promql: Add histograms to TestQueryStatistics
Also, fix the bugs exposed by the tests. Signed-off-by: beorn7 <beorn@grafana.com>
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beorn7
parent
f46dd34982
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f48c7a5503
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@ -1542,13 +1542,12 @@ func (ev *evaluator) eval(expr parser.Expr) (parser.Value, annotations.Annotatio
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histSamples := totalHPointSize(ss.Histograms)
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if len(ss.Floats)+histSamples > 0 {
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if ev.currentSamples+len(ss.Floats)+histSamples <= ev.maxSamples {
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mat = append(mat, ss)
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prevSS = &mat[len(mat)-1]
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ev.currentSamples += len(ss.Floats) + histSamples
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} else {
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if ev.currentSamples+len(ss.Floats)+histSamples > ev.maxSamples {
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ev.error(ErrTooManySamples(env))
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}
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mat = append(mat, ss)
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prevSS = &mat[len(mat)-1]
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ev.currentSamples += len(ss.Floats) + histSamples
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}
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ev.samplesStats.UpdatePeak(ev.currentSamples)
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@ -1711,26 +1710,28 @@ func (ev *evaluator) eval(expr parser.Expr) (parser.Value, annotations.Annotatio
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step++
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_, f, h, ok := ev.vectorSelectorSingle(it, e, ts)
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if ok {
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if ev.currentSamples < ev.maxSamples {
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if h == nil {
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if ss.Floats == nil {
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ss.Floats = reuseOrGetFPointSlices(prevSS, numSteps)
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}
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ss.Floats = append(ss.Floats, FPoint{F: f, T: ts})
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ev.currentSamples++
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ev.samplesStats.IncrementSamplesAtStep(step, 1)
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} else {
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if ss.Histograms == nil {
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ss.Histograms = reuseOrGetHPointSlices(prevSS, numSteps)
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}
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point := HPoint{H: h, T: ts}
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ss.Histograms = append(ss.Histograms, point)
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histSize := point.size()
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ev.currentSamples += histSize
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ev.samplesStats.IncrementSamplesAtStep(step, int64(histSize))
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if h == nil {
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ev.currentSamples++
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ev.samplesStats.IncrementSamplesAtStep(step, 1)
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if ev.currentSamples > ev.maxSamples {
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ev.error(ErrTooManySamples(env))
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}
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if ss.Floats == nil {
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ss.Floats = reuseOrGetFPointSlices(prevSS, numSteps)
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}
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ss.Floats = append(ss.Floats, FPoint{F: f, T: ts})
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} else {
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ev.error(ErrTooManySamples(env))
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point := HPoint{H: h, T: ts}
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histSize := point.size()
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ev.currentSamples += histSize
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ev.samplesStats.IncrementSamplesAtStep(step, int64(histSize))
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if ev.currentSamples > ev.maxSamples {
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ev.error(ErrTooManySamples(env))
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}
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if ss.Histograms == nil {
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ss.Histograms = reuseOrGetHPointSlices(prevSS, numSteps)
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}
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ss.Histograms = append(ss.Histograms, point)
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}
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}
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}
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@ -2170,10 +2171,10 @@ loop:
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histograms = histograms[:n]
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continue loop
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}
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if ev.currentSamples >= ev.maxSamples {
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ev.currentSamples += histograms[n].size()
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if ev.currentSamples > ev.maxSamples {
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ev.error(ErrTooManySamples(env))
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}
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ev.currentSamples += histograms[n].size()
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}
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case chunkenc.ValFloat:
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t, f := buf.At()
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@ -2182,10 +2183,10 @@ loop:
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}
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// Values in the buffer are guaranteed to be smaller than maxt.
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if t >= mintFloats {
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if ev.currentSamples >= ev.maxSamples {
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ev.currentSamples++
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if ev.currentSamples > ev.maxSamples {
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ev.error(ErrTooManySamples(env))
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}
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ev.currentSamples++
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if floats == nil {
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floats = getFPointSlice(16)
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}
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@ -2213,22 +2214,22 @@ loop:
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histograms = histograms[:n]
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break
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}
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if ev.currentSamples >= ev.maxSamples {
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ev.currentSamples += histograms[n].size()
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if ev.currentSamples > ev.maxSamples {
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ev.error(ErrTooManySamples(env))
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}
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ev.currentSamples += histograms[n].size()
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case chunkenc.ValFloat:
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t, f := it.At()
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if t == maxt && !value.IsStaleNaN(f) {
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if ev.currentSamples >= ev.maxSamples {
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ev.currentSamples++
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if ev.currentSamples > ev.maxSamples {
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ev.error(ErrTooManySamples(env))
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}
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if floats == nil {
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floats = getFPointSlice(16)
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}
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floats = append(floats, FPoint{T: t, F: f})
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ev.currentSamples++
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}
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}
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ev.samplesStats.UpdatePeak(ev.currentSamples)
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@ -755,7 +755,7 @@ load 10s
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metricWith3SampleEvery10Seconds{a="1",b="1"} 1+1x100
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metricWith3SampleEvery10Seconds{a="2",b="2"} 1+1x100
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metricWith3SampleEvery10Seconds{a="3",b="2"} 1+1x100
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metricWith1HistogramsEvery10Seconds {{schema:1 count:5 sum:20 buckets:[1 2 1 1]}}+{{schema:1 count:10 sum:5 buckets:[1 2 3 4]}}x100
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metricWith1HistogramEvery10Seconds {{schema:1 count:5 sum:20 buckets:[1 2 1 1]}}+{{schema:1 count:10 sum:5 buckets:[1 2 3 4]}}x100
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`)
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t.Cleanup(func() { storage.Close() })
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@ -799,10 +799,10 @@ load 10s
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{
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Query: "metricWith1HistogramEvery10Seconds",
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Start: time.Unix(21, 0),
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PeakSamples: 1,
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TotalSamples: 1, // 1 sample / 10 seconds
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PeakSamples: 12,
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TotalSamples: 12, // 1 histogram sample of size 12 / 10 seconds
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TotalSamplesPerStep: stats.TotalSamplesPerStep{
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21000: 1,
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21000: 12,
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},
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},
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{
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@ -815,6 +815,15 @@ load 10s
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21000: 1,
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},
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},
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{
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Query: "timestamp(metricWith1HistogramEvery10Seconds)",
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Start: time.Unix(21, 0),
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PeakSamples: 13, // histogram size 12 + 1 extra because of timestamp
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TotalSamples: 1, // 1 float sample (because of timestamp) / 10 seconds
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TotalSamplesPerStep: stats.TotalSamplesPerStep{
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21000: 1,
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},
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},
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{
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Query: "metricWith1SampleEvery10Seconds",
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Start: time.Unix(22, 0),
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@ -887,11 +896,20 @@ load 10s
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201000: 6,
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},
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},
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{
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Query: "metricWith1HistogramEvery10Seconds[60s]",
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Start: time.Unix(201, 0),
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PeakSamples: 72,
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TotalSamples: 72, // 1 histogram (size 12) / 10 seconds * 60 seconds
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TotalSamplesPerStep: stats.TotalSamplesPerStep{
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201000: 72,
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},
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},
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{
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Query: "max_over_time(metricWith1SampleEvery10Seconds[59s])[20s:5s]",
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Start: time.Unix(201, 0),
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PeakSamples: 10,
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TotalSamples: 24, // (1 sample / 10 seconds * 60 seconds) * 60/5 (using 59s so we always return 6 samples
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TotalSamples: 24, // (1 sample / 10 seconds * 60 seconds) * 20/5 (using 59s so we always return 6 samples
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// as if we run a query on 00 looking back 60 seconds we will return 7 samples;
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// see next test).
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TotalSamplesPerStep: stats.TotalSamplesPerStep{
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@ -902,12 +920,22 @@ load 10s
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Query: "max_over_time(metricWith1SampleEvery10Seconds[60s])[20s:5s]",
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Start: time.Unix(201, 0),
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PeakSamples: 11,
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TotalSamples: 26, // (1 sample / 10 seconds * 60 seconds) + 2 as
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TotalSamples: 26, // (1 sample / 10 seconds * 60 seconds) * 4 + 2 as
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// max_over_time(metricWith1SampleEvery10Seconds[60s]) @ 190 and 200 will return 7 samples.
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TotalSamplesPerStep: stats.TotalSamplesPerStep{
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201000: 26,
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},
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},
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{
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Query: "max_over_time(metricWith1HistogramEvery10Seconds[60s])[20s:5s]",
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Start: time.Unix(201, 0),
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PeakSamples: 72,
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TotalSamples: 312, // (1 histogram (size 12) / 10 seconds * 60 seconds) * 4 + 2 * 12 as
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// max_over_time(metricWith1SampleEvery10Seconds[60s]) @ 190 and 200 will return 7 samples.
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TotalSamplesPerStep: stats.TotalSamplesPerStep{
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201000: 312,
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},
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},
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{
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Query: "metricWith1SampleEvery10Seconds[60s] @ 30",
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Start: time.Unix(201, 0),
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@ -917,6 +945,15 @@ load 10s
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201000: 4,
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},
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},
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{
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Query: "metricWith1HistogramEvery10Seconds[60s] @ 30",
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Start: time.Unix(201, 0),
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PeakSamples: 48,
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TotalSamples: 48, // @ modifier force the evaluation to at 30 seconds - So it brings 4 datapoints (0, 10, 20, 30 seconds) * 1 series
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TotalSamplesPerStep: stats.TotalSamplesPerStep{
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201000: 48,
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},
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},
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{
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Query: "sum(max_over_time(metricWith3SampleEvery10Seconds[60s] @ 30))",
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Start: time.Unix(201, 0),
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@ -1045,7 +1082,21 @@ load 10s
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},
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},
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{
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// timestamp function as a special handling
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Query: `metricWith1HistogramEvery10Seconds`,
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Start: time.Unix(204, 0),
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End: time.Unix(223, 0),
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Interval: 5 * time.Second,
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PeakSamples: 48,
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TotalSamples: 48, // 1 histogram (size 12) per query * 4 steps
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TotalSamplesPerStep: stats.TotalSamplesPerStep{
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204000: 12, // aligned to the step time, not the sample time
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209000: 12,
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214000: 12,
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219000: 12,
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},
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},
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{
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// timestamp function has a special handling
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Query: "timestamp(metricWith1SampleEvery10Seconds)",
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Start: time.Unix(201, 0),
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End: time.Unix(220, 0),
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@ -1059,6 +1110,21 @@ load 10s
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216000: 1,
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},
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},
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{
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// timestamp function has a special handling
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Query: "timestamp(metricWith1HistogramEvery10Seconds)",
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Start: time.Unix(201, 0),
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End: time.Unix(220, 0),
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Interval: 5 * time.Second,
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PeakSamples: 16,
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TotalSamples: 4, // 1 sample per query * 4 steps
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TotalSamplesPerStep: stats.TotalSamplesPerStep{
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201000: 1,
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206000: 1,
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211000: 1,
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216000: 1,
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},
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},
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{
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Query: `max_over_time(metricWith3SampleEvery10Seconds{a="1"}[10s])`,
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Start: time.Unix(991, 0),
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