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Implement changes() function. changes() takes a range vector and returns the number of times a value has changed in the given time window for each time series as an instant vector.
2015-05-26 17:01:34 +00:00
# Testdata for resets() and changes().
Add resets() function to count counter resets. resets() returns for every range vector element how many counter resets there have been in the specified range.
2015-05-26 15:47:52 +00:00
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
Implement changes() function. changes() takes a range vector and returns the number of times a value has changed in the given time window for each time series as an instant vector.
2015-05-26 17:01:34 +00:00
http_requests{path="/biz"} 0 0 0 0 0 1 1 1 1 1
Add resets() function to count counter resets. resets() returns for every range vector element how many counter resets there have been in the specified range.
2015-05-26 15:47:52 +00:00
Implement changes() function. changes() takes a range vector and returns the number of times a value has changed in the given time window for each time series as an instant vector.
2015-05-26 17:01:34 +00:00
# Tests for resets().
Add resets() function to count counter resets. resets() returns for every range vector element how many counter resets there have been in the specified range.
2015-05-26 15:47:52 +00:00
eval instant at 50m resets(http_requests[5m])
{path="/foo"} 0
{path="/bar"} 0
Implement changes() function. changes() takes a range vector and returns the number of times a value has changed in the given time window for each time series as an instant vector.
2015-05-26 17:01:34 +00:00
{path="/biz"} 0
Add resets() function to count counter resets. resets() returns for every range vector element how many counter resets there have been in the specified range.
2015-05-26 15:47:52 +00:00
eval instant at 50m resets(http_requests[20m])
{path="/foo"} 1
{path="/bar"} 0
Implement changes() function. changes() takes a range vector and returns the number of times a value has changed in the given time window for each time series as an instant vector.
2015-05-26 17:01:34 +00:00
{path="/biz"} 0
Add resets() function to count counter resets. resets() returns for every range vector element how many counter resets there have been in the specified range.
2015-05-26 15:47:52 +00:00
eval instant at 50m resets(http_requests[30m])
{path="/foo"} 2
{path="/bar"} 1
Implement changes() function. changes() takes a range vector and returns the number of times a value has changed in the given time window for each time series as an instant vector.
2015-05-26 17:01:34 +00:00
{path="/biz"} 0
Add resets() function to count counter resets. resets() returns for every range vector element how many counter resets there have been in the specified range.
2015-05-26 15:47:52 +00:00
eval instant at 50m resets(http_requests[50m])
{path="/foo"} 3
{path="/bar"} 1
Implement changes() function. changes() takes a range vector and returns the number of times a value has changed in the given time window for each time series as an instant vector.
2015-05-26 17:01:34 +00:00
{path="/biz"} 0
Add resets() function to count counter resets. resets() returns for every range vector element how many counter resets there have been in the specified range.
2015-05-26 15:47:52 +00:00
eval instant at 50m resets(nonexistent_metric[50m])
Implement changes() function. changes() takes a range vector and returns the number of times a value has changed in the given time window for each time series as an instant vector.
2015-05-26 17:01:34 +00:00
# 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])
Add increase() function, to replace delta(..., 1). This calculates how much a counter increases over a given period of time, which is the area under the curve of it's rate. increase(x[5m]) is equivilent to rate(x[5m]) * 300.
2015-05-24 18:48:56 +00:00
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
promql: Add support for predict_linear(my_timeseries[1h], 7200) This will give a prediction for the value of my_timeseries in 2 hours, based on the last hour of data.
2015-07-28 11:30:57 +00:00
promql: Limit extrapolation of delta/rate/increase The new implementation detects the start and end of a series by looking at the average sample interval within the range. If the first (last) sample in the range is more than 1.1*interval distant from the beginning (end) of the range, it is considered the first (last) sample of the series as a whole, and extrapolation is limited to half the interval (rather than all the way to the beginning (end) of the range). In addition, if the extrapolated starting point of a counter (where it is zero) is within the range, it is used as the starting point of the series. Fixes #581
2015-11-28 21:13:41 +00:00
eval instant at 50m increase(http_requests[100m])
{path="/foo"} 100
{path="/bar"} 90
promql: Add irate() function irate is a rate function that only looks at the most recent two data points, and calucaltes a per-second value from that. This produces much more granular graphs for fast moving data, and works sanely across many scrape intervals. It doesn't do so well for slowly moving data.
2015-10-09 16:58:43 +00:00
clear
Assume counters start at zero after reset.
2016-08-12 18:21:04 +00:00
# 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
promql: Add irate() function irate is a rate function that only looks at the most recent two data points, and calucaltes a per-second value from that. This produces much more granular graphs for fast moving data, and works sanely across many scrape intervals. It doesn't do so well for slowly moving data.
2015-10-09 16:58:43 +00:00
# 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
promql: Add support for predict_linear(my_timeseries[1h], 7200) This will give a prediction for the value of my_timeseries in 2 hours, based on the last hour of data.
2015-07-28 11:30:57 +00:00
clear
move legacy test for delta function
2016-08-08 08:02:58 +00:00
# Tests for delta().
load 5m
http_requests{path="/foo"} 0 50 100 150 200
add idelta function similar to the irate function the idelta function calculates the delta function with the last two values
2016-08-08 08:40:50 +00:00
http_requests{path="/bar"} 200 150 100 50 0
move legacy test for delta function
2016-08-08 08:02:58 +00:00
eval instant at 20m delta(http_requests[20m])
{path="/foo"} 200
add idelta function similar to the irate function the idelta function calculates the delta function with the last two values
2016-08-08 08:40:50 +00:00
{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
move legacy test for delta function
2016-08-08 08:02:58 +00:00
clear
promql: Add support for predict_linear(my_timeseries[1h], 7200) This will give a prediction for the value of my_timeseries in 2 hours, based on the last hour of data.
2015-07-28 11:30:57 +00:00
# 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
Implement label_replace() Implements part of https://github.com/prometheus/prometheus/issues/959.
2015-08-17 21:25:53 +00:00
# deriv should return the same as rate in simple cases.
promql: Limit extrapolation of delta/rate/increase The new implementation detects the start and end of a series by looking at the average sample interval within the range. If the first (last) sample in the range is more than 1.1*interval distant from the beginning (end) of the range, it is considered the first (last) sample of the series as a whole, and extrapolation is limited to half the interval (rather than all the way to the beginning (end) of the range). In addition, if the extrapolated starting point of a counter (where it is zero) is within the range, it is used as the starting point of the series. Fixes #581
2015-11-28 21:13:41 +00:00
eval instant at 50m rate(http_requests{group="canary", instance="1", job="app-server"}[50m])
promql: Add support for predict_linear(my_timeseries[1h], 7200) This will give a prediction for the value of my_timeseries in 2 hours, based on the last hour of data.
2015-07-28 11:30:57 +00:00
{group="canary", instance="1", job="app-server"} 0.26666666666666666
promql: Limit extrapolation of delta/rate/increase The new implementation detects the start and end of a series by looking at the average sample interval within the range. If the first (last) sample in the range is more than 1.1*interval distant from the beginning (end) of the range, it is considered the first (last) sample of the series as a whole, and extrapolation is limited to half the interval (rather than all the way to the beginning (end) of the range). In addition, if the extrapolated starting point of a counter (where it is zero) is within the range, it is used as the starting point of the series. Fixes #581
2015-11-28 21:13:41 +00:00
eval instant at 50m deriv(http_requests{group="canary", instance="1", job="app-server"}[50m])
promql: Add support for predict_linear(my_timeseries[1h], 7200) This will give a prediction for the value of my_timeseries in 2 hours, based on the last hour of data.
2015-07-28 11:30:57 +00:00
{group="canary", instance="1", job="app-server"} 0.26666666666666666
Implement label_replace() Implements part of https://github.com/prometheus/prometheus/issues/959.
2015-08-17 21:25:53 +00:00
# deriv should return correct result.
promql: Add support for predict_linear(my_timeseries[1h], 7200) This will give a prediction for the value of my_timeseries in 2 hours, based on the last hour of data.
2015-07-28 11:30:57 +00:00
eval instant at 50m deriv(testcounter_reset_middle[100m])
{} 0.010606060606060607
Implement label_replace() Implements part of https://github.com/prometheus/prometheus/issues/959.
2015-08-17 21:25:53 +00:00
# predict_linear should return correct result.
Improve predict_linear Fixes https://github.com/prometheus/prometheus/issues/1401 This remove the last (and in fact bogus) use of BoundaryValues. Thus, a whole lot of unused (and arguably sub-optimal / ugly) code can be removed here, too.
2016-02-24 16:16:24 +00:00
# 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
promql: Add support for predict_linear(my_timeseries[1h], 7200) This will give a prediction for the value of my_timeseries in 2 hours, based on the last hour of data.
2015-07-28 11:30:57 +00:00
eval instant at 50m predict_linear(testcounter_reset_middle[100m], 3600)
Improve predict_linear Fixes https://github.com/prometheus/prometheus/issues/1401 This remove the last (and in fact bogus) use of BoundaryValues. Thus, a whole lot of unused (and arguably sub-optimal / ugly) code can be removed here, too.
2016-02-24 16:16:24 +00:00
{} 76.81818181818181
promql: Add support for predict_linear(my_timeseries[1h], 7200) This will give a prediction for the value of my_timeseries in 2 hours, based on the last hour of data.
2015-07-28 11:30:57 +00:00
Improve predict_linear Fixes https://github.com/prometheus/prometheus/issues/1401 This remove the last (and in fact bogus) use of BoundaryValues. Thus, a whole lot of unused (and arguably sub-optimal / ugly) code can be removed here, too.
2016-02-24 16:16:24 +00:00
# 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.
promql: Add support for predict_linear(my_timeseries[1h], 7200) This will give a prediction for the value of my_timeseries in 2 hours, based on the last hour of data.
2015-07-28 11:30:57 +00:00
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
Implement label_replace() Implements part of https://github.com/prometheus/prometheus/issues/959.
2015-08-17 21:25:53 +00:00
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
Change relabel_replace() to do full-string matches. THIS IS A BREAKING CHANGE. Fixes part of https://github.com/prometheus/prometheus/issues/996
2015-09-01 13:35:20 +00:00
# label_replace does a full-string match and replace.
eval instant at 0m label_replace(testmetric, "dst", "destination-value-$1", "src", "source-value-(.*)")
Implement label_replace() Implements part of https://github.com/prometheus/prometheus/issues/959.
2015-08-17 21:25:53 +00:00
testmetric{src="source-value-10",dst="destination-value-10"} 0
testmetric{src="source-value-20",dst="destination-value-20"} 1
Change relabel_replace() to do full-string matches. THIS IS A BREAKING CHANGE. Fixes part of https://github.com/prometheus/prometheus/issues/996
2015-09-01 13:35:20 +00:00
# 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
Implement label_replace() Implements part of https://github.com/prometheus/prometheus/issues/959.
2015-08-17 21:25:53 +00:00
# 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.
Change relabel_replace() to do full-string matches. THIS IS A BREAKING CHANGE. Fixes part of https://github.com/prometheus/prometheus/issues/996
2015-09-01 13:35:20 +00:00
eval instant at 0m label_replace(testmetric, "dst", "", "dst", ".*")
Implement label_replace() Implements part of https://github.com/prometheus/prometheus/issues/959.
2015-08-17 21:25:53 +00:00
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", "", "", "")
promql: Add vector function. Currently the only way to convert a scalar to a vector is to use absent(), which isn't very clean. This adds a vector() function that's the inverse of scalar() and lets your optionally set labels. Example usage would be vector(time() % 86400) < 3600 to filter to only the first hour of the day.
2015-09-11 11:09:34 +00:00
clear
# Tests for vector.
eval instant at 0m vector(1)
{} 1
eval instant at 60m vector(time())
{} 3600
promql: Add clamp_max/clamp_min functions.
2015-11-20 00:42:35 +00:00
clear
# 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
Move topk/bottomk tests from legacy.
2015-12-22 12:38:32 +00:00
Similar to topk/bottomk, have sort/sort_desc put NaN at end. This makes topk and bottomk consistent with the sorting functions, as per #1271.
2015-12-30 14:06:51 +00:00
# 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
Adds holt-winters query function
2016-03-10 03:29:02 +00:00
# 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
Add stddev_over_time and stdvar_over_time.
2016-07-15 23:34:44 +00:00
# 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
Add quantile_over_time function
2016-07-08 12:22:22 +00:00
# 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