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

# Filtering by a label that exists on both base metric and target_info should work.
# This is a regression test for https://github.com/prometheus/prometheus/issues/17813.
# Note: data="base" on base metric, data="info" on target_info - the filter matches target_info.
eval range from 0m to 10m step 5m info(metric_with_overlapping_label, {data="info"})
    metric_with_overlapping_label{data="base", instance="a", job="1", label="value"} 0 1 2

# Filtering by a label that exists on both base metric and target_info with regex should work.
eval range from 0m to 10m step 5m info(metric_with_overlapping_label, {data=~".+"})
    metric_with_overlapping_label{data="base", instance="a", job="1", label="value"} 0 1 2

# Filtering by a label that exists on both base metric and target_info with same value.
# The selector matches the target_info, and the join succeeds via identifying labels.
# Note: Only the instance label is considered for inclusion, but it already exists on base.
eval range from 0m to 10m step 5m info(metric_with_overlapping_label, {instance="a"})
    metric_with_overlapping_label{data="base", instance="a", job="1", label="value"} 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", another_data=~".+"})
    build_info{instance="a", job="1", build_data="build"} 1 1 1

# 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_info{instance="a", job="1", build_data="build"} 1 1 1

clear

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 1
    build_info{instance="a", job="1", build_data="build"} 1 1 1
    target_build{instance="a", job="1", build_data="build"} 1 1 1

# Multiple positive __name__ matchers.
eval range from 0m to 10m step 5m info(metric, {__name__=~"target_.+", __name__=~".+_info"})
    metric{instance="a", job="1", label="value", data="info", another_data="another info"} 0 1 2

# A positive and a negative __name__ matcher.
eval range from 0m to 10m step 5m info(metric, {__name__=~".+_info", __name__!~".*build.*"})
    metric{instance="a", job="1", label="value", data="info", another_data="another info"} 0 1 2

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

clear

load 1m
	data_metric{} 1 2 3

eval range from 0 to 2m step 1m info(data_metric, {__name__="info_metric"})
	data_metric{} 1 2 3

clear

load 1m
	data_metric{} 1 2 3
	data_metric{instance="a"} 4 5 6

eval range from 0 to 2m step 1m info(data_metric, {__name__="info_metric"})
	data_metric{} 1 2 3
	data_metric{instance="a"} 4 5 6

clear

load 1m
	data_metric{} 1 2 3
	data_metric{instance="a"} 4 5 6
	data_metric{job="1"} 7 8 9
	data_metric{instance="a", job="1"} 10 20 30

eval range from 0 to 2m step 1m info(data_metric, {__name__="info_metric"})
	data_metric{} 1 2 3
	data_metric{instance="a"} 4 5 6
	data_metric{job="1"} 7 8 9
	data_metric{instance="a", job="1"} 10 20 30