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517b81f927
Make the timestamp of instant vectors be the timestamp of the sample rather than the evaluation. We were not using this anywhere, so this is safe. Add a function to return the timestamp of samples in an instant vector. Fixes #1557
457 lines
14 KiB
Plaintext
457 lines
14 KiB
Plaintext
# Testdata for resets() and changes().
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load 5m
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http_requests{path="/foo"} 1 2 3 0 1 0 0 1 2 0
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http_requests{path="/bar"} 1 2 3 4 5 1 2 3 4 5
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http_requests{path="/biz"} 0 0 0 0 0 1 1 1 1 1
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# Tests for resets().
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eval instant at 50m resets(http_requests[5m])
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{path="/foo"} 0
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{path="/bar"} 0
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{path="/biz"} 0
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eval instant at 50m resets(http_requests[20m])
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{path="/foo"} 1
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{path="/bar"} 0
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{path="/biz"} 0
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eval instant at 50m resets(http_requests[30m])
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{path="/foo"} 2
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{path="/bar"} 1
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{path="/biz"} 0
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eval instant at 50m resets(http_requests[50m])
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{path="/foo"} 3
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{path="/bar"} 1
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{path="/biz"} 0
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eval instant at 50m resets(nonexistent_metric[50m])
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# Tests for changes().
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eval instant at 50m changes(http_requests[5m])
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{path="/foo"} 0
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{path="/bar"} 0
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{path="/biz"} 0
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eval instant at 50m changes(http_requests[20m])
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{path="/foo"} 3
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{path="/bar"} 3
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{path="/biz"} 0
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eval instant at 50m changes(http_requests[30m])
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{path="/foo"} 4
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{path="/bar"} 5
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{path="/biz"} 1
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eval instant at 50m changes(http_requests[50m])
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{path="/foo"} 8
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{path="/bar"} 9
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{path="/biz"} 1
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eval instant at 50m changes(nonexistent_metric[50m])
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clear
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load 5m
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x{a="b"} NaN NaN NaN
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x{a="c"} 0 NaN 0
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eval instant at 15m changes(x[15m])
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{a="b"} 0
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{a="c"} 2
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clear
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# Tests for increase().
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load 5m
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http_requests{path="/foo"} 0+10x10
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http_requests{path="/bar"} 0+10x5 0+10x5
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# Tests for increase().
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eval instant at 50m increase(http_requests[50m])
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{path="/foo"} 100
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{path="/bar"} 90
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eval instant at 50m increase(http_requests[100m])
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{path="/foo"} 100
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{path="/bar"} 90
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clear
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# Test for increase() with counter reset.
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# When the counter is reset, it always starts at 0.
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# So the sequence 3 2 (decreasing counter = reset) is interpreted the same as 3 0 1 2.
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# Prometheus assumes it missed the intermediate values 0 and 1.
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load 5m
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http_requests{path="/foo"} 0 1 2 3 2 3 4
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eval instant at 30m increase(http_requests[30m])
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{path="/foo"} 7
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clear
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# Tests for irate().
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load 5m
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http_requests{path="/foo"} 0+10x10
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http_requests{path="/bar"} 0+10x5 0+10x5
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eval instant at 50m irate(http_requests[50m])
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{path="/foo"} .03333333333333333333
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{path="/bar"} .03333333333333333333
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# Counter reset.
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eval instant at 30m irate(http_requests[50m])
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{path="/foo"} .03333333333333333333
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{path="/bar"} 0
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clear
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# Tests for delta().
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load 5m
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http_requests{path="/foo"} 0 50 100 150 200
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http_requests{path="/bar"} 200 150 100 50 0
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eval instant at 20m delta(http_requests[20m])
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{path="/foo"} 200
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{path="/bar"} -200
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clear
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# Tests for idelta().
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load 5m
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http_requests{path="/foo"} 0 50 100 150
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http_requests{path="/bar"} 0 50 100 50
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eval instant at 20m idelta(http_requests[20m])
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{path="/foo"} 50
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{path="/bar"} -50
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clear
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# Tests for deriv() and predict_linear().
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load 5m
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testcounter_reset_middle 0+10x4 0+10x5
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http_requests{job="app-server", instance="1", group="canary"} 0+80x10
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# deriv should return the same as rate in simple cases.
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eval instant at 50m rate(http_requests{group="canary", instance="1", job="app-server"}[50m])
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{group="canary", instance="1", job="app-server"} 0.26666666666666666
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eval instant at 50m deriv(http_requests{group="canary", instance="1", job="app-server"}[50m])
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{group="canary", instance="1", job="app-server"} 0.26666666666666666
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# deriv should return correct result.
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eval instant at 50m deriv(testcounter_reset_middle[100m])
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{} 0.010606060606060607
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# predict_linear should return correct result.
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# X/s = [ 0, 300, 600, 900,1200,1500,1800,2100,2400,2700,3000]
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# Y = [ 0, 10, 20, 30, 40, 0, 10, 20, 30, 40, 50]
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# sumX = 16500
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# sumY = 250
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# sumXY = 480000
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# sumX2 = 34650000
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# n = 11
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# covXY = 105000
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# varX = 9900000
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# slope = 0.010606060606060607
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# intercept at t=0: 6.818181818181818
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# intercept at t=3000: 38.63636363636364
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# intercept at t=3000+3600: 76.81818181818181
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eval instant at 50m predict_linear(testcounter_reset_middle[100m], 3600)
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{} 76.81818181818181
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# With http_requests, there is a sample value exactly at the end of
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# the range, and it has exactly the predicted value, so predict_linear
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# can be emulated with deriv.
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eval instant at 50m predict_linear(http_requests[50m], 3600) - (http_requests + deriv(http_requests[50m]) * 3600)
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{group="canary", instance="1", job="app-server"} 0
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clear
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# Tests for label_replace.
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load 5m
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testmetric{src="source-value-10",dst="original-destination-value"} 0
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testmetric{src="source-value-20",dst="original-destination-value"} 1
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# label_replace does a full-string match and replace.
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eval instant at 0m label_replace(testmetric, "dst", "destination-value-$1", "src", "source-value-(.*)")
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testmetric{src="source-value-10",dst="destination-value-10"} 0
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testmetric{src="source-value-20",dst="destination-value-20"} 1
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# label_replace does not do a sub-string match.
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eval instant at 0m label_replace(testmetric, "dst", "destination-value-$1", "src", "value-(.*)")
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testmetric{src="source-value-10",dst="original-destination-value"} 0
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testmetric{src="source-value-20",dst="original-destination-value"} 1
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# label_replace works with multiple capture groups.
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eval instant at 0m label_replace(testmetric, "dst", "$1-value-$2", "src", "(.*)-value-(.*)")
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testmetric{src="source-value-10",dst="source-value-10"} 0
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testmetric{src="source-value-20",dst="source-value-20"} 1
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# label_replace does not overwrite the destination label if the source label
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# does not exist.
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eval instant at 0m label_replace(testmetric, "dst", "value-$1", "nonexistent-src", "source-value-(.*)")
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testmetric{src="source-value-10",dst="original-destination-value"} 0
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testmetric{src="source-value-20",dst="original-destination-value"} 1
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# label_replace overwrites the destination label if the source label is empty,
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# but matched.
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eval instant at 0m label_replace(testmetric, "dst", "value-$1", "nonexistent-src", "(.*)")
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testmetric{src="source-value-10",dst="value-"} 0
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testmetric{src="source-value-20",dst="value-"} 1
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# label_replace does not overwrite the destination label if the source label
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# is not matched.
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eval instant at 0m label_replace(testmetric, "dst", "value-$1", "src", "non-matching-regex")
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testmetric{src="source-value-10",dst="original-destination-value"} 0
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testmetric{src="source-value-20",dst="original-destination-value"} 1
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# label_replace drops labels that are set to empty values.
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eval instant at 0m label_replace(testmetric, "dst", "", "dst", ".*")
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testmetric{src="source-value-10"} 0
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testmetric{src="source-value-20"} 1
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# label_replace fails when the regex is invalid.
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eval_fail instant at 0m label_replace(testmetric, "dst", "value-$1", "src", "(.*")
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# label_replace fails when the destination label name is not a valid Prometheus label name.
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eval_fail instant at 0m label_replace(testmetric, "invalid-label-name", "", "src", "(.*)")
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# label_replace fails when there would be duplicated identical output label sets.
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eval_fail instant at 0m label_replace(testmetric, "src", "", "", "")
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clear
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# Tests for vector, time and timestamp.
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load 10s
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metric 1 1
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eval instant at 0s timestamp(metric)
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{} 0
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eval instant at 5s timestamp(metric)
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{} 0
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eval instant at 10s timestamp(metric)
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{} 10
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eval instant at 0m vector(1)
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{} 1
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eval instant at 0s vector(time())
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{} 0
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eval instant at 5s vector(time())
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{} 5
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eval instant at 60m vector(time())
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{} 3600
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# Tests for clamp_max and clamp_min().
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load 5m
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test_clamp{src="clamp-a"} -50
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test_clamp{src="clamp-b"} 0
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test_clamp{src="clamp-c"} 100
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eval instant at 0m clamp_max(test_clamp, 75)
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{src="clamp-a"} -50
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{src="clamp-b"} 0
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{src="clamp-c"} 75
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eval instant at 0m clamp_min(test_clamp, -25)
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{src="clamp-a"} -25
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{src="clamp-b"} 0
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{src="clamp-c"} 100
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eval instant at 0m clamp_max(clamp_min(test_clamp, -20), 70)
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{src="clamp-a"} -20
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{src="clamp-b"} 0
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{src="clamp-c"} 70
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# Tests for sort/sort_desc.
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clear
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load 5m
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http_requests{job="api-server", instance="0", group="production"} 0+10x10
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http_requests{job="api-server", instance="1", group="production"} 0+20x10
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http_requests{job="api-server", instance="0", group="canary"} 0+30x10
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http_requests{job="api-server", instance="1", group="canary"} 0+40x10
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http_requests{job="api-server", instance="2", group="canary"} NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
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http_requests{job="app-server", instance="0", group="production"} 0+50x10
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http_requests{job="app-server", instance="1", group="production"} 0+60x10
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http_requests{job="app-server", instance="0", group="canary"} 0+70x10
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http_requests{job="app-server", instance="1", group="canary"} 0+80x10
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eval_ordered instant at 50m sort(http_requests)
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http_requests{group="production", instance="0", job="api-server"} 100
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http_requests{group="production", instance="1", job="api-server"} 200
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http_requests{group="canary", instance="0", job="api-server"} 300
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http_requests{group="canary", instance="1", job="api-server"} 400
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http_requests{group="production", instance="0", job="app-server"} 500
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http_requests{group="production", instance="1", job="app-server"} 600
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http_requests{group="canary", instance="0", job="app-server"} 700
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http_requests{group="canary", instance="1", job="app-server"} 800
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http_requests{group="canary", instance="2", job="api-server"} NaN
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eval_ordered instant at 50m sort_desc(http_requests)
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http_requests{group="canary", instance="1", job="app-server"} 800
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http_requests{group="canary", instance="0", job="app-server"} 700
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http_requests{group="production", instance="1", job="app-server"} 600
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http_requests{group="production", instance="0", job="app-server"} 500
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http_requests{group="canary", instance="1", job="api-server"} 400
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http_requests{group="canary", instance="0", job="api-server"} 300
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http_requests{group="production", instance="1", job="api-server"} 200
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http_requests{group="production", instance="0", job="api-server"} 100
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http_requests{group="canary", instance="2", job="api-server"} NaN
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# Tests for holt_winters
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clear
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# positive trends
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load 10s
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http_requests{job="api-server", instance="0", group="production"} 0+10x1000 100+30x1000
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http_requests{job="api-server", instance="1", group="production"} 0+20x1000 200+30x1000
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http_requests{job="api-server", instance="0", group="canary"} 0+30x1000 300+80x1000
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http_requests{job="api-server", instance="1", group="canary"} 0+40x2000
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eval instant at 8000s holt_winters(http_requests[1m], 0.01, 0.1)
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{job="api-server", instance="0", group="production"} 8000
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{job="api-server", instance="1", group="production"} 16000
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{job="api-server", instance="0", group="canary"} 24000
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{job="api-server", instance="1", group="canary"} 32000
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# negative trends
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clear
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load 10s
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http_requests{job="api-server", instance="0", group="production"} 8000-10x1000
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http_requests{job="api-server", instance="1", group="production"} 0-20x1000
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http_requests{job="api-server", instance="0", group="canary"} 0+30x1000 300-80x1000
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http_requests{job="api-server", instance="1", group="canary"} 0-40x1000 0+40x1000
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eval instant at 8000s holt_winters(http_requests[1m], 0.01, 0.1)
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{job="api-server", instance="0", group="production"} 0
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{job="api-server", instance="1", group="production"} -16000
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{job="api-server", instance="0", group="canary"} 24000
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{job="api-server", instance="1", group="canary"} -32000
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# Tests for stddev_over_time and stdvar_over_time.
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clear
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load 10s
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metric 0 8 8 2 3
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eval instant at 1m stdvar_over_time(metric[1m])
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{} 10.56
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eval instant at 1m stddev_over_time(metric[1m])
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{} 3.249615
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# Tests for quantile_over_time
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clear
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load 10s
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data{test="two samples"} 0 1
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data{test="three samples"} 0 1 2
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data{test="uneven samples"} 0 1 4
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eval instant at 1m quantile_over_time(0, data[1m])
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{test="two samples"} 0
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{test="three samples"} 0
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{test="uneven samples"} 0
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eval instant at 1m quantile_over_time(0.5, data[1m])
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{test="two samples"} 0.5
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{test="three samples"} 1
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{test="uneven samples"} 1
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eval instant at 1m quantile_over_time(0.75, data[1m])
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{test="two samples"} 0.75
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{test="three samples"} 1.5
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{test="uneven samples"} 2.5
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eval instant at 1m quantile_over_time(0.8, data[1m])
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{test="two samples"} 0.8
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{test="three samples"} 1.6
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{test="uneven samples"} 2.8
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eval instant at 1m quantile_over_time(1, data[1m])
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{test="two samples"} 1
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{test="three samples"} 2
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{test="uneven samples"} 4
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eval instant at 1m quantile_over_time(-1, data[1m])
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{test="two samples"} -Inf
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{test="three samples"} -Inf
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{test="uneven samples"} -Inf
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eval instant at 1m quantile_over_time(2, data[1m])
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{test="two samples"} +Inf
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{test="three samples"} +Inf
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{test="uneven samples"} +Inf
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clear
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# Test time-related functions.
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eval instant at 0m year()
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{} 1970
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eval instant at 0m year(vector(1136239445))
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{} 2006
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eval instant at 0m month()
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{} 1
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eval instant at 0m month(vector(1136239445))
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{} 1
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eval instant at 0m day_of_month()
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{} 1
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eval instant at 0m day_of_month(vector(1136239445))
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{} 2
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# Thursday.
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eval instant at 0m day_of_week()
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{} 4
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eval instant at 0m day_of_week(vector(1136239445))
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{} 1
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eval instant at 0m hour()
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{} 0
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eval instant at 0m hour(vector(1136239445))
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{} 22
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eval instant at 0m minute()
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{} 0
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eval instant at 0m minute(vector(1136239445))
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{} 4
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# 2008-12-31 23:59:59 just before leap second.
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eval instant at 0m year(vector(1230767999))
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{} 2008
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# 2009-01-01 00:00:00 just after leap second.
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eval instant at 0m year(vector(1230768000))
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{} 2009
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# 2016-02-29 23:59:59 Febuary 29th in leap year.
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eval instant at 0m month(vector(1456790399)) + day_of_month(vector(1456790399)) / 100
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{} 2.29
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# 2016-03-01 00:00:00 March 1st in leap year.
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eval instant at 0m month(vector(1456790400)) + day_of_month(vector(1456790400)) / 100
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{} 3.01
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# Febuary 1st 2016 in leap year.
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eval instant at 0m days_in_month(vector(1454284800))
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{} 29
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# Febuary 1st 2017 not in leap year.
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eval instant at 0m days_in_month(vector(1485907200))
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{} 28
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