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[BUGFIX] PromQL: Fix <aggr_over_time> functions with histograms (#15711)
fix aggr_over_time with histograms Signed-off-by: Neeraj Gartia <neerajgartia211002@gmail.com> --------- Signed-off-by: Neeraj Gartia <neerajgartia211002@gmail.com>
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Neeraj Gartia
GitHub
parent
d173c0b61c
commit
b3e30d52ce
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@ -691,9 +691,15 @@ func funcLastOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNod
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// === mad_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
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func funcMadOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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if len(vals[0].(Matrix)[0].Floats) == 0 {
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samples := vals[0].(Matrix)[0]
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var annos annotations.Annotations
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if len(samples.Floats) == 0 {
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return enh.Out, nil
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}
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if len(samples.Histograms) > 0 {
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metricName := samples.Metric.Get(labels.MetricName)
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annos.Add(annotations.NewHistogramIgnoredInMixedRangeInfo(metricName, args[0].PositionRange()))
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}
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return aggrOverTime(vals, enh, func(s Series) float64 {
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values := make(vectorByValueHeap, 0, len(s.Floats))
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for _, f := range s.Floats {
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@ -705,18 +711,20 @@ func funcMadOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNode
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values = append(values, Sample{F: math.Abs(f.F - median)})
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}
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return quantile(0.5, values)
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}), nil
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}), annos
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}
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// === max_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
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func funcMaxOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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if len(vals[0].(Matrix)[0].Floats) == 0 {
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// TODO(beorn7): The passed values only contain
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// histograms. max_over_time ignores histograms for now. If
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// there are only histograms, we have to return without adding
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// anything to enh.Out.
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samples := vals[0].(Matrix)[0]
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var annos annotations.Annotations
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if len(samples.Floats) == 0 {
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return enh.Out, nil
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}
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if len(samples.Histograms) > 0 {
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metricName := samples.Metric.Get(labels.MetricName)
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annos.Add(annotations.NewHistogramIgnoredInMixedRangeInfo(metricName, args[0].PositionRange()))
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}
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return aggrOverTime(vals, enh, func(s Series) float64 {
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maxVal := s.Floats[0].F
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for _, f := range s.Floats {
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@ -725,18 +733,20 @@ func funcMaxOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNode
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}
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}
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return maxVal
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}), nil
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}), annos
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}
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// === min_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
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func funcMinOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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if len(vals[0].(Matrix)[0].Floats) == 0 {
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// TODO(beorn7): The passed values only contain
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// histograms. min_over_time ignores histograms for now. If
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// there are only histograms, we have to return without adding
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// anything to enh.Out.
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samples := vals[0].(Matrix)[0]
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var annos annotations.Annotations
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if len(samples.Floats) == 0 {
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return enh.Out, nil
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}
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if len(samples.Histograms) > 0 {
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metricName := samples.Metric.Get(labels.MetricName)
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annos.Add(annotations.NewHistogramIgnoredInMixedRangeInfo(metricName, args[0].PositionRange()))
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}
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return aggrOverTime(vals, enh, func(s Series) float64 {
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minVal := s.Floats[0].F
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for _, f := range s.Floats {
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@ -745,7 +755,7 @@ func funcMinOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNode
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}
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}
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return minVal
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}), nil
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}), annos
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}
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// === sum_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
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@ -794,10 +804,6 @@ func funcQuantileOverTime(vals []parser.Value, args parser.Expressions, enh *Eva
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q := vals[0].(Vector)[0].F
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el := vals[1].(Matrix)[0]
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if len(el.Floats) == 0 {
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// TODO(beorn7): The passed values only contain
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// histograms. quantile_over_time ignores histograms for now. If
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// there are only histograms, we have to return without adding
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// anything to enh.Out.
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return enh.Out, nil
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}
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@ -805,7 +811,10 @@ func funcQuantileOverTime(vals []parser.Value, args parser.Expressions, enh *Eva
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if math.IsNaN(q) || q < 0 || q > 1 {
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annos.Add(annotations.NewInvalidQuantileWarning(q, args[0].PositionRange()))
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}
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if len(el.Histograms) > 0 {
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metricName := el.Metric.Get(labels.MetricName)
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annos.Add(annotations.NewHistogramIgnoredInAggregationInfo(metricName, args[0].PositionRange()))
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}
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values := make(vectorByValueHeap, 0, len(el.Floats))
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for _, f := range el.Floats {
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values = append(values, Sample{F: f.F})
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@ -815,13 +824,15 @@ func funcQuantileOverTime(vals []parser.Value, args parser.Expressions, enh *Eva
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// === stddev_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
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func funcStddevOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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if len(vals[0].(Matrix)[0].Floats) == 0 {
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// TODO(beorn7): The passed values only contain
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// histograms. stddev_over_time ignores histograms for now. If
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// there are only histograms, we have to return without adding
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// anything to enh.Out.
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samples := vals[0].(Matrix)[0]
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var annos annotations.Annotations
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if len(samples.Floats) == 0 {
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return enh.Out, nil
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}
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if len(samples.Histograms) > 0 {
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metricName := samples.Metric.Get(labels.MetricName)
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annos.Add(annotations.NewHistogramIgnoredInMixedRangeInfo(metricName, args[0].PositionRange()))
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}
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return aggrOverTime(vals, enh, func(s Series) float64 {
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var count float64
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var mean, cMean float64
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@ -833,18 +844,20 @@ func funcStddevOverTime(vals []parser.Value, args parser.Expressions, enh *EvalN
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aux, cAux = kahanSumInc(delta*(f.F-(mean+cMean)), aux, cAux)
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}
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return math.Sqrt((aux + cAux) / count)
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}), nil
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}), annos
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}
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// === stdvar_over_time(Matrix parser.ValueTypeMatrix) (Vector, Annotations) ===
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func funcStdvarOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
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if len(vals[0].(Matrix)[0].Floats) == 0 {
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// TODO(beorn7): The passed values only contain
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// histograms. stdvar_over_time ignores histograms for now. If
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// there are only histograms, we have to return without adding
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// anything to enh.Out.
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samples := vals[0].(Matrix)[0]
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var annos annotations.Annotations
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if len(samples.Floats) == 0 {
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return enh.Out, nil
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}
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if len(samples.Histograms) > 0 {
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metricName := samples.Metric.Get(labels.MetricName)
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annos.Add(annotations.NewHistogramIgnoredInMixedRangeInfo(metricName, args[0].PositionRange()))
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}
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return aggrOverTime(vals, enh, func(s Series) float64 {
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var count float64
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var mean, cMean float64
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@ -856,7 +869,7 @@ func funcStdvarOverTime(vals []parser.Value, args parser.Expressions, enh *EvalN
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aux, cAux = kahanSumInc(delta*(f.F-(mean+cMean)), aux, cAux)
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}
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return (aux + cAux) / count
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}), nil
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}), annos
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}
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// === absent(Vector parser.ValueTypeVector) (Vector, Annotations) ===
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78
promql/promqltest/testdata/functions.test
vendored
78
promql/promqltest/testdata/functions.test
vendored
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@ -929,35 +929,58 @@ eval instant at 1m avg_over_time(metric[2m])
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# Tests for stddev_over_time and stdvar_over_time.
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clear
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load 10s
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metric 0 8 8 2 3
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metric 0 8 8 2 3
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metric_histogram{type="only_histogram"} {{schema:1 sum:2 count:3}}x5
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metric_histogram{type="mix"} 1 1 1 {{schema:1 sum:2 count:3}} {{schema:1 sum:2 count:3}}
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eval instant at 1m stdvar_over_time(metric[2m])
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{} 10.56
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{} 10.56
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eval instant at 1m stddev_over_time(metric[2m])
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{} 3.249615
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{} 3.249615
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eval instant at 1m stddev_over_time((metric[2m]))
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{} 3.249615
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{} 3.249615
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# Tests for stddev_over_time and stdvar_over_time with histograms.
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eval instant at 1m stddev_over_time(metric_histogram{type="only_histogram"}[2m])
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#empty
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eval_info instant at 1m stddev_over_time(metric_histogram{type="mix"}[2m])
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{type="mix"} 0
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eval instant at 1m stdvar_over_time(metric_histogram{type="only_histogram"}[2m])
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#empty
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eval_info instant at 1m stdvar_over_time(metric_histogram{type="mix"}[2m])
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{type="mix"} 0
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# Tests for stddev_over_time and stdvar_over_time #4927.
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clear
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load 10s
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metric 1.5990505637277868 1.5990505637277868 1.5990505637277868
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metric 1.5990505637277868 1.5990505637277868 1.5990505637277868
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eval instant at 1m stdvar_over_time(metric[1m])
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{} 0
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{} 0
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eval instant at 1m stddev_over_time(metric[1m])
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{} 0
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{} 0
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# Tests for mad_over_time.
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clear
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load 10s
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metric 4 6 2 1 999 1 2
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metric 4 6 2 1 999 1 2
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metric_histogram{type="only_histogram"} {{schema:1 sum:2 count:3}}x5
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metric_histogram{type="mix"} 1 1 1 {{schema:1 sum:2 count:3}} {{schema:1 sum:2 count:3}}
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eval instant at 70s mad_over_time(metric[70s])
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{} 1
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{} 1
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eval instant at 70s mad_over_time(metric_histogram{type="only_histogram"}[70s])
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#empty
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eval_info instant at 70s mad_over_time(metric_histogram{type="mix"}[70s])
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{type="mix"} 0
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# Tests for quantile_over_time
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clear
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@ -966,6 +989,8 @@ load 10s
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data{test="two samples"} 0 1
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data{test="three samples"} 0 1 2
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data{test="uneven samples"} 0 1 4
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data_histogram{test="only histogram samples"} {{schema:0 sum:1 count:2}}x4
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data_histogram{test="mix samples"} 0 1 2 {{schema:0 sum:1 count:2}}x2
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eval instant at 1m quantile_over_time(0, data[2m])
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{test="two samples"} 0
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@ -1007,6 +1032,12 @@ eval_warn instant at 1m (quantile_over_time(2, (data[2m])))
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{test="three samples"} +Inf
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{test="uneven samples"} +Inf
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eval instant at 1m quantile_over_time(0.5, data_histogram{test="only histogram samples"}[2m])
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#empty
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eval_info instant at 1m quantile_over_time(0.5, data_histogram{test="mix samples"}[2m])
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{test="mix samples"} 1
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clear
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# Test time-related functions.
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@ -1120,15 +1151,17 @@ load 5m
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eval_fail instant at 0m changes({__name__=~'testmetric1|testmetric2'}[5m])
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# Tests for *_over_time
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clear
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# Tests for *_over_time
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load 10s
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data{type="numbers"} 2 0 3
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data{type="some_nan"} 2 0 NaN
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data{type="some_nan2"} 2 NaN 1
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data{type="some_nan3"} NaN 0 1
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data{type="only_nan"} NaN NaN NaN
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data_histogram{type="only_histogram"} {{schema:0 sum:1 count:2}} {{schema:0 sum:2 count:3}} {{schema:0 sum:3 count:4}}
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data_histogram{type="mix_samples"} 0 1 {{schema:0 sum:1 count:2}} {{schema:0 sum:2 count:3}}
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eval instant at 1m min_over_time(data[2m])
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{type="numbers"} 0
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@ -1137,6 +1170,12 @@ eval instant at 1m min_over_time(data[2m])
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{type="some_nan3"} 0
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{type="only_nan"} NaN
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eval instant at 1m min_over_time(data_histogram{type="only_histogram"}[2m])
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#empty
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eval_info instant at 1m min_over_time(data_histogram{type="mix_samples"}[2m])
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{type="mix_samples"} 0
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eval instant at 1m max_over_time(data[2m])
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{type="numbers"} 3
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{type="some_nan"} 2
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@ -1144,12 +1183,29 @@ eval instant at 1m max_over_time(data[2m])
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{type="some_nan3"} 1
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{type="only_nan"} NaN
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eval instant at 1m last_over_time(data[2m])
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eval instant at 1m max_over_time(data_histogram{type="only_histogram"}[2m])
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#empty
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eval_info instant at 1m max_over_time(data_histogram{type="mix_samples"}[2m])
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{type="mix_samples"} 1
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eval instant at 1m last_over_time({__name__=~"data(_histogram)?"}[2m])
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data{type="numbers"} 3
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data{type="some_nan"} NaN
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data{type="some_nan2"} 1
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data{type="some_nan3"} 1
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data{type="only_nan"} NaN
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data_histogram{type="only_histogram"} {{schema:0 sum:3 count:4}}
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data_histogram{type="mix_samples"} {{schema:0 sum:2 count:3}}
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eval instant at 1m count_over_time({__name__=~"data(_histogram)?"}[2m])
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{type="numbers"} 3
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{type="some_nan"} 3
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{type="some_nan2"} 3
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{type="some_nan3"} 3
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{type="only_nan"} 3
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{type="only_histogram"} 3
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{type="mix_samples"} 4
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clear
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