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9f4cb06a37
* use Welford/Knuth method to compute standard deviation and variance, avoids float precision issues * use better method for calculating avg and avg_over_time Signed-off-by: Dan Cech <dcech@grafana.com>
265 lines
10 KiB
Plaintext
265 lines
10 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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# 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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# 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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eval instant at 0m max(http_requests)
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{} 4
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eval instant at 0m min(http_requests)
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{} 1
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eval instant at 0m max by (group) (http_requests)
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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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{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
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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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eval_ordered instant at 50m topk(3, http_requests)
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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_ordered instant at 50m topk(5, http_requests{group="canary",job="app-server"})
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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_ordered instant at 50m bottomk(3, http_requests)
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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_ordered instant at 50m bottomk(5, http_requests{group="canary",job="app-server"})
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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_ordered instant at 50m bottomk by (group) (2, http_requests{group="production"})
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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_ordered instant at 50m topk(3, http_requests{job="api-server",group="production"})
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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_ordered instant at 50m bottomk(3, http_requests{job="api-server",group="production"})
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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_ordered instant at 50m bottomk(9999999999, http_requests{job="app-server",group="canary"})
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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_ordered instant at 50m topk(9999999999, http_requests{job="api-server",group="production"})
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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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clear
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# Tests for count_values.
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load 5m
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version{job="api-server", instance="0", group="production"} 6
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version{job="api-server", instance="1", group="production"} 6
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version{job="api-server", instance="2", group="production"} 6
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version{job="api-server", instance="0", group="canary"} 8
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version{job="api-server", instance="1", group="canary"} 8
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version{job="app-server", instance="0", group="production"} 6
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version{job="app-server", instance="1", group="production"} 6
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version{job="app-server", instance="0", group="canary"} 7
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version{job="app-server", instance="1", group="canary"} 7
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eval instant at 5m count_values("version", version)
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{version="6"} 5
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{version="7"} 2
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{version="8"} 2
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eval instant at 5m count_values without (instance)("version", version)
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{job="api-server", group="production", version="6"} 3
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{job="api-server", group="canary", version="8"} 2
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{job="app-server", group="production", version="6"} 2
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{job="app-server", group="canary", version="7"} 2
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# Overwrite label with output. Don't do this.
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eval instant at 5m count_values without (instance)("job", version)
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{job="6", group="production"} 5
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{job="8", group="canary"} 2
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{job="7", group="canary"} 2
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# Overwrite label with output. Don't do this.
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eval instant at 5m count_values by (job, group)("job", version)
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{job="6", group="production"} 5
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{job="8", group="canary"} 2
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{job="7", group="canary"} 2
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# Tests for quantile.
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clear
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load 10s
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data{test="two samples",point="a"} 0
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data{test="two samples",point="b"} 1
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data{test="three samples",point="a"} 0
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data{test="three samples",point="b"} 1
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data{test="three samples",point="c"} 2
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data{test="uneven samples",point="a"} 0
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data{test="uneven samples",point="b"} 1
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data{test="uneven samples",point="c"} 4
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eval instant at 1m quantile without(point)(0.8, data)
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{test="two samples"} 0.8
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{test="three samples"} 1.6
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{test="uneven samples"} 2.8
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