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125a90899c
promqltest: Complete the tests for info annotations So far, we did not test for the _absence_ of an info annotation (because many tests triggered info annotations, which we haven't taken into account so far). The test for info annotations was also missed for range queries. This completes the tests for info annotations (and refactors the many `if` statements into a somewhat more compact `switch` statement). It fixes most tests to not emit an info annotation anymore. Or it changes the `eval` to `eval_info` where we actually want to test for the info annotation. It also fixes a few spelling errors in comments. --------- Signed-off-by: beorn7 <beorn@grafana.com> Signed-off-by: Björn Rabenstein <github@rabenste.in> Co-authored-by: Arve Knudsen <arve.knudsen@gmail.com>
219 lines
6.6 KiB
Plaintext
219 lines
6.6 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 @ 100s
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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 @ 1m40s
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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 @ 100 offset 50
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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 offset 50 @ 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 @ 0 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 @ 0 offset -50
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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 @ 0
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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 -50 @ 0
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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 @ 0 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 @ 0 offset -50
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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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eval instant at 25s sum_over_time(metric{job="1"}[100] @ 100 offset 50)
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{job="1"} 15
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eval instant at 25s sum_over_time(metric{job="1"}[100] 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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# Note that this triggers an info annotation because we are rate'ing a metric that does not end in `_total`.
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eval_info 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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eval instant at 25s sum_over_time(metric{job="1"}[100] @ 100) + label_replace(sum_over_time(metric{job="2"}[100] @ 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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# 10*(1+2+...+7) + 8.
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eval instant at 25s sum_over_time(metric{job="1"}[100:1] offset 20 @ 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*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"} 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 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"} 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=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"} 21
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# Inner sum for subquery [100s:25s] @ 200 are
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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"} 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 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"} 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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{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"} 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=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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# time() is the eval time which is determined by subquery here.
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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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{} 295050
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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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{} 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"} 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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# 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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