# Testdata for resets() and changes().
load 5m
	http_requests{path="/foo"}	1 2 3 0 1 0 0 1 2 0
	http_requests{path="/bar"}	1 2 3 4 5 1 2 3 4 5
	http_requests{path="/biz"}	0 0 0 0 0 1 1 1 1 1

# Tests for resets().
eval instant at 50m resets(http_requests[5m])
	{path="/foo"} 0
	{path="/bar"} 0
	{path="/biz"} 0

eval instant at 50m resets(http_requests[20m])
	{path="/foo"} 1
	{path="/bar"} 0
	{path="/biz"} 0

eval instant at 50m resets(http_requests[30m])
	{path="/foo"} 2
	{path="/bar"} 1
	{path="/biz"} 0

eval instant at 50m resets(http_requests[50m])
	{path="/foo"} 3
	{path="/bar"} 1
	{path="/biz"} 0

eval instant at 50m resets(nonexistent_metric[50m])

# Tests for changes().
eval instant at 50m changes(http_requests[5m])
	{path="/foo"} 0
	{path="/bar"} 0
	{path="/biz"} 0

eval instant at 50m changes(http_requests[20m])
	{path="/foo"} 3
	{path="/bar"} 3
	{path="/biz"} 0

eval instant at 50m changes(http_requests[30m])
	{path="/foo"} 4
	{path="/bar"} 5
	{path="/biz"} 1

eval instant at 50m changes(http_requests[50m])
	{path="/foo"} 8
	{path="/bar"} 9
	{path="/biz"} 1

eval instant at 50m changes(nonexistent_metric[50m])

clear

load 5m
  x{a="b"} NaN NaN NaN
  x{a="c"} 0 NaN 0

eval instant at 15m changes(x[15m])
  {a="b"} 0
  {a="c"} 2

clear

# Tests for increase().
load 5m
	http_requests{path="/foo"}	0+10x10
	http_requests{path="/bar"}	0+10x5 0+10x5

# Tests for increase().
eval instant at 50m increase(http_requests[50m])
	{path="/foo"} 100
	{path="/bar"}  90

eval instant at 50m increase(http_requests[100m])
	{path="/foo"} 100
	{path="/bar"}  90

clear

# Test for increase() with counter reset.
# When the counter is reset, it always starts at 0.
# So the sequence 3 2 (decreasing counter = reset) is interpreted the same as 3 0 1 2.
# Prometheus assumes it missed the intermediate values 0 and 1.
load 5m
	http_requests{path="/foo"}	0 1 2 3 2 3 4

eval instant at 30m increase(http_requests[30m])
    {path="/foo"} 7

clear

# Tests for irate().
load 5m
	http_requests{path="/foo"}	0+10x10
	http_requests{path="/bar"}	0+10x5 0+10x5

eval instant at 50m irate(http_requests[50m])
	{path="/foo"} .03333333333333333333
	{path="/bar"} .03333333333333333333

# Counter reset.
eval instant at 30m irate(http_requests[50m])
	{path="/foo"} .03333333333333333333
	{path="/bar"} 0

clear

# Tests for delta().
load 5m
	http_requests{path="/foo"}	0 50 100 150 200
	http_requests{path="/bar"}	200 150 100 50 0

eval instant at 20m delta(http_requests[20m])
	{path="/foo"} 200
	{path="/bar"} -200

clear

# Tests for idelta().
load 5m
	http_requests{path="/foo"}	0 50 100 150
	http_requests{path="/bar"}	0 50 100 50

eval instant at 20m idelta(http_requests[20m])
	{path="/foo"} 50
	{path="/bar"} -50

clear

# Tests for deriv() and predict_linear().
load 5m
	testcounter_reset_middle	0+10x4 0+10x5
	http_requests{job="app-server", instance="1", group="canary"}		0+80x10

# deriv should return the same as rate in simple cases.
eval instant at 50m rate(http_requests{group="canary", instance="1", job="app-server"}[50m])
	{group="canary", instance="1", job="app-server"} 0.26666666666666666

eval instant at 50m deriv(http_requests{group="canary", instance="1", job="app-server"}[50m])
	{group="canary", instance="1", job="app-server"} 0.26666666666666666

# deriv should return correct result.
eval instant at 50m deriv(testcounter_reset_middle[100m])
	{} 0.010606060606060607

# predict_linear should return correct result.
# X/s = [  0, 300, 600, 900,1200,1500,1800,2100,2400,2700,3000]
# Y   = [  0,  10,  20,  30,  40,   0,  10,  20,  30,  40,  50]
# sumX  = 16500
# sumY  = 250
# sumXY = 480000
# sumX2 = 34650000
# n     = 11
# covXY = 105000
# varX  = 9900000
# slope = 0.010606060606060607
# intercept at t=0: 6.818181818181818
# intercept at t=3000: 38.63636363636364
# intercept at t=3000+3600: 76.81818181818181
eval instant at 50m predict_linear(testcounter_reset_middle[100m], 3600)
	{} 76.81818181818181

# With http_requests, there is a sample value exactly at the end of
# the range, and it has exactly the predicted value, so predict_linear
# can be emulated with deriv.
eval instant at 50m predict_linear(http_requests[50m], 3600) - (http_requests + deriv(http_requests[50m]) * 3600)
	{group="canary", instance="1", job="app-server"} 0

clear

# Tests for label_replace.
load 5m
  testmetric{src="source-value-10",dst="original-destination-value"} 0
  testmetric{src="source-value-20",dst="original-destination-value"} 1

# label_replace does a full-string match and replace.
eval instant at 0m label_replace(testmetric, "dst", "destination-value-$1", "src", "source-value-(.*)")
  testmetric{src="source-value-10",dst="destination-value-10"} 0
  testmetric{src="source-value-20",dst="destination-value-20"} 1

# label_replace does not do a sub-string match.
eval instant at 0m label_replace(testmetric, "dst", "destination-value-$1", "src", "value-(.*)")
  testmetric{src="source-value-10",dst="original-destination-value"} 0
  testmetric{src="source-value-20",dst="original-destination-value"} 1

# label_replace works with multiple capture groups.
eval instant at 0m label_replace(testmetric, "dst", "$1-value-$2", "src", "(.*)-value-(.*)")
  testmetric{src="source-value-10",dst="source-value-10"} 0
  testmetric{src="source-value-20",dst="source-value-20"} 1

# label_replace does not overwrite the destination label if the source label
# does not exist.
eval instant at 0m label_replace(testmetric, "dst", "value-$1", "nonexistent-src", "source-value-(.*)")
  testmetric{src="source-value-10",dst="original-destination-value"} 0
  testmetric{src="source-value-20",dst="original-destination-value"} 1

# label_replace overwrites the destination label if the source label is empty,
# but matched.
eval instant at 0m label_replace(testmetric, "dst", "value-$1", "nonexistent-src", "(.*)")
  testmetric{src="source-value-10",dst="value-"} 0
  testmetric{src="source-value-20",dst="value-"} 1

# label_replace does not overwrite the destination label if the source label
# is not matched.
eval instant at 0m label_replace(testmetric, "dst", "value-$1", "src", "non-matching-regex")
  testmetric{src="source-value-10",dst="original-destination-value"} 0
  testmetric{src="source-value-20",dst="original-destination-value"} 1

# label_replace drops labels that are set to empty values.
eval instant at 0m label_replace(testmetric, "dst", "", "dst", ".*")
  testmetric{src="source-value-10"} 0
  testmetric{src="source-value-20"} 1

# label_replace fails when the regex is invalid.
eval_fail instant at 0m label_replace(testmetric, "dst", "value-$1", "src", "(.*")

# label_replace fails when the destination label name is not a valid Prometheus label name.
eval_fail instant at 0m label_replace(testmetric, "invalid-label-name", "", "src", "(.*)")

# label_replace fails when there would be duplicated identical output label sets.
eval_fail instant at 0m label_replace(testmetric, "src", "", "", "")

clear

# Tests for vector, time and timestamp.
load 10s
  metric 1 1

eval instant at 0s timestamp(metric)
  {} 0

eval instant at 5s timestamp(metric)
  {} 0

eval instant at 10s timestamp(metric)
  {} 10

# Tests for label_join.
load 5m
  testmetric{src="a",src1="b",src2="c",dst="original-destination-value"} 0
  testmetric{src="d",src1="e",src2="f",dst="original-destination-value"} 1

# label_join joins all src values in order.
eval instant at 0m label_join(testmetric, "dst", "-", "src", "src1", "src2")
  testmetric{src="a",src1="b",src2="c",dst="a-b-c"} 0
  testmetric{src="d",src1="e",src2="f",dst="d-e-f"} 1

# label_join treats non existent src labels as empty strings.
eval instant at 0m label_join(testmetric, "dst", "-", "src", "src3", "src1")
  testmetric{src="a",src1="b",src2="c",dst="a--b"} 0
  testmetric{src="d",src1="e",src2="f",dst="d--e"} 1

# label_join overwrites the destination label even if the resulting dst label is empty string
eval instant at 0m label_join(testmetric, "dst", "", "emptysrc", "emptysrc1", "emptysrc2")
  testmetric{src="a",src1="b",src2="c"} 0
  testmetric{src="d",src1="e",src2="f"} 1

# test without src label for label_join
eval instant at 0m label_join(testmetric, "dst", ", ")
	  testmetric{src="a",src1="b",src2="c"} 0
	  testmetric{src="d",src1="e",src2="f"} 1

# test without dst label for label_join
load 5m
  testmetric1{src="foo",src1="bar",src2="foobar"} 0
  testmetric1{src="fizz",src1="buzz",src2="fizzbuzz"} 1

# label_join creates dst label if not present.
eval instant at 0m label_join(testmetric1, "dst", ", ", "src", "src1", "src2")
  testmetric1{src="foo",src1="bar",src2="foobar",dst="foo, bar, foobar"} 0
  testmetric1{src="fizz",src1="buzz",src2="fizzbuzz",dst="fizz, buzz, fizzbuzz"} 1

clear

# Tests for vector.
eval instant at 0m vector(1)
  {} 1

eval instant at 0s vector(time())
  {} 0

eval instant at 5s vector(time())
  {} 5

eval instant at 60m vector(time())
  {} 3600


# Tests for clamp_max and clamp_min().
load 5m
	test_clamp{src="clamp-a"}	-50
	test_clamp{src="clamp-b"}	0
	test_clamp{src="clamp-c"}	100

eval instant at 0m clamp_max(test_clamp, 75)
	{src="clamp-a"}	-50
	{src="clamp-b"}	0
	{src="clamp-c"}	75

eval instant at 0m clamp_min(test_clamp, -25)
	{src="clamp-a"}	-25
	{src="clamp-b"}	0
	{src="clamp-c"}	100

eval instant at 0m clamp_max(clamp_min(test_clamp, -20), 70)
	{src="clamp-a"}	-20
	{src="clamp-b"}	0
	{src="clamp-c"}	70


# Tests for sort/sort_desc.
clear
load 5m
	http_requests{job="api-server", instance="0", group="production"}	0+10x10
	http_requests{job="api-server", instance="1", group="production"}	0+20x10
	http_requests{job="api-server", instance="0", group="canary"}		0+30x10
	http_requests{job="api-server", instance="1", group="canary"}		0+40x10
	http_requests{job="api-server", instance="2", group="canary"}		NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
	http_requests{job="app-server", instance="0", group="production"}	0+50x10
	http_requests{job="app-server", instance="1", group="production"}	0+60x10
	http_requests{job="app-server", instance="0", group="canary"}		0+70x10
	http_requests{job="app-server", instance="1", group="canary"}		0+80x10

eval_ordered instant at 50m sort(http_requests)
	http_requests{group="production", instance="0", job="api-server"} 100
	http_requests{group="production", instance="1", job="api-server"} 200
	http_requests{group="canary", instance="0", job="api-server"} 300
	http_requests{group="canary", instance="1", job="api-server"} 400
	http_requests{group="production", instance="0", job="app-server"} 500
	http_requests{group="production", instance="1", job="app-server"} 600
	http_requests{group="canary", instance="0", job="app-server"} 700
	http_requests{group="canary", instance="1", job="app-server"} 800
	http_requests{group="canary", instance="2", job="api-server"} NaN

eval_ordered instant at 50m sort_desc(http_requests)
	http_requests{group="canary", instance="1", job="app-server"} 800
	http_requests{group="canary", instance="0", job="app-server"} 700
	http_requests{group="production", instance="1", job="app-server"} 600
	http_requests{group="production", instance="0", job="app-server"} 500
	http_requests{group="canary", instance="1", job="api-server"} 400
	http_requests{group="canary", instance="0", job="api-server"} 300
	http_requests{group="production", instance="1", job="api-server"} 200
	http_requests{group="production", instance="0", job="api-server"} 100
	http_requests{group="canary", instance="2", job="api-server"} NaN

# Tests for holt_winters
clear

# positive trends
load 10s
	http_requests{job="api-server", instance="0", group="production"}	0+10x1000 100+30x1000
	http_requests{job="api-server", instance="1", group="production"}	0+20x1000 200+30x1000
	http_requests{job="api-server", instance="0", group="canary"}		0+30x1000 300+80x1000
	http_requests{job="api-server", instance="1", group="canary"}		0+40x2000

eval instant at 8000s holt_winters(http_requests[1m], 0.01, 0.1)
	{job="api-server", instance="0", group="production"} 8000
	{job="api-server", instance="1", group="production"} 16000
	{job="api-server", instance="0", group="canary"} 24000
	{job="api-server", instance="1", group="canary"} 32000

# negative trends
clear
load 10s
	http_requests{job="api-server", instance="0", group="production"}	8000-10x1000
	http_requests{job="api-server", instance="1", group="production"}	0-20x1000
	http_requests{job="api-server", instance="0", group="canary"}		0+30x1000 300-80x1000
	http_requests{job="api-server", instance="1", group="canary"}		0-40x1000 0+40x1000

eval instant at 8000s holt_winters(http_requests[1m], 0.01, 0.1)
	{job="api-server", instance="0", group="production"} 0
	{job="api-server", instance="1", group="production"} -16000
	{job="api-server", instance="0", group="canary"} 24000
	{job="api-server", instance="1", group="canary"} -32000

# Tests for avg_over_time
clear
load 10s
  metric 1 2 3 4 5

eval instant at 1m avg_over_time(metric[1m])
  {} 3

# Tests for stddev_over_time and stdvar_over_time.
clear
load 10s
  metric 0 8 8 2 3

eval instant at 1m stdvar_over_time(metric[1m])
  {} 10.56

eval instant at 1m stddev_over_time(metric[1m])
  {} 3.249615

# Tests for stddev_over_time and stdvar_over_time #4927.
clear
load 10s
  metric 1.5990505637277868 1.5990505637277868 1.5990505637277868

eval instant at 1m stdvar_over_time(metric[1m])
  {} 0

eval instant at 1m stddev_over_time(metric[1m])
  {} 0

# Tests for quantile_over_time
clear

load 10s
	data{test="two samples"} 0 1
	data{test="three samples"} 0 1 2
	data{test="uneven samples"} 0 1 4

eval instant at 1m quantile_over_time(0, data[1m])
	{test="two samples"} 0
	{test="three samples"} 0
	{test="uneven samples"} 0

eval instant at 1m quantile_over_time(0.5, data[1m])
	{test="two samples"} 0.5
	{test="three samples"} 1
	{test="uneven samples"} 1

eval instant at 1m quantile_over_time(0.75, data[1m])
	{test="two samples"} 0.75
	{test="three samples"} 1.5
	{test="uneven samples"} 2.5

eval instant at 1m quantile_over_time(0.8, data[1m])
	{test="two samples"} 0.8
	{test="three samples"} 1.6
	{test="uneven samples"} 2.8

eval instant at 1m quantile_over_time(1, data[1m])
	{test="two samples"} 1
	{test="three samples"} 2
	{test="uneven samples"} 4

eval instant at 1m quantile_over_time(-1, data[1m])
	{test="two samples"} -Inf
	{test="three samples"} -Inf
	{test="uneven samples"} -Inf

eval instant at 1m quantile_over_time(2, data[1m])
	{test="two samples"} +Inf
	{test="three samples"} +Inf
	{test="uneven samples"} +Inf

clear

# Test time-related functions.
eval instant at 0m year()
  {} 1970

eval instant at 1ms time()
  0.001

eval instant at 0m year(vector(1136239445))
  {} 2006

eval instant at 0m month()
  {} 1

eval instant at 0m month(vector(1136239445))
  {} 1

eval instant at 0m day_of_month()
  {} 1

eval instant at 0m day_of_month(vector(1136239445))
  {} 2

# Thursday.
eval instant at 0m day_of_week()
  {} 4

eval instant at 0m day_of_week(vector(1136239445))
  {} 1

eval instant at 0m hour()
  {} 0

eval instant at 0m hour(vector(1136239445))
  {} 22

eval instant at 0m minute()
  {} 0

eval instant at 0m minute(vector(1136239445))
  {} 4

# 2008-12-31 23:59:59 just before leap second.
eval instant at 0m year(vector(1230767999))
  {} 2008

# 2009-01-01 00:00:00 just after leap second.
eval instant at 0m year(vector(1230768000))
  {} 2009

# 2016-02-29 23:59:59 February 29th in leap year.
eval instant at 0m month(vector(1456790399)) + day_of_month(vector(1456790399)) / 100
  {} 2.29

# 2016-03-01 00:00:00 March 1st in leap year.
eval instant at 0m month(vector(1456790400)) + day_of_month(vector(1456790400)) / 100
  {} 3.01

# February 1st 2016 in leap year.
eval instant at 0m days_in_month(vector(1454284800))
  {} 29

# February 1st 2017 not in leap year.
eval instant at 0m days_in_month(vector(1485907200))
  {} 28

clear

# Test duplicate labelset in promql output.
load 5m
  testmetric1{src="a",dst="b"} 0
  testmetric2{src="a",dst="b"} 1

eval_fail instant at 0m changes({__name__=~'testmetric1|testmetric2'}[5m])

# Tests for *_over_time
clear

load 10s
	data{type="numbers"} 2 0 3
	data{type="some_nan"} 2 0 NaN
	data{type="some_nan2"} 2 NaN 1
	data{type="some_nan3"} NaN 0 1
	data{type="only_nan"} NaN NaN NaN

eval instant at 1m min_over_time(data[1m])
	{type="numbers"} 0
	{type="some_nan"} 0
	{type="some_nan2"} 1
	{type="some_nan3"} 0
	{type="only_nan"} NaN

eval instant at 1m max_over_time(data[1m])
	{type="numbers"} 3
	{type="some_nan"} 2
	{type="some_nan2"} 2
	{type="some_nan3"} 1
	{type="only_nan"} NaN