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docs: reference links from key concepts (#2745)

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Signed-off-by: hagen1778 <roman@victoriametrics.com>

Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
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Roman Khavronenko 2022-06-19 22:12:10 +02:00 committed by Aliaksandr Valialkin
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app/vmalert/README.md
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@ -510,7 +510,7 @@ max range per request: 8h20m0s
In `replay` mode all groups are executed sequentially one-by-one. Rules within the group are In `replay` mode all groups are executed sequentially one-by-one. Rules within the group are
executed sequentially as well (`concurrency` setting is ignored). Vmalert sends rule's expression executed sequentially as well (`concurrency` setting is ignored). Vmalert sends rule's expression
to [/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries) endpoint to [/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) endpoint
of the configured `-datasource.url`. Returned data is then processed according to the rule type and of the configured `-datasource.url`. Returned data is then processed according to the rule type and
backfilled to `-remoteWrite.url` via [remote Write protocol](https://prometheus.io/docs/prometheus/latest/storage/#remote-storage-integrations). backfilled to `-remoteWrite.url` via [remote Write protocol](https://prometheus.io/docs/prometheus/latest/storage/#remote-storage-integrations).
Vmalert respects `evaluationInterval` value set by flag or per-group during the replay. Vmalert respects `evaluationInterval` value set by flag or per-group during the replay.

170
docs/MetricsQL.md
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@ -32,7 +32,7 @@ MetricsQL implements [PromQL](https://medium.com/@valyala/promql-tutorial-for-be
This functionality can be evaluated at [an editable Grafana dashboard](https://play-grafana.victoriametrics.com/d/4ome8yJmz/node-exporter-on-victoriametrics-demo) or at your own [VictoriaMetrics instance](https://docs.victoriametrics.com/#how-to-start-victoriametrics). This functionality can be evaluated at [an editable Grafana dashboard](https://play-grafana.victoriametrics.com/d/4ome8yJmz/node-exporter-on-victoriametrics-demo) or at your own [VictoriaMetrics instance](https://docs.victoriametrics.com/#how-to-start-victoriametrics).
* Graphite-compatible filters can be passed via `{__graphite__="foo.*.bar"}` syntax. See [these docs](https://docs.victoriametrics.com/#selecting-graphite-metrics). VictoriaMetrics also can be used as Graphite datasource in Grafana. See [these docs](https://docs.victoriametrics.com/#graphite-api-usage) for details. See also [label_graphite_group](#label_graphite_group) function, which can be used for extracting the given groups from Graphite metric name. * Graphite-compatible filters can be passed via `{__graphite__="foo.*.bar"}` syntax. See [these docs](https://docs.victoriametrics.com/#selecting-graphite-metrics). VictoriaMetrics also can be used as Graphite datasource in Grafana. See [these docs](https://docs.victoriametrics.com/#graphite-api-usage) for details. See also [label_graphite_group](#label_graphite_group) function, which can be used for extracting the given groups from Graphite metric name.
* Lookbehind window in square brackets may be omitted. VictoriaMetrics automatically selects the lookbehind window depending on the current step used for building the graph (e.g. `step` query arg passed to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries)). For instance, the following query is valid in VictoriaMetrics: `rate(node_network_receive_bytes_total)`. It is equivalent to `rate(node_network_receive_bytes_total[$__interval])` when used in Grafana. * Lookbehind window in square brackets may be omitted. VictoriaMetrics automatically selects the lookbehind window depending on the current step used for building the graph (e.g. `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query)). For instance, the following query is valid in VictoriaMetrics: `rate(node_network_receive_bytes_total)`. It is equivalent to `rate(node_network_receive_bytes_total[$__interval])` when used in Grafana.
* [Aggregate functions](#aggregate-functions) accept arbitrary number of args. For example, `avg(q1, q2, q3)` would return the average values for every point across time series returned by `q1`, `q2` and `q3`. * [Aggregate functions](#aggregate-functions) accept arbitrary number of args. For example, `avg(q1, q2, q3)` would return the average values for every point across time series returned by `q1`, `q2` and `q3`.
* [@ modifier](https://prometheus.io/docs/prometheus/latest/querying/basics/#modifier) can be put anywhere in the query. For example, `sum(foo) @ end()` calculates `sum(foo)` at the `end` timestamp of the selected time range `[start ... end]`. * [@ modifier](https://prometheus.io/docs/prometheus/latest/querying/basics/#modifier) can be put anywhere in the query. For example, `sum(foo) @ end()` calculates `sum(foo)` at the `end` timestamp of the selected time range `[start ... end]`.
* Arbitrary subexpression can be used as [@ modifier](https://prometheus.io/docs/prometheus/latest/querying/basics/#modifier). For example, `foo @ (end() - 1h)` calculates `foo` at the `end - 1 hour` timestamp on the selected time range `[start ... end]`. * Arbitrary subexpression can be used as [@ modifier](https://prometheus.io/docs/prometheus/latest/querying/basics/#modifier). For example, `foo @ (end() - 1h)` calculates `foo` at the `end - 1 hour` timestamp on the selected time range `[start ... end]`.
@ -69,13 +69,13 @@ MetricsQL provides the following functions:
### Rollup functions ### Rollup functions
**Rollup functions** (aka range functions or window functions) calculate rollups over **raw samples** on the given lookbehind window for the [selected time series](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). For example, `avg_over_time(temperature[24h])` calculates the average temperature over raw samples for the last 24 hours. Additional details: **Rollup functions** (aka range functions or window functions) calculate rollups over **raw samples** on the given lookbehind window for the [selected time series](https://docs.victoriametrics.com/keyConcepts.html#filtering). For example, `avg_over_time(temperature[24h])` calculates the average temperature over raw samples for the last 24 hours. Additional details:
* If rollup functions are used for building graphs in Grafana, then the rollup is calculated independently per each point on the graph. For example, every point for `avg_over_time(temperature[24h])` graph shows the average temperature for the last 24 hours ending at this point. The interval between points is set as `step` query arg passed by Grafana to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries). * If rollup functions are used for building graphs in Grafana, then the rollup is calculated independently per each point on the graph. For example, every point for `avg_over_time(temperature[24h])` graph shows the average temperature for the last 24 hours ending at this point. The interval between points is set as `step` query arg passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
* If the given [series selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) returns multiple time series, then rollups are calculated individually per each returned series. * If the given [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) returns multiple time series, then rollups are calculated individually per each returned series.
* If lookbehind window in square brackets is missing, then MetricsQL automatically sets the lookbehind window to the interval between points on the graph (aka `step` query arg at [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries), `$__interval` value from Grafana or `1i` duration in MetricsQL). For example, `rate(http_requests_total)` is equivalent to `rate(http_requests_total[$__interval])` in Grafana. It is also equivalent to `rate(http_requests_total[1i])`. * If lookbehind window in square brackets is missing, then MetricsQL automatically sets the lookbehind window to the interval between points on the graph (aka `step` query arg at [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query), `$__interval` value from Grafana or `1i` duration in MetricsQL). For example, `rate(http_requests_total)` is equivalent to `rate(http_requests_total[$__interval])` in Grafana. It is also equivalent to `rate(http_requests_total[1i])`.
* Every [series selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) in MetricsQL must be wrapped into a rollup function. Otherwise it is automatically wrapped into [default_rollup](#default_rollup). For example, `foo{bar="baz"}` is automatically converted to `default_rollup(foo{bar="baz"}[1i])` before performing the calculations. * Every [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) in MetricsQL must be wrapped into a rollup function. Otherwise it is automatically wrapped into [default_rollup](#default_rollup). For example, `foo{bar="baz"}` is automatically converted to `default_rollup(foo{bar="baz"}[1i])` before performing the calculations.
* If something other than [series selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) is passed to rollup function, then the inner arg is automatically converted to a [subquery](#subqueries). * If something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) is passed to rollup function, then the inner arg is automatically converted to a [subquery](#subqueries).
* All the rollup functions accept optional `keep_metric_names` modifier. If it is set, then the function keeps metric names in results. See [these docs](#keep_metric_names). * All the rollup functions accept optional `keep_metric_names` modifier. If it is set, then the function keeps metric names in results. See [these docs](#keep_metric_names).
See also [implicit query conversions](#implicit-query-conversions). See also [implicit query conversions](#implicit-query-conversions).
@ -86,91 +86,91 @@ See also [implicit query conversions](#implicit-query-conversions).
#### aggr_over_time #### aggr_over_time
`aggr_over_time(("rollup_func1", "rollup_func2", ...), series_selector[d])` calculates all the listed `rollup_func*` for raw samples on the given lookbehind window `d`. The calculations are perfomed individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). `rollup_func*` can contain any rollup function. For instance, `aggr_over_time(("min_over_time", "max_over_time", "rate"), m[d])` would calculate [min_over_time](#min_over_time), [max_over_time](#max_over_time) and [rate](#rate) for `m[d]`. `aggr_over_time(("rollup_func1", "rollup_func2", ...), series_selector[d])` calculates all the listed `rollup_func*` for raw samples on the given lookbehind window `d`. The calculations are perfomed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). `rollup_func*` can contain any rollup function. For instance, `aggr_over_time(("min_over_time", "max_over_time", "rate"), m[d])` would calculate [min_over_time](#min_over_time), [max_over_time](#max_over_time) and [rate](#rate) for `m[d]`.
#### ascent_over_time #### ascent_over_time
`ascent_over_time(series_selector[d])` calculates ascent of raw sample values on the given lookbehind window `d`. The calculations are performed individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Useful for tracking height gains in GPS tracking. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [descent_over_time](#descent_over_time). `ascent_over_time(series_selector[d])` calculates ascent of raw sample values on the given lookbehind window `d`. The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Useful for tracking height gains 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 [descent_over_time](#descent_over_time).
#### avg_over_time #### avg_over_time
`avg_over_time(series_selector[d])` calculates the average value over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). This function is supported by PromQL. See also [median_over_time](#median_over_time). `avg_over_time(series_selector[d])` calculates the average value over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is supported by PromQL. See also [median_over_time](#median_over_time).
#### changes #### changes
`changes(series_selector[d])` calculates the number of times the raw samples changed on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Unlike `changes()` in Prometheus it takes into account the change from the last sample before the given lookbehind window `d`. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [changes_prometheus](#changes_prometheus). `changes(series_selector[d])` calculates the number of times the raw samples changed on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Unlike `changes()` in Prometheus it takes into account the change from the last sample before the given lookbehind window `d`. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [changes_prometheus](#changes_prometheus).
#### changes_prometheus #### changes_prometheus
`changes_prometheus(series_selector[d])` calculates the number of times the raw samples changed on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). It doesn't take into account the change from the last sample before the given lookbehind window `d` in the same way as Prometheus does. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [changes](#changes). `changes_prometheus(series_selector[d])` calculates the number of times the raw samples changed on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). It doesn't take into account the change from the last sample before the given lookbehind window `d` in the same way as Prometheus does. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [changes](#changes).
#### count_eq_over_time #### count_eq_over_time
`count_eq_over_time(series_selector[d], eq)` calculates the number of raw samples on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_over_time](#count_over_time). `count_eq_over_time(series_selector[d], eq)` calculates the number of raw samples on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_over_time](#count_over_time).
#### count_gt_over_time #### count_gt_over_time
`count_gt_over_time(series_selector[d], gt)` calculates the number of raw samples on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_over_time](#count_over_time). `count_gt_over_time(series_selector[d], gt)` calculates the number of raw samples on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_over_time](#count_over_time).
#### count_le_over_time #### count_le_over_time
`count_le_over_time(series_selector[d], le)` calculates the number of raw samples on the given lookbehind window `d`, which don't exceed `le`. It is calculated independently per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_over_time](#count_over_time). `count_le_over_time(series_selector[d], le)` calculates the number of raw samples on the given lookbehind window `d`, which don't exceed `le`. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_over_time](#count_over_time).
#### count_ne_over_time #### count_ne_over_time
`count_ne_over_time(series_selector[d], ne)` calculates the number of raw samples on the given lookbehind window `d`, which aren't equal to `ne`. It is calculated independently per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_over_time](#count_over_time). `count_ne_over_time(series_selector[d], ne)` calculates the number of raw samples on the given lookbehind window `d`, which aren't equal to `ne`. It is calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [count_over_time](#count_over_time).
#### count_over_time #### count_over_time
`count_over_time(series_selector[d])` calculates the number of raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [count_le_over_time](#count_le_over_time), [count_gt_over_time](#count_gt_over_time), [count_eq_over_time](#count_eq_over_time) and [count_ne_over_time](#count_ne_over_time). `count_over_time(series_selector[d])` calculates the number of raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [count_le_over_time](#count_le_over_time), [count_gt_over_time](#count_gt_over_time), [count_eq_over_time](#count_eq_over_time) and [count_ne_over_time](#count_ne_over_time).
#### decreases_over_time #### decreases_over_time
`decreases_over_time(series_selector[d])` calculates the number of raw sample value decreases over the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [increases_over_time](#increases_over_time). `decreases_over_time(series_selector[d])` calculates the number of raw sample value decreases over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [increases_over_time](#increases_over_time).
#### default_rollup #### default_rollup
`default_rollup(series_selector[d])` returns the last raw sample value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). `default_rollup(series_selector[d])` returns the last raw sample value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
#### delta #### delta
`delta(series_selector[d])` 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://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). The behaviour of `delta()` function in MetricsQL is slighly 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(series_selector[d])` 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 slighly 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
`delta_prometheus(series_selector[d])` 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://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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). `delta_prometheus(series_selector[d])` 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
`deriv(series_selector[d])` calculates per-second derivative over the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). The derivative is calculated using linear regression. Metric names are stripped from the resulting rollups. This function is supported by PromQL. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [deriv_fast](#deriv_fast) and [ideriv](#ideriv). `deriv(series_selector[d])` calculates per-second derivative over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). The derivative is calculated using linear regression. Metric names are stripped from the resulting rollups. This function is supported by PromQL. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [deriv_fast](#deriv_fast) and [ideriv](#ideriv).
#### deriv_fast #### deriv_fast
`deriv_fast(series_selector[d])` calculates per-second derivative using the first and the last raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [deriv](#deriv) and [ideriv](#ideriv). `deriv_fast(series_selector[d])` calculates per-second derivative using the first and the last raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`descent_over_time(series_selector[d])` calculates descent of raw sample values on the given lookbehind window `d`. The calculations are performed individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Useful for tracking height loss in GPS tracking. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [ascent_over_time](#ascent_over_time). `descent_over_time(series_selector[d])` calculates descent of raw sample values on the given lookbehind window `d`. The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). 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
`distinct_over_time(series_selector[d])` returns the number of distinct raw sample values on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. `distinct_over_time(series_selector[d])` returns the number of distinct raw sample values on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`duration_over_time(series_selector[d], max_interval)` returns the duration in seconds when time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) were present over the given lookbehind window `d`. It is expected that intervals between adjacent samples per each series don't exceed the `max_interval`. Otherwise such intervals are considered as gaps and aren't counted. See also [lifetime](#lifetime) and [lag](#lag). `duration_over_time(series_selector[d], max_interval)` 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. See also [lifetime](#lifetime) and [lag](#lag).
#### first_over_time #### first_over_time
`first_over_time(series_selector[d])` returns the first raw sample value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). See also [last_over_time](#last_over_time) and [tfirst_over_time](#tfirst_over_time). `first_over_time(series_selector[d])` returns the first raw sample value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`geomean_over_time(series_selector[d])` calculates [geometric mean](https://en.wikipedia.org/wiki/Geometric_mean) over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. `geomean_over_time(series_selector[d])` calculates [geometric mean](https://en.wikipedia.org/wiki/Geometric_mean) over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`histogram_over_time(series_selector[d])` calculates [VictoriaMetrics histogram](https://godoc.org/github.com/VictoriaMetrics/metrics#Histogram) over raw samples on the given lookbehind window `d`. It is calculated individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). The resulting histograms are useful to pass to [histogram_quantile](#histogram_quantile) for calculating quantiles over multiple gauges. For example, the following query calculates median temperature by country over the last 24 hours: `histogram_quantile(0.5, sum(histogram_over_time(temperature[24h])) by (vmrange,country))`. `histogram_over_time(series_selector[d])` calculates [VictoriaMetrics histogram](https://godoc.org/github.com/VictoriaMetrics/metrics#Histogram) over raw samples on the given lookbehind window `d`. It is calculated individually per each time series returned from the given [series_selector](https://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
@ -182,71 +182,71 @@ See also [implicit query conversions](#implicit-query-conversions).
#### holt_winters #### holt_winters
`holt_winters(series_selector[d], sf, tf)` calculates Holt-Winters value (aka [double exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing#Double_exponential_smoothing)) for raw samples over the given lookbehind window `d` using the given smoothing factor `sf` and the given trend factor `tf`. Both `sf` and `tf` must be in the range `[0...1]`. It is expected that the [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) returns time series of [gauge type](https://prometheus.io/docs/concepts/metric_types/#gauge). This function is supported by PromQL. `holt_winters(series_selector[d], sf, tf)` calculates Holt-Winters value (aka [double exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing#Double_exponential_smoothing)) for raw samples over the given lookbehind window `d` using the given smoothing factor `sf` and the given trend factor `tf`. Both `sf` and `tf` must be in the range `[0...1]`. It is expected that the [series_selector](https://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.
#### idelta #### idelta
`idelta(series_selector[d])` calculates the difference between the last two raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. `idelta(series_selector[d])` calculates the difference between the last two raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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.
#### ideriv #### ideriv
`ideriv(series_selector[d])` calculates the per-second derivative based on the last two raw samples over the given lookbehind window `d`. The derivative is calculated independently per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [deriv](#deriv). `ideriv(series_selector[d])` calculates the per-second derivative based on the last two raw samples over the given lookbehind window `d`. The derivative is calculated independently per each time series returned from the given [series_selector](https://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
`increase(series_selector[d])` calculates the increase over the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). It is expected that the `series_selector` returns time series of [counter type](https://prometheus.io/docs/concepts/metric_types/#counter). 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(series_selector[d])` 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
`increase_prometheus(series_selector[d])` calculates the increase over the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). It is expected that the `series_selector` returns time series of [counter type](https://prometheus.io/docs/concepts/metric_types/#counter). 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_prometheus(series_selector[d])` 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
`increase_pure(series_selector[d])` works the same as [increase](#increase) except of the following corner case - it assumes that [counters](https://prometheus.io/docs/concepts/metric_types/#counter) always start from 0, while [increase](#increase) ignores the first value in a series if it is too big. `increase_pure(series_selector[d])` 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
`increases_over_time(series_selector[d])` calculates the number of raw sample value increases over the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [decreases_over_time](#decreases_over_time). `increases_over_time(series_selector[d])` calculates the number of raw sample value increases over the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`integrate(series_selector[d])` calculates the integral over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. `integrate(series_selector[d])` calculates the integral over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`irate(series_selector[d])` calculates the "instant" per-second increase rate over the last two raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). It is expected that the `series_selector` returns time series of [counter type](https://prometheus.io/docs/concepts/metric_types/#counter). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. See also [rate](#rate). `irate(series_selector[d])` calculates the "instant" per-second increase rate over the last two raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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).
#### lag #### lag
`lag(series_selector[d])` returns the duration in seconds between the last sample on the given lookbehind window `d` and the timestamp of the current point. It is calculated independently per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. 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). `lag(series_selector[d])` returns the duration in seconds between the last sample on the given lookbehind window `d` and the timestamp of the current point. It is calculated independently per each time series returned from the given [series_selector](https://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
`last_over_time(series_selector[d])` returns the last raw sample value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). This function is supported by PromQL. See also [first_over_time](#first_over_time) and [tlast_over_time](#tlast_over_time). `last_over_time(series_selector[d])` returns the last raw sample value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`lifetime(series_selector[d])` returns the duration in seconds between the last and the first sample on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. 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). `lifetime(series_selector[d])` returns the duration in seconds between the last and the first sample on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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).
#### max_over_time #### max_over_time
`max_over_time(series_selector[d])` calculates the maximum value over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). This function is supported by PromQL. See also [tmax_over_time](#tmax_over_time). `max_over_time(series_selector[d])` calculates the maximum value over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`median_over_time(series_selector[d])` calculates median value over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). See also [avg_over_time](#avg_over_time). `median_over_time(series_selector[d])` calculates median value over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). See also [avg_over_time](#avg_over_time).
#### min_over_time #### min_over_time
`min_over_time(series_selector[d])` calculates the minimum value over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). This function is supported by PromQL. See also [tmin_over_time](#tmin_over_time). `min_over_time(series_selector[d])` calculates the minimum value over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`mode_over_time(series_selector[d])` calculates [mode](https://en.wikipedia.org/wiki/Mode_(statistics)) for raw samples on the given lookbehind window `d`. It is calculated individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). It is expected that raw sample values are discrete. `mode_over_time(series_selector[d])` calculates [mode](https://en.wikipedia.org/wiki/Mode_(statistics)) for raw samples on the given lookbehind window `d`. It is calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). It is expected that raw sample values are discrete.
#### predict_linear #### predict_linear
`predict_linear(series_selector[d], t)` calculates the value `t` seconds in the future using linear interpolation over raw samples on the given lookbehind window `d`. The predicted value is calculated individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). This function is supported by PromQL. `predict_linear(series_selector[d], t)` calculates the value `t` seconds in the future using linear interpolation over raw samples on the given lookbehind window `d`. The predicted value is calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is supported by PromQL.
#### present_over_time #### present_over_time
@ -254,103 +254,103 @@ See also [implicit query conversions](#implicit-query-conversions).
#### quantile_over_time #### quantile_over_time
`quantile_over_time(phi, series_selector[d])` calculates `phi`-quantile over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). The `phi` value must be in the range `[0...1]`. This function is supported by PromQL. See also [quantiles_over_time](#quantiles_over_time). `quantile_over_time(phi, series_selector[d])` calculates `phi`-quantile over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`quantiles_over_time("phiLabel", phi1, ..., phiN, series_selector[d])` calculates `phi*`-quantiles over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). The function returns individual series per each `phi*` with `{phiLabel="phi*"}` label. `phi*` values must be in the range `[0...1]`. See also [quantile_over_time](#quantile_over_time). `quantiles_over_time("phiLabel", phi1, ..., phiN, series_selector[d])` calculates `phi*`-quantiles over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`range_over_time(series_selector[d])` calculates value range over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). E.g. it calculates `max_over_time(series_selector[d]) - min_over_time(series_selector[d])`. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. `range_over_time(series_selector[d])` calculates value range over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`rate(series_selector[d])` calculates the average per-second increase rate over the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). It is expected that the `series_selector` returns time series of [counter type](https://prometheus.io/docs/concepts/metric_types/#counter). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. `rate(series_selector[d])` calculates the average per-second increase rate over the given lookbehind window `d` per each time series returned from the given [series_selector](https://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.
#### rate_over_sum #### rate_over_sum
`rate_over_sum(series_selector[d])` calculates per-second rate over the sum of raw samples on the given lookbehind window `d`. The calculations are performed indiviually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. `rate_over_sum(series_selector[d])` calculates per-second rate over the sum of raw samples on the given lookbehind window `d`. The calculations are performed indiviually per each time series returned from the given [series_selector](https://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
`resets(series_selector[d])` returns the number of [counter](https://prometheus.io/docs/concepts/metric_types/#counter) resets over the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). It is expected that the `series_selector` returns time series of [counter type](https://prometheus.io/docs/concepts/metric_types/#counter). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. `resets(series_selector[d])` 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
`rollup(series_selector[d])` calculates `min`, `max` and `avg` values for raw samples on the given lookbehind window `d`. These values are calculated individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). `rollup(series_selector[d])` calculates `min`, `max` and `avg` values for raw samples on the given lookbehind window `d`. These values are calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
#### rollup_candlestick #### rollup_candlestick
`rollup_candlestick(series_selector[d])` calculates `open`, `high`, `low` and `close` values (aka OHLC) over raw samples on the given lookbehind window `d`. The calculations are perfomed individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). This function is useful for financial applications. `rollup_candlestick(series_selector[d])` calculates `open`, `high`, `low` and `close` values (aka OHLC) over raw samples on the given lookbehind window `d`. The calculations are perfomed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is useful for financial applications.
#### rollup_delta #### rollup_delta
`rollup_delta(series_selector[d])` calculates differences between adjancent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated differences. The calculations are performed individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [rollup_increase](#rollup_increase). `rollup_delta(series_selector[d])` calculates differences between adjancent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated differences. The calculations are performed individually per each time series returned from the given [series_selector](https://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_increase](#rollup_increase).
#### rollup_deriv #### rollup_deriv
`rollup_deriv(series_selector[d])` calculates per-second derivatives for adjancent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second derivatives. The calculations are performed individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. `rollup_deriv(series_selector[d])` calculates per-second derivatives for adjancent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second derivatives. The calculations are performed individually per each time series returned from the given [series_selector](https://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_increase #### rollup_increase
`rollup_increase(series_selector[d])` calculates increases for adjancent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated increases. The calculations are performed individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [rollup_delta](#rollup_delta). `rollup_increase(series_selector[d])` calculates increases for adjancent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated increases. The calculations are performed individually per each time series returned from the given [series_selector](https://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_delta](#rollup_delta).
#### rollup_rate #### rollup_rate
`rollup_rate(series_selector[d])` calculates per-second change rates for adjancent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second change rates. The calculations are perfomed individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. `rollup_rate(series_selector[d])` calculates per-second change rates for adjancent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second change rates. The calculations are perfomed individually per each time series returned from the given [series_selector](https://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
`rollup_scrape_interval(series_selector[d])` calculates the interval in seconds between adjancent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated interval. The calculations are perfomed individually per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [scrape_interval](#scrape_interval). `rollup_scrape_interval(series_selector[d])` calculates the interval in seconds between adjancent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated interval. The calculations are perfomed individually per each time series returned from the given [series_selector](https://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 [scrape_interval](#scrape_interval).
#### scrape_interval #### scrape_interval
`scrape_interval(series_selector[d])` calculates the average interval in seconds between raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [rollup_scrape_interval](#rollup_scrape_interval). `scrape_interval(series_selector[d])` calculates the average interval in seconds between raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`share_gt_over_time(series_selector[d], gt)` returns share (in the range `[0...1]`) of raw samples on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. Useful for calculating SLI and SLO. Example: `share_gt_over_time(up[24h], 0)` - returns service availability for the last 24 hours. See also [share_le_over_time](#share_le_over_time). `share_gt_over_time(series_selector[d], gt)` returns share (in the range `[0...1]`) of raw samples on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned from the given [series_selector](https://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. Useful for calculating SLI and SLO. Example: `share_gt_over_time(up[24h], 0)` - returns service availability for the last 24 hours. See also [share_le_over_time](#share_le_over_time).
#### share_le_over_time #### share_le_over_time
`share_le_over_time(series_selector[d], le)` returns share (in the range `[0...1]`) of raw samples on the given lookbehind window `d`, which are smaller or equal to `le`. It is calculated independently per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. Useful for calculating SLI and SLO. Example: `share_le_over_time(memory_usage_bytes[24h], 100*1024*1024)` returns the share of time series values for the last 24 hours when memory usage was below or equal to 100MB. See also [share_gt_over_time](#share_gt_over_time). `share_le_over_time(series_selector[d], le)` returns share (in the range `[0...1]`) of raw samples on the given lookbehind window `d`, which are smaller or equal to `le`. It is calculated independently per each time series returned from the given [series_selector](https://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. Useful for calculating SLI and SLO. Example: `share_le_over_time(memory_usage_bytes[24h], 100*1024*1024)` returns the share of time series values for the last 24 hours when memory usage was below or equal to 100MB. See also [share_gt_over_time](#share_gt_over_time).
#### stale_samples_over_time #### stale_samples_over_time
`stale_samples_over_time(series_selector[d])` 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://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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(series_selector[d])` 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
`stddev_over_time(series_selector[d])` calculates standard deviation over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. 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). `stddev_over_time(series_selector[d])` calculates standard deviation over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`stdvar_over_time(series_selector[d])` calculates stadnard variance over raw samples on the given lookbheind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. 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). `stdvar_over_time(series_selector[d])` calculates stadnard variance over raw samples on the given lookbheind window `d` per each time series returned from the given [series_selector](https://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
`sum_over_time(series_selector[d])` calculates the sum of raw sample values on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. This function is supported by PromQL. `sum_over_time(series_selector[d])` calculates the sum of raw sample values on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`sum2_over_time(series_selector[d])` calculates the sum of squares for raw sample values on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. `sum2_over_time(series_selector[d])` calculates the sum of squares for raw sample values on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`timestamp(series_selector[d])` returns the timestamp in seconds for the last raw sample on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. 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(series_selector[d])` returns the timestamp in seconds for the last raw sample on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`timestamp_with_name(series_selector[d])` returns the timestamp in seconds for the last raw sample on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are preserved in the resulting rollups. See also [timestamp](#timestamp). `timestamp_with_name(series_selector[d])` returns the timestamp in seconds for the last raw sample on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). Metric names are preserved in the resulting rollups. See also [timestamp](#timestamp).
#### tfirst_over_time #### tfirst_over_time
`tfirst_over_time(series_selector[d])` returns the timestamp in seconds for the first raw sample on the given lookbehind window `d` per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [first_over_time](#first_over_time). `tfirst_over_time(series_selector[d])` returns the timestamp in seconds for the first raw sample on the given lookbehind window `d` per each time series returned from the given [series_selector](https://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
`tlast_change_over_time(series_selector[d])` returns the timestamp in seconds for the last change per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) 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_change_over_time(series_selector[d])` 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
@ -358,21 +358,21 @@ See also [implicit query conversions](#implicit-query-conversions).
#### tmax_over_time #### tmax_over_time
`tmax_over_time(series_selector[d])` returns the timestamp in seconds for the raw sample with the maximum value on the given lookbehind window `d`. It is calculated independently per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). 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). `tmax_over_time(series_selector[d])` returns the timestamp in seconds for the raw sample with the maximum value on the given lookbehind window `d`. It is calculated independently per each time series returned from the given [series_selector](https://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
`tmin_over_time(series_selector[d])` returns the timestamp in seconds for the raw sample with the minimum value on the given lookbehind window `d`. It is calculated independently per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [min_over_time](#min_over_time). `tmin_over_time(series_selector[d])` returns the timestamp in seconds for the raw sample with the minimum value on the given lookbehind window `d`. It is calculated independently per each time series returned from the given [series_selector](https://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
`zscore_over_time(series_selector[d])` calculates returns [z-score](https://en.wikipedia.org/wiki/Standard_score) for raw samples on the given lookbehind window `d`. It is calculated independently per each time series returned from the given [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. `zscore_over_time(series_selector[d])` calculates returns [z-score](https://en.wikipedia.org/wiki/Standard_score) for raw samples on the given lookbehind window `d`. It is calculated independently per each time series returned from the given [series_selector](https://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.
### Transform functions ### Transform functions
**Transform functions** calculate transformations over rollup results. For example, `abs(delta(temperature[24h]))` calculates the absolute value for every point of every time series returned from the rollup `delta(temperature[24h])`. Additional details: **Transform functions** calculate transformations over rollup results. For example, `abs(delta(temperature[24h]))` calculates the absolute value for every point of every time series returned from the rollup `delta(temperature[24h])`. Additional details:
* If transform function is applied directly to a [series selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors), then the [default_rollup()](#default_rollup) function is automatically applied before calculating the transformations. For example, `abs(temperature)` is implicitly transformed to `abs(default_rollup(temperature[1i]))`. * 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). * 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). See also [implicit query conversions](#implicit-query-conversions).
@ -467,7 +467,7 @@ See also [implicit query conversions](#implicit-query-conversions).
#### end #### end
`end()` returns the unix timestamp in seconds for the last point. See also [start](#start). It is known as `end` query arg passed to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries). `end()` returns the unix timestamp in seconds for the last point. See also [start](#start). It is known as `end` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
#### exp #### exp
@ -679,11 +679,11 @@ See also [implicit query conversions](#implicit-query-conversions).
#### start #### start
`start()` returns unix timestamp in seconds for the first point. See also [end](#end). It is known as `start` query arg passed to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries). `start()` returns unix timestamp in seconds for the first point. See also [end](#end). It is known as `start` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
#### step #### step
`step()` returns the step in seconds (aka interval) between the returned points. It is known as `step` query arg passed to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries). `step()` returns the step in seconds (aka interval) between the returned points. It is known as `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
#### time #### time
@ -713,7 +713,7 @@ See also [implicit query conversions](#implicit-query-conversions).
**Label manipulation functions** perform manipulations with lables on the selected rollup results. Additional details: **Label manipulation functions** perform manipulations with lables on the selected rollup results. Additional details:
* If label manipulation function is applied directly to a [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors), then the [default_rollup()](#default_rollup) function is automatically applied before performing the label transformation. For example, `alias(temperature, "foo")` is implicitly transformed to `alias(default_rollup(temperature[1i]), "foo")`. * 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). See also [implicit query conversions](#implicit-query-conversions).
@ -797,7 +797,7 @@ sum by (__name__) (
**Aggregate functions** calculate aggregates over groups of rollup results. Additional details: **Aggregate functions** calculate aggregates over groups of rollup results. Additional details:
* By default a single group is used for aggregation. Multiple independent groups can be set up by specifying grouping labels in `by` and `without` modifiers. For example, `count(up) by (job)` would group rollup results by `job` label value and calculate the [count](#count) aggregate function independently per each group, while `count(up) without (instance)` would group rollup results by all the labels except `instance` before calculating [count](#count) aggregate function independently per each group. Multiple labels can be put in `by` and `without` modifiers. * By default a single group is used for aggregation. Multiple independent groups can be set up by specifying grouping labels in `by` and `without` modifiers. For example, `count(up) by (job)` would group rollup results by `job` label value and calculate the [count](#count) aggregate function independently per each group, while `count(up) without (instance)` would group rollup results by all the labels except `instance` before calculating [count](#count) aggregate function independently per each group. Multiple labels can be put in `by` and `without` modifiers.
* If the aggregate function is applied directly to a [series_selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors), then the [default_rollup()](#default_rollup) function is automatically applied before cacluating the aggregate. For example, `count(up)` is implicitly transformed to `count(default_rollup(up[1i]))`. * 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 cacluating the aggregate. For example, `count(up)` is implicitly transformed to `count(default_rollup(up[1i]))`.
* Aggregate functions accept arbitrary number of args. For example, `avg(q1, q2, q3)` would return the average values for every point across time series returned by `q1`, `q2` and `q3`. * Aggregate functions 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. * 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.
@ -945,22 +945,22 @@ See also [implicit query conversions](#implicit-query-conversions).
## Subqueries ## 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://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) 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). 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: 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 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://prometheus.io/docs/prometheus/latest/querying/api/#range-queries). * 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 ## Implicit query conversions
VictoriaMetrics performs the following implicit conversions for incoming queries before starting the calculations: VictoriaMetrics performs the following implicit conversions for incoming queries before starting the calculations:
* If lookbehind window in square brackets is missing inside [rollup function](#rollup-functions), then `[1i]` is automatically added there. The `[1i]` means one `step` value, which is passed to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries). It is also known as `$__interval` in Grafana. For example, `rate(http_requests_count)` is automatically transformed to `rate(http_requests_count[1i])`. * If lookbehind window in square brackets is missing inside [rollup function](#rollup-functions), then `[1i]` is automatically added there. The `[1i]` means one `step` value, which is passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query). It is also known as `$__interval` in Grafana. For example, `rate(http_requests_count)` is automatically transformed to `rate(http_requests_count[1i])`.
* All the [series selectors](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors), which aren't wrapped into [rollup functions](#rollup-functions), are automatically wrapped into [default_rollup](#default_rollup) function. Examples: * 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` is transformed to `default_rollup(foo[1i])`
* `foo + bar` is transformed to `default_rollup(foo[1i]) + default_rollup(bar[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) * `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) * `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 `step` in square brackets is missing inside [subquery](#subqueries), then `1i` step is automatically added there. For example, `avg_over_time(rate(http_requests_total[5m])[1h])` is automatically converted to `avg_over_time(rate(http_requests_total[5m])[1h:1i])`.
* If something other than [series selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) is passed to [rollup function](#rollup-functions), then a [subquery](#subqueries) with `1i` lookbehind window and `1i` step is automatically formed. For example, `rate(sum(up))` is automatically converted to `rate((sum(default_rollup(up[1i])))[1i:1i])`. * 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])`.

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@ -936,7 +936,7 @@ for metrics to export. Use `{__name__=~".*"}` selector for fetching all the time
On large databases you may experience problems with limit on the number of time series, which can be exported. In this case you need to adjust `-search.maxExportSeries` command-line flag: On large databases you may experience problems with limit on the number of time series, which can be exported. In this case you need to adjust `-search.maxExportSeries` command-line flag:
```console ```console
# count unique timeseries in database # count unique time series in database
wget -O- -q 'http://your_victoriametrics_instance:8428/api/v1/series/count' | jq '.data[0]' wget -O- -q 'http://your_victoriametrics_instance:8428/api/v1/series/count' | jq '.data[0]'
# relaunch victoriametrics with search.maxExportSeries more than value from previous command # relaunch victoriametrics with search.maxExportSeries more than value from previous command

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@ -940,7 +940,7 @@ for metrics to export. Use `{__name__=~".*"}` selector for fetching all the time
On large databases you may experience problems with limit on the number of time series, which can be exported. In this case you need to adjust `-search.maxExportSeries` command-line flag: On large databases you may experience problems with limit on the number of time series, which can be exported. In this case you need to adjust `-search.maxExportSeries` command-line flag:
```console ```console
# count unique timeseries in database # count unique time series in database
wget -O- -q 'http://your_victoriametrics_instance:8428/api/v1/series/count' | jq '.data[0]' wget -O- -q 'http://your_victoriametrics_instance:8428/api/v1/series/count' | jq '.data[0]'
# relaunch victoriametrics with search.maxExportSeries more than value from previous command # relaunch victoriametrics with search.maxExportSeries more than value from previous command

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@ -221,8 +221,8 @@ curl -G 'http://<vmselect>:8481/select/0/prometheus/api/v1/query?query=vm_http_r
Additional information: Additional information:
* [Prometheus querying API usage](https://docs.victoriametrics.com/#prometheus-querying-api-usage) * [Prometheus querying API usage](https://docs.victoriametrics.com/#prometheus-querying-api-usage)
* [Instant queries](https://prometheus.io/docs/prometheus/latest/querying/api/#instant-queries) * [Instant queries](https://docs.victoriametrics.com/keyConcepts.html#instant-query)
* [Instant vector selectors](https://prometheus.io/docs/prometheus/latest/querying/basics/#instant-vector-selectors) * [Query language](https://docs.victoriametrics.com/keyConcepts.html#metricsql)
## /api/v1/query_range ## /api/v1/query_range
@ -260,8 +260,8 @@ curl -G http://<vmselect>:8481/select/0/prometheus/api/v1/query_range --data-url
Additional information: Additional information:
* [Prometheus querying API usage](https://docs.victoriametrics.com/#prometheus-querying-api-usage) * [Prometheus querying API usage](https://docs.victoriametrics.com/#prometheus-querying-api-usage)
* [Range queries](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries) * [Range queries](https://docs.victoriametrics.com/keyConcepts.html#range-query)
* [Range Vector Selectors](https://prometheus.io/docs/prometheus/latest/querying/basics/#range-vector-selectors) * [Query language](https://docs.victoriametrics.com/keyConcepts.html#metricsql)
## /api/v1/series ## /api/v1/series

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@ -514,7 +514,7 @@ max range per request: 8h20m0s
In `replay` mode all groups are executed sequentially one-by-one. Rules within the group are In `replay` mode all groups are executed sequentially one-by-one. Rules within the group are
executed sequentially as well (`concurrency` setting is ignored). Vmalert sends rule's expression executed sequentially as well (`concurrency` setting is ignored). Vmalert sends rule's expression
to [/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries) endpoint to [/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) endpoint
of the configured `-datasource.url`. Returned data is then processed according to the rule type and of the configured `-datasource.url`. Returned data is then processed according to the rule type and
backfilled to `-remoteWrite.url` via [remote Write protocol](https://prometheus.io/docs/prometheus/latest/storage/#remote-storage-integrations). backfilled to `-remoteWrite.url` via [remote Write protocol](https://prometheus.io/docs/prometheus/latest/storage/#remote-storage-integrations).
Vmalert respects `evaluationInterval` value set by flag or per-group during the replay. Vmalert respects `evaluationInterval` value set by flag or per-group during the replay.