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promql: Add histograms to TestQueryStatistics 

Also, fix the bugs exposed by the tests.

Signed-off-by: beorn7 <beorn@grafana.com>
pull/13667/head
beorn7 9 months ago
parent
f46dd34982
commit
f48c7a5503
2 changed files with 105 additions and 38 deletions
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  1. 43
      promql/engine.go
  2. 80
      promql/engine_test.go

43
promql/engine.go
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@ -1542,13 +1542,12 @@ func (ev *evaluator) eval(expr parser.Expr) (parser.Value, annotations.Annotatio
histSamples := totalHPointSize(ss.Histograms)
if len(ss.Floats)+histSamples > 0 {
if ev.currentSamples+len(ss.Floats)+histSamples <= ev.maxSamples {
if ev.currentSamples+len(ss.Floats)+histSamples > ev.maxSamples {
ev.error(ErrTooManySamples(env))
}
mat = append(mat, ss)
prevSS = &mat[len(mat)-1]
ev.currentSamples += len(ss.Floats) + histSamples
} else {
ev.error(ErrTooManySamples(env))
}
}
ev.samplesStats.UpdatePeak(ev.currentSamples)
@ -1711,27 +1710,29 @@ func (ev *evaluator) eval(expr parser.Expr) (parser.Value, annotations.Annotatio
step++
_, f, h, ok := ev.vectorSelectorSingle(it, e, ts)
if ok {
if ev.currentSamples < ev.maxSamples {
if h == nil {
ev.currentSamples++
ev.samplesStats.IncrementSamplesAtStep(step, 1)
if ev.currentSamples > ev.maxSamples {
ev.error(ErrTooManySamples(env))
}
if ss.Floats == nil {
ss.Floats = reuseOrGetFPointSlices(prevSS, numSteps)
}
ss.Floats = append(ss.Floats, FPoint{F: f, T: ts})
ev.currentSamples++
ev.samplesStats.IncrementSamplesAtStep(step, 1)
} else {
if ss.Histograms == nil {
ss.Histograms = reuseOrGetHPointSlices(prevSS, numSteps)
}
point := HPoint{H: h, T: ts}
ss.Histograms = append(ss.Histograms, point)
histSize := point.size()
ev.currentSamples += histSize
ev.samplesStats.IncrementSamplesAtStep(step, int64(histSize))
}
} else {
if ev.currentSamples > ev.maxSamples {
ev.error(ErrTooManySamples(env))
}
if ss.Histograms == nil {
ss.Histograms = reuseOrGetHPointSlices(prevSS, numSteps)
}
ss.Histograms = append(ss.Histograms, point)
}
}
}
@ -2170,10 +2171,10 @@ loop:
histograms = histograms[:n]
continue loop
}
if ev.currentSamples >= ev.maxSamples {
ev.currentSamples += histograms[n].size()
if ev.currentSamples > ev.maxSamples {
ev.error(ErrTooManySamples(env))
}
ev.currentSamples += histograms[n].size()
}
case chunkenc.ValFloat:
t, f := buf.At()
@ -2182,10 +2183,10 @@ loop:
}
// Values in the buffer are guaranteed to be smaller than maxt.
if t >= mintFloats {
if ev.currentSamples >= ev.maxSamples {
ev.currentSamples++
if ev.currentSamples > ev.maxSamples {
ev.error(ErrTooManySamples(env))
}
ev.currentSamples++
if floats == nil {
floats = getFPointSlice(16)
}
@ -2213,22 +2214,22 @@ loop:
histograms = histograms[:n]
break
}
if ev.currentSamples >= ev.maxSamples {
ev.currentSamples += histograms[n].size()
if ev.currentSamples > ev.maxSamples {
ev.error(ErrTooManySamples(env))
}
ev.currentSamples += histograms[n].size()
case chunkenc.ValFloat:
t, f := it.At()
if t == maxt && !value.IsStaleNaN(f) {
if ev.currentSamples >= ev.maxSamples {
ev.currentSamples++
if ev.currentSamples > ev.maxSamples {
ev.error(ErrTooManySamples(env))
}
if floats == nil {
floats = getFPointSlice(16)
}
floats = append(floats, FPoint{T: t, F: f})
ev.currentSamples++
}
}
ev.samplesStats.UpdatePeak(ev.currentSamples)

80
promql/engine_test.go
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@ -755,7 +755,7 @@ load 10s
metricWith3SampleEvery10Seconds{a="1",b="1"} 1+1x100
metricWith3SampleEvery10Seconds{a="2",b="2"} 1+1x100
metricWith3SampleEvery10Seconds{a="3",b="2"} 1+1x100
metricWith1HistogramsEvery10Seconds {{schema:1 count:5 sum:20 buckets:[1 2 1 1]}}+{{schema:1 count:10 sum:5 buckets:[1 2 3 4]}}x100
metricWith1HistogramEvery10Seconds {{schema:1 count:5 sum:20 buckets:[1 2 1 1]}}+{{schema:1 count:10 sum:5 buckets:[1 2 3 4]}}x100
`)
t.Cleanup(func() { storage.Close() })
@ -799,10 +799,10 @@ load 10s
{
Query: "metricWith1HistogramEvery10Seconds",
Start: time.Unix(21, 0),
PeakSamples: 1,
TotalSamples: 1, // 1 sample / 10 seconds
PeakSamples: 12,
TotalSamples: 12, // 1 histogram sample of size 12 / 10 seconds
TotalSamplesPerStep: stats.TotalSamplesPerStep{
21000: 1,
21000: 12,
},
},
{
@ -815,6 +815,15 @@ load 10s
21000: 1,
},
},
{
Query: "timestamp(metricWith1HistogramEvery10Seconds)",
Start: time.Unix(21, 0),
PeakSamples: 13, // histogram size 12 + 1 extra because of timestamp
TotalSamples: 1, // 1 float sample (because of timestamp) / 10 seconds
TotalSamplesPerStep: stats.TotalSamplesPerStep{
21000: 1,
},
},
{
Query: "metricWith1SampleEvery10Seconds",
Start: time.Unix(22, 0),
@ -887,11 +896,20 @@ load 10s
201000: 6,
},
},
{
Query: "metricWith1HistogramEvery10Seconds[60s]",
Start: time.Unix(201, 0),
PeakSamples: 72,
TotalSamples: 72, // 1 histogram (size 12) / 10 seconds * 60 seconds
TotalSamplesPerStep: stats.TotalSamplesPerStep{
201000: 72,
},
},
{
Query: "max_over_time(metricWith1SampleEvery10Seconds[59s])[20s:5s]",
Start: time.Unix(201, 0),
PeakSamples: 10,
TotalSamples: 24, // (1 sample / 10 seconds * 60 seconds) * 60/5 (using 59s so we always return 6 samples
TotalSamples: 24, // (1 sample / 10 seconds * 60 seconds) * 20/5 (using 59s so we always return 6 samples
// as if we run a query on 00 looking back 60 seconds we will return 7 samples;
// see next test).
TotalSamplesPerStep: stats.TotalSamplesPerStep{
@ -902,12 +920,22 @@ load 10s
Query: "max_over_time(metricWith1SampleEvery10Seconds[60s])[20s:5s]",
Start: time.Unix(201, 0),
PeakSamples: 11,
TotalSamples: 26, // (1 sample / 10 seconds * 60 seconds) + 2 as
TotalSamples: 26, // (1 sample / 10 seconds * 60 seconds) * 4 + 2 as
// max_over_time(metricWith1SampleEvery10Seconds[60s]) @ 190 and 200 will return 7 samples.
TotalSamplesPerStep: stats.TotalSamplesPerStep{
201000: 26,
},
},
{
Query: "max_over_time(metricWith1HistogramEvery10Seconds[60s])[20s:5s]",
Start: time.Unix(201, 0),
PeakSamples: 72,
TotalSamples: 312, // (1 histogram (size 12) / 10 seconds * 60 seconds) * 4 + 2 * 12 as
// max_over_time(metricWith1SampleEvery10Seconds[60s]) @ 190 and 200 will return 7 samples.
TotalSamplesPerStep: stats.TotalSamplesPerStep{
201000: 312,
},
},
{
Query: "metricWith1SampleEvery10Seconds[60s] @ 30",
Start: time.Unix(201, 0),
@ -917,6 +945,15 @@ load 10s
201000: 4,
},
},
{
Query: "metricWith1HistogramEvery10Seconds[60s] @ 30",
Start: time.Unix(201, 0),
PeakSamples: 48,
TotalSamples: 48, // @ modifier force the evaluation to at 30 seconds - So it brings 4 datapoints (0, 10, 20, 30 seconds) * 1 series
TotalSamplesPerStep: stats.TotalSamplesPerStep{
201000: 48,
},
},
{
Query: "sum(max_over_time(metricWith3SampleEvery10Seconds[60s] @ 30))",
Start: time.Unix(201, 0),
@ -1045,7 +1082,21 @@ load 10s
},
},
{
// timestamp function as a special handling
Query: `metricWith1HistogramEvery10Seconds`,
Start: time.Unix(204, 0),
End: time.Unix(223, 0),
Interval: 5 * time.Second,
PeakSamples: 48,
TotalSamples: 48, // 1 histogram (size 12) per query * 4 steps
TotalSamplesPerStep: stats.TotalSamplesPerStep{
204000: 12, // aligned to the step time, not the sample time
209000: 12,
214000: 12,
219000: 12,
},
},
{
// timestamp function has a special handling
Query: "timestamp(metricWith1SampleEvery10Seconds)",
Start: time.Unix(201, 0),
End: time.Unix(220, 0),
@ -1059,6 +1110,21 @@ load 10s
216000: 1,
},
},
{
// timestamp function has a special handling
Query: "timestamp(metricWith1HistogramEvery10Seconds)",
Start: time.Unix(201, 0),
End: time.Unix(220, 0),
Interval: 5 * time.Second,
PeakSamples: 16,
TotalSamples: 4, // 1 sample per query * 4 steps
TotalSamplesPerStep: stats.TotalSamplesPerStep{
201000: 1,
206000: 1,
211000: 1,
216000: 1,
},
},
{
Query: `max_over_time(metricWith3SampleEvery10Seconds{a="1"}[10s])`,
Start: time.Unix(991, 0),

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