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 instantvector, which if not provided it will default to the value of the expressionvector(time()).

abs()

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

absent()

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

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

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

In the second example, absent() tries to be smart about deriving labels of the1-element output vector from the input vector.

ceil()

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

changes()

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

clamp_max()

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

clamp_min()

clamp_min(v instant-vector, min scalar) clamps the sample values of allelements 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 monthfor 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 foreach of the given times in UTC. Returned values are from 0 to 6, where 0 meansSunday etc.

days_in_month()

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

delta()

delta(v range-vector) calculates the difference between thefirst 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 inthe range vector selector, so that it is possible to get a non-integerresult even if the sample values are all integers.

The following example expression returns the difference in CPU temperaturebetween 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 rangevector 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 downto the nearest integer.

histogram_quantile()

histogram_quantile(φ float, b instant-vector) calculates the φ-quantile (0 ≤ φ≤ 1) from the buckets b of ahistogram. (Seehistograms and summaries fora detailed explanation of φ-quantiles and the usage of the histogram metric typein 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 inclusiveupper bound of the bucket. (Samples without such a label are silently ignored.)The histogram metric typeautomatically provides time series with the _bucket suffix and the appropriatelabels.

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

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

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

The quantile is calculated for each label combination inhttp_request_duration_seconds. To aggregate, use the sum() aggregatoraround the rate() function. Since the le label is required byhistogram_quantile(), it has to be included in the by clause. The followingexpression 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 byassuming a linear distribution within a bucket. The highest bucketmust have an upper bound of +Inf. (Otherwise, NaN is returned.) Ifa quantile is located in the highest bucket, the upper bound of thesecond highest bucket is returned. A lower limit of the lowest bucketis assumed to be 0 if the upper bound of that bucket is greater than0. In that case, the usual linear interpolation is applied within thatbucket. Otherwise, the upper bound of the lowest bucket is returnedfor quantiles located in the lowest bucket.

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

holt_winters()

holt_winters(v range-vector, sf scalar, tf scalar) produces a smoothed valuefor 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, themore trends in the data is considered. Both sf and tf must be between 0 and1.

holt_winters should only be used with gauges.

hour()

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

idelta()

idelta(v range-vector)

idelta(v range-vector) calculates the difference between the last two samplesin the range vector v, returning an instant vector with the given deltas andequivalent labels.

idelta should only be used with gauges.

increase()

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

The following example expression returns the number of HTTP requests as measuredover 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 sugarfor rate(v) multiplied by the number of seconds under the specifiedtime range window, and should be used primarily for human readability.Use rate in recording rules so that increases are tracked consistentlyon a per-second basis.

irate()

irate(v range-vector) calculates the per-second instant rate of increase ofthe 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) areautomatically adjusted for.

The following example expression returns the per-second rate of HTTP requestslooking up to 5 minutes back for the two most recent data points, per timeseries 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 changesin the rate can reset the FOR clause and graphs consisting entirely of rarespikes are hard to read.

Note that when combining irate() with anaggregation 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 detectcounter 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_labelsusing 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 regularexpression regex against the label src_label. If it matches, then thetimeseries is returned with the label dst_label replaced by the expansion ofreplacement. $1 is replaced with the first matching subgroup, $2 with thesecond etc. If the regular expression doesn't match then the timeseries isreturned unchanged.

This example will return a vector with each time series having a foolabel 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 eachof 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 eachof the given times in UTC. Returned values are from 1 to 12, where 1 meansJanuary etc.

predict_linear()

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

predict_linear should only be used with gauges.

rate()

rate(v range-vector) calculates the per-second average rate of increase of thetime series in the range vector. Breaks in monotonicity (such as counterresets due to target restarts) are automatically adjusted for. Also, thecalculation extrapolates to the ends of the time range, allowing for missedscrapes 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 measuredover 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 detectcounter resets when your target restarts.

resets()

For each input time series, resets(v range-vector) returns the number ofcounter resets within the provided time range as an instant vector. Anydecrease in the value between two consecutive samples is interpreted as acounter reset.

resets should only be used with counters.

round()

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

scalar()

Given a single-element input vector, scalar(v instant-vector) returns thesample value of that single element as a scalar. If the input vector does nothave 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 thatthis does not actually return the current time, but the time at which theexpression is to be evaluated.

timestamp()

timestamp(v instant-vector) returns the timestamp of each of the samples ofthe 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 yearfor each of the given times in UTC.

<aggregation>_over_time()

The following functions allow aggregating each series of a given range vectorover 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 theaggregation even if the values are not equally spaced throughout the interval.