--- sort: 13 --- # MetricsQL [VictoriaMetrics](https://github.com/VictoriaMetrics/VictoriaMetrics) implements MetricsQL - query language inspired by [PromQL](https://prometheus.io/docs/prometheus/latest/querying/basics/). 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](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) between these two languages. [Standalone MetricsQL package](https://godoc.org/github.com/VictoriaMetrics/metricsql) can be used for parsing MetricsQL in external apps. If you are unfamiliar with PromQL, then it is suggested reading [this tutorial for beginners](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085). 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](#rate) and [increase](#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](https://github.com/prometheus/prometheus/issues/3746). See technical details about VictoriaMetrics and Prometheus calculations for [rate](#rate) and [increase](#increase) [in this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1215#issuecomment-850305711). * MetricsQL returns the expected non-empty responses for [rate](#rate) with `step` values smaller than scrape interval. This addresses [this issue from Grafana](https://github.com/grafana/grafana/issues/11451). See also [this blog post](https://www.percona.com/blog/2020/02/28/better-prometheus-rate-function-with-victoriametrics/). * 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#expression-language-data-types) 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)](#min_over_time) or [round(foo)](#round) leaves `foo` metric name in the result. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/674) for details. Read more about the diffferences between PromQL and MetricsQL in [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e). Other PromQL functionality should work the same in MetricsQL. [File an issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues) if you notice discrepancies between PromQL and MetricsQL results other than mentioned above. ## MetricsQL features MetricsQL implements [PromQL](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085) and provides additional functionality mentioned below, which is aimed towards solving practical cases. Feel free [filing a feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues) if you think MetricsQL misses certain useful functionality. This functionality can be evaluated at [an editable Grafana dashboard](https://play-grafana.victoriametrics.com/d/4ome8yJmz/node-exporter-on-victoriametrics-demo) or at your own [VictoriaMetrics instance](https://docs.victoriametrics.com/#how-to-start-victoriametrics). - Graphite-compatible filters can be passed via `{__graphite__="foo.*.bar"}` syntax. See [these docs](https://docs.victoriametrics.com/#selecting-graphite-metrics). VictoriaMetrics also can be used as Graphite datasource in Grafana. See [these docs](https://docs.victoriametrics.com/#graphite-api-usage) for details. See also [label_graphite_group](#label_graphite_group) function, which can be used for extracting the given groups from Graphite metric name. - 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](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries)). 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](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#offset-modifier), lookbehind window in square brackets and `step` value for [subquery](#subqueries) 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#offset-modifier) may be put anywere in the query. For instance, `sum(foo) offset 24h`. - [offset](https://prometheus.io/docs/prometheus/latest/querying/basics/#offset-modifier) may be negative. For example, `q offset -1h`. - Lookbehind window in square brackets and [offset](https://prometheus.io/docs/prometheus/latest/querying/basics/#offset-modifier) 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](#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](https://github.com/prometheus/prometheus/issues/5706). - `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](https://play.victoriametrics.com/promql/expand-with-exprs) and try it. ## MetricsQL functions If you are unfamiliar with PromQL, then please read [this tutorial](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085) at first. MetricsQL provides the following functions: * [Rollup functions](#rollup-functions) * [Transform functions](#transform-functions) * [Label manipulation functions](#label-manipulation-functions) * [Aggregate functions](#aggregate-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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries). * If the given [series selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) 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](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries), `$__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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) in MetricsQL must be wrapped into a rollup function. Otherwise it is automatically wrapped into [default_rollup](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) is passed to rollup function, then the inner arg is automatically converted to a [subquery](#subqueries). See also [implicit query conversions](#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](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). `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](#min_over_time), [max_over_time](#max_over_time) and [rate](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Useful for tracking height gains in GPS tracking. Metric names are stripped from the resulting rollups. See also [descent_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). This function is supported by PromQL. See also [median_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [count_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [count_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [count_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [count_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. This function is supported by PromQL. See also [count_le_over_time](#count_le_over_time), [count_gt_over_time](#count_gt_over_time), [count_eq_over_time](#count_eq_over_time) and [count_ne_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [increases_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). #### 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. This function is supported by PromQL. See also [increase](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](#deriv_fast) and [ideriv](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [deriv](#deriv) and [ideriv](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Useful for tracking height loss in GPS tracking. Metric names are stripped from the resulting rollups. See also [ascent_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. #### duration_over_time `duration_over_time(series_selector[d], max_interval)` returns the duration in seconds when time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) 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. See also [lifetime](#lifetime) and [lag](#lag). #### 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). See also [last_over_time](#last_over_time) and [tfirst_over_time](#tfirst_over_time). #### geomean_over_time `geomean_over_time(series_selector[d])` calculates [geometric mean](https://en.wikipedia.org/wiki/Geometric_mean) over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. #### histogram_over_time `histogram_over_time(series_selector[d])` calculates [VictoriaMetrics histogram](https://godoc.org/github.com/VictoriaMetrics/metrics#Histogram) over raw samples on the given lookbehind window `d`. It is calculated individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). The resulting histograms are useful to pass to [histogram_quantile](#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](https://en.wikipedia.org/wiki/Hoeffding%27s_inequality) for the given `phi` in the range `[0...1]`. See also [hoeffding_bound_upper](#hoeffding_bound_upper). #### hoeffding_bound_upper `hoeffding_bound_upper(phi, series_selector[d])` calculates upper [Hoeffding bound](https://en.wikipedia.org/wiki/Hoeffding%27s_inequality) for the given `phi` in the range `[0...1]`. See also [hoeffding_bound_lower](#hoeffding_bound_lower). #### holt_winters `holt_winters(series_selector[d], sf, tf)` calculates Holt-Winters value (aka [double exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) returns time series of [gauge type](https://prometheus.io/docs/concepts/metric_types/#gauge). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [deriv](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). It is expected that the `series_selector` returns time series of [counter type](https://prometheus.io/docs/concepts/metric_types/#counter). Metric names are stripped from the resulting rollups. This function is supported by PromQL. See also [increase_pure](#increase_pure) and [delta](#delta). #### increase_pure `increase_pure(series_selector[d])` works the same as [increase](#increase) except of the following corner case - it assumes that [counters](https://prometheus.io/docs/concepts/metric_types/#counter) always start from 0, while [increase](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [decreases_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). It is expected that the `series_selector` returns time series of [counter type](https://prometheus.io/docs/concepts/metric_types/#counter). Metric names are stripped from the resulting rollups. This function is supported by PromQL. See also [rate](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [lifetime](#lifetime) and [duration_over_time](#duration_over_time). #### 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). This function is supported by PromQL. See also [first_over_time](#first_over_time) and [tlast_over_time](#tlast_over_time). #### lifetime `lifetime(series_selector[d])` 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [duration_over_time](#duration_over_time) and [lag](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). This function is supported by PromQL. See also [tmax_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). See also [avg_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). This function is supported by PromQL. See also [tmin_over_time](#tmin_over_time). #### mode_over_time `mode_over_time(series_selector[d])` calculates [mode](https://en.wikipedia.org/wiki/Mode_(statistics)) for raw samples on the given lookbehind window `d`. It is calculated individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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 `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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). It is expected that the `series_selector` returns time series of [counter type](https://prometheus.io/docs/concepts/metric_types/#counter). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. #### resets `resets(series_selector[d])` returns the number of [counter](https://prometheus.io/docs/concepts/metric_types/#counter) resets over the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). It is expected that the `series_selector` returns time series of [counter type](https://prometheus.io/docs/concepts/metric_types/#counter). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). #### 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [rollup_increase](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [rollup_delta](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [scrape_interval](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [rollup_scrape_interval](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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 `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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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 `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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. This function is supported by PromQL. See also [stddev_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [first_over_time](#first_over_time). #### tlast_over_time `tlast_over_time(series_selector[d])` is an alias for [timestamp](#timestamp). #### 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [max_over_time](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. See also [min_over_time](#min_over_time). #### zscore_over_time `zscore_over_time(series_selector[d])` calculates returns [z-score](https://en.wikipedia.org/wiki/Standard_score) for raw samples on the given lookbehind window `d`. It is calculated independently per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors), then the [default_rollup()](#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](#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](https://en.wikipedia.org/wiki/Inverse_trigonometric_functions) 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](#asin) and [cos](#cos). #### acosh `acosh(q)` returns [inverse hyperbolic cosine](https://en.wikipedia.org/wiki/Inverse_hyperbolic_functions#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](#cosh). #### asin `asin(q)` returns [inverse sine](https://en.wikipedia.org/wiki/Inverse_trigonometric_functions) 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](#acos) and [sin](#sin). #### asinh `asinh(q)` returns [inverse hyperbolic sine](https://en.wikipedia.org/wiki/Inverse_hyperbolic_functions#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](#sinh). #### atan `atan(q)` returns [inverse tangent](https://en.wikipedia.org/wiki/Inverse_trigonometric_functions) 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](#tan). #### atanh `atanh(q)` returns [inverse hyperbolic tangent](https://en.wikipedia.org/wiki/Inverse_hyperbolic_functions#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](#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](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350) to the given `limit`. See also [prometheus_buckets](#prometheus_buckets) and [histogram_quantile](#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](#floor) and [round](#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](#clamp_min) and [clamp_max](#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](#clamp) and [clamp_min](#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](#clamp) and [clamp_max](#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](#sin). #### cosh `cosh(q)` returns [hyperbolic cosine](https://en.wikipedia.org/wiki/Hyperbolic_functions) 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](#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](https://en.wikipedia.org/wiki/Radian#Conversions) 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](#rad). #### end `end()` returns the unix timestamp in seconds for the last point. See also [start](#start). It is known as `end` query arg passed to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries). #### 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](#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](#ceil) and [round](#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](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350). `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](https://github.com/prometheus/prometheus/issues/5706). This function is supported by PromQL (except of the `boundLabel` arg). See also [histogram_quantiles](#histogram_quantiles) and [histogram_share](#histogram_share). #### histogram_quantiles `histogram_quantiles("phiLabel", phi1, ..., phiN, buckets)` calculates the given `phi*`-quantiles over the given [histogram buckets](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350). `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_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_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](#keep_last_value) and [keep_next_value](#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](#keep_next_value) and [interpolate](#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](#keep_last_value) and [interpolate](#interpolate). #### limit_offset `limit_offset(limit, offset, q)` 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](#limitk). #### 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](#exp) and [log2](#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](#log10) and [ln](#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](#log2) and [ln](#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. #### now `now()` returns the current timestamp as a floating-point value in seconds. See also [time](#time). #### pi `pi()` returns [Pi number](https://en.wikipedia.org/wiki/Pi). This function is supported by PromQL. #### rad `rad(q)` converts [degrees to Radians](https://en.wikipedia.org/wiki/Radian#Conversions) 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](#deg). #### prometheus_buckets `prometheus_buckets(buckets)` converts [VictoriaMetrics histogram buckets](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350) with `vmrange` labels to Prometheus histogram buckets with `le` labels. This may be useful for building heatmaps in Grafana. See also [histogram_quantile](#histogram_quantile) and [buckets_limit](#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](#rand_normal) and [rand_exponential](#rand_exponential). #### rand_exponential `rand_exponential(seed)` returns pseudo-random numbers with [exponential distribution](https://en.wikipedia.org/wiki/Exponential_distribution). Optional `seed` can be used as a seed for pseudo-random number generator. See also [rand](#rand) and [rand_normal](#rand_normal). #### rand_normal `rand_normal(seed)` returns pesudo-random numbers with [normal distribution](https://en.wikipedia.org/wiki/Normal_distribution). Optional `seed` can be used as a seed for pseudo-random number generator. See also [rand](#rand) and [rand_exponential](#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](#floor) and [ceil](#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](https://github.com/prometheus/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](#cos). #### sinh `sinh(q)` returns [hyperbolic sine](https://en.wikipedia.org/wiki/Hyperbolic_functions) 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](#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](#atan). #### tanh `tanh(q)` returns [hyperbolic tangent](https://en.wikipedia.org/wiki/Hyperbolic_functions) 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](#atanh). #### smooth_exponential `smooth_exponential(q, sf)` smooths points per each time series returned by `q` using [exponential moving average](https://en.wikipedia.org/wiki/Moving_average#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_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 `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_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](#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](#end). It is known as `start` query arg passed to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries). #### 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](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries). #### 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](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones). #### 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](#alias) and [label_set](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors), then the [default_rollup()](#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](#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_graphite_group `label_graphite_group(q, groupNum1, ... groupNumN)` 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.bar.` metric names with `foo.`. This function is useful for aggregating Graphite metrics with [aggregate functions](#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")` 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](#rollup)-like functions, which may return multiple time series for every input series. See also [label_mismatch](#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](#rollup)-like functions, which may return multiple time series for every input series. See also [label_match](#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](#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](#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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors), then the [default_rollup()](#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](#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](#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](#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](#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](#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](#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](#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](#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](#any). #### histogram `histogram(q)` calculates [VictoriaMetrics histogram](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350) per each group of points with the same timestamp. Useful for visualizing big number of time series via a heatmap. See [this article](https://medium.com/@valyala/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350) 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. See also [limit_offset](#limit_offset). #### mad `mad(q) by (group_labels)` returns the [Median absolute deviation](https://en.wikipedia.org/wiki/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](#outliers_mad) and [stddev](#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](https://en.wikipedia.org/wiki/Mode_(statistics)) 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](https://en.wikipedia.org/wiki/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](#outliersk) and [mad](#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](#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 `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](#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](#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](#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](#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](#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](#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](#bottomk_min). #### zscore `zscore(q) by (group_labels)` returns [z-score](https://en.wikipedia.org/wiki/Standard_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](https://valyala.medium.com/prometheus-subqueries-in-victoriametrics-9b1492b720b3) for details. Any nested [rollup functions](#rollup-functions) form a subquery. Nested rollup functions can be implicit thanks to the [implicit query conversions](#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](#default_rollup) nested into [delta](#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](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries). ## 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](#rollup-functions), then `[1i]` is automatically added there. The `[1i]` means one `step` value, which is passed to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries). 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors), which aren't wrapped into [rollup functions](#rollup-functions), are automatically wrapped into [default_rollup](#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](#count) isn't a [rollup function](#rollup-functions) - it is [aggregate function](#aggregate-functions) * `abs(temperature)` is transformed to `abs(default_rollup(temperature[1i]))`, because [abs](#abs) isn't a [rollup function](#rollup-functions) - it is [transform function](#transform-functions) * If `step` in square brackets is missing inside [subquery](#subqueries), 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](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) is passed to [rollup function](#rollup-functions), then a [subquery](#subqueries) 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])`.