685 lines
29 KiB
Markdown
685 lines
29 KiB
Markdown
---
|
|
title: Query functions
|
|
nav_title: Functions
|
|
sort_rank: 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())`.
|
|
|
|
_Notes about the experimental native histograms:_
|
|
|
|
* Ingesting native histograms has to be enabled via a [feature
|
|
flag](../feature_flags.md#native-histograms). As long as no native histograms
|
|
have been ingested into the TSDB, all functions will behave as usual.
|
|
* Functions that do not explicitly mention native histograms in their
|
|
documentation (see below) will ignore histogram samples.
|
|
* Functions that do already act on native histograms might still change their
|
|
behavior in the future.
|
|
* If a function requires the same bucket layout between multiple native
|
|
histograms it acts on, it will automatically convert them
|
|
appropriately. (With the currently supported bucket schemas, that's always
|
|
possible.)
|
|
|
|
## `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 (floats or native histograms) 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 (floats or native histograms) 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()`
|
|
|
|
`clamp(v instant-vector, min scalar, max scalar)`
|
|
clamps the sample values of all elements in `v` to have a lower limit of `min` and an upper limit of `max`.
|
|
|
|
Special cases:
|
|
- Return an empty vector if `min > max`
|
|
- Return `NaN` if `min` or `max` is `NaN`
|
|
|
|
## `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.
|
|
|
|
## `day_of_year()`
|
|
|
|
`day_of_year(v=vector(time()) instant-vector)` returns the day of the year for
|
|
each of the given times in UTC. Returned values are from 1 to 365 for non-leap years,
|
|
and 1 to 366 in leap years.
|
|
|
|
## `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` acts on native histograms by calculating a new histogram where each
|
|
component (sum and count of observations, buckets) is the difference between
|
|
the respective component in the first and last native histogram in
|
|
`v`. However, each element in `v` that contains a mix of float and native
|
|
histogram samples within the range, will be missing from the result vector.
|
|
|
|
`delta` should only be used with gauges and native histograms where the
|
|
components behave like gauges (so-called gauge histograms).
|
|
|
|
## `deriv()`
|
|
|
|
`deriv(v range-vector)` calculates the per-second derivative of the time series in a range
|
|
vector `v`, using [simple linear regression](https://en.wikipedia.org/wiki/Simple_linear_regression).
|
|
The range vector must have at least two samples in order to perform the calculation. When `+Inf` or
|
|
`-Inf` are found in the range vector, the slope and offset value calculated will be `NaN`.
|
|
|
|
`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_count()` and `histogram_sum()`
|
|
|
|
_Both functions only act on native histograms, which are an experimental
|
|
feature. The behavior of these functions may change in future versions of
|
|
Prometheus, including their removal from PromQL._
|
|
|
|
`histogram_count(v instant-vector)` returns the count of observations stored in
|
|
a native histogram. Samples that are not native histograms are ignored and do
|
|
not show up in the returned vector.
|
|
|
|
Similarly, `histogram_sum(v instant-vector)` returns the sum of observations
|
|
stored in a native histogram.
|
|
|
|
Use `histogram_count` in the following way to calculate a rate of observations
|
|
(in this case corresponding to “requests per second”) from a native histogram:
|
|
|
|
histogram_count(rate(http_request_duration_seconds[10m]))
|
|
|
|
The additional use of `histogram_sum` enables the calculation of the average of
|
|
observed values (in this case corresponding to “average request duration”):
|
|
|
|
histogram_sum(rate(http_request_duration_seconds[10m]))
|
|
/
|
|
histogram_count(rate(http_request_duration_seconds[10m]))
|
|
|
|
## `histogram_fraction()`
|
|
|
|
_This function only acts on native histograms, which are an experimental
|
|
feature. The behavior of this function may change in future versions of
|
|
Prometheus, including its removal from PromQL._
|
|
|
|
For a native histogram, `histogram_fraction(lower scalar, upper scalar, v
|
|
instant-vector)` returns the estimated fraction of observations between the
|
|
provided lower and upper values. Samples that are not native histograms are
|
|
ignored and do not show up in the returned vector.
|
|
|
|
For example, the following expression calculates the fraction of HTTP requests
|
|
over the last hour that took 200ms or less:
|
|
|
|
histogram_fraction(0, 0.2, rate(http_request_duration_seconds[1h]))
|
|
|
|
The error of the estimation depends on the resolution of the underlying native
|
|
histogram and how closely the provided boundaries are aligned with the bucket
|
|
boundaries in the histogram.
|
|
|
|
`+Inf` and `-Inf` are valid boundary values. For example, if the histogram in
|
|
the expression above included negative observations (which shouldn't be the
|
|
case for request durations), the appropriate lower boundary to include all
|
|
observations less than or equal 0.2 would be `-Inf` rather than `0`.
|
|
|
|
Whether the provided boundaries are inclusive or exclusive is only relevant if
|
|
the provided boundaries are precisely aligned with bucket boundaries in the
|
|
underlying native histogram. In this case, the behavior depends on the schema
|
|
definition of the histogram. The currently supported schemas all feature
|
|
inclusive upper boundaries and exclusive lower boundaries for positive values
|
|
(and vice versa for negative values). Without a precise alignment of
|
|
boundaries, the function uses linear interpolation to estimate the
|
|
fraction. With the resulting uncertainty, it becomes irrelevant if the
|
|
boundaries are inclusive or exclusive.
|
|
|
|
## `histogram_quantile()`
|
|
|
|
`histogram_quantile(φ scalar, b instant-vector)` calculates the φ-quantile (0 ≤
|
|
φ ≤ 1) from a [classic
|
|
histogram](https://prometheus.io/docs/concepts/metric_types/#histogram) or from
|
|
a native histogram. (See [histograms and
|
|
summaries](https://prometheus.io/docs/practices/histograms) for a detailed
|
|
explanation of φ-quantiles and the usage of the (classic) histogram metric
|
|
type in general.)
|
|
|
|
_Note that native histograms are an experimental feature. The behavior of this
|
|
function when dealing with native histograms may change in future versions of
|
|
Prometheus._
|
|
|
|
The float samples in `b` are considered the counts of observations in each
|
|
bucket of one or more classic histograms. Each float sample must have a label
|
|
`le` where the label value denotes the inclusive upper bound of the bucket.
|
|
(Float samples without such a label are silently ignored.) The other labels and
|
|
the metric name are used to identify the buckets belonging to each classic
|
|
histogram. The [histogram metric
|
|
type](https://prometheus.io/docs/concepts/metric_types/#histogram)
|
|
automatically provides time series with the `_bucket` suffix and the
|
|
appropriate labels.
|
|
|
|
The native histogram samples in `b` are treated each individually as a separate
|
|
histogram to calculate the quantile from.
|
|
|
|
As long as no naming collisions arise, `b` may contain a mix of classic
|
|
and native histograms.
|
|
|
|
Use the `rate()` function to specify the time window for the quantile
|
|
calculation.
|
|
|
|
Example: A histogram metric is called `http_request_duration_seconds` (and
|
|
therefore the metric name for the buckets of a classic histogram is
|
|
`http_request_duration_seconds_bucket`). To calculate the 90th percentile of request
|
|
durations over the last 10m, use the following expression in case
|
|
`http_request_duration_seconds` is a classic histogram:
|
|
|
|
histogram_quantile(0.9, rate(http_request_duration_seconds_bucket[10m]))
|
|
|
|
For a native histogram, use the following expression instead:
|
|
|
|
histogram_quantile(0.9, rate(http_request_duration_seconds[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()` to deal with classic histograms, it has to be
|
|
included in the `by` clause. The following expression aggregates the 90th
|
|
percentile by `job` for classic histograms:
|
|
|
|
histogram_quantile(0.9, sum by (job, le) (rate(http_request_duration_seconds_bucket[10m])))
|
|
|
|
When aggregating native histograms, the expression simplifies to:
|
|
|
|
histogram_quantile(0.9, sum by (job) (rate(http_request_duration_seconds[10m])))
|
|
|
|
To aggregate all classic histograms, specify only the `le` label:
|
|
|
|
histogram_quantile(0.9, sum by (le) (rate(http_request_duration_seconds_bucket[10m])))
|
|
|
|
With native histograms, aggregating everything works as usual without any `by` clause:
|
|
|
|
histogram_quantile(0.9, sum(rate(http_request_duration_seconds[10m])))
|
|
|
|
The `histogram_quantile()` function interpolates quantile values by
|
|
assuming a linear distribution within a bucket.
|
|
|
|
If `b` has 0 observations, `NaN` is returned. For φ < 0, `-Inf` is
|
|
returned. For φ > 1, `+Inf` is returned. For φ = `NaN`, `NaN` is returned.
|
|
|
|
The following is only relevant for classic histograms: If `b` contains
|
|
fewer than two buckets, `NaN` is returned. 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.
|
|
|
|
You can use `histogram_quantile(0, v instant-vector)` to get the estimated minimum value stored in
|
|
a histogram.
|
|
|
|
You can use `histogram_quantile(1, v instant-vector)` to get the estimated maximum value stored in
|
|
a histogram.
|
|
|
|
Buckets of classic histograms are cumulative. Therefore, the following should always be the case:
|
|
|
|
- The counts in the buckets are monotonically increasing (strictly non-decreasing).
|
|
- A lack of observations between the upper limits of two consecutive buckets results in equal counts
|
|
in those two buckets.
|
|
|
|
However, floating point precision issues (e.g. small discrepancies introduced by computing of buckets
|
|
with `sum(rate(...))`) or invalid data might violate these assumptions. In that case,
|
|
`histogram_quantile` would be unable to return meaningful results. To mitigate the issue,
|
|
`histogram_quantile` assumes that tiny relative differences between consecutive buckets are happening
|
|
because of floating point precision errors and ignores them. (The threshold to ignore a difference
|
|
between two buckets is a trillionth (1e-12) of the sum of both buckets.) Furthermore, if there are
|
|
non-monotonic bucket counts even after this adjustment, they are increased to the value of the
|
|
previous buckets to enforce monotonicity. The latter is evidence for an actual issue with the input
|
|
data and is therefore flagged with an informational annotation reading `input to histogram_quantile
|
|
needed to be fixed for monotonicity`. If you encounter this annotation, you should find and remove
|
|
the source of the invalid data.
|
|
|
|
## `histogram_stddev()` and `histogram_stdvar()`
|
|
|
|
_Both functions only act on native histograms, which are an experimental
|
|
feature. The behavior of these functions may change in future versions of
|
|
Prometheus, including their removal from PromQL._
|
|
|
|
`histogram_stddev(v instant-vector)` returns the estimated standard deviation
|
|
of observations in a native histogram, based on the geometric mean of the buckets
|
|
where the observations lie. Samples that are not native histograms are ignored and
|
|
do not show up in the returned vector.
|
|
|
|
Similarly, `histogram_stdvar(v instant-vector)` returns the estimated standard
|
|
variance of observations in a native histogram.
|
|
|
|
## `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` acts on native histograms by calculating a new histogram where each
|
|
component (sum and count of observations, buckets) is the increase between
|
|
the respective component in the first and last native histogram in
|
|
`v`. However, each element in `v` that contains a mix of float and native
|
|
histogram samples within the range, will be missing from the result vector.
|
|
|
|
`increase` should only be used with counters and native histograms where the
|
|
components behave like 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](operators.md#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_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.
|
|
|
|
`label_join` acts on float and histogram samples in the same way.
|
|
|
|
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](https://github.com/google/re2/wiki/Syntax) `regex` against the value of the label `src_label`. If it
|
|
matches, the value of the label `dst_label` in the returned timeseries will be the expansion
|
|
of `replacement`, together with the original labels in the input. Capturing groups in the
|
|
regular expression can be referenced with `$1`, `$2`, etc. Named capturing groups in the regular expression can be referenced with `$name` (where `name` is the capturing group name). If the regular expression doesn't match then the timeseries is returned unchanged.
|
|
|
|
`label_replace` acts on float and histogram samples in the same way.
|
|
|
|
This example will return timeseries with the values `a:c` at label `service` and `a` at label `foo`:
|
|
|
|
```
|
|
label_replace(up{job="api-server",service="a:c"}, "foo", "$1", "service", "(.*):.*")
|
|
```
|
|
|
|
This second example has the same effect than the first example, and illustrates use of named capturing groups:
|
|
```
|
|
label_replace(up{job="api-server",service="a:c"}, "foo", "$name", "service", "(?P<name>.*):(?P<version>.*)")
|
|
```
|
|
|
|
## `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](https://en.wikipedia.org/wiki/Simple_linear_regression).
|
|
The range vector must have at least two samples in order to perform the
|
|
calculation. When `+Inf` or `-Inf` are found in the range vector,
|
|
the slope and offset value calculated will be `NaN`.
|
|
|
|
`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` acts on native histograms by calculating a new histogram where each
|
|
component (sum and count of observations, buckets) is the rate of increase
|
|
between the respective component in the first and last native histogram in
|
|
`v`. However, each element in `v` that contains a mix of float and native
|
|
histogram samples within the range, will be missing from the result vector.
|
|
|
|
`rate` should only be used with counters and native histograms where the
|
|
components behave like 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 float samples is interpreted as a
|
|
counter reset. A reset in a native histogram is detected in a more complex way:
|
|
Any decrease in any bucket, including the zero bucket, or in the count of
|
|
observation constitutes a counter reset, but also the disappearance of any
|
|
previously populated bucket, an increase in bucket resolution, or a decrease of
|
|
the zero-bucket width.
|
|
|
|
`resets` should only be used with counters and counter-like native
|
|
histograms.
|
|
|
|
If the range vector contains a mix of float and histogram samples for the same
|
|
series, counter resets are detected separately and their numbers added up. The
|
|
change from a float to a histogram sample is _not_ considered a counter
|
|
reset. Each float sample is compared to the next float sample, and each
|
|
histogram is comprared to the next histogram.
|
|
|
|
## `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`.
|
|
|
|
## `sgn()`
|
|
|
|
`sgn(v instant-vector)` returns a vector with all sample values converted to their sign, defined as this: 1 if v is positive, -1 if v is negative and 0 if v is equal to zero.
|
|
|
|
## `sort()`
|
|
|
|
`sort(v instant-vector)` returns vector elements sorted by their sample values,
|
|
in ascending order. Native histograms are sorted by their sum of observations.
|
|
|
|
## `sort_desc()`
|
|
|
|
Same as `sort`, but sorts in descending order.
|
|
|
|
## `sort_by_label()`
|
|
|
|
**This function has to be enabled via the [feature flag](../feature_flags/) `--enable-feature=promql-experimental-functions`.**
|
|
|
|
`sort_by_label(v instant-vector, label string, ...)` returns vector elements sorted by their label values and sample value in case of label values being equal, in ascending order.
|
|
|
|
Please note that the sort by label functions only affect the results of instant queries, as range query results always have a fixed output ordering.
|
|
|
|
This function uses [natural sort order](https://en.wikipedia.org/wiki/Natural_sort_order).
|
|
|
|
## `sort_by_label_desc()`
|
|
|
|
**This function has to be enabled via the [feature flag](../feature_flags/) `--enable-feature=promql-experimental-functions`.**
|
|
|
|
Same as `sort_by_label`, but sorts in descending order.
|
|
|
|
Please note that the sort by label functions only affect the results of instant queries, as range query results always have a fixed output ordering.
|
|
|
|
This function uses [natural sort order](https://en.wikipedia.org/wiki/Natural_sort_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. It also
|
|
works with histogram samples.
|
|
|
|
## `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.
|
|
* `last_over_time(range-vector)`: the most recent point value in the specified interval.
|
|
* `present_over_time(range-vector)`: the value 1 for any series in the specified interval.
|
|
|
|
If the [feature flag](../feature_flags/)
|
|
`--enable-feature=promql-experimental-functions` is set, the following
|
|
additional functions are available:
|
|
|
|
* `mad_over_time(range-vector)`: the median absolute deviation of all points 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.
|
|
|
|
`avg_over_time`, `sum_over_time`, `count_over_time`, `last_over_time`, and
|
|
`present_over_time` handle native histograms as expected. All other functions
|
|
ignore histogram samples.
|
|
|
|
## Trigonometric Functions
|
|
|
|
The trigonometric functions work in radians:
|
|
|
|
- `acos(v instant-vector)`: calculates the arccosine of all elements in `v` ([special cases](https://pkg.go.dev/math#Acos)).
|
|
- `acosh(v instant-vector)`: calculates the inverse hyperbolic cosine of all elements in `v` ([special cases](https://pkg.go.dev/math#Acosh)).
|
|
- `asin(v instant-vector)`: calculates the arcsine of all elements in `v` ([special cases](https://pkg.go.dev/math#Asin)).
|
|
- `asinh(v instant-vector)`: calculates the inverse hyperbolic sine of all elements in `v` ([special cases](https://pkg.go.dev/math#Asinh)).
|
|
- `atan(v instant-vector)`: calculates the arctangent of all elements in `v` ([special cases](https://pkg.go.dev/math#Atan)).
|
|
- `atanh(v instant-vector)`: calculates the inverse hyperbolic tangent of all elements in `v` ([special cases](https://pkg.go.dev/math#Atanh)).
|
|
- `cos(v instant-vector)`: calculates the cosine of all elements in `v` ([special cases](https://pkg.go.dev/math#Cos)).
|
|
- `cosh(v instant-vector)`: calculates the hyperbolic cosine of all elements in `v` ([special cases](https://pkg.go.dev/math#Cosh)).
|
|
- `sin(v instant-vector)`: calculates the sine of all elements in `v` ([special cases](https://pkg.go.dev/math#Sin)).
|
|
- `sinh(v instant-vector)`: calculates the hyperbolic sine of all elements in `v` ([special cases](https://pkg.go.dev/math#Sinh)).
|
|
- `tan(v instant-vector)`: calculates the tangent of all elements in `v` ([special cases](https://pkg.go.dev/math#Tan)).
|
|
- `tanh(v instant-vector)`: calculates the hyperbolic tangent of all elements in `v` ([special cases](https://pkg.go.dev/math#Tanh)).
|
|
|
|
The following are useful for converting between degrees and radians:
|
|
|
|
- `deg(v instant-vector)`: converts radians to degrees for all elements in `v`.
|
|
- `pi()`: returns pi.
|
|
- `rad(v instant-vector)`: converts degrees to radians for all elements in `v`.
|