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Query functions	Functions	3

Functions

Some functions have default arguments, e.g. year(v=vector(time()) instant-vector). This means that there is one argument v which is an instant vector, which if not provided it will default to the value of the expression vector(time()).

`abs()`

abs(v instant-vector) returns the input vector with all sample values converted to their absolute value.

`absent()`

absent(v instant-vector) returns an empty vector if the vector passed to it has any elements and a 1-element vector with the value 1 if the vector passed to it has no elements.

This is useful for alerting on when no time series exist for a given metric name and label combination.

absent(nonexistent{job="myjob"})
# => {job="myjob"}

absent(nonexistent{job="myjob",instance=~".*"})
# => {job="myjob"}

absent(sum(nonexistent{job="myjob"}))
# => {}

In the first two examples, absent() tries to be smart about deriving labels of the 1-element output vector from the input vector.

`absent_over_time()`

absent_over_time(v range-vector) returns an empty vector if the range vector passed to it has any elements and a 1-element vector with the value 1 if the range vector passed to it has no elements.

This is useful for alerting on when no time series exist for a given metric name and label combination for a certain amount of time.

absent_over_time(nonexistent{job="myjob"}[1h])
# => {job="myjob"}

absent_over_time(nonexistent{job="myjob",instance=~".*"}[1h])
# => {job="myjob"}

absent_over_time(sum(nonexistent{job="myjob"})[1h:])
# => {}

In the first two examples, absent_over_time() tries to be smart about deriving labels of the 1-element output vector from the input vector.

`ceil()`

ceil(v instant-vector) rounds the sample values of all elements in v up to the nearest integer.

`changes()`

For each input time series, changes(v range-vector) returns the number of times its value has changed within the provided time range as an instant vector.

`clamp_max()`

clamp_max(v instant-vector, max scalar) clamps the sample values of all elements in v to have an upper limit of max.

`clamp_min()`

clamp_min(v instant-vector, min scalar) clamps the sample values of all elements in v to have a lower limit of min.

`day_of_month()`

day_of_month(v=vector(time()) instant-vector) returns the day of the month for each of the given times in UTC. Returned values are from 1 to 31.

`day_of_week()`

day_of_week(v=vector(time()) instant-vector) returns the day of the week for each of the given times in UTC. Returned values are from 0 to 6, where 0 means Sunday etc.

`days_in_month()`

days_in_month(v=vector(time()) instant-vector) returns number of days in the month for each of the given times in UTC. Returned values are from 28 to 31.

`delta()`

delta(v range-vector) calculates the difference between the first and last value of each time series element in a range vector v, returning an instant vector with the given deltas and equivalent labels. The delta is extrapolated to cover the full time range as specified in the range vector selector, so that it is possible to get a non-integer result even if the sample values are all integers.

The following example expression returns the difference in CPU temperature between now and 2 hours ago:

delta(cpu_temp_celsius{host="zeus"}[2h])

delta should only be used with gauges.

`deriv()`

deriv(v range-vector) calculates the per-second derivative of the time series in a range vector v, using simple linear regression.

deriv should only be used with gauges.

`exp()`

exp(v instant-vector) calculates the exponential function for all elements in v. Special cases are:

Exp(+Inf) = +Inf
Exp(NaN) = NaN

`floor()`

floor(v instant-vector) rounds the sample values of all elements in v down to the nearest integer.

`histogram_quantile()`

histogram_quantile(φ float, b instant-vector) calculates the φ-quantile (0 ≤ φ ≤ 1) from the buckets b of a histogram. (See histograms and summaries for a detailed explanation of φ-quantiles and the usage of the histogram metric type in general.) The samples in b are the counts of observations in each bucket. Each sample must have a label le where the label value denotes the inclusive upper bound of the bucket. (Samples without such a label are silently ignored.) The histogram metric type automatically provides time series with the _bucket suffix and the appropriate labels.

Use the rate() function to specify the time window for the quantile calculation.

Example: A histogram metric is called http_request_duration_seconds. To calculate the 90th percentile of request durations over the last 10m, use the following expression:

histogram_quantile(0.9, rate(http_request_duration_seconds_bucket[10m]))

The quantile is calculated for each label combination in http_request_duration_seconds. To aggregate, use the sum() aggregator around the rate() function. Since the le label is required by histogram_quantile(), it has to be included in the by clause. The following expression aggregates the 90th percentile by job:

histogram_quantile(0.9, sum(rate(http_request_duration_seconds_bucket[10m])) by (job, le))

To aggregate everything, specify only the le label:

histogram_quantile(0.9, sum(rate(http_request_duration_seconds_bucket[10m])) by (le))

The histogram_quantile() function interpolates quantile values by assuming a linear distribution within a bucket. The highest bucket must have an upper bound of +Inf. (Otherwise, NaN is returned.) If a quantile is located in the highest bucket, the upper bound of the second highest bucket is returned. A lower limit of the lowest bucket is assumed to be 0 if the upper bound of that bucket is greater than 0. In that case, the usual linear interpolation is applied within that bucket. Otherwise, the upper bound of the lowest bucket is returned for quantiles located in the lowest bucket.

If b contains fewer than two buckets, NaN is returned. For φ < 0, -Inf is returned. For φ > 1, +Inf is returned.

`holt_winters()`

holt_winters(v range-vector, sf scalar, tf scalar) produces a smoothed value for time series based on the range in v. The lower the smoothing factor sf, the more importance is given to old data. The higher the trend factor tf, the more trends in the data is considered. Both sf and tf must be between 0 and 1.

holt_winters should only be used with gauges.

`hour()`

hour(v=vector(time()) instant-vector) returns the hour of the day for each of the given times in UTC. Returned values are from 0 to 23.

`idelta()`

idelta(v range-vector) calculates the difference between the last two samples in the range vector v, returning an instant vector with the given deltas and equivalent labels.

idelta should only be used with gauges.

`increase()`

increase(v range-vector) calculates the increase in the time series in the range vector. Breaks in monotonicity (such as counter resets due to target restarts) are automatically adjusted for. The increase is extrapolated to cover the full time range as specified in the range vector selector, so that it is possible to get a non-integer result even if a counter increases only by integer increments.

The following example expression returns the number of HTTP requests as measured over the last 5 minutes, per time series in the range vector:

increase(http_requests_total{job="api-server"}[5m])

increase should only be used with counters. It is syntactic sugar for rate(v) multiplied by the number of seconds under the specified time range window, and should be used primarily for human readability. Use rate in recording rules so that increases are tracked consistently on a per-second basis.

`irate()`

irate(v range-vector) calculates the per-second instant rate of increase of the time series in the range vector. This is based on the last two data points. Breaks in monotonicity (such as counter resets due to target restarts) are automatically adjusted for.

The following example expression returns the per-second rate of HTTP requests looking up to 5 minutes back for the two most recent data points, per time series in the range vector:

irate(http_requests_total{job="api-server"}[5m])

irate should only be used when graphing volatile, fast-moving counters. Use rate for alerts and slow-moving counters, as brief changes in the rate can reset the FOR clause and graphs consisting entirely of rare spikes are hard to read.

Note that when combining irate() with an aggregation operator (e.g. sum()) or a function aggregating over time (any function ending in _over_time), always take a irate() first, then aggregate. Otherwise irate() cannot detect counter resets when your target restarts.

`label_join()`

For each timeseries in v, label_join(v instant-vector, dst_label string, separator string, src_label_1 string, src_label_2 string, ...) joins all the values of all the src_labels using separator and returns the timeseries with the label dst_label containing the joined value. There can be any number of src_labels in this function.

This example will return a vector with each time series having a foo label with the value a,b,c added to it:

label_join(up{job="api-server",src1="a",src2="b",src3="c"}, "foo", ",", "src1", "src2", "src3")

`label_replace()`

For each timeseries in v, label_replace(v instant-vector, dst_label string, replacement string, src_label string, regex string) matches the regular expression regex against the label src_label. If it matches, then the timeseries is returned with the label dst_label replaced by the expansion of replacement. $1 is replaced with the first matching subgroup, $2 with the second etc. If the regular expression doesn't match then the timeseries is returned unchanged.

This example will return a vector with each time series having a foo label with the value a added to it:

label_replace(up{job="api-server",service="a:c"}, "foo", "$1", "service", "(.*):.*")

`ln()`

ln(v instant-vector) calculates the natural logarithm for all elements in v. Special cases are:

ln(+Inf) = +Inf
ln(0) = -Inf
ln(x < 0) = NaN
ln(NaN) = NaN

`log2()`

log2(v instant-vector) calculates the binary logarithm for all elements in v. The special cases are equivalent to those in ln.

`log10()`

log10(v instant-vector) calculates the decimal logarithm for all elements in v. The special cases are equivalent to those in ln.

`minute()`

minute(v=vector(time()) instant-vector) returns the minute of the hour for each of the given times in UTC. Returned values are from 0 to 59.

`month()`

month(v=vector(time()) instant-vector) returns the month of the year for each of the given times in UTC. Returned values are from 1 to 12, where 1 means January etc.

`predict_linear()`

predict_linear(v range-vector, t scalar) predicts the value of time series t seconds from now, based on the range vector v, using simple linear regression.

predict_linear should only be used with gauges.

`rate()`

rate(v range-vector) calculates the per-second average rate of increase of the time series in the range vector. Breaks in monotonicity (such as counter resets due to target restarts) are automatically adjusted for. Also, the calculation extrapolates to the ends of the time range, allowing for missed scrapes or imperfect alignment of scrape cycles with the range's time period.

The following example expression returns the per-second rate of HTTP requests as measured over the last 5 minutes, per time series in the range vector:

rate(http_requests_total{job="api-server"}[5m])

rate should only be used with counters. It is best suited for alerting, and for graphing of slow-moving counters.

Note that when combining rate() with an aggregation operator (e.g. sum()) or a function aggregating over time (any function ending in _over_time), always take a rate() first, then aggregate. Otherwise rate() cannot detect counter resets when your target restarts.

`resets()`

For each input time series, resets(v range-vector) returns the number of counter resets within the provided time range as an instant vector. Any decrease in the value between two consecutive samples is interpreted as a counter reset.

resets should only be used with counters.

`round()`

round(v instant-vector, to_nearest=1 scalar) rounds the sample values of all elements in v to the nearest integer. Ties are resolved by rounding up. The optional to_nearest argument allows specifying the nearest multiple to which the sample values should be rounded. This multiple may also be a fraction.

`scalar()`

Given a single-element input vector, scalar(v instant-vector) returns the sample value of that single element as a scalar. If the input vector does not have exactly one element, scalar will return NaN.

`sort()`

sort(v instant-vector) returns vector elements sorted by their sample values, in ascending order.

`sort_desc()`

Same as sort, but sorts in descending order.

`sqrt()`

sqrt(v instant-vector) calculates the square root of all elements in v.

`time()`

time() returns the number of seconds since January 1, 1970 UTC. Note that this does not actually return the current time, but the time at which the expression is to be evaluated.

`timestamp()`

timestamp(v instant-vector) returns the timestamp of each of the samples of the given vector as the number of seconds since January 1, 1970 UTC.

This function was added in Prometheus 2.0

`vector()`

vector(s scalar) returns the scalar s as a vector with no labels.

`year()`

year(v=vector(time()) instant-vector) returns the year for each of the given times in UTC.

`<aggregation>_over_time()`

The following functions allow aggregating each series of a given range vector over time and return an instant vector with per-series aggregation results:

avg_over_time(range-vector): the average value of all points in the specified interval.
min_over_time(range-vector): the minimum value of all points in the specified interval.
max_over_time(range-vector): the maximum value of all points in the specified interval.
sum_over_time(range-vector): the sum of all values in the specified interval.
count_over_time(range-vector): the count of all values in the specified interval.
quantile_over_time(scalar, range-vector): the φ-quantile (0 ≤ φ ≤ 1) of the values in the specified interval.
stddev_over_time(range-vector): the population standard deviation of the values in the specified interval.
stdvar_over_time(range-vector): the population standard variance of the values in the specified interval.

Note that all values in the specified interval have the same weight in the aggregation even if the values are not equally spaced throughout the interval.

15 KiB Raw Blame History

Functions

abs()

absent()

absent_over_time()

ceil()

changes()

clamp_max()

clamp_min()

day_of_month()

day_of_week()

days_in_month()

delta()

deriv()

exp()

floor()

histogram_quantile()

holt_winters()

hour()

idelta()

increase()

irate()

label_join()

label_replace()

ln()

log2()

log10()

minute()

month()

predict_linear()

rate()

resets()

round()

scalar()

sort()

sort_desc()

sqrt()

time()

timestamp()

vector()

year()

<aggregation>_over_time()