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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>
559 lines
20 KiB
Plaintext
559 lines
20 KiB
Plaintext
# Two histograms with 4 buckets each (x_sum and x_count not included,
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# only buckets). Lowest bucket for one histogram < 0, for the other >
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# 0. They have the same name, just separated by label. Not useful in
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# practice, but can happen (if clients change bucketing), and the
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# server has to cope with it.
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# Test histogram.
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load_with_nhcb 5m
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testhistogram_bucket{le="0.1", start="positive"} 0+5x10
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testhistogram_bucket{le=".2", start="positive"} 0+7x10
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testhistogram_bucket{le="1e0", start="positive"} 0+11x10
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testhistogram_bucket{le="+Inf", start="positive"} 0+12x10
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testhistogram_bucket{le="-.2", start="negative"} 0+1x10
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testhistogram_bucket{le="-0.1", start="negative"} 0+2x10
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testhistogram_bucket{le="0.3", start="negative"} 0+2x10
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testhistogram_bucket{le="+Inf", start="negative"} 0+3x10
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# Another test histogram, where q(1/6), q(1/2), and q(5/6) are each in
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# the middle of a bucket and should therefore be 1, 3, and 5,
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# respectively.
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load_with_nhcb 5m
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testhistogram2_bucket{le="0"} 0+0x10
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testhistogram2_bucket{le="2"} 0+1x10
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testhistogram2_bucket{le="4"} 0+2x10
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testhistogram2_bucket{le="6"} 0+3x10
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testhistogram2_bucket{le="+Inf"} 0+3x10
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# Another test histogram, this time without any observations in the +Inf bucket.
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# This enables a meaningful calculation of standard deviation and variance.
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load_with_nhcb 5m
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testhistogram3_bucket{le="0", start="positive"} 0+0x10
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testhistogram3_bucket{le="0.1", start="positive"} 0+5x10
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testhistogram3_bucket{le=".2", start="positive"} 0+7x10
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testhistogram3_bucket{le="1e0", start="positive"} 0+11x10
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testhistogram3_bucket{le="+Inf", start="positive"} 0+11x10
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testhistogram3_sum{start="positive"} 0+33x10
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testhistogram3_count{start="positive"} 0+11x10
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testhistogram3_bucket{le="-.25", start="negative"} 0+0x10
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testhistogram3_bucket{le="-.2", start="negative"} 0+1x10
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testhistogram3_bucket{le="-0.1", start="negative"} 0+2x10
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testhistogram3_bucket{le="0.3", start="negative"} 0+2x10
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testhistogram3_bucket{le="+Inf", start="negative"} 0+2x10
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testhistogram3_sum{start="negative"} 0+8x10
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testhistogram3_count{start="negative"} 0+2x10
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# Now a more realistic histogram per job and instance to test aggregation.
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load_with_nhcb 5m
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request_duration_seconds_bucket{job="job1", instance="ins1", le="0.1"} 0+1x10
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request_duration_seconds_bucket{job="job1", instance="ins1", le="0.2"} 0+3x10
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request_duration_seconds_bucket{job="job1", instance="ins1", le="+Inf"} 0+4x10
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request_duration_seconds_bucket{job="job1", instance="ins2", le="0.1"} 0+2x10
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request_duration_seconds_bucket{job="job1", instance="ins2", le="0.2"} 0+5x10
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request_duration_seconds_bucket{job="job1", instance="ins2", le="+Inf"} 0+6x10
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request_duration_seconds_bucket{job="job2", instance="ins1", le="0.1"} 0+3x10
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request_duration_seconds_bucket{job="job2", instance="ins1", le="0.2"} 0+4x10
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request_duration_seconds_bucket{job="job2", instance="ins1", le="+Inf"} 0+6x10
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request_duration_seconds_bucket{job="job2", instance="ins2", le="0.1"} 0+4x10
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request_duration_seconds_bucket{job="job2", instance="ins2", le="0.2"} 0+7x10
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request_duration_seconds_bucket{job="job2", instance="ins2", le="+Inf"} 0+9x10
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# Different le representations in one histogram.
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load_with_nhcb 5m
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mixed_bucket{job="job1", instance="ins1", le="0.1"} 0+1x10
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mixed_bucket{job="job1", instance="ins1", le="0.2"} 0+1x10
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mixed_bucket{job="job1", instance="ins1", le="2e-1"} 0+1x10
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mixed_bucket{job="job1", instance="ins1", le="2.0e-1"} 0+1x10
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mixed_bucket{job="job1", instance="ins1", le="+Inf"} 0+4x10
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mixed_bucket{job="job1", instance="ins2", le="+inf"} 0+0x10
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mixed_bucket{job="job1", instance="ins2", le="+Inf"} 0+0x10
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# Test histogram_count.
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eval instant at 50m histogram_count(testhistogram3)
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{start="positive"} 110
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{start="negative"} 20
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# Classic way of accessing the count still works.
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eval instant at 50m testhistogram3_count
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testhistogram3_count{start="positive"} 110
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testhistogram3_count{start="negative"} 20
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# Test histogram_sum.
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eval instant at 50m histogram_sum(testhistogram3)
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{start="positive"} 330
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{start="negative"} 80
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# Classic way of accessing the sum still works.
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eval instant at 50m testhistogram3_sum
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testhistogram3_sum{start="positive"} 330
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testhistogram3_sum{start="negative"} 80
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# Test histogram_avg. This has no classic equivalent.
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eval instant at 50m histogram_avg(testhistogram3)
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{start="positive"} 3
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{start="negative"} 4
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# Test histogram_stddev. This has no classic equivalent.
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eval instant at 50m histogram_stddev(testhistogram3)
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{start="positive"} 2.8189265757336734
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{start="negative"} 4.182715937754936
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# Test histogram_stdvar. This has no classic equivalent.
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eval instant at 50m histogram_stdvar(testhistogram3)
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{start="positive"} 7.946347039377573
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{start="negative"} 17.495112615949154
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# Test histogram_fraction.
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eval instant at 50m histogram_fraction(0, 0.2, testhistogram3)
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{start="positive"} 0.6363636363636364
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{start="negative"} 0
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eval instant at 50m histogram_fraction(0, 0.2, rate(testhistogram3[10m]))
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{start="positive"} 0.6363636363636364
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{start="negative"} 0
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# In the classic histogram, we can access the corresponding bucket (if
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# it exists) and divide by the count to get the same result.
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eval instant at 50m testhistogram3_bucket{le=".2"} / ignoring(le) testhistogram3_count
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{start="positive"} 0.6363636363636364
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eval instant at 50m rate(testhistogram3_bucket{le=".2"}[10m]) / ignoring(le) rate(testhistogram3_count[10m])
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{start="positive"} 0.6363636363636364
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# Test histogram_quantile, native and classic.
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eval instant at 50m histogram_quantile(0, testhistogram3)
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{start="positive"} 0
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{start="negative"} -0.25
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eval instant at 50m histogram_quantile(0, testhistogram3_bucket)
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{start="positive"} 0
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{start="negative"} -0.25
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eval instant at 50m histogram_quantile(0.25, testhistogram3)
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{start="positive"} 0.055
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{start="negative"} -0.225
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eval instant at 50m histogram_quantile(0.25, testhistogram3_bucket)
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{start="positive"} 0.055
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{start="negative"} -0.225
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eval instant at 50m histogram_quantile(0.5, testhistogram3)
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{start="positive"} 0.125
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{start="negative"} -0.2
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eval instant at 50m histogram_quantile(0.5, testhistogram3_bucket)
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{start="positive"} 0.125
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{start="negative"} -0.2
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eval instant at 50m histogram_quantile(0.75, testhistogram3)
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{start="positive"} 0.45
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{start="negative"} -0.15
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eval instant at 50m histogram_quantile(0.75, testhistogram3_bucket)
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{start="positive"} 0.45
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{start="negative"} -0.15
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eval instant at 50m histogram_quantile(1, testhistogram3)
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{start="positive"} 1
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{start="negative"} -0.1
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eval instant at 50m histogram_quantile(1, testhistogram3_bucket)
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{start="positive"} 1
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{start="negative"} -0.1
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# Quantile too low.
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eval_warn instant at 50m histogram_quantile(-0.1, testhistogram)
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{start="positive"} -Inf
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{start="negative"} -Inf
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eval_warn instant at 50m histogram_quantile(-0.1, testhistogram_bucket)
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{start="positive"} -Inf
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{start="negative"} -Inf
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# Quantile too high.
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eval_warn instant at 50m histogram_quantile(1.01, testhistogram)
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{start="positive"} +Inf
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{start="negative"} +Inf
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eval_warn instant at 50m histogram_quantile(1.01, testhistogram_bucket)
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{start="positive"} +Inf
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{start="negative"} +Inf
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# Quantile invalid.
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eval_warn instant at 50m histogram_quantile(NaN, testhistogram)
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{start="positive"} NaN
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{start="negative"} NaN
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eval_warn instant at 50m histogram_quantile(NaN, testhistogram_bucket)
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{start="positive"} NaN
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{start="negative"} NaN
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# Quantile value in lowest bucket.
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eval instant at 50m histogram_quantile(0, testhistogram)
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{start="positive"} 0
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{start="negative"} -0.2
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eval instant at 50m histogram_quantile(0, testhistogram_bucket)
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{start="positive"} 0
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{start="negative"} -0.2
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# Quantile value in highest bucket.
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eval instant at 50m histogram_quantile(1, testhistogram)
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{start="positive"} 1
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{start="negative"} 0.3
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eval instant at 50m histogram_quantile(1, testhistogram_bucket)
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{start="positive"} 1
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{start="negative"} 0.3
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# Finally some useful quantiles.
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eval instant at 50m histogram_quantile(0.2, testhistogram)
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{start="positive"} 0.048
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{start="negative"} -0.2
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eval instant at 50m histogram_quantile(0.2, testhistogram_bucket)
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{start="positive"} 0.048
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{start="negative"} -0.2
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eval instant at 50m histogram_quantile(0.5, testhistogram)
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{start="positive"} 0.15
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{start="negative"} -0.15
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eval instant at 50m histogram_quantile(0.5, testhistogram_bucket)
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{start="positive"} 0.15
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{start="negative"} -0.15
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eval instant at 50m histogram_quantile(0.8, testhistogram)
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{start="positive"} 0.72
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{start="negative"} 0.3
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eval instant at 50m histogram_quantile(0.8, testhistogram_bucket)
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{start="positive"} 0.72
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{start="negative"} 0.3
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# More realistic with rates.
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eval instant at 50m histogram_quantile(0.2, rate(testhistogram[10m]))
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{start="positive"} 0.048
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{start="negative"} -0.2
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eval instant at 50m histogram_quantile(0.2, rate(testhistogram_bucket[10m]))
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{start="positive"} 0.048
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{start="negative"} -0.2
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eval instant at 50m histogram_quantile(0.5, rate(testhistogram[10m]))
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{start="positive"} 0.15
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{start="negative"} -0.15
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eval instant at 50m histogram_quantile(0.5, rate(testhistogram_bucket[10m]))
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{start="positive"} 0.15
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{start="negative"} -0.15
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eval instant at 50m histogram_quantile(0.8, rate(testhistogram[10m]))
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{start="positive"} 0.72
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{start="negative"} 0.3
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eval instant at 50m histogram_quantile(0.8, rate(testhistogram_bucket[10m]))
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{start="positive"} 0.72
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{start="negative"} 0.3
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# Want results exactly in the middle of the bucket.
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eval instant at 7m histogram_quantile(1./6., testhistogram2)
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{} 1
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eval instant at 7m histogram_quantile(1./6., testhistogram2_bucket)
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{} 1
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eval instant at 7m histogram_quantile(0.5, testhistogram2)
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{} 3
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eval instant at 7m histogram_quantile(0.5, testhistogram2_bucket)
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{} 3
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eval instant at 7m histogram_quantile(5./6., testhistogram2)
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{} 5
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eval instant at 7m histogram_quantile(5./6., testhistogram2_bucket)
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{} 5
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eval instant at 47m histogram_quantile(1./6., rate(testhistogram2[15m]))
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{} 1
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eval instant at 47m histogram_quantile(1./6., rate(testhistogram2_bucket[15m]))
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{} 1
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eval instant at 47m histogram_quantile(0.5, rate(testhistogram2[15m]))
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{} 3
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eval instant at 47m histogram_quantile(0.5, rate(testhistogram2_bucket[15m]))
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{} 3
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eval instant at 47m histogram_quantile(5./6., rate(testhistogram2[15m]))
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{} 5
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eval instant at 47m histogram_quantile(5./6., rate(testhistogram2_bucket[15m]))
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{} 5
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# Aggregated histogram: Everything in one. Note how native histograms
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# don't require aggregation by le.
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eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds[10m])))
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{} 0.075
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eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[10m])) by (le))
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{} 0.075
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eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds[10m])))
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{} 0.1277777777777778
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eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[10m])) by (le))
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{} 0.1277777777777778
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# Aggregated histogram: Everything in one. Now with avg, which does not change anything.
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eval instant at 50m histogram_quantile(0.3, avg(rate(request_duration_seconds[10m])))
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{} 0.075
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eval instant at 50m histogram_quantile(0.3, avg(rate(request_duration_seconds_bucket[10m])) by (le))
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{} 0.075
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eval instant at 50m histogram_quantile(0.5, avg(rate(request_duration_seconds[10m])))
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{} 0.12777777777777778
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eval instant at 50m histogram_quantile(0.5, avg(rate(request_duration_seconds_bucket[10m])) by (le))
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{} 0.12777777777777778
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# Aggregated histogram: By instance.
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eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds[10m])) by (instance))
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{instance="ins1"} 0.075
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{instance="ins2"} 0.075
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eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[10m])) by (le, instance))
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{instance="ins1"} 0.075
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{instance="ins2"} 0.075
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eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds[10m])) by (instance))
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{instance="ins1"} 0.1333333333
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{instance="ins2"} 0.125
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eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[10m])) by (le, instance))
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{instance="ins1"} 0.1333333333
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{instance="ins2"} 0.125
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# Aggregated histogram: By job.
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eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds[10m])) by (job))
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{job="job1"} 0.1
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{job="job2"} 0.0642857142857143
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eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[10m])) by (le, job))
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{job="job1"} 0.1
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{job="job2"} 0.0642857142857143
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eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds[10m])) by (job))
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{job="job1"} 0.14
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{job="job2"} 0.1125
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eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[10m])) by (le, job))
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{job="job1"} 0.14
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{job="job2"} 0.1125
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# Aggregated histogram: By job and instance.
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eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds[10m])) by (job, instance))
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{instance="ins1", job="job1"} 0.11
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{instance="ins2", job="job1"} 0.09
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{instance="ins1", job="job2"} 0.06
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{instance="ins2", job="job2"} 0.0675
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eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[10m])) by (le, job, instance))
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{instance="ins1", job="job1"} 0.11
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{instance="ins2", job="job1"} 0.09
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{instance="ins1", job="job2"} 0.06
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{instance="ins2", job="job2"} 0.0675
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eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds[10m])) by (job, instance))
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{instance="ins1", job="job1"} 0.15
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{instance="ins2", job="job1"} 0.1333333333333333
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{instance="ins1", job="job2"} 0.1
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{instance="ins2", job="job2"} 0.1166666666666667
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eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[10m])) by (le, job, instance))
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{instance="ins1", job="job1"} 0.15
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{instance="ins2", job="job1"} 0.1333333333333333
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{instance="ins1", job="job2"} 0.1
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{instance="ins2", job="job2"} 0.1166666666666667
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# The unaggregated histogram for comparison. Same result as the previous one.
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eval instant at 50m histogram_quantile(0.3, rate(request_duration_seconds[10m]))
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{instance="ins1", job="job1"} 0.11
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{instance="ins2", job="job1"} 0.09
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{instance="ins1", job="job2"} 0.06
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{instance="ins2", job="job2"} 0.0675
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eval instant at 50m histogram_quantile(0.3, rate(request_duration_seconds_bucket[10m]))
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{instance="ins1", job="job1"} 0.11
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{instance="ins2", job="job1"} 0.09
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{instance="ins1", job="job2"} 0.06
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{instance="ins2", job="job2"} 0.0675
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eval instant at 50m histogram_quantile(0.5, rate(request_duration_seconds[10m]))
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{instance="ins1", job="job1"} 0.15
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{instance="ins2", job="job1"} 0.13333333333333333
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{instance="ins1", job="job2"} 0.1
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{instance="ins2", job="job2"} 0.11666666666666667
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eval instant at 50m histogram_quantile(0.5, rate(request_duration_seconds_bucket[10m]))
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{instance="ins1", job="job1"} 0.15
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{instance="ins2", job="job1"} 0.13333333333333333
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{instance="ins1", job="job2"} 0.1
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{instance="ins2", job="job2"} 0.11666666666666667
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# All NHCBs summed into one.
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|
eval instant at 50m sum(request_duration_seconds)
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{} {{schema:-53 count:250 custom_values:[0.1 0.2] buckets:[100 90 60]}}
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|
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eval instant at 50m sum(request_duration_seconds{job="job1",instance="ins1"} + ignoring(job,instance) request_duration_seconds{job="job1",instance="ins2"} + ignoring(job,instance) request_duration_seconds{job="job2",instance="ins1"} + ignoring(job,instance) request_duration_seconds{job="job2",instance="ins2"})
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{} {{schema:-53 count:250 custom_values:[0.1 0.2] buckets:[100 90 60]}}
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|
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eval instant at 50m avg(request_duration_seconds)
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{} {{schema:-53 count:62.5 custom_values:[0.1 0.2] buckets:[25 22.5 15]}}
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|
|
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# To verify the result above, calculate from classic histogram as well.
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eval instant at 50m avg (request_duration_seconds_bucket{le="0.1"})
|
|
{} 25
|
|
|
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eval instant at 50m avg (request_duration_seconds_bucket{le="0.2"}) - avg (request_duration_seconds_bucket{le="0.1"})
|
|
{} 22.5
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|
|
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eval instant at 50m avg (request_duration_seconds_bucket{le="+Inf"}) - avg (request_duration_seconds_bucket{le="0.2"})
|
|
{} 15
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|
|
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eval instant at 50m count(request_duration_seconds)
|
|
{} 4
|
|
|
|
# A histogram with nonmonotonic bucket counts. This may happen when recording
|
|
# rule evaluation or federation races scrape ingestion, causing some buckets
|
|
# counts to be derived from fewer samples.
|
|
|
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load 5m
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|
nonmonotonic_bucket{le="0.1"} 0+2x10
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|
nonmonotonic_bucket{le="1"} 0+1x10
|
|
nonmonotonic_bucket{le="10"} 0+5x10
|
|
nonmonotonic_bucket{le="100"} 0+4x10
|
|
nonmonotonic_bucket{le="1000"} 0+9x10
|
|
nonmonotonic_bucket{le="+Inf"} 0+8x10
|
|
|
|
# Nonmonotonic buckets, triggering an info annotation.
|
|
eval_info instant at 50m histogram_quantile(0.01, nonmonotonic_bucket)
|
|
{} 0.0045
|
|
|
|
eval_info instant at 50m histogram_quantile(0.5, nonmonotonic_bucket)
|
|
{} 8.5
|
|
|
|
eval_info 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[10m]))
|
|
{instance="ins1", job="job1"} 0.15
|
|
{instance="ins2", job="job1"} NaN
|
|
|
|
eval instant at 50m histogram_quantile(0.5, rate(mixed[10m]))
|
|
{instance="ins1", job="job1"} 0.2
|
|
{instance="ins2", job="job1"} NaN
|
|
|
|
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[10m]))
|
|
{instance="ins1", job="job1"} 0.2
|
|
{instance="ins2", job="job1"} NaN
|
|
|
|
load_with_nhcb 5m
|
|
empty_bucket{le="0.1", job="job1", instance="ins1"} 0x10
|
|
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[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.
|
|
# https://github.com/prometheus/prometheus/issues/9910
|
|
load_with_nhcb 5m
|
|
request_duration_seconds2_bucket{job="job1", instance="ins1", le="0.1"} 0+1x10
|
|
request_duration_seconds2_bucket{job="job1", instance="ins1", le="0.2"} 0+3x10
|
|
request_duration_seconds2_bucket{job="job1", instance="ins1", le="+Inf"} 0+4x10
|
|
|
|
eval_fail instant at 50m histogram_quantile(0.99, {__name__=~"request_duration_seconds\\d*_bucket"})
|
|
|
|
eval_fail instant at 50m histogram_quantile(0.99, {__name__=~"request_duration_seconds\\d*"})
|
|
|
|
# Histogram with constant buckets.
|
|
load_with_nhcb 1m
|
|
const_histogram_bucket{le="0.0"} 1 1 1 1 1
|
|
const_histogram_bucket{le="1.0"} 1 1 1 1 1
|
|
const_histogram_bucket{le="2.0"} 1 1 1 1 1
|
|
const_histogram_bucket{le="+Inf"} 1 1 1 1 1
|
|
|
|
# There is no change to the bucket count over time, thus rate is 0 in each bucket.
|
|
eval instant at 5m rate(const_histogram_bucket[5m])
|
|
{le="0.0"} 0
|
|
{le="1.0"} 0
|
|
{le="2.0"} 0
|
|
{le="+Inf"} 0
|
|
|
|
# Native histograms do not represent empty buckets, so here the zeros are implicit.
|
|
eval instant at 5m rate(const_histogram[5m])
|
|
{} {{schema:-53 sum:0 count:0 custom_values:[0.0 1.0 2.0]}}
|
|
|
|
# Zero buckets mean no observations, so there is no value that observations fall below,
|
|
# which means that any quantile is a NaN.
|
|
eval instant at 5m histogram_quantile(1.0, sum by (le) (rate(const_histogram_bucket[5m])))
|
|
{} NaN
|
|
|
|
eval instant at 5m histogram_quantile(1.0, sum(rate(const_histogram[5m])))
|
|
{} NaN
|
|
|
|
load_with_nhcb 1m
|
|
histogram_over_time_bucket{le="0"} 0 1 3 9
|
|
histogram_over_time_bucket{le="1"} 2 3 3 9
|
|
histogram_over_time_bucket{le="2"} 3 8 5 10
|
|
histogram_over_time_bucket{le="4"} 3 10 6 18
|
|
|
|
# Test custom buckets with sum_over_time, avg_over_time.
|
|
eval instant at 3m sum_over_time(histogram_over_time[4m:1m])
|
|
{} {{schema:-53 count:37 custom_values:[0 1 2 4] buckets:[13 4 9 11]}}
|
|
|
|
eval instant at 3m avg_over_time(histogram_over_time[4m:1m])
|
|
{} {{schema:-53 count:9.25 custom_values:[0 1 2 4] buckets:[3.25 1 2.25 2.75]}}
|
|
|
|
# Test custom buckets with counter reset
|
|
load_with_nhcb 5m
|
|
histogram_with_reset_bucket{le="1"} 1 3 9
|
|
histogram_with_reset_bucket{le="2"} 3 3 9
|
|
histogram_with_reset_bucket{le="4"} 8 5 12
|
|
histogram_with_reset_bucket{le="8"} 10 6 18
|
|
histogram_with_reset_sum{} 36 16 61
|
|
|
|
eval instant at 10m increase(histogram_with_reset[15m])
|
|
{} {{schema:-53 count:27 sum:91.5 custom_values:[1 2 4 8] counter_reset_hint:gauge buckets:[13.5 0 4.5 9]}}
|
|
|
|
eval instant at 10m resets(histogram_with_reset[15m])
|
|
{} 1
|
|
|
|
eval instant at 10m histogram_count(increase(histogram_with_reset[15m]))
|
|
{} 27
|
|
|
|
eval instant at 10m histogram_sum(increase(histogram_with_reset[15m]))
|
|
{} 91.5
|