
// to avoid floating point accuracy issues, see
// https://github.com/prometheus/prometheus/issues/2674
slope, _ := linearRegression(samples.Floats, samples.Floats[0].T)
if len(samples.Histograms) > 0 {
return append(enh.Out, Sample{F: slope}), annotations.New().Add(annotations.NewHistogramIgnoredInMixedRangeInfo(metricName, args[0].PositionRange()))
}
return append(enh.Out, Sample{F: slope}), nil
}
@ -1128,13 +1142,22 @@ func funcDeriv(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper
func funcPredictLinear(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
samples := vals[0].(Matrix)[0]
duration := vals[1].(Vector)[0].F
// No sense in trying to predict anything without at least two points.
metricName := samples.Metric.Get(labels.MetricName)
// No sense in trying to predict anything without at least two float points.
// Drop this Vector element.
if len(samples.Floats) < 2 {
// Annotate mix of float and histogram.
if len(samples.Floats) == 1 && len(samples.Histograms) > 0 {
return enh.Out, annotations.New().Add(annotations.NewHistogramIgnoredInMixedRangeInfo(metricName, args[0].PositionRange()))
}
return enh.Out, nil
}
slope, intercept := linearRegression(samples.Floats, enh.Ts)
slope, intercept := linearRegression(samples.Floats, enh.Ts)
if len(samples.Histograms) > 0 {
return append(enh.Out, Sample{F: slope*duration + intercept}), annotations.New().Add(annotations.NewHistogramIgnoredInMixedRangeInfo(metricName, args[0].PositionRange()))
}
return append(enh.Out, Sample{F: slope*duration + intercept}), nil
}

40
promql/promqltest/testdata/functions.test vendored
View file

@ -259,6 +259,7 @@ load 5m
testcounter_reset_middle_mix 0+10x4 0+10x5 {{schema:0 sum:1 count:1}} {{schema:1 sum:2 count:2}}
http_requests_mix{job="app-server", instance="1", group="canary"} 0+80x10 {{schema:0 sum:1 count:1}}
http_requests_histogram{job="app-server", instance="1", group="canary"} {{schema:0 sum:1 count:2}}x10
http_requests_inf{job="app-server", instance="1", group="canary"} -Inf 0+80x10 Inf
# deriv should return the same as rate in simple cases.
eval instant at 50m rate(http_requests_total{group="canary", instance="1", job="app-server"}[50m])
@ -271,16 +272,20 @@ eval instant at 50m deriv(http_requests_total{group="canary", instance="1", job=
eval instant at 50m deriv(testcounter_reset_middle_total[100m])
{} 0.010606060606060607
# deriv should ignore histograms.
eval instant at 110m deriv(http_requests_mix{group="canary", instance="1", job="app-server"}[110m])
# deriv should ignore histograms with info annotation.
eval_info instant at 110m deriv(http_requests_mix{group="canary", instance="1", job="app-server"}[110m])
{group="canary", instance="1", job="app-server"} 0.26666666666666666
eval instant at 100m deriv(testcounter_reset_middle_mix[110m])
eval_info instant at 100m deriv(testcounter_reset_middle_mix[110m])
{} 0.010606060606060607
eval instant at 50m deriv(http_requests_histogram[60m])
#empty
# deriv should return NaN in case of +Inf or -Inf found.
eval instant at 100m deriv(http_requests_inf[100m])
{job="app-server", instance="1", group="canary"} NaN
# predict_linear should return correct result.
# X/s = [ 0, 300, 600, 900,1200,1500,1800,2100,2400,2700,3000]
# Y = [ 0, 10, 20, 30, 40, 0, 10, 20, 30, 40, 50]
@ -316,6 +321,20 @@ eval instant at 10m predict_linear(testcounter_reset_middle_total[55m] @ 3000, 3
eval instant at 70m predict_linear(testcounter_reset_middle_total[55m] @ 3000, 3600)
{} 89.54545454545455
# predict_linear should ignore histogram with info annotation.
eval_info instant at 60m predict_linear(testcounter_reset_middle_mix[60m], 3000)
{} 70
eval_info instant at 60m predict_linear(testcounter_reset_middle_mix[60m], 50m)
{} 70
eval instant at 60m predict_linear(http_requests_histogram[60m], 50m)
#empty
# predict_linear should return NaN in case of +Inf or -Inf found.
eval instant at 100m predict_linear(http_requests_inf[100m], 6000)
{job="app-server", instance="1", group="canary"} NaN
# With http_requests_total, there is a sample value exactly at the end of
# the range, and it has exactly the predicted value, so predict_linear
# can be emulated with deriv.
@ -719,6 +738,11 @@ load 10s
http_requests{job="api-server", instance="1", group="production"} 0+20x1000 200+30x1000
http_requests{job="api-server", instance="0", group="canary"} 0+30x1000 300+80x1000
http_requests{job="api-server", instance="1", group="canary"} 0+40x2000
http_requests_mix{job="api-server", instance="0", group="production"} 0+10x1000 100+30x1000 {{schema:0 count:1 sum:2}}x1000
http_requests_mix{job="api-server", instance="1", group="production"} 0+20x1000 200+30x1000 {{schema:0 count:1 sum:2}}x1000
http_requests_mix{job="api-server", instance="0", group="canary"} 0+30x1000 300+80x1000 {{schema:0 count:1 sum:2}}x1000
http_requests_mix{job="api-server", instance="1", group="canary"} 0+40x2000 {{schema:0 count:1 sum:2}}x1000
http_requests_histogram{job="api-server", instance="1", group="canary"} {{schema:0 count:1 sum:2}}x1000
eval instant at 8000s double_exponential_smoothing(http_requests[1m], 0.01, 0.1)
{job="api-server", instance="0", group="production"} 8000
@ -726,6 +750,16 @@ eval instant at 8000s double_exponential_smoothing(http_requests[1m], 0.01, 0.1)
{job="api-server", instance="0", group="canary"} 24000
{job="api-server", instance="1", group="canary"} 32000
# double_exponential_smoothing should ignore histogram with info annotation.
eval_info instant at 20010s double_exponential_smoothing(http_requests_mix[1m], 0.01, 0.1)
{job="api-server", instance="0", group="production"} 30100
{job="api-server", instance="1", group="production"} 30200
{job="api-server", instance="0", group="canary"} 80300
{job="api-server", instance="1", group="canary"} 80000
eval instant at 10000s double_exponential_smoothing(http_requests_histogram[1m], 0.01, 0.1)
#empty
# negative trends
clear
load 10s

8
util/annotations/annotations.go
View file

@ -148,6 +148,7 @@ var (
HistogramQuantileForcedMonotonicityInfo = fmt.Errorf("%w: input to histogram_quantile needed to be fixed for monotonicity (see https://prometheus.io/docs/prometheus/latest/querying/functions/#histogram_quantile) for metric name", PromQLInfo)
IncompatibleTypesInBinOpInfo = fmt.Errorf("%w: incompatible sample types encountered for binary operator", PromQLInfo)
HistogramIgnoredInAggregationInfo = fmt.Errorf("%w: ignored histogram in", PromQLInfo)
HistogramIgnoredInMixedRangeInfo = fmt.Errorf("%w: ignored histograms in a range containing both floats and histograms for metric name", PromQLInfo)
)
type annoErr struct {
@ -293,3 +294,10 @@ func NewHistogramIgnoredInAggregationInfo(aggregation string, pos posrange.Posit
Err: fmt.Errorf("%w %s aggregation", HistogramIgnoredInAggregationInfo, aggregation),
}
}
func NewHistogramIgnoredInMixedRangeInfo(metricName string, pos posrange.PositionRange) error {
return annoErr{
PositionRange: pos,
Err: fmt.Errorf("%w %q", HistogramIgnoredInMixedRangeInfo, metricName),
}
}