mirror of https://github.com/prometheus/prometheus
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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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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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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# 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"})
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{} 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"})
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{} 22.5
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eval instant at 50m avg (request_duration_seconds_bucket{le="+Inf"}) - avg (request_duration_seconds_bucket{le="0.2"})
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{} 15
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eval instant at 50m count(request_duration_seconds)
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{} 4
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# A histogram with nonmonotonic bucket counts. This may happen when recording
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# rule evaluation or federation races scrape ingestion, causing some buckets
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# 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
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nonmonotonic_bucket{le="10"} 0+5x10
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nonmonotonic_bucket{le="100"} 0+4x10
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nonmonotonic_bucket{le="1000"} 0+9x10
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nonmonotonic_bucket{le="+Inf"} 0+8x10
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# Nonmonotonic buckets
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eval instant at 50m histogram_quantile(0.01, nonmonotonic_bucket)
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{} 0.0045
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eval instant at 50m histogram_quantile(0.5, nonmonotonic_bucket)
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{} 8.5
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eval instant at 50m histogram_quantile(0.99, nonmonotonic_bucket)
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{} 979.75
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# Buckets with different representations of the same upper bound.
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eval instant at 50m histogram_quantile(0.5, rate(mixed_bucket[10m]))
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{instance="ins1", job="job1"} 0.15
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{instance="ins2", job="job1"} NaN
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eval instant at 50m histogram_quantile(0.5, rate(mixed[10m]))
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{instance="ins1", job="job1"} 0.2
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|
{instance="ins2", job="job1"} NaN
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eval instant at 50m histogram_quantile(0.75, rate(mixed_bucket[10m]))
|
|
{instance="ins1", job="job1"} 0.2
|
|
{instance="ins2", job="job1"} NaN
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|
|
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eval instant at 50m histogram_quantile(1, rate(mixed_bucket[10m]))
|
|
{instance="ins1", job="job1"} 0.2
|
|
{instance="ins2", job="job1"} NaN
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|
|
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load_with_nhcb 5m
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empty_bucket{le="0.1", job="job1", instance="ins1"} 0x10
|
|
empty_bucket{le="0.2", job="job1", instance="ins1"} 0x10
|
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empty_bucket{le="+Inf", job="job1", instance="ins1"} 0x10
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|
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eval instant at 50m histogram_quantile(0.2, rate(empty_bucket[10m]))
|
|
{instance="ins1", job="job1"} NaN
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|
|
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# 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
|
|
|
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eval_fail instant at 50m histogram_quantile(0.99, {__name__=~"request_duration_seconds\\d*_bucket"})
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|
|
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eval_fail instant at 50m histogram_quantile(0.99, {__name__=~"request_duration_seconds\\d*"})
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|
|
|
# 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
|