--- sort: 23 weight: 23 title: MetricsQL menu: docs: parent: 'victoriametrics' weight: 23 aliases: - /ExtendedPromQL.html - /MetricsQL.html --- # 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) and introduction into [basic querying via MetricsQL](https://docs.victoriametrics.com/keyConcepts.html#metricsql). The following functionality is implemented differently in MetricsQL compared to PromQL. This improves user experience: * MetricsQL takes into account the last [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples) before the lookbehind window in square brackets for [increase](#increase) and [rate](#rate) functions. This allows returning the exact results users expect for `increase(metric[$__interval])` queries instead of incomplete results Prometheus returns for such queries. Prometheus misses the increase between the last sample before the lookbehind window and the first sample inside the lookbehind window. * MetricsQL doesn't extrapolate [rate](#rate) and [increase](#increase) function results, so it always returns the expected results. For example, it returns integer results from `increase()` over slow-changing integer counter. Prometheus in this case returns unexpected fractional results, which may significantly differ from the expected results. This addresses [this issue from Prometheus](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) function when Grafana or [vmui](https://docs.victoriametrics.com/#vmui) passes `step` values smaller than the interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query). 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 differences 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 [VictoriaMetrics playground](https://play.victoriametrics.com/select/accounting/1/6a716b0f-38bc-4856-90ce-448fd713e3fe/prometheus/graph/) or at your own [VictoriaMetrics instance](https://docs.victoriametrics.com/#how-to-start-victoriametrics). The list of MetricsQL features on top of PromQL: * Graphite-compatible filters can be passed via `{__graphite__="foo.*.bar"}` syntax. See [these docs](https://docs.victoriametrics.com/#selecting-graphite-metrics). VictoriaMetrics 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 for [rollup functions](#rollup-functions) may be omitted. VictoriaMetrics automatically selects the lookbehind window depending on the `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) and the real interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) (aka `scrape_interval`). For instance, the following query is valid in VictoriaMetrics: `rate(node_network_receive_bytes_total)`. It is roughly equivalent to `rate(node_network_receive_bytes_total[$__interval])` when used in Grafana. The difference is documented in [rate() docs](#rate). * Numeric values can contain `_` delimiters for better readability. For example, `1_234_567_890` can be used in queries instead of `1234567890`. * [Series selectors](https://docs.victoriametrics.com/keyConcepts.html#filtering) accept multiple `or` filters. For example, `{env="prod",job="a" or env="dev",job="b"}` selects series with `{env="prod",job="a"}` or `{env="dev",job="b"}` labels. See [these docs](https://docs.victoriametrics.com/keyConcepts.html#filtering-by-multiple-or-filters) for details. * Support for `group_left(*)` and `group_right(*)` for copying all the labels from time series on the `one` side of [many-to-one operations](https://prometheus.io/docs/prometheus/latest/querying/operators/#many-to-one-and-one-to-many-vector-matches). The copied label names may clash with the existing label names, so MetricsQL provides an ability to add prefix to the copied metric names via `group_left(*) prefix "..."` syntax. For example, the following query copies all the `namespace`-related labels from `kube_namespace_labels` to `kube_pod_info` series, while adding `ns_` prefix to the copied labels: `kube_pod_info * on(namespace) group_left(*) prefix "ns_" kube_namespace_labels`. Labels from the `on()` list aren't copied. * [Aggregate functions](#aggregate-functions) accept arbitrary number of args. For example, `avg(q1, q2, q3)` would return the average values for every point across time series returned by `q1`, `q2` and `q3`. * [@ modifier](https://prometheus.io/docs/prometheus/latest/querying/basics/#modifier) can be put anywhere in the query. For example, `sum(foo) @ end()` calculates `sum(foo)` at the `end` timestamp of the selected time range `[start ... end]`. * Arbitrary subexpression can be used as [@ modifier](https://prometheus.io/docs/prometheus/latest/querying/basics/#modifier). For example, `foo @ (end() - 1h)` calculates `foo` at the `end - 1 hour` timestamp on the selected time range `[start ... end]`. * [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 anywhere in the query. For instance, `sum(foo) offset 24h`. * 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`. * Numeric values can have `K`, `Ki`, `M`, `Mi`, `G`, `Gi`, `T` and `Ti` suffixes. For example, `8K` is equivalent to `8000`, while `1.2Mi` is equivalent to `1.2*1024*1024`. * Trailing commas on all the lists are allowed - label filters, function args and with expressions. For instance, the following queries are valid: `m{foo="bar",}`, `f(a, b,)`, `WITH (x=y,) x`. This simplifies maintenance of multi-line queries. * Metric names and label names may contain any unicode letter. For example `температура{город="Київ"}` is a value MetricsQL expression. * Metric names and labels names may contain escaped chars. For example, `foo\-bar{baz\=aa="b"}` is valid expression. It returns time series with name `foo-bar` containing label `baz=aa` with value `b`. Additionally, the following escape sequences are supported: - `\xXX`, where `XX` is hexadecimal representation of the escaped ascii char. - `\uXXXX`, where `XXXX` is a hexadecimal representation of the escaped unicode char. * Aggregate functions support optional `limit N` suffix in order to limit the number of output series. For example, `sum(x) by (y) limit 3` limits the number of output time series after the aggregation to 3. All the other time series are dropped. * [histogram_quantile](#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`. See also [drop_empty_series](#drop_empty_series). * `if` binary operator. `q1 if q2` removes values from `q1` for missing values from `q2`. * `ifnot` binary operator. `q1 ifnot q2` removes values from `q1` for existing values from `q2`. * `WITH` templates. This feature simplifies writing and managing complex queries. Go to [WITH templates playground](https://play.victoriametrics.com/select/accounting/1/6a716b0f-38bc-4856-90ce-448fd713e3fe/expand-with-exprs) and try it. * String literals may be concatenated. This is useful with `WITH` templates: `WITH (commonPrefix="long_metric_prefix_") {__name__=commonPrefix+"suffix1"} / {__name__=commonPrefix+"suffix2"}`. * `keep_metric_names` modifier can be applied to all the [rollup functions](#rollup-functions), [transform functions](#transform-functions) and [binary operators](https://prometheus.io/docs/prometheus/latest/querying/operators/#binary-operators). This modifier prevents from dropping metric names in function results. See [these docs](#keep_metric_names). ## keep_metric_names By default, metric names are dropped after applying functions or [binary operators](https://prometheus.io/docs/prometheus/latest/querying/operators/#binary-operators), since they may change the meaning of the original time series. This may result in `duplicate time series` error when the function is applied to multiple time series with different names. This error can be fixed by applying `keep_metric_names` modifier to the function or binary operator. For example: - `rate({__name__=~"foo|bar"}) keep_metric_names` leaves `foo` and `bar` metric names in the returned time series. - `({__name__=~"foo|bar"} / 10) keep_metric_names` leaves `foo` and `bar` metric names in the returned time series. ## MetricsQL functions If you are unfamiliar with PromQL, then please read [this tutorial](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://docs.victoriametrics.com/keyConcepts.html#filtering). 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://docs.victoriametrics.com/keyConcepts.html#range-query). * If the given [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) returns multiple time series, then rollups are calculated individually per each returned series. * If lookbehind window in square brackets is missing, then it is automatically set to the following value: - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query) for all the [rollup functions](#rollup-functions) except of [default_rollup](#default_rollup) and [rate](#rate). This value is known as `$__interval` in Grafana or `1i` in MetricsQL. For example, `avg_over_time(temperature)` is automatically transformed to `avg_over_time(temperature[1i])`. - To the `max(step, scrape_interval)`, where `scrape_interval` is the interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) for [default_rollup](#default_rollup) and [rate](#rate) functions. This allows avoiding unexpected gaps on the graph when `step` is smaller than `scrape_interval`. * Every [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) 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"})` before performing the calculations. * If something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) is passed to rollup function, then the inner arg is automatically converted to a [subquery](#subqueries). * All the rollup functions accept optional `keep_metric_names` modifier. If it is set, then the function keeps metric names in results. See [these docs](#keep_metric_names). See also [implicit query conversions](#implicit-query-conversions). The list of supported rollup functions: #### absent_over_time `absent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns 1 if the given lookbehind window `d` doesn't contain raw samples. Otherwise, it returns an empty result. This function is supported by PromQL. See also [present_over_time](#present_over_time). #### aggr_over_time `aggr_over_time(("rollup_func1", "rollup_func2", ...), series_selector[d])` is a [rollup function](#rollup-functions), which calculates all the listed `rollup_func*` for raw samples on the given lookbehind window `d`. The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). `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])` is a [rollup function](#rollup-functions), which calculates ascent of raw sample values on the given lookbehind window `d`. The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is useful for tracking height gains in GPS tracking. Metric names are stripped from the resulting rollups. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [descent_over_time](#descent_over_time). #### avg_over_time `avg_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average value over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is supported by PromQL. See also [median_over_time](#median_over_time). #### changes `changes(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times the raw samples changed on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Unlike `changes()` in Prometheus it takes into account the change from the last sample before the given lookbehind window `d`. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [changes_prometheus](#changes_prometheus). #### changes_prometheus `changes_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times the raw samples changed on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). It doesn't take into account the change from the last sample before the given lookbehind window `d` in the same way as Prometheus does. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [changes](#changes). #### count_eq_over_time `count_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which calculates the number of raw samples on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_over_time](#count_over_time), [share_eq_over_time](#share_eq_over_time) and [count_values_over_time](#count_values_over_time). #### count_gt_over_time `count_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which calculates the number of raw samples on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_over_time](#count_over_time) and [share_gt_over_time](#share_gt_over_time). #### count_le_over_time `count_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which calculates the number of raw samples on the given lookbehind window `d`, which don't exceed `le`. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_over_time](#count_over_time) and [share_le_over_time](#share_le_over_time). #### count_ne_over_time `count_ne_over_time(series_selector[d], ne)` is a [rollup function](#rollup-functions), which calculates the number of raw samples on the given lookbehind window `d`, which aren't equal to `ne`. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_over_time](#count_over_time). #### count_over_time `count_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. 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). #### count_values_over_time `count_values_over_time("label", series_selector[d])` is a [rollup function](#rollup-functions), which counts the number of raw samples with the same value over the given lookbehind window and stores the counts in a time series with an additional `label`, which contains each initial value. The results are calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_eq_over_time](#count_eq_over_time), [count_values](#count_values) and [distinct_over_time](#distinct_over_time) and [label_match](#label_match). #### decreases_over_time `decreases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of raw sample value decreases over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [increases_over_time](#increases_over_time). #### default_rollup `default_rollup(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). If the lookbehind window is skipped in square brackets, then it is automatically calculated as `max(step, scrape_interval)`, where `step` is the query arg value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query), while `scrape_interval` is the interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) for the selected time series. This allows avoiding unexpected gaps on the graph when `step` is smaller than the `scrape_interval`. #### delta `delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between the last sample before the given lookbehind window `d` and the last sample at the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). The behaviour of `delta()` function in MetricsQL is slightly different to the behaviour of `delta()` function in Prometheus. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [increase](#increase) and [delta_prometheus](#delta_prometheus). #### delta_prometheus `delta_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between the first and the last samples at the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). The behaviour of `delta_prometheus()` is close to the behaviour of `delta()` function in Prometheus. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [delta](#delta). #### deriv `deriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivative over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). The derivative is calculated using linear regression. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [deriv_fast](#deriv_fast) and [ideriv](#ideriv). #### deriv_fast `deriv_fast(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivative using the first and the last raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [deriv](#deriv) and [ideriv](#ideriv). #### descent_over_time `descent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates descent of raw sample values on the given lookbehind window `d`. The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is useful for tracking height loss in GPS tracking. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [ascent_over_time](#ascent_over_time). #### distinct_over_time `distinct_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which 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://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_values_over_time](#count_values_over_time). #### duration_over_time `duration_over_time(series_selector[d], max_interval)` is a [rollup function](#rollup-functions), which returns the duration in seconds when time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) 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. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [lifetime](#lifetime) and [lag](#lag). #### first_over_time `first_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the first raw sample value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). See also [last_over_time](#last_over_time) and [tfirst_over_time](#tfirst_over_time). #### geomean_over_time `geomean_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which 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://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. #### histogram_over_time `histogram_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which 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://docs.victoriametrics.com/keyConcepts.html#filtering). The resulting histograms are useful to pass to [histogram_quantile](#histogram_quantile) for calculating quantiles over multiple [gauges](https://docs.victoriametrics.com/keyConcepts.html#gauge). 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])` is a [rollup function](#rollup-functions), which 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])` is a [rollup function](#rollup-functions), which 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)` is a [rollup function](#rollup-functions), which 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://docs.victoriametrics.com/keyConcepts.html#filtering) returns time series of [gauge type](https://docs.victoriametrics.com/keyConcepts.html#gauge). This function is supported by PromQL. See also [range_linear_regression](#range_linear_regression). #### idelta `idelta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between the last two raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [delta](#delta). #### ideriv `ideriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the per-second derivative based on the last two raw samples over the given lookbehind window `d`. The derivative is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [deriv](#deriv). #### increase `increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter). Unlike Prometheus, it takes into account the last sample before the given lookbehind window `d` when calculating the result. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [increase_pure](#increase_pure), [increase_prometheus](#increase_prometheus) and [delta](#delta). #### increase_prometheus `increase_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter). It doesn't take into account the last sample before the given lookbehind window `d` when calculating the result in the same way as Prometheus does. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [increase_pure](#increase_pure) and [increase](#increase). #### increase_pure `increase_pure(series_selector[d])` is a [rollup function](#rollup-functions), which works the same as [increase](#increase) except of the following corner case - it assumes that [counters](https://docs.victoriametrics.com/keyConcepts.html#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])` is a [rollup function](#rollup-functions), which calculates the number of raw sample value increases over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [decreases_over_time](#decreases_over_time). #### integrate `integrate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the integral over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. #### irate `irate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the "instant" per-second increase rate over the last two raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [rate](#rate) and [rollup_rate](#rollup_rate). #### lag `lag(series_selector[d])` is a [rollup function](#rollup-functions), which returns the duration in seconds between the last sample on the given lookbehind window `d` and the timestamp of the current point. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [lifetime](#lifetime) and [duration_over_time](#duration_over_time). #### last_over_time `last_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). 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])` is a [rollup function](#rollup-functions), which returns the duration in seconds between the last and the first sample on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [duration_over_time](#duration_over_time) and [lag](#lag). #### mad_over_time `mad_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation) over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). See also [mad](#mad), [range_mad](#range_mad) and [outlier_iqr_over_time](#outlier_iqr_over_time). #### max_over_time `max_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the maximum value over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is supported by PromQL. See also [tmax_over_time](#tmax_over_time). #### median_over_time `median_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates median value over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). See also [avg_over_time](#avg_over_time). #### min_over_time `min_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the minimum value over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is supported by PromQL. See also [tmin_over_time](#tmin_over_time). #### mode_over_time `mode_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which 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://docs.victoriametrics.com/keyConcepts.html#filtering). It is expected that raw sample values are discrete. #### outlier_iqr_over_time `outlier_iqr_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last sample on the given lookbehind window `d` if its value is either smaller than the `q25-1.5*iqr` or bigger than `q75+1.5*iqr` where: - `iqr` is an [Interquartile range](https://en.wikipedia.org/wiki/Interquartile_range) over raw samples on the lookbehind window `d` - `q25` and `q75` are 25th and 75th [percentiles](https://en.wikipedia.org/wiki/Percentile) over raw samples on the lookbehind window `d`. The `outlier_iqr_over_time()` is useful for detecting anomalies in gauge values based on the previous history of values. For example, `outlier_iqr_over_time(memory_usage_bytes[1h])` triggers when `memory_usage_bytes` suddenly goes outside the usual value range for the last 24 hours. See also [outliers_iqr](#outliers_iqr). #### predict_linear `predict_linear(series_selector[d], t)` is a [rollup function](#rollup-functions), which calculates the value `t` seconds in the future using linear interpolation over raw samples on the given lookbehind window `d`. The predicted value is calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is supported by PromQL. See also [range_linear_regression](#range_linear_regression). #### present_over_time `present_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which 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. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. #### quantile_over_time `quantile_over_time(phi, series_selector[d])` is a [rollup function](#rollup-functions), which calculates `phi`-quantile over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). 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])` is a [rollup function](#rollup-functions), which calculates `phi*`-quantiles over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). 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])` is a [rollup function](#rollup-functions), which calculates value range over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). E.g. it calculates `max_over_time(series_selector[d]) - min_over_time(series_selector[d])`. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. #### rate `rate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average per-second increase rate over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter). If the lookbehind window is skipped in square brackets, then it is automatically calculated as `max(step, scrape_interval)`, where `step` is the query arg value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query), while `scrape_interval` is the interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) for the selected time series. This allows avoiding unexpected gaps on the graph when `step` is smaller than the `scrape_interval`. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [irate](#irate) and [rollup_rate](#rollup_rate). #### rate_over_sum `rate_over_sum(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second rate over the sum of raw samples on the given lookbehind window `d`. The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. #### resets `resets(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number of [counter](https://docs.victoriametrics.com/keyConcepts.html#counter) resets over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. #### rollup `rollup(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `min`, `max` and `avg` values for raw samples on the given lookbehind window `d` and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels. These values are calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label. See also [label_match](#label_match). #### rollup_candlestick `rollup_candlestick(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `open`, `high`, `low` and `close` values (aka OHLC) over raw samples on the given lookbehind window `d` and returns them in time series with `rollup="open"`, `rollup="high"`, `rollup="low"` and `rollup="close"` additional labels. The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is useful for financial applications. Optional 2nd argument `"open"`, `"high"` or `"low"` or `"close"` can be passed to keep only one calculation result and without adding a label. See also [label_match](#label_match). #### rollup_delta `rollup_delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates differences between adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated differences and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels. The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label. See also [label_match](#label_match). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [rollup_increase](#rollup_increase). #### rollup_deriv `rollup_deriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivatives for adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second derivatives and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels. The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label. See also [label_match](#label_match). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. #### rollup_increase `rollup_increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates increases for adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated increases and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels. The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label. See also [label_match](#label_match). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [rollup_delta](#rollup_delta). #### rollup_rate `rollup_rate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second change rates for adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second change rates and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels. See [this article](https://valyala.medium.com/why-irate-from-prometheus-doesnt-capture-spikes-45f9896d7832) in order to understand better when to use `rollup_rate()`. Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label. See also [label_match](#label_match). The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. #### rollup_scrape_interval `rollup_scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the interval in seconds between adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated interval and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels. The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label. See also [label_match](#label_match). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [scrape_interval](#scrape_interval). #### scrape_interval `scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average interval in seconds between raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [rollup_scrape_interval](#rollup_scrape_interval). #### share_gt_over_time `share_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is useful for calculating SLI and SLO. Example: `share_gt_over_time(up[24h], 0)` - returns service availability for the last 24 hours. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [share_le_over_time](#share_le_over_time) and [count_gt_over_time](#count_gt_over_time). #### share_le_over_time `share_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples on the given lookbehind window `d`, which are smaller or equal to `le`. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is useful for calculating SLI and SLO. Example: `share_le_over_time(memory_usage_bytes[24h], 100*1024*1024)` returns the share of time series values for the last 24 hours when memory usage was below or equal to 100MB. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [share_gt_over_time](#share_gt_over_time) and [count_le_over_time](#count_le_over_time). #### share_eq_over_time `share_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_eq_over_time](#count_eq_over_time). #### stale_samples_over_time `stale_samples_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of [staleness markers](https://docs.victoriametrics.com/vmagent.html#prometheus-staleness-markers) on the given lookbehind window `d` per each time series matching the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. #### stddev_over_time `stddev_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard deviation over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [stdvar_over_time](#stdvar_over_time). #### stdvar_over_time `stdvar_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard variance over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [stddev_over_time](#stddev_over_time). #### sum_eq_over_time `sum_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-function), which calculates the sum of raw sample values equal to `eq` on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [sum_over_time](#sum_over_time) and [count_eq_over_time](#count_eq_over_time). #### sum_gt_over_time `sum_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-function), which calculates the sum of raw sample values bigger than `gt` on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [sum_over_time](#sum_over_time) and [count_gt_over_time](#count_gt_over_time). #### sum_le_over_time `sum_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-function), which calculates the sum of raw sample values smaller or equal to `le` on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [sum_over_time](#sum_over_time) and [count_le_over_time](#count_le_over_time). #### sum_over_time `sum_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of raw sample values on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. #### sum2_over_time `sum2_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of squares for raw sample values on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. #### timestamp `timestamp(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last raw sample on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [time](#time) and [now](#now). #### timestamp_with_name `timestamp_with_name(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last raw sample on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are preserved in the resulting rollups. See also [timestamp](#timestamp) and [keep_metric_names](#keep_metric_names) modifier. #### tfirst_over_time `tfirst_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the first raw sample on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [first_over_time](#first_over_time). #### tlast_change_over_time `tlast_change_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last change per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) on the given lookbehind window `d`. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [last_over_time](#last_over_time). #### tlast_over_time `tlast_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which is an alias for [timestamp](#timestamp). See also [tlast_change_over_time](#tlast_change_over_time). #### tmax_over_time `tmax_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the raw sample with the maximum value on the given lookbehind window `d`. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [max_over_time](#max_over_time). #### tmin_over_time `tmin_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the raw sample with the minimum value on the given lookbehind window `d`. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [min_over_time](#min_over_time). #### zscore_over_time `zscore_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which 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://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [zscore](#zscore), [range_trim_zscore](#range_trim_zscore) and [outlier_iqr_over_time](#outlier_iqr_over_time). ### Transform functions **Transform functions** calculate transformations over [rollup results](#rollup-functions). 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://docs.victoriametrics.com/keyConcepts.html#filtering), 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))`. * All the transform functions accept optional `keep_metric_names` modifier. If it is set, then the function doesn't drop metric names from the resulting time series. See [these docs](#keep_metric_names). See also [implicit query conversions](#implicit-query-conversions). The list of supported transform functions: #### abs `abs(q)` is a [transform function](#transform-functions), which calculates the absolute value for every point of every time series returned by `q`. This function is supported by PromQL. #### absent `absent(q)` is a [transform function](#transform-functions), which returns 1 if `q` has no points. Otherwise, returns an empty result. This function is supported by PromQL. See also [absent_over_time](#absent_over_time). #### acos `acos(q)` is a [transform function](#transform-functions), which 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. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [asin](#asin) and [cos](#cos). #### acosh `acosh(q)` is a [transform function](#transform-functions), which 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. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [sinh](#cosh). #### asin `asin(q)` is a [transform function](#transform-functions), which 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. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [acos](#acos) and [sin](#sin). #### asinh `asinh(q)` is a [transform function](#transform-functions), which 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. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [sinh](#sinh). #### atan `atan(q)` is a [transform function](#transform-functions), which 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. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [tan](#tan). #### atanh `atanh(q)` is a [transform function](#transform-functions), which 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. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [tanh](#tanh). #### bitmap_and `bitmap_and(q, mask)` is a [transform function](#transform-functions), which calculates bitwise `v & mask` for every `v` point of every time series returned from `q`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. #### bitmap_or `bitmap_or(q, mask)` is a [transform function](#transform-functions), which calculates bitwise `v | mask` for every `v` point of every time series returned from `q`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. #### bitmap_xor `bitmap_xor(q, mask)` is a [transform function](#transform-functions), which calculates bitwise `v ^ mask` for every `v` point of every time series returned from `q`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. #### buckets_limit `buckets_limit(limit, buckets)` is a [transform function](#transform-functions), which 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)` is a [transform function](#transform-functions), which 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)` is a [transform function](#transform-functions), which clamps every point for every time series returned by `q` with the given `min` and `max` values. This function is supported by PromQL. See also [clamp_min](#clamp_min) and [clamp_max](#clamp_max). #### clamp_max `clamp_max(q, max)` is a [transform function](#transform-functions), which 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)` is a [transform function](#transform-functions), which clamps every point 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)` is a [transform function](#transform-functions), which returns `cos(v)` for every `v` point of every time series returned by `q`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [sin](#sin). #### cosh `cosh(q)` is a [transform function](#transform-functions), which 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. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [acosh](#acosh). #### day_of_month `day_of_month(q)` is a [transform function](#transform-functions), which returns the day of month for every point of every time series returned by `q`. It is expected that `q` returns unix timestamps. The returned values are in the range `[1...31]`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [day_of_week](#day_of_week) and [day_of_year](#day_of_year). #### day_of_week `day_of_week(q)` is a [transform function](#transform-functions), which returns the day of week for every point of every time series returned by `q`. It is expected that `q` returns unix timestamps. The returned values are in the range `[0...6]`, where `0` means Sunday and `6` means Saturday. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [day_of_month](#day_of_month) and [day_of_year](#day_of_year). #### day_of_year `day_of_year(q)` is a [transform function](#transform-functions), which returns the day of year for every point of every time series returned by `q`. It is expected that `q` returns unix timestamps. The returned values are in the range `[1...365]` for non-leap years, and `[1 to 366]` in leap years. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [day_of_week](#day_of_week) and [day_of_month](#day_of_month). #### days_in_month `days_in_month(q)` is a [transform function](#transform-functions), which returns the number of days in the month identified by every point of every time series returned by `q`. It is expected that `q` returns unix timestamps. The returned values are in the range `[28...31]`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. #### deg `deg(q)` is a [transform function](#transform-functions), which 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. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [rad](#rad). #### drop_empty_series `drop_empty_series(q)` is a [transform function](#transform-functions), which drops empty series from `q`. This function can be used when `default` operator should be applied only to non-empty series. For example, `drop_empty_series(temperature < 30) default 42` returns series, which have at least a single sample smaller than 30 on the selected time range, while filling gaps in the returned series with 42. On the other hand `(temperature < 30) default 40` returns all the `temperature` series, even if they have no samples smaller than 30, by replacing all the values bigger or equal to 30 with 40. #### end `end()` is a [transform function](#transform-functions), which returns the unix timestamp in seconds for the last point. It is known as `end` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query). See also [start](#start), [time](#time) and [now](#now). #### exp `exp(q)` is a [transform function](#transform-functions), which calculates the `e^v` for every point `v` of every time series returned by `q`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [ln](#ln). #### floor `floor(q)` is a [transform function](#transform-functions), which rounds every point for every time series returned by `q` to the lower nearest integer. This function is supported by PromQL. See also [ceil](#ceil) and [round](#round). #### histogram_avg `histogram_avg(buckets)` is a [transform function](#transform-functions), which calculates the average value for the given `buckets`. It can be used for calculating the average over the given time range across multiple time series. For example, `histogram_avg(sum(histogram_over_time(response_time_duration_seconds[5m])) by (vmrange,job))` would return the average response time per each `job` over the last 5 minutes. #### histogram_quantile `histogram_quantile(phi, buckets)` is a [transform function](#transform-functions), which calculates `phi`-[percentile](https://en.wikipedia.org/wiki/Percentile) 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. The function accepts optional third arg - `boundsLabel`. In this case it returns `lower` and `upper` bounds for the estimated percentile with the given `boundsLabel` label. See [this issue for details](https://github.com/prometheus/prometheus/issues/5706). When the [percentile](https://en.wikipedia.org/wiki/Percentile) is calculated over multiple histograms, then all the input histograms **must** have buckets with identical boundaries, e.g. they must have the same set of `le` or `vmrange` labels. Otherwise, the returned result may be invalid. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3231) for details. This function is supported by PromQL (except of the `boundLabel` arg). See also [histogram_quantiles](#histogram_quantiles), [histogram_share](#histogram_share) and [quantile](#quantile). #### histogram_quantiles `histogram_quantiles("phiLabel", phi1, ..., phiN, buckets)` is a [transform function](#transform-functions), which calculates the given `phi*`-quantiles over the given [histogram buckets](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350). Argument `phi*` must be in the range `[0...1]`. For example, `histogram_quantiles('le', 0.3, 0.5, sum(rate(http_request_duration_seconds_bucket[5m]) by (le))`. Each calculated quantile is returned in a separate time series with the corresponding `{phiLabel="phi*"}` label. See also [histogram_quantile](#histogram_quantile). #### histogram_share `histogram_share(le, buckets)` is a [transform function](#transform-functions), which calculates the share (in the range `[0...1]`) for `buckets` that fall below `le`. This function is useful for calculating SLI and SLO. This is inverse to [histogram_quantile](#histogram_quantile). The function accepts optional third arg - `boundsLabel`. In this case it returns `lower` and `upper` bounds for the estimated share with the given `boundsLabel` label. #### histogram_stddev `histogram_stddev(buckets)` is a [transform function](#transform-functions), which calculates standard deviation for the given `buckets`. #### histogram_stdvar `histogram_stdvar(buckets)` is a [transform function](#transform-functions), which calculates standard variance for the given `buckets`. It can be used for calculating standard deviation over the given time range across multiple time series. For example, `histogram_stdvar(sum(histogram_over_time(temperature[24])) by (vmrange,country))` would return standard deviation for the temperature per each country over the last 24 hours. #### hour `hour(q)` is a [transform function](#transform-functions), which returns the hour for every point of every time series returned by `q`. It is expected that `q` returns unix timestamps. The returned values are in the range `[0...23]`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. #### interpolate `interpolate(q)` is a [transform function](#transform-functions), which fills gaps with linearly interpolated values calculated from the last and the next non-empty points per each time series returned by `q`. See also [keep_last_value](#keep_last_value) and [keep_next_value](#keep_next_value). #### keep_last_value `keep_last_value(q)` is a [transform function](#transform-functions), which 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)` is a [transform function](#transform-functions), which 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)` is a [transform function](#transform-functions), which skips `offset` time series from series returned by `q` and then returns up to `limit` of the remaining time series per each group. This allows implementing simple paging for `q` time series. See also [limitk](#limitk). #### ln `ln(q)` is a [transform function](#transform-functions), which calculates `ln(v)` for every point `v` of every time series returned by `q`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [exp](#exp) and [log2](#log2). #### log2 `log2(q)` is a [transform function](#transform-functions), which calculates `log2(v)` for every point `v` of every time series returned by `q`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [log10](#log10) and [ln](#ln). #### log10 `log10(q)` is a [transform function](#transform-functions), which calculates `log10(v)` for every point `v` of every time series returned by `q`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [log2](#log2) and [ln](#ln). #### minute `minute(q)` is a [transform function](#transform-functions), which returns the minute for every point of every time series returned by `q`. It is expected that `q` returns unix timestamps. The returned values are in the range `[0...59]`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. #### month `month(q)` is a [transform function](#transform-functions), which returns the month for every point of every time series returned by `q`. It is expected that `q` returns unix timestamps. The returned values are in the range `[1...12]`, where `1` means January and `12` means December. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. #### now `now()` is a [transform function](#transform-functions), which returns the current timestamp as a floating-point value in seconds. See also [time](#time). #### pi `pi()` is a [transform function](#transform-functions), which returns [Pi number](https://en.wikipedia.org/wiki/Pi). This function is supported by PromQL. #### rad `rad(q)` is a [transform function](#transform-functions), which 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. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [deg](#deg). #### prometheus_buckets `prometheus_buckets(buckets)` is a [transform function](#transform-functions), which 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)` is a [transform function](#transform-functions), which returns pseudo-random numbers on the range `[0...1]` with even distribution. Optional `seed` can be used as a seed for pseudo-random number generator. See also [rand_normal](#rand_normal) and [rand_exponential](#rand_exponential). #### rand_exponential `rand_exponential(seed)` is a [transform function](#transform-functions), which 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)` is a [transform function](#transform-functions), which returns pseudo-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)` is a [transform function](#transform-functions), which calculates the avg value across points per each time series returned by `q`. #### range_first `range_first(q)` is a [transform function](#transform-functions), which returns the value for the first point per each time series returned by `q`. #### range_last `range_last(q)` is a [transform function](#transform-functions), which returns the value for the last point per each time series returned by `q`. #### range_linear_regression `range_linear_regression(q)` is a [transform function](#transform-functions), which calculates [simple linear regression](https://en.wikipedia.org/wiki/Simple_linear_regression) over the selected time range per each time series returned by `q`. This function is useful for capacity planning and predictions. #### range_mad `range_mad(q)` is a [transform function](#transform-functions), which calculates the [median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation) across points per each time series returned by `q`. See also [mad](#mad) and [mad_over_time](#mad_over_time). #### range_max `range_max(q)` is a [transform function](#transform-functions), which calculates the max value across points per each time series returned by `q`. #### range_median `range_median(q)` is a [transform function](#transform-functions), which calculates the median value across points per each time series returned by `q`. #### range_min `range_min(q)` is a [transform function](#transform-functions), which calculates the min value across points per each time series returned by `q`. #### range_normalize `range_normalize(q1, ...)` is a [transform function](#transform-functions), which normalizes values for time series returned by `q1, ...` into `[0 ... 1]` range. This function is useful for correlating time series with distinct value ranges. See also [share](#share). #### range_quantile `range_quantile(phi, q)` is a [transform function](#transform-functions), which returns `phi`-quantile across points per each time series returned by `q`. `phi` must be in the range `[0...1]`. #### range_stddev `range_stddev(q)` is a [transform function](#transform-functions), which calculates [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) per each time series returned by `q` on the selected time range. #### range_stdvar `range_stdvar(q)` is a [transform function](#transform-functions), which calculates [standard variance](https://en.wikipedia.org/wiki/Variance) per each time series returned by `q` on the selected time range. #### range_sum `range_sum(q)` is a [transform function](#transform-functions), which calculates the sum of points per each time series returned by `q`. #### range_trim_outliers `range_trim_outliers(k, q)` is a [transform function](#transform-functions), which drops points located farther than `k*range_mad(q)` from the `range_median(q)`. E.g. it is equivalent to the following query: `q ifnot (abs(q - range_median(q)) > k*range_mad(q))`. See also [range_trim_spikes](#range_trim_spikes) and [range_trim_zscore](#range_trim_zscore). #### range_trim_spikes `range_trim_spikes(phi, q)` is a [transform function](#transform-functions), which drops `phi` percent of biggest spikes from time series returned by `q`. The `phi` must be in the range `[0..1]`, where `0` means `0%` and `1` means `100%`. See also [range_trim_outliers](#range_trim_outliers) and [range_trim_zscore](#range_trim_zscore). #### range_trim_zscore `range_trim_zscore(z, q)` is a [transform function](#transform-functions), which drops points located farther than `z*range_stddev(q)` from the `range_avg(q)`. E.g. it is equivalent to the following query: `q ifnot (abs(q - range_avg(q)) > z*range_avg(q))`. See also [range_trim_outliers](#range_trim_outliers) and [range_trim_spikes](#range_trim_spikes). #### range_zscore `range_zscore(q)` is a [transform function](#transform-functions), which calculates [z-score](https://en.wikipedia.org/wiki/Standard_score) for points returned by `q`, e.g. it is equivalent to the following query: `(q - range_avg(q)) / range_stddev(q)`. #### remove_resets `remove_resets(q)` is a [transform function](#transform-functions), which removes counter resets from time series returned by `q`. #### round `round(q, nearest)` is a [transform function](#transform-functions), which rounds every point of every time series returned by `q` to the `nearest` multiple. If `nearest` is missing then the rounding is performed to the nearest integer. This function is supported by PromQL. See also [floor](#floor) and [ceil](#ceil). #### ru `ru(free, max)` is a [transform function](#transform-functions), which calculates resource utilization in the range `[0%...100%]` for the given `free` and `max` resources. For instance, `ru(node_memory_MemFree_bytes, node_memory_MemTotal_bytes)` returns memory utilization over [node_exporter](https://github.com/prometheus/node_exporter) metrics. #### running_avg `running_avg(q)` is a [transform function](#transform-functions), which calculates the running avg per each time series returned by `q`. #### running_max `running_max(q)` is a [transform function](#transform-functions), which calculates the running max per each time series returned by `q`. #### running_min `running_min(q)` is a [transform function](#transform-functions), which calculates the running min per each time series returned by `q`. #### running_sum `running_sum(q)` is a [transform function](#transform-functions), which calculates the running sum per each time series returned by `q`. #### scalar `scalar(q)` is a [transform function](#transform-functions), which returns `q` if `q` contains only a single time series. Otherwise, it returns nothing. This function is supported by PromQL. #### sgn `sgn(q)` is a [transform function](#transform-functions), which returns `1` if `v>0`, `-1` if `v<0` and `0` if `v==0` for every point `v` of every time series returned by `q`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. #### sin `sin(q)` is a [transform function](#transform-functions), which returns `sin(v)` for every `v` point of every time series returned by `q`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by MetricsQL. See also [cos](#cos). #### sinh `sinh(q)` is a [transform function](#transform-functions), which 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. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by MetricsQL. See also [cosh](#cosh). #### tan `tan(q)` is a [transform function](#transform-functions), which returns `tan(v)` for every `v` point of every time series returned by `q`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by MetricsQL. See also [atan](#atan). #### tanh `tanh(q)` is a [transform function](#transform-functions), which 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. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by MetricsQL. See also [atanh](#atanh). #### smooth_exponential `smooth_exponential(q, sf)` is a [transform function](#transform-functions), which 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)` is a [transform function](#transform-functions), which sorts series in ascending order by the last point in every time series returned by `q`. This function is supported by PromQL. See also [sort_desc](#sort_desc) and [sort_by_label](#sort_by_label). #### sort_desc `sort_desc(q)` is a [transform function](#transform-functions), which sorts series in descending order by the last point in every time series returned by `q`. This function is supported by PromQL. See also [sort](#sort) and [sort_by_label](#sort_by_label_desc). #### sqrt `sqrt(q)` is a [transform function](#transform-functions), which calculates square root for every point of every time series returned by `q`. Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. #### start `start()` is a [transform function](#transform-functions), which returns unix timestamp in seconds for the first point. It is known as `start` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query). See also [end](#end), [time](#time) and [now](#now). #### step `step()` is a [transform function](#transform-functions), which returns the step in seconds (aka interval) between the returned points. It is known as `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query). See also [start](#start) and [end](#end). #### time `time()` is a [transform function](#transform-functions), which returns unix timestamp for every returned point. This function is supported by PromQL. See also [timestamp](#timestamp), [now](#now), [start](#start) and [end](#end). #### timezone_offset `timezone_offset(tz)` is a [transform function](#transform-functions), which returns offset in seconds for the given timezone `tz` relative to UTC. This can be useful when combining with datetime-related functions. For example, `day_of_week(time()+timezone_offset("America/Los_Angeles"))` would return weekdays for `America/Los_Angeles` time zone. Special `Local` time zone can be used for returning an offset for the time zone set on the host where VictoriaMetrics runs. See [the list of supported timezones](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones). #### ttf `ttf(free)` is a [transform function](#transform-functions), which estimates the time in seconds needed to exhaust `free` resources. For instance, `ttf(node_filesystem_avail_byte)` returns the time to storage space exhaustion. This function may be useful for capacity planning. #### union `union(q1, ..., qN)` is a [transform function](#transform-functions), which returns a union of time series returned from `q1`, ..., `qN`. The `union` function name can be skipped - the following queries are equivalent: `union(q1, q2)` and `(q1, q2)`. It is expected that each `q*` query returns time series with unique sets of labels. Otherwise, only the first time series out of series with identical set of labels is returned. Use [alias](#alias) and [label_set](#label_set) functions for giving unique labelsets per each `q*` query: #### vector `vector(q)` is a [transform function](#transform-functions), which returns `q`, e.g. it does nothing in MetricsQL. This function is supported by PromQL. #### year `year(q)` is a [transform function](#transform-functions), which 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. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. ### Label manipulation functions **Label manipulation functions** perform manipulations with labels on the selected [rollup results](#rollup-functions). Additional details: * If label manipulation function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering), 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), "foo")`. See also [implicit query conversions](#implicit-query-conversions). The list of supported label manipulation functions: #### alias `alias(q, "name")` is [label manipulation function](#label-manipulation-functions), which sets the given `name` to all the time series returned by `q`. For example, `alias(up, "foobar")` would rename `up` series to `foobar` series. #### drop_common_labels `drop_common_labels(q1, ...., qN)` is [label manipulation function](#label-manipulation-functions), which drops common `label="value"` pairs among time series returned from `q1, ..., qN`. #### label_copy `label_copy(q, "src_label1", "dst_label1", ..., "src_labelN", "dst_labelN")` is [label manipulation function](#label-manipulation-functions), which copies label values from `src_label*` to `dst_label*` for all the time series returned by `q`. If `src_label` is empty, then the corresponding `dst_label` is left untouched. #### label_del `label_del(q, "label1", ..., "labelN")` is [label manipulation function](#label-manipulation-functions), which deletes the given `label*` labels from all the time series returned by `q`. #### label_graphite_group `label_graphite_group(q, groupNum1, ... groupNumN)` is [label manipulation function](#label-manipulation-functions), which replaces metric names returned from `q` with the given Graphite group values concatenated via `.` char. For example, `label_graphite_group({__graphite__="foo*.bar.*"}, 0, 2)` would substitute `foo.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")` is [label manipulation function](#label-manipulation-functions), which joins `src_label*` values with the given `separator` and stores the result in `dst_label`. This is performed individually per each time series returned by `q`. For example, `label_join(up{instance="xxx",job="yyy"}, "foo", "-", "instance", "job")` would store `xxx-yyy` label value into `foo` label. This function is supported by PromQL. #### label_keep `label_keep(q, "label1", ..., "labelN")` is [label manipulation function](#label-manipulation-functions), which deletes all the labels except of the listed `label*` labels in all the time series returned by `q`. #### label_lowercase `label_lowercase(q, "label1", ..., "labelN")` is [label manipulation function](#label-manipulation-functions), which lowercases values for the given `label*` labels in all the time series returned by `q`. #### label_map `label_map(q, "label", "src_value1", "dst_value1", ..., "src_valueN", "dst_valueN")` is [label manipulation function](#label-manipulation-functions), which maps `label` values from `src_*` to `dst*` for all the time series returned by `q`. #### label_match `label_match(q, "label", "regexp")` is [label manipulation function](#label-manipulation-functions), which 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) and [labels_equal](#labels_equal). #### label_mismatch `label_mismatch(q, "label", "regexp")` is [label manipulation function](#label-manipulation-functions), which 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) and [labels_equal](#labels_equal). #### label_move `label_move(q, "src_label1", "dst_label1", ..., "src_labelN", "dst_labelN")` is [label manipulation function](#label-manipulation-functions), which moves label values from `src_label*` to `dst_label*` for all the time series returned by `q`. If `src_label` is empty, then the corresponding `dst_label` is left untouched. #### label_replace `label_replace(q, "dst_label", "replacement", "src_label", "regex")` is [label manipulation function](#label-manipulation-functions), which applies the given `regex` to `src_label` and stores the `replacement` in `dst_label` if the given `regex` matches `src_label`. The `replacement` may contain references to regex captures such as `$1`, `$2`, etc. These references are substituted by the corresponding regex captures. For example, `label_replace(up{job="node-exporter"}, "foo", "bar-$1", "job", "node-(.+)")` would store `bar-exporter` label value into `foo` label. This function is supported by PromQL. #### label_set `label_set(q, "label1", "value1", ..., "labelN", "valueN")` is [label manipulation function](#label-manipulation-functions), which sets `{label1="value1", ..., labelN="valueN"}` labels to all the time series returned by `q`. #### label_transform `label_transform(q, "label", "regexp", "replacement")` is [label manipulation function](#label-manipulation-functions), which substitutes all the `regexp` occurrences by the given `replacement` in the given `label`. #### label_uppercase `label_uppercase(q, "label1", ..., "labelN")` is [label manipulation function](#label-manipulation-functions), which uppercases values for the given `label*` labels in all the time series returned by `q`. See also [label_lowercase](#label_lowercase). #### label_value `label_value(q, "label")` is [label manipulation function](#label-manipulation-functions), which returns numeric values for the given `label` for every time series returned by `q`. For example, if `label_value(foo, "bar")` is applied to `foo{bar="1.234"}`, then it will return a time series `foo{bar="1.234"}` with `1.234` value. Function will return no data for non-numeric label values. #### labels_equal `labels_equal(q, "label1", "label2", ...)` is [label manipulation function](#label-manipulation-functions), which returns `q` series with identical values for the listed labels "label1", "label2", etc. See also [label_match](#label_match) and [label_mismatch](#label_mismatch). #### sort_by_label `sort_by_label(q, "label1", ... "labelN")` is [label manipulation function](#label-manipulation-functions), which sorts series in ascending order by the given set of labels. For example, `sort_by_label(foo, "bar")` would sort `foo` series by values of the label `bar` in these series. See also [sort_by_label_desc](#sort_by_label_desc) and [sort_by_label_numeric](#sort_by_label_numeric). #### sort_by_label_desc `sort_by_label_desc(q, "label1", ... "labelN")` is [label manipulation function](#label-manipulation-functions), which sorts series in descending order by the given set of labels. For example, `sort_by_label(foo, "bar")` would sort `foo` series by values of the label `bar` in these series. See also [sort_by_label](#sort_by_label) and [sort_by_label_numeric_desc](#sort_by_label_numeric_desc). #### sort_by_label_numeric `sort_by_label_numeric(q, "label1", ... "labelN")` is [label manipulation function](#label-manipulation-functions), which sorts series in ascending order by the given set of labels using [numeric sort](https://www.gnu.org/software/coreutils/manual/html_node/Version-sort-is-not-the-same-as-numeric-sort.html). For example, if `foo` series have `bar` label with values `1`, `101`, `15` and `2`, then `sort_by_label_numeric(foo, "bar")` would return series in the following order of `bar` label values: `1`, `2`, `15` and `101`. See also [sort_by_label_numeric_desc](#sort_by_label_numeric_desc) and [sort_by_label](#sort_by_label). #### sort_by_label_numeric_desc `sort_by_label_numeric_desc(q, "label1", ... "labelN")` is [label manipulation function](#label-manipulation-functions), which sorts series in descending order by the given set of labels using [numeric sort](https://www.gnu.org/software/coreutils/manual/html_node/Version-sort-is-not-the-same-as-numeric-sort.html). For example, if `foo` series have `bar` label with values `1`, `101`, `15` and `2`, then `sort_by_label_numeric(foo, "bar")` would return series in the following order of `bar` label values: `101`, `15`, `2` and `1`. See also [sort_by_label_numeric](#sort_by_label_numeric) and [sort_by_label_desc](#sort_by_label_desc). ### Aggregate functions **Aggregate functions** calculate aggregates over groups of [rollup results](#rollup-functions). 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](#rollup-functions) by `job` label value and calculate the [count](#count) aggregate function independently per each group, while `count(up) without (instance)` would group [rollup results](#rollup-functions) 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://docs.victoriametrics.com/keyConcepts.html#filtering), then the [default_rollup()](#default_rollup) function is automatically applied before calculating the aggregate. For example, `count(up)` is implicitly transformed to `count(default_rollup(up))`. * Aggregate functions accept arbitrary number of args. For example, `avg(q1, q2, q3)` would return the average values for every point across time series returned by `q1`, `q2` and `q3`. * Aggregate functions support optional `limit N` suffix, which can be used for limiting the number of output groups. For example, `sum(x) by (y) limit 3` limits the number of groups for the aggregation to 3. All the other groups are ignored. See also [implicit query conversions](#implicit-query-conversions). The list of supported aggregate functions: #### any `any(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns a single series per `group_labels` out of time series returned by `q`. See also [group](#group). #### avg `avg(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns the average value per `group_labels` for time series returned by `q`. The aggregate is calculated individually per each group of points with the same timestamp. This function is supported by PromQL. #### bottomk `bottomk(k, q)` is [aggregate function](#aggregate-functions), which returns up to `k` points with the smallest values across all the time series returned by `q`. The aggregate is calculated individually per each group of points with the same timestamp. This function is supported by PromQL. See also [topk](#topk), [bottomk_min](#bottomk_min) and [#bottomk_last](#bottomk_last). #### bottomk_avg `bottomk_avg(k, q, "other_label=other_value")` is [aggregate function](#aggregate-functions), which returns up to `k` time series from `q` with the smallest averages. If an optional `other_label=other_value` arg is set, then the sum of the remaining time series is returned with the given label. For example, `bottomk_avg(3, sum(process_resident_memory_bytes) by (job), "job=other")` would return up to 3 time series with the smallest averages plus a time series with `{job="other"}` label with the sum of the remaining series if any. See also [topk_avg](#topk_avg). #### bottomk_last `bottomk_last(k, q, "other_label=other_value")` is [aggregate function](#aggregate-functions), which returns up to `k` time series from `q` with the smallest last values. If an optional `other_label=other_value` arg is set, then the sum of the remaining time series is returned with the given label. For example, `bottomk_max(3, sum(process_resident_memory_bytes) by (job), "job=other")` would return up to 3 time series with the smallest maximums plus a time series with `{job="other"}` label with the sum of the remaining series if any. See also [topk_last](#topk_last). #### bottomk_max `bottomk_max(k, q, "other_label=other_value")` is [aggregate function](#aggregate-functions), which returns up to `k` time series from `q` with the smallest maximums. If an optional `other_label=other_value` arg is set, then the sum of the remaining time series is returned with the given label. For example, `bottomk_max(3, sum(process_resident_memory_bytes) by (job), "job=other")` would return up to 3 time series with the smallest maximums plus a time series with `{job="other"}` label with the sum of the remaining series if any. See also [topk_max](#topk_max). #### bottomk_median `bottomk_median(k, q, "other_label=other_value")` is [aggregate function](#aggregate-functions), which returns up to `k` time series from `q` with the smallest medians. If an optional`other_label=other_value` arg is set, then the sum of the remaining time series is returned with the given label. For example, `bottomk_median(3, sum(process_resident_memory_bytes) by (job), "job=other")` would return up to 3 time series with the smallest medians plus a time series with `{job="other"}` label with the sum of the remaining series if any. See also [topk_median](#topk_median). #### bottomk_min `bottomk_min(k, q, "other_label=other_value")` is [aggregate function](#aggregate-functions), which returns up to `k` time series from `q` with the smallest minimums. If an optional `other_label=other_value` arg is set, then the sum of the remaining time series is returned with the given label. For example, `bottomk_min(3, sum(process_resident_memory_bytes) by (job), "job=other")` would return up to 3 time series with the smallest minimums plus a time series with `{job="other"}` label with the sum of the remaining series if any. See also [topk_min](#topk_min). #### count `count(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns the number of non-empty points per `group_labels` for time series returned by `q`. The aggregate is calculated individually per each group of points with the same timestamp. This function is supported by PromQL. #### count_values `count_values("label", q)` is [aggregate function](#aggregate-functions), which counts the number of points with the same value and stores the counts in a time series with an additional `label`, which contains each initial value. The aggregate is calculated individually per each group of points with the same timestamp. This function is supported by PromQL. See also [count_values_over_time](#count_values_over_time) and [label_match](#label_match). #### distinct `distinct(q)` is [aggregate function](#aggregate-functions), which calculates the number of unique values per each group of points with the same timestamp. See also [distinct_over_time](#distinct_over_time). #### geomean `geomean(q)` is [aggregate function](#aggregate-functions), which calculates geometric mean per each group of points with the same timestamp. #### group `group(q) by (group_labels)` is [aggregate function](#aggregate-functions), which 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)` is [aggregate function](#aggregate-functions), which 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. See also [histogram_over_time](#histogram_over_time) and [histogram_quantile](#histogram_quantile). #### limitk `limitk(k, q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns up to `k` time series per each `group_labels` out of time series returned by `q`. The returned set of time series remain the same across calls. See also [limit_offset](#limit_offset). #### mad `mad(q) by (group_labels)` is [aggregate function](#aggregate-functions), which 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 [range_mad](#range_mad), [mad_over_time](#mad_over_time), [outliers_mad](#outliers_mad) and [stddev](#stddev). #### max `max(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns the maximum value per each `group_labels` for all the time series returned by `q`. The aggregate is calculated individually per each group of points with the same timestamp. This function is supported by PromQL. #### median `median(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns the median value per each `group_labels` for all the time series returned by `q`. The aggregate is calculated individually per each group of points with the same timestamp. #### min `min(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns the minimum value per each `group_labels` for all the time series returned by `q`. The aggregate is calculated individually per each group of points with the same timestamp. This function is supported by PromQL. #### mode `mode(q) by (group_labels)` is [aggregate function](#aggregate-functions), which 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_iqr `outliers_iqr(q)` is [aggregate function](#aggregate-functions), which returns time series from `q` with at least a single point outside e.g. [Interquartile range outlier bounds](https://en.wikipedia.org/wiki/Interquartile_range) `[q25-1.5*iqr .. q75+1.5*iqr]` comparing to other time series at the given point, where: - `iqr` is an [Interquartile range](https://en.wikipedia.org/wiki/Interquartile_range) calculated independently per each point on the graph across `q` series. - `q25` and `q75` are 25th and 75th [percentiles](https://en.wikipedia.org/wiki/Percentile) calculated independently per each point on the graph across `q` series. The `outliers_iqr()` is useful for detecting anomalous series in the group of series. For example, `outliers_iqr(temperature) by (country)` returns per-country series with anomalous outlier values comparing to the rest of per-country series. See also [outliers_mad](#outliers_mad), [outliersk](#outliersk) and [outlier_iqr_over_time](#outlier_iqr_over_time). #### outliers_mad `outliers_mad(tolerance, q)` is [aggregate function](#aggregate-functions), which 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 [outliers_iqr](#outliers_iqr), [outliersk](#outliersk) and [mad](#mad). #### outliersk `outliersk(k, q)` is [aggregate function](#aggregate-functions), which returns up to `k` time series with the biggest standard deviation (aka outliers) out of time series returned by `q`. See also [outliers_iqr](#outliers_iqr) and [outliers_mad](#outliers_mad). #### quantile `quantile(phi, q) by (group_labels)` is [aggregate function](#aggregate-functions), which calculates `phi`-quantile per each `group_labels` for all the time series returned by `q`. `phi` must be in the range `[0...1]`. The aggregate is calculated individually per each group of points with the same timestamp. This function is supported by PromQL. See also [quantiles](#quantiles) and [histogram_quantile](#histogram_quantile). #### quantiles `quantiles("phiLabel", phi1, ..., phiN, q)` is [aggregate function](#aggregate-functions), which calculates `phi*`-quantiles for all the time series returned by `q` and return them in time series with `{phiLabel="phi*"}` label. `phi*` must be in the range `[0...1]`. The aggregate is calculated individually per each group of points with the same timestamp. See also [quantile](#quantile). #### share `share(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns shares in the range `[0..1]` for every non-negative points returned by `q` per each timestamp, so the sum of shares per each `group_labels` equals 1. This function is useful for normalizing [histogram bucket](https://docs.victoriametrics.com/keyConcepts.html#histogram) shares into `[0..1]` range: ```metricsql share( sum( rate(http_request_duration_seconds_bucket[5m]) ) by (le, vmrange) ) ``` See also [range_normalize](#range_normalize). #### stddev `stddev(q) by (group_labels)` is [aggregate function](#aggregate-functions), which calculates standard deviation per each `group_labels` for all the time series returned by `q`. The aggregate is calculated individually per each group of points with the same timestamp. This function is supported by PromQL. #### stdvar `stdvar(q) by (group_labels)` is [aggregate function](#aggregate-functions), which calculates standard variance per each `group_labels` for all the time series returned by `q`. The aggregate is calculated individually per each group of points with the same timestamp. This function is supported by PromQL. #### sum `sum(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns the sum per each `group_labels` for all the time series returned by `q`. The aggregate is calculated individually per each group of points with the same timestamp. This function is supported by PromQL. #### sum2 `sum2(q) by (group_labels)` is [aggregate function](#aggregate-functions), which calculates the sum of squares per each `group_labels` for all the time series returned by `q`. The aggregate is calculated individually per each group of points with the same timestamp. #### topk `topk(k, q)` is [aggregate function](#aggregate-functions), which returns up to `k` points with the biggest values across all the time series returned by `q`. The aggregate is calculated individually per each group of points with the same timestamp. This function is supported by PromQL. See also [bottomk](#bottomk), [topk_max](#topk_max) and [topk_last](#topk_last). #### topk_avg `topk_avg(k, q, "other_label=other_value")` is [aggregate function](#aggregate-functions), which returns up to `k` time series from `q` with the biggest averages. If an optional `other_label=other_value` arg is set, then the sum of the remaining time series is returned with the given label. For example, `topk_avg(3, sum(process_resident_memory_bytes) by (job), "job=other")` would return up to 3 time series with the biggest averages plus a time series with `{job="other"}` label with the sum of the remaining series if any. See also [bottomk_avg](#bottomk_avg). #### topk_last `topk_last(k, q, "other_label=other_value")` is [aggregate function](#aggregate-functions), which returns up to `k` time series from `q` with the biggest last values. If an optional `other_label=other_value` arg is set, then the sum of the remaining time series is returned with the given label. For example, `topk_max(3, sum(process_resident_memory_bytes) by (job), "job=other")` would return up to 3 time series with the biggest maximums plus a time series with `{job="other"}` label with the sum of the remaining series if any. See also [bottomk_last](#bottomk_last). #### topk_max `topk_max(k, q, "other_label=other_value")` is [aggregate function](#aggregate-functions), which returns up to `k` time series from `q` with the biggest maximums. If an optional `other_label=other_value` arg is set, then the sum of the remaining time series is returned with the given label. For example, `topk_max(3, sum(process_resident_memory_bytes) by (job), "job=other")` would return up to 3 time series with the biggest maximums plus a time series with `{job="other"}` label with the sum of the remaining series if any. See also [bottomk_max](#bottomk_max). #### topk_median `topk_median(k, q, "other_label=other_value")` is [aggregate function](#aggregate-functions), which returns up to `k` time series from `q` with the biggest medians. If an optional `other_label=other_value` arg is set, then the sum of the remaining time series is returned with the given label. For example, `topk_median(3, sum(process_resident_memory_bytes) by (job), "job=other")` would return up to 3 time series with the biggest medians plus a time series with `{job="other"}` label with the sum of the remaining series if any. See also [bottomk_median](#bottomk_median). #### topk_min `topk_min(k, q, "other_label=other_value")` is [aggregate function](#aggregate-functions), which returns up to `k` time series from `q` with the biggest minimums. If an optional `other_label=other_value` arg is set, then the sum of the remaining time series is returned with the given label. For example, `topk_min(3, sum(process_resident_memory_bytes) by (job), "job=other")` would return up to 3 time series with the biggest minimums plus a time series with `{job="other"}` label with the sum of the remaining series if any. See also [bottomk_min](#bottomk_min). #### zscore `zscore(q) by (group_labels)` is [aggregate function](#aggregate-functions), which 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. This function is useful for detecting anomalies in the group of related time series. See also [zscore_over_time](#zscore_over_time), [range_trim_zscore](#range_trim_zscore) and [outliers_iqr](#outliers_iqr). ## Subqueries MetricsQL supports and extends PromQL subqueries. See [this article](https://valyala.medium.com/prometheus-subqueries-in-victoriametrics-9b1492b720b3) for details. Any [rollup function](#rollup-functions) for something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) 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])`, 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, for expression `max_over_time(rate(http_requests_total[5m])[1h:30s])` the inner function `rate(http_requests_total[5m])` is calculated with `step=30s`. The resulting data points are aligned by the `step`. * It calculates the outer rollup function over the results of the inner rollup function using the `step` value passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query). ## 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 it is automatically set to the following value: - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query) for all the [rollup functions](#rollup-functions) except of [default_rollup](#default_rollup) and [rate](#rate). This value is known as `$__interval` in Grafana or `1i` in MetricsQL. For example, `avg_over_time(temperature)` is automatically transformed to `avg_over_time(temperature[1i])`. - To the `max(step, scrape_interval)`, where `scrape_interval` is the interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) for [default_rollup](#default_rollup) and [rate](#rate) functions. This allows avoiding unexpected gaps on the graph when `step` is smaller than `scrape_interval`. * All the [series selectors](https://docs.victoriametrics.com/keyConcepts.html#filtering), 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)` * `foo + bar` is transformed to `default_rollup(foo) + default_rollup(bar)` * `count(up)` is transformed to `count(default_rollup(up))`, 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))`, 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://docs.victoriametrics.com/keyConcepts.html#filtering) 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])`.