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matrixIterSlice shall drop float and histogram at left bound
Signed-off-by: Zhang Zhanpeng <zhangzhanpeng.zzp@alibaba-inc.com>
This commit is contained in:
Zhang Zhanpeng
parent
1081e336a0
commit
381f8d52e0
6
cmd/promtool/testdata/unittest.yml
vendored
6
cmd/promtool/testdata/unittest.yml
vendored
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@ -113,7 +113,7 @@ tests:
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- expr: count_over_time(fixed_data[1h])
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eval_time: 1h
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exp_samples:
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- value: 61
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- value: 60
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- expr: timestamp(fixed_data)
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eval_time: 1h
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exp_samples:
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@ -183,7 +183,7 @@ tests:
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- expr: job:test:count_over_time1m
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eval_time: 1m
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exp_samples:
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- value: 61
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- value: 60
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labels: 'job:test:count_over_time1m{job="test"}'
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- expr: timestamp(job:test:count_over_time1m)
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eval_time: 1m10s
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@ -194,7 +194,7 @@ tests:
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- expr: job:test:count_over_time1m
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eval_time: 2m
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exp_samples:
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- value: 61
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- value: 60
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labels: 'job:test:count_over_time1m{job="test"}'
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- expr: timestamp(job:test:count_over_time1m)
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eval_time: 2m59s999ms
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@ -2207,20 +2207,20 @@ func (ev *evaluator) matrixIterSlice(
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mintFloats, mintHistograms := mint, mint
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// First floats...
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if len(floats) > 0 && floats[len(floats)-1].T >= mint {
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if len(floats) > 0 && floats[len(floats)-1].T > mint {
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// There is an overlap between previous and current ranges, retain common
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// points. In most such cases:
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// (a) the overlap is significantly larger than the eval step; and/or
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// (b) the number of samples is relatively small.
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// so a linear search will be as fast as a binary search.
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var drop int
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for drop = 0; floats[drop].T < mint; drop++ {
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for drop = 0; floats[drop].T <= mint; drop++ {
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}
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ev.currentSamples -= drop
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copy(floats, floats[drop:])
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floats = floats[:len(floats)-drop]
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// Only append points with timestamps after the last timestamp we have.
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mintFloats = floats[len(floats)-1].T + 1
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mintFloats = floats[len(floats)-1].T
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} else {
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ev.currentSamples -= len(floats)
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if floats != nil {
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@ -2229,14 +2229,14 @@ func (ev *evaluator) matrixIterSlice(
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}
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// ...then the same for histograms. TODO(beorn7): Use generics?
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if len(histograms) > 0 && histograms[len(histograms)-1].T >= mint {
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if len(histograms) > 0 && histograms[len(histograms)-1].T > mint {
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// There is an overlap between previous and current ranges, retain common
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// points. In most such cases:
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// (a) the overlap is significantly larger than the eval step; and/or
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// (b) the number of samples is relatively small.
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// so a linear search will be as fast as a binary search.
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var drop int
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for drop = 0; histograms[drop].T < mint; drop++ {
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for drop = 0; histograms[drop].T <= mint; drop++ {
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}
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// Rotate the buffer around the drop index so that points before mint can be
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// reused to store new histograms.
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@ -2247,7 +2247,7 @@ func (ev *evaluator) matrixIterSlice(
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histograms = histograms[:len(histograms)-drop]
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ev.currentSamples -= totalHPointSize(histograms)
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// Only append points with timestamps after the last timestamp we have.
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mintHistograms = histograms[len(histograms)-1].T + 1
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mintHistograms = histograms[len(histograms)-1].T
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} else {
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ev.currentSamples -= totalHPointSize(histograms)
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if histograms != nil {
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@ -2271,7 +2271,7 @@ loop:
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case chunkenc.ValFloatHistogram, chunkenc.ValHistogram:
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t := buf.AtT()
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// Values in the buffer are guaranteed to be smaller than maxt.
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if t >= mintHistograms {
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if t > mintHistograms {
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if histograms == nil {
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histograms = getMatrixSelectorHPoints()
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}
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@ -2297,7 +2297,7 @@ loop:
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continue 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 t > mintFloats {
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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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File diff suppressed because it is too large
Load diff
64
promql/promqltest/testdata/at_modifier.test
vendored
64
promql/promqltest/testdata/at_modifier.test
vendored
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@ -76,45 +76,43 @@ eval instant at 25s sum_over_time(metric{job="1"}[100s:1s] offset 20s @ 100)
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# Since vector selector has timestamp, the result value does not depend on the timestamp of subqueries.
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# Inner most sum=1+2+...+10=55.
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# With [100s:25s] subquery, it's 55*5.
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# With [100s:25s] subquery, it's 55*4.
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eval instant at 100s sum_over_time(sum_over_time(metric{job="1"}[100s] @ 100)[100s:25s] @ 50)
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{job="1"} 275
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{job="1"} 220
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# Nested subqueries with different timestamps on both.
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# Since vector selector has timestamp, the result value does not depend on the timestamp of subqueries.
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# Sum of innermost subquery is 275 as above. The outer subquery repeats it 4 times.
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# Sum of innermost subquery is 220 as above. The outer subquery repeats it 3 times.
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eval instant at 0s sum_over_time(sum_over_time(sum_over_time(metric{job="1"}[100s] @ 100)[100s:25s] @ 50)[3s:1s] @ 3000)
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{job="1"} 1100
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{job="1"} 660
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# Testing the inner subquery timestamp since vector selector does not have @.
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# Inner sum for subquery [100s:25s] @ 50 are
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# at -50 nothing, at -25 nothing, at 0=0, at 25=2, at 50=4+5=9.
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# This sum of 11 is repeated 4 times by outer subquery.
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# at -50 nothing, at -25 nothing, at 0=0, at 25=2, at 50=5.
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# This sum of 7 is repeated 3 times by outer subquery.
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eval instant at 0s sum_over_time(sum_over_time(sum_over_time(metric{job="1"}[10s])[100s:25s] @ 50)[3s:1s] @ 200)
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{job="1"} 44
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{job="1"} 21
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# Inner sum for subquery [100s:25s] @ 200 are
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# at 100=9+10, at 125=12, at 150=14+15, at 175=17, at 200=19+20.
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# This sum of 116 is repeated 4 times by outer subquery.
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# at 125=12, at 150=15, at 175=17, at 200=20.
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# This sum of 64 is repeated 3 times by outer subquery.
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eval instant at 0s sum_over_time(sum_over_time(sum_over_time(metric{job="1"}[10s])[100s:25s] @ 200)[3s:1s] @ 50)
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{job="1"} 464
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{job="1"} 192
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# Nested subqueries with timestamp only on outer subquery.
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# Outer most subquery:
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# at 900=783
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# inner subquery: at 870=87+86+85, at 880=88+87+86, at 890=89+88+87
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# at 925=537
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# inner subquery: at 895=89+88, at 905=90+89, at 915=90+91
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# at 950=828
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# inner subquery: at 920=92+91+90, at 930=93+92+91, at 940=94+93+92
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# at 975=567
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# inner subquery: at 945=94+93, at 955=95+94, at 965=96+95
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# at 1000=873
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# inner subquery: at 970=97+96+95, at 980=98+97+96, at 990=99+98+97
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# at 925=360
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# inner subquery: at 905=90+89, at 915=91+90
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# at 950=372
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# inner subquery: at 930=93+92, at 940=94+93
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# at 975=380
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# inner subquery: at 955=95+94, at 965=96+95
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# at 1000=392
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# inner subquery: at 980=98+97, at 990=99+98
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eval instant at 0s sum_over_time(sum_over_time(sum_over_time(metric{job="1"}[20s])[20s:10s] offset 10s)[100s:25s] @ 1000)
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{job="1"} 3588
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{job="1"} 1504
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# minute is counted on the value of the sample.
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eval instant at 10s minute(metric @ 1500)
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@ -137,32 +135,32 @@ eval instant at 15m timestamp(timestamp(metric{job="1"} @ 10))
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# minute is counted on the value of the sample.
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eval instant at 0s sum_over_time(minute(metric @ 1500)[100s:10s])
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{job="1"} 22
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{job="2"} 55
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{job="1"} 20
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{job="2"} 50
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# If nothing passed, minute() takes eval time.
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# Here the eval time is determined by the subquery.
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# [50m:1m] at 6000, i.e. 100m, is 50m to 100m.
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# sum=50+51+52+...+59+0+1+2+...+40.
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# sum=51+52+...+59+0+1+2+...+40.
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eval instant at 0s sum_over_time(minute()[50m:1m] @ 6000)
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{} 1315
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# sum=46+47+...+59+0+1+2+...+35.
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eval instant at 0s sum_over_time(minute()[50m:1m] @ 6000 offset 5m)
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{} 1365
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# sum=45+46+47+...+59+0+1+2+...+35.
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eval instant at 0s sum_over_time(minute()[50m:1m] @ 6000 offset 5m)
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{} 1410
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# time() is the eval time which is determined by subquery here.
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# 2900+2901+...+3000 = (3000*3001 - 2899*2900)/2.
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# 2901+...+3000 = (3000*3001 - 2899*2900)/2.
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eval instant at 0s sum_over_time(vector(time())[100s:1s] @ 3000)
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{} 297950
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{} 295050
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# 2300+2301+...+2400 = (2400*2401 - 2299*2300)/2.
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# 2301+...+2400 = (2400*2401 - 2299*2300)/2.
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eval instant at 0s sum_over_time(vector(time())[100s:1s] @ 3000 offset 600s)
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{} 237350
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{} 235050
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# timestamp() takes the time of the sample and not the evaluation time.
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eval instant at 0s sum_over_time(timestamp(metric{job="1"} @ 10)[100s:10s] @ 3000)
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{job="1"} 110
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{job="1"} 100
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# The result of inner timestamp() will have the timestamp as the
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# eval time, hence entire expression is not step invariant and depends on eval time.
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112
promql/promqltest/testdata/functions.test
vendored
112
promql/promqltest/testdata/functions.test
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@ -6,9 +6,6 @@ load 5m
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# Tests for resets().
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eval instant at 50m resets(http_requests[5m])
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{path="/foo"} 0
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{path="/bar"} 0
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{path="/biz"} 0
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eval instant at 50m resets(http_requests[20m])
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{path="/foo"} 1
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@ -16,8 +13,8 @@ eval instant at 50m resets(http_requests[20m])
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{path="/biz"} 0
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eval instant at 50m resets(http_requests[30m])
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{path="/foo"} 2
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{path="/bar"} 1
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{path="/foo"} 1
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{path="/bar"} 0
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{path="/biz"} 0
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eval instant at 50m resets(http_requests[50m])
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@ -29,28 +26,25 @@ eval instant at 50m resets(nonexistent_metric[50m])
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# Tests for changes().
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eval instant at 50m changes(http_requests[5m])
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{path="/foo"} 0
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{path="/bar"} 0
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{path="/biz"} 0
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eval instant at 50m changes(http_requests[20m])
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{path="/foo"} 3
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{path="/bar"} 3
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{path="/foo"} 2
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{path="/bar"} 2
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{path="/biz"} 0
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eval instant at 50m changes(http_requests[30m])
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{path="/foo"} 4
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{path="/bar"} 5
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{path="/biz"} 1
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{path="/foo"} 3
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{path="/bar"} 4
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{path="/biz"} 0
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eval instant at 50m changes(http_requests[50m])
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{path="/foo"} 8
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{path="/bar"} 9
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{path="/foo"} 7
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{path="/bar"} 8
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{path="/biz"} 1
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eval instant at 50m changes((http_requests[50m]))
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{path="/foo"} 8
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{path="/bar"} 9
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{path="/foo"} 7
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{path="/bar"} 8
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{path="/biz"} 1
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eval instant at 50m changes(nonexistent_metric[50m])
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@ -63,7 +57,7 @@ load 5m
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eval instant at 15m changes(x[15m])
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{a="b"} 0
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{a="c"} 2
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{a="c"} 1
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clear
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@ -77,7 +71,7 @@ load 5m
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# Tests for increase().
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eval instant at 50m increase(http_requests[50m])
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{path="/foo"} 100
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{path="/bar"} 90
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{path="/bar"} 88.88888888888889
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{path="/dings"} 100
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{path="/bumms"} 100
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@ -115,11 +109,10 @@ load 5m
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# Counter resets at in the middle of range are handled correctly by rate().
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eval instant at 50m rate(testcounter_reset_middle[50m])
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{} 0.03
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{} 0.02962962962962963
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# Counter resets at end of range are ignored by rate().
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eval instant at 50m rate(testcounter_reset_end[5m])
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{} 0
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clear
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@ -237,19 +230,19 @@ eval instant at 50m deriv(testcounter_reset_middle[100m])
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# intercept at t=3000: 38.63636363636364
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# intercept at t=3000+3600: 76.81818181818181
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eval instant at 50m predict_linear(testcounter_reset_middle[50m], 3600)
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{} 76.81818181818181
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{} 70
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# intercept at t = 3000+3600 = 6600
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eval instant at 50m predict_linear(testcounter_reset_middle[50m] @ 3000, 3600)
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{} 76.81818181818181
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{} 70
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# intercept at t = 600+3600 = 4200
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eval instant at 10m predict_linear(testcounter_reset_middle[50m] @ 3000, 3600)
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{} 51.36363636363637
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{} 48.18181818181818
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# intercept at t = 4200+3600 = 7800
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eval instant at 70m predict_linear(testcounter_reset_middle[50m] @ 3000, 3600)
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{} 89.54545454545455
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{} 80.9090909090909
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# With http_requests, there is a sample value exactly at the end of
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# the range, and it has exactly the predicted value, so predict_linear
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@ -678,10 +671,10 @@ load 10s
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metric10 -9.988465674311579e+307 9.988465674311579e+307
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eval instant at 1m avg_over_time(metric[1m])
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{} 3
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{} 3.5
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eval instant at 1m sum_over_time(metric[1m])/count_over_time(metric[1m])
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{} 3
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{} 3.5
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eval instant at 1m avg_over_time(metric2[1m])
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{} Inf
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@ -748,8 +741,8 @@ eval instant at 1m avg_over_time(metric8[1m])
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{} 9.988465674311579e+307
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# This overflows float64.
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eval instant at 1m sum_over_time(metric8[1m])/count_over_time(metric8[1m])
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{} Inf
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eval instant at 1m sum_over_time(metric8[2m])/count_over_time(metric8[2m])
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{} +Inf
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eval instant at 1m avg_over_time(metric9[1m])
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{} -9.988465674311579e+307
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@ -758,10 +751,16 @@ eval instant at 1m avg_over_time(metric9[1m])
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eval instant at 1m sum_over_time(metric9[1m])/count_over_time(metric9[1m])
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{} -Inf
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eval instant at 1m avg_over_time(metric10[1m])
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eval instant at 45s avg_over_time(metric10[1m])
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{} 0
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eval instant at 1m sum_over_time(metric10[1m])/count_over_time(metric10[1m])
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eval instant at 1m avg_over_time(metric10[2m])
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{} 0
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eval instant at 45s sum_over_time(metric10[1m])/count_over_time(metric10[1m])
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{} 0
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eval instant at 1m sum_over_time(metric10[2m])/count_over_time(metric10[2m])
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{} 0
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# Test if very big intermediate values cause loss of detail.
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@ -770,7 +769,7 @@ load 10s
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metric 1 1e100 1 -1e100
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eval instant at 1m sum_over_time(metric[1m])
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{} 2
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{} 1
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# Tests for stddev_over_time and stdvar_over_time.
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clear
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@ -778,13 +777,13 @@ load 10s
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metric 0 8 8 2 3
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eval instant at 1m stdvar_over_time(metric[1m])
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{} 10.56
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{} 7.6875
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eval instant at 1m stddev_over_time(metric[1m])
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{} 3.249615
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{} 2.7726341266023544
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eval instant at 1m stddev_over_time((metric[1m]))
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{} 3.249615
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{} 2.7726341266023544
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# Tests for stddev_over_time and stdvar_over_time #4927.
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clear
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@ -814,24 +813,24 @@ load 10s
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data{test="uneven samples"} 0 1 4
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eval instant at 1m quantile_over_time(0, data[1m])
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{test="two samples"} 0
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{test="three samples"} 0
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{test="uneven samples"} 0
|
||||
|
||||
eval instant at 1m quantile_over_time(0.5, data[1m])
|
||||
{test="two samples"} 0.5
|
||||
{test="two samples"} 1
|
||||
{test="three samples"} 1
|
||||
{test="uneven samples"} 1
|
||||
|
||||
eval instant at 1m quantile_over_time(0.75, data[1m])
|
||||
{test="two samples"} 0.75
|
||||
eval instant at 1m quantile_over_time(0.5, data[1m])
|
||||
{test="two samples"} 1
|
||||
{test="three samples"} 1.5
|
||||
{test="uneven samples"} 2.5
|
||||
|
||||
eval instant at 1m quantile_over_time(0.75, data[1m])
|
||||
{test="two samples"} 1
|
||||
{test="three samples"} 1.75
|
||||
{test="uneven samples"} 3.25
|
||||
|
||||
eval instant at 1m quantile_over_time(0.8, data[1m])
|
||||
{test="two samples"} 0.8
|
||||
{test="three samples"} 1.6
|
||||
{test="uneven samples"} 2.8
|
||||
{test="two samples"} 1
|
||||
{test="three samples"} 1.8
|
||||
{test="uneven samples"} 3.4000000000000004
|
||||
|
||||
eval instant at 1m quantile_over_time(1, data[1m])
|
||||
{test="two samples"} 1
|
||||
|
|
@ -965,8 +964,8 @@ eval instant at 1m min_over_time(data[1m])
|
|||
|
||||
eval instant at 1m max_over_time(data[1m])
|
||||
{type="numbers"} 3
|
||||
{type="some_nan"} 2
|
||||
{type="some_nan2"} 2
|
||||
{type="some_nan"} 0
|
||||
{type="some_nan2"} 1
|
||||
{type="some_nan3"} 1
|
||||
{type="only_nan"} NaN
|
||||
|
||||
|
|
@ -1063,13 +1062,19 @@ eval instant at 1m absent_over_time(httpd_log_lines_total[30s])
|
|||
{} 1
|
||||
|
||||
eval instant at 15m absent_over_time(http_requests[5m])
|
||||
|
||||
eval instant at 16m absent_over_time(http_requests[5m])
|
||||
{} 1
|
||||
|
||||
eval instant at 15m absent_over_time(http_requests[10m])
|
||||
|
||||
eval instant at 16m absent_over_time(http_requests[6m])
|
||||
{} 1
|
||||
|
||||
eval instant at 16m absent_over_time(http_requests[16m])
|
||||
|
||||
eval instant at 16m absent_over_time(httpd_handshake_failures_total[1m])
|
||||
{} 1
|
||||
|
||||
eval instant at 16m absent_over_time(httpd_handshake_failures_total[2m])
|
||||
|
||||
eval instant at 16m absent_over_time({instance="127.0.0.1"}[5m])
|
||||
|
||||
|
|
@ -1125,17 +1130,18 @@ eval instant at 0m present_over_time(httpd_log_lines_total[30s])
|
|||
eval instant at 1m present_over_time(httpd_log_lines_total[30s])
|
||||
|
||||
eval instant at 15m present_over_time(http_requests[5m])
|
||||
|
||||
eval instant at 15m present_over_time(http_requests[10m])
|
||||
{instance="127.0.0.1", job="httpd", path="/bar"} 1
|
||||
{instance="127.0.0.1", job="httpd", path="/foo"} 1
|
||||
|
||||
eval instant at 16m present_over_time(http_requests[5m])
|
||||
|
||||
eval instant at 16m present_over_time(http_requests[6m])
|
||||
|
||||
eval instant at 16m present_over_time(http_requests[16m])
|
||||
{instance="127.0.0.1", job="httpd", path="/bar"} 1
|
||||
{instance="127.0.0.1", job="httpd", path="/foo"} 1
|
||||
|
||||
eval instant at 16m present_over_time(httpd_handshake_failures_total[1m])
|
||||
{instance="127.0.0.1", job="node"} 1
|
||||
|
||||
eval instant at 16m present_over_time({instance="127.0.0.1"}[5m])
|
||||
{instance="127.0.0.1",job="node"} 1
|
||||
|
|
|
|||
38
promql/promqltest/testdata/histograms.test
vendored
38
promql/promqltest/testdata/histograms.test
vendored
|
|
@ -93,15 +93,15 @@ eval instant at 50m histogram_quantile(0.8, testhistogram_bucket)
|
|||
{start="negative"} 0.3
|
||||
|
||||
# More realistic with rates.
|
||||
eval instant at 50m histogram_quantile(0.2, rate(testhistogram_bucket[5m]))
|
||||
eval instant at 50m histogram_quantile(0.2, rate(testhistogram_bucket[10m]))
|
||||
{start="positive"} 0.048
|
||||
{start="negative"} -0.2
|
||||
|
||||
eval instant at 50m histogram_quantile(0.5, rate(testhistogram_bucket[5m]))
|
||||
eval instant at 50m histogram_quantile(0.5, rate(testhistogram_bucket[10m]))
|
||||
{start="positive"} 0.15
|
||||
{start="negative"} -0.15
|
||||
|
||||
eval instant at 50m histogram_quantile(0.8, rate(testhistogram_bucket[5m]))
|
||||
eval instant at 50m histogram_quantile(0.8, rate(testhistogram_bucket[10m]))
|
||||
{start="positive"} 0.72
|
||||
{start="negative"} 0.3
|
||||
|
||||
|
|
@ -125,58 +125,58 @@ eval instant at 47m histogram_quantile(5./6., rate(testhistogram2_bucket[15m]))
|
|||
{} 5
|
||||
|
||||
# Aggregated histogram: Everything in one.
|
||||
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le))
|
||||
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[10m])) by (le))
|
||||
{} 0.075
|
||||
|
||||
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le))
|
||||
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[10m])) by (le))
|
||||
{} 0.1277777777777778
|
||||
|
||||
# Aggregated histogram: Everything in one. Now with avg, which does not change anything.
|
||||
eval instant at 50m histogram_quantile(0.3, avg(rate(request_duration_seconds_bucket[5m])) by (le))
|
||||
eval instant at 50m histogram_quantile(0.3, avg(rate(request_duration_seconds_bucket[10m])) by (le))
|
||||
{} 0.075
|
||||
|
||||
eval instant at 50m histogram_quantile(0.5, avg(rate(request_duration_seconds_bucket[5m])) by (le))
|
||||
eval instant at 50m histogram_quantile(0.5, avg(rate(request_duration_seconds_bucket[10m])) by (le))
|
||||
{} 0.12777777777777778
|
||||
|
||||
# Aggregated histogram: By instance.
|
||||
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))
|
||||
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[10m])) by (le, instance))
|
||||
{instance="ins1"} 0.075
|
||||
{instance="ins2"} 0.075
|
||||
|
||||
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))
|
||||
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[10m])) by (le, instance))
|
||||
{instance="ins1"} 0.1333333333
|
||||
{instance="ins2"} 0.125
|
||||
|
||||
# Aggregated histogram: By job.
|
||||
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))
|
||||
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[10m])) by (le, job))
|
||||
{job="job1"} 0.1
|
||||
{job="job2"} 0.0642857142857143
|
||||
|
||||
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))
|
||||
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[10m])) by (le, job))
|
||||
{job="job1"} 0.14
|
||||
{job="job2"} 0.1125
|
||||
|
||||
# Aggregated histogram: By job and instance.
|
||||
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))
|
||||
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[10m])) by (le, job, instance))
|
||||
{instance="ins1", job="job1"} 0.11
|
||||
{instance="ins2", job="job1"} 0.09
|
||||
{instance="ins1", job="job2"} 0.06
|
||||
{instance="ins2", job="job2"} 0.0675
|
||||
|
||||
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))
|
||||
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[10m])) by (le, job, instance))
|
||||
{instance="ins1", job="job1"} 0.15
|
||||
{instance="ins2", job="job1"} 0.1333333333333333
|
||||
{instance="ins1", job="job2"} 0.1
|
||||
{instance="ins2", job="job2"} 0.1166666666666667
|
||||
|
||||
# The unaggregated histogram for comparison. Same result as the previous one.
|
||||
eval instant at 50m histogram_quantile(0.3, rate(request_duration_seconds_bucket[5m]))
|
||||
eval instant at 50m histogram_quantile(0.3, rate(request_duration_seconds_bucket[10m]))
|
||||
{instance="ins1", job="job1"} 0.11
|
||||
{instance="ins2", job="job1"} 0.09
|
||||
{instance="ins1", job="job2"} 0.06
|
||||
{instance="ins2", job="job2"} 0.0675
|
||||
|
||||
eval instant at 50m histogram_quantile(0.5, rate(request_duration_seconds_bucket[5m]))
|
||||
eval instant at 50m histogram_quantile(0.5, rate(request_duration_seconds_bucket[10m]))
|
||||
{instance="ins1", job="job1"} 0.15
|
||||
{instance="ins2", job="job1"} 0.13333333333333333
|
||||
{instance="ins1", job="job2"} 0.1
|
||||
|
|
@ -205,15 +205,15 @@ eval instant at 50m histogram_quantile(0.99, nonmonotonic_bucket)
|
|||
{} 979.75
|
||||
|
||||
# Buckets with different representations of the same upper bound.
|
||||
eval instant at 50m histogram_quantile(0.5, rate(mixed_bucket[5m]))
|
||||
eval instant at 50m histogram_quantile(0.5, rate(mixed_bucket[10m]))
|
||||
{instance="ins1", job="job1"} 0.15
|
||||
{instance="ins2", job="job1"} NaN
|
||||
|
||||
eval instant at 50m histogram_quantile(0.75, rate(mixed_bucket[5m]))
|
||||
eval instant at 50m histogram_quantile(0.75, rate(mixed_bucket[10m]))
|
||||
{instance="ins1", job="job1"} 0.2
|
||||
{instance="ins2", job="job1"} NaN
|
||||
|
||||
eval instant at 50m histogram_quantile(1, rate(mixed_bucket[5m]))
|
||||
eval instant at 50m histogram_quantile(1, rate(mixed_bucket[10m]))
|
||||
{instance="ins1", job="job1"} 0.2
|
||||
{instance="ins2", job="job1"} NaN
|
||||
|
||||
|
|
@ -222,7 +222,7 @@ load 5m
|
|||
empty_bucket{le="0.2", job="job1", instance="ins1"} 0x10
|
||||
empty_bucket{le="+Inf", job="job1", instance="ins1"} 0x10
|
||||
|
||||
eval instant at 50m histogram_quantile(0.2, rate(empty_bucket[5m]))
|
||||
eval instant at 50m histogram_quantile(0.2, rate(empty_bucket[10m]))
|
||||
{instance="ins1", job="job1"} NaN
|
||||
|
||||
# Load a duplicate histogram with a different name to test failure scenario on multiple histograms with the same label set
|
||||
|
|
|
|||
|
|
@ -133,7 +133,7 @@ eval instant at 50m histogram_quantile(0.5, incr_histogram)
|
|||
{} 1.5
|
||||
|
||||
# Per-second average rate of increase should be 1/(5*60) for count and buckets, then 2/(5*60) for sum.
|
||||
eval instant at 50m rate(incr_histogram[5m])
|
||||
eval instant at 50m rate(incr_histogram[10m])
|
||||
{} {{count:0.0033333333333333335 sum:0.006666666666666667 offset:1 buckets:[0.0033333333333333335]}}
|
||||
|
||||
# Calculate the 50th percentile of observations over the last 10m.
|
||||
|
|
|
|||
2
promql/promqltest/testdata/operators.test
vendored
2
promql/promqltest/testdata/operators.test
vendored
|
|
@ -113,7 +113,7 @@ eval instant at 50m http_requests{job="api-server", group="canary"}
|
|||
http_requests{group="canary", instance="0", job="api-server"} 300
|
||||
http_requests{group="canary", instance="1", job="api-server"} 400
|
||||
|
||||
eval instant at 50m http_requests{job="api-server", group="canary"} + rate(http_requests{job="api-server"}[5m]) * 5 * 60
|
||||
eval instant at 50m http_requests{job="api-server", group="canary"} + rate(http_requests{job="api-server"}[10m]) * 5 * 60
|
||||
{group="canary", instance="0", job="api-server"} 330
|
||||
{group="canary", instance="1", job="api-server"} 440
|
||||
|
||||
|
|
|
|||
2
promql/promqltest/testdata/staleness.test
vendored
2
promql/promqltest/testdata/staleness.test
vendored
|
|
@ -30,6 +30,8 @@ eval instant at 10s count_over_time(metric[1s])
|
|||
eval instant at 20s count_over_time(metric[1s])
|
||||
|
||||
eval instant at 20s count_over_time(metric[10s])
|
||||
|
||||
eval instant at 20s count_over_time(metric[20s])
|
||||
{} 1
|
||||
|
||||
|
||||
|
|
|
|||
28
promql/promqltest/testdata/subquery.test
vendored
28
promql/promqltest/testdata/subquery.test
vendored
|
|
@ -10,18 +10,18 @@ eval instant at 10s sum_over_time(metric[50s:5s])
|
|||
|
||||
# Every evaluation yields the last value, i.e. 2
|
||||
eval instant at 5m sum_over_time(metric[50s:10s])
|
||||
{} 12
|
||||
{} 10
|
||||
|
||||
# Series becomes stale at 5m10s (5m after last sample)
|
||||
# Hence subquery gets a single sample at 6m-50s=5m10s.
|
||||
eval instant at 6m sum_over_time(metric[50s:10s])
|
||||
# Hence subquery gets a single sample at 5m10s.
|
||||
eval instant at 6m sum_over_time(metric[60s:10s])
|
||||
{} 2
|
||||
|
||||
eval instant at 10s rate(metric[20s:10s])
|
||||
{} 0.1
|
||||
|
||||
eval instant at 20s rate(metric[20s:5s])
|
||||
{} 0.05
|
||||
{} 0.06666666666666667
|
||||
|
||||
clear
|
||||
|
||||
|
|
@ -49,16 +49,16 @@ load 10s
|
|||
metric3 0+3x1000
|
||||
|
||||
eval instant at 1000s sum_over_time(metric1[30s:10s])
|
||||
{} 394
|
||||
{} 297
|
||||
|
||||
# This is (394*2 - 100), because other than the last 100 at 1000s,
|
||||
# everything else is repeated with the 5s step.
|
||||
eval instant at 1000s sum_over_time(metric1[30s:5s])
|
||||
{} 688
|
||||
{} 591
|
||||
|
||||
# Offset is aligned with the step.
|
||||
eval instant at 1010s sum_over_time(metric1[30s:10s] offset 10s)
|
||||
{} 394
|
||||
{} 297
|
||||
|
||||
# Same result for different offsets due to step alignment.
|
||||
eval instant at 1010s sum_over_time(metric1[30s:10s] offset 9s)
|
||||
|
|
@ -78,16 +78,16 @@ eval instant at 1010s sum_over_time((metric1)[30s:10s] offset 3s)
|
|||
|
||||
# Nested subqueries
|
||||
eval instant at 1000s rate(sum_over_time(metric1[30s:10s])[50s:10s])
|
||||
{} 0.4
|
||||
{} 0.30000000000000004
|
||||
|
||||
eval instant at 1000s rate(sum_over_time(metric2[30s:10s])[50s:10s])
|
||||
{} 0.8
|
||||
{} 0.6000000000000001
|
||||
|
||||
eval instant at 1000s rate(sum_over_time(metric3[30s:10s])[50s:10s])
|
||||
{} 1.2
|
||||
{} 0.9
|
||||
|
||||
eval instant at 1000s rate(sum_over_time((metric1+metric2+metric3)[30s:10s])[30s:10s])
|
||||
{} 2.4
|
||||
{} 1.8
|
||||
|
||||
clear
|
||||
|
||||
|
|
@ -102,15 +102,15 @@ eval instant at 80s rate(metric[1m])
|
|||
|
||||
# No extrapolation, [2@20, 144@80]: (144 - 2) / 60
|
||||
eval instant at 80s rate(metric[1m:10s])
|
||||
{} 2.366666667
|
||||
{} 2.4
|
||||
|
||||
# Only one value between 10s and 20s, 2@14
|
||||
eval instant at 20s min_over_time(metric[10s])
|
||||
{} 2
|
||||
|
||||
# min(1@10, 2@20)
|
||||
# min(2@20)
|
||||
eval instant at 20s min_over_time(metric[10s:10s])
|
||||
{} 1
|
||||
{} 2
|
||||
|
||||
eval instant at 20m min_over_time(rate(metric[5m])[20m:1m])
|
||||
{} 0.12119047619047618
|
||||
|
|
|
|||
Loading…
Reference in a new issue