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prometheus/promql/testdata/aggregators.test

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Break aggregation tests out. Add missing tests.
2016-02-07 10:00:23 -08:00
load 5m
http_requests{job="api-server", instance="0", group="production"} 0+10x10
http_requests{job="api-server", instance="1", group="production"} 0+20x10
http_requests{job="api-server", instance="0", group="canary"} 0+30x10
http_requests{job="api-server", instance="1", group="canary"} 0+40x10
http_requests{job="app-server", instance="0", group="production"} 0+50x10
http_requests{job="app-server", instance="1", group="production"} 0+60x10
http_requests{job="app-server", instance="0", group="canary"} 0+70x10
http_requests{job="app-server", instance="1", group="canary"} 0+80x10
Add without aggregator modifier. This has the advantage that the user doesn't need to list all labels they want to keep (as with "by") but without having to worry about inconsistent labels as when there's only one time series (as with "keeping_common"). Almost all aggregation should use this rather than the existing two options as it's much less error prone and easier to maintain due to not having to always add in "job" plus whatever other common job-level labels you have like "region".
2016-02-07 10:03:16 -08:00
load 5m
foo{job="api-server", instance="0", region="europe"} 0+90x10
foo{job="api-server"} 0+100x10
Break aggregation tests out. Add missing tests.
2016-02-07 10:00:23 -08:00
# Simple sum.
eval instant at 50m SUM BY (group) (http_requests{job="api-server"})
{group="canary"} 700
{group="production"} 300
# Test alternative "by"-clause order.
eval instant at 50m sum by (group) (http_requests{job="api-server"})
{group="canary"} 700
{group="production"} 300
# Simple average.
eval instant at 50m avg by (group) (http_requests{job="api-server"})
{group="canary"} 350
{group="production"} 150
# Simple count.
eval instant at 50m count by (group) (http_requests{job="api-server"})
{group="canary"} 2
{group="production"} 2
Add without aggregator modifier. This has the advantage that the user doesn't need to list all labels they want to keep (as with "by") but without having to worry about inconsistent labels as when there's only one time series (as with "keeping_common"). Almost all aggregation should use this rather than the existing two options as it's much less error prone and easier to maintain due to not having to always add in "job" plus whatever other common job-level labels you have like "region".
2016-02-07 10:03:16 -08:00
# Simple without.
eval instant at 50m sum without (instance) (http_requests{job="api-server"})
{group="canary",job="api-server"} 700
{group="production",job="api-server"} 300
Allow on, ignoring, by and without wit empty laberls. This offers new semantics in allowing on() for matching two single-element vectors with no known common labels. Previosuly this was often done using on(dummy). This also allows making it explict that you meant to do an aggregation without labels via by(). Fixes #1597.
2016-06-23 09:49:22 -07:00
# Empty by.
eval instant at 50m sum by () (http_requests{job="api-server"})
{} 1000
# No by/without.
Remove keep_common modifier. See #3060
2017-10-05 05:19:52 -07:00
eval instant at 50m sum(http_requests{job="api-server"})
Allow on, ignoring, by and without wit empty laberls. This offers new semantics in allowing on() for matching two single-element vectors with no known common labels. Previosuly this was often done using on(dummy). This also allows making it explict that you meant to do an aggregation without labels via by(). Fixes #1597.
2016-06-23 09:49:22 -07:00
{} 1000
# Empty without.
eval instant at 50m sum without () (http_requests{job="api-server",group="production"})
{group="production",job="api-server",instance="0"} 100
{group="production",job="api-server",instance="1"} 200
Add without aggregator modifier. This has the advantage that the user doesn't need to list all labels they want to keep (as with "by") but without having to worry about inconsistent labels as when there's only one time series (as with "keeping_common"). Almost all aggregation should use this rather than the existing two options as it's much less error prone and easier to maintain due to not having to always add in "job" plus whatever other common job-level labels you have like "region".
2016-02-07 10:03:16 -08:00
# Without with mismatched and missing labels. Do not do this.
eval instant at 50m sum without (instance) (http_requests{job="api-server"} or foo)
{group="canary",job="api-server"} 700
{group="production",job="api-server"} 300
{region="europe",job="api-server"} 900
{job="api-server"} 1000
Break aggregation tests out. Add missing tests.
2016-02-07 10:00:23 -08:00
# Lower-cased aggregation operators should work too.
eval instant at 50m sum(http_requests) by (job) + min(http_requests) by (job) + max(http_requests) by (job) + avg(http_requests) by (job)
{job="app-server"} 4550
{job="api-server"} 1750
Remove keep_common modifier. See #3060
2017-10-05 05:19:52 -07:00
# Test alternative "by"-clause order.
eval instant at 50m sum by (group) (http_requests{job="api-server"})
{group="canary"} 700
{group="production"} 300
Break aggregation tests out. Add missing tests.
2016-02-07 10:00:23 -08:00
# Test both alternative "by"-clause orders in one expression.
# Public health warning: stick to one form within an expression (or even
# in an organization), or risk serious user confusion.
Remove keep_common modifier. See #3060
2017-10-05 05:19:52 -07:00
eval instant at 50m sum(sum by (group) (http_requests{job="api-server"})) by (job)
{} 1000
Break aggregation tests out. Add missing tests.
2016-02-07 10:00:23 -08:00
Add without aggregator modifier. This has the advantage that the user doesn't need to list all labels they want to keep (as with "by") but without having to worry about inconsistent labels as when there's only one time series (as with "keeping_common"). Almost all aggregation should use this rather than the existing two options as it's much less error prone and easier to maintain due to not having to always add in "job" plus whatever other common job-level labels you have like "region".
2016-02-07 10:03:16 -08:00
Break aggregation tests out. Add missing tests.
2016-02-07 10:00:23 -08:00
# Standard deviation and variance.
eval instant at 50m stddev(http_requests)
{} 229.12878474779
eval instant at 50m stddev by (instance)(http_requests)
{instance="0"} 223.60679774998
{instance="1"} 223.60679774998
eval instant at 50m stdvar(http_requests)
{} 52500
eval instant at 50m stdvar by (instance)(http_requests)
{instance="0"} 50000
{instance="1"} 50000
Add without aggregator modifier. This has the advantage that the user doesn't need to list all labels they want to keep (as with "by") but without having to worry about inconsistent labels as when there's only one time series (as with "keeping_common"). Almost all aggregation should use this rather than the existing two options as it's much less error prone and easier to maintain due to not having to always add in "job" plus whatever other common job-level labels you have like "region".
2016-02-07 10:03:16 -08:00
Break aggregation tests out. Add missing tests.
2016-02-07 10:00:23 -08:00
# Regression test for missing separator byte in labelsToGroupingKey.
clear
load 5m
label_grouping_test{a="aa", b="bb"} 0+10x10
label_grouping_test{a="a", b="abb"} 0+20x10
eval instant at 50m sum(label_grouping_test) by (a, b)
{a="a", b="abb"} 200
{a="aa", b="bb"} 100
Add without aggregator modifier. This has the advantage that the user doesn't need to list all labels they want to keep (as with "by") but without having to worry about inconsistent labels as when there's only one time series (as with "keeping_common"). Almost all aggregation should use this rather than the existing two options as it's much less error prone and easier to maintain due to not having to always add in "job" plus whatever other common job-level labels you have like "region".
2016-02-07 10:03:16 -08:00
Break aggregation tests out. Add missing tests.
2016-02-07 10:00:23 -08:00
# Tests for min/max.
clear
load 5m
http_requests{job="api-server", instance="0", group="production"} 1
http_requests{job="api-server", instance="1", group="production"} 2
http_requests{job="api-server", instance="0", group="canary"} NaN
http_requests{job="api-server", instance="1", group="canary"} 3
http_requests{job="api-server", instance="2", group="canary"} 4
eval instant at 0m max(http_requests)
{} 4
eval instant at 0m min(http_requests)
{} 1
eval instant at 0m max by (group) (http_requests)
{group="production"} 2
{group="canary"} 4
eval instant at 0m min by (group) (http_requests)
{group="production"} 1
{group="canary"} 3
Move topk/bottomk unittests over to aggregators.
2016-07-04 05:12:12 -07:00
clear
# Tests for topk/bottomk.
load 5m
http_requests{job="api-server", instance="0", group="production"} 0+10x10
http_requests{job="api-server", instance="1", group="production"} 0+20x10
http_requests{job="api-server", instance="2", group="production"} NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
http_requests{job="api-server", instance="0", group="canary"} 0+30x10
http_requests{job="api-server", instance="1", group="canary"} 0+40x10
http_requests{job="app-server", instance="0", group="production"} 0+50x10
http_requests{job="app-server", instance="1", group="production"} 0+60x10
http_requests{job="app-server", instance="0", group="canary"} 0+70x10
http_requests{job="app-server", instance="1", group="canary"} 0+80x10
eval_ordered instant at 50m topk(3, http_requests)
http_requests{group="canary", instance="1", job="app-server"} 800
http_requests{group="canary", instance="0", job="app-server"} 700
http_requests{group="production", instance="1", job="app-server"} 600
eval_ordered instant at 50m topk(5, http_requests{group="canary",job="app-server"})
http_requests{group="canary", instance="1", job="app-server"} 800
http_requests{group="canary", instance="0", job="app-server"} 700
eval_ordered instant at 50m bottomk(3, http_requests)
http_requests{group="production", instance="0", job="api-server"} 100
http_requests{group="production", instance="1", job="api-server"} 200
http_requests{group="canary", instance="0", job="api-server"} 300
eval_ordered instant at 50m bottomk(5, http_requests{group="canary",job="app-server"})
http_requests{group="canary", instance="0", job="app-server"} 700
http_requests{group="canary", instance="1", job="app-server"} 800
Add topk/bottomk tests for multiple buckets.
2016-07-04 05:17:30 -07:00
eval instant at 50m topk by (group) (1, http_requests)
http_requests{group="production", instance="1", job="app-server"} 600
http_requests{group="canary", instance="1", job="app-server"} 800
eval instant at 50m bottomk by (group) (2, http_requests)
http_requests{group="canary", instance="0", job="api-server"} 300
http_requests{group="canary", instance="1", job="api-server"} 400
http_requests{group="production", instance="0", job="api-server"} 100
http_requests{group="production", instance="1", job="api-server"} 200
eval_ordered instant at 50m bottomk by (group) (2, http_requests{group="production"})
http_requests{group="production", instance="0", job="api-server"} 100
http_requests{group="production", instance="1", job="api-server"} 200
Move topk/bottomk unittests over to aggregators.
2016-07-04 05:12:12 -07:00
# Test NaN is sorted away from the top/bottom.
eval_ordered instant at 50m topk(3, http_requests{job="api-server",group="production"})
http_requests{job="api-server", instance="1", group="production"} 200
http_requests{job="api-server", instance="0", group="production"} 100
http_requests{job="api-server", instance="2", group="production"} NaN
eval_ordered instant at 50m bottomk(3, http_requests{job="api-server",group="production"})
http_requests{job="api-server", instance="0", group="production"} 100
http_requests{job="api-server", instance="1", group="production"} 200
http_requests{job="api-server", instance="2", group="production"} NaN
Add count_values() aggregator. This is useful for counting how many instances of a job are running a particular version/build. Fixes #622
2016-07-05 09:12:19 -07:00
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
eval instant at 5m count_values("version", version)
{version="6"} 5
{version="7"} 2
{version="8"} 2
eval instant at 5m 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
# Overwrite label with output. Don't do this.
eval instant at 5m count_values without (instance)("job", version)
{job="6", group="production"} 5
{job="8", group="canary"} 2
{job="7", group="canary"} 2
# Overwrite label with output. Don't do this.
eval instant at 5m count_values by (job, group)("job", version)
{job="6", group="production"} 5
{job="8", group="canary"} 2
{job="7", group="canary"} 2
Add quantile aggregator.
2016-07-08 05:48:48 -07:00
# 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
eval instant at 1m quantile without(point)(0.8, data)
{test="two samples"} 0.8
{test="three samples"} 1.6
{test="uneven samples"} 2.8
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