mudhorn/prometheus
1
0
Fork 0
mirror of https://github.com/prometheus/prometheus.git synced 2024-11-15 18:14:06 -08:00
Code Issues Actions 5 Packages Projects Releases Wiki Activity
prometheus/promql/promqltest/testdata/aggregators.test

575 lines
20 KiB
Plaintext
Raw Normal View History

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
PromQL: Fix string and parentheses handling in engine (#6612) * WIP: PromQL: Allow engine to return strings Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Add test suggested by @roidelapluie Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix typo in React UI Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix parenthesis handling for functions and aggregator params Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Add more tests Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix React UI test Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com>
2020-01-15 09:31:58 -08:00
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 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
promql: Organize legacy tests into appropriate files (#7989) * Separate tests into: ** Aggregators ** Functions ** Operators ** Selectors * Remove simple files and place tests into other files. * Eliminate some of the _over_time tests since there are already plenty of edge cases present in the functions.test file. Relates to #740 Signed-off-by: Harold Dost <harolddost@gmail.com>
2020-10-07 02:09:20 -07:00
eval instant at 50m SUM(http_requests)
{} 3600
eval instant at 50m SUM(http_requests{instance="0"}) BY(job)
{job="api-server"} 400
{job="app-server"} 1200
eval instant at 50m SUM(http_requests) BY (job)
{job="api-server"} 1000
{job="app-server"} 2600
# Non-existent labels mentioned in BY-clauses shouldn't propagate to output.
eval instant at 50m SUM(http_requests) BY (job, nonexistent)
{job="api-server"} 1000
{job="app-server"} 2600
eval instant at 50m COUNT(http_requests) BY (job)
{job="api-server"} 4
{job="app-server"} 4
eval instant at 50m SUM(http_requests) BY (job, group)
{group="canary", job="api-server"} 700
{group="canary", job="app-server"} 1500
{group="production", job="api-server"} 300
{group="production", job="app-server"} 1100
eval instant at 50m AVG(http_requests) BY (job)
{job="api-server"} 250
{job="app-server"} 650
eval instant at 50m MIN(http_requests) BY (job)
{job="api-server"} 100
{job="app-server"} 500
eval instant at 50m MAX(http_requests) BY (job)
{job="api-server"} 400
{job="app-server"} 800
eval instant at 50m abs(-1 * http_requests{group="production",job="api-server"})
{group="production", instance="0", job="api-server"} 100
{group="production", instance="1", job="api-server"} 200
eval instant at 50m floor(0.004 * http_requests{group="production",job="api-server"})
{group="production", instance="0", job="api-server"} 0
{group="production", instance="1", job="api-server"} 0
eval instant at 50m ceil(0.004 * http_requests{group="production",job="api-server"})
{group="production", instance="0", job="api-server"} 1
{group="production", instance="1", job="api-server"} 1
eval instant at 50m round(0.004 * http_requests{group="production",job="api-server"})
{group="production", instance="0", job="api-server"} 0
{group="production", instance="1", job="api-server"} 1
# Round should correctly handle negative numbers.
eval instant at 50m round(-1 * (0.004 * http_requests{group="production",job="api-server"}))
{group="production", instance="0", job="api-server"} 0
{group="production", instance="1", job="api-server"} -1
# Round should round half up.
eval instant at 50m round(0.005 * http_requests{group="production",job="api-server"})
{group="production", instance="0", job="api-server"} 1
{group="production", instance="1", job="api-server"} 1
eval instant at 50m round(-1 * (0.005 * http_requests{group="production",job="api-server"}))
{group="production", instance="0", job="api-server"} 0
{group="production", instance="1", job="api-server"} -1
eval instant at 50m round(1 + 0.005 * http_requests{group="production",job="api-server"})
{group="production", instance="0", job="api-server"} 2
{group="production", instance="1", job="api-server"} 2
eval instant at 50m round(-1 * (1 + 0.005 * http_requests{group="production",job="api-server"}))
{group="production", instance="0", job="api-server"} -1
{group="production", instance="1", job="api-server"} -2
# Round should accept the number to round nearest to.
eval instant at 50m round(0.0005 * http_requests{group="production",job="api-server"}, 0.1)
{group="production", instance="0", job="api-server"} 0.1
{group="production", instance="1", job="api-server"} 0.1
eval instant at 50m round(2.1 + 0.0005 * http_requests{group="production",job="api-server"}, 0.1)
{group="production", instance="0", job="api-server"} 2.2
{group="production", instance="1", job="api-server"} 2.2
eval instant at 50m round(5.2 + 0.0005 * http_requests{group="production",job="api-server"}, 0.1)
{group="production", instance="0", job="api-server"} 5.3
{group="production", instance="1", job="api-server"} 5.3
# Round should work correctly with negative numbers and multiple decimal places.
eval instant at 50m round(-1 * (5.2 + 0.0005 * http_requests{group="production",job="api-server"}), 0.1)
{group="production", instance="0", job="api-server"} -5.2
{group="production", instance="1", job="api-server"} -5.3
# Round should work correctly with big toNearests.
eval instant at 50m round(0.025 * http_requests{group="production",job="api-server"}, 5)
{group="production", instance="0", job="api-server"} 5
{group="production", instance="1", job="api-server"} 5
eval instant at 50m round(0.045 * http_requests{group="production",job="api-server"}, 5)
{group="production", instance="0", job="api-server"} 5
{group="production", instance="1", job="api-server"} 10
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
use Welford/Knuth method to compute standard deviation and variance (#4533) * 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>
2018-08-26 02:28:47 -07:00
# Float precision test for standard deviation and variance
clear
load 5m
http_requests{job="api-server", instance="0", group="production"} 0+1.33x10
http_requests{job="api-server", instance="1", group="production"} 0+1.33x10
http_requests{job="api-server", instance="0", group="canary"} 0+1.33x10
eval instant at 50m stddev(http_requests)
{} 0.0
eval instant at 50m stdvar(http_requests)
{} 0.0
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
make matrix selection and lookback left-open and right-closed Signed-off-by: Zhang Zhanpeng <zhangzhanpeng.zzp@alibaba-inc.com> Signed-off-by: beorn7 <beorn@grafana.com> Co-authored-by: beorn7 <beorn@grafana.com>
2024-04-08 09:46:52 -07:00
http_requests{job="api-server", instance="2", group="production"} NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Move topk/bottomk unittests over to aggregators.
2016-07-04 05:12:12 -07:00
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
Fix panic when aggregator param is not a literal. The return value for checkForSeriesSetExpansion is always nil, simplify. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
2019-03-01 04:00:14 -08:00
foo 3+0x10
Move topk/bottomk unittests over to aggregators.
2016-07-04 05:12:12 -07:00
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
PromQL: Fix string and parentheses handling in engine (#6612) * WIP: PromQL: Allow engine to return strings Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Add test suggested by @roidelapluie Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix typo in React UI Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix parenthesis handling for functions and aggregator params Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Add more tests Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix React UI test Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com>
2020-01-15 09:31:58 -08:00
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
Move topk/bottomk unittests over to aggregators.
2016-07-04 05:12:12 -07:00
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
Fix OOM when a large K is used in topk queries (#4087) This attempts to close #3973. Handles cases where the length of the input vector to an aggregate topk / bottomk function is less than the K paramater. The change updates Prometheus to allocate a result vector the same length as the input vector in these cases. Previously Prometheus would out-of-memory panic for large K values. This change makes that unlikely unless the size of the input vector is equally large. Signed-off-by: David King <dave@davbo.org>
2018-04-16 01:03:04 -07:00
# Test topk and bottomk allocate min(k, input_vector) for results vector
eval_ordered instant at 50m bottomk(9999999999, http_requests{job="app-server",group="canary"})
http_requests{group="canary", instance="0", job="app-server"} 700
http_requests{group="canary", instance="1", job="app-server"} 800
eval_ordered instant at 50m topk(9999999999, 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
Fix panic when aggregator param is not a literal. The return value for checkForSeriesSetExpansion is always nil, simplify. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
2019-03-01 04:00:14 -08:00
# Bug #5276.
eval_ordered instant at 50m topk(scalar(foo), 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
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
make matrix selection and lookback left-open and right-closed Signed-off-by: Zhang Zhanpeng <zhangzhanpeng.zzp@alibaba-inc.com> Signed-off-by: beorn7 <beorn@grafana.com> Co-authored-by: beorn7 <beorn@grafana.com>
2024-04-08 09:46:52 -07:00
eval instant at 1m count_values("version", version)
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
{version="6"} 5
{version="7"} 2
{version="8"} 2
PromQL: Fix string and parentheses handling in engine (#6612) * WIP: PromQL: Allow engine to return strings Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Add test suggested by @roidelapluie Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix typo in React UI Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix parenthesis handling for functions and aggregator params Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Add more tests Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix React UI test Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com>
2020-01-15 09:31:58 -08:00
make matrix selection and lookback left-open and right-closed Signed-off-by: Zhang Zhanpeng <zhangzhanpeng.zzp@alibaba-inc.com> Signed-off-by: beorn7 <beorn@grafana.com> Co-authored-by: beorn7 <beorn@grafana.com>
2024-04-08 09:46:52 -07:00
eval instant at 1m count_values(((("version"))), version)
PromQL: Fix string and parentheses handling in engine (#6612) * WIP: PromQL: Allow engine to return strings Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Add test suggested by @roidelapluie Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix typo in React UI Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix parenthesis handling for functions and aggregator params Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Add more tests Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix React UI test Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com>
2020-01-15 09:31:58 -08:00
{version="6"} 5
{version="7"} 2
{version="8"} 2
make matrix selection and lookback left-open and right-closed Signed-off-by: Zhang Zhanpeng <zhangzhanpeng.zzp@alibaba-inc.com> Signed-off-by: beorn7 <beorn@grafana.com> Co-authored-by: beorn7 <beorn@grafana.com>
2024-04-08 09:46:52 -07:00
eval instant at 1m count_values without (instance)("version", version)
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
{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.
make matrix selection and lookback left-open and right-closed Signed-off-by: Zhang Zhanpeng <zhangzhanpeng.zzp@alibaba-inc.com> Signed-off-by: beorn7 <beorn@grafana.com> Co-authored-by: beorn7 <beorn@grafana.com>
2024-04-08 09:46:52 -07:00
eval instant at 1m count_values without (instance)("job", version)
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
{job="6", group="production"} 5
{job="8", group="canary"} 2
{job="7", group="canary"} 2
# Overwrite label with output. Don't do this.
make matrix selection and lookback left-open and right-closed Signed-off-by: Zhang Zhanpeng <zhangzhanpeng.zzp@alibaba-inc.com> Signed-off-by: beorn7 <beorn@grafana.com> Co-authored-by: beorn7 <beorn@grafana.com>
2024-04-08 09:46:52 -07:00
eval instant at 1m count_values by (job, group)("job", version)
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
{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
Fix panic when aggregator param is not a literal. The return value for checkForSeriesSetExpansion is always nil, simplify. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
2019-03-01 04:00:14 -08:00
foo .8
Add quantile aggregator.
2016-07-08 05:48:48 -07:00
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
Fix panic when aggregator param is not a literal. The return value for checkForSeriesSetExpansion is always nil, simplify. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
2019-03-01 04:00:14 -08:00
# 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
PromQL: Fix string and parentheses handling in engine (#6612) * WIP: PromQL: Allow engine to return strings Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Add test suggested by @roidelapluie Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix typo in React UI Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix parenthesis handling for functions and aggregator params Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Add more tests Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com> * Fix React UI test Signed-off-by: Tobias Guggenmos <tguggenm@redhat.com>
2020-01-15 09:31:58 -08:00
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
Add group() aggregator (#7480) * Add group() aggregator Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
2020-06-30 07:51:18 -07:00
[nhcb branch] Enhance unit test framework for nhcb and warnings (#14009) * process custom values in histogram unit test framework * check for warnings when evaluating in unit test framework * add test cases for custom buckets in test framework Signed-off-by: Jeanette Tan <jeanette.tan@grafana.com>
2024-05-08 04:58:24 -07:00
eval_warn instant at 1m quantile without(point)(NaN, data)
address comment Signed-off-by: jyz0309 <45495947@qq.com>
2022-02-15 06:09:17 -08:00
{test="two samples"} NaN
{test="three samples"} NaN
{test="uneven samples"} NaN
Add group() aggregator (#7480) * Add group() aggregator Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
2020-06-30 07:51:18 -07:00
# 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
foo .8
eval instant at 1m group without(point)(data)
{test="two samples"} 1
{test="three samples"} 1
{test="uneven samples"} 1
eval instant at 1m group(foo)
{} 1
Fix avg_over_time for nan and float64 overflows (#7346) * Fix avg_over_time with Inf and NaN values Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
2020-07-13 08:30:50 -07:00
# 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
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
make aggregations deterministic (#9459) * Add deterministic test for aggregations Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu> * Make aggregations deterministic Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu> * Increase testing Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
2021-10-17 02:46:38 -07:00
promql: make avg aggregation more precise and less expensive The basic idea here is that the previous code was always doing incremental calculation of the mean value, which is more costly and can be less precise. It protects against overflows, but in most cases, an overflow doesn't happen anyway. The other idea applied here is to expand on #14074, where Kahan summation was applied to sum(). With this commit, the average is calculated in a conventional way (adding everything up and divide in the end) as long as the sum isn't overflowing float64. This is combined with Kahan summation so that the avg aggregation, in most cases, is really equivalent to the sum aggregation with a following division (which is the user's expectation as avg is supposed to be syntactic sugar for sum with a following divison). If the sum hits ±Inf, the calculation reverts to incremental calculation of the mean value. Kahan summation is also applied here, although it cannot fully compensate for the numerical errors introduced by the incremental mean calculation. (The tests added in this commit would fail if incremental mean calculation was always used.) Signed-off-by: beorn7 <beorn@grafana.com>
2024-07-04 06:13:35 -07:00
# Test summing and averaging extreme values.
[ENHANCEMENT] PromQL: use Kahan summation for sum() This can give a more precise result, by keeping a separate running compensation value to accumulate small errors. See https://en.wikipedia.org/wiki/Kahan_summation_algorithm Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
2024-05-09 06:29:34 -07:00
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
Fix issue where summation of +/- infinity returns NaN instead of infinity Signed-off-by: Charles Korn <charles.korn@grafana.com>
2024-06-27 18:19:27 -07:00
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
[ENHANCEMENT] PromQL: use Kahan summation for sum() This can give a more precise result, by keeping a separate running compensation value to accumulate small errors. See https://en.wikipedia.org/wiki/Kahan_summation_algorithm Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
2024-05-09 06:29:34 -07:00
eval instant at 1m sum(data{test="ten"})
{} 10
promql: make avg aggregation more precise and less expensive The basic idea here is that the previous code was always doing incremental calculation of the mean value, which is more costly and can be less precise. It protects against overflows, but in most cases, an overflow doesn't happen anyway. The other idea applied here is to expand on #14074, where Kahan summation was applied to sum(). With this commit, the average is calculated in a conventional way (adding everything up and divide in the end) as long as the sum isn't overflowing float64. This is combined with Kahan summation so that the avg aggregation, in most cases, is really equivalent to the sum aggregation with a following division (which is the user's expectation as avg is supposed to be syntactic sugar for sum with a following divison). If the sum hits ±Inf, the calculation reverts to incremental calculation of the mean value. Kahan summation is also applied here, although it cannot fully compensate for the numerical errors introduced by the incremental mean calculation. (The tests added in this commit would fail if incremental mean calculation was always used.) Signed-off-by: beorn7 <beorn@grafana.com>
2024-07-04 06:13:35 -07:00
eval instant at 1m avg(data{test="ten"})
{} 2.5
Fix issue where summation of +/- infinity returns NaN instead of infinity Signed-off-by: Charles Korn <charles.korn@grafana.com>
2024-06-27 18:19:27 -07:00
eval instant at 1m sum by (group) (data{test="pos_inf"})
{group="1"} Inf
{group="2"} Inf
promql: make avg aggregation more precise and less expensive The basic idea here is that the previous code was always doing incremental calculation of the mean value, which is more costly and can be less precise. It protects against overflows, but in most cases, an overflow doesn't happen anyway. The other idea applied here is to expand on #14074, where Kahan summation was applied to sum(). With this commit, the average is calculated in a conventional way (adding everything up and divide in the end) as long as the sum isn't overflowing float64. This is combined with Kahan summation so that the avg aggregation, in most cases, is really equivalent to the sum aggregation with a following division (which is the user's expectation as avg is supposed to be syntactic sugar for sum with a following divison). If the sum hits ±Inf, the calculation reverts to incremental calculation of the mean value. Kahan summation is also applied here, although it cannot fully compensate for the numerical errors introduced by the incremental mean calculation. (The tests added in this commit would fail if incremental mean calculation was always used.) Signed-off-by: beorn7 <beorn@grafana.com>
2024-07-04 06:13:35 -07:00
eval instant at 1m avg by (group) (data{test="pos_inf"})
{group="1"} Inf
{group="2"} Inf
Fix issue where summation of +/- infinity returns NaN instead of infinity Signed-off-by: Charles Korn <charles.korn@grafana.com>
2024-06-27 18:19:27 -07:00
eval instant at 1m sum by (group) (data{test="neg_inf"})
{group="1"} -Inf
{group="2"} -Inf
promql: make avg aggregation more precise and less expensive The basic idea here is that the previous code was always doing incremental calculation of the mean value, which is more costly and can be less precise. It protects against overflows, but in most cases, an overflow doesn't happen anyway. The other idea applied here is to expand on #14074, where Kahan summation was applied to sum(). With this commit, the average is calculated in a conventional way (adding everything up and divide in the end) as long as the sum isn't overflowing float64. This is combined with Kahan summation so that the avg aggregation, in most cases, is really equivalent to the sum aggregation with a following division (which is the user's expectation as avg is supposed to be syntactic sugar for sum with a following divison). If the sum hits ±Inf, the calculation reverts to incremental calculation of the mean value. Kahan summation is also applied here, although it cannot fully compensate for the numerical errors introduced by the incremental mean calculation. (The tests added in this commit would fail if incremental mean calculation was always used.) Signed-off-by: beorn7 <beorn@grafana.com>
2024-07-04 06:13:35 -07:00
eval instant at 1m avg by (group) (data{test="neg_inf"})
{group="1"} -Inf
{group="2"} -Inf
Fix issue where summation of +/- infinity returns NaN instead of infinity Signed-off-by: Charles Korn <charles.korn@grafana.com>
2024-06-27 18:19:27 -07:00
eval instant at 1m sum(data{test="inf_inf"})
{} NaN
promql: make avg aggregation more precise and less expensive The basic idea here is that the previous code was always doing incremental calculation of the mean value, which is more costly and can be less precise. It protects against overflows, but in most cases, an overflow doesn't happen anyway. The other idea applied here is to expand on #14074, where Kahan summation was applied to sum(). With this commit, the average is calculated in a conventional way (adding everything up and divide in the end) as long as the sum isn't overflowing float64. This is combined with Kahan summation so that the avg aggregation, in most cases, is really equivalent to the sum aggregation with a following division (which is the user's expectation as avg is supposed to be syntactic sugar for sum with a following divison). If the sum hits ±Inf, the calculation reverts to incremental calculation of the mean value. Kahan summation is also applied here, although it cannot fully compensate for the numerical errors introduced by the incremental mean calculation. (The tests added in this commit would fail if incremental mean calculation was always used.) Signed-off-by: beorn7 <beorn@grafana.com>
2024-07-04 06:13:35 -07:00
eval instant at 1m avg(data{test="inf_inf"})
{} NaN
Fix issue where summation of +/- infinity returns NaN instead of infinity Signed-off-by: Charles Korn <charles.korn@grafana.com>
2024-06-27 18:19:27 -07:00
eval instant at 1m sum by (group) (data{test="nan"})
{group="1"} NaN
{group="2"} NaN
promql: make avg aggregation more precise and less expensive The basic idea here is that the previous code was always doing incremental calculation of the mean value, which is more costly and can be less precise. It protects against overflows, but in most cases, an overflow doesn't happen anyway. The other idea applied here is to expand on #14074, where Kahan summation was applied to sum(). With this commit, the average is calculated in a conventional way (adding everything up and divide in the end) as long as the sum isn't overflowing float64. This is combined with Kahan summation so that the avg aggregation, in most cases, is really equivalent to the sum aggregation with a following division (which is the user's expectation as avg is supposed to be syntactic sugar for sum with a following divison). If the sum hits ±Inf, the calculation reverts to incremental calculation of the mean value. Kahan summation is also applied here, although it cannot fully compensate for the numerical errors introduced by the incremental mean calculation. (The tests added in this commit would fail if incremental mean calculation was always used.) Signed-off-by: beorn7 <beorn@grafana.com>
2024-07-04 06:13:35 -07:00
eval instant at 1m avg by (group) (data{test="nan"})
{group="1"} NaN
{group="2"} NaN
make aggregations deterministic (#9459) * Add deterministic test for aggregations Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu> * Make aggregations deterministic Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu> * Increase testing Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
2021-10-17 02:46:38 -07:00
clear
# Test that aggregations are deterministic.
PromQL: Comment flaky test (#9545) Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>
2021-10-19 04:13:56 -07:00
# 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
Reference in a new issue Copy permalink