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1dcdb07d30
As for float samples, Kahan summation is used for the `sum` and `avg` aggregation and for the respective `_over_time` functions. Kahan summation is not perfect. This commit also adds tests that even Kahan summation cannot reliably pass. These tests are commented out. Note that the behavior might be different on other hardware platforms. We have to keep an eye on test failing on other hardware platforms and adjust them accordingly. Signed-off-by: Aleksandr Smirnov <5targazer@mail.ru>
968 lines
30 KiB
Plaintext
968 lines
30 KiB
Plaintext
load 5m
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http_requests{job="api-server", instance="0", group="production"} 0+10x10
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http_requests{job="api-server", instance="1", group="production"} 0+20x10
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http_requests{job="api-server", instance="0", group="canary"} 0+30x10
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http_requests{job="api-server", instance="1", group="canary"} 0+40x10
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http_requests{job="app-server", instance="0", group="production"} 0+50x10
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http_requests{job="app-server", instance="1", group="production"} 0+60x10
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http_requests{job="app-server", instance="0", group="canary"} 0+70x10
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http_requests{job="app-server", instance="1", group="canary"} 0+80x10
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load 5m
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foo{job="api-server", instance="0", region="europe"} 0+90x10
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foo{job="api-server"} 0+100x10
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# Simple sum.
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eval instant at 50m SUM BY (group) (http_requests{job="api-server"})
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{group="canary"} 700
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{group="production"} 300
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eval instant at 50m SUM BY (group) (((http_requests{job="api-server"})))
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{group="canary"} 700
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{group="production"} 300
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# Test alternative "by"-clause order.
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eval instant at 50m sum by (group) (http_requests{job="api-server"})
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{group="canary"} 700
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{group="production"} 300
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# Simple average.
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eval instant at 50m avg by (group) (http_requests{job="api-server"})
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{group="canary"} 350
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{group="production"} 150
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# Simple count.
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eval instant at 50m count by (group) (http_requests{job="api-server"})
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{group="canary"} 2
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{group="production"} 2
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# Simple without.
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eval instant at 50m sum without (instance) (http_requests{job="api-server"})
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{group="canary",job="api-server"} 700
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{group="production",job="api-server"} 300
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# Empty by.
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eval instant at 50m sum by () (http_requests{job="api-server"})
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{} 1000
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# No by/without.
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eval instant at 50m sum(http_requests{job="api-server"})
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{} 1000
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# Empty without.
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eval instant at 50m sum without () (http_requests{job="api-server",group="production"})
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{group="production",job="api-server",instance="0"} 100
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{group="production",job="api-server",instance="1"} 200
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# Without with mismatched and missing labels. Do not do this.
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eval instant at 50m sum without (instance) (http_requests{job="api-server"} or foo)
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{group="canary",job="api-server"} 700
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{group="production",job="api-server"} 300
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{region="europe",job="api-server"} 900
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{job="api-server"} 1000
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# Lower-cased aggregation operators should work too.
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eval instant at 50m sum(http_requests) by (job) + min(http_requests) by (job) + max(http_requests) by (job) + avg(http_requests) by (job)
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{job="app-server"} 4550
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{job="api-server"} 1750
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# Test alternative "by"-clause order.
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eval instant at 50m sum by (group) (http_requests{job="api-server"})
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{group="canary"} 700
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{group="production"} 300
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# Test both alternative "by"-clause orders in one expression.
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# Public health warning: stick to one form within an expression (or even
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# in an organization), or risk serious user confusion.
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eval instant at 50m sum(sum by (group) (http_requests{job="api-server"})) by (job)
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{} 1000
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eval instant at 50m SUM(http_requests)
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{} 3600
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eval instant at 50m SUM(http_requests{instance="0"}) BY(job)
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{job="api-server"} 400
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{job="app-server"} 1200
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eval instant at 50m SUM(http_requests) BY (job)
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{job="api-server"} 1000
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{job="app-server"} 2600
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# Non-existent labels mentioned in BY-clauses shouldn't propagate to output.
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eval instant at 50m SUM(http_requests) BY (job, nonexistent)
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{job="api-server"} 1000
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{job="app-server"} 2600
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eval instant at 50m COUNT(http_requests) BY (job)
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{job="api-server"} 4
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{job="app-server"} 4
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eval instant at 50m SUM(http_requests) BY (job, group)
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{group="canary", job="api-server"} 700
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{group="canary", job="app-server"} 1500
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{group="production", job="api-server"} 300
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{group="production", job="app-server"} 1100
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eval instant at 50m AVG(http_requests) BY (job)
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{job="api-server"} 250
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{job="app-server"} 650
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eval instant at 50m MIN(http_requests) BY (job)
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{job="api-server"} 100
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{job="app-server"} 500
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eval instant at 50m MAX(http_requests) BY (job)
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{job="api-server"} 400
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{job="app-server"} 800
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eval instant at 50m abs(-1 * http_requests{group="production",job="api-server"})
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{group="production", instance="0", job="api-server"} 100
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{group="production", instance="1", job="api-server"} 200
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eval instant at 50m floor(0.004 * http_requests{group="production",job="api-server"})
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{group="production", instance="0", job="api-server"} 0
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{group="production", instance="1", job="api-server"} 0
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eval instant at 50m ceil(0.004 * http_requests{group="production",job="api-server"})
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{group="production", instance="0", job="api-server"} 1
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{group="production", instance="1", job="api-server"} 1
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eval instant at 50m round(0.004 * http_requests{group="production",job="api-server"})
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{group="production", instance="0", job="api-server"} 0
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{group="production", instance="1", job="api-server"} 1
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# Round should correctly handle negative numbers.
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eval instant at 50m round(-1 * (0.004 * http_requests{group="production",job="api-server"}))
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{group="production", instance="0", job="api-server"} 0
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{group="production", instance="1", job="api-server"} -1
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# Round should round half up.
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eval instant at 50m round(0.005 * http_requests{group="production",job="api-server"})
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{group="production", instance="0", job="api-server"} 1
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{group="production", instance="1", job="api-server"} 1
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eval instant at 50m round(-1 * (0.005 * http_requests{group="production",job="api-server"}))
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{group="production", instance="0", job="api-server"} 0
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{group="production", instance="1", job="api-server"} -1
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eval instant at 50m round(1 + 0.005 * http_requests{group="production",job="api-server"})
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{group="production", instance="0", job="api-server"} 2
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{group="production", instance="1", job="api-server"} 2
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eval instant at 50m round(-1 * (1 + 0.005 * http_requests{group="production",job="api-server"}))
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{group="production", instance="0", job="api-server"} -1
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{group="production", instance="1", job="api-server"} -2
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# Round should accept the number to round nearest to.
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eval instant at 50m round(0.0005 * http_requests{group="production",job="api-server"}, 0.1)
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{group="production", instance="0", job="api-server"} 0.1
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{group="production", instance="1", job="api-server"} 0.1
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eval instant at 50m round(2.1 + 0.0005 * http_requests{group="production",job="api-server"}, 0.1)
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{group="production", instance="0", job="api-server"} 2.2
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{group="production", instance="1", job="api-server"} 2.2
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eval instant at 50m round(5.2 + 0.0005 * http_requests{group="production",job="api-server"}, 0.1)
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{group="production", instance="0", job="api-server"} 5.3
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{group="production", instance="1", job="api-server"} 5.3
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# Round should work correctly with negative numbers and multiple decimal places.
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eval instant at 50m round(-1 * (5.2 + 0.0005 * http_requests{group="production",job="api-server"}), 0.1)
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{group="production", instance="0", job="api-server"} -5.2
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{group="production", instance="1", job="api-server"} -5.3
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# Round should work correctly with big toNearests.
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eval instant at 50m round(0.025 * http_requests{group="production",job="api-server"}, 5)
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{group="production", instance="0", job="api-server"} 5
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{group="production", instance="1", job="api-server"} 5
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eval instant at 50m round(0.045 * http_requests{group="production",job="api-server"}, 5)
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{group="production", instance="0", job="api-server"} 5
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{group="production", instance="1", job="api-server"} 10
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# Standard deviation and variance.
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eval instant at 50m stddev(http_requests)
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{} 229.12878474779
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eval instant at 50m stddev by (instance)(http_requests)
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{instance="0"} 223.60679774998
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{instance="1"} 223.60679774998
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eval instant at 50m stdvar(http_requests)
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{} 52500
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eval instant at 50m stdvar by (instance)(http_requests)
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{instance="0"} 50000
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{instance="1"} 50000
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# Float precision test for standard deviation and variance
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clear
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load 5m
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http_requests{job="api-server", instance="0", group="production"} 0+1.33x10
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http_requests{job="api-server", instance="1", group="production"} 0+1.33x10
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http_requests{job="api-server", instance="0", group="canary"} 0+1.33x10
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eval instant at 50m stddev(http_requests)
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{} 0.0
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eval instant at 50m stdvar(http_requests)
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{} 0.0
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# Regression test for missing separator byte in labelsToGroupingKey.
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clear
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load 5m
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label_grouping_test{a="aa", b="bb"} 0+10x10
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label_grouping_test{a="a", b="abb"} 0+20x10
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eval instant at 50m sum(label_grouping_test) by (a, b)
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{a="a", b="abb"} 200
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{a="aa", b="bb"} 100
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# Tests for min/max.
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clear
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load 5m
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http_requests{job="api-server", instance="0", group="production"} 1
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http_requests{job="api-server", instance="1", group="production"} 2
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http_requests{job="api-server", instance="0", group="canary"} NaN
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http_requests{job="api-server", instance="1", group="canary"} 3
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http_requests{job="api-server", instance="2", group="canary"} 4
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http_requests_histogram{job="api-server", instance="3", group="canary"} {{schema:2 count:4 sum:10 buckets:[1 0 0 0 1 0 0 1 1]}}
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eval instant at 0m max(http_requests)
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expect no_info
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{} 4
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# The histogram is ignored here so the result doesn't change but it has an info annotation now.
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eval instant at 0m max({job="api-server"})
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expect info
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{} 4
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# The histogram is ignored here so there is no result but it has an info annotation now.
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eval instant at 0m max(http_requests_histogram)
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expect info
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eval instant at 0m min(http_requests)
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expect no_info
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{} 1
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# The histogram is ignored here so the result doesn't change but it has an info annotation now.
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eval instant at 0m min({job="api-server"})
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expect info
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{} 1
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# The histogram is ignored here so there is no result but it has an info annotation now.
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eval instant at 0m min(http_requests_histogram)
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expect info
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eval instant at 0m max by (group) (http_requests)
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expect no_info
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{group="production"} 2
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{group="canary"} 4
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eval instant at 0m min by (group) (http_requests)
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expect no_info
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{group="production"} 1
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{group="canary"} 3
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clear
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# Tests for topk/bottomk.
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load 5m
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http_requests{job="api-server", instance="0", group="production"} 0+10x10
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http_requests{job="api-server", instance="1", group="production"} 0+20x10
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http_requests{job="api-server", instance="2", group="production"} NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
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http_requests{job="api-server", instance="0", group="canary"} 0+30x10
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http_requests{job="api-server", instance="1", group="canary"} 0+40x10
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http_requests{job="app-server", instance="0", group="production"} 0+50x10
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http_requests{job="app-server", instance="1", group="production"} 0+60x10
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http_requests{job="app-server", instance="0", group="canary"} 0+70x10
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http_requests{job="app-server", instance="1", group="canary"} 0+80x10
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http_requests_histogram{job="app-server", instance="2", group="canary"} {{schema:0 sum:10 count:10}}x11
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http_requests_histogram{job="api-server", instance="3", group="production"} {{schema:0 sum:20 count:20}}x11
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foo 1+1x9 3
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eval instant at 50m topk(3, http_requests)
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expect ordered
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http_requests{group="canary", instance="1", job="app-server"} 800
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http_requests{group="canary", instance="0", job="app-server"} 700
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http_requests{group="production", instance="1", job="app-server"} 600
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eval instant at 50m topk((3), (http_requests))
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expect ordered
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http_requests{group="canary", instance="1", job="app-server"} 800
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http_requests{group="canary", instance="0", job="app-server"} 700
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http_requests{group="production", instance="1", job="app-server"} 600
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eval instant at 50m topk(5, http_requests{group="canary",job="app-server"})
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expect ordered
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http_requests{group="canary", instance="1", job="app-server"} 800
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http_requests{group="canary", instance="0", job="app-server"} 700
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eval instant at 50m bottomk(3, http_requests)
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expect ordered
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http_requests{group="production", instance="0", job="api-server"} 100
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http_requests{group="production", instance="1", job="api-server"} 200
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http_requests{group="canary", instance="0", job="api-server"} 300
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eval instant at 50m bottomk(5, http_requests{group="canary",job="app-server"})
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expect ordered
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http_requests{group="canary", instance="0", job="app-server"} 700
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http_requests{group="canary", instance="1", job="app-server"} 800
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eval instant at 50m topk by (group) (1, http_requests)
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http_requests{group="production", instance="1", job="app-server"} 600
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http_requests{group="canary", instance="1", job="app-server"} 800
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eval instant at 50m bottomk by (group) (2, http_requests)
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http_requests{group="canary", instance="0", job="api-server"} 300
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http_requests{group="canary", instance="1", job="api-server"} 400
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http_requests{group="production", instance="0", job="api-server"} 100
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http_requests{group="production", instance="1", job="api-server"} 200
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eval instant at 50m bottomk by (group) (2, http_requests{group="production"})
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expect ordered
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http_requests{group="production", instance="0", job="api-server"} 100
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http_requests{group="production", instance="1", job="api-server"} 200
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# Test NaN is sorted away from the top/bottom.
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eval instant at 50m topk(3, http_requests{job="api-server",group="production"})
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expect ordered
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http_requests{job="api-server", instance="1", group="production"} 200
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http_requests{job="api-server", instance="0", group="production"} 100
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http_requests{job="api-server", instance="2", group="production"} NaN
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eval instant at 50m bottomk(3, http_requests{job="api-server",group="production"})
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expect ordered
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http_requests{job="api-server", instance="0", group="production"} 100
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http_requests{job="api-server", instance="1", group="production"} 200
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http_requests{job="api-server", instance="2", group="production"} NaN
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# Test topk and bottomk allocate min(k, input_vector) for results vector
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eval instant at 50m bottomk(9999999999, http_requests{job="app-server",group="canary"})
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expect ordered
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http_requests{group="canary", instance="0", job="app-server"} 700
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http_requests{group="canary", instance="1", job="app-server"} 800
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eval instant at 50m topk(9999999999, http_requests{job="api-server",group="production"})
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expect ordered
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http_requests{job="api-server", instance="1", group="production"} 200
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http_requests{job="api-server", instance="0", group="production"} 100
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http_requests{job="api-server", instance="2", group="production"} NaN
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# Bug #5276.
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eval instant at 50m topk(scalar(foo), http_requests)
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expect ordered
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http_requests{group="canary", instance="1", job="app-server"} 800
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http_requests{group="canary", instance="0", job="app-server"} 700
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http_requests{group="production", instance="1", job="app-server"} 600
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# Bug #15971.
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eval range from 0m to 50m step 5m count(topk(scalar(foo), http_requests))
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{} 1 2 3 4 5 6 7 8 9 9 3
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eval range from 0m to 50m step 5m count(bottomk(scalar(foo), http_requests))
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{} 1 2 3 4 5 6 7 8 9 9 3
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# Tests for histogram: should ignore histograms.
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eval instant at 50m topk(100, http_requests_histogram)
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expect info
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#empty
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eval range from 0 to 50m step 5m topk(100, http_requests_histogram)
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expect info
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#empty
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eval instant at 50m topk(1, {__name__=~"http_requests(_histogram)?"})
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expect info
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{__name__="http_requests", group="canary", instance="1", job="app-server"} 800
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eval instant at 50m count(topk(1000, {__name__=~"http_requests(_histogram)?"}))
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expect info
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{} 9
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eval range from 0 to 50m step 5m count(topk(1000, {__name__=~"http_requests(_histogram)?"}))
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expect info
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{} 9x10
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eval instant at 50m topk by (instance) (1, {__name__=~"http_requests(_histogram)?"})
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expect info
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{__name__="http_requests", group="canary", instance="0", job="app-server"} 700
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{__name__="http_requests", group="canary", instance="1", job="app-server"} 800
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{__name__="http_requests", group="production", instance="2", job="api-server"} NaN
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eval instant at 50m bottomk(100, http_requests_histogram)
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expect info
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#empty
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eval range from 0 to 50m step 5m bottomk(100, http_requests_histogram)
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expect info
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#empty
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eval instant at 50m bottomk(1, {__name__=~"http_requests(_histogram)?"})
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expect info
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{__name__="http_requests", group="production", instance="0", job="api-server"} 100
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eval instant at 50m count(bottomk(1000, {__name__=~"http_requests(_histogram)?"}))
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expect info
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{} 9
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eval range from 0 to 50m step 5m count(bottomk(1000, {__name__=~"http_requests(_histogram)?"}))
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expect info
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{} 9x10
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eval instant at 50m bottomk by (instance) (1, {__name__=~"http_requests(_histogram)?"})
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expect info
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{__name__="http_requests", group="production", instance="0", job="api-server"} 100
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{__name__="http_requests", group="production", instance="1", job="api-server"} 200
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{__name__="http_requests", group="production", instance="2", job="api-server"} NaN
|
|
|
|
eval instant at 50m topk(NaN, non_existent)
|
|
expect fail msg: Parameter value is NaN
|
|
|
|
eval instant at 50m limitk(NaN, non_existent)
|
|
expect fail msg: Parameter value is NaN
|
|
|
|
eval instant at 50m limit_ratio(NaN, non_existent)
|
|
expect fail msg: Ratio value is NaN
|
|
|
|
clear
|
|
|
|
# Tests for count_values.
|
|
load 5m
|
|
version{job="api-server", instance="0", group="production"} 6
|
|
version{job="api-server", instance="1", group="production"} 6
|
|
version{job="api-server", instance="2", group="production"} 6
|
|
version{job="api-server", instance="0", group="canary"} 8
|
|
version{job="api-server", instance="1", group="canary"} 8
|
|
version{job="app-server", instance="0", group="production"} 6
|
|
version{job="app-server", instance="1", group="production"} 6
|
|
version{job="app-server", instance="0", group="canary"} 7
|
|
version{job="app-server", instance="1", group="canary"} 7
|
|
version{job="app-server", instance="2", group="canary"} {{schema:0 sum:10 count:20 z_bucket_w:0.001 z_bucket:2 buckets:[1 2] n_buckets:[1 2]}}
|
|
version{job="app-server", instance="3", group="canary"} {{schema:0 sum:10 count:20 z_bucket_w:0.001 z_bucket:2 buckets:[1 2] n_buckets:[1 2]}}
|
|
|
|
eval instant at 1m count_values("version", version)
|
|
{version="6"} 5
|
|
{version="7"} 2
|
|
{version="8"} 2
|
|
{version="{count:20, sum:10, [-2,-1):2, [-1,-0.5):1, [-0.001,0.001]:2, (0.5,1]:1, (1,2]:2}"} 2
|
|
|
|
eval instant at 1m count_values(((("version"))), version)
|
|
{version="6"} 5
|
|
{version="7"} 2
|
|
{version="8"} 2
|
|
{version="{count:20, sum:10, [-2,-1):2, [-1,-0.5):1, [-0.001,0.001]:2, (0.5,1]:1, (1,2]:2}"} 2
|
|
|
|
eval instant at 1m count_values without (instance)("version", version)
|
|
{job="api-server", group="production", version="6"} 3
|
|
{job="api-server", group="canary", version="8"} 2
|
|
{job="app-server", group="production", version="6"} 2
|
|
{job="app-server", group="canary", version="7"} 2
|
|
{job="app-server", group="canary", version="{count:20, sum:10, [-2,-1):2, [-1,-0.5):1, [-0.001,0.001]:2, (0.5,1]:1, (1,2]:2}"} 2
|
|
|
|
# Overwrite label with output. Don't do this.
|
|
eval instant at 1m count_values without (instance)("job", version)
|
|
{job="6", group="production"} 5
|
|
{job="8", group="canary"} 2
|
|
{job="7", group="canary"} 2
|
|
{job="{count:20, sum:10, [-2,-1):2, [-1,-0.5):1, [-0.001,0.001]:2, (0.5,1]:1, (1,2]:2}", group="canary"} 2
|
|
|
|
# Overwrite label with output. Don't do this.
|
|
eval instant at 1m count_values by (job, group)("job", version)
|
|
{job="6", group="production"} 5
|
|
{job="8", group="canary"} 2
|
|
{job="7", group="canary"} 2
|
|
{job="{count:20, sum:10, [-2,-1):2, [-1,-0.5):1, [-0.001,0.001]:2, (0.5,1]:1, (1,2]:2}", group="canary"} 2
|
|
|
|
# Test an invalid label value.
|
|
eval instant at 0 count_values("a\xc5z", version)
|
|
expect fail msg:invalid label name "a\xc5z"
|
|
|
|
# Tests for quantile.
|
|
clear
|
|
|
|
load 10s
|
|
data{test="two samples",point="a"} 0
|
|
data{test="two samples",point="b"} 1
|
|
data{test="three samples",point="a"} 0
|
|
data{test="three samples",point="b"} 1
|
|
data{test="three samples",point="c"} 2
|
|
data{test="uneven samples",point="a"} 0
|
|
data{test="uneven samples",point="b"} 1
|
|
data{test="uneven samples",point="c"} 4
|
|
data{test="NaN sample",point="a"} 0
|
|
data{test="NaN sample",point="b"} 1
|
|
data{test="NaN sample",point="c"} NaN
|
|
data_histogram{test="histogram sample", point="c"} {{schema:2 count:4 sum:10 buckets:[1 0 0 0 1 0 0 1 1]}}
|
|
foo 0 1 0 1 0 1 0.8
|
|
|
|
# 80th percentile.
|
|
# The NaN sample is treated as the smallest possible value.
|
|
eval instant at 1m quantile without(point)(0.8, data)
|
|
expect no_info
|
|
{test="two samples"} 0.8
|
|
{test="three samples"} 1.6
|
|
{test="uneven samples"} 2.8
|
|
{test="NaN sample"} 0.6
|
|
|
|
# 20th percentile.
|
|
# A quantile between NaN and 0 is interpolated as NaN.
|
|
eval instant at 1m quantile without(point)(0.2, data)
|
|
{test="two samples"} 0.2
|
|
{test="three samples"} 0.4
|
|
{test="uneven samples"} 0.4
|
|
{test="NaN sample"} NaN
|
|
|
|
# The histogram is ignored here so the result doesn't change but it has an info annotation now.
|
|
eval instant at 1m quantile without(point)(0.8, {__name__=~"data(_histogram)?"})
|
|
expect info
|
|
{test="two samples"} 0.8
|
|
{test="three samples"} 1.6
|
|
{test="uneven samples"} 2.8
|
|
{test="NaN sample"} 0.6
|
|
|
|
# The histogram is ignored here so there is no result but it has an info annotation now.
|
|
eval instant at 1m quantile(0.8, data_histogram)
|
|
expect info
|
|
|
|
# Bug #5276.
|
|
eval instant at 1m quantile without(point)(scalar(foo), data)
|
|
{test="two samples"} 0.8
|
|
{test="three samples"} 1.6
|
|
{test="uneven samples"} 2.8
|
|
{test="NaN sample"} 0.6
|
|
|
|
eval instant at 1m quantile without(point)((scalar(foo)), data)
|
|
{test="two samples"} 0.8
|
|
{test="three samples"} 1.6
|
|
{test="uneven samples"} 2.8
|
|
{test="NaN sample"} 0.6
|
|
|
|
eval instant at 1m quantile without(point)(NaN, data)
|
|
expect warn msg: PromQL warning: quantile value should be between 0 and 1, got NaN
|
|
{test="two samples"} NaN
|
|
{test="three samples"} NaN
|
|
{test="uneven samples"} NaN
|
|
{test="NaN sample"} NaN
|
|
|
|
# Bug #15971.
|
|
eval range from 0m to 1m step 10s quantile without(point) (scalar(foo), data)
|
|
{test="two samples"} 0 1 0 1 0 1 0.8
|
|
{test="three samples"} 0 2 0 2 0 2 1.6
|
|
{test="uneven samples"} 0 4 0 4 0 4 2.8
|
|
{test="NaN sample"} NaN 1 NaN 1 NaN 1 0.6
|
|
|
|
# Tests for group.
|
|
clear
|
|
|
|
load 10s
|
|
data{test="two samples",point="a"} 0
|
|
data{test="two samples",point="b"} 1
|
|
data{test="three samples",point="a"} 0
|
|
data{test="three samples",point="b"} 1
|
|
data{test="three samples",point="c"} 2
|
|
data{test="uneven samples",point="a"} 0
|
|
data{test="uneven samples",point="b"} 1
|
|
data{test="uneven samples",point="c"} 4
|
|
data{test="histogram sample",point="c"} {{schema:0 sum:0 count:0}}
|
|
foo .8
|
|
|
|
eval instant at 1m group without(point)(data)
|
|
{test="two samples"} 1
|
|
{test="three samples"} 1
|
|
{test="uneven samples"} 1
|
|
{test="histogram sample"} 1
|
|
|
|
eval instant at 1m group(foo)
|
|
{} 1
|
|
|
|
# Tests for avg.
|
|
clear
|
|
|
|
load 10s
|
|
data{test="ten",point="a"} 8
|
|
data{test="ten",point="b"} 10
|
|
data{test="ten",point="c"} 12
|
|
data{test="inf",point="a"} 0
|
|
data{test="inf",point="b"} Inf
|
|
data{test="inf",point="d"} Inf
|
|
data{test="inf",point="c"} 0
|
|
data{test="-inf",point="a"} -Inf
|
|
data{test="-inf",point="b"} -Inf
|
|
data{test="-inf",point="c"} 0
|
|
data{test="inf2",point="a"} Inf
|
|
data{test="inf2",point="b"} 0
|
|
data{test="inf2",point="c"} Inf
|
|
data{test="-inf2",point="a"} -Inf
|
|
data{test="-inf2",point="b"} 0
|
|
data{test="-inf2",point="c"} -Inf
|
|
data{test="inf3",point="b"} Inf
|
|
data{test="inf3",point="d"} Inf
|
|
data{test="inf3",point="c"} Inf
|
|
data{test="inf3",point="d"} -Inf
|
|
data{test="-inf3",point="b"} -Inf
|
|
data{test="-inf3",point="d"} -Inf
|
|
data{test="-inf3",point="c"} -Inf
|
|
data{test="-inf3",point="c"} Inf
|
|
data{test="nan",point="a"} -Inf
|
|
data{test="nan",point="b"} 0
|
|
data{test="nan",point="c"} Inf
|
|
data{test="big",point="a"} 9.988465674311579e+307
|
|
data{test="big",point="b"} 9.988465674311579e+307
|
|
data{test="big",point="c"} 9.988465674311579e+307
|
|
data{test="big",point="d"} 9.988465674311579e+307
|
|
data{test="-big",point="a"} -9.988465674311579e+307
|
|
data{test="-big",point="b"} -9.988465674311579e+307
|
|
data{test="-big",point="c"} -9.988465674311579e+307
|
|
data{test="-big",point="d"} -9.988465674311579e+307
|
|
data{test="bigzero",point="a"} -9.988465674311579e+307
|
|
data{test="bigzero",point="b"} -9.988465674311579e+307
|
|
data{test="bigzero",point="c"} 9.988465674311579e+307
|
|
data{test="bigzero",point="d"} 9.988465674311579e+307
|
|
data{test="value is nan"} NaN
|
|
|
|
eval instant at 1m avg(data{test="ten"})
|
|
{} 10
|
|
|
|
eval instant at 1m avg(data{test="inf"})
|
|
{} Inf
|
|
|
|
eval instant at 1m avg(data{test="inf2"})
|
|
{} Inf
|
|
|
|
eval instant at 1m avg(data{test="inf3"})
|
|
{} NaN
|
|
|
|
eval instant at 1m avg(data{test="-inf"})
|
|
{} -Inf
|
|
|
|
eval instant at 1m avg(data{test="-inf2"})
|
|
{} -Inf
|
|
|
|
eval instant at 1m avg(data{test="-inf3"})
|
|
{} NaN
|
|
|
|
eval instant at 1m avg(data{test="nan"})
|
|
{} NaN
|
|
|
|
eval instant at 1m avg(data{test="big"})
|
|
{} 9.988465674311579e+307
|
|
|
|
eval instant at 1m avg(data{test="-big"})
|
|
{} -9.988465674311579e+307
|
|
|
|
eval instant at 1m avg(data{test="bigzero"})
|
|
{} 0
|
|
|
|
# If NaN is in the mix, the result is NaN.
|
|
eval instant at 1m avg(data)
|
|
{} NaN
|
|
|
|
# Test summing and averaging extreme values.
|
|
clear
|
|
|
|
load 10s
|
|
data{test="ten",point="a"} 2
|
|
data{test="ten",point="b"} 8
|
|
data{test="ten",point="c"} 1e+100
|
|
data{test="ten",point="d"} -1e100
|
|
data{test="pos_inf",group="1",point="a"} Inf
|
|
data{test="pos_inf",group="1",point="b"} 2
|
|
data{test="pos_inf",group="2",point="a"} 2
|
|
data{test="pos_inf",group="2",point="b"} Inf
|
|
data{test="neg_inf",group="1",point="a"} -Inf
|
|
data{test="neg_inf",group="1",point="b"} 2
|
|
data{test="neg_inf",group="2",point="a"} 2
|
|
data{test="neg_inf",group="2",point="b"} -Inf
|
|
data{test="inf_inf",point="a"} Inf
|
|
data{test="inf_inf",point="b"} -Inf
|
|
data{test="nan",group="1",point="a"} NaN
|
|
data{test="nan",group="1",point="b"} 2
|
|
data{test="nan",group="2",point="a"} 2
|
|
data{test="nan",group="2",point="b"} NaN
|
|
|
|
eval instant at 1m sum(data{test="ten"})
|
|
{} 10
|
|
|
|
# Plain addition doesn't use Kahan summation, so operations involving very large magnitudes
|
|
# (±1e+100) lose precision. The smaller values are absorbed, leading to an incorrect result.
|
|
# eval instant at 1m sum(data{test="ten",point="a"}) + sum(data{test="ten",point="b"}) + sum(data{test="ten",point="c"}) + sum(data{test="ten",point="d"})
|
|
# {} 10
|
|
|
|
eval instant at 1m avg(data{test="ten"})
|
|
{} 2.5
|
|
|
|
eval instant at 1m sum by (group) (data{test="pos_inf"})
|
|
{group="1"} Inf
|
|
{group="2"} Inf
|
|
|
|
eval instant at 1m avg by (group) (data{test="pos_inf"})
|
|
{group="1"} Inf
|
|
{group="2"} Inf
|
|
|
|
eval instant at 1m sum by (group) (data{test="neg_inf"})
|
|
{group="1"} -Inf
|
|
{group="2"} -Inf
|
|
|
|
eval instant at 1m avg by (group) (data{test="neg_inf"})
|
|
{group="1"} -Inf
|
|
{group="2"} -Inf
|
|
|
|
eval instant at 1m sum(data{test="inf_inf"})
|
|
{} NaN
|
|
|
|
eval instant at 1m avg(data{test="inf_inf"})
|
|
{} NaN
|
|
|
|
eval instant at 1m sum by (group) (data{test="nan"})
|
|
{group="1"} NaN
|
|
{group="2"} NaN
|
|
|
|
eval instant at 1m avg by (group) (data{test="nan"})
|
|
{group="1"} NaN
|
|
{group="2"} NaN
|
|
|
|
clear
|
|
|
|
# Demonstrate robustness of direct mean calculation vs. incremental mean calculation.
|
|
# The tests below are prone to small inaccuracies with incremental mean calculation.
|
|
# The exact number of aggregated values that trigger an inaccuracy depends on the
|
|
# hardware.
|
|
# See also discussion in https://github.com/prometheus/prometheus/issues/16714
|
|
load 5m
|
|
foo{idx="0"} 52
|
|
foo{idx="1"} 52
|
|
foo{idx="2"} 52
|
|
foo{idx="3"} 52
|
|
foo{idx="4"} 52
|
|
foo{idx="5"} 52
|
|
foo{idx="6"} 52
|
|
foo{idx="7"} 52
|
|
foo{idx="8"} 52
|
|
foo{idx="9"} 52
|
|
foo{idx="10"} 52
|
|
foo{idx="11"} 52
|
|
|
|
eval instant at 0 avg(foo) - 52
|
|
{} 0
|
|
|
|
eval instant at 0 avg(topk(11, foo)) - 52
|
|
{} 0
|
|
|
|
eval instant at 0 avg(topk(10, foo)) - 52
|
|
{} 0
|
|
|
|
eval instant at 0 avg(topk(9, foo)) - 52
|
|
{} 0
|
|
|
|
eval instant at 0 avg(topk(8, foo)) - 52
|
|
{} 0
|
|
|
|
# The following tests do not utilize the tolerance built into the
|
|
# testing framework but rely on the exact equality implemented in
|
|
# PromQL. They currently pass, but we should keep in mind that this is
|
|
# not a hard requirement, and generally it is a bad idea in practice
|
|
# to rely on exact equality like this in alerting rules etc.
|
|
|
|
eval instant at 0 avg(foo) == 52
|
|
{} 52
|
|
|
|
eval instant at 0 avg(topk(11, foo)) == 52
|
|
{} 52
|
|
|
|
eval instant at 0 avg(topk(10, foo)) == 52
|
|
{} 52
|
|
|
|
eval instant at 0 avg(topk(9, foo)) == 52
|
|
{} 52
|
|
|
|
eval instant at 0 avg(topk(8, foo)) == 52
|
|
{} 52
|
|
|
|
clear
|
|
|
|
# Test that aggregations are deterministic.
|
|
# Commented because it is flaky in range mode.
|
|
#load 10s
|
|
# up{job="prometheus"} 1
|
|
# up{job="prometheus2"} 1
|
|
#
|
|
#eval instant at 1m count(topk(1,max(up) without()) == topk(1,max(up) without()) == topk(1,max(up) without()) == topk(1,max(up) without()) == topk(1,max(up) without()))
|
|
# {} 1
|
|
|
|
clear
|
|
|
|
# Test stddev produces consistent results regardless the order the data is loaded in.
|
|
load 5m
|
|
series{label="a"} 1
|
|
series{label="b"} 2
|
|
series{label="c"} {{schema:1 sum:15 count:10 buckets:[3 2 5 7 9]}}
|
|
|
|
# The histogram is ignored here so the result doesn't change but it has an info annotation now.
|
|
eval instant at 0m stddev(series)
|
|
expect info
|
|
{} 0.5
|
|
|
|
eval instant at 0m stdvar(series)
|
|
expect info
|
|
{} 0.25
|
|
|
|
# The histogram is ignored here so there is no result but it has an info annotation now.
|
|
eval instant at 0m stddev({label="c"})
|
|
expect info
|
|
|
|
eval instant at 0m stdvar({label="c"})
|
|
expect info
|
|
|
|
eval instant at 0m stddev by (label) (series)
|
|
expect info
|
|
{label="a"} 0
|
|
{label="b"} 0
|
|
|
|
eval instant at 0m stdvar by (label) (series)
|
|
expect info
|
|
{label="a"} 0
|
|
{label="b"} 0
|
|
|
|
clear
|
|
|
|
load 5m
|
|
series{label="a"} {{schema:1 sum:15 count:10 buckets:[3 2 5 7 9]}}
|
|
series{label="b"} 1
|
|
series{label="c"} 2
|
|
|
|
eval instant at 0m stddev(series)
|
|
expect info
|
|
{} 0.5
|
|
|
|
eval instant at 0m stdvar(series)
|
|
expect info
|
|
{} 0.25
|
|
|
|
eval instant at 0m stddev by (label) (series)
|
|
expect info
|
|
{label="b"} 0
|
|
{label="c"} 0
|
|
|
|
eval instant at 0m stdvar by (label) (series)
|
|
expect info
|
|
{label="b"} 0
|
|
{label="c"} 0
|
|
|
|
clear
|
|
|
|
load 5m
|
|
series{label="a"} 1
|
|
series{label="b"} 2
|
|
series{label="c"} NaN
|
|
|
|
eval instant at 0m stddev(series)
|
|
{} NaN
|
|
|
|
eval instant at 0m stdvar(series)
|
|
{} NaN
|
|
|
|
eval instant at 0m stddev by (label) (series)
|
|
{label="a"} 0
|
|
{label="b"} 0
|
|
{label="c"} NaN
|
|
|
|
eval instant at 0m stdvar by (label) (series)
|
|
{label="a"} 0
|
|
{label="b"} 0
|
|
{label="c"} NaN
|
|
|
|
clear
|
|
|
|
load 5m
|
|
series{label="a"} NaN
|
|
series{label="b"} 1
|
|
series{label="c"} 2
|
|
|
|
eval instant at 0m stddev(series)
|
|
{} NaN
|
|
|
|
eval instant at 0m stdvar(series)
|
|
{} NaN
|
|
|
|
eval instant at 0m stddev by (label) (series)
|
|
{label="a"} NaN
|
|
{label="b"} 0
|
|
{label="c"} 0
|
|
|
|
eval instant at 0m stdvar by (label) (series)
|
|
{label="a"} NaN
|
|
{label="b"} 0
|
|
{label="c"} 0
|
|
|
|
clear
|
|
|
|
load 5m
|
|
series NaN
|
|
|
|
eval instant at 0m stddev(series)
|
|
{} NaN
|
|
|
|
eval instant at 0m stdvar(series)
|
|
{} NaN
|
|
|
|
clear
|
|
|
|
load 5m
|
|
series{label="a"} 1
|
|
series{label="b"} 2
|
|
series{label="c"} inf
|
|
|
|
eval instant at 0m stddev (series)
|
|
{} NaN
|
|
|
|
eval instant at 0m stdvar (series)
|
|
{} NaN
|
|
|
|
eval instant at 0m stddev by (label) (series)
|
|
{label="a"} 0
|
|
{label="b"} 0
|
|
{label="c"} NaN
|
|
|
|
eval instant at 0m stdvar by (label) (series)
|
|
{label="a"} 0
|
|
{label="b"} 0
|
|
{label="c"} NaN
|
|
|
|
clear
|
|
|
|
load 5m
|
|
series{label="a"} inf
|
|
series{label="b"} 1
|
|
series{label="c"} 2
|
|
|
|
eval instant at 0m stddev(series)
|
|
{} NaN
|
|
|
|
eval instant at 0m stdvar(series)
|
|
{} NaN
|
|
|
|
eval instant at 0m stddev by (label) (series)
|
|
{label="a"} NaN
|
|
{label="b"} 0
|
|
{label="c"} 0
|
|
|
|
eval instant at 0m stdvar by (label) (series)
|
|
{label="a"} NaN
|
|
{label="b"} 0
|
|
{label="c"} 0
|
|
|
|
clear
|
|
|
|
load 5m
|
|
series inf
|
|
|
|
eval instant at 0m stddev(series)
|
|
{} NaN
|
|
|
|
eval instant at 0m stdvar(series)
|
|
{} NaN
|