mirror of https://github.com/prometheus/prometheus
[BUGFIX] PromQL: Fix behaviour of some aggregations with histograms (#15432)
promql: fix some aggregations for histograms This PR fixes the behaviour of `topk`,`bottomk`, `limitk` and `limit_ratio` with histograms. The fixed behaviour are as follows: - For `topk` and `bottomk` histograms are ignored and add info annotations added. - For `limitk` and `limit_ratio` histograms are included in the results(if applicable). Signed-off-by: Neeraj Gartia <neerajgartia211002@gmail.com>pull/15473/head
Neeraj Gartia
GitHub
parent
634afbc130
commit
38bb6ece25
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@ -3187,18 +3187,19 @@ func (ev *evaluator) aggregationK(e *parser.AggregateExpr, k int, r float64, inp
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seriesLoop:
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for si := range inputMatrix {
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f, _, ok := ev.nextValues(enh.Ts, &inputMatrix[si])
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f, h, ok := ev.nextValues(enh.Ts, &inputMatrix[si])
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if !ok {
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continue
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}
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s = Sample{Metric: inputMatrix[si].Metric, F: f, DropName: inputMatrix[si].DropName}
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s = Sample{Metric: inputMatrix[si].Metric, F: f, H: h, DropName: inputMatrix[si].DropName}
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group := &groups[seriesToResult[si]]
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// Initialize this group if it's the first time we've seen it.
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if !group.seen {
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// LIMIT_RATIO is a special case, as we may not add this very sample to the heap,
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// while we also don't know the final size of it.
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if op == parser.LIMIT_RATIO {
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switch op {
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case parser.LIMIT_RATIO:
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*group = groupedAggregation{
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seen: true,
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heap: make(vectorByValueHeap, 0),
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@ -3206,12 +3207,34 @@ seriesLoop:
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if ratiosampler.AddRatioSample(r, &s) {
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heap.Push(&group.heap, &s)
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}
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} else {
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case parser.LIMITK:
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*group = groupedAggregation{
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seen: true,
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heap: make(vectorByValueHeap, 1, k),
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}
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group.heap[0] = s
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case parser.TOPK:
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*group = groupedAggregation{
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seen: true,
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heap: make(vectorByValueHeap, 0, k),
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}
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if s.H != nil {
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group.seen = false
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annos.Add(annotations.NewHistogramIgnoredInAggregationInfo("topk", e.PosRange))
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} else {
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heap.Push(&group.heap, &s)
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}
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case parser.BOTTOMK:
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*group = groupedAggregation{
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seen: true,
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heap: make(vectorByValueHeap, 0, k),
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}
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if s.H != nil {
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group.seen = false
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annos.Add(annotations.NewHistogramIgnoredInAggregationInfo("bottomk", e.PosRange))
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} else {
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heap.Push(&group.heap, &s)
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}
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}
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continue
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}
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@ -3220,6 +3243,9 @@ seriesLoop:
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case parser.TOPK:
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// We build a heap of up to k elements, with the smallest element at heap[0].
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switch {
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case s.H != nil:
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// Ignore histogram sample and add info annotation.
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annos.Add(annotations.NewHistogramIgnoredInAggregationInfo("topk", e.PosRange))
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case len(group.heap) < k:
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heap.Push(&group.heap, &s)
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case group.heap[0].F < s.F || (math.IsNaN(group.heap[0].F) && !math.IsNaN(s.F)):
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@ -3233,6 +3259,9 @@ seriesLoop:
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case parser.BOTTOMK:
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// We build a heap of up to k elements, with the biggest element at heap[0].
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switch {
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case s.H != nil:
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// Ignore histogram sample and add info annotation.
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annos.Add(annotations.NewHistogramIgnoredInAggregationInfo("bottomk", e.PosRange))
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case len(group.heap) < k:
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heap.Push((*vectorByReverseValueHeap)(&group.heap), &s)
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case group.heap[0].F > s.F || (math.IsNaN(group.heap[0].F) && !math.IsNaN(s.F)):
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@ -3275,10 +3304,14 @@ seriesLoop:
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mat = make(Matrix, 0, len(groups))
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}
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add := func(lbls labels.Labels, f float64, dropName bool) {
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add := func(lbls labels.Labels, f float64, h *histogram.FloatHistogram, dropName bool) {
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// If this could be an instant query, add directly to the matrix so the result is in consistent order.
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if ev.endTimestamp == ev.startTimestamp {
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mat = append(mat, Series{Metric: lbls, Floats: []FPoint{{T: enh.Ts, F: f}}, DropName: dropName})
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if h != nil {
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mat = append(mat, Series{Metric: lbls, Histograms: []HPoint{{T: enh.Ts, H: h}}, DropName: dropName})
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} else {
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mat = append(mat, Series{Metric: lbls, Floats: []FPoint{{T: enh.Ts, F: f}}, DropName: dropName})
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}
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} else {
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// Otherwise the results are added into seriess elements.
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hash := lbls.Hash()
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@ -3286,7 +3319,7 @@ seriesLoop:
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if !ok {
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ss = Series{Metric: lbls, DropName: dropName}
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}
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addToSeries(&ss, enh.Ts, f, nil, numSteps)
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addToSeries(&ss, enh.Ts, f, h, numSteps)
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seriess[hash] = ss
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}
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}
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@ -3301,7 +3334,7 @@ seriesLoop:
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sort.Sort(sort.Reverse(aggr.heap))
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}
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for _, v := range aggr.heap {
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add(v.Metric, v.F, v.DropName)
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add(v.Metric, v.F, v.H, v.DropName)
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}
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case parser.BOTTOMK:
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@ -3310,12 +3343,12 @@ seriesLoop:
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sort.Sort(sort.Reverse((*vectorByReverseValueHeap)(&aggr.heap)))
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}
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for _, v := range aggr.heap {
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add(v.Metric, v.F, v.DropName)
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add(v.Metric, v.F, v.H, v.DropName)
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}
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case parser.LIMITK, parser.LIMIT_RATIO:
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for _, v := range aggr.heap {
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add(v.Metric, v.F, v.DropName)
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add(v.Metric, v.F, v.H, v.DropName)
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}
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}
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}
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@ -272,6 +272,8 @@ load 5m
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http_requests{job="app-server", instance="1", group="production"} 0+60x10
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http_requests{job="app-server", instance="0", group="canary"} 0+70x10
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http_requests{job="app-server", instance="1", group="canary"} 0+80x10
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http_requests_histogram{job="app-server", instance="2", group="canary"} {{schema:0 sum:10 count:10}}x11
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http_requests_histogram{job="api-server", instance="3", group="production"} {{schema:0 sum:20 count:20}}x11
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foo 3+0x10
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eval_ordered instant at 50m topk(3, http_requests)
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@ -338,6 +340,47 @@ eval_ordered instant at 50m topk(scalar(foo), http_requests)
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http_requests{group="canary", instance="0", job="app-server"} 700
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http_requests{group="production", instance="1", job="app-server"} 600
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# Tests for histogram: should ignore histograms.
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eval_info instant at 50m topk(100, http_requests_histogram)
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#empty
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eval_info range from 0 to 50m step 5m topk(100, http_requests_histogram)
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#empty
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eval_info instant at 50m topk(1, {__name__=~"http_requests(_histogram)?"})
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{__name__="http_requests", group="canary", instance="1", job="app-server"} 800
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eval_info instant at 50m count(topk(1000, {__name__=~"http_requests(_histogram)?"}))
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{} 9
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eval_info range from 0 to 50m step 5m count(topk(1000, {__name__=~"http_requests(_histogram)?"}))
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{} 9x10
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eval_info instant at 50m topk by (instance) (1, {__name__=~"http_requests(_histogram)?"})
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{__name__="http_requests", group="canary", instance="0", job="app-server"} 700
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{__name__="http_requests", group="canary", instance="1", job="app-server"} 800
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{__name__="http_requests", group="production", instance="2", job="api-server"} NaN
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eval_info instant at 50m bottomk(100, http_requests_histogram)
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#empty
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eval_info range from 0 to 50m step 5m bottomk(100, http_requests_histogram)
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#empty
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eval_info instant at 50m bottomk(1, {__name__=~"http_requests(_histogram)?"})
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{__name__="http_requests", group="production", instance="0", job="api-server"} 100
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eval_info instant at 50m count(bottomk(1000, {__name__=~"http_requests(_histogram)?"}))
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{} 9
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eval_info range from 0 to 50m step 5m count(bottomk(1000, {__name__=~"http_requests(_histogram)?"}))
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{} 9x10
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eval_info instant at 50m bottomk by (instance) (1, {__name__=~"http_requests(_histogram)?"})
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{__name__="http_requests", group="production", instance="0", job="api-server"} 100
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{__name__="http_requests", group="production", instance="1", job="api-server"} 200
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{__name__="http_requests", group="production", instance="2", job="api-server"} NaN
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clear
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# Tests for count_values.
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@ -9,6 +9,8 @@ load 5m
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http_requests{job="api-server", instance="1", group="canary"} 0+40x10
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http_requests{job="api-server", instance="2", group="canary"} 0+50x10
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http_requests{job="api-server", instance="3", group="canary"} 0+60x10
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http_requests{job="api-server", instance="histogram_1", group="canary"} {{schema:0 sum:10 count:10}}x11
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http_requests{job="api-server", instance="histogram_2", group="canary"} {{schema:0 sum:20 count:20}}x11
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eval instant at 50m count(limitk by (group) (0, http_requests))
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# empty
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@ -16,25 +18,56 @@ eval instant at 50m count(limitk by (group) (0, http_requests))
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eval instant at 50m count(limitk by (group) (-1, http_requests))
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# empty
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# Exercise k==1 special case (as sample is added before the main series loop
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# Exercise k==1 special case (as sample is added before the main series loop).
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eval instant at 50m count(limitk by (group) (1, http_requests) and http_requests)
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{} 2
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{} 2
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eval instant at 50m count(limitk by (group) (2, http_requests) and http_requests)
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{} 4
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{} 4
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eval instant at 50m count(limitk(100, http_requests) and http_requests)
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{} 6
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{} 8
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# Exercise k==1 special case (as sample is added before the main series loop
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# Exercise k==1 special case (as sample is added before the main series loop).
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eval instant at 50m count(limitk by (group) (1, http_requests) and http_requests)
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{} 2
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{} 2
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eval instant at 50m count(limitk by (group) (2, http_requests) and http_requests)
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{} 4
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{} 4
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eval instant at 50m count(limitk(100, http_requests) and http_requests)
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{} 6
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{} 8
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# Test for histograms.
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# k==1: verify that histogram is included in the result.
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eval instant at 50m limitk(1, http_requests{instance="histogram_1"})
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{__name__="http_requests", group="canary", instance="histogram_1", job="api-server"} {{count:10 sum:10}}
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eval range from 0 to 50m step 5m limitk(1, http_requests{instance="histogram_1"})
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{__name__="http_requests", group="canary", instance="histogram_1", job="api-server"} {{count:10 sum:10}}x10
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# Histogram is included with mix of floats as well.
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eval instant at 50m limitk(8, http_requests{instance=~"(histogram_2|0)"})
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{__name__="http_requests", group="canary", instance="histogram_2", job="api-server"} {{count:20 sum:20}}
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{__name__="http_requests", group="production", instance="0", job="api-server"} 100
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{__name__="http_requests", group="canary", instance="0", job="api-server"} 300
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eval range from 0 to 50m step 5m limitk(8, http_requests{instance=~"(histogram_2|0)"})
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{__name__="http_requests", group="canary", instance="histogram_2", job="api-server"} {{count:20 sum:20}}x10
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{__name__="http_requests", group="production", instance="0", job="api-server"} 0+10x10
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{__name__="http_requests", group="canary", instance="0", job="api-server"} 0+30x10
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eval instant at 50m count(limitk(2, http_requests{instance=~"histogram_[0-9]"}))
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{} 2
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eval range from 0 to 50m step 5m count(limitk(2, http_requests{instance=~"histogram_[0-9]"}))
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{} 2+0x10
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eval instant at 50m count(limitk(1000, http_requests{instance=~"histogram_[0-9]"}))
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{} 2
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eval range from 0 to 50m step 5m count(limitk(1000, http_requests{instance=~"histogram_[0-9]"}))
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{} 2+0x10
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# limit_ratio
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eval range from 0 to 50m step 5m count(limit_ratio(0.0, http_requests))
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@ -42,9 +75,9 @@ eval range from 0 to 50m step 5m count(limit_ratio(0.0, http_requests))
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# limitk(2, ...) should always return a 2-count subset of the timeseries (hence the AND'ing)
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eval range from 0 to 50m step 5m count(limitk(2, http_requests) and http_requests)
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{} 2+0x10
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{} 2+0x10
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# Tests for limit_ratio
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# Tests for limit_ratio.
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#
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# NB: below 0.5 ratio will depend on some hashing "luck" (also there's no guarantee that
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# an integer comes from: total number of series * ratio), as it depends on:
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@ -56,50 +89,50 @@ eval range from 0 to 50m step 5m count(limitk(2, http_requests) and http_request
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#
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# See `AddRatioSample()` in promql/engine.go for more details.
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# Half~ish samples: verify we get "near" 3 (of 0.5 * 6)
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eval range from 0 to 50m step 5m count(limit_ratio(0.5, http_requests) and http_requests) <= bool (3+1)
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{} 1+0x10
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# Half~ish samples: verify we get "near" 3 (of 0.5 * 6).
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eval range from 0 to 50m step 5m count(limit_ratio(0.5, http_requests) and http_requests) <= bool (4+1)
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{} 1+0x10
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eval range from 0 to 50m step 5m count(limit_ratio(0.5, http_requests) and http_requests) >= bool (3-1)
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{} 1+0x10
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eval range from 0 to 50m step 5m count(limit_ratio(0.5, http_requests) and http_requests) >= bool (4-1)
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{} 1+0x10
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# All samples
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# All samples.
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eval range from 0 to 50m step 5m count(limit_ratio(1.0, http_requests) and http_requests)
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{} 6+0x10
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{} 8+0x10
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# All samples
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# All samples.
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eval range from 0 to 50m step 5m count(limit_ratio(-1.0, http_requests) and http_requests)
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{} 6+0x10
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{} 8+0x10
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# Capped to 1.0 -> all samples
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# Capped to 1.0 -> all samples.
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eval_warn range from 0 to 50m step 5m count(limit_ratio(1.1, http_requests) and http_requests)
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{} 6+0x10
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{} 8+0x10
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# Capped to -1.0 -> all samples
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# Capped to -1.0 -> all samples.
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eval_warn range from 0 to 50m step 5m count(limit_ratio(-1.1, http_requests) and http_requests)
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{} 6+0x10
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{} 8+0x10
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# Verify that limit_ratio(value) and limit_ratio(1.0-value) return the "complement" of each other
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# Complement below for [0.2, -0.8]
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# Verify that limit_ratio(value) and limit_ratio(1.0-value) return the "complement" of each other.
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# Complement below for [0.2, -0.8].
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#
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# Complement 1of2: `or` should return all samples
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# Complement 1of2: `or` should return all samples.
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eval range from 0 to 50m step 5m count(limit_ratio(0.2, http_requests) or limit_ratio(-0.8, http_requests))
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{} 6+0x10
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{} 8+0x10
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# Complement 2of2: `and` should return no samples
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# Complement 2of2: `and` should return no samples.
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eval range from 0 to 50m step 5m count(limit_ratio(0.2, http_requests) and limit_ratio(-0.8, http_requests))
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# empty
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# Complement below for [0.5, -0.5]
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# Complement below for [0.5, -0.5].
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eval range from 0 to 50m step 5m count(limit_ratio(0.5, http_requests) or limit_ratio(-0.5, http_requests))
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{} 6+0x10
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{} 8+0x10
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eval range from 0 to 50m step 5m count(limit_ratio(0.5, http_requests) and limit_ratio(-0.5, http_requests))
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# empty
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# Complement below for [0.8, -0.2]
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# Complement below for [0.8, -0.2].
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eval range from 0 to 50m step 5m count(limit_ratio(0.8, http_requests) or limit_ratio(-0.2, http_requests))
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{} 6+0x10
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{} 8+0x10
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eval range from 0 to 50m step 5m count(limit_ratio(0.8, http_requests) and limit_ratio(-0.2, http_requests))
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# empty
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@ -107,13 +140,19 @@ eval range from 0 to 50m step 5m count(limit_ratio(0.8, http_requests) and limit
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# Complement below for [some_ratio, 1.0 - some_ratio], some_ratio derived from time(),
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# using a small prime number to avoid rounded ratio values, and a small set of them.
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eval range from 0 to 50m step 5m count(limit_ratio(time() % 17/17, http_requests) or limit_ratio(1.0 - (time() % 17/17), http_requests))
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{} 6+0x10
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{} 8+0x10
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eval range from 0 to 50m step 5m count(limit_ratio(time() % 17/17, http_requests) and limit_ratio(1.0 - (time() % 17/17), http_requests))
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# empty
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# Poor man's normality check: ok (loaded samples follow a nice linearity over labels and time)
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# The check giving: 1 (i.e. true)
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eval range from 0 to 50m step 5m abs(avg(limit_ratio(0.5, http_requests)) - avg(limit_ratio(-0.5, http_requests))) <= bool stddev(http_requests)
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# Poor man's normality check: ok (loaded samples follow a nice linearity over labels and time).
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# The check giving: 1 (i.e. true).
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eval range from 0 to 50m step 5m abs(avg(limit_ratio(0.5, http_requests{instance!~"histogram_[0-9]"})) - avg(limit_ratio(-0.5, http_requests{instance!~"histogram_[0-9]"}))) <= bool stddev(http_requests{instance!~"histogram_[0-9]"})
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{} 1+0x10
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# All specified histograms are included for r=1.
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eval instant at 50m limit_ratio(1, http_requests{instance="histogram_1"})
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{__name__="http_requests", group="canary", instance="histogram_1", job="api-server"} {{count:10 sum:10}}
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eval range from 0 to 50m step 5m limit_ratio(1, http_requests{instance="histogram_1"})
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{__name__="http_requests", group="canary", instance="histogram_1", job="api-server"} {{count:10 sum:10}}x10
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|
|
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Reference in New Issue