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9199fcb8d1
This commit adds `@ <timestamp>` modifier as per this design doc: https://docs.google.com/document/d/1uSbD3T2beM-iX4-Hp7V074bzBRiRNlqUdcWP6JTDQSs/edit. An example query: ``` rate(process_cpu_seconds_total[1m]) and topk(7, rate(process_cpu_seconds_total[1h] @ 1234)) ``` which ranks based on last 1h rate and w.r.t. unix timestamp 1234 but actually plots the 1m rate. Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
167 lines
5.5 KiB
Plaintext
167 lines
5.5 KiB
Plaintext
load 10s
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metric{job="1"} 0+1x1000
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metric{job="2"} 0+2x1000
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load 1ms
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metric_ms 0+1x10000
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# Instant vector selectors.
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eval instant at 10s metric @ 100
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metric{job="1"} 10
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metric{job="2"} 20
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eval instant at 10s metric @ 100 offset 50s
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metric{job="1"} 5
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metric{job="2"} 10
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eval instant at 10s metric offset 50s @ 100
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metric{job="1"} 5
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metric{job="2"} 10
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eval instant at 10s -metric @ 100
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{job="1"} -10
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{job="2"} -20
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eval instant at 10s ---metric @ 100
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{job="1"} -10
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{job="2"} -20
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# Millisecond precision.
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eval instant at 100s metric_ms @ 1.234
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metric_ms 1234
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# Range vector selectors.
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eval instant at 25s sum_over_time(metric{job="1"}[100s] @ 100)
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{job="1"} 55
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eval instant at 25s sum_over_time(metric{job="1"}[100s] @ 100 offset 50s)
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{job="1"} 15
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eval instant at 25s sum_over_time(metric{job="1"}[100s] offset 50s @ 100)
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{job="1"} 15
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# Different timestamps.
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eval instant at 25s metric{job="1"} @ 50 + metric{job="1"} @ 100
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{job="1"} 15
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eval instant at 25s rate(metric{job="1"}[100s] @ 100) + label_replace(rate(metric{job="2"}[123s] @ 200), "job", "1", "", "")
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{job="1"} 0.3
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eval instant at 25s sum_over_time(metric{job="1"}[100s] @ 100) + label_replace(sum_over_time(metric{job="2"}[100s] @ 100), "job", "1", "", "")
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{job="1"} 165
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# Subqueries.
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# 10*(1+2+...+9) + 10.
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eval instant at 25s sum_over_time(metric{job="1"}[100s:1s] @ 100)
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{job="1"} 460
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# 10*(1+2+...+7) + 8.
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eval instant at 25s sum_over_time(metric{job="1"}[100s:1s] @ 100 offset 20s)
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{job="1"} 288
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# 10*(1+2+...+7) + 8.
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eval instant at 25s sum_over_time(metric{job="1"}[100s:1s] offset 20s @ 100)
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{job="1"} 288
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# Subquery with different timestamps.
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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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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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# 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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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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# 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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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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# 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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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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# 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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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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# minute is counted on the value of the sample.
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eval instant at 10s minute(metric @ 1500)
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{job="1"} 2
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{job="2"} 5
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# timestamp() takes the time of the sample and not the evaluation time.
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eval instant at 10m timestamp(metric{job="1"} @ 10)
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{job="1"} 10
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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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eval instant at 10m timestamp(timestamp(metric{job="1"} @ 10))
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{job="1"} 600
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eval instant at 15m timestamp(timestamp(metric{job="1"} @ 10))
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{job="1"} 900
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# Time functions inside a subquery.
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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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# 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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eval instant at 0s sum_over_time(minute()[50m:1m] @ 6000)
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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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eval instant at 0s sum_over_time(vector(time())[100s:1s] @ 3000)
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{} 297950
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# 2300+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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# 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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# 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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# Here eval time is determined by the subquery.
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eval instant at 0s sum_over_time(timestamp(timestamp(metric{job="1"} @ 999))[10s:1s] @ 10)
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{job="1"} 55
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clear
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