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The `info` function is an experiment to improve UX around including labels from info metrics. `info` has to be enabled via the feature flag `--enable-feature=promql-experimental-functions`. This MVP of info simplifies the implementation by assuming: * Only support for the target_info metric * That target_info's identifying labels are job and instance Also: * Encode info samples' original timestamp as sample value * Deduce info series select hints from top-most VectorSelector --------- Signed-off-by: Arve Knudsen <arve.knudsen@gmail.com> Co-authored-by: Ying WANG <ying.wang@grafana.com> Co-authored-by: Augustin Husson <augustin.husson@amadeus.com> Co-authored-by: Bartlomiej Plotka <bwplotka@gmail.com> Co-authored-by: Björn Rabenstein <github@rabenste.in> Co-authored-by: Bryan Boreham <bjboreham@gmail.com>
141 lines
6.4 KiB
Go
141 lines
6.4 KiB
Go
// Copyright 2024 The Prometheus Authors
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package promql_test
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import (
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"testing"
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"github.com/prometheus/prometheus/promql/promqltest"
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)
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// The "info" function is experimental. This is why we write those tests here for now instead of promqltest/testdata/info.test.
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func TestInfo(t *testing.T) {
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engine := promqltest.NewTestEngine(t, false, 0, promqltest.DefaultMaxSamplesPerQuery)
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promqltest.RunTest(t, `
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load 5m
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metric{instance="a", job="1", label="value"} 0 1 2
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metric_not_matching_target_info{instance="a", job="2", label="value"} 0 1 2
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metric_with_overlapping_label{instance="a", job="1", label="value", data="base"} 0 1 2
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target_info{instance="a", job="1", data="info", another_data="another info"} 1 1 1
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build_info{instance="a", job="1", build_data="build"} 1 1 1
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# Include one info metric data label.
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eval range from 0m to 10m step 5m info(metric, {data=~".+"})
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metric{data="info", instance="a", job="1", label="value"} 0 1 2
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# Include all info metric data labels.
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eval range from 0m to 10m step 5m info(metric)
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metric{data="info", instance="a", job="1", label="value", another_data="another info"} 0 1 2
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# Try including all info metric data labels, but non-matching identifying labels.
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eval range from 0m to 10m step 5m info(metric_not_matching_target_info)
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metric_not_matching_target_info{instance="a", job="2", label="value"} 0 1 2
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# Try including a certain info metric data label with a non-matching matcher not accepting empty labels.
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# Metric is ignored, due there being a data label matcher not matching empty labels,
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# and there being no info series matches.
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eval range from 0m to 10m step 5m info(metric, {non_existent=~".+"})
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# Include a certain info metric data label together with a non-matching matcher accepting empty labels.
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# Since the non_existent matcher matches empty labels, it's simply ignored when there's no match.
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# XXX: This case has to include a matcher not matching empty labels, due the PromQL limitation
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# that vector selectors have to contain at least one matcher not accepting empty labels.
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# We might need another construct than vector selector to get around this limitation.
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eval range from 0m to 10m step 5m info(metric, {data=~".+", non_existent=~".*"})
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metric{data="info", instance="a", job="1", label="value"} 0 1 2
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# Info series data labels overlapping with those of base series are ignored.
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eval range from 0m to 10m step 5m info(metric_with_overlapping_label)
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metric_with_overlapping_label{data="base", instance="a", job="1", label="value", another_data="another info"} 0 1 2
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# Include data labels from target_info specifically.
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eval range from 0m to 10m step 5m info(metric, {__name__="target_info"})
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metric{data="info", instance="a", job="1", label="value", another_data="another info"} 0 1 2
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# Try to include all data labels from a non-existent info metric.
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eval range from 0m to 10m step 5m info(metric, {__name__="non_existent"})
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metric{instance="a", job="1", label="value"} 0 1 2
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# Try to include a certain data label from a non-existent info metric.
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eval range from 0m to 10m step 5m info(metric, {__name__="non_existent", data=~".+"})
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# Include data labels from build_info.
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eval range from 0m to 10m step 5m info(metric, {__name__="build_info"})
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metric{instance="a", job="1", label="value", build_data="build"} 0 1 2
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# Include data labels from build_info and target_info.
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eval range from 0m to 10m step 5m info(metric, {__name__=~".+_info"})
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metric{instance="a", job="1", label="value", build_data="build", data="info", another_data="another info"} 0 1 2
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# Info metrics themselves are ignored when it comes to enriching with info metric data labels.
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eval range from 0m to 10m step 5m info(build_info, {__name__=~".+_info", build_data=~".+"})
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build_info{instance="a", job="1", build_data="build"} 1 1 1
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clear
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# Overlapping target_info series.
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load 5m
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metric{instance="a", job="1", label="value"} 0 1 2
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target_info{instance="a", job="1", data="info", another_data="another info"} 1 1 _
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target_info{instance="a", job="1", data="updated info", another_data="another info"} _ _ 1
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# Conflicting info series are resolved through picking the latest sample.
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eval range from 0m to 10m step 5m info(metric)
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metric{data="info", instance="a", job="1", label="value", another_data="another info"} 0 1 _
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metric{data="updated info", instance="a", job="1", label="value", another_data="another info"} _ _ 2
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clear
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# Non-overlapping target_info series.
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load 5m
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metric{instance="a", job="1", label="value"} 0 1 2
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target_info{instance="a", job="1", data="info"} 1 1 stale
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target_info{instance="a", job="1", data="updated info"} _ _ 1
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# Include info metric data labels from a metric which data labels change over time.
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eval range from 0m to 10m step 5m info(metric)
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metric{data="info", instance="a", job="1", label="value"} 0 1 _
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metric{data="updated info", instance="a", job="1", label="value"} _ _ 2
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clear
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# Info series selector matches histogram series, info metrics should be float type.
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load 5m
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metric{instance="a", job="1", label="value"} 0 1 2
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histogram{instance="a", job="1"} {{schema:1 sum:3 count:22 buckets:[5 10 7]}}
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eval_fail range from 0m to 10m step 5m info(metric, {__name__="histogram"})
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clear
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# Series with skipped scrape.
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load 1m
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metric{instance="a", job="1", label="value"} 0 _ 2 3 4
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target_info{instance="a", job="1", data="info"} 1 _ 1 1 1
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# Lookback works also for the info series.
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eval range from 1m to 4m step 1m info(metric)
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metric{data="info", instance="a", job="1", label="value"} 0 2 3 4
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# @ operator works also with info.
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# Note that we pick the timestamp missing a sample, lookback should pick previous sample.
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eval range from 1m to 4m step 1m info(metric @ 60)
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metric{data="info", instance="a", job="1", label="value"} 0 0 0 0
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# offset operator works also with info.
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eval range from 1m to 4m step 1m info(metric offset 1m)
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metric{data="info", instance="a", job="1", label="value"} 0 0 2 3
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`, engine)
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}
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