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114 KiB
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2015 lines
114 KiB
Markdown
---
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sort: 23
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weight: 23
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title: MetricsQL
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menu:
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docs:
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parent: 'victoriametrics'
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weight: 23
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aliases:
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- /ExtendedPromQL.html
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- /MetricsQL.html
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---
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# MetricsQL
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[VictoriaMetrics](https://github.com/VictoriaMetrics/VictoriaMetrics) implements MetricsQL -
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query language inspired by [PromQL](https://prometheus.io/docs/prometheus/latest/querying/basics/).
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MetricsQL is backwards-compatible with PromQL, so Grafana dashboards backed by Prometheus datasource should work
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the same after switching from Prometheus to VictoriaMetrics.
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However, there are some [intentional differences](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) between these two languages.
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[Standalone MetricsQL package](https://godoc.org/github.com/VictoriaMetrics/metricsql) can be used for parsing MetricsQL in external apps.
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If you are unfamiliar with PromQL, then it is suggested reading [this tutorial for beginners](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085).
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The following functionality is implemented differently in MetricsQL compared to PromQL. This improves user experience:
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* MetricsQL takes into account the previous point before the window in square brackets for range functions such as [rate](#rate) and [increase](#increase).
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This allows returning the exact results users expect for `increase(metric[$__interval])` queries instead of incomplete results Prometheus returns for such queries.
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* MetricsQL doesn't extrapolate range function results. This addresses [this issue from Prometheus](https://github.com/prometheus/prometheus/issues/3746).
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See technical details about VictoriaMetrics and Prometheus calculations for [rate](#rate)
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and [increase](#increase) [in this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1215#issuecomment-850305711).
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* MetricsQL returns the expected non-empty responses for [rate](#rate) with `step` values smaller than scrape interval.
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This addresses [this issue from Grafana](https://github.com/grafana/grafana/issues/11451).
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See also [this blog post](https://www.percona.com/blog/2020/02/28/better-prometheus-rate-function-with-victoriametrics/).
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* MetricsQL treats `scalar` type the same as `instant vector` without labels, since subtle differences between these types usually confuse users.
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See [the corresponding Prometheus docs](https://prometheus.io/docs/prometheus/latest/querying/basics/#expression-language-data-types) for details.
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* MetricsQL removes all the `NaN` values from the output, so some queries like `(-1)^0.5` return empty results in VictoriaMetrics,
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while returning a series of `NaN` values in Prometheus. Note that Grafana doesn't draw any lines or dots for `NaN` values,
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so the end result looks the same for both VictoriaMetrics and Prometheus.
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* MetricsQL keeps metric names after applying functions, which don't change the meaning of the original time series.
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For example, [min_over_time(foo)](#min_over_time) or [round(foo)](#round) leaves `foo` metric name in the result.
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See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/674) for details.
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Read more about the differences between PromQL and MetricsQL in [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e).
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Other PromQL functionality should work the same in MetricsQL.
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[File an issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues) if you notice discrepancies between PromQL and MetricsQL results other than mentioned above.
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## MetricsQL features
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MetricsQL implements [PromQL](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085)
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and provides additional functionality mentioned below, which is aimed towards solving practical cases.
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Feel free [filing a feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues) if you think MetricsQL misses certain useful functionality.
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This functionality can be evaluated at [VictoriaMetrics playground](https://play.victoriametrics.com/select/accounting/1/6a716b0f-38bc-4856-90ce-448fd713e3fe/prometheus/graph/)
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or at your own [VictoriaMetrics instance](https://docs.victoriametrics.com/#how-to-start-victoriametrics).
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The list of MetricsQL features on top of PromQL:
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* Graphite-compatible filters can be passed via `{__graphite__="foo.*.bar"}` syntax.
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See [these docs](https://docs.victoriametrics.com/#selecting-graphite-metrics).
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VictoriaMetrics also can be used as Graphite datasource in Grafana.
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See [these docs](https://docs.victoriametrics.com/#graphite-api-usage) for details.
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See also [label_graphite_group](#label_graphite_group) function, which can be used for extracting the given groups from Graphite metric name.
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* Lookbehind window in square brackets may be omitted. VictoriaMetrics automatically selects the lookbehind window
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depending on the current step used for building the graph (e.g. `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query)).
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For instance, the following query is valid in VictoriaMetrics: `rate(node_network_receive_bytes_total)`.
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It is equivalent to `rate(node_network_receive_bytes_total[$__interval])` when used in Grafana.
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* Numeric values can contain `_` delimiters for better readability. For example, `1_234_567_890` can be used in queries instead of `1234567890`.
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* [Series selectors](https://docs.victoriametrics.com/keyConcepts.html#filtering) accept multiple `or` filters. For example, `{env="prod",job="a" or env="dev",job="b"}`
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selects series with either `{env="prod",job="a"}` or `{env="dev",job="b"}` labels.
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See [these docs](https://docs.victoriametrics.com/keyConcepts.html#filtering-by-multiple-or-filters) for details.
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* Support for `group_left(*)` and `group_right(*)` for copying all the labels from time series on the `one` side
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of [many-to-one operations](https://prometheus.io/docs/prometheus/latest/querying/operators/#many-to-one-and-one-to-many-vector-matches).
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The copied label names may clash with the existing label names, so MetricsQL provides an ability to add prefix to the copied metric names
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via `group_left(*) prefix "..."` syntax.
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For example, the following query copies all the `namespace`-related labels from `kube_namespace_labels` to `kube_pod_info` series,
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while adding `ns_` prefix to the copied labels: `kube_pod_info * on(namespace) group_left(*) prefix "ns_" kube_namespace_labels`.
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Labels from the `on()` list aren't copied.
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* [Aggregate functions](#aggregate-functions) accept arbitrary number of args.
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For example, `avg(q1, q2, q3)` would return the average values for every point across time series returned by `q1`, `q2` and `q3`.
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* [@ modifier](https://prometheus.io/docs/prometheus/latest/querying/basics/#modifier) can be put anywhere in the query.
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For example, `sum(foo) @ end()` calculates `sum(foo)` at the `end` timestamp of the selected time range `[start ... end]`.
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* Arbitrary subexpression can be used as [@ modifier](https://prometheus.io/docs/prometheus/latest/querying/basics/#modifier).
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For example, `foo @ (end() - 1h)` calculates `foo` at the `end - 1 hour` timestamp on the selected time range `[start ... end]`.
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* [offset](https://prometheus.io/docs/prometheus/latest/querying/basics/#offset-modifier), lookbehind window in square brackets
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and `step` value for [subquery](#subqueries) may refer to the current step aka `$__interval` value from Grafana with `[Ni]` syntax.
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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.
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* [offset](https://prometheus.io/docs/prometheus/latest/querying/basics/#offset-modifier) may be put anywhere in the query. For instance, `sum(foo) offset 24h`.
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* Lookbehind window in square brackets and [offset](https://prometheus.io/docs/prometheus/latest/querying/basics/#offset-modifier) may be fractional.
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For instance, `rate(node_network_receive_bytes_total[1.5m] offset 0.5d)`.
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* The duration suffix is optional. The duration is in seconds if the suffix is missing.
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For example, `rate(m[300] offset 1800)` is equivalent to `rate(m[5m]) offset 30m`.
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* 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`.
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* 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`.
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* Trailing commas on all the lists are allowed - label filters, function args and with expressions.
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For instance, the following queries are valid: `m{foo="bar",}`, `f(a, b,)`, `WITH (x=y,) x`.
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This simplifies maintenance of multi-line queries.
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* Metric names and label names may contain any unicode letter. For example `температура{город="Киев"}` is a value MetricsQL expression.
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* Metric names and labels names may contain escaped chars. For example, `foo\-bar{baz\=aa="b"}` is valid expression.
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It returns time series with name `foo-bar` containing label `baz=aa` with value `b`.
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Additionally, the following escape sequences are supported:
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- `\xXX`, where `XX` is hexadecimal representation of the escaped ascii char.
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- `\uXXXX`, where `XXXX` is a hexadecimal representation of the escaped unicode char.
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* Aggregate functions support optional `limit N` suffix in order to limit the number of output series.
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For example, `sum(x) by (y) limit 3` limits the number of output time series after the aggregation to 3.
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All the other time series are dropped.
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* [histogram_quantile](#histogram_quantile) accepts optional third arg - `boundsLabel`.
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In this case it returns `lower` and `upper` bounds for the estimated percentile.
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See [this issue for details](https://github.com/prometheus/prometheus/issues/5706).
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* `default` binary operator. `q1 default q2` fills gaps in `q1` with the corresponding values from `q2`.
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* `if` binary operator. `q1 if q2` removes values from `q1` for missing values from `q2`.
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* `ifnot` binary operator. `q1 ifnot q2` removes values from `q1` for existing values from `q2`.
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* `WITH` templates. This feature simplifies writing and managing complex queries.
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Go to [WITH templates playground](https://play.victoriametrics.com/select/accounting/1/6a716b0f-38bc-4856-90ce-448fd713e3fe/expand-with-exprs) and try it.
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* String literals may be concatenated. This is useful with `WITH` templates:
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`WITH (commonPrefix="long_metric_prefix_") {__name__=commonPrefix+"suffix1"} / {__name__=commonPrefix+"suffix2"}`.
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* `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).
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This modifier prevents from dropping metric names in function results. See [these docs](#keep_metric_names).
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## keep_metric_names
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By default, metric names are dropped after applying functions or [binary operators](https://prometheus.io/docs/prometheus/latest/querying/operators/#binary-operators),
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since they may change the meaning of the original time series.
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This may result in `duplicate time series` error when the function is applied to multiple time series with different names.
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This error can be fixed by applying `keep_metric_names` modifier to the function or binary operator.
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For example:
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- `rate({__name__=~"foo|bar"}) keep_metric_names` leaves `foo` and `bar` metric names in the returned time series.
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- `({__name__=~"foo|bar"} / 10) keep_metric_names` leaves `foo` and `bar` metric names in the returned time series.
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## MetricsQL functions
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If you are unfamiliar with PromQL, then please read [this tutorial](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085) at first.
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MetricsQL provides the following functions:
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* [Rollup functions](#rollup-functions)
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* [Transform functions](#transform-functions)
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* [Label manipulation functions](#label-manipulation-functions)
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* [Aggregate functions](#aggregate-functions)
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### Rollup functions
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**Rollup functions** (aka range functions or window functions) calculate rollups over **raw samples**
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on the given lookbehind window for the [selected time series](https://docs.victoriametrics.com/keyConcepts.html#filtering).
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For example, `avg_over_time(temperature[24h])` calculates the average temperature over raw samples for the last 24 hours.
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Additional details:
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* If rollup functions are used for building graphs in Grafana, then the rollup is calculated independently per each point on the graph.
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For example, every point for `avg_over_time(temperature[24h])` graph shows the average temperature for the last 24 hours ending at this point.
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The interval between points is set as `step` query arg passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
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* If the given [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) returns multiple time series,
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then rollups are calculated individually per each returned series.
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* If lookbehind window in square brackets is missing, then MetricsQL automatically sets the lookbehind window
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to the interval between points on the graph (aka `step` query arg at [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query),
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`$__interval` value from Grafana or `1i` duration in MetricsQL).
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For example, `rate(http_requests_total)` is equivalent to `rate(http_requests_total[$__interval])` in Grafana.
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It is also equivalent to `rate(http_requests_total[1i])`.
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* Every [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) in MetricsQL must be wrapped into a rollup function.
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Otherwise, it is automatically wrapped into [default_rollup](#default_rollup). For example, `foo{bar="baz"}`
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is automatically converted to `default_rollup(foo{bar="baz"}[1i])` before performing the calculations.
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* If something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) is passed to rollup function,
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then the inner arg is automatically converted to a [subquery](#subqueries).
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* All the rollup functions accept optional `keep_metric_names` modifier. If it is set, then the function keeps metric names in results.
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See [these docs](#keep_metric_names).
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See also [implicit query conversions](#implicit-query-conversions).
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The list of supported rollup functions:
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#### absent_over_time
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`absent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns 1
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if the given lookbehind window `d` doesn't contain raw samples. Otherwise, it returns an empty result.
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This function is supported by PromQL. See also [present_over_time](#present_over_time).
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#### aggr_over_time
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`aggr_over_time(("rollup_func1", "rollup_func2", ...), series_selector[d])` is a [rollup function](#rollup-functions),
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which calculates all the listed `rollup_func*` for raw samples on the given lookbehind window `d`.
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The calculations are performed individually per each time series returned
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from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
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`rollup_func*` can contain any rollup function. For instance, `aggr_over_time(("min_over_time", "max_over_time", "rate"), m[d])`
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would calculate [min_over_time](#min_over_time), [max_over_time](#max_over_time) and [rate](#rate) for `m[d]`.
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#### ascent_over_time
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`ascent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates
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ascent of raw sample values on the given lookbehind window `d`. The calculations are performed individually
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per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
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This function is useful for tracking height gains in GPS tracking. Metric names are stripped from the resulting rollups.
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Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
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See also [descent_over_time](#descent_over_time).
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#### avg_over_time
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`avg_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average value
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over raw samples on the given lookbehind window `d` per each time series returned
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from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
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This function is supported by PromQL. See also [median_over_time](#median_over_time).
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#### changes
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`changes(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times
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the raw samples changed on the given lookbehind window `d` per each time series returned
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from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
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Unlike `changes()` in Prometheus it takes into account the change from the last sample before the given lookbehind window `d`.
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See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
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Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
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This function is supported by PromQL. See also [changes_prometheus](#changes_prometheus).
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#### changes_prometheus
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`changes_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times
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the raw samples changed on the given lookbehind window `d` per each time series returned
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from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
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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.
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See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
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Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
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This function is supported by PromQL. See also [changes](#changes).
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#### count_eq_over_time
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`count_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
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on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned
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from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
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Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
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See also [count_over_time](#count_over_time).
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#### count_gt_over_time
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`count_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
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on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned
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from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
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Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
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See also [count_over_time](#count_over_time).
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#### count_le_over_time
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`count_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
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on the given lookbehind window `d`, which don't exceed `le`. It is calculated independently per each time series returned
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from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
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Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
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See also [count_over_time](#count_over_time).
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#### count_ne_over_time
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`count_ne_over_time(series_selector[d], ne)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
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on the given lookbehind window `d`, which aren't equal to `ne`. It is calculated independently per each time series returned
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from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
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Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
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See also [count_over_time](#count_over_time).
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#### count_over_time
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`count_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of raw samples
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on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
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Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
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This function is supported by PromQL. See also [count_le_over_time](#count_le_over_time), [count_gt_over_time](#count_gt_over_time),
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[count_eq_over_time](#count_eq_over_time) and [count_ne_over_time](#count_ne_over_time).
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#### decreases_over_time
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`decreases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of raw sample value decreases
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over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
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Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
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See also [increases_over_time](#increases_over_time).
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#### default_rollup
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`default_rollup(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d`
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per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
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#### delta
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`delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between
|
|
the last sample before the given lookbehind window `d` and the last sample at the given lookbehind window `d`
|
|
per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
The behaviour of `delta()` function in MetricsQL is slightly different to the behaviour of `delta()` function in Prometheus.
|
|
See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [increase](#increase) and [delta_prometheus](#delta_prometheus).
|
|
|
|
#### delta_prometheus
|
|
|
|
`delta_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between
|
|
the first and the last samples at the given lookbehind window `d` per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
The behaviour of `delta_prometheus()` is close to the behaviour of `delta()` function in Prometheus.
|
|
See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [delta](#delta).
|
|
|
|
#### deriv
|
|
|
|
`deriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivative over the given lookbehind window `d`
|
|
per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
The derivative is calculated using linear regression.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [deriv_fast](#deriv_fast) and [ideriv](#ideriv).
|
|
|
|
#### deriv_fast
|
|
|
|
`deriv_fast(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivative
|
|
using the first and the last raw samples on the given lookbehind window `d` per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [deriv](#deriv) and [ideriv](#ideriv).
|
|
|
|
#### descent_over_time
|
|
|
|
`descent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates descent of raw sample values
|
|
on the given lookbehind window `d`. The calculations are performed individually per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
This function is useful for tracking height loss in GPS tracking.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [ascent_over_time](#ascent_over_time).
|
|
|
|
#### distinct_over_time
|
|
|
|
`distinct_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number of distinct raw sample values
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
#### duration_over_time
|
|
|
|
`duration_over_time(series_selector[d], max_interval)` is a [rollup function](#rollup-functions), which returns the duration in seconds
|
|
when time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) were present
|
|
over the given lookbehind window `d`. It is expected that intervals between adjacent samples per each series don't exceed the `max_interval`.
|
|
Otherwise, such intervals are considered as gaps and aren't counted.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [lifetime](#lifetime) and [lag](#lag).
|
|
|
|
#### first_over_time
|
|
|
|
`first_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the first raw sample value
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
See also [last_over_time](#last_over_time) and [tfirst_over_time](#tfirst_over_time).
|
|
|
|
#### geomean_over_time
|
|
|
|
`geomean_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [geometric mean](https://en.wikipedia.org/wiki/Geometric_mean)
|
|
over raw samples on the given lookbehind window `d` per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
#### histogram_over_time
|
|
|
|
`histogram_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates
|
|
[VictoriaMetrics histogram](https://godoc.org/github.com/VictoriaMetrics/metrics#Histogram) over raw samples on the given lookbehind window `d`.
|
|
It is calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
The resulting histograms are useful to pass to [histogram_quantile](#histogram_quantile) for calculating quantiles
|
|
over multiple [gauges](https://docs.victoriametrics.com/keyConcepts.html#gauge).
|
|
For example, the following query calculates median temperature by country over the last 24 hours:
|
|
|
|
`histogram_quantile(0.5, sum(histogram_over_time(temperature[24h])) by (vmrange,country))`.
|
|
|
|
#### hoeffding_bound_lower
|
|
|
|
`hoeffding_bound_lower(phi, series_selector[d])` is a [rollup function](#rollup-functions), which calculates
|
|
lower [Hoeffding bound](https://en.wikipedia.org/wiki/Hoeffding%27s_inequality) for the given `phi` in the range `[0...1]`.
|
|
|
|
See also [hoeffding_bound_upper](#hoeffding_bound_upper).
|
|
|
|
#### hoeffding_bound_upper
|
|
|
|
`hoeffding_bound_upper(phi, series_selector[d])` is a [rollup function](#rollup-functions), which calculates
|
|
upper [Hoeffding bound](https://en.wikipedia.org/wiki/Hoeffding%27s_inequality) for the given `phi` in the range `[0...1]`.
|
|
|
|
See also [hoeffding_bound_lower](#hoeffding_bound_lower).
|
|
|
|
#### holt_winters
|
|
|
|
`holt_winters(series_selector[d], sf, tf)` is a [rollup function](#rollup-functions), which calculates Holt-Winters value
|
|
(aka [double exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing#Double_exponential_smoothing)) for raw samples
|
|
over the given lookbehind window `d` using the given smoothing factor `sf` and the given trend factor `tf`.
|
|
Both `sf` and `tf` must be in the range `[0...1]`. It is expected that the [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering)
|
|
returns time series of [gauge type](https://docs.victoriametrics.com/keyConcepts.html#gauge).
|
|
|
|
This function is supported by PromQL. See also [range_linear_regression](#range_linear_regression).
|
|
|
|
#### idelta
|
|
|
|
`idelta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between the last two raw samples
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [delta](#delta).
|
|
|
|
#### ideriv
|
|
|
|
`ideriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the per-second derivative based on the last two raw samples
|
|
over the given lookbehind window `d`. The derivative is calculated independently per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [deriv](#deriv).
|
|
|
|
#### increase
|
|
|
|
`increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase over the given lookbehind window `d`
|
|
per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
|
|
|
|
Unlike Prometheus, it takes into account the last sample before the given lookbehind window `d` when calculating the result.
|
|
See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [increase_pure](#increase_pure), [increase_prometheus](#increase_prometheus) and [delta](#delta).
|
|
|
|
#### increase_prometheus
|
|
|
|
`increase_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase
|
|
over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
|
|
It doesn't take into account the last sample before the given lookbehind window `d` when calculating the result in the same way as Prometheus does.
|
|
See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [increase_pure](#increase_pure) and [increase](#increase).
|
|
|
|
#### increase_pure
|
|
|
|
`increase_pure(series_selector[d])` is a [rollup function](#rollup-functions), which works the same as [increase](#increase) except
|
|
of the following corner case - it assumes that [counters](https://docs.victoriametrics.com/keyConcepts.html#counter) always start from 0,
|
|
while [increase](#increase) ignores the first value in a series if it is too big.
|
|
|
|
#### increases_over_time
|
|
|
|
`increases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of raw sample value increases
|
|
over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [decreases_over_time](#decreases_over_time).
|
|
|
|
#### integrate
|
|
|
|
`integrate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the integral over raw samples on the given lookbehind window `d`
|
|
per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
#### irate
|
|
|
|
`irate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the "instant" per-second increase rate over the last two raw samples
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [rate](#rate) and [rollup_rate](#rollup_rate).
|
|
|
|
#### lag
|
|
|
|
`lag(series_selector[d])` is a [rollup function](#rollup-functions), which returns the duration in seconds between the last sample
|
|
on the given lookbehind window `d` and the timestamp of the current point. It is calculated independently per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [lifetime](#lifetime) and [duration_over_time](#duration_over_time).
|
|
|
|
#### last_over_time
|
|
|
|
`last_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d`
|
|
per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
This function is supported by PromQL. See also [first_over_time](#first_over_time) and [tlast_over_time](#tlast_over_time).
|
|
|
|
#### lifetime
|
|
|
|
`lifetime(series_selector[d])` is a [rollup function](#rollup-functions), which returns the duration in seconds between the last and the first sample
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [duration_over_time](#duration_over_time) and [lag](#lag).
|
|
|
|
#### mad_over_time
|
|
|
|
`mad_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation)
|
|
over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
See also [mad](#mad) and [range_mad](#range_mad).
|
|
|
|
#### max_over_time
|
|
|
|
`max_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the maximum value over raw samples
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
This function is supported by PromQL. See also [tmax_over_time](#tmax_over_time).
|
|
|
|
#### median_over_time
|
|
|
|
`median_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates median value over raw samples
|
|
on the given lookbehind window `d` per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
See also [avg_over_time](#avg_over_time).
|
|
|
|
#### min_over_time
|
|
|
|
`min_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the minimum value over raw samples
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
This function is supported by PromQL. See also [tmin_over_time](#tmin_over_time).
|
|
|
|
#### mode_over_time
|
|
|
|
`mode_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [mode](https://en.wikipedia.org/wiki/Mode_(statistics))
|
|
for raw samples on the given lookbehind window `d`. It is calculated individually per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). It is expected that raw sample values are discrete.
|
|
|
|
#### predict_linear
|
|
|
|
`predict_linear(series_selector[d], t)` is a [rollup function](#rollup-functions), which calculates the value `t` seconds in the future using
|
|
linear interpolation over raw samples on the given lookbehind window `d`. The predicted value is calculated individually per each time series
|
|
returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
This function is supported by PromQL. See also [range_linear_regression](#range_linear_regression).
|
|
|
|
#### present_over_time
|
|
|
|
`present_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns 1 if there is at least a single raw sample
|
|
on the given lookbehind window `d`. Otherwise, an empty result is returned.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL.
|
|
|
|
#### quantile_over_time
|
|
|
|
`quantile_over_time(phi, series_selector[d])` is a [rollup function](#rollup-functions), which calculates `phi`-quantile over raw samples
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
The `phi` value must be in the range `[0...1]`.
|
|
|
|
This function is supported by PromQL. See also [quantiles_over_time](#quantiles_over_time).
|
|
|
|
#### quantiles_over_time
|
|
|
|
`quantiles_over_time("phiLabel", phi1, ..., phiN, series_selector[d])` is a [rollup function](#rollup-functions), which calculates `phi*`-quantiles
|
|
over raw samples on the given lookbehind window `d` per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
The function returns individual series per each `phi*` with `{phiLabel="phi*"}` label. `phi*` values must be in the range `[0...1]`.
|
|
|
|
See also [quantile_over_time](#quantile_over_time).
|
|
|
|
#### range_over_time
|
|
|
|
`range_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates value range over raw samples
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
E.g. it calculates `max_over_time(series_selector[d]) - min_over_time(series_selector[d])`.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
#### rate
|
|
|
|
`rate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average per-second increase rate
|
|
over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [irate](#irate) and [rollup_rate](#rollup_rate).
|
|
|
|
#### rate_over_sum
|
|
|
|
`rate_over_sum(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second rate over the sum of raw samples
|
|
on the given lookbehind window `d`. The calculations are performed individually per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
#### resets
|
|
|
|
`resets(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number
|
|
of [counter](https://docs.victoriametrics.com/keyConcepts.html#counter) resets over the given lookbehind window `d`
|
|
per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL.
|
|
|
|
#### rollup
|
|
|
|
`rollup(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `min`, `max` and `avg` values for raw samples
|
|
on the given lookbehind window `d` and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
|
|
These values are calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
|
|
|
|
#### rollup_candlestick
|
|
|
|
`rollup_candlestick(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `open`, `high`, `low` and `close` values (aka OHLC)
|
|
over raw samples on the given lookbehind window `d` and returns them in time series with `rollup="open"`, `rollup="high"`, `rollup="low"` and `rollup="close"` additional labels.
|
|
The calculations are performed individually per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is useful for financial applications.
|
|
|
|
Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
|
|
|
|
#### rollup_delta
|
|
|
|
`rollup_delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates differences between adjacent raw samples
|
|
on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated differences
|
|
and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
|
|
The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [rollup_increase](#rollup_increase).
|
|
|
|
#### rollup_deriv
|
|
|
|
`rollup_deriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivatives
|
|
for adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second derivatives
|
|
and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
|
|
The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
#### rollup_increase
|
|
|
|
`rollup_increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates increases for adjacent raw samples
|
|
on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated increases
|
|
and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
|
|
The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
|
|
|
|
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.
|
|
|
|
The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
#### rollup_scrape_interval
|
|
|
|
`rollup_scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the interval in seconds between
|
|
adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated interval
|
|
and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
|
|
The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [scrape_interval](#scrape_interval).
|
|
|
|
#### scrape_interval
|
|
|
|
`scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average interval in seconds between raw samples
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [rollup_scrape_interval](#rollup_scrape_interval).
|
|
|
|
#### share_gt_over_time
|
|
|
|
`share_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
|
|
on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
This function is useful for calculating SLI and SLO. Example: `share_gt_over_time(up[24h], 0)` - returns service availability for the last 24 hours.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [share_le_over_time](#share_le_over_time).
|
|
|
|
#### share_le_over_time
|
|
|
|
`share_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
|
|
on the given lookbehind window `d`, which are smaller or equal to `le`. It is calculated independently per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
This function is useful for calculating SLI and SLO. Example: `share_le_over_time(memory_usage_bytes[24h], 100*1024*1024)` returns
|
|
the share of time series values for the last 24 hours when memory usage was below or equal to 100MB.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [share_gt_over_time](#share_gt_over_time).
|
|
|
|
#### share_eq_over_time
|
|
|
|
`share_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
|
|
on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
#### stale_samples_over_time
|
|
|
|
`stale_samples_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number
|
|
of [staleness markers](https://docs.victoriametrics.com/vmagent.html#prometheus-staleness-markers) on the given lookbehind window `d`
|
|
per each time series matching the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
#### stddev_over_time
|
|
|
|
`stddev_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard deviation over raw samples
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [stdvar_over_time](#stdvar_over_time).
|
|
|
|
#### stdvar_over_time
|
|
|
|
`stdvar_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard variance over raw samples
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [stddev_over_time](#stddev_over_time).
|
|
|
|
#### sum_over_time
|
|
|
|
`sum_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of raw sample values
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL.
|
|
|
|
#### sum2_over_time
|
|
|
|
`sum2_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of squares for raw sample values
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
#### timestamp
|
|
|
|
`timestamp(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds for the last raw sample
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [timestamp_with_name](#timestamp_with_name).
|
|
|
|
#### timestamp_with_name
|
|
|
|
`timestamp_with_name(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds for the last raw sample
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are preserved in the resulting rollups.
|
|
|
|
See also [timestamp](#timestamp).
|
|
|
|
#### tfirst_over_time
|
|
|
|
`tfirst_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds for the first raw sample
|
|
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [first_over_time](#first_over_time).
|
|
|
|
#### tlast_change_over_time
|
|
|
|
`tlast_change_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds for the last change
|
|
per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) on the given lookbehind window `d`.
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [last_over_time](#last_over_time).
|
|
|
|
#### tlast_over_time
|
|
|
|
`tlast_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which is an alias for [timestamp](#timestamp).
|
|
|
|
See also [tlast_change_over_time](#tlast_change_over_time).
|
|
|
|
#### tmax_over_time
|
|
|
|
`tmax_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds for the raw sample
|
|
with the maximum value on the given lookbehind window `d`. It is calculated independently per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [max_over_time](#max_over_time).
|
|
|
|
#### tmin_over_time
|
|
|
|
`tmin_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds for the raw sample
|
|
with the minimum value on the given lookbehind window `d`. It is calculated independently per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [min_over_time](#min_over_time).
|
|
|
|
#### zscore_over_time
|
|
|
|
`zscore_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns [z-score](https://en.wikipedia.org/wiki/Standard_score)
|
|
for raw samples on the given lookbehind window `d`. It is calculated independently per each time series returned
|
|
from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
|
|
|
|
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
See also [zscore](#zscore) and [range_trim_zscore](#range_trim_zscore).
|
|
|
|
|
|
### Transform functions
|
|
|
|
**Transform functions** calculate transformations over [rollup results](#rollup-functions).
|
|
For example, `abs(delta(temperature[24h]))` calculates the absolute value for every point of every time series
|
|
returned from the rollup `delta(temperature[24h])`.
|
|
|
|
Additional details:
|
|
|
|
* If transform function is applied directly to a [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
|
|
then the [default_rollup()](#default_rollup) function is automatically applied before calculating the transformations.
|
|
For example, `abs(temperature)` is implicitly transformed to `abs(default_rollup(temperature[1i]))`.
|
|
* 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. 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.
|
|
|
|
#### 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.
|
|
|
|
#### 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).
|
|
|
|
#### end
|
|
|
|
`end()` is a [transform function](#transform-functions), which returns the unix timestamp in seconds for the last point.
|
|
It is known as `end` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
|
|
|
|
See also [start](#start), [time](#time) and [now](#now).
|
|
|
|
#### exp
|
|
|
|
`exp(q)` is a [transform function](#transform-functions), which calculates the `e^v` for every point `v` of every time series returned by `q`.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [ln](#ln).
|
|
|
|
#### floor
|
|
|
|
`floor(q)` is a [transform function](#transform-functions), which rounds every point for every time series returned by `q` to the lower nearest integer.
|
|
|
|
This function is supported by PromQL. See also [ceil](#ceil) and [round](#round).
|
|
|
|
#### histogram_avg
|
|
|
|
`histogram_avg(buckets)` is a [transform function](#transform-functions), which calculates the average value for the given `buckets`.
|
|
It can be used for calculating the average over the given time range across multiple time series.
|
|
For example, `histogram_avg(sum(histogram_over_time(response_time_duration_seconds[5m])) by (vmrange,job))` would return the average response time
|
|
per each `job` over the last 5 minutes.
|
|
|
|
#### histogram_quantile
|
|
|
|
`histogram_quantile(phi, buckets)` is a [transform function](#transform-functions), which calculates `phi`-[percentile](https://en.wikipedia.org/wiki/Percentile)
|
|
over the given [histogram buckets](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350).
|
|
`phi` must be in the range `[0...1]`. For example, `histogram_quantile(0.5, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))`
|
|
would return median request duration for all the requests during the last 5 minutes.
|
|
|
|
The function accepts optional third arg - `boundsLabel`. In this case it returns `lower` and `upper` bounds for the estimated percentile with the given `boundsLabel` label.
|
|
See [this issue for details](https://github.com/prometheus/prometheus/issues/5706).
|
|
|
|
When the [percentile](https://en.wikipedia.org/wiki/Percentile) is calculated over multiple histograms,
|
|
then all the input histograms **must** have buckets with identical boundaries, e.g. they must have the same set of `le` or `vmrange` labels.
|
|
Otherwise, the returned result may be invalid. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3231) for details.
|
|
|
|
This function is supported by PromQL (except of the `boundLabel` arg). See also [histogram_quantiles](#histogram_quantiles), [histogram_share](#histogram_share)
|
|
and [quantile](#quantile).
|
|
|
|
#### histogram_quantiles
|
|
|
|
`histogram_quantiles("phiLabel", phi1, ..., phiN, buckets)` is a [transform function](#transform-functions), which calculates the given `phi*`-quantiles
|
|
over the given [histogram buckets](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350).
|
|
Argument `phi*` must be in the range `[0...1]`. For example, `histogram_quantiles('le', 0.3, 0.5, sum(rate(http_request_duration_seconds_bucket[5m]) by (le))`.
|
|
Each calculated quantile is returned in a separate time series with the corresponding `{phiLabel="phi*"}` label.
|
|
|
|
See also [histogram_quantile](#histogram_quantile).
|
|
|
|
#### histogram_share
|
|
|
|
`histogram_share(le, buckets)` is a [transform function](#transform-functions), which calculates the share (in the range `[0...1]`)
|
|
for `buckets` that fall below `le`. This function is useful for calculating SLI and SLO. This is inverse to [histogram_quantile](#histogram_quantile).
|
|
|
|
The function accepts optional third arg - `boundsLabel`. In this case it returns `lower` and `upper` bounds for the estimated share with the given `boundsLabel` label.
|
|
|
|
#### histogram_stddev
|
|
|
|
`histogram_stddev(buckets)` is a [transform function](#transform-functions), which calculates standard deviation for the given `buckets`.
|
|
|
|
#### histogram_stdvar
|
|
|
|
`histogram_stdvar(buckets)` is a [transform function](#transform-functions), which calculates standard variance for the given `buckets`.
|
|
It can be used for calculating standard deviation over the given time range across multiple time series.
|
|
For example, `histogram_stdvar(sum(histogram_over_time(temperature[24])) by (vmrange,country))` would return standard deviation
|
|
for the temperature per each country over the last 24 hours.
|
|
|
|
#### hour
|
|
|
|
`hour(q)` is a [transform function](#transform-functions), which returns the hour for every point of every time series returned by `q`.
|
|
It is expected that `q` returns unix timestamps. The returned values are in the range `[0...23]`.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL.
|
|
|
|
#### interpolate
|
|
|
|
`interpolate(q)` is a [transform function](#transform-functions), which fills gaps with linearly interpolated values calculated
|
|
from the last and the next non-empty points per each time series returned by `q`.
|
|
|
|
See also [keep_last_value](#keep_last_value) and [keep_next_value](#keep_next_value).
|
|
|
|
#### keep_last_value
|
|
|
|
`keep_last_value(q)` is a [transform function](#transform-functions), which fills gaps with the value of the last non-empty point
|
|
in every time series returned by `q`.
|
|
|
|
See also [keep_next_value](#keep_next_value) and [interpolate](#interpolate).
|
|
|
|
#### keep_next_value
|
|
|
|
`keep_next_value(q)` is a [transform function](#transform-functions), which fills gaps with the value of the next non-empty point
|
|
in every time series returned by `q`.
|
|
|
|
See also [keep_last_value](#keep_last_value) and [interpolate](#interpolate).
|
|
|
|
#### limit_offset
|
|
|
|
`limit_offset(limit, offset, q)` is a [transform function](#transform-functions), which skips `offset` time series from series returned by `q`
|
|
and then returns up to `limit` of the remaining time series per each group.
|
|
|
|
This allows implementing simple paging for `q` time series. See also [limitk](#limitk).
|
|
|
|
#### ln
|
|
|
|
`ln(q)` is a [transform function](#transform-functions), which calculates `ln(v)` for every point `v` of every time series returned by `q`.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [exp](#exp) and [log2](#log2).
|
|
|
|
#### log2
|
|
|
|
`log2(q)` is a [transform function](#transform-functions), which calculates `log2(v)` for every point `v` of every time series returned by `q`.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [log10](#log10) and [ln](#ln).
|
|
|
|
#### log10
|
|
|
|
`log10(q)` is a [transform function](#transform-functions), which calculates `log10(v)` for every point `v` of every time series returned by `q`.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [log2](#log2) and [ln](#ln).
|
|
|
|
#### minute
|
|
|
|
`minute(q)` is a [transform function](#transform-functions), which returns the minute for every point of every time series returned by `q`.
|
|
It is expected that `q` returns unix timestamps. The returned values are in the range `[0...59]`.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL.
|
|
|
|
#### month
|
|
|
|
`month(q)` is a [transform function](#transform-functions), which returns the month for every point of every time series returned by `q`.
|
|
It is expected that `q` returns unix timestamps. The returned values are in the range `[1...12]`, where `1` means January and `12` means December.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL.
|
|
|
|
#### now
|
|
|
|
`now()` is a [transform function](#transform-functions), which returns the current timestamp as a floating-point value in seconds.
|
|
|
|
See also [time](#time).
|
|
|
|
#### pi
|
|
|
|
`pi()` is a [transform function](#transform-functions), which returns [Pi number](https://en.wikipedia.org/wiki/Pi).
|
|
|
|
This function is supported by PromQL.
|
|
|
|
#### rad
|
|
|
|
`rad(q)` is a [transform function](#transform-functions), which converts [degrees to Radians](https://en.wikipedia.org/wiki/Radian#Conversions)
|
|
for every point of every time series returned by `q`.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL. See also [deg](#deg).
|
|
|
|
#### prometheus_buckets
|
|
|
|
`prometheus_buckets(buckets)` is a [transform function](#transform-functions), which converts
|
|
[VictoriaMetrics histogram buckets](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350) with `vmrange` labels
|
|
to Prometheus histogram buckets with `le` labels. This may be useful for building heatmaps in Grafana.
|
|
|
|
See also [histogram_quantile](#histogram_quantile) and [buckets_limit](#buckets_limit).
|
|
|
|
#### rand
|
|
|
|
`rand(seed)` is a [transform function](#transform-functions), which returns pseudo-random numbers on the range `[0...1]` with even distribution.
|
|
Optional `seed` can be used as a seed for pseudo-random number generator.
|
|
|
|
See also [rand_normal](#rand_normal) and [rand_exponential](#rand_exponential).
|
|
|
|
#### rand_exponential
|
|
|
|
`rand_exponential(seed)` is a [transform function](#transform-functions), which returns pseudo-random numbers
|
|
with [exponential distribution](https://en.wikipedia.org/wiki/Exponential_distribution). Optional `seed` can be used as a seed for pseudo-random number generator.
|
|
|
|
See also [rand](#rand) and [rand_normal](#rand_normal).
|
|
|
|
#### rand_normal
|
|
|
|
`rand_normal(seed)` is a [transform function](#transform-functions), which returns pseudo-random numbers
|
|
with [normal distribution](https://en.wikipedia.org/wiki/Normal_distribution). Optional `seed` can be used as a seed for pseudo-random number generator.
|
|
|
|
See also [rand](#rand) and [rand_exponential](#rand_exponential).
|
|
|
|
#### range_avg
|
|
|
|
`range_avg(q)` is a [transform function](#transform-functions), which calculates the avg value across points per each time series returned by `q`.
|
|
|
|
#### range_first
|
|
|
|
`range_first(q)` is a [transform function](#transform-functions), which returns the value for the first point per each time series returned by `q`.
|
|
|
|
#### range_last
|
|
|
|
`range_last(q)` is a [transform function](#transform-functions), which returns the value for the last point per each time series returned by `q`.
|
|
|
|
#### range_linear_regression
|
|
|
|
`range_linear_regression(q)` is a [transform function](#transform-functions), which calculates [simple linear regression](https://en.wikipedia.org/wiki/Simple_linear_regression)
|
|
over the selected time range per each time series returned by `q`. This function is useful for capacity planning and predictions.
|
|
|
|
#### range_mad
|
|
|
|
`range_mad(q)` is a [transform function](#transform-functions), which calculates the [median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation)
|
|
across points per each time series returned by `q`.
|
|
|
|
See also [mad](#mad) and [mad_over_time](#mad_over_time).
|
|
|
|
#### range_max
|
|
|
|
`range_max(q)` is a [transform function](#transform-functions), which calculates the max value across points per each time series returned by `q`.
|
|
|
|
#### range_median
|
|
|
|
`range_median(q)` is a [transform function](#transform-functions), which calculates the median value across points per each time series returned by `q`.
|
|
|
|
#### range_min
|
|
|
|
`range_min(q)` is a [transform function](#transform-functions), which calculates the min value across points per each time series returned by `q`.
|
|
|
|
#### range_normalize
|
|
|
|
`range_normalize(q1, ...)` is a [transform function](#transform-functions), which normalizes values for time series returned by `q1, ...` into `[0 ... 1]` range.
|
|
This function is useful for correlating time series with distinct value ranges.
|
|
|
|
See also [share](#share).
|
|
|
|
#### range_quantile
|
|
|
|
`range_quantile(phi, q)` is a [transform function](#transform-functions), which returns `phi`-quantile across points per each time series returned by `q`.
|
|
`phi` must be in the range `[0...1]`.
|
|
|
|
#### range_stddev
|
|
|
|
`range_stddev(q)` is a [transform function](#transform-functions), which calculates [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation)
|
|
per each time series returned by `q` on the selected time range.
|
|
|
|
#### range_stdvar
|
|
|
|
`range_stdvar(q)` is a [transform function](#transform-functions), which calculates [standard variance](https://en.wikipedia.org/wiki/Variance)
|
|
per each time series returned by `q` on the selected time range.
|
|
|
|
#### range_sum
|
|
|
|
`range_sum(q)` is a [transform function](#transform-functions), which calculates the sum of points per each time series returned by `q`.
|
|
|
|
#### range_trim_outliers
|
|
|
|
`range_trim_outliers(k, q)` is a [transform function](#transform-functions), which drops points located farther than `k*range_mad(q)`
|
|
from the `range_median(q)`. E.g. it is equivalent to the following query: `q ifnot (abs(q - range_median(q)) > k*range_mad(q))`.
|
|
|
|
See also [range_trim_spikes](#range_trim_spikes) and [range_trim_zscore](#range_trim_zscore).
|
|
|
|
#### range_trim_spikes
|
|
|
|
`range_trim_spikes(phi, q)` is a [transform function](#transform-functions), which drops `phi` percent of biggest spikes from time series returned by `q`.
|
|
The `phi` must be in the range `[0..1]`, where `0` means `0%` and `1` means `100%`.
|
|
|
|
See also [range_trim_outliers](#range_trim_outliers) and [range_trim_zscore](#range_trim_zscore).
|
|
|
|
#### range_trim_zscore
|
|
|
|
`range_trim_zscore(z, q)` is a [transform function](#transform-functions), which drops points located farther than `z*range_stddev(q)`
|
|
from the `range_avg(q)`. E.g. it is equivalent to the following query: `q ifnot (abs(q - range_avg(q)) > z*range_avg(q))`.
|
|
|
|
See also [range_trim_outliers](#range_trim_outliers) and [range_trim_spikes](#range_trim_spikes).
|
|
|
|
#### range_zscore
|
|
|
|
`range_zscore(q)` is a [transform function](#transform-functions), which calculates [z-score](https://en.wikipedia.org/wiki/Standard_score)
|
|
for points returned by `q`, e.g. it is equivalent to the following query: `(q - range_avg(q)) / range_stddev(q)`.
|
|
|
|
#### remove_resets
|
|
|
|
`remove_resets(q)` is a [transform function](#transform-functions), which removes counter resets from time series returned by `q`.
|
|
|
|
#### round
|
|
|
|
`round(q, nearest)` is a [transform function](#transform-functions), which rounds every point of every time series returned by `q` to the `nearest` multiple.
|
|
If `nearest` is missing then the rounding is performed to the nearest integer.
|
|
|
|
This function is supported by PromQL. See also [floor](#floor) and [ceil](#ceil).
|
|
|
|
#### ru
|
|
|
|
`ru(free, max)` is a [transform function](#transform-functions), which calculates resource utilization in the range `[0%...100%]` for the given `free` and `max` resources.
|
|
For instance, `ru(node_memory_MemFree_bytes, node_memory_MemTotal_bytes)` returns memory utilization over [node_exporter](https://github.com/prometheus/node_exporter) metrics.
|
|
|
|
#### running_avg
|
|
|
|
`running_avg(q)` is a [transform function](#transform-functions), which calculates the running avg per each time series returned by `q`.
|
|
|
|
#### running_max
|
|
|
|
`running_max(q)` is a [transform function](#transform-functions), which calculates the running max per each time series returned by `q`.
|
|
|
|
#### running_min
|
|
|
|
`running_min(q)` is a [transform function](#transform-functions), which calculates the running min per each time series returned by `q`.
|
|
|
|
#### running_sum
|
|
|
|
`running_sum(q)` is a [transform function](#transform-functions), which calculates the running sum per each time series returned by `q`.
|
|
|
|
#### scalar
|
|
|
|
`scalar(q)` is a [transform function](#transform-functions), which returns `q` if `q` contains only a single time series. Otherwise, it returns nothing.
|
|
|
|
This function is supported by PromQL.
|
|
|
|
#### sgn
|
|
|
|
`sgn(q)` is a [transform function](#transform-functions), which returns `1` if `v>0`, `-1` if `v<0` and `0` if `v==0` for every point `v`
|
|
of every time series returned by `q`.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL.
|
|
|
|
#### sin
|
|
|
|
`sin(q)` is a [transform function](#transform-functions), which returns `sin(v)` for every `v` point of every time series returned by `q`.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by MetricsQL. See also [cos](#cos).
|
|
|
|
#### sinh
|
|
|
|
`sinh(q)` is a [transform function](#transform-functions), which returns [hyperbolic sine](https://en.wikipedia.org/wiki/Hyperbolic_functions)
|
|
for every point of every time series returned by `q`.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by MetricsQL. See also [cosh](#cosh).
|
|
|
|
#### tan
|
|
|
|
`tan(q)` is a [transform function](#transform-functions), which returns `tan(v)` for every `v` point of every time series returned by `q`.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by MetricsQL. See also [atan](#atan).
|
|
|
|
#### tanh
|
|
|
|
`tanh(q)` is a [transform function](#transform-functions), which returns [hyperbolic tangent](https://en.wikipedia.org/wiki/Hyperbolic_functions)
|
|
for every point of every time series returned by `q`.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by MetricsQL. See also [atanh](#atanh).
|
|
|
|
#### smooth_exponential
|
|
|
|
`smooth_exponential(q, sf)` is a [transform function](#transform-functions), which smooths points per each time series returned
|
|
by `q` using [exponential moving average](https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average) with the given smooth factor `sf`.
|
|
|
|
#### sort
|
|
|
|
`sort(q)` is a [transform function](#transform-functions), which sorts series in ascending order by the last point in every time series returned by `q`.
|
|
|
|
This function is supported by PromQL. See also [sort_desc](#sort_desc) and [sort_by_label](#sort_by_label).
|
|
|
|
#### sort_desc
|
|
|
|
`sort_desc(q)` is a [transform function](#transform-functions), which sorts series in descending order by the last point in every time series returned by `q`.
|
|
|
|
This function is supported by PromQL. See also [sort](#sort) and [sort_by_label](#sort_by_label_desc).
|
|
|
|
#### sqrt
|
|
|
|
`sqrt(q)` is a [transform function](#transform-functions), which calculates square root for every point of every time series returned by `q`.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL.
|
|
|
|
#### start
|
|
|
|
`start()` is a [transform function](#transform-functions), which returns unix timestamp in seconds for the first point.
|
|
|
|
It is known as `start` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
|
|
|
|
See also [end](#end), [time](#time) and [now](#now).
|
|
|
|
#### step
|
|
|
|
`step()` is a [transform function](#transform-functions), which returns the step in seconds (aka interval) between the returned points.
|
|
It is known as `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
|
|
|
|
See also [start](#start) and [end](#end).
|
|
|
|
#### time
|
|
|
|
`time()` is a [transform function](#transform-functions), which returns unix timestamp for every returned point.
|
|
|
|
This function is supported by PromQL. See also [now](#now), [start](#start) and [end](#end).
|
|
|
|
#### timezone_offset
|
|
|
|
`timezone_offset(tz)` is a [transform function](#transform-functions), which returns offset in seconds for the given timezone `tz` relative to UTC.
|
|
This can be useful when combining with datetime-related functions. For example, `day_of_week(time()+timezone_offset("America/Los_Angeles"))`
|
|
would return weekdays for `America/Los_Angeles` time zone.
|
|
|
|
Special `Local` time zone can be used for returning an offset for the time zone set on the host where VictoriaMetrics runs.
|
|
|
|
See [the list of supported timezones](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones).
|
|
|
|
#### ttf
|
|
|
|
`ttf(free)` is a [transform function](#transform-functions), which estimates the time in seconds needed to exhaust `free` resources.
|
|
For instance, `ttf(node_filesystem_avail_byte)` returns the time to storage space exhaustion. This function may be useful for capacity planning.
|
|
|
|
#### union
|
|
|
|
`union(q1, ..., qN)` is a [transform function](#transform-functions), which returns a union of time series returned from `q1`, ..., `qN`.
|
|
The `union` function name can be skipped - the following queries are equivalent: `union(q1, q2)` and `(q1, q2)`.
|
|
|
|
It is expected that each `q*` query returns time series with unique sets of labels.
|
|
Otherwise, only the first time series out of series with identical set of labels is returned.
|
|
Use [alias](#alias) and [label_set](#label_set) functions for giving unique labelsets per each `q*` query:
|
|
|
|
#### vector
|
|
|
|
`vector(q)` is a [transform function](#transform-functions), which returns `q`, e.g. it does nothing in MetricsQL.
|
|
|
|
This function is supported by PromQL.
|
|
|
|
#### year
|
|
|
|
`year(q)` is a [transform function](#transform-functions), which returns the year for every point of every time series returned by `q`.
|
|
It is expected that `q` returns unix timestamps.
|
|
|
|
Metric names are stripped from the resulting series. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
|
|
|
|
This function is supported by PromQL.
|
|
|
|
### Label manipulation functions
|
|
|
|
**Label manipulation functions** perform manipulations with labels on the selected [rollup results](#rollup-functions).
|
|
|
|
Additional details:
|
|
|
|
* If label manipulation function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
|
|
then the [default_rollup()](#default_rollup) function is automatically applied before performing the label transformation.
|
|
For example, `alias(temperature, "foo")` is implicitly transformed to `alias(default_rollup(temperature[1i]), "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<any_value>.bar.<other_value>` metric names with `foo<any_value>.<other_value>`.
|
|
|
|
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).
|
|
|
|
#### 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).
|
|
|
|
#### 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.
|
|
|
|
#### sort_by_label
|
|
|
|
`sort_by_label(q, label1, ... labelN)` is [label manipulation function](#label-manipulation-functions), which sorts series in ascending order by the given set of labels.
|
|
For example, `sort_by_label(foo, "bar")` would sort `foo` series by values of the label `bar` in these series.
|
|
|
|
See also [sort_by_label_desc](#sort_by_label_desc) and [sort_by_label_numeric](#sort_by_label_numeric).
|
|
|
|
#### sort_by_label_desc
|
|
|
|
`sort_by_label_desc(q, label1, ... labelN)` is [label manipulation function](#label-manipulation-functions), which sorts series in descending order by the given set of labels.
|
|
For example, `sort_by_label(foo, "bar")` would sort `foo` series by values of the label `bar` in these series.
|
|
|
|
See also [sort_by_label](#sort_by_label) and [sort_by_label_numeric_desc](#sort_by_label_numeric_desc).
|
|
|
|
#### sort_by_label_numeric
|
|
|
|
`sort_by_label_numeric(q, label1, ... labelN)` is [label manipulation function](#label-manipulation-functions), which sorts series in ascending order by the given set of labels
|
|
using [numeric sort](https://www.gnu.org/software/coreutils/manual/html_node/Version-sort-is-not-the-same-as-numeric-sort.html).
|
|
For example, if `foo` series have `bar` label with values `1`, `101`, `15` and `2`, then `sort_by_label_numeric(foo, "bar")` would return series
|
|
in the following order of `bar` label values: `1`, `2`, `15` and `101`.
|
|
|
|
See also [sort_by_label_numeric_desc](#sort_by_label_numeric_desc) and [sort_by_label](#sort_by_label).
|
|
|
|
#### sort_by_label_numeric_desc
|
|
|
|
`sort_by_label_numeric_desc(q, label1, ... labelN)` is [label manipulation function](#label-manipulation-functions), which sorts series in descending order
|
|
by the given set of labels using [numeric sort](https://www.gnu.org/software/coreutils/manual/html_node/Version-sort-is-not-the-same-as-numeric-sort.html).
|
|
For example, if `foo` series have `bar` label with values `1`, `101`, `15` and `2`, then `sort_by_label_numeric(foo, "bar")`
|
|
would return series in the following order of `bar` label values: `101`, `15`, `2` and `1`.
|
|
|
|
See also [sort_by_label_numeric](#sort_by_label_numeric) and [sort_by_label_desc](#sort_by_label_desc).
|
|
|
|
|
|
### Aggregate functions
|
|
|
|
**Aggregate functions** calculate aggregates over groups of [rollup results](#rollup-functions).
|
|
|
|
Additional details:
|
|
|
|
* By default, a single group is used for aggregation. Multiple independent groups can be set up by specifying grouping labels
|
|
in `by` and `without` modifiers. For example, `count(up) by (job)` would group [rollup results](#rollup-functions) by `job` label value
|
|
and calculate the [count](#count) aggregate function independently per each group, while `count(up) without (instance)`
|
|
would group [rollup results](#rollup-functions) by all the labels except `instance` before calculating [count](#count) aggregate function independently per each group.
|
|
Multiple labels can be put in `by` and `without` modifiers.
|
|
* If the aggregate function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
|
|
then the [default_rollup()](#default_rollup) function is automatically applied before calculating the aggregate.
|
|
For example, `count(up)` is implicitly transformed to `count(default_rollup(up[1i]))`.
|
|
* Aggregate functions accept arbitrary number of args. For example, `avg(q1, q2, q3)` would return the average values for every point
|
|
across time series returned by `q1`, `q2` and `q3`.
|
|
* Aggregate functions support optional `limit N` suffix, which can be used for limiting the number of output groups.
|
|
For example, `sum(x) by (y) limit 3` limits the number of groups for the aggregation to 3. All the other groups are ignored.
|
|
|
|
See also [implicit query conversions](#implicit-query-conversions).
|
|
|
|
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_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.
|
|
|
|
#### distinct
|
|
|
|
`distinct(q)` is [aggregate function](#aggregate-functions), which calculates the number of unique values per each group of points with the same timestamp.
|
|
|
|
#### 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.
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#### min
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`min(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns the minimum value per each `group_labels`
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for all the time series returned by `q`. The aggregate is calculated individually per each group of points with the same timestamp.
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This function is supported by PromQL.
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#### mode
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`mode(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns [mode](https://en.wikipedia.org/wiki/Mode_(statistics))
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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.
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#### outliers_mad
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|
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`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`.
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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)`.
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See also [outliersk](#outliersk) and [mad](#mad).
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#### outliersk
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`outliersk(k, q)` is [aggregate function](#aggregate-functions), which returns up to `k` time series with the biggest standard deviation (aka outliers)
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|
out of time series returned by `q`.
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|
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|
See also [outliers_mad](#outliers_mad).
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#### quantile
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`quantile(phi, q) by (group_labels)` is [aggregate function](#aggregate-functions), which calculates `phi`-quantile per each `group_labels`
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|
for all the time series returned by `q`. `phi` must be in the range `[0...1]`.
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|
The aggregate is calculated individually per each group of points with the same timestamp.
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|
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This function is supported by PromQL. See also [quantiles](#quantiles) and [histogram_quantile](#histogram_quantile).
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|
|
|
#### quantiles
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|
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`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.
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|
|
|
See also [quantile](#quantile).
|
|
|
|
#### share
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|
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|
`share(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns shares in the range `[0..1]`
|
|
for every non-negative points returned by `q` per each timestamp, so the sum of shares per each `group_labels` equals 1.
|
|
|
|
This function is useful for normalizing [histogram bucket](https://docs.victoriametrics.com/keyConcepts.html#histogram) shares
|
|
into `[0..1]` range:
|
|
|
|
```metricsql
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|
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_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) and [range_trim_zscore](#range_trim_zscore).
|
|
|
|
## Subqueries
|
|
|
|
MetricsQL supports and extends PromQL subqueries. See [this article](https://valyala.medium.com/prometheus-subqueries-in-victoriametrics-9b1492b720b3) for details.
|
|
Any [rollup function](#rollup-functions) for something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) form a subquery.
|
|
Nested rollup functions can be implicit thanks to the [implicit query conversions](#implicit-query-conversions).
|
|
For example, `delta(sum(m))` is implicitly converted to `delta(sum(default_rollup(m[1i]))[1i:1i])`, so it becomes a subquery,
|
|
since it contains [default_rollup](#default_rollup) nested into [delta](#delta).
|
|
|
|
VictoriaMetrics performs subqueries in the following way:
|
|
|
|
* It calculates the inner rollup function using the `step` value from the outer rollup function.
|
|
For example, for expression `max_over_time(rate(http_requests_total[5m])[1h:30s])` the inner function `rate(http_requests_total[5m])`
|
|
is calculated with `step=30s`. The resulting data points are aligned by the `step`.
|
|
* It calculates the outer rollup function over the results of the inner rollup function using the `step` value
|
|
passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
|
|
|
|
## Implicit query conversions
|
|
|
|
VictoriaMetrics performs the following implicit conversions for incoming queries before starting the calculations:
|
|
|
|
* If lookbehind window in square brackets is missing inside [rollup function](#rollup-functions),
|
|
then `[1i]` is automatically added there. The `[1i]` means one `step` value, which is passed
|
|
to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
|
|
It is also known as `$__interval` in Grafana. For example, `rate(http_requests_count)` is automatically transformed to `rate(http_requests_count[1i])`.
|
|
* All the [series selectors](https://docs.victoriametrics.com/keyConcepts.html#filtering),
|
|
which aren't wrapped into [rollup functions](#rollup-functions), are automatically wrapped into [default_rollup](#default_rollup) function.
|
|
Examples:
|
|
* `foo` is transformed to `default_rollup(foo[1i])`
|
|
* `foo + bar` is transformed to `default_rollup(foo[1i]) + default_rollup(bar[1i])`
|
|
* `count(up)` is transformed to `count(default_rollup(up[1i]))`, because [count](#count) isn't a [rollup function](#rollup-functions) -
|
|
it is [aggregate function](#aggregate-functions)
|
|
* `abs(temperature)` is transformed to `abs(default_rollup(temperature[1i]))`, because [abs](#abs) isn't a [rollup function](#rollup-functions) -
|
|
it is [transform function](#transform-functions)
|
|
* If `step` in square brackets is missing inside [subquery](#subqueries), then `1i` step is automatically added there.
|
|
For example, `avg_over_time(rate(http_requests_total[5m])[1h])` is automatically converted to `avg_over_time(rate(http_requests_total[5m])[1h:1i])`.
|
|
* If something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering)
|
|
is passed to [rollup function](#rollup-functions), then a [subquery](#subqueries) with `1i` lookbehind window and `1i` step is automatically formed.
|
|
For example, `rate(sum(up))` is automatically converted to `rate((sum(default_rollup(up[1i])))[1i:1i])`.
|