# Two histograms with 4 buckets each (x_sum and x_count not included,
# only buckets). Lowest bucket for one histogram < 0, for the other >
# 0. They have the same name, just separated by label. Not useful in
# practice, but can happen (if clients change bucketing), and the
# server has to cope with it.

# Test histogram.
load_with_nhcb 5m
	testhistogram_bucket{le="0.1", start="positive"}	0+5x10
	testhistogram_bucket{le=".2", start="positive"}		0+7x10
	testhistogram_bucket{le="1e0", start="positive"}	0+11x10
	testhistogram_bucket{le="+Inf", start="positive"}	0+12x10
	testhistogram_bucket{le="-.2", start="negative"}	0+1x10
	testhistogram_bucket{le="-0.1", start="negative"}	0+2x10
	testhistogram_bucket{le="0.3", start="negative"}	0+2x10
	testhistogram_bucket{le="+Inf", start="negative"}	0+3x10

# Another test histogram, where q(1/6), q(1/2), and q(5/6) are each in
# the middle of a bucket and should therefore be 1, 3, and 5,
# respectively.
load_with_nhcb 5m
	testhistogram2_bucket{le="0"}	 0+0x10
	testhistogram2_bucket{le="2"}	 0+1x10
	testhistogram2_bucket{le="4"}    0+2x10
	testhistogram2_bucket{le="6"}	 0+3x10
	testhistogram2_bucket{le="+Inf"} 0+3x10

# Another test histogram, this time without any observations in the +Inf bucket.
# This enables a meaningful calculation of standard deviation and variance.
load_with_nhcb 5m
	testhistogram3_bucket{le="0", start="positive"}	0+0x10
	testhistogram3_bucket{le="0.1", start="positive"}	0+5x10
	testhistogram3_bucket{le=".2", start="positive"}		0+7x10
	testhistogram3_bucket{le="1e0", start="positive"}	0+11x10
	testhistogram3_bucket{le="+Inf", start="positive"}	0+11x10
	testhistogram3_sum{start="positive"}	0+33x10
	testhistogram3_count{start="positive"}	0+11x10
	testhistogram3_bucket{le="-.25", start="negative"}	0+0x10
	testhistogram3_bucket{le="-.2", start="negative"}	0+1x10
	testhistogram3_bucket{le="-0.1", start="negative"}	0+2x10
	testhistogram3_bucket{le="0.3", start="negative"}	0+2x10
	testhistogram3_bucket{le="+Inf", start="negative"}	0+2x10
	testhistogram3_sum{start="negative"}	0+8x10
	testhistogram3_count{start="negative"}	0+2x10

# Now a more realistic histogram per job and instance to test aggregation.
load_with_nhcb 5m
	request_duration_seconds_bucket{job="job1", instance="ins1", le="0.1"}	0+1x10
	request_duration_seconds_bucket{job="job1", instance="ins1", le="0.2"}	0+3x10
	request_duration_seconds_bucket{job="job1", instance="ins1", le="+Inf"}	0+4x10
	request_duration_seconds_bucket{job="job1", instance="ins2", le="0.1"}	0+2x10
	request_duration_seconds_bucket{job="job1", instance="ins2", le="0.2"}	0+5x10
	request_duration_seconds_bucket{job="job1", instance="ins2", le="+Inf"}	0+6x10
	request_duration_seconds_bucket{job="job2", instance="ins1", le="0.1"}	0+3x10
	request_duration_seconds_bucket{job="job2", instance="ins1", le="0.2"}	0+4x10
	request_duration_seconds_bucket{job="job2", instance="ins1", le="+Inf"}	0+6x10
	request_duration_seconds_bucket{job="job2", instance="ins2", le="0.1"}	0+4x10
	request_duration_seconds_bucket{job="job2", instance="ins2", le="0.2"}	0+7x10
	request_duration_seconds_bucket{job="job2", instance="ins2", le="+Inf"}	0+9x10

# Different le representations in one histogram.
load_with_nhcb 5m
	mixed_bucket{job="job1", instance="ins1", le="0.1"}	0+1x10
	mixed_bucket{job="job1", instance="ins1", le="0.2"}	0+1x10
	mixed_bucket{job="job1", instance="ins1", le="2e-1"}	0+1x10
	mixed_bucket{job="job1", instance="ins1", le="2.0e-1"}	0+1x10
	mixed_bucket{job="job1", instance="ins1", le="+Inf"}	0+4x10
	mixed_bucket{job="job1", instance="ins2", le="+inf"}	0+0x10
	mixed_bucket{job="job1", instance="ins2", le="+Inf"}	0+0x10

# Test histogram_count.
eval instant at 50m histogram_count(testhistogram3)
	{start="positive"} 110
	{start="negative"} 20

# Classic way of accessing the count still works.
eval instant at 50m testhistogram3_count
	testhistogram3_count{start="positive"} 110
	testhistogram3_count{start="negative"} 20

# Test histogram_sum.
eval instant at 50m histogram_sum(testhistogram3)
	{start="positive"} 330
	{start="negative"} 80

# Classic way of accessing the sum still works.
eval instant at 50m testhistogram3_sum
	testhistogram3_sum{start="positive"} 330
	testhistogram3_sum{start="negative"} 80

# Test histogram_avg. This has no classic equivalent.
eval instant at 50m histogram_avg(testhistogram3)
	{start="positive"} 3
	{start="negative"} 4

# Test histogram_stddev. This has no classic equivalent.
eval instant at 50m histogram_stddev(testhistogram3)
	{start="positive"} 2.8189265757336734
	{start="negative"} 4.182715937754936

# Test histogram_stdvar. This has no classic equivalent.
eval instant at 50m histogram_stdvar(testhistogram3)
	{start="positive"} 7.946347039377573
	{start="negative"} 17.495112615949154

# Test histogram_fraction.

eval instant at 50m histogram_fraction(0, 0.2, testhistogram3)
	{start="positive"} 0.6363636363636364
	{start="negative"} 0
	
eval instant at 50m histogram_fraction(0, 0.2, rate(testhistogram3[5m]))
	{start="positive"} 0.6363636363636364
	{start="negative"} 0

# In the classic histogram, we can access the corresponding bucket (if
# it exists) and divide by the count to get the same result.

eval instant at 50m testhistogram3_bucket{le=".2"} / ignoring(le) testhistogram3_count
	{start="positive"} 0.6363636363636364
	
eval instant at 50m rate(testhistogram3_bucket{le=".2"}[5m]) / ignoring(le) rate(testhistogram3_count[5m])
	{start="positive"} 0.6363636363636364

# Test histogram_quantile, native and classic.

eval instant at 50m histogram_quantile(0, testhistogram3)
	{start="positive"} 0
	{start="negative"} -0.25

eval instant at 50m histogram_quantile(0, testhistogram3_bucket)
	{start="positive"} 0
	{start="negative"} -0.25

eval instant at 50m histogram_quantile(0.25, testhistogram3)
	{start="positive"} 0.055
	{start="negative"} -0.225

eval instant at 50m histogram_quantile(0.25, testhistogram3_bucket)
	{start="positive"} 0.055
	{start="negative"} -0.225

eval instant at 50m histogram_quantile(0.5, testhistogram3)
	{start="positive"} 0.125
	{start="negative"} -0.2

eval instant at 50m histogram_quantile(0.5, testhistogram3_bucket)
	{start="positive"} 0.125
	{start="negative"} -0.2

eval instant at 50m histogram_quantile(0.75, testhistogram3)
	{start="positive"} 0.45
	{start="negative"} -0.15

eval instant at 50m histogram_quantile(0.75, testhistogram3_bucket)
	{start="positive"} 0.45
	{start="negative"} -0.15

eval instant at 50m histogram_quantile(1, testhistogram3)
	{start="positive"} 1
	{start="negative"} -0.1

eval instant at 50m histogram_quantile(1, testhistogram3_bucket)
	{start="positive"} 1
	{start="negative"} -0.1

# Quantile too low.

eval_warn instant at 50m histogram_quantile(-0.1, testhistogram)
	{start="positive"} -Inf
	{start="negative"} -Inf

eval_warn instant at 50m histogram_quantile(-0.1, testhistogram_bucket)
	{start="positive"} -Inf
	{start="negative"} -Inf

# Quantile too high.

eval_warn instant at 50m histogram_quantile(1.01, testhistogram)
	{start="positive"} +Inf
	{start="negative"} +Inf

eval_warn instant at 50m histogram_quantile(1.01, testhistogram_bucket)
	{start="positive"} +Inf
	{start="negative"} +Inf

# Quantile invalid.

eval_warn instant at 50m histogram_quantile(NaN, testhistogram)
	{start="positive"} NaN
	{start="negative"} NaN

eval_warn instant at 50m histogram_quantile(NaN, testhistogram_bucket)
	{start="positive"} NaN
	{start="negative"} NaN

# Quantile value in lowest bucket.

eval instant at 50m histogram_quantile(0, testhistogram)
	{start="positive"} 0
	{start="negative"} -0.2

eval instant at 50m histogram_quantile(0, testhistogram_bucket)
	{start="positive"} 0
	{start="negative"} -0.2

# Quantile value in highest bucket.

eval instant at 50m histogram_quantile(1, testhistogram)
	{start="positive"} 1
	{start="negative"} 0.3

eval instant at 50m histogram_quantile(1, testhistogram_bucket)
	{start="positive"} 1
	{start="negative"} 0.3

# Finally some useful quantiles.

eval instant at 50m histogram_quantile(0.2, testhistogram)
	{start="positive"} 0.048
	{start="negative"} -0.2

eval instant at 50m histogram_quantile(0.2, testhistogram_bucket)
	{start="positive"} 0.048
	{start="negative"} -0.2

eval instant at 50m histogram_quantile(0.5, testhistogram)
	{start="positive"} 0.15
	{start="negative"} -0.15

eval instant at 50m histogram_quantile(0.5, testhistogram_bucket)
	{start="positive"} 0.15
	{start="negative"} -0.15

eval instant at 50m histogram_quantile(0.8, testhistogram)
	{start="positive"} 0.72
	{start="negative"} 0.3

eval instant at 50m histogram_quantile(0.8, testhistogram_bucket)
	{start="positive"} 0.72
	{start="negative"} 0.3

# More realistic with rates.

eval instant at 50m histogram_quantile(0.2, rate(testhistogram[5m]))
	{start="positive"} 0.048
	{start="negative"} -0.2

eval instant at 50m histogram_quantile(0.2, rate(testhistogram_bucket[5m]))
	{start="positive"} 0.048
	{start="negative"} -0.2

eval instant at 50m histogram_quantile(0.5, rate(testhistogram[5m]))
	{start="positive"} 0.15
	{start="negative"} -0.15

eval instant at 50m histogram_quantile(0.5, rate(testhistogram_bucket[5m]))
	{start="positive"} 0.15
	{start="negative"} -0.15

eval instant at 50m histogram_quantile(0.8, rate(testhistogram[5m]))
	{start="positive"} 0.72
	{start="negative"} 0.3

eval instant at 50m histogram_quantile(0.8, rate(testhistogram_bucket[5m]))
	{start="positive"} 0.72
	{start="negative"} 0.3

# Want results exactly in the middle of the bucket.

eval instant at 7m histogram_quantile(1./6., testhistogram2)
	{} 1

eval instant at 7m histogram_quantile(1./6., testhistogram2_bucket)
	{} 1

eval instant at 7m histogram_quantile(0.5, testhistogram2)
	{} 3

eval instant at 7m histogram_quantile(0.5, testhistogram2_bucket)
	{} 3

eval instant at 7m histogram_quantile(5./6., testhistogram2)
	{} 5

eval instant at 7m histogram_quantile(5./6., testhistogram2_bucket)
	{} 5

eval instant at 47m histogram_quantile(1./6., rate(testhistogram2[15m]))
	{} 1

eval instant at 47m histogram_quantile(1./6., rate(testhistogram2_bucket[15m]))
	{} 1

eval instant at 47m histogram_quantile(0.5, rate(testhistogram2[15m]))
	{} 3

eval instant at 47m histogram_quantile(0.5, rate(testhistogram2_bucket[15m]))
	{} 3

eval instant at 47m histogram_quantile(5./6., rate(testhistogram2[15m]))
	{} 5

eval instant at 47m histogram_quantile(5./6., rate(testhistogram2_bucket[15m]))
	{} 5

# Aggregated histogram: Everything in one. Note how native histograms
# don't require aggregation by le.

eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds[5m])))
	{} 0.075

eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le))
	{} 0.075

eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds[5m])))
	{} 0.1277777777777778

eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le))
	{} 0.1277777777777778

# Aggregated histogram: Everything in one. Now with avg, which does not change anything.

eval instant at 50m histogram_quantile(0.3, avg(rate(request_duration_seconds[5m])))
	{} 0.075

eval instant at 50m histogram_quantile(0.3, avg(rate(request_duration_seconds_bucket[5m])) by (le))
	{} 0.075

eval instant at 50m histogram_quantile(0.5, avg(rate(request_duration_seconds[5m])))
	{} 0.12777777777777778

eval instant at 50m histogram_quantile(0.5, avg(rate(request_duration_seconds_bucket[5m])) by (le))
	{} 0.12777777777777778

# Aggregated histogram: By instance.

eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds[5m])) by (instance))
	{instance="ins1"} 0.075
	{instance="ins2"} 0.075

eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))
	{instance="ins1"} 0.075
	{instance="ins2"} 0.075

eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds[5m])) by (instance))
	{instance="ins1"} 0.1333333333
	{instance="ins2"} 0.125

eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))
	{instance="ins1"} 0.1333333333
	{instance="ins2"} 0.125

# Aggregated histogram: By job.

eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds[5m])) by (job))
	{job="job1"} 0.1
	{job="job2"} 0.0642857142857143

eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))
	{job="job1"} 0.1
	{job="job2"} 0.0642857142857143

eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds[5m])) by (job))
	{job="job1"} 0.14
	{job="job2"} 0.1125

eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))
	{job="job1"} 0.14
	{job="job2"} 0.1125

# Aggregated histogram: By job and instance.

eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds[5m])) by (job, instance))
	{instance="ins1", job="job1"} 0.11
	{instance="ins2", job="job1"} 0.09
	{instance="ins1", job="job2"} 0.06
	{instance="ins2", job="job2"} 0.0675

eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))
	{instance="ins1", job="job1"} 0.11
	{instance="ins2", job="job1"} 0.09
	{instance="ins1", job="job2"} 0.06
	{instance="ins2", job="job2"} 0.0675

eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds[5m])) by (job, instance))
	{instance="ins1", job="job1"} 0.15
	{instance="ins2", job="job1"} 0.1333333333333333
	{instance="ins1", job="job2"} 0.1
	{instance="ins2", job="job2"} 0.1166666666666667

eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))
	{instance="ins1", job="job1"} 0.15
	{instance="ins2", job="job1"} 0.1333333333333333
	{instance="ins1", job="job2"} 0.1
	{instance="ins2", job="job2"} 0.1166666666666667

# The unaggregated histogram for comparison. Same result as the previous one.

eval instant at 50m histogram_quantile(0.3, rate(request_duration_seconds[5m]))
	{instance="ins1", job="job1"} 0.11
	{instance="ins2", job="job1"} 0.09
	{instance="ins1", job="job2"} 0.06
	{instance="ins2", job="job2"} 0.0675

eval instant at 50m histogram_quantile(0.3, rate(request_duration_seconds_bucket[5m]))
	{instance="ins1", job="job1"} 0.11
	{instance="ins2", job="job1"} 0.09
	{instance="ins1", job="job2"} 0.06
	{instance="ins2", job="job2"} 0.0675

eval instant at 50m histogram_quantile(0.5, rate(request_duration_seconds[5m]))
	{instance="ins1", job="job1"} 0.15
	{instance="ins2", job="job1"} 0.13333333333333333
	{instance="ins1", job="job2"} 0.1
	{instance="ins2", job="job2"} 0.11666666666666667

eval instant at 50m histogram_quantile(0.5, rate(request_duration_seconds_bucket[5m]))
	{instance="ins1", job="job1"} 0.15
	{instance="ins2", job="job1"} 0.13333333333333333
	{instance="ins1", job="job2"} 0.1
	{instance="ins2", job="job2"} 0.11666666666666667

# All NHCBs summed into one.
eval instant at 50m sum(request_duration_seconds)
	{} {{schema:-53 count:250 custom_values:[0.1 0.2] buckets:[100 90 60]}}

# A histogram with nonmonotonic bucket counts. This may happen when recording
# rule evaluation or federation races scrape ingestion, causing some buckets
# counts to be derived from fewer samples.

load 5m
    nonmonotonic_bucket{le="0.1"}   0+2x10
    nonmonotonic_bucket{le="1"}     0+1x10
    nonmonotonic_bucket{le="10"}    0+5x10
    nonmonotonic_bucket{le="100"}   0+4x10
    nonmonotonic_bucket{le="1000"}  0+9x10
    nonmonotonic_bucket{le="+Inf"}  0+8x10

# Nonmonotonic buckets
eval instant at 50m histogram_quantile(0.01, nonmonotonic_bucket)
    {} 0.0045

eval instant at 50m histogram_quantile(0.5, nonmonotonic_bucket)
    {} 8.5

eval instant at 50m histogram_quantile(0.99, nonmonotonic_bucket)
    {} 979.75

# Buckets with different representations of the same upper bound.
eval instant at 50m histogram_quantile(0.5, rate(mixed_bucket[5m]))
	{instance="ins1", job="job1"} 0.15
	{instance="ins2", job="job1"} NaN

eval instant at 50m histogram_quantile(0.5, rate(mixed[5m]))
	{instance="ins1", job="job1"} 0.2
	{instance="ins2", job="job1"} NaN

eval instant at 50m histogram_quantile(0.75, rate(mixed_bucket[5m]))
	{instance="ins1", job="job1"} 0.2
	{instance="ins2", job="job1"} NaN

eval instant at 50m histogram_quantile(1, rate(mixed_bucket[5m]))
	{instance="ins1", job="job1"} 0.2
	{instance="ins2", job="job1"} NaN

load_with_nhcb 5m
	empty_bucket{le="0.1", job="job1", instance="ins1"}    0x10
	empty_bucket{le="0.2", job="job1", instance="ins1"}    0x10
	empty_bucket{le="+Inf", job="job1", instance="ins1"}   0x10

eval instant at 50m histogram_quantile(0.2, rate(empty_bucket[5m]))
	{instance="ins1", job="job1"} NaN

# Load a duplicate histogram with a different name to test failure scenario on multiple histograms with the same label set.
# https://github.com/prometheus/prometheus/issues/9910
load_with_nhcb 5m
	request_duration_seconds2_bucket{job="job1", instance="ins1", le="0.1"}	 0+1x10
	request_duration_seconds2_bucket{job="job1", instance="ins1", le="0.2"}	 0+3x10
	request_duration_seconds2_bucket{job="job1", instance="ins1", le="+Inf"} 0+4x10

eval_fail instant at 50m histogram_quantile(0.99, {__name__=~"request_duration_seconds\\d*_bucket"})

eval_fail instant at 50m histogram_quantile(0.99, {__name__=~"request_duration_seconds\\d*"})

# Histogram with constant buckets.
load_with_nhcb 1m
    const_histogram_bucket{le="0.0"} 1 1 1 1 1
    const_histogram_bucket{le="1.0"} 1 1 1 1 1
    const_histogram_bucket{le="2.0"} 1 1 1 1 1
    const_histogram_bucket{le="+Inf"} 1 1 1 1 1

# There is no change to the bucket count over time, thus rate is 0 in each bucket.
eval instant at 5m rate(const_histogram_bucket[5m])
    {le="0.0"} 0
    {le="1.0"} 0
    {le="2.0"} 0
    {le="+Inf"} 0

# There is no change to the bucket count over time, thus rate is 0 in each bucket.
# However native histograms do not represent empty buckets, so here the zeros are implicit.
eval instant at 5m rate(const_histogram[5m])
    {} {{schema:-53 sum:0 count:0 custom_values:[0.0 1.0 2.0]}}

# Zero buckets mean no observations, so there is no value that observations fall below,
# which means that any quantile is a NaN.
eval instant at 5m histogram_quantile(1.0, sum by (le) (rate(const_histogram_bucket[5m])))
    {} NaN

# Zero buckets mean no observations, so there is no value that observations fall below,
# which means that any quantile is a NaN.
eval instant at 5m histogram_quantile(1.0, sum(rate(const_histogram[5m])))
    {} NaN