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* Add new test cases for multiple name matchers in PromQL info function * Fix handling of multiple name matchers in PromQL info function --------- Signed-off-by: Jeanette Tan <jeanette.tan@grafana.com>
221 lines
9.8 KiB
Plaintext
221 lines
9.8 KiB
Plaintext
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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# Filtering by a label that exists on both base metric and target_info should work.
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# This is a regression test for https://github.com/prometheus/prometheus/issues/17813.
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# Note: data="base" on base metric, data="info" on target_info - the filter matches target_info.
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eval range from 0m to 10m step 5m info(metric_with_overlapping_label, {data="info"})
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metric_with_overlapping_label{data="base", instance="a", job="1", label="value"} 0 1 2
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# Filtering by a label that exists on both base metric and target_info with regex should work.
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eval range from 0m to 10m step 5m info(metric_with_overlapping_label, {data=~".+"})
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metric_with_overlapping_label{data="base", instance="a", job="1", label="value"} 0 1 2
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# Filtering by a label that exists on both base metric and target_info with same value.
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# The selector matches the target_info, and the join succeeds via identifying labels.
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# Note: Only the instance label is considered for inclusion, but it already exists on base.
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eval range from 0m to 10m step 5m info(metric_with_overlapping_label, {instance="a"})
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metric_with_overlapping_label{data="base", instance="a", job="1", label="value"} 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", another_data=~".+"})
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build_info{instance="a", job="1", build_data="build"} 1 1 1
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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"})
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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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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 1
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build_info{instance="a", job="1", build_data="build"} 1 1 1
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target_build{instance="a", job="1", build_data="build"} 1 1 1
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# Multiple positive __name__ matchers.
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eval range from 0m to 10m step 5m info(metric, {__name__=~"target_.+", __name__=~".+_info"})
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metric{instance="a", job="1", label="value", data="info", another_data="another info"} 0 1 2
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# A positive and a negative __name__ matcher.
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eval range from 0m to 10m step 5m info(metric, {__name__=~".+_info", __name__!~".*build.*"})
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metric{instance="a", job="1", label="value", data="info", another_data="another info"} 0 1 2
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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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clear
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# info_metric churn:
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load 1m
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data_metric{instance="a", job="work"} 10 20 30
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data_metric{instance="b", job="work"} 11 21 31
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info_metric{instance="b", job="work", state="stopped"} 1 1 _
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info_metric{instance="b", job="work", state="running"} _ _ 1
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info_metric{instance="a", job="work", state="running"} 1 1 1
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eval range from 0 to 2m step 1m info(data_metric, {__name__="info_metric"})
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data_metric{instance="a", job="work", state="running"} 10 20 30
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data_metric{instance="b", job="work", state="stopped"} 11 21 _
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data_metric{instance="b", job="work", state="running"} _ _ 31
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clear
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# data_metric churn:
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load 1m
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data_metric{instance="a", job="work"} 10 20 stale
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data_metric{instance="b", job="work"} 11 21 31
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data_metric{instance="a", job="work", label="new"} _ _ 30
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info_metric{instance="b", job="work", state="stopped"} 1 1 1
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info_metric{instance="a", job="work", state="running"} 1 1 1
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eval range from 0 to 2m step 1m info(data_metric, {__name__="info_metric"})
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data_metric{instance="a", job="work", state="running"} 10 20 _
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data_metric{instance="b", job="work", state="stopped"} 11 21 31
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data_metric{instance="a", job="work", state="running", label="new"} _ _ 30
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eval range from 0 to 2m step 1m info({job="work"}, {__name__="info_metric"})
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data_metric{instance="a", job="work", state="running"} 10 20 _
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data_metric{instance="b", job="work", state="stopped"} 11 21 31
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data_metric{instance="a", job="work", state="running", label="new"} _ _ 30
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info_metric{instance="b", job="work", state="stopped"} 1 1 1
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info_metric{instance="a", job="work", state="running"} 1 1 1
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clear
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load 1m
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data_metric{} 1 2 3
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eval range from 0 to 2m step 1m info(data_metric, {__name__="info_metric"})
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data_metric{} 1 2 3
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clear
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load 1m
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data_metric{} 1 2 3
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data_metric{instance="a"} 4 5 6
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eval range from 0 to 2m step 1m info(data_metric, {__name__="info_metric"})
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data_metric{} 1 2 3
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data_metric{instance="a"} 4 5 6
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clear
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load 1m
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data_metric{} 1 2 3
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data_metric{instance="a"} 4 5 6
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data_metric{job="1"} 7 8 9
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data_metric{instance="a", job="1"} 10 20 30
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eval range from 0 to 2m step 1m info(data_metric, {__name__="info_metric"})
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data_metric{} 1 2 3
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data_metric{instance="a"} 4 5 6
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data_metric{job="1"} 7 8 9
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data_metric{instance="a", job="1"} 10 20 30
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