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app/vlselect/vmui: run make vmui-logs-update

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<!doctype html><html lang="en"><head><meta charset="utf-8"/><link rel="icon" href="./favicon.ico"/><meta name="viewport" content="width=device-width,initial-scale=1,maximum-scale=5"/><meta name="theme-color" content="#000000"/><meta name="description" content="UI for VictoriaMetrics"/><link rel="apple-touch-icon" href="./apple-touch-icon.png"/><link rel="icon" type="image/png" sizes="32x32" href="./favicon-32x32.png"><link rel="manifest" href="./manifest.json"/><title>VM UI</title><script src="./dashboards/index.js" type="module"></script><meta name="twitter:card" content="summary_large_image"><meta name="twitter:image" content="./preview.jpg"><meta name="twitter:title" content="UI for VictoriaMetrics"><meta name="twitter:description" content="Explore and troubleshoot your VictoriaMetrics data"><meta name="twitter:site" content="@VictoriaMetrics"><meta property="og:title" content="Metric explorer for VictoriaMetrics"><meta property="og:description" content="Explore and troubleshoot your VictoriaMetrics data"><meta property="og:image" content="./preview.jpg"><meta property="og:type" content="website"><script defer="defer" src="./static/js/main.8e7757ef.js"></script><link href="./static/css/main.bc07cc78.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div></body></html>
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app/vlselect/vmui/static/js/main.8e7757ef.js.LICENSE.txt → app/vlselect/vmui/static/js/main.c3285306.js.LICENSE.txt
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app/vlselect/vmui/static/media/MetricsQL.da86c2db4f0b05e286b0.md → app/vlselect/vmui/static/media/MetricsQL.df7574389d8f8bbcf0c7.md
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@ -79,6 +79,8 @@ The list of MetricsQL features on top of PromQL:
* [Series selectors](https://docs.victoriametrics.com/keyconcepts/#filtering) accept multiple `or` filters. For example, `{env="prod",job="a" or env="dev",job="b"}`
selects series with `{env="prod",job="a"}` or `{env="dev",job="b"}` labels.
See [these docs](https://docs.victoriametrics.com/keyconcepts/#filtering-by-multiple-or-filters) for details.
* Support for matching against multiple numeric constants via `q == (C1, ..., CN)` and `q != (C1, ..., CN)` syntax. For example, `status_code == (300, 301, 304)`
returns `status_code` metrics with one of `300`, `301` or `304` values.
* Support for `group_left(*)` and `group_right(*)` for copying all the labels from time series on the `one` side
of [many-to-one operations](https://prometheus.io/docs/prometheus/latest/querying/operators/#many-to-one-and-one-to-many-vector-matches).
The copied label names may clash with the existing label names, so MetricsQL provides an ability to add prefix to the copied metric names
@ -152,9 +154,9 @@ MetricsQL provides the following functions:
### Rollup functions
**Rollup functions** (aka range functions or window functions) calculate rollups over **raw samples**
**Rollup functions** (aka range functions or window functions) calculate rollups over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window for the [selected time series](https://docs.victoriametrics.com/keyconcepts/#filtering).
For example, `avg_over_time(temperature[24h])` calculates the average temperature over raw samples for the last 24 hours.
For example, `avg_over_time(temperature[24h])` calculates the average temperature over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) for the last 24 hours.
Additional details:
@ -184,7 +186,7 @@ The list of supported rollup functions:
#### absent_over_time
`absent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns 1
if the given lookbehind window `d` doesn't contain raw samples. Otherwise, it returns an empty result.
if the given lookbehind window `d` doesn't contain [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples). Otherwise, it returns an empty result.
This function is supported by PromQL.
@ -193,7 +195,7 @@ See also [present_over_time](#present_over_time).
#### aggr_over_time
`aggr_over_time(("rollup_func1", "rollup_func2", ...), series_selector[d])` is a [rollup function](#rollup-functions),
which calculates all the listed `rollup_func*` for raw samples on the given lookbehind window `d`.
which calculates all the listed `rollup_func*` for [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d`.
The calculations are performed individually per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -203,7 +205,7 @@ would calculate [min_over_time](#min_over_time), [max_over_time](#max_over_time)
#### ascent_over_time
`ascent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates
ascent of raw sample values on the given lookbehind window `d`. The calculations are performed individually
ascent of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples) values on the given lookbehind window `d`. The calculations are performed individually
per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
This function is useful for tracking height gains in GPS tracking. Metric names are stripped from the resulting rollups.
@ -215,7 +217,7 @@ See also [descent_over_time](#descent_over_time).
#### avg_over_time
`avg_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average value
over raw samples on the given lookbehind window `d` per each time series returned
over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
This function is supported by PromQL.
@ -225,7 +227,7 @@ See also [median_over_time](#median_over_time).
#### changes
`changes(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times
the raw samples changed on the given lookbehind window `d` per each time series returned
the [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) changed on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Unlike `changes()` in Prometheus it takes into account the change from the last sample before the given lookbehind window `d`.
@ -240,7 +242,7 @@ See also [changes_prometheus](#changes_prometheus).
#### changes_prometheus
`changes_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times
the raw samples changed on the given lookbehind window `d` per each time series returned
the [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) changed on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
It doesn't take into account the change from the last sample before the given lookbehind window `d` in the same way as Prometheus does.
@ -254,7 +256,7 @@ See also [changes](#changes).
#### count_eq_over_time
`count_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
`count_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which calculates the number of [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -264,7 +266,7 @@ See also [count_over_time](#count_over_time), [share_eq_over_time](#share_eq_ove
#### count_gt_over_time
`count_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
`count_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which calculates the number of [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -274,7 +276,7 @@ See also [count_over_time](#count_over_time) and [share_gt_over_time](#share_gt_
#### count_le_over_time
`count_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
`count_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which calculates the number of [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d`, which don't exceed `le`. It is calculated independently per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -284,7 +286,7 @@ See also [count_over_time](#count_over_time) and [share_le_over_time](#share_le_
#### count_ne_over_time
`count_ne_over_time(series_selector[d], ne)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
`count_ne_over_time(series_selector[d], ne)` is a [rollup function](#rollup-functions), which calculates the number of [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d`, which aren't equal to `ne`. It is calculated independently per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -294,7 +296,7 @@ See also [count_over_time](#count_over_time).
#### count_over_time
`count_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of raw samples
`count_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -305,7 +307,7 @@ See also [count_le_over_time](#count_le_over_time), [count_gt_over_time](#count_
#### count_values_over_time
`count_values_over_time("label", series_selector[d])` is a [rollup function](#rollup-functions), which counts the number of raw samples
`count_values_over_time("label", series_selector[d])` is a [rollup function](#rollup-functions), which counts the number of [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
with the same value over the given lookbehind window and stores the counts in a time series with an additional `label`, which contains each initial value.
The results are calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -315,8 +317,8 @@ See also [count_eq_over_time](#count_eq_over_time), [count_values](#count_values
#### decreases_over_time
`decreases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of raw sample value decreases
over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
`decreases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
value decreases over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -324,8 +326,8 @@ See also [increases_over_time](#increases_over_time).
#### default_rollup
`default_rollup(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d`
per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
`default_rollup(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
If the lookbehind window is skipped in square brackets, then it is automatically calculated as `max(step, scrape_interval)`, where `step` is the query arg value
passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query),
@ -375,7 +377,7 @@ See also [deriv_fast](#deriv_fast) and [ideriv](#ideriv).
#### deriv_fast
`deriv_fast(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivative
using the first and the last raw samples on the given lookbehind window `d` per each time series returned
using the first and the last [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -384,8 +386,8 @@ See also [deriv](#deriv) and [ideriv](#ideriv).
#### descent_over_time
`descent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates descent of raw sample values
on the given lookbehind window `d`. The calculations are performed individually per each time series returned
`descent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates descent of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
values on the given lookbehind window `d`. The calculations are performed individually per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
This function is useful for tracking height loss in GPS tracking.
@ -396,8 +398,8 @@ See also [ascent_over_time](#ascent_over_time).
#### distinct_over_time
`distinct_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number of distinct raw sample values
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
`distinct_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number of unique [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
values on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -416,15 +418,15 @@ See also [lifetime](#lifetime) and [lag](#lag).
#### first_over_time
`first_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the first raw sample value
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
`first_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the first [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
See also [last_over_time](#last_over_time) and [tfirst_over_time](#tfirst_over_time).
#### geomean_over_time
`geomean_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [geometric mean](https://en.wikipedia.org/wiki/Geometric_mean)
over raw samples on the given lookbehind window `d` per each time series returned
over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -432,8 +434,8 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
#### histogram_over_time
`histogram_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates
[VictoriaMetrics histogram](https://godoc.org/github.com/VictoriaMetrics/metrics#Histogram) over raw samples on the given lookbehind window `d`.
It is calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
[VictoriaMetrics histogram](https://godoc.org/github.com/VictoriaMetrics/metrics#Histogram) over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d`. It is calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
The resulting histograms are useful to pass to [histogram_quantile](#histogram_quantile) for calculating quantiles
over multiple [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge).
For example, the following query calculates median temperature by country over the last 24 hours:
@ -457,7 +459,7 @@ See also [hoeffding_bound_lower](#hoeffding_bound_lower).
#### holt_winters
`holt_winters(series_selector[d], sf, tf)` is a [rollup function](#rollup-functions), which calculates Holt-Winters value
(aka [double exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing#Double_exponential_smoothing)) for raw samples
(aka [double exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing#Double_exponential_smoothing)) for [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
over the given lookbehind window `d` using the given smoothing factor `sf` and the given trend factor `tf`.
Both `sf` and `tf` must be in the range `[0...1]`. It is expected that the [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering)
returns time series of [gauge type](https://docs.victoriametrics.com/keyconcepts/#gauge).
@ -468,7 +470,7 @@ See also [range_linear_regression](#range_linear_regression).
#### idelta
`idelta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between the last two raw samples
`idelta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between the last two [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -479,7 +481,8 @@ See also [delta](#delta).
#### ideriv
`ideriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the per-second derivative based on the last two raw samples
`ideriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the per-second derivative based
on the last two [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
over the given lookbehind window `d`. The derivative is calculated independently per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -522,8 +525,8 @@ while [increase](#increase) ignores the first value in a series if it is too big
#### increases_over_time
`increases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of raw sample value increases
over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
`increases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
value increases over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -531,14 +534,15 @@ See also [decreases_over_time](#decreases_over_time).
#### integrate
`integrate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the integral over raw samples on the given lookbehind window `d`
per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
`integrate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the integral over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
#### irate
`irate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the "instant" per-second increase rate over the last two raw samples
`irate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the "instant" per-second increase rate over
the last two [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
@ -560,8 +564,8 @@ See also [lifetime](#lifetime) and [duration_over_time](#duration_over_time).
#### last_over_time
`last_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d`
per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
`last_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
value on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
This function is supported by PromQL.
@ -579,13 +583,14 @@ See also [duration_over_time](#duration_over_time) and [lag](#lag).
#### mad_over_time
`mad_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation)
over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
See also [mad](#mad), [range_mad](#range_mad) and [outlier_iqr_over_time](#outlier_iqr_over_time).
#### max_over_time
`max_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the maximum value over raw samples
`max_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the maximum value over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
This function is supported by PromQL.
@ -594,7 +599,7 @@ See also [tmax_over_time](#tmax_over_time).
#### median_over_time
`median_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates median value over raw samples
`median_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates median value over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -602,7 +607,7 @@ See also [avg_over_time](#avg_over_time).
#### min_over_time
`min_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the minimum value over raw samples
`min_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the minimum value over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
This function is supported by PromQL.
@ -612,15 +617,16 @@ See also [tmin_over_time](#tmin_over_time).
#### mode_over_time
`mode_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [mode](https://en.wikipedia.org/wiki/Mode_(statistics))
for raw samples on the given lookbehind window `d`. It is calculated individually per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). It is expected that raw sample values are discrete.
for [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d`. It is calculated individually per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). It is expected that [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
values are discrete.
#### outlier_iqr_over_time
`outlier_iqr_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last sample on the given lookbehind window `d`
if its value is either smaller than the `q25-1.5*iqr` or bigger than `q75+1.5*iqr` where:
- `iqr` is an [Interquartile range](https://en.wikipedia.org/wiki/Interquartile_range) over raw samples on the lookbehind window `d`
- `q25` and `q75` are 25th and 75th [percentiles](https://en.wikipedia.org/wiki/Percentile) over raw samples on the lookbehind window `d`.
- `iqr` is an [Interquartile range](https://en.wikipedia.org/wiki/Interquartile_range) over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the lookbehind window `d`
- `q25` and `q75` are 25th and 75th [percentiles](https://en.wikipedia.org/wiki/Percentile) over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the lookbehind window `d`.
The `outlier_iqr_over_time()` is useful for detecting anomalies in gauge values based on the previous history of values.
For example, `outlier_iqr_over_time(memory_usage_bytes[1h])` triggers when `memory_usage_bytes` suddenly goes outside the usual value range for the last hour.
@ -630,8 +636,8 @@ See also [outliers_iqr](#outliers_iqr).
#### predict_linear
`predict_linear(series_selector[d], t)` is a [rollup function](#rollup-functions), which calculates the value `t` seconds in the future using
linear interpolation over raw samples on the given lookbehind window `d`. The predicted value is calculated individually per each time series
returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
linear interpolation over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d`.
The predicted value is calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
This function is supported by PromQL.
@ -639,7 +645,7 @@ See also [range_linear_regression](#range_linear_regression).
#### present_over_time
`present_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns 1 if there is at least a single raw sample
`present_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns 1 if there is at least a single [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d`. Otherwise, an empty result is returned.
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -648,7 +654,7 @@ This function is supported by PromQL.
#### quantile_over_time
`quantile_over_time(phi, series_selector[d])` is a [rollup function](#rollup-functions), which calculates `phi`-quantile over raw samples
`quantile_over_time(phi, series_selector[d])` is a [rollup function](#rollup-functions), which calculates `phi`-quantile over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
The `phi` value must be in the range `[0...1]`.
@ -659,7 +665,7 @@ See also [quantiles_over_time](#quantiles_over_time).
#### quantiles_over_time
`quantiles_over_time("phiLabel", phi1, ..., phiN, series_selector[d])` is a [rollup function](#rollup-functions), which calculates `phi*`-quantiles
over raw samples on the given lookbehind window `d` per each time series returned
over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
The function returns individual series per each `phi*` with `{phiLabel="phi*"}` label. `phi*` values must be in the range `[0...1]`.
@ -667,7 +673,7 @@ See also [quantile_over_time](#quantile_over_time).
#### range_over_time
`range_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates value range over raw samples
`range_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates value range over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
E.g. it calculates `max_over_time(series_selector[d]) - min_over_time(series_selector[d])`.
@ -692,7 +698,7 @@ See also [irate](#irate) and [rollup_rate](#rollup_rate).
#### rate_over_sum
`rate_over_sum(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second rate over the sum of raw samples
`rate_over_sum(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second rate over the sum of [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d`. The calculations are performed individually per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -711,7 +717,7 @@ This function is supported by PromQL.
#### rollup
`rollup(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `min`, `max` and `avg` values for raw samples
`rollup(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `min`, `max` and `avg` values for [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
These values are calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -721,7 +727,8 @@ See also [label_match](#label_match).
#### rollup_candlestick
`rollup_candlestick(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `open`, `high`, `low` and `close` values (aka OHLC)
over raw samples on the given lookbehind window `d` and returns them in time series with `rollup="open"`, `rollup="high"`, `rollup="low"` and `rollup="close"` additional labels.
over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` and returns them in time series
with `rollup="open"`, `rollup="high"`, `rollup="low"` and `rollup="close"` additional labels.
The calculations are performed individually per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). This function is useful for financial applications.
@ -730,7 +737,7 @@ See also [label_match](#label_match).
#### rollup_delta
`rollup_delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates differences between adjacent raw samples
`rollup_delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates differences between adjacent [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated differences
and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -745,8 +752,8 @@ See also [rollup_increase](#rollup_increase).
#### rollup_deriv
`rollup_deriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivatives
for adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second derivatives
and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
for adjacent [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` and returns `min`, `max` and `avg` values
for the calculated per-second derivatives and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
@ -756,7 +763,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
#### rollup_increase
`rollup_increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates increases for adjacent raw samples
`rollup_increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates increases for adjacent [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated increases
and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -768,7 +775,8 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
#### rollup_rate
`rollup_rate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second change rates for adjacent raw samples
`rollup_rate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second change rates
for adjacent [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second change rates
and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
@ -785,7 +793,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
#### rollup_scrape_interval
`rollup_scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the interval in seconds between
adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated interval
adjacent [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated interval
and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -796,7 +804,8 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
#### scrape_interval
`scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average interval in seconds between raw samples
`scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average interval in seconds
between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -805,7 +814,8 @@ See also [rollup_scrape_interval](#rollup_scrape_interval).
#### share_gt_over_time
`share_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
`share_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`)
of [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -817,7 +827,8 @@ See also [share_le_over_time](#share_le_over_time) and [count_gt_over_time](#cou
#### share_le_over_time
`share_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
`share_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`)
of [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d`, which are smaller or equal to `le`. It is calculated independently per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -830,7 +841,8 @@ See also [share_gt_over_time](#share_gt_over_time) and [count_le_over_time](#cou
#### share_eq_over_time
`share_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
`share_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`)
of [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -848,7 +860,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
#### stddev_over_time
`stddev_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard deviation over raw samples
`stddev_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard deviation over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -859,7 +871,7 @@ See also [stdvar_over_time](#stdvar_over_time).
#### stdvar_over_time
`stdvar_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard variance over raw samples
`stdvar_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard variance over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -870,8 +882,8 @@ See also [stddev_over_time](#stddev_over_time).
#### sum_eq_over_time
`sum_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-function), which calculates the sum of raw sample values equal to `eq`
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
`sum_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-function), which calculates the sum of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
values equal to `eq` on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -879,8 +891,8 @@ See also [sum_over_time](#sum_over_time) and [count_eq_over_time](#count_eq_over
#### sum_gt_over_time
`sum_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-function), which calculates the sum of raw sample values bigger than `gt`
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
`sum_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-function), which calculates the sum of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
values bigger than `gt` on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -888,8 +900,8 @@ See also [sum_over_time](#sum_over_time) and [count_gt_over_time](#count_gt_over
#### sum_le_over_time
`sum_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-function), which calculates the sum of raw sample values smaller or equal to `le`
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
`sum_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-function), which calculates the sum of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
values smaller or equal to `le` on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -897,7 +909,7 @@ See also [sum_over_time](#sum_over_time) and [count_le_over_time](#count_le_over
#### sum_over_time
`sum_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of raw sample values
`sum_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples) values
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -906,14 +918,15 @@ This function is supported by PromQL.
#### sum2_over_time
`sum2_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of squares for raw sample values
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
`sum2_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of squares for [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
values on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
#### timestamp
`timestamp(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last raw sample
`timestamp(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision
for the last [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -924,7 +937,8 @@ See also [time](#time) and [now](#now).
#### timestamp_with_name
`timestamp_with_name(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last raw sample
`timestamp_with_name(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision
for the last [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are preserved in the resulting rollups.
@ -933,7 +947,8 @@ See also [timestamp](#timestamp) and [keep_metric_names](#keep_metric_names) mod
#### tfirst_over_time
`tfirst_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the first raw sample
`tfirst_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision
for the first [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -957,7 +972,8 @@ See also [tlast_change_over_time](#tlast_change_over_time).
#### tmax_over_time
`tmax_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the raw sample
`tmax_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision
for the [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
with the maximum value on the given lookbehind window `d`. It is calculated independently per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -967,7 +983,8 @@ See also [max_over_time](#max_over_time).
#### tmin_over_time
`tmin_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the raw sample
`tmin_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision
for the [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
with the minimum value on the given lookbehind window `d`. It is calculated independently per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
@ -978,7 +995,7 @@ See also [min_over_time](#min_over_time).
#### zscore_over_time
`zscore_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns [z-score](https://en.wikipedia.org/wiki/Standard_score)
for raw samples on the given lookbehind window `d`. It is calculated independently per each time series returned
for [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d`. It is calculated independently per each time series returned
from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@ -2212,6 +2229,7 @@ Any [rollup function](#rollup-functions) for something other than [series select
Nested rollup functions can be implicit thanks to the [implicit query conversions](#implicit-query-conversions).
For example, `delta(sum(m))` is implicitly converted to `delta(sum(default_rollup(m))[1i:1i])`, so it becomes a subquery,
since it contains [default_rollup](#default_rollup) nested into [delta](#delta).
This behavior can be disabled or logged via cmd-line flags `-search.disableImplicitConversion` and `-search.logImplicitConversion` since v1.101.0.
VictoriaMetrics performs subqueries in the following way:
@ -2244,4 +2262,5 @@ VictoriaMetrics performs the following implicit conversions for incoming queries
For example, `avg_over_time(rate(http_requests_total[5m])[1h])` is automatically converted to `avg_over_time(rate(http_requests_total[5m])[1h:1i])`.
* If something other than [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering)
is passed to [rollup function](#rollup-functions), then a [subquery](#subqueries) with `1i` lookbehind window and `1i` step is automatically formed.
For example, `rate(sum(up))` is automatically converted to `rate((sum(default_rollup(up)))[1i:1i])`.
For example, `rate(sum(up))` is automatically converted to `rate((sum(default_rollup(up)))[1i:1i])`.
This behavior can be disabled or logged via cmd-line flags `-search.disableImplicitConversion` and `-search.logImplicitConversion` since v1.101.0.