prometheus/promql/promqltest/testdata/info.test

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load 5m
metric{instance="a", job="1", label="value"} 0 1 2
metric_not_matching_target_info{instance="a", job="2", label="value"} 0 1 2
metric_with_overlapping_label{instance="a", job="1", label="value", data="base"} 0 1 2
target_info{instance="a", job="1", data="info", another_data="another info"} 1 1 1
build_info{instance="a", job="1", build_data="build"} 1 1 1
# Include one info metric data label.
eval range from 0m to 10m step 5m info(metric, {data=~".+"})
metric{data="info", instance="a", job="1", label="value"} 0 1 2
# Include all info metric data labels.
eval range from 0m to 10m step 5m info(metric)
metric{data="info", instance="a", job="1", label="value", another_data="another info"} 0 1 2
# Try including all info metric data labels, but non-matching identifying labels.
eval range from 0m to 10m step 5m info(metric_not_matching_target_info)
metric_not_matching_target_info{instance="a", job="2", label="value"} 0 1 2
# Try including a certain info metric data label with a non-matching matcher not accepting empty labels.
# Metric is ignored, due there being a data label matcher not matching empty labels,
# and there being no info series matches.
eval range from 0m to 10m step 5m info(metric, {non_existent=~".+"})
# Include a certain info metric data label together with a non-matching matcher accepting empty labels.
# Since the non_existent matcher matches empty labels, it's simply ignored when there's no match.
# XXX: This case has to include a matcher not matching empty labels, due the PromQL limitation
# that vector selectors have to contain at least one matcher not accepting empty labels.
# We might need another construct than vector selector to get around this limitation.
eval range from 0m to 10m step 5m info(metric, {data=~".+", non_existent=~".*"})
metric{data="info", instance="a", job="1", label="value"} 0 1 2
# Info series data labels overlapping with those of base series are ignored.
eval range from 0m to 10m step 5m info(metric_with_overlapping_label)
metric_with_overlapping_label{data="base", instance="a", job="1", label="value", another_data="another info"} 0 1 2
# Include data labels from target_info specifically.
eval range from 0m to 10m step 5m info(metric, {__name__="target_info"})
metric{data="info", instance="a", job="1", label="value", another_data="another info"} 0 1 2
# Try to include all data labels from a non-existent info metric.
eval range from 0m to 10m step 5m info(metric, {__name__="non_existent"})
metric{instance="a", job="1", label="value"} 0 1 2
# Try to include a certain data label from a non-existent info metric.
eval range from 0m to 10m step 5m info(metric, {__name__="non_existent", data=~".+"})
# Include data labels from build_info.
eval range from 0m to 10m step 5m info(metric, {__name__="build_info"})
metric{instance="a", job="1", label="value", build_data="build"} 0 1 2
# Include data labels from build_info and target_info.
eval range from 0m to 10m step 5m info(metric, {__name__=~".+_info"})
metric{instance="a", job="1", label="value", build_data="build", data="info", another_data="another info"} 0 1 2
# Info metrics themselves are ignored when it comes to enriching with info metric data labels.
eval range from 0m to 10m step 5m info(build_info, {__name__=~".+_info", build_data=~".+"})
build_info{instance="a", job="1", build_data="build"} 1 1 1
clear
# Overlapping target_info series.
load 5m
metric{instance="a", job="1", label="value"} 0 1 2
target_info{instance="a", job="1", data="info", another_data="another info"} 1 1 _
target_info{instance="a", job="1", data="updated info", another_data="another info"} _ _ 1
# Conflicting info series are resolved through picking the latest sample.
eval range from 0m to 10m step 5m info(metric)
metric{data="info", instance="a", job="1", label="value", another_data="another info"} 0 1 _
metric{data="updated info", instance="a", job="1", label="value", another_data="another info"} _ _ 2
clear
# Non-overlapping target_info series.
load 5m
metric{instance="a", job="1", label="value"} 0 1 2
target_info{instance="a", job="1", data="info"} 1 1 stale
target_info{instance="a", job="1", data="updated info"} _ _ 1
# Include info metric data labels from a metric which data labels change over time.
eval range from 0m to 10m step 5m info(metric)
metric{data="info", instance="a", job="1", label="value"} 0 1 _
metric{data="updated info", instance="a", job="1", label="value"} _ _ 2
clear
# Info series selector matches histogram series, info metrics should be float type.
load 5m
metric{instance="a", job="1", label="value"} 0 1 2
histogram{instance="a", job="1"} {{schema:1 sum:3 count:22 buckets:[5 10 7]}}
eval_fail range from 0m to 10m step 5m info(metric, {__name__="histogram"})
clear
# Series with skipped scrape.
load 1m
metric{instance="a", job="1", label="value"} 0 _ 2 3 4
target_info{instance="a", job="1", data="info"} 1 _ 1 1 1
# Lookback works also for the info series.
eval range from 1m to 4m step 1m info(metric)
metric{data="info", instance="a", job="1", label="value"} 0 2 3 4
# @ operator works also with info.
# Note that we pick the timestamp missing a sample, lookback should pick previous sample.
eval range from 1m to 4m step 1m info(metric @ 60)
metric{data="info", instance="a", job="1", label="value"} 0 0 0 0
# offset operator works also with info.
eval range from 1m to 4m step 1m info(metric offset 1m)
metric{data="info", instance="a", job="1", label="value"} 0 0 2 3
clear
# info_metric churn:
load 1m
data_metric{instance="a", job="work"} 10 20 30
data_metric{instance="b", job="work"} 11 21 31
info_metric{instance="b", job="work", state="stopped"} 1 1 _
info_metric{instance="b", job="work", state="running"} _ _ 1
info_metric{instance="a", job="work", state="running"} 1 1 1
eval range from 0 to 2m step 1m info(data_metric, {__name__="info_metric"})
data_metric{instance="a", job="work", state="running"} 10 20 30
data_metric{instance="b", job="work", state="stopped"} 11 21 _
data_metric{instance="b", job="work", state="running"} _ _ 31
clear
# data_metric churn:
load 1m
data_metric{instance="a", job="work"} 10 20 stale
data_metric{instance="b", job="work"} 11 21 31
data_metric{instance="a", job="work", label="new"} _ _ 30
info_metric{instance="b", job="work", state="stopped"} 1 1 1
info_metric{instance="a", job="work", state="running"} 1 1 1
eval range from 0 to 2m step 1m info(data_metric, {__name__="info_metric"})
data_metric{instance="a", job="work", state="running"} 10 20 _
data_metric{instance="b", job="work", state="stopped"} 11 21 31
data_metric{instance="a", job="work", state="running", label="new"} _ _ 30
eval range from 0 to 2m step 1m info({job="work"}, {__name__="info_metric"})
data_metric{instance="a", job="work", state="running"} 10 20 _
data_metric{instance="b", job="work", state="stopped"} 11 21 31
data_metric{instance="a", job="work", state="running", label="new"} _ _ 30
info_metric{instance="b", job="work", state="stopped"} 1 1 1
info_metric{instance="a", job="work", state="running"} 1 1 1