VictoriaMetrics implements MetricsQL - query language inspired by PromQL. MetricsQL is backwards-compatible with PromQL, so Grafana dashboards backed by Prometheus datasource should work the same after switching from Prometheus to VictoriaMetrics. However, there are some intentional differences between these two languages.

Standalone MetricsQL package can be used for parsing MetricsQL in external apps.

If you are unfamiliar with PromQL, then it is suggested reading this tutorial for beginners and introduction into basic querying via MetricsQL.

The following functionality is implemented differently in MetricsQL compared to PromQL. This improves user experience:

- MetricsQL takes into account the last raw sample before the lookbehind window
in square brackets for increase and rate functions. This allows returning the exact results users expect for
`increase(metric[$__interval])`

queries instead of incomplete results Prometheus returns for such queries. Prometheus misses the increase between the last sample before the lookbehind window and the first sample inside the lookbehind window. - MetricsQL doesn’t extrapolate rate and increase function results, so it always returns the expected results. For example, it returns
integer results from
`increase()`

over slow-changing integer counter. Prometheus in this case returns unexpected fractional results, which may significantly differ from the expected results. This addresses this issue from Prometheus. See technical details about VictoriaMetrics and Prometheus calculations for rate and increase in this issue. - MetricsQL returns the expected non-empty responses for rate function when Grafana or vmui
passes
`step`

values smaller than the interval between raw samples to /api/v1/query_range. This addresses this issue from Grafana. See also this blog post. - MetricsQL treats
`scalar`

type the same as`instant vector`

without labels, since subtle differences between these types usually confuse users. See the corresponding Prometheus docs for details. - MetricsQL removes all the
`NaN`

values from the output, so some queries like`(-1)^0.5`

return empty results in VictoriaMetrics, while returning a series of`NaN`

values in Prometheus. Note that Grafana doesn’t draw any lines or dots for`NaN`

values, so the end result looks the same for both VictoriaMetrics and Prometheus. - MetricsQL keeps metric names after applying functions, which don’t change the meaning of the original time series.
For example, min_over_time(foo) or round(foo) leaves
`foo`

metric name in the result. See this issue for details.

Read more about the differences between PromQL and MetricsQL in this article.

Other PromQL functionality should work the same in MetricsQL. File an issue if you notice discrepancies between PromQL and MetricsQL results other than mentioned above.

## MetricsQL features#

MetricsQL implements PromQL and provides additional functionality mentioned below, which is aimed towards solving practical cases. Feel free filing a feature request if you think MetricsQL misses certain useful functionality.

This functionality can be evaluated at VictoriaMetrics playground or at your own VictoriaMetrics instance.

The list of MetricsQL features on top of PromQL:

- Graphite-compatible filters can be passed via
`{__graphite__="foo.*.bar"}`

syntax. See these docs. VictoriaMetrics can be used as Graphite datasource in Grafana. See these docs for details. See also label_graphite_group function, which can be used for extracting the given groups from Graphite metric name. - Lookbehind window in square brackets for rollup functions may be omitted. VictoriaMetrics automatically selects the lookbehind window
depending on the
`step`

query arg passed to /api/v1/query_range and the real interval between raw samples (aka`scrape_interval`

). For instance, the following query is valid in VictoriaMetrics:`rate(node_network_receive_bytes_total)`

. It is roughly equivalent to`rate(node_network_receive_bytes_total[$__interval])`

when used in Grafana. The difference is documented in rate() docs. - Numeric values can contain
`_`

delimiters for better readability. For example,`1_234_567_890`

can be used in queries instead of`1234567890`

. - Series selectors accept multiple
`or`

filters. For example,`{env="prod",job="a" or env="dev",job="b"}`

selects series with`{env="prod",job="a"}`

or`{env="dev",job="b"}`

labels. See these docs for details. - Support for matching against multiple numeric constants via
`q == (C1, ..., CN)`

and`q != (C1, ..., CN)`

syntax. For example,`status_code == (300, 301, 304)`

returns`status_code`

metrics with one of`300`

,`301`

or`304`

values. - Support for
`group_left(*)`

and`group_right(*)`

for copying all the labels from time series on the`one`

side of many-to-one operations. The copied label names may clash with the existing label names, so MetricsQL provides an ability to add prefix to the copied metric names via`group_left(*) prefix "..."`

syntax. For example, the following query copies all the`namespace`

-related labels from`kube_namespace_labels`

to`kube_pod_info`

series, while adding`ns_`

prefix to the copied labels:`kube_pod_info * on(namespace) group_left(*) prefix "ns_" kube_namespace_labels`

. Labels from the`on()`

list aren’t copied. - Aggregate functions accept arbitrary number of args.
For example,
`avg(q1, q2, q3)`

would return the average values for every point across time series returned by`q1`

,`q2`

and`q3`

. - @ modifier can be put anywhere in the query.
For example,
`sum(foo) @ end()`

calculates`sum(foo)`

at the`end`

timestamp of the selected time range`[start ... end]`

. - Arbitrary subexpression can be used as @ modifier.
For example,
`foo @ (end() - 1h)`

calculates`foo`

at the`end - 1 hour`

timestamp on the selected time range`[start ... end]`

. - offset, lookbehind window in square brackets
and
`step`

value for subquery may refer to the current step aka`$__interval`

value from Grafana with`[Ni]`

syntax. For instance,`rate(metric[10i] offset 5i)`

would return per-second rate over a range covering 10 previous steps with the offset of 5 steps. - offset may be put anywhere in the query. For instance,
`sum(foo) offset 24h`

. - Lookbehind window in square brackets and offset may be fractional.
For instance,
`rate(node_network_receive_bytes_total[1.5m] offset 0.5d)`

. - The duration suffix is optional. The duration is in seconds if the suffix is missing.
For example,
`rate(m[300] offset 1800)`

is equivalent to`rate(m[5m]) offset 30m`

. - The duration can be placed anywhere in the query. For example,
`sum_over_time(m[1h]) / 1h`

is equivalent to`sum_over_time(m[1h]) / 3600`

. - Numeric values can have
`K`

,`Ki`

,`M`

,`Mi`

,`G`

,`Gi`

,`T`

and`Ti`

suffixes. For example,`8K`

is equivalent to`8000`

, while`1.2Mi`

is equivalent to`1.2*1024*1024`

. - Trailing commas on all the lists are allowed - label filters, function args and with expressions.
For instance, the following queries are valid:
`m{foo="bar",}`

,`f(a, b,)`

,`WITH (x=y,) x`

. This simplifies maintenance of multi-line queries. - Metric names and label names may contain any unicode letter. For example
`температура{город="Київ"}`

is a valid MetricsQL expression. - Metric names and labels names may contain escaped chars. For example,
`foo\-bar{baz\=aa="b"}`

is valid expression. It returns time series with name`foo-bar`

containing label`baz=aa`

with value`b`

. Additionally, the following escape sequences are supported:`\xXX`

, where`XX`

is hexadecimal representation of the escaped ascii char.`\uXXXX`

, where`XXXX`

is a hexadecimal representation of the escaped unicode char.

- Aggregate functions support optional
`limit N`

suffix in order to limit the number of output series. For example,`sum(x) by (y) limit 3`

limits the number of output time series after the aggregation to 3. All the other time series are dropped. - histogram_quantile accepts optional third arg -
`boundsLabel`

. In this case it returns`lower`

and`upper`

bounds for the estimated percentile. See this issue for details. `default`

binary operator.`q1 default q2`

fills gaps in`q1`

with the corresponding values from`q2`

. See also drop_empty_series.`if`

binary operator.`q1 if q2`

removes values from`q1`

for missing values from`q2`

.`ifnot`

binary operator.`q1 ifnot q2`

removes values from`q1`

for existing values from`q2`

.`WITH`

templates. This feature simplifies writing and managing complex queries. Go to WITH templates playground and try it.- String literals may be concatenated. This is useful with
`WITH`

templates:`WITH (commonPrefix="long_metric_prefix_") {__name__=commonPrefix+"suffix1"} / {__name__=commonPrefix+"suffix2"}`

. `keep_metric_names`

modifier can be applied to all the rollup functions, transform functions and binary operators. This modifier prevents from dropping metric names in function results. See these docs.

## keep_metric_names#

By default, metric names are dropped after applying functions or binary operators,
since they may change the meaning of the original time series.
This may result in `duplicate time series`

error when the function is applied to multiple time series with different names.
This error can be fixed by applying `keep_metric_names`

modifier to the function or binary operator.

For example:

`rate({__name__=~"foo|bar"}) keep_metric_names`

leaves`foo`

and`bar`

metric names in the returned time series.`({__name__=~"foo|bar"} / 10) keep_metric_names`

leaves`foo`

and`bar`

metric names in the returned time series.

## MetricsQL functions#

If you are unfamiliar with PromQL, then please read this tutorial at first.

MetricsQL provides the following functions:

### Rollup functions#

**Rollup functions** (aka range functions or window functions) calculate rollups over raw samples
on the given lookbehind window for the selected time series.
For example, `avg_over_time(temperature[24h])`

calculates the average temperature over raw samples for the last 24 hours.

Additional details:

- If rollup functions are used for building graphs in Grafana, then the rollup is calculated independently per each point on the graph.
For example, every point for
`avg_over_time(temperature[24h])`

graph shows the average temperature for the last 24 hours ending at this point. The interval between points is set as`step`

query arg passed by Grafana to /api/v1/query_range. - If the given series selector returns multiple time series, then rollups are calculated individually per each returned series.
- If lookbehind window in square brackets is missing, then it is automatically set to the following value:
- To
`step`

value passed to /api/v1/query_range or /api/v1/query for all the rollup functions except of default_rollup and rate. This value is known as`$__interval`

in Grafana or`1i`

in MetricsQL. For example,`avg_over_time(temperature)`

is automatically transformed to`avg_over_time(temperature[1i])`

. - To the
`max(step, scrape_interval)`

, where`scrape_interval`

is the interval between raw samples for default_rollup and rate functions. This allows avoiding unexpected gaps on the graph when`step`

is smaller than`scrape_interval`

.

- To
- Every series selector in MetricsQL must be wrapped into a rollup function.
Otherwise, it is automatically wrapped into default_rollup. For example,
`foo{bar="baz"}`

is automatically converted to`default_rollup(foo{bar="baz"})`

before performing the calculations. - If something other than series selector is passed to rollup function, then the inner arg is automatically converted to a subquery.
- All the rollup functions accept optional
`keep_metric_names`

modifier. If it is set, then the function keeps metric names in results. See these docs.

See also implicit query conversions.

The list of supported rollup functions:

#### absent_over_time#

`absent_over_time(series_selector[d])`

is a rollup function, which returns 1
if the given lookbehind window `d`

doesn’t contain raw samples. Otherwise, it returns an empty result.

This function is supported by PromQL.

See also present_over_time.

#### aggr_over_time#

`aggr_over_time(("rollup_func1", "rollup_func2", ...), series_selector[d])`

is a rollup function,
which calculates all the listed `rollup_func*`

for raw samples on the given lookbehind window `d`

.
The calculations are performed individually per each time series returned
from the given series_selector.

`rollup_func*`

can contain any rollup function. For instance, `aggr_over_time(("min_over_time", "max_over_time", "rate"), m[d])`

would calculate min_over_time, max_over_time and rate for `m[d]`

.

#### ascent_over_time#

`ascent_over_time(series_selector[d])`

is a rollup function, which calculates
ascent of raw sample values on the given lookbehind window `d`

. The calculations are performed individually
per each time series returned from the given series_selector.

This function is useful for tracking height gains in GPS tracking. Metric names are stripped from the resulting rollups.

Metric names are stripped from the resulting rollups. Add keep_metric_names modifier in order to keep metric names.

See also descent_over_time.

#### avg_over_time#

`avg_over_time(series_selector[d])`

is a rollup function, which calculates the average value
over raw samples on the given lookbehind window `d`

per each time series returned
from the given series_selector.

This function is usually applied to gauges.

This function is supported by PromQL.

See also median_over_time, min_over_time and max_over_time.

#### changes#

`changes(series_selector[d])`

is a rollup function, which calculates the number of times
the raw samples changed on the given lookbehind window `d`

per each time series returned
from the given series_selector.

Unlike `changes()`

in Prometheus it takes into account the change from the last sample before the given lookbehind window `d`

.
See this article for details.

Metric names are stripped from the resulting rollups. Add keep_metric_names modifier in order to keep metric names.

This function is supported by PromQL.

See also changes_prometheus.

#### changes_prometheus#

`changes_prometheus(series_selector[d])`

is a rollup function, which calculates the number of times
the raw samples changed on the given lookbehind window `d`

per each time series returned
from the given series_selector.

It doesn’t take into account the change from the last sample before the given lookbehind window `d`

in the same way as Prometheus does.
See this article for details.

Metric names are stripped from the resulting rollups. Add keep_metric_names modifier in order to keep metric names.

This function is supported by PromQL.

See also changes.

#### count_eq_over_time#

`count_eq_over_time(series_selector[d], eq)`

is a rollup function, which calculates the number of raw samples
on the given lookbehind window `d`

, which are equal to `eq`

. It is calculated independently per each time series returned
from the given series_selector.

This function is usually applied to gauges.

See also count_over_time, share_eq_over_time and count_values_over_time.

#### count_gt_over_time#

`count_gt_over_time(series_selector[d], gt)`

is a rollup function, which calculates the number of raw samples
on the given lookbehind window `d`

, which are bigger than `gt`

. It is calculated independently per each time series returned
from the given series_selector.

This function is usually applied to gauges.

See also count_over_time and share_gt_over_time.

#### count_le_over_time#

`count_le_over_time(series_selector[d], le)`

is a rollup function, which calculates the number of raw samples
on the given lookbehind window `d`

, which don’t exceed `le`

. It is calculated independently per each time series returned
from the given series_selector.

This function is usually applied to gauges.

See also count_over_time and share_le_over_time.

#### count_ne_over_time#

`count_ne_over_time(series_selector[d], ne)`

is a rollup function, which calculates the number of raw samples
on the given lookbehind window `d`

, which aren’t equal to `ne`

. It is calculated independently per each time series returned
from the given series_selector.

This function is usually applied to gauges.

See also count_over_time and count_eq_over_time.

#### count_over_time#

`count_over_time(series_selector[d])`

is a rollup function, which calculates the number of raw samples
on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is supported by PromQL.

See also count_le_over_time, count_gt_over_time, count_eq_over_time and count_ne_over_time.

#### count_values_over_time#

`count_values_over_time("label", series_selector[d])`

is a rollup function, which counts the number of raw samples
with the same value over the given lookbehind window and stores the counts in a time series with an additional `label`

, which contains each initial value.
The results are calculated independently per each time series returned from the given series_selector.

This function is usually applied to gauges.

See also count_eq_over_time, count_values and distinct_over_time and label_match.

#### decreases_over_time#

`decreases_over_time(series_selector[d])`

is a rollup function, which calculates the number of raw sample
value decreases over the given lookbehind window `d`

per each time series returned from the given series_selector.

See also increases_over_time.

#### default_rollup#

`default_rollup(series_selector[d])`

is a rollup function, which returns the last raw sample
value on the given lookbehind window `d`

per each time series returned from the given series_selector.
Compared to last_over_time it accounts for staleness markers to detect stale series.

If the lookbehind window is skipped in square brackets, then it is automatically calculated as `max(step, scrape_interval)`

, where `step`

is the query arg value
passed to /api/v1/query_range or /api/v1/query,
while `scrape_interval`

is the interval between raw samples for the selected time series.
This allows avoiding unexpected gaps on the graph when `step`

is smaller than the `scrape_interval`

.

#### delta#

`delta(series_selector[d])`

is a rollup function, which calculates the difference between
the last sample before the given lookbehind window `d`

and the last sample at the given lookbehind window `d`

per each time series returned from the given series_selector.

The behaviour of `delta()`

function in MetricsQL is slightly different to the behaviour of `delta()`

function in Prometheus.
See this article for details.

This function is supported by PromQL.

See also increase and delta_prometheus.

#### delta_prometheus#

`delta_prometheus(series_selector[d])`

is a rollup function, which calculates the difference between
the first and the last samples at the given lookbehind window `d`

per each time series returned
from the given series_selector.

The behaviour of `delta_prometheus()`

is close to the behaviour of `delta()`

function in Prometheus.
See this article for details.

See also delta.

#### deriv#

`deriv(series_selector[d])`

is a rollup function, which calculates per-second derivative over the given lookbehind window `d`

per each time series returned from the given series_selector.
The derivative is calculated using linear regression.

This function is supported by PromQL.

See also deriv_fast and ideriv.

#### deriv_fast#

`deriv_fast(series_selector[d])`

is a rollup function, which calculates per-second derivative
using the first and the last raw samples on the given lookbehind window `d`

per each time series returned
from the given series_selector.

#### descent_over_time#

`descent_over_time(series_selector[d])`

is a rollup function, which calculates descent of raw sample
values on the given lookbehind window `d`

. The calculations are performed individually per each time series returned
from the given series_selector.

This function is useful for tracking height loss in GPS tracking.

See also ascent_over_time.

#### distinct_over_time#

`distinct_over_time(series_selector[d])`

is a rollup function, which returns the number of unique raw sample
values on the given lookbehind window `d`

per each time series returned from the given series_selector.

See also count_values_over_time.

#### duration_over_time#

`duration_over_time(series_selector[d], max_interval)`

is a rollup function, which returns the duration in seconds
when time series returned from the given series_selector were present
over the given lookbehind window `d`

. It is expected that intervals between adjacent samples per each series don’t exceed the `max_interval`

.
Otherwise, such intervals are considered as gaps and aren’t counted.

#### first_over_time#

`first_over_time(series_selector[d])`

is a rollup function, which returns the first raw sample
value on the given lookbehind window `d`

per each time series returned from the given series_selector.

See also last_over_time and tfirst_over_time.

#### geomean_over_time#

`geomean_over_time(series_selector[d])`

is a rollup function, which calculates geometric mean
over raw samples on the given lookbehind window `d`

per each time series returned
from the given series_selector.

This function is usually applied to gauges.

#### histogram_over_time#

`histogram_over_time(series_selector[d])`

is a rollup function, which calculates
VictoriaMetrics histogram over raw samples
on the given lookbehind window `d`

. It is calculated individually per each time series returned from the given series_selector.
The resulting histograms are useful to pass to histogram_quantile for calculating quantiles
over multiple gauges.
For example, the following query calculates median temperature by country over the last 24 hours:

`histogram_quantile(0.5, sum(histogram_over_time(temperature[24h])) by (vmrange,country))`

.

This function is usually applied to gauges.

#### hoeffding_bound_lower#

`hoeffding_bound_lower(phi, series_selector[d])`

is a rollup function, which calculates
lower Hoeffding bound for the given `phi`

in the range `[0...1]`

.

This function is usually applied to gauges.

See also hoeffding_bound_upper.

#### hoeffding_bound_upper#

`hoeffding_bound_upper(phi, series_selector[d])`

is a rollup function, which calculates
upper Hoeffding bound for the given `phi`

in the range `[0...1]`

.

This function is usually applied to gauges.

See also hoeffding_bound_lower.

#### holt_winters#

`holt_winters(series_selector[d], sf, tf)`

is a rollup function, which calculates Holt-Winters value
(aka double exponential smoothing) for raw samples
over the given lookbehind window `d`

using the given smoothing factor `sf`

and the given trend factor `tf`

.
Both `sf`

and `tf`

must be in the range `[0...1]`

.

This function is usually applied to gauges.

This function is supported by PromQL.

See also range_linear_regression.

#### idelta#

`idelta(series_selector[d])`

is a rollup function, which calculates the difference between the last two raw samples
on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is supported by PromQL.

See also delta.

#### ideriv#

`ideriv(series_selector[d])`

is a rollup function, which calculates the per-second derivative based
on the last two raw samples
over the given lookbehind window `d`

. The derivative is calculated independently per each time series returned
from the given series_selector.

See also deriv.

#### increase#

`increase(series_selector[d])`

is a rollup function, which calculates the increase over the given lookbehind window `d`

per each time series returned from the given series_selector.

Unlike Prometheus, it takes into account the last sample before the given lookbehind window `d`

when calculating the result.
See this article for details.

This function is usually applied to counters.

This function is supported by PromQL.

See also increase_pure, increase_prometheus and delta.

#### increase_prometheus#

`increase_prometheus(series_selector[d])`

is a rollup function, which calculates the increase
over the given lookbehind window `d`

per each time series returned from the given series_selector.
It doesn’t take into account the last sample before the given lookbehind window `d`

when calculating the result in the same way as Prometheus does.
See this article for details.

This function is usually applied to counters.

See also increase_pure and increase.

#### increase_pure#

`increase_pure(series_selector[d])`

is a rollup function, which works the same as increase except
of the following corner case - it assumes that counters always start from 0,
while increase ignores the first value in a series if it is too big.

This function is usually applied to counters.

See also increase and increase_prometheus.

#### increases_over_time#

`increases_over_time(series_selector[d])`

is a rollup function, which calculates the number of raw sample
value increases over the given lookbehind window `d`

per each time series returned from the given series_selector.

See also decreases_over_time.

#### integrate#

`integrate(series_selector[d])`

is a rollup function, which calculates the integral over raw samples
on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is usually applied to gauges.

#### irate#

`irate(series_selector[d])`

is a rollup function, which calculates the “instant” per-second increase rate over
the last two raw samples
on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is usually applied to counters.

This function is supported by PromQL.

See also rate and rollup_rate.

#### lag#

`lag(series_selector[d])`

is a rollup function, which returns the duration in seconds between the last sample
on the given lookbehind window `d`

and the timestamp of the current point. It is calculated independently per each time series returned
from the given series_selector.

See also lifetime and duration_over_time.

#### last_over_time#

`last_over_time(series_selector[d])`

is a rollup function, which returns the last raw sample
value on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is supported by PromQL.

See also first_over_time and tlast_over_time.

#### lifetime#

`lifetime(series_selector[d])`

is a rollup function, which returns the duration in seconds between the last and the first sample
on the given lookbehind window `d`

per each time series returned from the given series_selector.

See also duration_over_time and lag.

#### mad_over_time#

`mad_over_time(series_selector[d])`

is a rollup function, which calculates median absolute deviation
over raw samples on the given lookbehind window `d`

per each time series returned
from the given series_selector.

This function is usually applied to gauges.

See also mad, range_mad and outlier_iqr_over_time.

#### max_over_time#

`max_over_time(series_selector[d])`

is a rollup function, which calculates the maximum value over raw samples
on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is usually applied to gauges.

This function is supported by PromQL.

See also tmax_over_time and min_over_time.

#### median_over_time#

`median_over_time(series_selector[d])`

is a rollup function, which calculates median value over raw samples
on the given lookbehind window `d`

per each time series returned
from the given series_selector.

This function is usually applied to gauges.

See also avg_over_time.

#### min_over_time#

`min_over_time(series_selector[d])`

is a rollup function, which calculates the minimum value over raw samples
on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is usually applied to gauges.

This function is supported by PromQL.

See also tmin_over_time and max_over_time.

#### mode_over_time#

`mode_over_time(series_selector[d])`

is a rollup function, which calculates mode
for raw samples on the given lookbehind window `d`

. It is calculated individually per each time series returned
from the given series_selector. It is expected that raw sample
values are discrete.

This function is usually applied to gauges.

#### outlier_iqr_over_time#

`outlier_iqr_over_time(series_selector[d])`

is a rollup function, which returns the last sample on the given lookbehind window `d`

if its value is either smaller than the `q25-1.5*iqr`

or bigger than `q75+1.5*iqr`

where:

`iqr`

is an Interquartile range over raw samples on the lookbehind window`d`

`q25`

and`q75`

are 25th and 75th percentiles over raw samples on the lookbehind window`d`

.

The `outlier_iqr_over_time()`

is useful for detecting anomalies in gauge values based on the previous history of values.
For example, `outlier_iqr_over_time(memory_usage_bytes[1h])`

triggers when `memory_usage_bytes`

suddenly goes outside the usual value range for the last hour.

This function is usually applied to gauges.

See also outliers_iqr.

#### predict_linear#

`predict_linear(series_selector[d], t)`

is a rollup function, which calculates the value `t`

seconds in the future using
linear interpolation over raw samples on the given lookbehind window `d`

.
The predicted value is calculated individually per each time series returned from the given series_selector.

This function is supported by PromQL.

See also range_linear_regression.

#### present_over_time#

`present_over_time(series_selector[d])`

is a rollup function, which returns 1 if there is at least a single raw sample
on the given lookbehind window `d`

. Otherwise, an empty result is returned.

This function is supported by PromQL.

#### quantile_over_time#

`quantile_over_time(phi, series_selector[d])`

is a rollup function, which calculates `phi`

-quantile over raw samples
on the given lookbehind window `d`

per each time series returned from the given series_selector.
The `phi`

value must be in the range `[0...1]`

.

This function is usually applied to gauges.

This function is supported by PromQL.

See also quantiles_over_time.

#### quantiles_over_time#

`quantiles_over_time("phiLabel", phi1, ..., phiN, series_selector[d])`

is a rollup function, which calculates `phi*`

-quantiles
over raw samples on the given lookbehind window `d`

per each time series returned
from the given series_selector.
The function returns individual series per each `phi*`

with `{phiLabel="phi*"}`

label. `phi*`

values must be in the range `[0...1]`

.

This function is usually applied to gauges.

See also quantile_over_time.

#### range_over_time#

`range_over_time(series_selector[d])`

is a rollup function, which calculates value range over raw samples
on the given lookbehind window `d`

per each time series returned from the given series_selector.
E.g. it calculates `max_over_time(series_selector[d]) - min_over_time(series_selector[d])`

.

This function is usually applied to gauges.

#### rate#

`rate(series_selector[d])`

is a rollup function, which calculates the average per-second increase rate
over the given lookbehind window `d`

per each time series returned from the given series_selector.

If the lookbehind window is skipped in square brackets, then it is automatically calculated as `max(step, scrape_interval)`

, where `step`

is the query arg value
passed to /api/v1/query_range or /api/v1/query,
while `scrape_interval`

is the interval between raw samples for the selected time series.
This allows avoiding unexpected gaps on the graph when `step`

is smaller than the `scrape_interval`

.

This function is supported by PromQL.

See also irate and rollup_rate.

#### rate_over_sum#

`rate_over_sum(series_selector[d])`

is a rollup function, which calculates per-second rate over the sum of raw samples
on the given lookbehind window `d`

. The calculations are performed individually per each time series returned
from the given series_selector.

This function is usually applied to gauges.

#### resets#

`resets(series_selector[d])`

is a rollup function, which returns the number
of counter resets over the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is usually applied to counters.

This function is supported by PromQL.

#### rollup#

`rollup(series_selector[d])`

is a rollup function, which calculates `min`

, `max`

and `avg`

values for raw samples
on the given lookbehind window `d`

and returns them in time series with `rollup="min"`

, `rollup="max"`

and `rollup="avg"`

additional labels.
These values are calculated individually per each time series returned from the given series_selector.

Optional 2nd argument `"min"`

, `"max"`

or `"avg"`

can be passed to keep only one calculation result and without adding a label.
See also label_match.

This function is usually applied to gauges.

See also rollup_rate.

#### rollup_candlestick#

`rollup_candlestick(series_selector[d])`

is a rollup function, which calculates `open`

, `high`

, `low`

and `close`

values (aka OHLC)
over raw samples on the given lookbehind window `d`

and returns them in time series
with `rollup="open"`

, `rollup="high"`

, `rollup="low"`

and `rollup="close"`

additional labels.
The calculations are performed individually per each time series returned
from the given series_selector. This function is useful for financial applications.

Optional 2nd argument `"open"`

, `"high"`

or `"low"`

or `"close"`

can be passed to keep only one calculation result and without adding a label.
See also label_match.

This function is usually applied to gauges.

#### rollup_delta#

`rollup_delta(series_selector[d])`

is a rollup function, which calculates differences between adjacent raw samples
on the given lookbehind window `d`

and returns `min`

, `max`

and `avg`

values for the calculated differences
and returns them in time series with `rollup="min"`

, `rollup="max"`

and `rollup="avg"`

additional labels.
The calculations are performed individually per each time series returned from the given series_selector.

Optional 2nd argument `"min"`

, `"max"`

or `"avg"`

can be passed to keep only one calculation result and without adding a label.
See also label_match.

See also rollup_increase.

#### rollup_deriv#

`rollup_deriv(series_selector[d])`

is a rollup function, which calculates per-second derivatives
for adjacent raw samples on the given lookbehind window `d`

and returns `min`

, `max`

and `avg`

values
for the calculated per-second derivatives and returns them in time series with `rollup="min"`

, `rollup="max"`

and `rollup="avg"`

additional labels.
The calculations are performed individually per each time series returned from the given series_selector.

Optional 2nd argument `"min"`

, `"max"`

or `"avg"`

can be passed to keep only one calculation result and without adding a label.
See also label_match.

See also rollup and rollup_rate.

#### rollup_increase#

`rollup_increase(series_selector[d])`

is a rollup function, which calculates increases for adjacent raw samples
on the given lookbehind window `d`

and returns `min`

, `max`

and `avg`

values for the calculated increases
and returns them in time series with `rollup="min"`

, `rollup="max"`

and `rollup="avg"`

additional labels.
The calculations are performed individually per each time series returned from the given series_selector.

`"min"`

, `"max"`

or `"avg"`

can be passed to keep only one calculation result and without adding a label.
See also label_match.

Metric names are stripped from the resulting rollups. Add keep_metric_names modifier in order to keep metric names. See also rollup_delta.

This function is usually applied to counters.

See also rollup and rollup_rate.

#### rollup_rate#

`rollup_rate(series_selector[d])`

is a rollup function, which calculates per-second change rates
for adjacent raw samples
on the given lookbehind window `d`

and returns `min`

, `max`

and `avg`

values for the calculated per-second change rates
and returns them in time series with `rollup="min"`

, `rollup="max"`

and `rollup="avg"`

additional labels.
The calculations are performed individually per each time series returned from the given series_selector.

See this article in order to understand better
when to use `rollup_rate()`

.

`"min"`

, `"max"`

or `"avg"`

can be passed to keep only one calculation result and without adding a label.
See also label_match.

This function is usually applied to counters.

See also rollup and rollup_increase.

#### rollup_scrape_interval#

`rollup_scrape_interval(series_selector[d])`

is a rollup function, which calculates the interval in seconds between
adjacent raw samples on the given lookbehind window `d`

and returns `min`

, `max`

and `avg`

values for the calculated interval
and returns them in time series with `rollup="min"`

, `rollup="max"`

and `rollup="avg"`

additional labels.
The calculations are performed individually per each time series returned from the given series_selector.

`"min"`

, `"max"`

or `"avg"`

can be passed to keep only one calculation result and without adding a label.
See also label_match.

Metric names are stripped from the resulting rollups. Add keep_metric_names modifier in order to keep metric names. See also scrape_interval.

#### scrape_interval#

`scrape_interval(series_selector[d])`

is a rollup function, which calculates the average interval in seconds
between raw samples
on the given lookbehind window `d`

per each time series returned from the given series_selector.

See also rollup_scrape_interval.

#### share_gt_over_time#

`share_gt_over_time(series_selector[d], gt)`

is a rollup function, which returns share (in the range `[0...1]`

)
of raw samples
on the given lookbehind window `d`

, which are bigger than `gt`

. It is calculated independently per each time series returned
from the given series_selector.

This function is useful for calculating SLI and SLO. Example: `share_gt_over_time(up[24h], 0)`

- returns service availability for the last 24 hours.

This function is usually applied to gauges.

See also share_le_over_time and count_gt_over_time.

#### share_le_over_time#

`share_le_over_time(series_selector[d], le)`

is a rollup function, which returns share (in the range `[0...1]`

)
of raw samples
on the given lookbehind window `d`

, which are smaller or equal to `le`

. It is calculated independently per each time series returned
from the given series_selector.

This function is useful for calculating SLI and SLO. Example: `share_le_over_time(memory_usage_bytes[24h], 100*1024*1024)`

returns
the share of time series values for the last 24 hours when memory usage was below or equal to 100MB.

This function is usually applied to gauges.

See also share_gt_over_time and count_le_over_time.

#### share_eq_over_time#

`share_eq_over_time(series_selector[d], eq)`

is a rollup function, which returns share (in the range `[0...1]`

)
of raw samples
on the given lookbehind window `d`

, which are equal to `eq`

. It is calculated independently per each time series returned
from the given series_selector.

This function is usually applied to gauges.

See also count_eq_over_time.

#### stale_samples_over_time#

`stale_samples_over_time(series_selector[d])`

is a rollup function, which calculates the number
of staleness markers on the given lookbehind window `d`

per each time series matching the given series_selector.

#### stddev_over_time#

`stddev_over_time(series_selector[d])`

is a rollup function, which calculates standard deviation over raw samples
on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is usually applied to gauges.

This function is supported by PromQL.

See also stdvar_over_time.

#### stdvar_over_time#

`stdvar_over_time(series_selector[d])`

is a rollup function, which calculates standard variance over raw samples
on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is usually applied to gauges.

This function is supported by PromQL.

See also stddev_over_time.

#### sum_eq_over_time#

`sum_eq_over_time(series_selector[d], eq)`

is a rollup function, which calculates the sum of raw sample
values equal to `eq`

on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is usually applied to gauges.

See also sum_over_time and count_eq_over_time.

#### sum_gt_over_time#

`sum_gt_over_time(series_selector[d], gt)`

is a rollup function, which calculates the sum of raw sample
values bigger than `gt`

on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is usually applied to gauges.

See also sum_over_time and count_gt_over_time.

#### sum_le_over_time#

`sum_le_over_time(series_selector[d], le)`

is a rollup function, which calculates the sum of raw sample
values smaller or equal to `le`

on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is usually applied to gauges.

See also sum_over_time and count_le_over_time.

#### sum_over_time#

`sum_over_time(series_selector[d])`

is a rollup function, which calculates the sum of raw sample values
on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is usually applied to gauges.

This function is supported by PromQL.

#### sum2_over_time#

`sum2_over_time(series_selector[d])`

is a rollup function, which calculates the sum of squares for raw sample
values on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is usually applied to gauges.

#### timestamp#

`timestamp(series_selector[d])`

is a rollup function, which returns the timestamp in seconds with millisecond precision
for the last raw sample
on the given lookbehind window `d`

per each time series returned from the given series_selector.

This function is supported by PromQL.

#### timestamp_with_name#

`timestamp_with_name(series_selector[d])`

is a rollup function, which returns the timestamp in seconds with millisecond precision
for the last raw sample
on the given lookbehind window `d`

per each time series returned from the given series_selector.

Metric names are preserved in the resulting rollups.

See also timestamp and keep_metric_names modifier.

#### tfirst_over_time#

`tfirst_over_time(series_selector[d])`

is a rollup function, which returns the timestamp in seconds with millisecond precision
for the first raw sample
on the given lookbehind window `d`

per each time series returned from the given series_selector.

See also first_over_time.

#### tlast_change_over_time#

`tlast_change_over_time(series_selector[d])`

is a rollup function, which returns the timestamp in seconds with millisecond precision for the last change
per each time series returned from the given series_selector on the given lookbehind window `d`

.

See also last_over_time.

#### tlast_over_time#

`tlast_over_time(series_selector[d])`

is a rollup function, which is an alias for timestamp.

See also tlast_change_over_time.

#### tmax_over_time#

`tmax_over_time(series_selector[d])`

is a rollup function, which returns the timestamp in seconds with millisecond precision
for the raw sample
with the maximum value on the given lookbehind window `d`

. It is calculated independently per each time series returned
from the given series_selector.

See also max_over_time.

#### tmin_over_time#

`tmin_over_time(series_selector[d])`

is a rollup function, which returns the timestamp in seconds with millisecond precision
for the raw sample
with the minimum value on the given lookbehind window `d`

. It is calculated independently per each time series returned
from the given series_selector.

See also min_over_time.

#### zscore_over_time#

`zscore_over_time(series_selector[d])`

is a rollup function, which returns z-score
for raw samples on the given lookbehind window `d`

. It is calculated independently per each time series returned
from the given series_selector.

This function is usually applied to gauges.

See also zscore, range_trim_zscore and outlier_iqr_over_time.

### Transform functions#

**Transform functions** calculate transformations over rollup results.
For example, `abs(delta(temperature[24h]))`

calculates the absolute value for every point of every time series
returned from the rollup `delta(temperature[24h])`

.

Additional details:

- If transform function is applied directly to a series selector,
then the default_rollup() function is automatically applied before calculating the transformations.
For example,
`abs(temperature)`

is implicitly transformed to`abs(default_rollup(temperature))`

. - All the transform functions accept optional
`keep_metric_names`

modifier. If it is set, then the function doesn’t drop metric names from the resulting time series. See these docs.

See also implicit query conversions.

The list of supported transform functions:

#### abs#

`abs(q)`

is a transform function, which calculates the absolute value for every point of every time series returned by `q`

.

This function is supported by PromQL.

#### absent#

`absent(q)`

is a transform function, which returns 1 if `q`

has no points. Otherwise, returns an empty result.

This function is supported by PromQL.

See also absent_over_time.

#### acos#

`acos(q)`

is a transform function, which returns inverse cosine
for every point of every time series returned by `q`

.

Metric names are stripped from the resulting series. Add keep_metric_names modifier in order to keep metric names.

This function is supported by PromQL.

#### acosh#

`acosh(q)`

is a transform function, which returns
inverse hyperbolic cosine for every point of every time series returned by `q`

.

Metric names are stripped from the resulting series. Add keep_metric_names modifier in order to keep metric names.

This function is supported by PromQL.

See also sinh.

#### asin#

`asin(q)`

is a transform function, which returns inverse sine
for every point of every time series returned by `q`

.

Metric names are stripped from the resulting series. Add keep_metric_names modifier in order to keep metric names.

This function is supported by PromQL.

#### asinh#

`asinh(q)`

is a transform function, which returns
inverse hyperbolic sine for every point of every time series returned by `q`

.

This function is supported by PromQL.

See also sinh.

#### atan#

`atan(q)`

is a transform function, which returns inverse tangent
for every point of every time series returned by `q`

.

This function is supported by PromQL.

See also tan.

#### atanh#

`atanh(q)`

is a transform function, which returns
inverse hyperbolic tangent for every point of every time series returned by `q`

.

This function is supported by PromQL.

See also tanh.

#### bitmap_and#

`bitmap_and(q, mask)`

is a transform function, which calculates bitwise `v & mask`

for every `v`

point of every time series returned from `q`

.

#### bitmap_or#

`bitmap_or(q, mask)`

is a transform function, which calculates bitwise `v | mask`

for every `v`

point of every time series returned from `q`

.

#### bitmap_xor#

`bitmap_xor(q, mask)`

is a transform function, which calculates bitwise `v ^ mask`

for every `v`

point of every time series returned from `q`

.

#### buckets_limit#

`buckets_limit(limit, buckets)`

is a transform function, which limits the number
of histogram buckets to the given `limit`

.

See also prometheus_buckets and histogram_quantile.

#### ceil#

`ceil(q)`

is a transform function, which rounds every point for every time series returned by `q`

to the upper nearest integer.

This function is supported by PromQL.

#### clamp#

`clamp(q, min, max)`

is a transform function, which clamps every point for every time series returned by `q`

with the given `min`

and `max`

values.

This function is supported by PromQL.

See also clamp_min and clamp_max.

#### clamp_max#

`clamp_max(q, max)`

is a transform function, which clamps every point for every time series returned by `q`

with the given `max`

value.

This function is supported by PromQL.

#### clamp_min#

`clamp_min(q, min)`

is a transform function, which clamps every point for every time series returned by `q`

with the given `min`

value.

This function is supported by PromQL.

#### cos#

`cos(q)`

is a transform function, which returns `cos(v)`

for every `v`

point of every time series returned by `q`

.

This function is supported by PromQL.

See also sin.

#### cosh#

`cosh(q)`

is a transform function, which returns hyperbolic cosine
for every point of every time series returned by `q`

.

This function is supported by PromQL.

See also acosh.

#### day_of_month#

`day_of_month(q)`

is a transform function, which returns the day of month for every point of every time series returned by `q`

.
It is expected that `q`

returns unix timestamps. The returned values are in the range `[1...31]`

.

This function is supported by PromQL.

See also day_of_week and day_of_year.

#### day_of_week#

`day_of_week(q)`

is a transform function, which returns the day of week for every point of every time series returned by `q`

.
It is expected that `q`

returns unix timestamps. The returned values are in the range `[0...6]`

, where `0`

means Sunday and `6`

means Saturday.

This function is supported by PromQL.

See also day_of_month and day_of_year.

#### day_of_year#

`day_of_year(q)`

is a transform function, which returns the day of year for every point of every time series returned by `q`

.
It is expected that `q`

returns unix timestamps. The returned values are in the range `[1...365]`

for non-leap years, and `[1 to 366]`

in leap years.

This function is supported by PromQL.

See also day_of_week and day_of_month.

#### days_in_month#

`days_in_month(q)`

is a transform function, which returns the number of days in the month identified
by every point of every time series returned by `q`

. It is expected that `q`

returns unix timestamps.
The returned values are in the range `[28...31]`

.

This function is supported by PromQL.

#### deg#

`deg(q)`

is a transform function, which converts Radians to degrees
for every point of every time series returned by `q`

.

This function is supported by PromQL.

See also rad.

#### drop_empty_series#

`drop_empty_series(q)`

is a transform function, which drops empty series from `q`

.

This function can be used when `default`

operator should be applied only to non-empty series. For example,
`drop_empty_series(temperature < 30) default 42`

returns series, which have at least a single sample smaller than 30 on the selected time range,
while filling gaps in the returned series with 42.

On the other hand `(temperature < 30) default 40`

returns all the `temperature`

series, even if they have no samples smaller than 30,
by replacing all the values bigger or equal to 30 with 40.

#### end#

`end()`

is a transform function, which returns the unix timestamp in seconds for the last point.
It is known as `end`

query arg passed to /api/v1/query_range.

#### exp#

`exp(q)`

is a transform function, which calculates the `e^v`

for every point `v`

of every time series returned by `q`

.

This function is supported by PromQL.

See also ln.

#### floor#

`floor(q)`

is a transform function, which rounds every point for every time series returned by `q`

to the lower nearest integer.

This function is supported by PromQL.

#### histogram_avg#

`histogram_avg(buckets)`

is a transform function, which calculates the average value for the given `buckets`

.
It can be used for calculating the average over the given time range across multiple time series.
For example, `histogram_avg(sum(histogram_over_time(response_time_duration_seconds[5m])) by (vmrange,job))`

would return the average response time
per each `job`

over the last 5 minutes.

#### histogram_quantile#

`histogram_quantile(phi, buckets)`

is a transform function, which calculates `phi`

-percentile
over the given histogram buckets.
`phi`

must be in the range `[0...1]`

. For example, `histogram_quantile(0.5, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))`

would return median request duration for all the requests during the last 5 minutes.

The function accepts optional third arg - `boundsLabel`

. In this case it returns `lower`

and `upper`

bounds for the estimated percentile with the given `boundsLabel`

label.
See this issue for details.

When the percentile is calculated over multiple histograms,
then all the input histograms **must** have buckets with identical boundaries, e.g. they must have the same set of `le`

or `vmrange`

labels.
Otherwise, the returned result may be invalid. See this issue for details.

This function is supported by PromQL (except of the `boundLabel`

arg).

See also histogram_quantiles, histogram_share and quantile.

#### histogram_quantiles#

`histogram_quantiles("phiLabel", phi1, ..., phiN, buckets)`

is a transform function, which calculates the given `phi*`

-quantiles
over the given histogram buckets.
Argument `phi*`

must be in the range `[0...1]`

. For example, `histogram_quantiles('le', 0.3, 0.5, sum(rate(http_request_duration_seconds_bucket[5m]) by (le))`

.
Each calculated quantile is returned in a separate time series with the corresponding `{phiLabel="phi*"}`

label.

See also histogram_quantile.

#### histogram_share#

`histogram_share(le, buckets)`

is a transform function, which calculates the share (in the range `[0...1]`

)
for `buckets`

that fall below `le`

. This function is useful for calculating SLI and SLO. This is inverse to histogram_quantile.

The function accepts optional third arg - `boundsLabel`

. In this case it returns `lower`

and `upper`

bounds for the estimated share with the given `boundsLabel`

label.

#### histogram_stddev#

`histogram_stddev(buckets)`

is a transform function, which calculates standard deviation for the given `buckets`

.

#### histogram_stdvar#

`histogram_stdvar(buckets)`

is a transform function, which calculates standard variance for the given `buckets`

.
It can be used for calculating standard deviation over the given time range across multiple time series.
For example, `histogram_stdvar(sum(histogram_over_time(temperature[24])) by (vmrange,country))`

would return standard deviation
for the temperature per each country over the last 24 hours.

#### hour#

`hour(q)`

is a transform function, which returns the hour for every point of every time series returned by `q`

.
It is expected that `q`

returns unix timestamps. The returned values are in the range `[0...23]`

.

This function is supported by PromQL.

#### interpolate#

`interpolate(q)`

is a transform function, which fills gaps with linearly interpolated values calculated
from the last and the next non-empty points per each time series returned by `q`

.

See also keep_last_value and keep_next_value.

#### keep_last_value#

`keep_last_value(q)`

is a transform function, which fills gaps with the value of the last non-empty point
in every time series returned by `q`

.

See also keep_next_value and interpolate.

#### keep_next_value#

`keep_next_value(q)`

is a transform function, which fills gaps with the value of the next non-empty point
in every time series returned by `q`

.

See also keep_last_value and interpolate.

#### limit_offset#

`limit_offset(limit, offset, q)`

is a transform function, which skips `offset`

time series from series returned by `q`

and then returns up to `limit`

of the remaining time series per each group.

This allows implementing simple paging for `q`

time series. See also limitk.

#### ln#

`ln(q)`

is a transform function, which calculates `ln(v)`

for every point `v`

of every time series returned by `q`

.

This function is supported by PromQL.

#### log2#

`log2(q)`

is a transform function, which calculates `log2(v)`

for every point `v`

of every time series returned by `q`

.

This function is supported by PromQL.

#### log10#

`log10(q)`

is a transform function, which calculates `log10(v)`

for every point `v`

of every time series returned by `q`

.

This function is supported by PromQL.

#### minute#

`minute(q)`

is a transform function, which returns the minute for every point of every time series returned by `q`

.
It is expected that `q`

returns unix timestamps. The returned values are in the range `[0...59]`

.

This function is supported by PromQL.

#### month#

`month(q)`

is a transform function, which returns the month for every point of every time series returned by `q`

.
It is expected that `q`

returns unix timestamps. The returned values are in the range `[1...12]`

, where `1`

means January and `12`

means December.

This function is supported by PromQL.

#### now#

`now()`

is a transform function, which returns the current timestamp as a floating-point value in seconds.

See also time.

#### pi#

`pi()`

is a transform function, which returns Pi number.

This function is supported by PromQL.

#### rad#

`rad(q)`

is a transform function, which converts degrees to Radians
for every point of every time series returned by `q`

.

This function is supported by PromQL.

See also deg.

#### prometheus_buckets#

`prometheus_buckets(buckets)`

is a transform function, which converts
VictoriaMetrics histogram buckets with `vmrange`

labels
to Prometheus histogram buckets with `le`

labels. This may be useful for building heatmaps in Grafana.

See also histogram_quantile and buckets_limit.

#### rand#

`rand(seed)`

is a transform function, which returns pseudo-random numbers on the range `[0...1]`

with even distribution.
Optional `seed`

can be used as a seed for pseudo-random number generator.

See also rand_normal and rand_exponential.

#### rand_exponential#

`rand_exponential(seed)`

is a transform function, which returns pseudo-random numbers
with exponential distribution. Optional `seed`

can be used as a seed for pseudo-random number generator.

See also rand and rand_normal.

#### rand_normal#

`rand_normal(seed)`

is a transform function, which returns pseudo-random numbers
with normal distribution. Optional `seed`

can be used as a seed for pseudo-random number generator.

See also rand and rand_exponential.

#### range_avg#

`range_avg(q)`

is a transform function, which calculates the avg value across points per each time series returned by `q`

.

#### range_first#

`range_first(q)`

is a transform function, which returns the value for the first point per each time series returned by `q`

.

#### range_last#

`range_last(q)`

is a transform function, which returns the value for the last point per each time series returned by `q`

.

#### range_linear_regression#

`range_linear_regression(q)`

is a transform function, which calculates simple linear regression
over the selected time range per each time series returned by `q`

. This function is useful for capacity planning and predictions.

#### range_mad#

`range_mad(q)`

is a transform function, which calculates the median absolute deviation
across points per each time series returned by `q`

.

See also mad and mad_over_time.

#### range_max#

`range_max(q)`

is a transform function, which calculates the max value across points per each time series returned by `q`

.

#### range_median#

`range_median(q)`

is a transform function, which calculates the median value across points per each time series returned by `q`

.

#### range_min#

`range_min(q)`

is a transform function, which calculates the min value across points per each time series returned by `q`

.

#### range_normalize#

`range_normalize(q1, ...)`

is a transform function, which normalizes values for time series returned by `q1, ...`

into `[0 ... 1]`

range.
This function is useful for correlating time series with distinct value ranges.

See also share.

#### range_quantile#

`range_quantile(phi, q)`

is a transform function, which returns `phi`

-quantile across points per each time series returned by `q`

.
`phi`

must be in the range `[0...1]`

.

#### range_stddev#

`range_stddev(q)`

is a transform function, which calculates standard deviation
per each time series returned by `q`

on the selected time range.

#### range_stdvar#

`range_stdvar(q)`

is a transform function, which calculates standard variance
per each time series returned by `q`

on the selected time range.

#### range_sum#

`range_sum(q)`

is a transform function, which calculates the sum of points per each time series returned by `q`

.

#### range_trim_outliers#

`range_trim_outliers(k, q)`

is a transform function, which drops points located farther than `k*range_mad(q)`

from the `range_median(q)`

. E.g. it is equivalent to the following query: `q ifnot (abs(q - range_median(q)) > k*range_mad(q))`

.

See also range_trim_spikes and range_trim_zscore.

#### range_trim_spikes#

`range_trim_spikes(phi, q)`

is a transform function, which drops `phi`

percent of biggest spikes from time series returned by `q`

.
The `phi`

must be in the range `[0..1]`

, where `0`

means `0%`

and `1`

means `100%`

.

See also range_trim_outliers and range_trim_zscore.

#### range_trim_zscore#

`range_trim_zscore(z, q)`

is a transform function, which drops points located farther than `z*range_stddev(q)`

from the `range_avg(q)`

. E.g. it is equivalent to the following query: `q ifnot (abs(q - range_avg(q)) > z*range_avg(q))`

.

See also range_trim_outliers and range_trim_spikes.

#### range_zscore#

`range_zscore(q)`

is a transform function, which calculates z-score
for points returned by `q`

, e.g. it is equivalent to the following query: `(q - range_avg(q)) / range_stddev(q)`

.

#### remove_resets#

`remove_resets(q)`

is a transform function, which removes counter resets from time series returned by `q`

.

#### round#

`round(q, nearest)`

is a transform function, which rounds every point of every time series returned by `q`

to the `nearest`

multiple.
If `nearest`

is missing then the rounding is performed to the nearest integer.

This function is supported by PromQL.

#### ru#

`ru(free, max)`

is a transform function, which calculates resource utilization in the range `[0%...100%]`

for the given `free`

and `max`

resources.
For instance, `ru(node_memory_MemFree_bytes, node_memory_MemTotal_bytes)`

returns memory utilization over node_exporter metrics.

#### running_avg#

`running_avg(q)`

is a transform function, which calculates the running avg per each time series returned by `q`

.

#### running_max#

`running_max(q)`

is a transform function, which calculates the running max per each time series returned by `q`

.

#### running_min#

`running_min(q)`

is a transform function, which calculates the running min per each time series returned by `q`

.

#### running_sum#

`running_sum(q)`

is a transform function, which calculates the running sum per each time series returned by `q`

.

#### scalar#

`scalar(q)`

is a transform function, which returns `q`

if `q`

contains only a single time series. Otherwise, it returns nothing.

This function is supported by PromQL.

#### sgn#

`sgn(q)`

is a transform function, which returns `1`

if `v>0`

, `-1`

if `v<0`

and `0`

if `v==0`

for every point `v`

of every time series returned by `q`

.

This function is supported by PromQL.

#### sin#

`sin(q)`

is a transform function, which returns `sin(v)`

for every `v`

point of every time series returned by `q`

.

This function is supported by MetricsQL.

See also cos.

#### sinh#

`sinh(q)`

is a transform function, which returns hyperbolic sine
for every point of every time series returned by `q`

.

This function is supported by MetricsQL.

See also cosh.

#### tan#

`tan(q)`

is a transform function, which returns `tan(v)`

for every `v`

point of every time series returned by `q`

.

This function is supported by MetricsQL.

See also atan.

#### tanh#

`tanh(q)`

is a transform function, which returns hyperbolic tangent
for every point of every time series returned by `q`

.

This function is supported by MetricsQL.

See also atanh.

#### smooth_exponential#

`smooth_exponential(q, sf)`

is a transform function, which smooths points per each time series returned
by `q`

using exponential moving average with the given smooth factor `sf`

.

#### sort#

`sort(q)`

is a transform function, which sorts series in ascending order by the last point in every time series returned by `q`

.

This function is supported by PromQL.

See also sort_desc and sort_by_label.

#### sort_desc#

`sort_desc(q)`

is a transform function, which sorts series in descending order by the last point in every time series returned by `q`

.

This function is supported by PromQL.

See also sort and sort_by_label.

#### sqrt#

`sqrt(q)`

is a transform function, which calculates square root for every point of every time series returned by `q`

.

This function is supported by PromQL.

#### start#

`start()`

is a transform function, which returns unix timestamp in seconds for the first point.

It is known as `start`

query arg passed to /api/v1/query_range.

#### step#

`step()`

is a transform function, which returns the step in seconds (aka interval) between the returned points.
It is known as `step`

query arg passed to /api/v1/query_range.

#### time#

`time()`

is a transform function, which returns unix timestamp for every returned point.

This function is supported by PromQL.

See also timestamp, now, start and end.

#### timezone_offset#

`timezone_offset(tz)`

is a transform function, which returns offset in seconds for the given timezone `tz`

relative to UTC.
This can be useful when combining with datetime-related functions. For example, `day_of_week(time()+timezone_offset("America/Los_Angeles"))`

would return weekdays for `America/Los_Angeles`

time zone.

Special `Local`

time zone can be used for returning an offset for the time zone set on the host where VictoriaMetrics runs.

See the list of supported timezones.

#### ttf#

`ttf(free)`

is a transform function, which estimates the time in seconds needed to exhaust `free`

resources.
For instance, `ttf(node_filesystem_avail_byte)`

returns the time to storage space exhaustion. This function may be useful for capacity planning.

#### union#

`union(q1, ..., qN)`

is a transform function, which returns a union of time series returned from `q1`

, …, `qN`

.
The `union`

function name can be skipped - the following queries are equivalent: `union(q1, q2)`

and `(q1, q2)`

.

It is expected that each `q*`

query returns time series with unique sets of labels.
Otherwise, only the first time series out of series with identical set of labels is returned.
Use alias and label_set functions for giving unique labelsets per each `q*`

query:

#### vector#

`vector(q)`

is a transform function, which returns `q`

, e.g. it does nothing in MetricsQL.

This function is supported by PromQL.

#### year#

`year(q)`

is a transform function, which returns the year for every point of every time series returned by `q`

.
It is expected that `q`

returns unix timestamps.

This function is supported by PromQL.

### Label manipulation functions#

**Label manipulation functions** perform manipulations with labels on the selected rollup results.

Additional details:

- If label manipulation function is applied directly to a series_selector,
then the default_rollup() function is automatically applied before performing the label transformation.
For example,
`alias(temperature, "foo")`

is implicitly transformed to`alias(default_rollup(temperature), "foo")`

.

See also implicit query conversions.

The list of supported label manipulation functions:

#### alias#

`alias(q, "name")`

is label manipulation function, which sets the given `name`

to all the time series returned by `q`

.
For example, `alias(up, "foobar")`

would rename `up`

series to `foobar`

series.

#### drop_common_labels#

`drop_common_labels(q1, ...., qN)`

is label manipulation function, which drops common `label="value"`

pairs
among time series returned from `q1, ..., qN`

.

#### label_copy#

`label_copy(q, "src_label1", "dst_label1", ..., "src_labelN", "dst_labelN")`

is label manipulation function,
which copies label values from `src_label*`

to `dst_label*`

for all the time series returned by `q`

.
If `src_label`

is empty, then the corresponding `dst_label`

is left untouched.

#### label_del#

`label_del(q, "label1", ..., "labelN")`

is label manipulation function, which deletes the given `label*`

labels
from all the time series returned by `q`

.

#### label_graphite_group#

`label_graphite_group(q, groupNum1, ... groupNumN)`

is label manipulation function, which replaces metric names
returned from `q`

with the given Graphite group values concatenated via `.`

char.

For example, `label_graphite_group({__graphite__="foo*.bar.*"}, 0, 2)`

would substitute `foo<any_value>.bar.<other_value>`

metric names with `foo<any_value>.<other_value>`

.

This function is useful for aggregating Graphite metrics with aggregate functions. For example, the following query would return per-app memory usage:

```
sum by (__name__) (
label_graphite_group({__graphite__="app*.host*.memory_usage"}, 0)
)
```

#### label_join#

`label_join(q, "dst_label", "separator", "src_label1", ..., "src_labelN")`

is label manipulation function,
which joins `src_label*`

values with the given `separator`

and stores the result in `dst_label`

.
This is performed individually per each time series returned by `q`

.
For example, `label_join(up{instance="xxx",job="yyy"}, "foo", "-", "instance", "job")`

would store `xxx-yyy`

label value into `foo`

label.

This function is supported by PromQL.

#### label_keep#

`label_keep(q, "label1", ..., "labelN")`

is label manipulation function, which deletes all the labels
except of the listed `label*`

labels in all the time series returned by `q`

.

#### label_lowercase#

`label_lowercase(q, "label1", ..., "labelN")`

is label manipulation function, which lowercases values
for the given `label*`

labels in all the time series returned by `q`

.

#### label_map#

`label_map(q, "label", "src_value1", "dst_value1", ..., "src_valueN", "dst_valueN")`

is label manipulation function,
which maps `label`

values from `src_*`

to `dst*`

for all the time series returned by `q`

.

#### label_match#

`label_match(q, "label", "regexp")`

is label manipulation function,
which drops time series from `q`

with `label`

not matching the given `regexp`

.
This function can be useful after rollup-like functions, which may return multiple time series for every input series.

See also label_mismatch and labels_equal.

#### label_mismatch#

`label_mismatch(q, "label", "regexp")`

is label manipulation function,
which drops time series from `q`

with `label`

matching the given `regexp`

.
This function can be useful after rollup-like functions, which may return multiple time series for every input series.

See also label_match and labels_equal.

#### label_move#

`label_move(q, "src_label1", "dst_label1", ..., "src_labelN", "dst_labelN")`

is label manipulation function,
which moves label values from `src_label*`

to `dst_label*`

for all the time series returned by `q`

.
If `src_label`

is empty, then the corresponding `dst_label`

is left untouched.

#### label_replace#

`label_replace(q, "dst_label", "replacement", "src_label", "regex")`

is label manipulation function,
which applies the given `regex`

to `src_label`

and stores the `replacement`

in `dst_label`

if the given `regex`

matches `src_label`

.
The `replacement`

may contain references to regex captures such as `$1`

, `$2`

, etc.
These references are substituted by the corresponding regex captures.
For example, `label_replace(up{job="node-exporter"}, "foo", "bar-$1", "job", "node-(.+)")`

would store `bar-exporter`

label value into `foo`

label.

This function is supported by PromQL.

#### label_set#

`label_set(q, "label1", "value1", ..., "labelN", "valueN")`

is label manipulation function,
which sets `{label1="value1", ..., labelN="valueN"}`

labels to all the time series returned by `q`

.

#### label_transform#

`label_transform(q, "label", "regexp", "replacement")`

is label manipulation function,
which substitutes all the `regexp`

occurrences by the given `replacement`

in the given `label`

.

#### label_uppercase#

`label_uppercase(q, "label1", ..., "labelN")`

is label manipulation function,
which uppercases values for the given `label*`

labels in all the time series returned by `q`

.

See also label_lowercase.

#### label_value#

`label_value(q, "label")`

is label manipulation function, which returns numeric values
for the given `label`

for every time series returned by `q`

.

For example, if `label_value(foo, "bar")`

is applied to `foo{bar="1.234"}`

, then it will return a time series
`foo{bar="1.234"}`

with `1.234`

value. Function will return no data for non-numeric label values.

#### labels_equal#

`labels_equal(q, "label1", "label2", ...)`

is label manipulation function, which returns `q`

series with identical values for the listed labels
“label1”, “label2”, etc.

See also label_match and label_mismatch.

#### sort_by_label#

`sort_by_label(q, "label1", ... "labelN")`

is label manipulation function, which sorts series in ascending order by the given set of labels.
For example, `sort_by_label(foo, "bar")`

would sort `foo`

series by values of the label `bar`

in these series.

See also sort_by_label_desc and sort_by_label_numeric.

#### sort_by_label_desc#

`sort_by_label_desc(q, "label1", ... "labelN")`

is label manipulation function, which sorts series in descending order by the given set of labels.
For example, `sort_by_label(foo, "bar")`

would sort `foo`

series by values of the label `bar`

in these series.

See also sort_by_label and sort_by_label_numeric_desc.

#### sort_by_label_numeric#

`sort_by_label_numeric(q, "label1", ... "labelN")`

is label manipulation function, which sorts series in ascending order by the given set of labels
using numeric sort.
For example, if `foo`

series have `bar`

label with values `1`

, `101`

, `15`

and `2`

, then `sort_by_label_numeric(foo, "bar")`

would return series
in the following order of `bar`

label values: `1`

, `2`

, `15`

and `101`

.

See also sort_by_label_numeric_desc and sort_by_label.

#### sort_by_label_numeric_desc#

`sort_by_label_numeric_desc(q, "label1", ... "labelN")`

is label manipulation function, which sorts series in descending order
by the given set of labels using numeric sort.
For example, if `foo`

series have `bar`

label with values `1`

, `101`

, `15`

and `2`

, then `sort_by_label_numeric(foo, "bar")`

would return series in the following order of `bar`

label values: `101`

, `15`

, `2`

and `1`

.

See also sort_by_label_numeric and sort_by_label_desc.

### Aggregate functions#

**Aggregate functions** calculate aggregates over groups of rollup results.

Additional details:

- By default, a single group is used for aggregation. Multiple independent groups can be set up by specifying grouping labels
in
`by`

and`without`

modifiers. For example,`count(up) by (job)`

would group rollup results by`job`

label value and calculate the count aggregate function independently per each group, while`count(up) without (instance)`

would group rollup results by all the labels except`instance`

before calculating count aggregate function independently per each group. Multiple labels can be put in`by`

and`without`

modifiers. - If the aggregate function is applied directly to a series_selector,
then the default_rollup() function is automatically applied before calculating the aggregate.
For example,
`count(up)`

is implicitly transformed to`count(default_rollup(up))`

. - Aggregate functions accept arbitrary number of args. For example,
`avg(q1, q2, q3)`

would return the average values for every point across time series returned by`q1`

,`q2`

and`q3`

. - Aggregate functions support optional
`limit N`

suffix, which can be used for limiting the number of output groups. For example,`sum(x) by (y) limit 3`

limits the number of groups for the aggregation to 3. All the other groups are ignored.

See also implicit query conversions.

The list of supported aggregate functions:

#### any#

`any(q) by (group_labels)`

is aggregate function, which returns a single series per `group_labels`

out of time series returned by `q`

.

See also group.

#### avg#

`avg(q) by (group_labels)`

is aggregate function, which returns the average value per `group_labels`

for time series returned by `q`

.
The aggregate is calculated individually per each group of points with the same timestamp.

This function is supported by PromQL.

#### bottomk#

`bottomk(k, q)`

is aggregate function, which returns up to `k`

points with the smallest values across all the time series returned by `q`

.
The aggregate is calculated individually per each group of points with the same timestamp.

This function is supported by PromQL.

See also topk, bottomk_min and #bottomk_last.

#### bottomk_avg#

`bottomk_avg(k, q, "other_label=other_value")`

is aggregate function, which returns up to `k`

time series from `q`

with the smallest averages.
If an optional `other_label=other_value`

arg is set, then the sum of the remaining time series is returned with the given label.
For example, `bottomk_avg(3, sum(process_resident_memory_bytes) by (job), "job=other")`

would return up to 3 time series
with the smallest averages plus a time series with `{job="other"}`

label with the sum of the remaining series if any.

See also topk_avg.

#### bottomk_last#

`bottomk_last(k, q, "other_label=other_value")`

is aggregate function, which returns up to `k`

time series from `q`

with the smallest last values.
If an optional `other_label=other_value`

arg is set, then the sum of the remaining time series is returned with the given label.
For example, `bottomk_max(3, sum(process_resident_memory_bytes) by (job), "job=other")`

would return up to 3 time series
with the smallest maximums plus a time series with `{job="other"}`

label with the sum of the remaining series if any.

See also topk_last.

#### bottomk_max#

`bottomk_max(k, q, "other_label=other_value")`

is aggregate function, which returns up to `k`

time series from `q`

with the smallest maximums.
If an optional `other_label=other_value`

arg is set, then the sum of the remaining time series is returned with the given label.
For example, `bottomk_max(3, sum(process_resident_memory_bytes) by (job), "job=other")`

would return up to 3 time series
with the smallest maximums plus a time series with `{job="other"}`

label with the sum of the remaining series if any.

See also topk_max.

#### bottomk_median#

`bottomk_median(k, q, "other_label=other_value")`

is aggregate function, which returns up to `k`

time series from `q`

with the smallest medians.
If an optional`other_label=other_value`

arg is set, then the sum of the remaining time series is returned with the given label.
For example, `bottomk_median(3, sum(process_resident_memory_bytes) by (job), "job=other")`

would return up to 3 time series
with the smallest medians plus a time series with `{job="other"}`

label with the sum of the remaining series if any.

See also topk_median.

#### bottomk_min#

`bottomk_min(k, q, "other_label=other_value")`

is aggregate function, which returns up to `k`

time series from `q`

with the smallest minimums.
If an optional `other_label=other_value`

arg is set, then the sum of the remaining time series is returned with the given label.
For example, `bottomk_min(3, sum(process_resident_memory_bytes) by (job), "job=other")`

would return up to 3 time series
with the smallest minimums plus a time series with `{job="other"}`

label with the sum of the remaining series if any.

See also topk_min.

#### count#

`count(q) by (group_labels)`

is aggregate function, which returns the number of non-empty points per `group_labels`

for time series returned by `q`

. The aggregate is calculated individually per each group of points with the same timestamp.

This function is supported by PromQL.

#### count_values#

`count_values("label", q)`

is aggregate function, which counts the number of points with the same value
and stores the counts in a time series with an additional `label`

, which contains each initial value.
The aggregate is calculated individually per each group of points with the same timestamp.

This function is supported by PromQL.

See also count_values_over_time and label_match.

#### distinct#

`distinct(q)`

is aggregate function, which calculates the number of unique values per each group of points with the same timestamp.

See also distinct_over_time.

#### geomean#

`geomean(q)`

is aggregate function, which calculates geometric mean per each group of points with the same timestamp.

#### group#

`group(q) by (group_labels)`

is aggregate function, which returns `1`

per each `group_labels`

for time series returned by `q`

.

This function is supported by PromQL. See also any.

#### histogram#

`histogram(q)`

is aggregate function, which calculates
VictoriaMetrics histogram
per each group of points with the same timestamp. Useful for visualizing big number of time series via a heatmap.
See this article for more details.

See also histogram_over_time and histogram_quantile.

#### limitk#

`limitk(k, q) by (group_labels)`

is aggregate function, which returns up to `k`

time series per each `group_labels`

out of time series returned by `q`

. The returned set of time series remain the same across calls.

See also limit_offset.

#### mad#

`mad(q) by (group_labels)`

is aggregate function, which returns the Median absolute deviation
per each `group_labels`

for all the time series returned by `q`

. The aggregate is calculated individually per each group of points with the same timestamp.

See also range_mad, mad_over_time, outliers_mad and stddev.

#### max#

`max(q) by (group_labels)`

is aggregate function, which returns the maximum value per each `group_labels`

for all the time series returned by `q`

. The aggregate is calculated individually per each group of points with the same timestamp.

This function is supported by PromQL.

#### median#

`median(q) by (group_labels)`

is aggregate function, which returns the median value per each `group_labels`

for all the time series returned by `q`

. The aggregate is calculated individually per each group of points with the same timestamp.

#### min#

`min(q) by (group_labels)`

is aggregate function, which returns the minimum value per each `group_labels`

for all the time series returned by `q`

. The aggregate is calculated individually per each group of points with the same timestamp.

This function is supported by PromQL.

#### mode#

`mode(q) by (group_labels)`

is aggregate function, which returns mode
per each `group_labels`

for all the time series returned by `q`

. The aggregate is calculated individually per each group of points with the same timestamp.

#### outliers_iqr#

`outliers_iqr(q)`

is aggregate function, which returns time series from `q`

with at least a single point
outside e.g. Interquartile range outlier bounds `[q25-1.5*iqr .. q75+1.5*iqr]`

comparing to other time series at the given point, where:

`iqr`

is an Interquartile range calculated independently per each point on the graph across`q`

series.`q25`

and`q75`

are 25th and 75th percentiles calculated independently per each point on the graph across`q`

series.

The `outliers_iqr()`

is useful for detecting anomalous series in the group of series. For example, `outliers_iqr(temperature) by (country)`

returns
per-country series with anomalous outlier values comparing to the rest of per-country series.

See also outliers_mad, outliersk and outlier_iqr_over_time.

#### outliers_mad#

`outliers_mad(tolerance, q)`

is aggregate function, which returns time series from `q`

with at least
a single point outside Median absolute deviation (aka MAD) multiplied by `tolerance`

.
E.g. it returns time series with at least a single point below `median(q) - mad(q)`

or a single point above `median(q) + mad(q)`

.

See also outliers_iqr, outliersk and mad.

#### outliersk#

`outliersk(k, q)`

is aggregate function, which returns up to `k`

time series with the biggest standard deviation (aka outliers)
out of time series returned by `q`

.

See also outliers_iqr and outliers_mad.

#### quantile#

`quantile(phi, q) by (group_labels)`

is aggregate function, which calculates `phi`

-quantile per each `group_labels`

for all the time series returned by `q`

. `phi`

must be in the range `[0...1]`

.
The aggregate is calculated individually per each group of points with the same timestamp.

This function is supported by PromQL.

See also quantiles and histogram_quantile.

#### quantiles#

`quantiles("phiLabel", phi1, ..., phiN, q)`

is aggregate function, which calculates `phi*`

-quantiles for all the time series
returned by `q`

and return them in time series with `{phiLabel="phi*"}`

label. `phi*`

must be in the range `[0...1]`

.
The aggregate is calculated individually per each group of points with the same timestamp.

See also quantile.

#### share#

`share(q) by (group_labels)`

is aggregate function, which returns shares in the range `[0..1]`

for every non-negative points returned by `q`

per each timestamp, so the sum of shares per each `group_labels`

equals 1.

This function is useful for normalizing histogram bucket shares
into `[0..1]`

range:

```
share(
sum(
rate(http_request_duration_seconds_bucket[5m])
) by (le, vmrange)
)
```

See also range_normalize.

#### stddev#

`stddev(q) by (group_labels)`

is aggregate function, which calculates standard deviation per each `group_labels`

for all the time series returned by `q`

. The aggregate is calculated individually per each group of points with the same timestamp.

This function is supported by PromQL.

#### stdvar#

`stdvar(q) by (group_labels)`

is aggregate function, which calculates standard variance per each `group_labels`

for all the time series returned by `q`

. The aggregate is calculated individually per each group of points with the same timestamp.

This function is supported by PromQL.

#### sum#

`sum(q) by (group_labels)`

is aggregate function, which returns the sum per each `group_labels`

for all the time series returned by `q`

. The aggregate is calculated individually per each group of points with the same timestamp.

This function is supported by PromQL.

#### sum2#

`sum2(q) by (group_labels)`

is aggregate function, which calculates the sum of squares per each `group_labels`

for all the time series returned by `q`

. The aggregate is calculated individually per each group of points with the same timestamp.

#### topk#

`topk(k, q)`

is aggregate function, which returns up to `k`

points with the biggest values across all the time series returned by `q`

.
The aggregate is calculated individually per each group of points with the same timestamp.

This function is supported by PromQL.

See also bottomk, topk_max and topk_last.

#### topk_avg#

`topk_avg(k, q, "other_label=other_value")`

is aggregate function, which returns up to `k`

time series from `q`

with the biggest averages.
If an optional `other_label=other_value`

arg is set, then the sum of the remaining time series is returned with the given label.
For example, `topk_avg(3, sum(process_resident_memory_bytes) by (job), "job=other")`

would return up to 3 time series with the biggest averages
plus a time series with `{job="other"}`

label with the sum of the remaining series if any.

See also bottomk_avg.

#### topk_last#

`topk_last(k, q, "other_label=other_value")`

is aggregate function, which returns up to `k`

time series from `q`

with the biggest last values.
If an optional `other_label=other_value`

arg is set, then the sum of the remaining time series is returned with the given label.
For example, `topk_max(3, sum(process_resident_memory_bytes) by (job), "job=other")`

would return up to 3 time series with the biggest maximums
plus a time series with `{job="other"}`

label with the sum of the remaining series if any.

See also bottomk_last.

#### topk_max#

`topk_max(k, q, "other_label=other_value")`

is aggregate function, which returns up to `k`

time series from `q`

with the biggest maximums.
If an optional `other_label=other_value`

arg is set, then the sum of the remaining time series is returned with the given label.
For example, `topk_max(3, sum(process_resident_memory_bytes) by (job), "job=other")`

would return up to 3 time series with the biggest maximums
plus a time series with `{job="other"}`

label with the sum of the remaining series if any.

See also bottomk_max.

#### topk_median#

`topk_median(k, q, "other_label=other_value")`

is aggregate function, which returns up to `k`

time series from `q`

with the biggest medians.
If an optional `other_label=other_value`

arg is set, then the sum of the remaining time series is returned with the given label.
For example, `topk_median(3, sum(process_resident_memory_bytes) by (job), "job=other")`

would return up to 3 time series with the biggest medians
plus a time series with `{job="other"}`

label with the sum of the remaining series if any.

See also bottomk_median.

#### topk_min#

`topk_min(k, q, "other_label=other_value")`

is aggregate function, which returns up to `k`

time series from `q`

with the biggest minimums.
If an optional `other_label=other_value`

arg is set, then the sum of the remaining time series is returned with the given label.
For example, `topk_min(3, sum(process_resident_memory_bytes) by (job), "job=other")`

would return up to 3 time series with the biggest minimums
plus a time series with `{job="other"}`

label with the sum of the remaining series if any.

See also bottomk_min.

#### zscore#

`zscore(q) by (group_labels)`

is aggregate function, which returns z-score values
per each `group_labels`

for all the time series returned by `q`

. The aggregate is calculated individually per each group of points with the same timestamp.
This function is useful for detecting anomalies in the group of related time series.

See also zscore_over_time, range_trim_zscore and outliers_iqr.

## Subqueries#

MetricsQL supports and extends PromQL subqueries. See this article for details.
Any rollup function for something other than series selector form a subquery.
Nested rollup functions can be implicit thanks to the implicit query conversions.
For example, `delta(sum(m))`

is implicitly converted to `delta(sum(default_rollup(m))[1i:1i])`

, so it becomes a subquery,
since it contains default_rollup nested into delta.
This behavior can be disabled or logged via `-search.disableImplicitConversion`

and `-search.logImplicitConversion`

command-line flags
starting from `v1.101.0`

release.

VictoriaMetrics performs subqueries in the following way:

- It calculates the inner rollup function using the
`step`

value from the outer rollup function. For example, for expression`max_over_time(rate(http_requests_total[5m])[1h:30s])`

the inner function`rate(http_requests_total[5m])`

is calculated with`step=30s`

. The resulting data points are aligned by the`step`

. - It calculates the outer rollup function over the results of the inner rollup function using the
`step`

value passed by Grafana to /api/v1/query_range.

## Implicit query conversions#

VictoriaMetrics performs the following implicit conversions for incoming queries before starting the calculations:

- If lookbehind window in square brackets is missing inside rollup function, then it is automatically set to the following value:
- To
`step`

value passed to /api/v1/query_range or /api/v1/query for all the rollup functions except of default_rollup and rate. This value is known as`$__interval`

in Grafana or`1i`

in MetricsQL. For example,`avg_over_time(temperature)`

is automatically transformed to`avg_over_time(temperature[1i])`

. - To the
`max(step, scrape_interval)`

, where`scrape_interval`

is the interval between raw samples for default_rollup and rate functions. This allows avoiding unexpected gaps on the graph when`step`

is smaller than`scrape_interval`

.

- To
- All the series selectors,
which aren’t wrapped into rollup functions, are automatically wrapped into default_rollup function.
Examples:
`foo`

is transformed to`default_rollup(foo)`

`foo + bar`

is transformed to`default_rollup(foo) + default_rollup(bar)`

`count(up)`

is transformed to`count(default_rollup(up))`

, because count isn’t a rollup function - it is aggregate function`abs(temperature)`

is transformed to`abs(default_rollup(temperature))`

, because abs isn’t a rollup function - it is transform function

- If
`step`

in square brackets is missing inside subquery, then`1i`

step is automatically added there. For example,`avg_over_time(rate(http_requests_total[5m])[1h])`

is automatically converted to`avg_over_time(rate(http_requests_total[5m])[1h:1i])`

. - If something other than series selector
is passed to rollup function, then a subquery with
`1i`

lookbehind window and`1i`

step is automatically formed. For example,`rate(sum(up))`

is automatically converted to`rate((sum(default_rollup(up)))[1i:1i])`

. This behavior can be disabled or logged via`-search.disableImplicitConversion`

and`-search.logImplicitConversion`

command-line flags starting from`v1.101.0`

release.