---
title: Query functions
nav_title: Functions
sort_rank: 3
---

# Functions

## `abs()`

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

## `absent()`

`absent(v 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.

In the second case, `absent()` tries to be smart about deriving labels of
the 1-element output vector from the input vector:

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

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

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

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

## `bottomk()`

`bottomk(k integer, v instant-vector)` returns the `k` smallest elements of `v`
by sample value.


## `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`.

## `count_scalar()`

`count_scalar(v instant-vector)` returns the number of elements in a time series
vector as a scalar. This is in contrast to the `count()`
[aggregation operator](/docs/querying/operators/#aggregation-operators), which
always returns a vector (an empty one if the input vector is empty) and allows
grouping by labels via a `by` clause.

## `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 interpolated to cover the full time range.

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 derivative of the time series in a range
vector `v`, using [simple linear regression](http://en.wikipedia.org/wiki/Simple_linear_regression).

`deriv` should only be used with gauges.

## `drop_common_labels()`

`drop_common_labels(instant-vector)` drops all labels that have the same name
and value across all series in the input vector.

## `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](/docs/concepts/metric_types/#histogram). (See [histograms
and summaries](/docs/practices/histograms) 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](/docs/concepts/metric_types/#histogram) 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.

## `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 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 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](/docs/querying/operators/#aggregation-operators) (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_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 not returned.


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`.

## `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](http://en.wikipedia.org/wiki/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.

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.

## `topk()`

`topk(k integer, v instant-vector)` returns the `k` largest elements of `v` by
sample value.

## `vector()`

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

## `<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 under the specified interval.
* `min_over_time(range-vector)`: the minimum value of all points under the specified interval.
* `max_over_time(range-vector)`: the maximum value of all points under the specified interval.
* `sum_over_time(range-vector)`: the sum of all values under the specified interval.
* `count_over_time(range-vector)`: the count of all values under the specified interval.
