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Ct("div",{className:"vm-active-queries",children:[o&&Ct(Dd,{}),Ct("div",{className:"vm-active-queries-header",children:[!c.length&&!l&&Ct(Qr,{variant:"info",children:"There are currently no active queries running"}),l&&Ct(Qr,{variant:"error",children:l}),Ct("div",{className:"vm-active-queries-header-controls",children:[Ct(aa,{variant:"contained",onClick:async()=>{s().catch(console.error)},startIcon:Ct(jn,{}),children:"Update"}),Ct("div",{className:"vm-active-queries-header__update-msg",children:["Last updated: ",a]})]})]}),!!c.length&&Ct("div",{className:xr()({"vm-block":!0,"vm-block_mobile":e}),children:Ct(Qm,{rows:c,columns:u,defaultOrderBy:"duration",copyToClipboard:"data",paginationOffset:{startIndex:0,endIndex:1/0}})})]})},Gm=e=>{let{onClose:t,onUpload:n}=e;const{isMobile:a}=Wr(),[i,o]=(0,r.useState)(""),[l,s]=(0,r.useState)(""),c=(0,r.useMemo)((()=>{try{return JSON.parse(i),""}catch(ap){return ap instanceof Error?ap.message:"Unknown 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t,n;let{data:a,period:o}=e;const l=(0,r.useMemo)((()=>a.filter((e=>e.stats&&"matrix"===e.data.resultType))),[a]),s=(0,r.useMemo)((()=>{var e,t;return null===(e=a.find((e=>{var t;return null===e||void 0===e||null===(t=e.vmui)||void 0===t?void 0:t.comment})))||void 0===e||null===(t=e.vmui)||void 0===t?void 0:t.comment}),[a]),c=(0,r.useMemo)((()=>{if(!o)return"";const e=i()(1e3*o.start).tz().format(Lt),t=i()(1e3*o.end).tz().format(Lt);return"".concat(e," - ").concat(t)}),[o]),{value:u,setTrue:d,setFalse:h}=oa(!1);return Ct(xt.FK,{children:[Ct("div",{className:"vm-query-analyzer-info-header",children:[Ct(aa,{startIcon:Ct(Rn,{}),variant:"outlined",color:"warning",onClick:d,children:"Show report info"}),o&&Ct(xt.FK,{children:[Ct("div",{className:"vm-query-analyzer-info-header__period",children:[Ct(or,{})," step: ",o.step]}),Ct("div",{className:"vm-query-analyzer-info-header__period",children:[Ct(Vn,{})," ",c]})]})]}),u&&Ct(pa,{title:"Report 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manually"})]})})]})},ep=()=>{const[e,t]=(0,r.useState)([]),[n,a]=(0,r.useState)(""),i=(0,r.useMemo)((()=>!!e.length),[e]),{value:o,setTrue:l,setFalse:s}=oa(!1),c=(0,r.useMemo)((()=>{var t,n;if(!e)return;const r=null===(t=e[0])||void 0===t||null===(n=t.vmui)||void 0===n?void 0:n.params,a={start:+((null===r||void 0===r?void 0:r.start)||0),end:+((null===r||void 0===r?void 0:r.end)||0),step:null===r||void 0===r?void 0:r.step,date:""};if(!r){const t=e.filter((e=>"matrix"===e.data.resultType)).map((e=>e.data.result)).flat().map((e=>{var t;return e.values?null===(t=e.values)||void 0===t?void 0:t.map((e=>e[0])):[0]})).flat(),n=Array.from(new Set(t.filter(Boolean))).sort(((e,t)=>e-t));a.start=n[0],a.end=n[n.length-1],a.step=Bt((e=>{const t=e.slice(1).map(((t,n)=>t-e[n])),n={};t.forEach((e=>{const t=e.toString();n[t]=(n[t]||0)+1}));let r=0,a=0;for(const i in n)n[i]>a&&(a=n[i],r=Number(i));return r})(n))}return a.date=Zt(Xt(a.end)),a}),[e]),u=e=>{try{const 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Ct(xt.FK,{children:Ct(Le,{children:Ct(ta,{children:Ct(xt.FK,{children:[Ct(_m,{onLoaded:t}),e&&Ct(ke,{children:Ct(be,{path:"/",element:Ct(Ai,{}),children:[Ct(be,{path:Ye.home,element:Ct(Lh,{})}),Ct(be,{path:Ye.metrics,element:Ct(zm,{})}),Ct(be,{path:Ye.cardinality,element:Ct(sm,{})}),Ct(be,{path:Ye.topQueries,element:Ct(pm,{})}),Ct(be,{path:Ye.trace,element:Ct(km,{})}),Ct(be,{path:Ye.queryAnalyzer,element:Ct(ep,{})}),Ct(be,{path:Ye.dashboards,element:Ct(Ih,{})}),Ct(be,{path:Ye.withTemplate,element:Ct(qm,{})}),Ct(be,{path:Ye.relabel,element:Ct(Km,{})}),Ct(be,{path:Ye.activeQueries,element:Ct(Zm,{})}),Ct(be,{path:Ye.icons,element:Ct(Fm,{})})]})})]})})})})},np=e=>{e&&n.e(685).then(n.bind(n,685)).then((t=>{let{getCLS:n,getFID:r,getFCP:a,getLCP:i,getTTFB:o}=t;n(e),r(e),a(e),i(e),o(e)}))},rp=document.getElementById("root");rp&&(0,r.render)(Ct(tp,{}),rp),np()})()})();
\ No newline at end of file
diff --git a/app/vmselect/vmui/static/js/main.202937c2.js.LICENSE.txt b/app/vmselect/vmui/static/js/main.202937c2.js.LICENSE.txt
deleted file mode 100644
index db76a5f622aa..000000000000
--- a/app/vmselect/vmui/static/js/main.202937c2.js.LICENSE.txt
+++ /dev/null
@@ -1,38 +0,0 @@
-/*!
- Copyright (c) 2018 Jed Watson.
- Licensed under the MIT License (MIT), see
- http://jedwatson.github.io/classnames
-*/
-
-/**
- * @remix-run/router v1.15.1
- *
- * Copyright (c) Remix Software Inc.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE.md file in the root directory of this source tree.
- *
- * @license MIT
- */
-
-/**
- * React Router DOM v6.22.1
- *
- * Copyright (c) Remix Software Inc.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE.md file in the root directory of this source tree.
- *
- * @license MIT
- */
-
-/**
- * React Router v6.22.1
- *
- * Copyright (c) Remix Software Inc.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE.md file in the root directory of this source tree.
- *
- * @license MIT
- */
diff --git a/app/vmselect/vmui/static/media/MetricsQL.da86c2db4f0b05e286b0.md b/app/vmselect/vmui/static/media/MetricsQL.da86c2db4f0b05e286b0.md
deleted file mode 100644
index 7f3ccc498b19..000000000000
--- a/app/vmselect/vmui/static/media/MetricsQL.da86c2db4f0b05e286b0.md
+++ /dev/null
@@ -1,2247 +0,0 @@
----
-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/#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/#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/#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/#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/#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/#range-query).
-* If the given [series selector](https://docs.victoriametrics.com/keyconcepts/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#filtering).
-The resulting histograms are useful to pass to [histogram_quantile](#histogram_quantile) for calculating quantiles
-over multiple [gauges](https://docs.victoriametrics.com/keyconcepts/#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/#filtering)
-returns time series of [gauge type](https://docs.victoriametrics.com/keyconcepts/#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/#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/#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/#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#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/#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/#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/#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/#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#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/#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/#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/#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/#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/#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/#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/#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/#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 hour.
-
-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/#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/#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/#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/#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/#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#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/#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/#counter) resets over the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#prometheus-staleness-markers) on the given lookbehind window `d`
-per each time series matching the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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/#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])`.
diff --git a/app/vmui/packages/vmui/src/assets/MetricsQL.md b/app/vmui/packages/vmui/src/assets/MetricsQL.md
index 5c517e4ed701..fec10e5dc0bb 100644
--- a/app/vmui/packages/vmui/src/assets/MetricsQL.md
+++ b/app/vmui/packages/vmui/src/assets/MetricsQL.md
@@ -79,6 +79,8 @@ The list of MetricsQL features on top of PromQL:
* [Series selectors](https://docs.victoriametrics.com/keyconcepts/#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/#filtering-by-multiple-or-filters) for details.
+* Support for matching against multiple numeric constants via `q == (C1, ..., CN)` and `q != (C1, ..., CN)` syntax. For example, `status_code == (300, 301, 304)`
+ returns `status_code` metrics with one of `300`, `301` or `304` values.
* Support for `group_left(*)` and `group_right(*)` for copying all the labels from time series on the `one` side
of [many-to-one operations](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
@@ -152,9 +154,9 @@ MetricsQL provides the following functions:
### Rollup functions
-**Rollup functions** (aka range functions or window functions) calculate rollups over **raw samples**
+**Rollup functions** (aka range functions or window functions) calculate rollups over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window for the [selected time series](https://docs.victoriametrics.com/keyconcepts/#filtering).
-For example, `avg_over_time(temperature[24h])` calculates the average temperature over raw samples for the last 24 hours.
+For example, `avg_over_time(temperature[24h])` calculates the average temperature over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) for the last 24 hours.
Additional details:
@@ -184,7 +186,7 @@ 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.
+if the given lookbehind window `d` doesn't contain [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples). Otherwise, it returns an empty result.
This function is supported by PromQL.
@@ -193,7 +195,7 @@ 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`.
+which calculates all the listed `rollup_func*` for [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -203,7 +205,7 @@ would calculate [min_over_time](#min_over_time), [max_over_time](#max_over_time)
#### 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
+ascent of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples) 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/#filtering).
This function is useful for tracking height gains in GPS tracking. Metric names are stripped from the resulting rollups.
@@ -215,7 +217,7 @@ 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
+over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
This function is supported by PromQL.
@@ -225,7 +227,7 @@ 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
+the [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) changed on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Unlike `changes()` in Prometheus it takes into account the change from the last sample before the given lookbehind window `d`.
@@ -240,7 +242,7 @@ 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
+the [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) changed on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#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.
@@ -254,7 +256,7 @@ 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
+`count_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which calculates the number of [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -264,7 +266,7 @@ See also [count_over_time](#count_over_time), [share_eq_over_time](#share_eq_ove
#### 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
+`count_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which calculates the number of [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -274,7 +276,7 @@ See also [count_over_time](#count_over_time) and [share_gt_over_time](#share_gt_
#### 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
+`count_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which calculates the number of [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -284,7 +286,7 @@ See also [count_over_time](#count_over_time) and [share_le_over_time](#share_le_
#### 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
+`count_ne_over_time(series_selector[d], ne)` is a [rollup function](#rollup-functions), which calculates the number of [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -294,7 +296,7 @@ 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
+`count_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -305,7 +307,7 @@ See also [count_le_over_time](#count_le_over_time), [count_gt_over_time](#count_
#### 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
+`count_values_over_time("label", series_selector[d])` is a [rollup function](#rollup-functions), which counts the number of [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -315,8 +317,8 @@ See also [count_eq_over_time](#count_eq_over_time), [count_values](#count_values
#### 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/#filtering).
+`decreases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+value decreases over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -324,8 +326,8 @@ 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/#filtering).
+`default_rollup(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#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),
@@ -375,7 +377,7 @@ 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
+using the first and the last [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -384,8 +386,8 @@ 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
+`descent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates descent of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+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/#filtering).
This function is useful for tracking height loss in GPS tracking.
@@ -396,8 +398,8 @@ 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/#filtering).
+`distinct_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number of unique [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+values on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -416,15 +418,15 @@ 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/#filtering).
+`first_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the first [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#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
+over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -432,8 +434,8 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
#### 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/#filtering).
+[VictoriaMetrics histogram](https://godoc.org/github.com/VictoriaMetrics/metrics#Histogram) over [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
The resulting histograms are useful to pass to [histogram_quantile](#histogram_quantile) for calculating quantiles
over multiple [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge).
For example, the following query calculates median temperature by country over the last 24 hours:
@@ -457,7 +459,7 @@ 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
+(aka [double exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing#Double_exponential_smoothing)) for [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering)
returns time series of [gauge type](https://docs.victoriametrics.com/keyconcepts/#gauge).
@@ -468,7 +470,7 @@ 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
+`idelta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between the last two [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -479,7 +481,8 @@ 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
+`ideriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the per-second derivative based
+on the last two [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -522,8 +525,8 @@ 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/#filtering).
+`increases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+value increases over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -531,14 +534,15 @@ 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/#filtering).
+`integrate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the integral over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#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
+`irate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the "instant" per-second increase rate over
+the last two [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
@@ -560,8 +564,8 @@ 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/#filtering).
+`last_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
This function is supported by PromQL.
@@ -579,13 +583,14 @@ 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/#filtering).
+over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#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
+`max_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the maximum value over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
This function is supported by PromQL.
@@ -594,7 +599,7 @@ 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
+`median_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates median value over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@@ -602,7 +607,7 @@ 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
+`min_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the minimum value over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
This function is supported by PromQL.
@@ -612,15 +617,16 @@ 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/#filtering). It is expected that raw sample values are discrete.
+for [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering). It is expected that [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+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`.
+- `iqr` is an [Interquartile range](https://en.wikipedia.org/wiki/Interquartile_range) over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the lookbehind window `d`
+- `q25` and `q75` are 25th and 75th [percentiles](https://en.wikipedia.org/wiki/Percentile) over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the lookbehind window `d`.
The `outlier_iqr_over_time()` is useful for detecting anomalies in gauge values based on the previous history of values.
For example, `outlier_iqr_over_time(memory_usage_bytes[1h])` triggers when `memory_usage_bytes` suddenly goes outside the usual value range for the last hour.
@@ -630,8 +636,8 @@ 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/#filtering).
+linear interpolation over [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
This function is supported by PromQL.
@@ -639,7 +645,7 @@ 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
+`present_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns 1 if there is at least a single [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
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.
@@ -648,7 +654,7 @@ 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
+`quantile_over_time(phi, series_selector[d])` is a [rollup function](#rollup-functions), which calculates `phi`-quantile over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
The `phi` value must be in the range `[0...1]`.
@@ -659,7 +665,7 @@ 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
+over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
The function returns individual series per each `phi*` with `{phiLabel="phi*"}` label. `phi*` values must be in the range `[0...1]`.
@@ -667,7 +673,7 @@ 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
+`range_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates value range over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
E.g. it calculates `max_over_time(series_selector[d]) - min_over_time(series_selector[d])`.
@@ -692,7 +698,7 @@ 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
+`rate_over_sum(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second rate over the sum of [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -711,7 +717,7 @@ 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
+`rollup(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `min`, `max` and `avg` values for [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -721,7 +727,8 @@ 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.
+over [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering). This function is useful for financial applications.
@@ -730,7 +737,7 @@ 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
+`rollup_delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates differences between adjacent [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -745,8 +752,8 @@ 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.
+for adjacent [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
@@ -756,7 +763,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
#### rollup_increase
-`rollup_increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates increases for adjacent raw samples
+`rollup_increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates increases for adjacent [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -768,7 +775,8 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
#### rollup_rate
-`rollup_rate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second change rates for adjacent raw samples
+`rollup_rate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second change rates
+for adjacent [raw samples](https://docs.victoriametrics.com/keyconcepts/#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.
@@ -785,7 +793,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
#### 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
+adjacent [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -796,7 +804,8 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
#### scrape_interval
-`scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average interval in seconds between raw samples
+`scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average interval in seconds
+between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -805,7 +814,8 @@ 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
+`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](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -817,7 +827,8 @@ See also [share_le_over_time](#share_le_over_time) and [count_gt_over_time](#cou
#### 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
+`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](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -830,7 +841,8 @@ See also [share_gt_over_time](#share_gt_over_time) and [count_le_over_time](#cou
#### 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
+`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](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
@@ -848,7 +860,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
#### stddev_over_time
-`stddev_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard deviation over raw samples
+`stddev_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard deviation over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -859,7 +871,7 @@ 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
+`stdvar_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard variance over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -870,8 +882,8 @@ 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/#filtering).
+`sum_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-function), which calculates the sum of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+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/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -879,8 +891,8 @@ See also [sum_over_time](#sum_over_time) and [count_eq_over_time](#count_eq_over
#### 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/#filtering).
+`sum_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-function), which calculates the sum of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+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/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -888,8 +900,8 @@ See also [sum_over_time](#sum_over_time) and [count_gt_over_time](#count_gt_over
#### 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/#filtering).
+`sum_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-function), which calculates the sum of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+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/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -897,7 +909,7 @@ See also [sum_over_time](#sum_over_time) and [count_le_over_time](#count_le_over
#### sum_over_time
-`sum_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of raw sample values
+`sum_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples) values
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -906,14 +918,15 @@ 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/#filtering).
+`sum2_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of squares for [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+values on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#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
+`timestamp(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision
+for the last [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -924,7 +937,8 @@ 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
+`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](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are preserved in the resulting rollups.
@@ -933,7 +947,8 @@ See also [timestamp](#timestamp) and [keep_metric_names](#keep_metric_names) mod
#### 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
+`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](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -957,7 +972,8 @@ 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
+`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](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
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/#filtering).
@@ -967,7 +983,8 @@ 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
+`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](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
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/#filtering).
@@ -978,7 +995,7 @@ 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
+for [raw samples](https://docs.victoriametrics.com/keyconcepts/#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/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -2211,7 +2228,7 @@ MetricsQL supports and extends PromQL subqueries. See [this article](https://val
Any [rollup function](#rollup-functions) for something other than [series selector](https://docs.victoriametrics.com/keyconcepts/#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).
+since it contains [default_rollup](#default_rollup) nested into [delta](#delta).
This behavior can be disabled or logged via cmd-line flags `-search.disableImplicitConversion` and `-search.logImplicitConversion` since v1.101.0.
VictoriaMetrics performs subqueries in the following way:
@@ -2242,8 +2259,8 @@ VictoriaMetrics performs the following implicit conversions for incoming queries
* `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])`.
+ 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/#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])`.
+ For example, `rate(sum(up))` is automatically converted to `rate((sum(default_rollup(up)))[1i:1i])`.
This behavior can be disabled or logged via cmd-line flags `-search.disableImplicitConversion` and `-search.logImplicitConversion` since v1.101.0.