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159 lines
6.4 KiB
159 lines
6.4 KiB
# 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 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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# Now a more realistic histogram per job and instance to test aggregation. |
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load 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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# Quantile too low. |
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eval 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 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 value in lowest bucket, which is positive. |
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eval instant at 50m histogram_quantile(0, testhistogram_bucket{start="positive"}) |
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{start="positive"} 0 |
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# Quantile value in lowest bucket, which is negative. |
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eval instant at 50m histogram_quantile(0, testhistogram_bucket{start="negative"}) |
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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_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_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_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_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_bucket[5m])) |
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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_bucket[5m])) |
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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_bucket[5m])) |
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{start="positive"} 0.72 |
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{start="negative"} 0.3 |
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# Aggregated histogram: Everything in one. |
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eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) 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_bucket[5m])) 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_bucket[5m])) 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_bucket[5m])) by (le)) |
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{} 0.12777777777777778 |
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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_bucket[5m])) 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_bucket[5m])) 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 instance. |
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eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) 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_bucket[5m])) 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_bucket[5m])) 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_bucket[5m])) 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_bucket[5m])) |
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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_bucket[5m])) |
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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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# 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. The wrong answer we want to avoid |
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# is for histogram_quantile(0.99, nonmonotonic_bucket) to return ~1000 instead |
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# of 1. |
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load 5m |
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nonmonotonic_bucket{le="0.1"} 0+1x10 |
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nonmonotonic_bucket{le="1"} 0+9x10 |
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nonmonotonic_bucket{le="10"} 0+8x10 |
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nonmonotonic_bucket{le="100"} 0+8x10 |
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nonmonotonic_bucket{le="1000"} 0+9x10 |
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nonmonotonic_bucket{le="+Inf"} 0+9x10 |
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# Nonmonotonic buckets |
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eval instant at 50m histogram_quantile(0.99, nonmonotonic_bucket) |
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{} 0.989875
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