# MetricsQL

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.

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

- MetricsQL takes into account the previous point before the window in square brackets for range functions such as rate and increase. This allows returning the exact results users expect for
`increase(metric[$__interval])`

queries instead of incomplete results Prometheus returns for such queries. - MetricsQL doesn't extrapolate range function 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 with
`step`

values smaller than scrape interval. 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 meaining 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 diffferences 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 an editable Grafana dashboard or at your own VictoriaMetrics instance.

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

syntax. This is equivalent to`{__name__=~"foo[.][^.]*[.]bar"}`

, but usually works faster and is easier to use when migrating from Graphite. VictoriaMetrics also can be used as Graphite datasource in Grafana. See these docs for details. - Lookbehind window in square brackets may be omitted. VictoriaMetrics automatically selects the lookbehind window depending on the current step used for building the graph (e.g.
`step`

query arg passed to /api/v1/query_range). For instance, the following query is valid in VictoriaMetrics:`rate(node_network_receive_bytes_total)`

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

when used in Grafana. - 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`

. - 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 anywere in the query. For instance,
`sum(foo) offset 24h`

. - offset may be negative. For example,
`q offset -1h`

. - 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`

. - 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 metric labels may contain escaped chars. For instance,
`foo\-bar{baz\=aa="b"}`

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

containing label`baz=aa`

with value`b`

. Additionally,`\xXX`

escape sequence is supported, where`XX`

is hexadecimal representation of escaped 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`

.`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`

.- String literals may be concatenated. This is useful with
`WITH`

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

. `WITH`

templates. This feature simplifies writing and managing complex queries. Go to WITH templates playground and try it.

## 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 MetricsQL automatically sets the lookbehind window to the interval between points on the graph (aka
`step`

query arg at /api/v1/query_range,`$__interval`

value from Grafana or`1i`

duration in MetricsQL). For example,`rate(http_requests_total)`

is equivalent to`rate(http_requests_total[$__interval])`

in Grafana. It is also equivalent to`rate(http_requests_total[1i])`

. - 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"}[1i])`

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.

See also implicit query conversions.

#### absent_over_time

`absent_over_time(series_selector[d])`

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])`

calculates all the listed `rollup_func*`

for raw samples on the given lookbehind window `d`

. The calculations are perfomed 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])`

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. Useful for tracking height gains in GPS tracking. Metric names are stripped from the resulting rollups. See also descent_over_time.

#### avg_over_time

`avg_over_time(series_selector[d])`

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 supported by PromQL. See also median_over_time.

#### changes

`changes(series_selector[d])`

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. Metric names are stripped from the resulting rollups. This function is supported by PromQL.

#### count_eq_over_time

`count_eq_over_time(series_selector[d], eq)`

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. Metric names are stripped from the resulting rollups. See also count_over_time.

#### count_gt_over_time

`count_gt_over_time(series_selector[d], gt)`

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. Metric names are stripped from the resulting rollups. See also count_over_time.

#### count_le_over_time

`count_le_over_time(series_selector[d], le)`

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. Metric names are stripped from the resulting rollups. See also count_over_time.

#### count_ne_over_time

`count_ne_over_time(series_selector[d], ne)`

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. Metric names are stripped from the resulting rollups. See also count_over_time.

#### count_over_time

`count_over_time(series_selector[d])`

calculates the number of raw samples on the given lookbehind window `d`

per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups. 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.

#### decreases_over_time

`decreases_over_time(series_selector[d])`

calculates the number of raw sample value decreases over the given lookbehind window `d`

per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups. See also increases_over_time.

#### default_rollup

`default_rollup(series_selector[d])`

returns the last raw sample value on the given lookbehind window `d`

per each time series returned from the given series_selector.

#### delta

`delta(series_selector[d])`

calculates the difference between the first and the last point over the given lookbehind window `d`

per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups. This function is supported by PromQL. See also increase.

#### deriv

`deriv(series_selector[d])`

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. Metric names are stripped from the resulting rollups. This function is supported by PromQL. See also deriv_fast and ideriv.

#### deriv_fast

`deriv_fast(series_selector[d])`

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. Metric names are stripped from the resulting rollups. See also deriv and ideriv.

#### descent_over_time

`descent_over_time(series_selector[d])`

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. Useful for tracking height loss in GPS tracking. Metric names are stripped from the resulting rollups. See also ascent_over_time.

#### distinct_over_time

`distinct_over_time(series_selector[d])`

returns the number of distinct raw sample values on the given lookbehind window `d`

per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups.

#### first_over_time

`first_over_time(series_selector[d])`

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])`

calculates geometric mean over raw samples on the given lookbehind window `d`

per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups.

#### histogram_over_time

`histogram_over_time(series_selector[d])`

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))`

.

#### hoeffding_bound_lower

`hoeffding_bound_lower(phi, series_selector[d])`

calculates lower Hoeffding bound for the given `phi`

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

. See also hoeffding_bound_upper.

#### hoeffding_bound_upper

`hoeffding_bound_upper(phi, series_selector[d])`

calculates upper Hoeffding bound for the given `phi`

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

. See also hoeffding_bound_lower.

#### holt_winters

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

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]`

. It is expected that the series_selector returns time series of gauge type. This function is supported by PromQL.

#### idelta

`idelta(series_selector[d])`

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. Metric names are stripped from the resulting rollups. This function is supported by PromQL.

#### ideriv

`ideriv(series_selector[d])`

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. Metric names are stripped from the resulting rollups. See also deriv.

#### increase

`increase(series_selector[d])`

calculates the increase over the given lookbehind window `d`

per each time series returned from the given series_selector. It is expected that the `series_selector`

returns time series of counter type. Metric names are stripped from the resulting rollups. This function is supported by PromQL. See also increase_pure and delta.

#### increase_pure

`increase_pure(series_selector[d])`

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.

#### increases_over_time

`increases_over_time(series_selector[d])`

calculates the number of raw sample value increases over the given lookbehind window `d`

per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups. See also decreases_over_time.

#### integrate

`integrate(series_selector[d])`

calculates the integral over raw samples on the given lookbehind window `d`

per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups.

#### irate

`irate(series_selector[d])`

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. It is expected that the `series_selector`

returns time series of counter type. Metric names are stripped from the resulting rollups. This function is supported by PromQL. See also rate.

#### lag

`lag(series_selector[d])`

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. Metric names are stripped from the resulting rollups. See also lifetime.

#### last_over_time

`last_over_time(series_selector[d])`

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])`

calculates the lifetime in seconds per each time series returned from the given series_selector. The returned lifetime is limited by the given lookbehind window `d`

. Metric names are stripped from the resulting rollups. See also lag.

#### max_over_time

`max_over_time(series_selector[d])`

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 supported by PromQL. See also tmax_over_time.

#### median_over_time

`median_over_time(series_selector[d])`

calculates median value over raw samples on the given lookbehind window `d`

per each time series returned from the given series_selector. See also avg_over_time.

#### min_over_time

`min_over_time(series_selector[d])`

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 supported by PromQL. See also tmin_over_time.

#### mode_over_time

`mode_over_time(series_selector[d])`

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.

#### predict_linear

`predict_linear(series_selector[d], t)`

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.

#### present_over_time

`present_over_time(series_selector[d])`

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

. Otherwise an empty result is returned. Metric names are stripped from the resulting rollups. This function is supported by PromQL.

#### quantile_over_time

`quantile_over_time(phi, series_selector[d])`

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 supported by PromQL. See also quantiles_over_time.

#### quantiles_over_time

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

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]`

. See also quantile_over_time.

#### range_over_time

`range_over_time(series_selector[d])`

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])`

. Metric names are stripped from the resulting rollups.

#### rate

`rate(series_selector[d])`

calculates the average per-second increase rate over the given lookbehind window `d`

per each time series returned from the given series_selector. It is expected that the `series_selector`

returns time series of counter type. Metric names are stripped from the resulting rollups. This function is supported by PromQL.

#### rate_over_sum

`rate_over_sum(series_selector[d])`

calculates per-second rate over the sum of raw samples on the given lookbehind window `d`

. The calculations are performed indiviually per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups.

#### resets

`resets(series_selector[d])`

returns the number of counter resets over the given lookbehind window `d`

per each time series returned from the given series_selector. It is expected that the `series_selector`

returns time series of counter type. Metric names are stripped from the resulting rollups. This function is supported by PromQL.

#### rollup

`rollup(series_selector[d])`

calculates `min`

, `max`

and `avg`

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

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

#### rollup_candlestick

`rollup_candlestick(series_selector[d])`

calculates `open`

, `high`

, `low`

and `close`

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

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

#### rollup_delta

`rollup_delta(series_selector[d])`

calculates differences between adjancent raw samples on the given lookbehind window `d`

and returns `min`

, `max`

and `avg`

values for the calculated differences. The calculations are performed individually per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups. See also rollup_increase.

#### rollup_deriv

`rollup_deriv(series_selector[d])`

calculates per-second derivatives for adjancent raw samples on the given lookbehind window `d`

and returns `min`

, `max`

and `avg`

values for the calculated per-second derivatives. The calculations are performed individually per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups.

#### rollup_increase

`rollup_increase(series_selector[d])`

calculates increases for adjancent raw samples on the given lookbehind window `d`

and returns `min`

, `max`

and `avg`

values for the calculated increases. The calculations are performed individually per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups. See also rollup_delta.

#### rollup_rate

`rollup_rate(series_selector[d])`

calculates per-second change rates for adjancent raw samples on the given lookbehind window `d`

and returns `min`

, `max`

and `avg`

values for the calculated per-second change rates. The calculations are perfomed individually per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups.

#### rollup_scrape_interval

`rollup_scrape_interval(series_selector[d])`

calculates the interval in seconds between adjancent raw samples on the given lookbehind window `d`

and returns `min`

, `max`

and `avg`

values for the calculated interval. The calculations are perfomed individually per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups. See also scrape_interval.

#### scrape_interval

`scrape_interval(series_selector[d])`

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. Metric names are stripped from the resulting rollups. See also rollup_scrape_interval.

#### share_gt_over_time

`share_gt_over_time(series_selector[d], gt)`

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. Metric names are stripped from the resulting rollups. Useful for calculating SLI and SLO. Example: `share_gt_over_time(up[24h], 0)`

- returns service availability for the last 24 hours. See also share_le_over_time.

#### share_le_over_time

`share_le_over_time(series_selector[d], le)`

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. Metric names are stripped from the resulting rollups. 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. See also share_gt_over_time.

#### stddev_over_time

`stddev_over_time(series_selector[d])`

calculates standard deviation over raw samples on the given lookbehind window `d`

per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups. This function is supported by PromQL. See also stdvar_over_time.

#### stdvar_over_time

`stdvar_over_time(series_selector[d])`

calculates stadnard variance over raw samples on the given lookbheind window `d`

per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups. This function is supported by PromQL. See also stddev_over_time.

#### sum_over_time

`sum_over_time(series_selector[d])`

calculates the sum of raw sample values on the given lookbehind window `d`

per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups. This function is supported by PromQL.

#### sum2_over_time

`sum2_over_time(series_selector[d])`

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. Metric names are stripped from the resulting rollups.

#### timestamp

`timestamp(series_selector[d])`

returns the timestamp in seconds for the last raw sample on the given lookbehind window `d`

per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups. This function is supported by PromQL.

#### tfirst_over_time

`tfirst_over_time(series_selector[d])`

returns the timestamp in seconds for the first raw sample on the given lookbehind window `d`

per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups. See also first_over_time.

#### tlast_over_time

`tlast_over_time(series_selector[d])`

returns the timestamp in seconds for the last raw sample on the given lookbehind window `d`

per each time series returned from the given series_selector. Metric names are stripped from the resulting rollups. See also last_over_time.

#### tmax_over_time

`tmax_over_time(series_selector[d])`

returns the timestamp in seconds 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. Metric names are stripped from the resulting rollups. See also max_over_time.

#### tmin_over_time

`tmin_over_time(series_selector[d])`

returns the timestamp in seconds 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. Metric names are stripped from the resulting rollups. See also min_over_time.

#### zscore_over_time

`zscore_over_time(series_selector[d])`

calculates 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. Metric names are stripped from the resulting rollups.

### 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[1i]))`

.

See also implicit query conversions.

#### abs

`abs(q)`

calculates the absolute value for every point of every time series returned by `q`

. This function is supported by PromQL.

#### absent

`absent(q)`

returns 1 if `q`

has no points. Otherwise returns an empty result. This function is supported by PromQL.

#### acos

`acos(q)`

returns inverse cosine for every point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL. See also asin and cos.

#### acosh

`acosh(q)`

returns inverse hyperbolic cosine for every point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL. See also sinh.

#### asin

`asin(q)`

returns inverse sine for every point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL. See also acos and sin.

#### asinh

`asinh(q)`

returns inverse hyperbolic sine for every point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL. See also sinh.

#### atan

`atan(q)`

returns inverse tangent for every point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL. See also tan.

#### atanh

`atanh(q)`

returns inverse hyperbolic tangent for every point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL. See also tanh.

#### bitmap_and

`bitmap_and(q, mask)`

- calculates bitwise `v & mask`

for every `v`

point of every time series returned from `q`

. Metric names are stripped from the resulting series.

#### bitmap_or

`bitmap_or(q, mask)`

calculates bitwise `v | mask`

for every `v`

point of every time series returned from `q`

. Metric names are stripped from the resulting series.

#### bitmap_xor

`bitmap_xor(q, mask)`

calculates bitwise `v ^ mask`

for every `v`

point of every time series returned from `q`

. Metric names are stripped from the resulting series.

#### buckets_limit

`buckets_limit(limit, buckets)`

limits the number of histogram buckets to the given `limit`

. See also prometheus_buckets and histogram_quantile.

#### ceil

`ceil(q)`

rounds every point for every time series returned by `q`

to the upper nearest integer. This function is supported by PromQL. See also floor and round.

#### clamp

`clamp(q, min, max)`

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)`

clamps every point for every time series returned by `q`

with the given `max`

value. This function is supported by PromQL. See also clamp and clamp_min.

#### clamp_min

`clamp_min(q, min)`

clamps every pount for every time series returned by `q`

with the given `min`

value. This function is supported by PromQL. See also clamp and clamp_max.

#### cos

`cos(q)`

returns `cos(v)`

for every `v`

point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL. See also sin.

#### cosh

`cosh(q)`

returns hyperbolic cosine for every point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL. This function is supported by PromQL. See also acosh.

#### day_of_month

`day_of_month(q)`

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]`

. Metric names are stripped from the resulting series. This function is supported by PromQL.

#### day_of_week

`day_of_week(q)`

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. Metric names are stripped from the resulting series. This function is supported by PromQL.

#### days_in_month

`days_in_month(q)`

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]`

. Metric names are stripped from the resulting series. This function is supported by PromQL.

#### deg

`deg(q)`

converts Radians to degrees for every point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL. See also rad.

#### end

`end()`

returns the unix timestamp in seconds for the last point. See also start. It is known as `end`

query arg passed to /api/v1/query_range.

#### exp

`exp(q)`

calculates the `e^v`

for every point `v`

of every time series returned by `q`

. Metric names are stripped from the resulting series. See also ln. This function is supported by PromQL.

#### floor

`floor(q)`

rounds every point for every time series returned by `q`

to the lower nearest integer. See also ceil and round. This function is supported by PromQL.

#### histogram_avg

`histogram_avg(buckets)`

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 exmple, `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)`

calculates `phi`

-quantile 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. It accepts optional third arg - `boundsLabel`

. In this case it returns `lower`

and `upper`

bounds for the estimated percentile. See this issue for details. This function is supported by PromQL (except of the `boundLabel`

arg). See also histogram_quantiles and histogram_share.

#### histogram_quantiles

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

calculates the given `phi*`

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

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

. 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)`

calculates the share (in the range `[0...1]`

) for `buckets`

that fall below `le`

. Useful for calculating SLI and SLO. This is inverse to histogram_quantile.

#### histogram_stddev

`histogram_stddev(buckets)`

calculates standard deviation for the given `buckets`

.

#### histogram_stdvar

`histogram_stdvar(buckets)`

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)`

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]`

. Metric names are stripped from the resulting series. This function is supported by PromQL.

#### interpolate

`interpolate(q)`

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)`

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)`

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.

#### ln

`ln(q)`

calculates `ln(v)`

for every point `v`

of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL. See also exp and log2.

#### log2

`log2(q)`

calculates `log2(v)`

for every point `v`

of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL. See also log10 and ln.

#### log10

`log10(q)`

calculates `log10(v)`

for every point `v`

of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL. See also log2 and ln.

#### minute

`minute(q)`

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]`

. Metric names are stripped from the resulting series. This function is supported by PromQL.

#### month

`month(q)`

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. Metric names are stripped from the resulting series. This function is supported by PromQL.

#### pi

`pi()`

returns Pi number. This function is supported by PromQL.

#### rad

`rad(q)`

converts degrees to Radians for every point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL. See also deg.

#### prometheus_buckets

`prometheus_buckets(buckets)`

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)`

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)`

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)`

returns pesudo-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)`

calculates the avg value across points per each time series returned by `q`

.

#### range_first

`range_first(q)`

returns the value for the first point per each time series returned by `q`

.

#### range_last

`range_last(q)`

returns the value for the last point per each time series returned by `q`

.

#### range_max

`range_max(q)`

calculates the max value across points per each time series returned by `q`

.

#### range_median

`range_median(q)`

calculates the median value across points per each time series returned by `q`

.

#### range_min

`range_min(q)`

calculates the min value across points per each time series returned by `q`

.

#### range_quantile

`range_quantile(phi, q)`

returns `phi`

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

. `phi`

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

.

#### range_sum

`range_sum(q)`

calculates the sum of points per each time series returned by `q`

. Metric names are stripped from the resulting series.

#### remove_resets

`remove_resets(q)`

removes counter resets from time series returned by `q`

.

#### round

`round(q, nearest)`

round 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. See also floor and ceil.

#### ru

`ru(free, max)`

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)`

calculates the running avg per each time series returned by `q`

.

#### running_max

`running_max(q)`

calculates the running max per each time series returned by `q`

.

#### running_min

`running_min(q)`

calculates the running min per each time series returned by `q`

.

#### running_sum

`running_sum(q)`

calculates the running sum per each time series returned by `q`

. Metric names are stripped from the resulting series.

#### scalar

`scalar(q)`

returns `q`

if `q`

contains only a single time series. Otherwise it returns nothing. This function is supported by PromQL.

#### sgn

`sgn(q)`

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`

. Metric names are stripped from the resulting series. This function is supported by PromQL.

#### sin

`sin(q)`

returns `sin(v)`

for every `v`

point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by MetricsQL. See also cos.

#### sinh

`sinh(q)`

returns hyperbolic sine for every point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by MetricsQL. See also cosh.

#### tan

`tan(q)`

returns `tan(v)`

for every `v`

point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by MetricsQL. See also atan.

#### tanh

`tanh(q)`

returns hyperbolic tangent for every point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by MetricsQL. See also atanh.

#### smooth_exponential

`smooth_exponential(q, sf)`

smooths points per each time series returned by `q`

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

.

#### sort

`sort(q)`

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.

#### sort_by_label

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

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.

#### sort_by_label_desc

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

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.

#### sort_desc

`sort_desc(q)`

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.

#### sqrt

`sqrt(q)`

calculates square root for every point of every time series returned by `q`

. Metric names are stripped from the resulting series. This function is supported by PromQL.

#### start

`start()`

returns unix timestamp in seconds for the first point. See also end. It is known as `start`

query arg passed to /api/v1/query_range.

#### step

`step()`

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()`

returns unix timestamp for every returned point. This function is supported by PromQL.

#### timezone_offset

`timezone_offset(tz)`

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)`

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)`

returns a union of time series returned from `q1`

, …, `qN`

. The `union`

function name can be skipped - the following queries are quivalent: `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)`

returns `q`

, e.g. it does nothing in MetricsQL. This function is supported by PromQL.

#### year

`year(q)`

returns the year for every point of every time series returned by `q`

. It is expected that `q`

returns unix timestamps. Metric names are stripped from the resulting series. This function is supported by PromQL.

### Label manipulation functions

**Label manipulation functions** perform manipulations with lables 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[1i]), "foo")`

.

See also implicit query conversions.

#### alias

`alias(q, "name")`

sets the given `name`

to all the time series returned by `q`

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

would rename `up`

series to `foobar`

series.

#### label_copy

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

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")`

deletes the given `label*`

labels from all the time series returned by `q`

.

#### label_join

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

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")`

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")`

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")`

maps `label`

values from `src_*`

to `dst*`

for all the time seires returned by `q`

.

#### label_match

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

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.

#### label_mismatch

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

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.

#### label_move

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

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")`

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-node-exporter`

label value into `foo`

label. This function is supported by PromQL.

#### label_set

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

sets `{label1="value1", ..., labelN="valueN"}`

labels to all the time series returned by `q`

.

#### label_transform

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

substitutes all the `regexp`

occurences by the given `replacement`

in the given `label`

.

#### label_uppercase

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

uppercases values for the given `label*`

labels in all the time series returned by `q`

.

#### label_value

`label_value(q, "label")`

returns number 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.

### 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 cacluating the aggregate. For example,
`count(up)`

is implicitly transformed to`count(default_rollup(up[1i]))`

. - 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.

#### any

`any(q) by (group_labels)`

returns a single series per `group_labels`

out of time series returned by `q`

. See also group.

#### avg

`avg(q) by (group_labels)`

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)`

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_avg

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

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")`

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")`

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")`

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")`

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)`

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)`

counts the number of points with the same value and stores the counts in a time series with an additional `label`

, wich contains each initial value. The aggregate is calculated individually per each group of points with the same timestamp. This function is supported by PromQL.

#### distinct

`distinct(q)`

calculates the number of unique values per each group of points with the same timestamp.

#### geomean

`geomean(q)`

calculates geometric mean per each group of points with the same timestamp.

#### group

`group(q) by (group_labels)`

returns `1`

per each `group_labels`

for time series returned by `q`

. This function is supported by PromQL. See also any.

#### histogram

`histogram(q)`

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.

#### limitk

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

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.

#### mad

`mad(q) by (group_labels)`

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 outliers_mad and stddev.

#### max

`max(q) by (group_labels)`

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)`

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)`

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)`

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_mad

`outliers_mad(tolerance, q)`

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 outliersk and mad.

#### outliersk

`outliersk(k, q)`

returns up to `k`

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

. See also outliers_mad.

#### quantile

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

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.

#### quantiles

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

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.

#### stddev

`stddev(q) by (group_labels)`

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)`

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)`

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)`

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)`

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_avg

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

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")`

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 amaximums 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")`

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 amaximums 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")`

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")`

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)`

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. Useful for detecting anomalies in the group of related time series.

## Subqueries

MetricsQL supports and extends PromQL subqueries. See this article for details. Any nested rollup functions 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:1i])`

, so it becomes a subquery, since it contains default_rollup nested into delta.

VictoriaMetrics performs subqueries in the following way:

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

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

is executed, then the`rate(http_requests_total[5m])`

is calculated with the`step`

equal to`30s`

. The resulting data points are algined 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
`[1i]`

is automatically added there. The`[1i]`

means one`step`

value, which is passed to /api/v1/query_range. It is also known as`$__interval`

in Grafana. For example,`rate(http_requests_count)`

is automatically transformed to`rate(http_requests_count[1i])`

. - 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[1i])`

`foo + bar`

is transformed to`default_rollup(foo[1i]) + default_rollup(bar[1i])`

`count(up)`

is transformed to`count(default_rollup(up[1i]))`

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

is transformed to`abs(default_rollup(temperature[1i]))`

, 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:1i])`

.