vmui/logs: add support for head|limit in expressions

See https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.97.4

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -8,7 +8,7 @@ body:
         Before filling a bug report it would be great to [upgrade](https://docs.victoriametrics.com/#how-to-upgrade) 
         to [the latest available release](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/latest)
         and verify whether the bug is reproducible there.
-        It's also recommended to read the [troubleshooting docs](https://docs.victoriametrics.com/Troubleshooting.html) first.
+        It's also recommended to read the [troubleshooting docs](https://docs.victoriametrics.com/troubleshooting/) first.
   - type: textarea
     id: describe-the-bug
     attributes:
@@ -65,7 +65,7 @@ body:
         
         See how to setup monitoring here:
           * [monitoring for single-node VictoriaMetrics](https://docs.victoriametrics.com/#monitoring)
-          * [monitoring for VictoriaMetrics cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#monitoring)
+          * [monitoring for VictoriaMetrics cluster](https://docs.victoriametrics.com/cluster-victoriametrics/#monitoring)
     validations:
       required: false
   - type: textarea

.github/ISSUE_TEMPLATE/question.yml

@@ -24,9 +24,9 @@ body:
       label: Troubleshooting docs
       description: I am familiar with the following troubleshooting docs
       options:
-        - label: General - https://docs.victoriametrics.com/Troubleshooting.html
+        - label: General - https://docs.victoriametrics.com/troubleshooting/
           required: false
-        - label: vmagent - https://docs.victoriametrics.com/vmagent.html#troubleshooting
+        - label: vmagent - https://docs.victoriametrics.com/vmagent/#troubleshooting
+          required: false
+        - label: vmalert - https://docs.victoriametrics.com/vmalert/#troubleshooting
           required: false
-        - label: vmalert - https://docs.victoriametrics.com/vmalert.html#troubleshooting
-          required: false

.github/PULL_REQUEST_TEMPLATE/pull_request_template.md

@@ -30,6 +30,6 @@ Further checks are optional for External Contributions:
 
 Examples of good changelog messages:
 
-1. FEATURE: [vmagent](https://docs.victoriametrics.com/vmagent.html): add support for [VictoriaMetrics remote write protocol](https://docs.victoriametrics.com/vmagent.html#victoriametrics-remote-write-protocol) when [sending / receiving data to / from Kafka](https://docs.victoriametrics.com/vmagent.html#kafka-integration). This protocol allows saving egress network bandwidth costs when sending data from `vmagent` to `Kafka` located in another datacenter or availability zone. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1225).
+1. FEATURE: [vmagent](https://docs.victoriametrics.com/vmagent/): add support for [VictoriaMetrics remote write protocol](https://docs.victoriametrics.com/vmagent/#victoriametrics-remote-write-protocol) when [sending / receiving data to / from Kafka](https://docs.victoriametrics.com/vmagent/#kafka-integration). This protocol allows saving egress network bandwidth costs when sending data from `vmagent` to `Kafka` located in another datacenter or availability zone. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1225).
 
-2. BUGFIX: [stream aggregation](https://docs.victoriametrics.com/stream-aggregation.html): suppress `series after dedup` error message in logs when `-remoteWrite.streamAggr.dedupInterval` command-line flag is set at [vmagent](https://docs.victoriametrics.com/vmgent.html) or when `-streamAggr.dedupInterval` command-line flag is set at [single-node VictoriaMetrics](https://docs.victoriametrics.com/).
+2. BUGFIX: [stream aggregation](https://docs.victoriametrics.com/stream-aggregation/): suppress `series after dedup` error message in logs when `-remoteWrite.streamAggr.dedupInterval` command-line flag is set at [vmagent](https://docs.victoriametrics.com/vmgent.html) or when `-streamAggr.dedupInterval` command-line flag is set at [single-node VictoriaMetrics](https://docs.victoriametrics.com/).

SECURITY.md

@@ -6,7 +6,7 @@ The following versions of VictoriaMetrics receive regular security fixes:
 
 | Version | Supported          |
 |---------|--------------------|
-| [latest release](https://docs.victoriametrics.com/CHANGELOG.html) | :white_check_mark: |
+| [latest release](https://docs.victoriametrics.com/changelog/) | :white_check_mark: |
 | v1.97.x [LTS line](https://docs.victoriametrics.com/lts-releases/) | :white_check_mark: |
 | v1.93.x [LTS line](https://docs.victoriametrics.com/lts-releases/) | :white_check_mark: |
 | other releases  | :x:                |

app/vlselect/logsql/logsql.go

@@ -48,29 +48,8 @@ func ProcessQueryRequest(w http.ResponseWriter, r *http.Request, stopCh <-chan s
 		}
 		rowsCount := len(columns[0].Values)
 
-		// skip entries with empty _stream column
-		// _stream is empty in case indexdb entry was not flushed to the storage yet
-		// skipping such entries makes the result more consistent
-		streamCol := 0
-
-		// fast path
-		// _stream column is a built-in column and it is always supposed to be at the same position
-		if len(columns) >= 2 && columns[1].Name == "_stream" {
-			streamCol = 1
-		} else {
-			for i := 1; i < len(columns); i++ {
-				if columns[i].Name == "_stream" {
-					streamCol = i
-					break
-				}
-			}
-		}
-
 		bb := blockResultPool.Get()
 		for rowIdx := 0; rowIdx < rowsCount; rowIdx++ {
-			if columns[streamCol].Values[rowIdx] == "" {
-				continue
-			}
 			WriteJSONRow(bb, columns, rowIdx)
 		}
 

app/vlselect/vmui/asset-manifest.json

@@ -1,13 +1,13 @@
 {
   "files": {
     "main.css": "./static/css/main.bc07cc78.css",
-    "main.js": "./static/js/main.034044a7.js",
+    "main.js": "./static/js/main.8e7757ef.js",
     "static/js/685.bebe1265.chunk.js": "./static/js/685.bebe1265.chunk.js",
-    "static/media/MetricsQL.md": "./static/media/MetricsQL.10add6e7bdf0f1d98cf7.md",
+    "static/media/MetricsQL.md": "./static/media/MetricsQL.da86c2db4f0b05e286b0.md",
     "index.html": "./index.html"
   },
   "entrypoints": [
     "static/css/main.bc07cc78.css",
-    "static/js/main.034044a7.js"
+    "static/js/main.8e7757ef.js"
   ]
 }

app/vlselect/vmui/index.html

@@ -1 +1 @@
-<!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.034044a7.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>
+<!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>

app/vlselect/vmui/static/media/MetricsQL.da86c2db4f0b05e286b0.md

@@ -22,7 +22,7 @@ However, there are some [intentional differences](https://medium.com/@romanhavro
 [Standalone MetricsQL package](https://godoc.org/github.com/VictoriaMetrics/metricsql) can be used for parsing MetricsQL in external apps.
 
 If you are unfamiliar with PromQL, then it is suggested reading [this tutorial for beginners](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085)
-and introduction into [basic querying via MetricsQL](https://docs.victoriametrics.com/keyConcepts.html#metricsql).
+and introduction into [basic querying via MetricsQL](https://docs.victoriametrics.com/keyconcepts/#metricsql).
 
 The following functionality is implemented differently in MetricsQL compared to PromQL. This improves user experience:
 
@@ -70,15 +70,15 @@ The list of MetricsQL features on top of PromQL:
   VictoriaMetrics can be used as Graphite datasource in Grafana. See [these docs](https://docs.victoriametrics.com/#graphite-api-usage) for details.
   See also [label_graphite_group](#label_graphite_group) function, which can be used for extracting the given groups from Graphite metric name.
 * Lookbehind window in square brackets for [rollup functions](#rollup-functions) may be omitted. VictoriaMetrics automatically selects the lookbehind window
-  depending on the `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query)
+  depending on the `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query)
   and the real interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) (aka `scrape_interval`).
   For instance, the following query is valid in VictoriaMetrics: `rate(node_network_receive_bytes_total)`.
   It is roughly equivalent to `rate(node_network_receive_bytes_total[$__interval])` when used in Grafana.
   The difference is documented in [rate() docs](#rate).
 * Numeric values can contain `_` delimiters for better readability. For example, `1_234_567_890` can be used in queries instead of `1234567890`.
-* [Series selectors](https://docs.victoriametrics.com/keyConcepts.html#filtering) accept multiple `or` filters. For example, `{env="prod",job="a" or env="dev",job="b"}`
+* [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.html#filtering-by-multiple-or-filters) for details.
+  See [these docs](https://docs.victoriametrics.com/keyconcepts/#filtering-by-multiple-or-filters) for details.
 * Support for `group_left(*)` and `group_right(*)` for copying all the labels from time series on the `one` side
   of [many-to-one operations](https://prometheus.io/docs/prometheus/latest/querying/operators/#many-to-one-and-one-to-many-vector-matches).
   The copied label names may clash with the existing label names, so MetricsQL provides an ability to add prefix to the copied metric names
@@ -153,26 +153,26 @@ MetricsQL provides the following functions:
 ### Rollup functions
 
 **Rollup functions** (aka range functions or window functions) calculate rollups over **raw samples**
-on the given lookbehind window for the [selected time series](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window for the [selected time series](https://docs.victoriametrics.com/keyconcepts/#filtering).
 For example, `avg_over_time(temperature[24h])` calculates the average temperature over raw samples for the last 24 hours.
 
 Additional details:
 
 * If rollup functions are used for building graphs in Grafana, then the rollup is calculated independently per each point on the graph.
   For example, every point for `avg_over_time(temperature[24h])` graph shows the average temperature for the last 24 hours ending at this point.
-  The interval between points is set as `step` query arg passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
-* If the given [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) returns multiple time series,
+  The interval between points is set as `step` query arg passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
+* If the given [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) returns multiple time series,
   then rollups are calculated individually per each returned series.
 * If lookbehind window in square brackets is missing, then it is automatically set to the following value:
-  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
+  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
     for all the [rollup functions](#rollup-functions) except of [default_rollup](#default_rollup) and [rate](#rate). This value is known as `$__interval` in Grafana or `1i` in MetricsQL.
     For example, `avg_over_time(temperature)` is automatically transformed to `avg_over_time(temperature[1i])`.
   - To the `max(step, scrape_interval)`, where `scrape_interval` is the interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
     for [default_rollup](#default_rollup) and [rate](#rate) functions. This allows avoiding unexpected gaps on the graph when `step` is smaller than `scrape_interval`.
-* Every [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) in MetricsQL must be wrapped into a rollup function.
+* Every [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) in MetricsQL must be wrapped into a rollup function.
   Otherwise, it is automatically wrapped into [default_rollup](#default_rollup). For example, `foo{bar="baz"}`
   is automatically converted to `default_rollup(foo{bar="baz"})` before performing the calculations.
-* If something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) is passed to rollup function,
+* If something other than [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) is passed to rollup function,
   then the inner arg is automatically converted to a [subquery](#subqueries).
 * All the rollup functions accept optional `keep_metric_names` modifier. If it is set, then the function keeps metric names in results.
   See [these docs](#keep_metric_names).
@@ -195,7 +195,7 @@ See also [present_over_time](#present_over_time).
 `aggr_over_time(("rollup_func1", "rollup_func2", ...), series_selector[d])` is a [rollup function](#rollup-functions),
 which calculates all the listed `rollup_func*` for raw samples on the given lookbehind window `d`.
 The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 `rollup_func*` can contain any rollup function. For instance, `aggr_over_time(("min_over_time", "max_over_time", "rate"), m[d])`
 would calculate [min_over_time](#min_over_time), [max_over_time](#max_over_time) and [rate](#rate) for `m[d]`.
@@ -204,7 +204,7 @@ would calculate [min_over_time](#min_over_time), [max_over_time](#max_over_time)
 
 `ascent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates
 ascent of raw sample values on the given lookbehind window `d`. The calculations are performed individually
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -216,7 +216,7 @@ See also [descent_over_time](#descent_over_time).
 
 `avg_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average value
 over raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is supported by PromQL.
 
@@ -226,7 +226,7 @@ See also [median_over_time](#median_over_time).
 
 `changes(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times
 the raw samples changed on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Unlike `changes()` in Prometheus it takes into account the change from the last sample before the given lookbehind window `d`.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -241,7 +241,7 @@ See also [changes_prometheus](#changes_prometheus).
 
 `changes_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times
 the raw samples changed on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 It doesn't take into account the change from the last sample before the given lookbehind window `d` in the same way as Prometheus does.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -256,7 +256,7 @@ See also [changes](#changes).
 
 `count_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -266,7 +266,7 @@ See also [count_over_time](#count_over_time), [share_eq_over_time](#share_eq_ove
 
 `count_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -276,7 +276,7 @@ See also [count_over_time](#count_over_time) and [share_gt_over_time](#share_gt_
 
 `count_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which don't exceed `le`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -286,7 +286,7 @@ See also [count_over_time](#count_over_time) and [share_le_over_time](#share_le_
 
 `count_ne_over_time(series_selector[d], ne)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which aren't equal to `ne`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -295,7 +295,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -307,7 +307,7 @@ See also [count_le_over_time](#count_le_over_time), [count_gt_over_time](#count_
 
 `count_values_over_time("label", series_selector[d])` is a [rollup function](#rollup-functions), which counts the number of raw samples
 with the same value over the given lookbehind window and stores the counts in a time series with an additional `label`, which contains each initial value.
-The results are calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The results are calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -316,7 +316,7 @@ 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.html#filtering).
+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.
 
@@ -325,7 +325,7 @@ See also [increases_over_time](#increases_over_time).
 #### default_rollup
 
 `default_rollup(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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),
@@ -336,7 +336,7 @@ This allows avoiding unexpected gaps on the graph when `step` is smaller than th
 
 `delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between
 the last sample before the given lookbehind window `d` and the last sample at the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 The behaviour of `delta()` function in MetricsQL is slightly different to the behaviour of `delta()` function in Prometheus.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -351,7 +351,7 @@ See also [increase](#increase) and [delta_prometheus](#delta_prometheus).
 
 `delta_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between
 the first and the last samples at the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 The behaviour of `delta_prometheus()` is close to the behaviour of `delta()` function in Prometheus.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -363,7 +363,7 @@ See also [delta](#delta).
 #### deriv
 
 `deriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivative over the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 The derivative is calculated using linear regression.
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -376,7 +376,7 @@ See also [deriv_fast](#deriv_fast) and [ideriv](#ideriv).
 
 `deriv_fast(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivative
 using the first and the last raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -386,7 +386,7 @@ See also [deriv](#deriv) and [ideriv](#ideriv).
 
 `descent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates descent of raw sample values
 on the given lookbehind window `d`. The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is useful for tracking height loss in GPS tracking.
 
@@ -397,7 +397,7 @@ See also [ascent_over_time](#ascent_over_time).
 #### distinct_over_time
 
 `distinct_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number of distinct raw sample values
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -406,7 +406,7 @@ See also [count_values_over_time](#count_values_over_time).
 #### duration_over_time
 
 `duration_over_time(series_selector[d], max_interval)` is a [rollup function](#rollup-functions), which returns the duration in seconds
-when time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) were present
+when time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering) were present
 over the given lookbehind window `d`. It is expected that intervals between adjacent samples per each series don't exceed the `max_interval`.
 Otherwise, such intervals are considered as gaps and aren't counted.
 
@@ -417,7 +417,7 @@ See also [lifetime](#lifetime) and [lag](#lag).
 #### first_over_time
 
 `first_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the first raw sample value
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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).
 
@@ -425,7 +425,7 @@ See also [last_over_time](#last_over_time) and [tfirst_over_time](#tfirst_over_t
 
 `geomean_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [geometric mean](https://en.wikipedia.org/wiki/Geometric_mean)
 over raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -433,9 +433,9 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 
 `histogram_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates
 [VictoriaMetrics histogram](https://godoc.org/github.com/VictoriaMetrics/metrics#Histogram) over raw samples on the given lookbehind window `d`.
-It is calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.html#gauge).
+over multiple [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge).
 For example, the following query calculates median temperature by country over the last 24 hours:
 
 `histogram_quantile(0.5, sum(histogram_over_time(temperature[24h])) by (vmrange,country))`.
@@ -459,8 +459,8 @@ See also [hoeffding_bound_lower](#hoeffding_bound_lower).
 `holt_winters(series_selector[d], sf, tf)` is a [rollup function](#rollup-functions), which calculates Holt-Winters value
 (aka [double exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing#Double_exponential_smoothing)) for raw samples
 over the given lookbehind window `d` using the given smoothing factor `sf` and the given trend factor `tf`.
-Both `sf` and `tf` must be in the range `[0...1]`. It is expected that the [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering)
-returns time series of [gauge type](https://docs.victoriametrics.com/keyConcepts.html#gauge).
+Both `sf` and `tf` must be in the range `[0...1]`. It is expected that the [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering)
+returns time series of [gauge type](https://docs.victoriametrics.com/keyconcepts/#gauge).
 
 This function is supported by PromQL.
 
@@ -469,7 +469,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -481,7 +481,7 @@ See also [delta](#delta).
 
 `ideriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the per-second derivative based on the last two raw samples
 over the given lookbehind window `d`. The derivative is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -490,8 +490,8 @@ See also [deriv](#deriv).
 #### increase
 
 `increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase over the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 Unlike Prometheus, it takes into account the last sample before the given lookbehind window `d` when calculating the result.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -505,8 +505,8 @@ See also [increase_pure](#increase_pure), [increase_prometheus](#increase_promet
 #### increase_prometheus
 
 `increase_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase
-over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 It doesn't take into account the last sample before the given lookbehind window `d` when calculating the result in the same way as Prometheus does.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
 
@@ -517,13 +517,13 @@ See also [increase_pure](#increase_pure) and [increase](#increase).
 #### increase_pure
 
 `increase_pure(series_selector[d])` is a [rollup function](#rollup-functions), which works the same as [increase](#increase) except
-of the following corner case - it assumes that [counters](https://docs.victoriametrics.com/keyConcepts.html#counter) always start from 0,
+of the following corner case - it assumes that [counters](https://docs.victoriametrics.com/keyconcepts/#counter) always start from 0,
 while [increase](#increase) ignores the first value in a series if it is too big.
 
 #### increases_over_time
 
 `increases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of raw sample value increases
-over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -532,15 +532,15 @@ 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.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### irate
 
 `irate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the "instant" per-second increase rate over the last two raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -552,7 +552,7 @@ See also [rate](#rate) and [rollup_rate](#rollup_rate).
 
 `lag(series_selector[d])` is a [rollup function](#rollup-functions), which returns the duration in seconds between the last sample
 on the given lookbehind window `d` and the timestamp of the current point. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -561,7 +561,7 @@ See also [lifetime](#lifetime) and [duration_over_time](#duration_over_time).
 #### last_over_time
 
 `last_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is supported by PromQL.
 
@@ -570,7 +570,7 @@ See also [first_over_time](#first_over_time) and [tlast_over_time](#tlast_over_t
 #### lifetime
 
 `lifetime(series_selector[d])` is a [rollup function](#rollup-functions), which returns the duration in seconds between the last and the first sample
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -579,14 +579,14 @@ 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.html#filtering).
+over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 See also [mad](#mad), [range_mad](#range_mad) and [outlier_iqr_over_time](#outlier_iqr_over_time).
 
 #### max_over_time
 
 `max_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the maximum value over raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -596,14 +596,14 @@ See also [tmax_over_time](#tmax_over_time).
 
 `median_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates median value over raw samples
 on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 See also [avg_over_time](#avg_over_time).
 
 #### min_over_time
 
 `min_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the minimum value over raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -613,7 +613,7 @@ See also [tmin_over_time](#tmin_over_time).
 
 `mode_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [mode](https://en.wikipedia.org/wiki/Mode_(statistics))
 for raw samples on the given lookbehind window `d`. It is calculated individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). It is expected that raw sample values are discrete.
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). It is expected that raw sample values are discrete.
 
 #### outlier_iqr_over_time
 
@@ -631,7 +631,7 @@ See also [outliers_iqr](#outliers_iqr).
 
 `predict_linear(series_selector[d], t)` is a [rollup function](#rollup-functions), which calculates the value `t` seconds in the future using
 linear interpolation over raw samples on the given lookbehind window `d`. The predicted value is calculated individually per each time series
-returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is supported by PromQL.
 
@@ -649,7 +649,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 The `phi` value must be in the range `[0...1]`.
 
 This function is supported by PromQL.
@@ -660,7 +660,7 @@ See also [quantiles_over_time](#quantiles_over_time).
 
 `quantiles_over_time("phiLabel", phi1, ..., phiN, series_selector[d])` is a [rollup function](#rollup-functions), which calculates `phi*`-quantiles
 over raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 The function returns individual series per each `phi*` with `{phiLabel="phi*"}` label. `phi*` values must be in the range `[0...1]`.
 
 See also [quantile_over_time](#quantile_over_time).
@@ -668,7 +668,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 E.g. it calculates `max_over_time(series_selector[d]) - min_over_time(series_selector[d])`.
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -676,8 +676,8 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 #### rate
 
 `rate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average per-second increase rate
-over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 If the lookbehind window is skipped in square brackets, then it is automatically calculated as `max(step, scrape_interval)`, where `step` is the query arg value
 passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query),
@@ -694,16 +694,16 @@ See also [irate](#irate) and [rollup_rate](#rollup_rate).
 
 `rate_over_sum(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second rate over the sum of raw samples
 on the given lookbehind window `d`. The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### resets
 
 `resets(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number
-of [counter](https://docs.victoriametrics.com/keyConcepts.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).
+of [counter](https://docs.victoriametrics.com/keyconcepts/#counter) resets over the given lookbehind window `d`
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -713,7 +713,7 @@ This function is supported by PromQL.
 
 `rollup(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `min`, `max` and `avg` values for raw samples
 on the given lookbehind window `d` and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-These values are calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+These values are calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -723,7 +723,7 @@ See also [label_match](#label_match).
 `rollup_candlestick(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `open`, `high`, `low` and `close` values (aka OHLC)
 over raw samples on the given lookbehind window `d` and returns them in time series with `rollup="open"`, `rollup="high"`, `rollup="low"` and `rollup="close"` additional labels.
 The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is useful for financial applications.
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). This function is useful for financial applications.
 
 Optional 2nd argument `"open"`, `"high"` or `"low"` or `"close"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -733,7 +733,7 @@ See also [label_match](#label_match).
 `rollup_delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates differences between adjacent raw samples
 on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated differences
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -747,7 +747,7 @@ See also [rollup_increase](#rollup_increase).
 `rollup_deriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivatives
 for adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second derivatives
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -759,7 +759,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 `rollup_increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates increases for adjacent raw samples
 on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated increases
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -778,7 +778,7 @@ when to use `rollup_rate()`.
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
 
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -787,7 +787,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 `rollup_scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the interval in seconds between
 adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated interval
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -797,7 +797,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -807,7 +807,7 @@ See also [rollup_scrape_interval](#rollup_scrape_interval).
 
 `share_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
 on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is useful for calculating SLI and SLO. Example: `share_gt_over_time(up[24h], 0)` - returns service availability for the last 24 hours.
 
@@ -819,7 +819,7 @@ See also [share_le_over_time](#share_le_over_time) and [count_gt_over_time](#cou
 
 `share_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
 on the given lookbehind window `d`, which are smaller or equal to `le`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is useful for calculating SLI and SLO. Example: `share_le_over_time(memory_usage_bytes[24h], 100*1024*1024)` returns
 the share of time series values for the last 24 hours when memory usage was below or equal to 100MB.
@@ -832,7 +832,7 @@ See also [share_gt_over_time](#share_gt_over_time) and [count_le_over_time](#cou
 
 `share_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
 on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -841,15 +841,15 @@ See also [count_eq_over_time](#count_eq_over_time).
 #### stale_samples_over_time
 
 `stale_samples_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number
-of [staleness markers](https://docs.victoriametrics.com/vmagent.html#prometheus-staleness-markers) on the given lookbehind window `d`
-per each time series matching the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+of [staleness markers](https://docs.victoriametrics.com/vmagent/#prometheus-staleness-markers) on the given lookbehind window `d`
+per each time series matching the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### stddev_over_time
 
 `stddev_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard deviation over raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -860,7 +860,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -871,7 +871,7 @@ See also [stddev_over_time](#stddev_over_time).
 #### sum_eq_over_time
 
 `sum_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-function), which calculates the sum of raw sample values equal to `eq`
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -880,7 +880,7 @@ 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.html#filtering).
+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.
 
@@ -889,7 +889,7 @@ 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.html#filtering).
+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.
 
@@ -898,7 +898,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -907,14 +907,14 @@ This function is supported by PromQL.
 #### sum2_over_time
 
 `sum2_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of squares for raw sample values
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### timestamp
 
 `timestamp(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last raw sample
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -925,7 +925,7 @@ See also [time](#time) and [now](#now).
 #### timestamp_with_name
 
 `timestamp_with_name(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last raw sample
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -934,7 +934,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -943,7 +943,7 @@ See also [first_over_time](#first_over_time).
 #### tlast_change_over_time
 
 `tlast_change_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last change
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) on the given lookbehind window `d`.
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering) on the given lookbehind window `d`.
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -959,7 +959,7 @@ See also [tlast_change_over_time](#tlast_change_over_time).
 
 `tmax_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the raw sample
 with the maximum value on the given lookbehind window `d`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -969,7 +969,7 @@ See also [max_over_time](#max_over_time).
 
 `tmin_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the raw sample
 with the minimum value on the given lookbehind window `d`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -979,7 +979,7 @@ See also [min_over_time](#min_over_time).
 
 `zscore_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns [z-score](https://en.wikipedia.org/wiki/Standard_score)
 for raw samples on the given lookbehind window `d`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -994,7 +994,7 @@ returned from the rollup `delta(temperature[24h])`.
 
 Additional details:
 
-* If transform function is applied directly to a [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* If transform function is applied directly to a [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering),
   then the [default_rollup()](#default_rollup) function is automatically applied before calculating the transformations.
   For example, `abs(temperature)` is implicitly transformed to `abs(default_rollup(temperature))`.
 * All the transform functions accept optional `keep_metric_names` modifier. If it is set,
@@ -1230,7 +1230,7 @@ by replacing all the values bigger or equal to 30 with 40.
 #### end
 
 `end()` is a [transform function](#transform-functions), which returns the unix timestamp in seconds for the last point.
-It is known as `end` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+It is known as `end` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 See also [start](#start), [time](#time) and [now](#now).
 
@@ -1653,14 +1653,14 @@ This function is supported by PromQL.
 
 `start()` is a [transform function](#transform-functions), which returns unix timestamp in seconds for the first point.
 
-It is known as `start` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+It is known as `start` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 See also [end](#end), [time](#time) and [now](#now).
 
 #### step
 
 `step()` is a [transform function](#transform-functions), which returns the step in seconds (aka interval) between the returned points.
-It is known as `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+It is known as `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 See also [start](#start) and [end](#end).
 
@@ -1717,7 +1717,7 @@ This function is supported by PromQL.
 
 Additional details:
 
-* If label manipulation function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* If label manipulation function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering),
   then the [default_rollup()](#default_rollup) function is automatically applied before performing the label transformation.
   For example, `alias(temperature, "foo")` is implicitly transformed to `alias(default_rollup(temperature), "foo")`.
 
@@ -1894,7 +1894,7 @@ Additional details:
   and calculate the [count](#count) aggregate function independently per each group, while `count(up) without (instance)`
   would group [rollup results](#rollup-functions) by all the labels except `instance` before calculating [count](#count) aggregate function independently per each group.
   Multiple labels can be put in `by` and `without` modifiers.
-* If the aggregate function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* If the aggregate function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering),
   then the [default_rollup()](#default_rollup) function is automatically applied before calculating the aggregate.
   For example, `count(up)` is implicitly transformed to `count(default_rollup(up))`.
 * Aggregate functions accept arbitrary number of args. For example, `avg(q1, q2, q3)` would return the average values for every point
@@ -2104,7 +2104,7 @@ See also [quantile](#quantile).
 `share(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns shares in the range `[0..1]`
 for every non-negative points returned by `q` per each timestamp, so the sum of shares per each `group_labels` equals 1.
 
-This function is useful for normalizing [histogram bucket](https://docs.victoriametrics.com/keyConcepts.html#histogram) shares
+This function is useful for normalizing [histogram bucket](https://docs.victoriametrics.com/keyconcepts/#histogram) shares
 into `[0..1]` range:
 
 ```metricsql
@@ -2208,7 +2208,7 @@ See also [zscore_over_time](#zscore_over_time), [range_trim_zscore](#range_trim_
 ## Subqueries
 
 MetricsQL supports and extends PromQL subqueries. See [this article](https://valyala.medium.com/prometheus-subqueries-in-victoriametrics-9b1492b720b3) for details.
-Any [rollup function](#rollup-functions) for something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) form a subquery.
+Any [rollup function](#rollup-functions) for something other than [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) form a subquery.
 Nested rollup functions can be implicit thanks to the [implicit query conversions](#implicit-query-conversions).
 For example, `delta(sum(m))` is implicitly converted to `delta(sum(default_rollup(m))[1i:1i])`, so it becomes a subquery,
 since it contains [default_rollup](#default_rollup) nested into [delta](#delta).
@@ -2219,19 +2219,19 @@ VictoriaMetrics performs subqueries in the following way:
   For example, for expression `max_over_time(rate(http_requests_total[5m])[1h:30s])` the inner function `rate(http_requests_total[5m])`
   is calculated with `step=30s`. The resulting data points are aligned by the `step`.
 * It calculates the outer rollup function over the results of the inner rollup function using the `step` value
-  passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+  passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 ## Implicit query conversions
 
 VictoriaMetrics performs the following implicit conversions for incoming queries before starting the calculations:
 
 * If lookbehind window in square brackets is missing inside [rollup function](#rollup-functions), then it is automatically set to the following value:
-  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
+  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
     for all the [rollup functions](#rollup-functions) except of [default_rollup](#default_rollup) and [rate](#rate). This value is known as `$__interval` in Grafana or `1i` in MetricsQL.
     For example, `avg_over_time(temperature)` is automatically transformed to `avg_over_time(temperature[1i])`.
   - To the `max(step, scrape_interval)`, where `scrape_interval` is the interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
     for [default_rollup](#default_rollup) and [rate](#rate) functions. This allows avoiding unexpected gaps on the graph when `step` is smaller than `scrape_interval`.
-* All the [series selectors](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* All the [series selectors](https://docs.victoriametrics.com/keyconcepts/#filtering),
   which aren't wrapped into [rollup functions](#rollup-functions), are automatically wrapped into [default_rollup](#default_rollup) function.
   Examples:
   * `foo` is transformed to `default_rollup(foo)`
@@ -2242,6 +2242,6 @@ VictoriaMetrics performs the following implicit conversions for incoming queries
     it is [transform function](#transform-functions)
 * If `step` in square brackets is missing inside [subquery](#subqueries), then `1i` step is automatically added there.
   For example, `avg_over_time(rate(http_requests_total[5m])[1h])` is automatically converted to `avg_over_time(rate(http_requests_total[5m])[1h:1i])`.
-* If something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering)
+* 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])`.

app/vmagent/README.md

@@ -1,3 +1,3 @@
-See vmagent docs [here](https://docs.victoriametrics.com/vmagent.html).
+See vmagent docs [here](https://docs.victoriametrics.com/vmagent/).
 
-vmagent docs can be edited at [docs/vmagent.md](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/docs/vmagent.md).
+vmagent docs can be edited at [docs/vmagent.md](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/docs/vmagent.md).

app/vmagent/main.go

@@ -225,7 +225,7 @@ func requestHandler(w http.ResponseWriter, r *http.Request) bool {
 		}
 		w.Header().Add("Content-Type", "text/html; charset=utf-8")
 		fmt.Fprintf(w, "<h2>vmagent</h2>")
-		fmt.Fprintf(w, "See docs at <a href='https://docs.victoriametrics.com/vmagent.html'>https://docs.victoriametrics.com/vmagent.html</a></br>")
+		fmt.Fprintf(w, "See docs at <a href='https://docs.victoriametrics.com/vmagent/'>https://docs.victoriametrics.com/vmagent/</a></br>")
 		fmt.Fprintf(w, "Useful endpoints:</br>")
 		httpserver.WriteAPIHelp(w, [][2]string{
 			{"targets", "status for discovered active targets"},
@@ -718,7 +718,7 @@ func usage() {
 	const s = `
 vmagent collects metrics data via popular data ingestion protocols and routes it to VictoriaMetrics.
 
-See the docs at https://docs.victoriametrics.com/vmagent.html .
+See the docs at https://docs.victoriametrics.com/vmagent/ .
 `
 	flagutil.Usage(s)
 }

app/vmagent/remotewrite/client.go

@@ -25,9 +25,9 @@ import (
 
 var (
 	forcePromProto = flagutil.NewArrayBool("remoteWrite.forcePromProto", "Whether to force Prometheus remote write protocol for sending data "+
-		"to the corresponding -remoteWrite.url . See https://docs.victoriametrics.com/vmagent.html#victoriametrics-remote-write-protocol")
+		"to the corresponding -remoteWrite.url . See https://docs.victoriametrics.com/vmagent/#victoriametrics-remote-write-protocol")
 	forceVMProto = flagutil.NewArrayBool("remoteWrite.forceVMProto", "Whether to force VictoriaMetrics remote write protocol for sending data "+
-		"to the corresponding -remoteWrite.url . See https://docs.victoriametrics.com/vmagent.html#victoriametrics-remote-write-protocol")
+		"to the corresponding -remoteWrite.url . See https://docs.victoriametrics.com/vmagent/#victoriametrics-remote-write-protocol")
 
 	rateLimit = flagutil.NewArrayInt("remoteWrite.rateLimit", 0, "Optional rate limit in bytes per second for data sent to the corresponding -remoteWrite.url. "+
 		"By default, the rate limit is disabled. It can be useful for limiting load on remote storage when big amounts of buffered data "+
@@ -164,7 +164,7 @@ func newHTTPClient(argIdx int, remoteWriteURL, sanitizedURL string, fq *persiste
 		useVMProto = common.HandleVMProtoClientHandshake(c.remoteWriteURL, doRequest)
 		if !useVMProto {
 			logger.Infof("the remote storage at %q doesn't support VictoriaMetrics remote write protocol. Switching to Prometheus remote write protocol. "+
-				"See https://docs.victoriametrics.com/vmagent.html#victoriametrics-remote-write-protocol", sanitizedURL)
+				"See https://docs.victoriametrics.com/vmagent/#victoriametrics-remote-write-protocol", sanitizedURL)
 		}
 	}
 	c.useVMProto = useVMProto

app/vmagent/remotewrite/pendingseries.go

@@ -28,7 +28,7 @@ var (
 	maxRowsPerBlock      = flag.Int("remoteWrite.maxRowsPerBlock", 10000, "The maximum number of samples to send in each block to remote storage. Higher number may improve performance at the cost of the increased memory usage. See also -remoteWrite.maxBlockSize")
 	vmProtoCompressLevel = flag.Int("remoteWrite.vmProtoCompressLevel", 0, "The compression level for VictoriaMetrics remote write protocol. "+
 		"Higher values reduce network traffic at the cost of higher CPU usage. Negative values reduce CPU usage at the cost of increased network traffic. "+
-		"See https://docs.victoriametrics.com/vmagent.html#victoriametrics-remote-write-protocol")
+		"See https://docs.victoriametrics.com/vmagent/#victoriametrics-remote-write-protocol")
 )
 
 type pendingSeries struct {

app/vmagent/remotewrite/relabel.go

@@ -19,10 +19,10 @@ var (
 	relabelConfigPathGlobal = flag.String("remoteWrite.relabelConfig", "", "Optional path to file with relabeling configs, which are applied "+
 		"to all the metrics before sending them to -remoteWrite.url. See also -remoteWrite.urlRelabelConfig. "+
 		"The path can point either to local file or to http url. "+
-		"See https://docs.victoriametrics.com/vmagent.html#relabeling")
+		"See https://docs.victoriametrics.com/vmagent/#relabeling")
 	relabelConfigPaths = flagutil.NewArrayString("remoteWrite.urlRelabelConfig", "Optional path to relabel configs for the corresponding -remoteWrite.url. "+
 		"See also -remoteWrite.relabelConfig. The path can point either to local file or to http url. "+
-		"See https://docs.victoriametrics.com/vmagent.html#relabeling")
+		"See https://docs.victoriametrics.com/vmagent/#relabeling")
 
 	usePromCompatibleNaming = flag.Bool("usePromCompatibleNaming", false, "Whether to replace characters unsupported by Prometheus with underscores "+
 		"in the ingested metric names and label names. For example, foo.bar{a.b='c'} is transformed into foo_bar{a_b='c'} during data ingestion if this flag is set. "+

app/vmagent/remotewrite/remotewrite.go

@@ -40,21 +40,26 @@ var (
 		"Pass multiple -remoteWrite.url options in order to replicate the collected data to multiple remote storage systems. "+
 		"The data can be sharded among the configured remote storage systems if -remoteWrite.shardByURL flag is set")
 	remoteWriteMultitenantURLs = flagutil.NewArrayString("remoteWrite.multitenantURL", "Base path for multitenant remote storage URL to write data to. "+
-		"See https://docs.victoriametrics.com/vmagent.html#multitenancy for details. Example url: http://<vminsert>:8480 . "+
+		"See https://docs.victoriametrics.com/vmagent/#multitenancy for details. Example url: http://<vminsert>:8480 . "+
 		"Pass multiple -remoteWrite.multitenantURL flags in order to replicate data to multiple remote storage systems. "+
-		"This flag is deprecated in favor of -enableMultitenantHandlers . See https://docs.victoriametrics.com/vmagent.html#multitenancy")
+		"This flag is deprecated in favor of -enableMultitenantHandlers . See https://docs.victoriametrics.com/vmagent/#multitenancy")
 	enableMultitenantHandlers = flag.Bool("enableMultitenantHandlers", false, "Whether to process incoming data via multitenant insert handlers according to "+
-		"https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#url-format . By default incoming data is processed via single-node insert handlers "+
+		"https://docs.victoriametrics.com/cluster-victoriametrics/#url-format . By default incoming data is processed via single-node insert handlers "+
 		"according to https://docs.victoriametrics.com/#how-to-import-time-series-data ."+
-		"See https://docs.victoriametrics.com/vmagent.html#multitenancy for details")
+		"See https://docs.victoriametrics.com/vmagent/#multitenancy for details")
+
 	shardByURL = flag.Bool("remoteWrite.shardByURL", false, "Whether to shard outgoing series across all the remote storage systems enumerated via -remoteWrite.url . "+
-		"By default the data is replicated across all the -remoteWrite.url . See https://docs.victoriametrics.com/vmagent.html#sharding-among-remote-storages")
+		"By default the data is replicated across all the -remoteWrite.url . See https://docs.victoriametrics.com/vmagent/#sharding-among-remote-storages . "+
+		"See also -remoteWrite.shardByURLReplicas")
+	shardByURLReplicas = flag.Int("remoteWrite.shardByURLReplicas", 1, "How many copies of data to make among remote storage systems enumerated via -remoteWrite.url "+
+		"when -remoteWrite.shardByURL is set. See https://docs.victoriametrics.com/vmagent/#sharding-among-remote-storages")
 	shardByURLLabels = flagutil.NewArrayString("remoteWrite.shardByURL.labels", "Optional list of labels, which must be used for sharding outgoing samples "+
 		"among remote storage systems if -remoteWrite.shardByURL command-line flag is set. By default all the labels are used for sharding in order to gain "+
 		"even distribution of series over the specified -remoteWrite.url systems. See also -remoteWrite.shardByURL.ignoreLabels")
 	shardByURLIgnoreLabels = flagutil.NewArrayString("remoteWrite.shardByURL.ignoreLabels", "Optional list of labels, which must be ignored when sharding outgoing samples "+
 		"among remote storage systems if -remoteWrite.shardByURL command-line flag is set. By default all the labels are used for sharding in order to gain "+
 		"even distribution of series over the specified -remoteWrite.url systems. See also -remoteWrite.shardByURL.labels")
+
 	tmpDataPath = flag.String("remoteWrite.tmpDataPath", "vmagent-remotewrite-data", "Path to directory for storing pending data, which isn't sent to the configured -remoteWrite.url . "+
 		"See also -remoteWrite.maxDiskUsagePerURL and -remoteWrite.disableOnDiskQueue")
 	keepDanglingQueues = flag.Bool("remoteWrite.keepDanglingQueues", false, "Keep persistent queues contents at -remoteWrite.tmpDataPath in case there are no matching -remoteWrite.url. "+
@@ -81,33 +86,33 @@ var (
 		`For example, if m{k1="v1",k2="v2"} may be sent as m{k2="v2",k1="v1"}`+
 		`Enabled sorting for labels can slow down ingestion performance a bit`)
 	maxHourlySeries = flag.Int("remoteWrite.maxHourlySeries", 0, "The maximum number of unique series vmagent can send to remote storage systems during the last hour. "+
-		"Excess series are logged and dropped. This can be useful for limiting series cardinality. See https://docs.victoriametrics.com/vmagent.html#cardinality-limiter")
+		"Excess series are logged and dropped. This can be useful for limiting series cardinality. See https://docs.victoriametrics.com/vmagent/#cardinality-limiter")
 	maxDailySeries = flag.Int("remoteWrite.maxDailySeries", 0, "The maximum number of unique series vmagent can send to remote storage systems during the last 24 hours. "+
-		"Excess series are logged and dropped. This can be useful for limiting series churn rate. See https://docs.victoriametrics.com/vmagent.html#cardinality-limiter")
+		"Excess series are logged and dropped. This can be useful for limiting series churn rate. See https://docs.victoriametrics.com/vmagent/#cardinality-limiter")
 	maxIngestionRate = flag.Int("maxIngestionRate", 0, "The maximum number of samples vmagent can receive per second. Data ingestion is paused when the limit is exceeded. "+
 		"By default there are no limits on samples ingestion rate. See also -remoteWrite.rateLimit")
 
 	streamAggrConfig = flagutil.NewArrayString("remoteWrite.streamAggr.config", "Optional path to file with stream aggregation config. "+
-		"See https://docs.victoriametrics.com/stream-aggregation.html . "+
+		"See https://docs.victoriametrics.com/stream-aggregation/ . "+
 		"See also -remoteWrite.streamAggr.keepInput, -remoteWrite.streamAggr.dropInput and -remoteWrite.streamAggr.dedupInterval")
 	streamAggrKeepInput = flagutil.NewArrayBool("remoteWrite.streamAggr.keepInput", "Whether to keep all the input samples after the aggregation "+
 		"with -remoteWrite.streamAggr.config. By default, only aggregates samples are dropped, while the remaining samples "+
-		"are written to the corresponding -remoteWrite.url . See also -remoteWrite.streamAggr.dropInput and https://docs.victoriametrics.com/stream-aggregation.html")
+		"are written to the corresponding -remoteWrite.url . See also -remoteWrite.streamAggr.dropInput and https://docs.victoriametrics.com/stream-aggregation/")
 	streamAggrDropInput = flagutil.NewArrayBool("remoteWrite.streamAggr.dropInput", "Whether to drop all the input samples after the aggregation "+
 		"with -remoteWrite.streamAggr.config. By default, only aggregates samples are dropped, while the remaining samples "+
-		"are written to the corresponding -remoteWrite.url . See also -remoteWrite.streamAggr.keepInput and https://docs.victoriametrics.com/stream-aggregation.html")
+		"are written to the corresponding -remoteWrite.url . See also -remoteWrite.streamAggr.keepInput and https://docs.victoriametrics.com/stream-aggregation/")
 	streamAggrDedupInterval = flagutil.NewArrayDuration("remoteWrite.streamAggr.dedupInterval", 0, "Input samples are de-duplicated with this interval before optional aggregation "+
-		"with -remoteWrite.streamAggr.config . See also -dedup.minScrapeInterval and https://docs.victoriametrics.com/stream-aggregation.html#deduplication")
+		"with -remoteWrite.streamAggr.config . See also -dedup.minScrapeInterval and https://docs.victoriametrics.com/stream-aggregation/#deduplication")
 	streamAggrIgnoreOldSamples = flagutil.NewArrayBool("remoteWrite.streamAggr.ignoreOldSamples", "Whether to ignore input samples with old timestamps outside the current aggregation interval "+
-		"for the corresponding -remoteWrite.streamAggr.config . See https://docs.victoriametrics.com/stream-aggregation.html#ignoring-old-samples")
+		"for the corresponding -remoteWrite.streamAggr.config . See https://docs.victoriametrics.com/stream-aggregation/#ignoring-old-samples")
 	streamAggrDropInputLabels = flagutil.NewArrayString("streamAggr.dropInputLabels", "An optional list of labels to drop from samples "+
-		"before stream de-duplication and aggregation . See https://docs.victoriametrics.com/stream-aggregation.html#dropping-unneeded-labels")
+		"before stream de-duplication and aggregation . See https://docs.victoriametrics.com/stream-aggregation/#dropping-unneeded-labels")
 
 	disableOnDiskQueue = flag.Bool("remoteWrite.disableOnDiskQueue", false, "Whether to disable storing pending data to -remoteWrite.tmpDataPath "+
-		"when the configured remote storage systems cannot keep up with the data ingestion rate. See https://docs.victoriametrics.com/vmagent.html#disabling-on-disk-persistence ."+
+		"when the configured remote storage systems cannot keep up with the data ingestion rate. See https://docs.victoriametrics.com/vmagent/#disabling-on-disk-persistence ."+
 		"See also -remoteWrite.dropSamplesOnOverload")
 	dropSamplesOnOverload = flag.Bool("remoteWrite.dropSamplesOnOverload", false, "Whether to drop samples when -remoteWrite.disableOnDiskQueue is set and if the samples "+
-		"cannot be pushed into the configured remote storage systems in a timely manner. See https://docs.victoriametrics.com/vmagent.html#disabling-on-disk-persistence")
+		"cannot be pushed into the configured remote storage systems in a timely manner. See https://docs.victoriametrics.com/vmagent/#disabling-on-disk-persistence")
 )
 
 var (
@@ -125,7 +130,7 @@ var (
 	ErrQueueFullHTTPRetry = &httpserver.ErrorWithStatusCode{
 		Err: fmt.Errorf("remote storage systems cannot keep up with the data ingestion rate; retry the request later " +
 			"or remove -remoteWrite.disableOnDiskQueue from vmagent command-line flags, so it could save pending data to -remoteWrite.tmpDataPath; " +
-			"see https://docs.victoriametrics.com/vmagent.html#disabling-on-disk-persistence"),
+			"see https://docs.victoriametrics.com/vmagent/#disabling-on-disk-persistence"),
 		StatusCode: http.StatusTooManyRequests,
 	}
 )
@@ -338,7 +343,7 @@ func newRemoteWriteCtxs(at *auth.Token, urls []string) []*remoteWriteCtx {
 		}
 		sanitizedURL := fmt.Sprintf("%d:secret-url", i+1)
 		if at != nil {
-			// Construct full remote_write url for the given tenant according to https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#url-format
+			// Construct full remote_write url for the given tenant according to https://docs.victoriametrics.com/cluster-victoriametrics/#url-format
 			remoteWriteURL.Path = fmt.Sprintf("%s/insert/%d:%d/prometheus/api/v1/write", remoteWriteURL.Path, at.AccountID, at.ProjectID)
 			sanitizedURL = fmt.Sprintf("%s:%d:%d", sanitizedURL, at.AccountID, at.ProjectID)
 		}
@@ -562,58 +567,17 @@ func tryPushBlockToRemoteStorages(rwctxs []*remoteWriteCtx, tssBlock []prompbmar
 
 	// We need to push tssBlock to multiple remote storages.
 	// This is either sharding or replication depending on -remoteWrite.shardByURL command-line flag value.
-	if *shardByURL {
-		// Shard the data among rwctxs
-		tssByURL := make([][]prompbmarshal.TimeSeries, len(rwctxs))
-		tmpLabels := promutils.GetLabels()
-		for _, ts := range tssBlock {
-			hashLabels := ts.Labels
-			if len(shardByURLLabelsMap) > 0 {
-				hashLabels = tmpLabels.Labels[:0]
-				for _, label := range ts.Labels {
-					if _, ok := shardByURLLabelsMap[label.Name]; ok {
-						hashLabels = append(hashLabels, label)
-					}
-				}
-				tmpLabels.Labels = hashLabels
-			} else if len(shardByURLIgnoreLabelsMap) > 0 {
-				hashLabels = tmpLabels.Labels[:0]
-				for _, label := range ts.Labels {
-					if _, ok := shardByURLIgnoreLabelsMap[label.Name]; !ok {
-						hashLabels = append(hashLabels, label)
-					}
-				}
-				tmpLabels.Labels = hashLabels
-			}
-			h := getLabelsHash(hashLabels)
-			idx := h % uint64(len(tssByURL))
-			tssByURL[idx] = append(tssByURL[idx], ts)
+	if *shardByURL && *shardByURLReplicas < len(rwctxs) {
+		// Shard tssBlock samples among rwctxs.
+		replicas := *shardByURLReplicas
+		if replicas <= 0 {
+			replicas = 1
 		}
-		promutils.PutLabels(tmpLabels)
-
-		// Push sharded data to remote storages in parallel in order to reduce
-		// the time needed for sending the data to multiple remote storage systems.
-		var wg sync.WaitGroup
-		var anyPushFailed atomic.Bool
-		for i, rwctx := range rwctxs {
-			tssShard := tssByURL[i]
-			if len(tssShard) == 0 {
-				continue
-			}
-			wg.Add(1)
-			go func(rwctx *remoteWriteCtx, tss []prompbmarshal.TimeSeries) {
-				defer wg.Done()
-				if !rwctx.TryPush(tss) {
-					anyPushFailed.Store(true)
-				}
-			}(rwctx, tssShard)
-		}
-		wg.Wait()
-		return !anyPushFailed.Load()
+		return tryShardingBlockAmongRemoteStorages(rwctxs, tssBlock, replicas)
 	}
 
-	// Replicate data among rwctxs.
-	// Push block to remote storages in parallel in order to reduce
+	// Replicate tssBlock samples among rwctxs.
+	// Push tssBlock to remote storage systems in parallel in order to reduce
 	// the time needed for sending the data to multiple remote storage systems.
 	var wg sync.WaitGroup
 	wg.Add(len(rwctxs))
@@ -630,6 +594,97 @@ func tryPushBlockToRemoteStorages(rwctxs []*remoteWriteCtx, tssBlock []prompbmar
 	return !anyPushFailed.Load()
 }
 
+func tryShardingBlockAmongRemoteStorages(rwctxs []*remoteWriteCtx, tssBlock []prompbmarshal.TimeSeries, replicas int) bool {
+	x := getTSSShards(len(rwctxs))
+	defer putTSSShards(x)
+
+	shards := x.shards
+	tmpLabels := promutils.GetLabels()
+	for _, ts := range tssBlock {
+		hashLabels := ts.Labels
+		if len(shardByURLLabelsMap) > 0 {
+			hashLabels = tmpLabels.Labels[:0]
+			for _, label := range ts.Labels {
+				if _, ok := shardByURLLabelsMap[label.Name]; ok {
+					hashLabels = append(hashLabels, label)
+				}
+			}
+			tmpLabels.Labels = hashLabels
+		} else if len(shardByURLIgnoreLabelsMap) > 0 {
+			hashLabels = tmpLabels.Labels[:0]
+			for _, label := range ts.Labels {
+				if _, ok := shardByURLIgnoreLabelsMap[label.Name]; !ok {
+					hashLabels = append(hashLabels, label)
+				}
+			}
+			tmpLabels.Labels = hashLabels
+		}
+		h := getLabelsHash(hashLabels)
+		idx := h % uint64(len(shards))
+		i := 0
+		for {
+			shards[idx] = append(shards[idx], ts)
+			i++
+			if i >= replicas {
+				break
+			}
+			idx++
+			if idx >= uint64(len(shards)) {
+				idx = 0
+			}
+		}
+	}
+	promutils.PutLabels(tmpLabels)
+
+	// Push sharded samples to remote storage systems in parallel in order to reduce
+	// the time needed for sending the data to multiple remote storage systems.
+	var wg sync.WaitGroup
+	var anyPushFailed atomic.Bool
+	for i, rwctx := range rwctxs {
+		shard := shards[i]
+		if len(shard) == 0 {
+			continue
+		}
+		wg.Add(1)
+		go func(rwctx *remoteWriteCtx, tss []prompbmarshal.TimeSeries) {
+			defer wg.Done()
+			if !rwctx.TryPush(tss) {
+				anyPushFailed.Store(true)
+			}
+		}(rwctx, shard)
+	}
+	wg.Wait()
+	return !anyPushFailed.Load()
+}
+
+type tssShards struct {
+	shards [][]prompbmarshal.TimeSeries
+}
+
+func getTSSShards(n int) *tssShards {
+	v := tssShardsPool.Get()
+	if v == nil {
+		v = &tssShards{}
+	}
+	x := v.(*tssShards)
+	if cap(x.shards) < n {
+		x.shards = make([][]prompbmarshal.TimeSeries, n)
+	}
+	x.shards = x.shards[:n]
+	return x
+}
+
+func putTSSShards(x *tssShards) {
+	shards := x.shards
+	for i := range shards {
+		clear(shards[i])
+		shards[i] = shards[i][:0]
+	}
+	tssShardsPool.Put(x)
+}
+
+var tssShardsPool sync.Pool
+
 // sortLabelsIfNeeded sorts labels if -sortLabels command-line flag is set.
 func sortLabelsIfNeeded(tss []prompbmarshal.TimeSeries) {
 	if !*sortLabels {

app/vmagent/remotewrite/remotewrite_test.go

@@ -0,0 +1,48 @@
+package remotewrite
+
+import (
+	"fmt"
+	"math"
+	"testing"
+
+	"github.com/VictoriaMetrics/VictoriaMetrics/lib/prompbmarshal"
+)
+
+func TestGetLabelsHash_Distribution(t *testing.T) {
+	f := func(bucketsCount int) {
+		t.Helper()
+
+		// Distribute itemsCount hashes returned by getLabelsHash() across bucketsCount buckets.
+		itemsCount := 1_000 * bucketsCount
+		m := make([]int, bucketsCount)
+		var labels []prompbmarshal.Label
+		for i := 0; i < itemsCount; i++ {
+			labels = append(labels[:0], prompbmarshal.Label{
+				Name:  "__name__",
+				Value: fmt.Sprintf("some_name_%d", i),
+			})
+			for j := 0; j < 10; j++ {
+				labels = append(labels, prompbmarshal.Label{
+					Name:  fmt.Sprintf("label_%d", j),
+					Value: fmt.Sprintf("value_%d_%d", i, j),
+				})
+			}
+			h := getLabelsHash(labels)
+			m[h%uint64(bucketsCount)]++
+		}
+
+		// Verify that the distribution is even
+		expectedItemsPerBucket := itemsCount / bucketsCount
+		for _, n := range m {
+			if math.Abs(1-float64(n)/float64(expectedItemsPerBucket)) > 0.04 {
+				t.Fatalf("unexpected items in the bucket for %d buckets; got %d; want around %d", bucketsCount, n, expectedItemsPerBucket)
+			}
+		}
+	}
+
+	f(2)
+	f(3)
+	f(4)
+	f(5)
+	f(10)
+}

app/vmagent/remotewrite/statconn.go

@@ -3,34 +3,15 @@ package remotewrite
 import (
 	"context"
 	"net"
-	"sync"
 	"sync/atomic"
-	"time"
 
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/netutil"
 	"github.com/VictoriaMetrics/metrics"
 )
 
-func getStdDialer() *net.Dialer {
-	stdDialerOnce.Do(func() {
-		stdDialer = &net.Dialer{
-			Timeout:   30 * time.Second,
-			KeepAlive: 30 * time.Second,
-			DualStack: netutil.TCP6Enabled(),
-		}
-	})
-	return stdDialer
-}
-
-var (
-	stdDialer     *net.Dialer
-	stdDialerOnce sync.Once
-)
-
 func statDial(ctx context.Context, _, addr string) (conn net.Conn, err error) {
 	network := netutil.GetTCPNetwork()
-	d := getStdDialer()
-	conn, err = d.DialContext(ctx, network, addr)
+	conn, err = netutil.DialMaybeSRV(ctx, network, addr)
 	dialsTotal.Inc()
 	if err != nil {
 		dialErrors.Inc()

app/vmalert/README.md

@@ -1,3 +1,3 @@
-See vmalert docs [here](https://docs.victoriametrics.com/vmalert.html).
+See vmalert docs [here](https://docs.victoriametrics.com/vmalert/).
 
 vmalert docs can be edited at [docs/vmalert.md](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/docs/vmalert.md).

app/vmalert/main.go

@@ -44,7 +44,7 @@ Enterprise version of vmalert supports S3 and GCS paths to rules.
 For example: gs://bucket/path/to/rules, s3://bucket/path/to/rules
 S3 and GCS paths support only matching by prefix, e.g. s3://bucket/dir/rule_ matches
 all files with prefix rule_ in folder dir.
-See https://docs.victoriametrics.com/vmalert.html#reading-rules-from-object-storage
+See https://docs.victoriametrics.com/vmalert/#reading-rules-from-object-storage
 `)
 
 	ruleTemplatesPath = flagutil.NewArrayString("rule.templates", `Path or glob pattern to location with go template definitions `+
@@ -71,7 +71,7 @@ absolute path to all .tpl files in root.
 	externalURL         = flag.String("external.url", "", "External URL is used as alert's source for sent alerts to the notifier. By default, hostname is used as address.")
 	externalAlertSource = flag.String("external.alert.source", "", `External Alert Source allows to override the Source link for alerts sent to AlertManager `+
 		`for cases where you want to build a custom link to Grafana, Prometheus or any other service. `+
-		`Supports templating - see https://docs.victoriametrics.com/vmalert.html#templating . `+
+		`Supports templating - see https://docs.victoriametrics.com/vmalert/#templating . `+
 		`For example, link to Grafana: -external.alert.source='explore?orgId=1&left={"datasource":"VictoriaMetrics","queries":[{"expr":{{$expr|jsonEscape|queryEscape}},"refId":"A"}],"range":{"from":"now-1h","to":"now"}}'. `+
 		`Link to VMUI: -external.alert.source='vmui/#/?g0.expr={{.Expr|queryEscape}}'. `+
 		`If empty 'vmalert/alert?group_id={{.GroupID}}&alert_id={{.AlertID}}' is used.`)
@@ -319,7 +319,7 @@ func usage() {
 	const s = `
 vmalert processes alerts and recording rules.
 
-See the docs at https://docs.victoriametrics.com/vmalert.html .
+See the docs at https://docs.victoriametrics.com/vmalert/ .
 `
 	flagutil.Usage(s)
 }

app/vmalert/rule/alerting.go

@@ -314,23 +314,20 @@ func (ar *AlertingRule) execRange(ctx context.Context, start, end time.Time) ([]
 		return nil, fmt.Errorf("`query` template isn't supported in replay mode")
 	}
 	for _, s := range res.Data {
-		ls, err := ar.toLabels(s, qFn)
+		ls, as, err := ar.expandTemplates(s, qFn, time.Time{})
 		if err != nil {
-			return nil, fmt.Errorf("failed to expand labels: %s", err)
-		}
-		h := hash(ls.processed)
-		a, err := ar.newAlert(s, nil, time.Time{}, qFn) // initial alert
-		if err != nil {
-			return nil, fmt.Errorf("failed to create alert: %w", err)
+			return nil, fmt.Errorf("failed to expand templates: %s", err)
 		}
+		alertID := hash(ls.processed)
+		a := ar.newAlert(s, time.Time{}, ls.processed, as) // initial alert
 
 		prevT := time.Time{}
 		for i := range s.Values {
 			at := time.Unix(s.Timestamps[i], 0)
 			// try to restore alert's state on the first iteration
 			if at.Equal(start) {
-				if _, ok := ar.alerts[h]; ok {
-					a = ar.alerts[h]
+				if _, ok := ar.alerts[alertID]; ok {
+					a = ar.alerts[alertID]
 					prevT = at
 				}
 			}
@@ -352,7 +349,7 @@ func (ar *AlertingRule) execRange(ctx context.Context, start, end time.Time) ([]
 
 			// save alert's state on last iteration, so it can be used on the next execRange call
 			if at.Equal(end) {
-				holdAlertState[h] = a
+				holdAlertState[alertID] = a
 			}
 		}
 	}
@@ -386,15 +383,34 @@ func (ar *AlertingRule) exec(ctx context.Context, ts time.Time, limit int) ([]pr
 		}
 	}()
 
-	ar.alertsMu.Lock()
-	defer ar.alertsMu.Unlock()
-
 	if err != nil {
 		return nil, fmt.Errorf("failed to execute query %q: %w", ar.Expr, err)
 	}
-
 	ar.logDebugf(ts, nil, "query returned %d samples (elapsed: %s)", curState.Samples, curState.Duration)
 
+	qFn := func(query string) ([]datasource.Metric, error) {
+		res, _, err := ar.q.Query(ctx, query, ts)
+		return res.Data, err
+	}
+
+	// template labels and annotations before updating ar.alerts,
+	// since they could use `query` function which takes a while to execute,
+	// see https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6079.
+	expandedLabels := make([]*labelSet, len(res.Data))
+	expandedAnnotations := make([]map[string]string, len(res.Data))
+	for i, m := range res.Data {
+		ls, as, err := ar.expandTemplates(m, qFn, ts)
+		if err != nil {
+			curState.Err = fmt.Errorf("failed to expand templates: %w", err)
+			return nil, curState.Err
+		}
+		expandedLabels[i] = ls
+		expandedAnnotations[i] = as
+	}
+
+	ar.alertsMu.Lock()
+	defer ar.alertsMu.Unlock()
+
 	for h, a := range ar.alerts {
 		// cleanup inactive alerts from previous Exec
 		if a.State == notifier.StateInactive && ts.Sub(a.ResolvedAt) > resolvedRetention {
@@ -403,26 +419,18 @@ func (ar *AlertingRule) exec(ctx context.Context, ts time.Time, limit int) ([]pr
 		}
 	}
 
-	qFn := func(query string) ([]datasource.Metric, error) {
-		res, _, err := ar.q.Query(ctx, query, ts)
-		return res.Data, err
-	}
 	updated := make(map[uint64]struct{})
 	// update list of active alerts
-	for _, m := range res.Data {
-		ls, err := ar.toLabels(m, qFn)
-		if err != nil {
-			curState.Err = fmt.Errorf("failed to expand labels: %w", err)
-			return nil, curState.Err
-		}
-		h := hash(ls.processed)
-		if _, ok := updated[h]; ok {
+	for i, m := range res.Data {
+		labels, annotations := expandedLabels[i], expandedAnnotations[i]
+		alertID := hash(labels.processed)
+		if _, ok := updated[alertID]; ok {
 			// duplicate may be caused the removal of `__name__` label
-			curState.Err = fmt.Errorf("labels %v: %w", ls.processed, errDuplicate)
+			curState.Err = fmt.Errorf("labels %v: %w", labels.processed, errDuplicate)
 			return nil, curState.Err
 		}
-		updated[h] = struct{}{}
-		if a, ok := ar.alerts[h]; ok {
+		updated[alertID] = struct{}{}
+		if a, ok := ar.alerts[alertID]; ok {
 			if a.State == notifier.StateInactive {
 				// alert could be in inactive state for resolvedRetention
 				// so when we again receive metrics for it - we switch it
@@ -432,22 +440,17 @@ func (ar *AlertingRule) exec(ctx context.Context, ts time.Time, limit int) ([]pr
 				ar.logDebugf(ts, a, "INACTIVE => PENDING")
 			}
 			a.Value = m.Values[0]
-			// re-exec template since Value or query can be used in annotations
-			a.Annotations, err = a.ExecTemplate(qFn, ls.origin, ar.Annotations)
+			a.Annotations = annotations
 			if err != nil {
 				return nil, err
 			}
 			a.KeepFiringSince = time.Time{}
 			continue
 		}
-		a, err := ar.newAlert(m, ls, ts, qFn)
-		if err != nil {
-			curState.Err = fmt.Errorf("failed to create alert: %w", err)
-			return nil, curState.Err
-		}
-		a.ID = h
+		a := ar.newAlert(m, ts, labels.processed, annotations)
+		a.ID = alertID
 		a.State = notifier.StatePending
-		ar.alerts[h] = a
+		ar.alerts[alertID] = a
 		ar.logDebugf(ts, a, "created in state PENDING")
 	}
 	var numActivePending int
@@ -497,6 +500,28 @@ func (ar *AlertingRule) exec(ctx context.Context, ts time.Time, limit int) ([]pr
 	return ar.toTimeSeries(ts.Unix()), nil
 }
 
+func (ar *AlertingRule) expandTemplates(m datasource.Metric, qFn templates.QueryFn, ts time.Time) (*labelSet, map[string]string, error) {
+	ls, err := ar.toLabels(m, qFn)
+	if err != nil {
+		return nil, nil, fmt.Errorf("failed to expand labels: %w", err)
+	}
+
+	tplData := notifier.AlertTplData{
+		Value:    m.Values[0],
+		Labels:   ls.origin,
+		Expr:     ar.Expr,
+		AlertID:  hash(ls.processed),
+		GroupID:  ar.GroupID,
+		ActiveAt: ts,
+		For:      ar.For,
+	}
+	as, err := notifier.ExecTemplate(qFn, ar.Annotations, tplData)
+	if err != nil {
+		return nil, nil, fmt.Errorf("failed to template annotations: %w", err)
+	}
+	return ls, as, nil
+}
+
 func (ar *AlertingRule) toTimeSeries(timestamp int64) []prompbmarshal.TimeSeries {
 	var tss []prompbmarshal.TimeSeries
 	for _, a := range ar.alerts {
@@ -530,25 +555,25 @@ func hash(labels map[string]string) uint64 {
 	return hash.Sum64()
 }
 
-func (ar *AlertingRule) newAlert(m datasource.Metric, ls *labelSet, start time.Time, qFn templates.QueryFn) (*notifier.Alert, error) {
-	var err error
-	if ls == nil {
-		ls, err = ar.toLabels(m, qFn)
-		if err != nil {
-			return nil, fmt.Errorf("failed to expand labels: %w", err)
-		}
+func (ar *AlertingRule) newAlert(m datasource.Metric, start time.Time, labels, annotations map[string]string) *notifier.Alert {
+	as := make(map[string]string)
+	if annotations != nil {
+		as = annotations
 	}
-	a := &notifier.Alert{
-		GroupID:  ar.GroupID,
-		Name:     ar.Name,
-		Labels:   ls.processed,
-		Value:    m.Values[0],
-		ActiveAt: start,
-		Expr:     ar.Expr,
-		For:      ar.For,
+	ls := make(map[string]string)
+	if labels != nil {
+		ls = labels
+	}
+	return &notifier.Alert{
+		GroupID:     ar.GroupID,
+		Name:        ar.Name,
+		Expr:        ar.Expr,
+		For:         ar.For,
+		ActiveAt:    start,
+		Value:       m.Values[0],
+		Labels:      ls,
+		Annotations: as,
 	}
-	a.Annotations, err = a.ExecTemplate(qFn, ls.origin, ar.Annotations)
-	return a, err
 }
 
 const (
@@ -604,9 +629,6 @@ func (ar *AlertingRule) restore(ctx context.Context, q datasource.Querier, ts ti
 		return nil
 	}
 
-	ar.alertsMu.Lock()
-	defer ar.alertsMu.Unlock()
-
 	if len(ar.alerts) < 1 {
 		return nil
 	}
@@ -631,6 +653,10 @@ func (ar *AlertingRule) restore(ctx context.Context, q datasource.Querier, ts ti
 		ar.logDebugf(ts, nil, "no response was received from restore query")
 		return nil
 	}
+
+	ar.alertsMu.Lock()
+	defer ar.alertsMu.Unlock()
+
 	for _, series := range res.Data {
 		series.DelLabel("__name__")
 		labelSet := make(map[string]string, len(series.Labels))

app/vmalert/rule/group_test.go

@@ -217,20 +217,21 @@ func TestGroupStart(t *testing.T) {
 
 	const evalInterval = time.Millisecond
 	g := NewGroup(groups[0], fs, evalInterval, map[string]string{"cluster": "east-1"})
-	g.Concurrency = 2
 
 	const inst1, inst2, job = "foo", "bar", "baz"
 	m1 := metricWithLabels(t, "instance", inst1, "job", job)
 	m2 := metricWithLabels(t, "instance", inst2, "job", job)
 
 	r := g.Rules[0].(*AlertingRule)
-	alert1, err := r.newAlert(m1, nil, time.Now(), nil)
-	if err != nil {
-		t.Fatalf("faield to create alert: %s", err)
-	}
+	alert1 := r.newAlert(m1, time.Now(), nil, nil)
 	alert1.State = notifier.StateFiring
+	// add annotations
+	alert1.Annotations["summary"] = "1"
 	// add external label
 	alert1.Labels["cluster"] = "east-1"
+	// add labels from response
+	alert1.Labels["job"] = job
+	alert1.Labels["instance"] = inst1
 	// add rule labels
 	alert1.Labels["label"] = "bar"
 	alert1.Labels["host"] = inst1
@@ -239,13 +240,15 @@ func TestGroupStart(t *testing.T) {
 	alert1.Labels[alertGroupNameLabel] = g.Name
 	alert1.ID = hash(alert1.Labels)
 
-	alert2, err := r.newAlert(m2, nil, time.Now(), nil)
-	if err != nil {
-		t.Fatalf("faield to create alert: %s", err)
-	}
+	alert2 := r.newAlert(m2, time.Now(), nil, nil)
 	alert2.State = notifier.StateFiring
+	// add annotations
+	alert2.Annotations["summary"] = "1"
 	// add external label
 	alert2.Labels["cluster"] = "east-1"
+	// add labels from response
+	alert2.Labels["job"] = job
+	alert2.Labels["instance"] = inst2
 	// add rule labels
 	alert2.Labels["label"] = "bar"
 	alert2.Labels["host"] = inst2
@@ -262,8 +265,25 @@ func TestGroupStart(t *testing.T) {
 		close(finished)
 	}()
 
-	// wait for multiple evals
-	time.Sleep(20 * evalInterval)
+	waitForIterations := func(n int, interval time.Duration) {
+		t.Helper()
+
+		var cur uint64
+		prev := g.metrics.iterationTotal.Get()
+		for i := 0; ; i++ {
+			if i > 40 {
+				t.Fatalf("group wasn't able to perform %d evaluations during %d eval intervals", n, i)
+			}
+			cur = g.metrics.iterationTotal.Get()
+			if int(cur-prev) >= n {
+				return
+			}
+			time.Sleep(interval)
+		}
+	}
+
+	// wait for multiple evaluation iterations
+	waitForIterations(4, evalInterval)
 
 	gotAlerts := fn.GetAlerts()
 	expectedAlerts := []notifier.Alert{*alert1, *alert2}
@@ -280,8 +300,8 @@ func TestGroupStart(t *testing.T) {
 	// and set only one datapoint for response
 	fs.Add(m1)
 
-	// wait for multiple evals
-	time.Sleep(20 * evalInterval)
+	// wait for multiple evaluation iterations
+	waitForIterations(4, evalInterval)
 
 	gotAlerts = fn.GetAlerts()
 	alert2.State = notifier.StateInactive

app/vmalert/rule/rule.go

@@ -32,7 +32,7 @@ type Rule interface {
 	close()
 }
 
-var errDuplicate = errors.New("result contains metrics with the same labelset after applying rule labels. See https://docs.victoriametrics.com/vmalert.html#series-with-the-same-labelset for details")
+var errDuplicate = errors.New("result contains metrics with the same labelset after applying rule labels. See https://docs.victoriametrics.com/vmalert/#series-with-the-same-labelset for details")
 
 type ruleState struct {
 	sync.RWMutex

app/vmalert/web.go

@@ -39,7 +39,7 @@ var (
 		{Name: "Groups", Url: "groups"},
 		{Name: "Alerts", Url: "alerts"},
 		{Name: "Notifiers", Url: "notifiers"},
-		{Name: "Docs", Url: "https://docs.victoriametrics.com/vmalert.html"},
+		{Name: "Docs", Url: "https://docs.victoriametrics.com/vmalert/"},
 	}
 )
 

app/vmauth/README.md

@@ -1,3 +1,3 @@
-See vmauth docs [here](https://docs.victoriametrics.com/vmauth.html).
+See vmauth docs [here](https://docs.victoriametrics.com/vmauth/).
 
 vmauth docs can be edited at [docs/vmauth.md](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/docs/vmauth.md).

app/vmauth/auth_config.go

@@ -7,7 +7,6 @@ import (
 	"flag"
 	"fmt"
 	"math"
-	"net"
 	"net/http"
 	"net/url"
 	"os"
@@ -27,20 +26,21 @@ import (
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/flagutil"
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/fs/fscore"
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/logger"
+	"github.com/VictoriaMetrics/VictoriaMetrics/lib/netutil"
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/procutil"
 )
 
 var (
 	authConfigPath = flag.String("auth.config", "", "Path to auth config. It can point either to local file or to http url. "+
-		"See https://docs.victoriametrics.com/vmauth.html for details on the format of this auth config")
+		"See https://docs.victoriametrics.com/vmauth/ for details on the format of this auth config")
 	configCheckInterval = flag.Duration("configCheckInterval", 0, "interval for config file re-read. "+
 		"Zero value disables config re-reading. By default, refreshing is disabled, send SIGHUP for config refresh.")
 	defaultRetryStatusCodes = flagutil.NewArrayInt("retryStatusCodes", 0, "Comma-separated list of default HTTP response status codes when vmauth re-tries the request on other backends. "+
-		"See https://docs.victoriametrics.com/vmauth.html#load-balancing for details")
+		"See https://docs.victoriametrics.com/vmauth/#load-balancing for details")
 	defaultLoadBalancingPolicy = flag.String("loadBalancingPolicy", "least_loaded", "The default load balancing policy to use for backend urls specified inside url_prefix section. "+
-		"Supported policies: least_loaded, first_available. See https://docs.victoriametrics.com/vmauth.html#load-balancing")
+		"Supported policies: least_loaded, first_available. See https://docs.victoriametrics.com/vmauth/#load-balancing")
 	discoverBackendIPsGlobal = flag.Bool("discoverBackendIPs", false, "Whether to discover backend IPs via periodic DNS queries to hostnames specified in url_prefix. "+
-		"This may be useful when url_prefix points to a hostname with dynamically scaled instances behind it. See https://docs.victoriametrics.com/vmauth.html#discovering-backend-ips")
+		"This may be useful when url_prefix points to a hostname with dynamically scaled instances behind it. See https://docs.victoriametrics.com/vmauth/#discovering-backend-ips")
 	discoverBackendIPsInterval = flag.Duration("discoverBackendIPsInterval", 10*time.Second, "The interval for re-discovering backend IPs if -discoverBackendIPs command-line flag is set. "+
 		"Too low value may lead to DNS errors")
 	httpAuthHeader = flagutil.NewArrayString("httpAuthHeader", "HTTP request header to use for obtaining authorization tokens. By default auth tokens are read from Authorization request header")
@@ -73,15 +73,18 @@ type UserInfo struct {
 	RetryStatusCodes       []int       `yaml:"retry_status_codes,omitempty"`
 	LoadBalancingPolicy    string      `yaml:"load_balancing_policy,omitempty"`
 	DropSrcPathPrefixParts *int        `yaml:"drop_src_path_prefix_parts,omitempty"`
-	TLSInsecureSkipVerify  *bool       `yaml:"tls_insecure_skip_verify,omitempty"`
 	TLSCAFile              string      `yaml:"tls_ca_file,omitempty"`
+	TLSCertFile            string      `yaml:"tls_cert_file,omitempty"`
+	TLSKeyFile             string      `yaml:"tls_key_file,omitempty"`
+	TLSServerName          string      `yaml:"tls_server_name,omitempty"`
+	TLSInsecureSkipVerify  *bool       `yaml:"tls_insecure_skip_verify,omitempty"`
 
 	MetricLabels map[string]string `yaml:"metric_labels,omitempty"`
 
 	concurrencyLimitCh      chan struct{}
 	concurrencyLimitReached *metrics.Counter
 
-	httpTransport *http.Transport
+	rt http.RoundTripper
 
 	requests         *metrics.Counter
 	backendErrors    *metrics.Counter
@@ -90,8 +93,8 @@ type UserInfo struct {
 
 // HeadersConf represents config for request and response headers.
 type HeadersConf struct {
-	RequestHeaders  []Header `yaml:"headers,omitempty"`
-	ResponseHeaders []Header `yaml:"response_headers,omitempty"`
+	RequestHeaders  []*Header `yaml:"headers,omitempty"`
+	ResponseHeaders []*Header `yaml:"response_headers,omitempty"`
 }
 
 func (ui *UserInfo) beginConcurrencyLimit() error {
@@ -155,10 +158,10 @@ type URLMap struct {
 	SrcHosts []*Regex `yaml:"src_hosts,omitempty"`
 
 	// SrcQueryArgs is an optional list of query args, which must match request URL query args.
-	SrcQueryArgs []QueryArg `yaml:"src_query_args,omitempty"`
+	SrcQueryArgs []*QueryArg `yaml:"src_query_args,omitempty"`
 
 	// SrcHeaders is an optional list of headers, which must match request headers.
-	SrcHeaders []Header `yaml:"src_headers,omitempty"`
+	SrcHeaders []*Header `yaml:"src_headers,omitempty"`
 
 	// UrlPrefix contains backend url prefixes for the proxied request url.
 	URLPrefix *URLPrefix `yaml:"url_prefix,omitempty"`
@@ -179,22 +182,15 @@ type URLMap struct {
 	DropSrcPathPrefixParts *int `yaml:"drop_src_path_prefix_parts,omitempty"`
 }
 
-// Regex represents a regex
-type Regex struct {
-	re *regexp.Regexp
-
-	sOriginal string
-}
-
 // QueryArg represents HTTP query arg
 type QueryArg struct {
 	Name  string
-	Value string
+	Value *Regex
 
 	sOriginal string
 }
 
-// UnmarshalYAML unmarshals up from yaml.
+// UnmarshalYAML unmarshals qa from yaml.
 func (qa *QueryArg) UnmarshalYAML(f func(interface{}) error) error {
 	var s string
 	if err := f(&s); err != nil {
@@ -203,14 +199,27 @@ func (qa *QueryArg) UnmarshalYAML(f func(interface{}) error) error {
 	qa.sOriginal = s
 
 	n := strings.IndexByte(s, '=')
-	if n >= 0 {
-		qa.Name = s[:n]
-		qa.Value = s[n+1:]
+	if n < 0 {
+		return nil
 	}
+
+	qa.Name = s[:n]
+	expr := s[n+1:]
+	if !strings.HasPrefix(expr, "~") {
+		expr = regexp.QuoteMeta(expr)
+	} else {
+		expr = expr[1:]
+	}
+
+	var re Regex
+	if err := yaml.Unmarshal([]byte(expr), &re); err != nil {
+		return fmt.Errorf("cannot unmarshal regex for %q query arg: %w", qa.Name, err)
+	}
+	qa.Value = &re
 	return nil
 }
 
-// MarshalYAML marshals up to yaml.
+// MarshalYAML marshals qa to yaml.
 func (qa *QueryArg) MarshalYAML() (interface{}, error) {
 	return qa.sOriginal, nil
 }
@@ -293,7 +302,7 @@ func (up *URLPrefix) getBackendsCount() int {
 //
 // backendURL.put() must be called on the returned backendURL after the request is complete.
 func (up *URLPrefix) getBackendURL() *backendURL {
-	up.discoverBackendIPsIfNeeded()
+	up.discoverBackendAddrsIfNeeded()
 
 	pbus := up.bus.Load()
 	bus := *pbus
@@ -303,7 +312,7 @@ func (up *URLPrefix) getBackendURL() *backendURL {
 	return getLeastLoadedBackendURL(bus, &up.n)
 }
 
-func (up *URLPrefix) discoverBackendIPsIfNeeded() {
+func (up *URLPrefix) discoverBackendAddrsIfNeeded() {
 	if !up.discoverBackendIPs {
 		// The discovery is disabled.
 		return
@@ -328,27 +337,42 @@ func (up *URLPrefix) discoverBackendIPsIfNeeded() {
 
 	// Discover ips for all the backendURLs
 	ctx, cancel := context.WithTimeout(context.Background(), time.Second*time.Duration(intervalSec))
-	hostToIPs := make(map[string][]string)
+	hostToAddrs := make(map[string][]string)
 	for _, bu := range up.busOriginal {
 		host := bu.Hostname()
-		if hostToIPs[host] != nil {
+		if hostToAddrs[host] != nil {
 			// ips for the given host have been already discovered
 			continue
 		}
-		addrs, err := resolver.LookupIPAddr(ctx, host)
-		var ips []string
-		if err != nil {
-			logger.Warnf("cannot discover backend IPs for %s: %s; use it literally", bu, err)
-			ips = []string{host}
-		} else {
-			ips = make([]string, len(addrs))
-			for i, addr := range addrs {
-				ips[i] = addr.String()
+		var resolvedAddrs []string
+		if strings.HasPrefix(host, "srv+") {
+			// The host has the format 'srv+realhost'. Strip 'srv+' prefix before performing the lookup.
+			host = strings.TrimPrefix(host, "srv+")
+			_, addrs, err := netutil.Resolver.LookupSRV(ctx, "", "", host)
+			if err != nil {
+				logger.Warnf("cannot discover backend SRV records for %s: %s; use it literally", bu, err)
+				resolvedAddrs = []string{host}
+			} else {
+				resolvedAddrs := make([]string, len(addrs))
+				for i, addr := range addrs {
+					resolvedAddrs[i] = fmt.Sprintf("%s:%d", addr.Target, addr.Port)
+				}
+			}
+		} else {
+			addrs, err := netutil.Resolver.LookupIPAddr(ctx, host)
+			if err != nil {
+				logger.Warnf("cannot discover backend IPs for %s: %s; use it literally", bu, err)
+				resolvedAddrs = []string{host}
+			} else {
+				resolvedAddrs = make([]string, len(addrs))
+				for i, addr := range addrs {
+					resolvedAddrs[i] = addr.String()
+				}
 			}
-			// sort ips, so they could be compared below in areEqualBackendURLs()
-			sort.Strings(ips)
 		}
-		hostToIPs[host] = ips
+		// sort resolvedAddrs, so they could be compared below in areEqualBackendURLs()
+		sort.Strings(resolvedAddrs)
+		hostToAddrs[host] = resolvedAddrs
 	}
 	cancel()
 
@@ -357,10 +381,14 @@ func (up *URLPrefix) discoverBackendIPsIfNeeded() {
 	for _, bu := range up.busOriginal {
 		host := bu.Hostname()
 		port := bu.Port()
-		for _, ip := range hostToIPs[host] {
+		for _, addr := range hostToAddrs[host] {
 			buCopy := *bu
-			buCopy.Host = ip
+			buCopy.Host = addr
 			if port != "" {
+				if n := strings.IndexByte(buCopy.Host, ':'); n >= 0 {
+					// Drop the discovered port and substitute it the the port specified in bu.
+					buCopy.Host = buCopy.Host[:n]
+				}
 				buCopy.Host += ":" + port
 			}
 			busNew = append(busNew, &backendURL{
@@ -391,11 +419,6 @@ func areEqualBackendURLs(a, b []*backendURL) bool {
 	return true
 }
 
-var resolver = &net.Resolver{
-	PreferGo:     true,
-	StrictErrors: true,
-}
-
 // getFirstAvailableBackendURL returns the first available backendURL, which isn't broken.
 //
 // backendURL.put() must be called on the returned backendURL after the request is complete.
@@ -508,6 +531,13 @@ func (up *URLPrefix) MarshalYAML() (interface{}, error) {
 	return up.vOriginal, nil
 }
 
+// Regex represents a regex
+type Regex struct {
+	re *regexp.Regexp
+
+	sOriginal string
+}
+
 func (r *Regex) match(s string) bool {
 	prefix, ok := r.re.LiteralPrefix()
 	if ok {
@@ -716,11 +746,11 @@ func parseAuthConfig(data []byte) (*AuthConfig, error) {
 			return float64(len(ui.concurrencyLimitCh))
 		})
 
-		tr, err := getTransport(ui.TLSInsecureSkipVerify, ui.TLSCAFile)
+		rt, err := newRoundTripper(ui.TLSCAFile, ui.TLSCertFile, ui.TLSKeyFile, ui.TLSServerName, ui.TLSInsecureSkipVerify)
 		if err != nil {
-			return nil, fmt.Errorf("cannot initialize HTTP transport: %w", err)
+			return nil, fmt.Errorf("cannot initialize HTTP RoundTripper: %w", err)
 		}
-		ui.httpTransport = tr
+		ui.rt = rt
 	}
 	return ac, nil
 }
@@ -764,11 +794,11 @@ func parseAuthConfigUsers(ac *AuthConfig) (map[string]*UserInfo, error) {
 			return float64(len(ui.concurrencyLimitCh))
 		})
 
-		tr, err := getTransport(ui.TLSInsecureSkipVerify, ui.TLSCAFile)
+		rt, err := newRoundTripper(ui.TLSCAFile, ui.TLSCertFile, ui.TLSKeyFile, ui.TLSServerName, ui.TLSInsecureSkipVerify)
 		if err != nil {
-			return nil, fmt.Errorf("cannot initialize HTTP transport: %w", err)
+			return nil, fmt.Errorf("cannot initialize HTTP RoundTripper: %w", err)
 		}
-		ui.httpTransport = tr
+		ui.rt = rt
 
 		for _, at := range ats {
 			byAuthToken[at] = ui

app/vmauth/auth_config_test.go

@@ -4,10 +4,11 @@ import (
 	"bytes"
 	"fmt"
 	"net/url"
-	"regexp"
 	"testing"
 
 	"gopkg.in/yaml.v2"
+
+	"github.com/VictoriaMetrics/VictoriaMetrics/lib/logger"
 )
 
 func TestParseAuthConfigFailure(t *testing.T) {
@@ -315,43 +316,54 @@ users:
 	f(`
 users:
 - auth_token: foo
-  url_prefix: http://aaa:343/bbb
+  url_prefix: https://aaa:343/bbb
   max_concurrent_requests: 5
   tls_insecure_skip_verify: true
+  tls_server_name: "foo.bar"
+  tls_ca_file: "foo/bar"
+  tls_cert_file: "foo/baz"
+  tls_key_file: "foo/foo"
 `, map[string]*UserInfo{
 		getHTTPAuthToken("foo"): {
 			AuthToken:             "foo",
-			URLPrefix:             mustParseURL("http://aaa:343/bbb"),
+			URLPrefix:             mustParseURL("https://aaa:343/bbb"),
 			MaxConcurrentRequests: 5,
 			TLSInsecureSkipVerify: &insecureSkipVerifyTrue,
+			TLSServerName:         "foo.bar",
+			TLSCAFile:             "foo/bar",
+			TLSCertFile:           "foo/baz",
+			TLSKeyFile:            "foo/foo",
 		},
 	})
 
 	// Multiple url_prefix entries
 	insecureSkipVerifyFalse := false
+	discoverBackendIPsTrue := true
 	f(`
 users:
 - username: foo
   password: bar
   url_prefix:
   - http://node1:343/bbb
-  - http://node2:343/bbb
+  - http://srv+node2:343/bbb
   tls_insecure_skip_verify: false
   retry_status_codes: [500, 501]
   load_balancing_policy: first_available
   drop_src_path_prefix_parts: 1
+  discover_backend_ips: true
 `, map[string]*UserInfo{
 		getHTTPAuthBasicToken("foo", "bar"): {
 			Username: "foo",
 			Password: "bar",
 			URLPrefix: mustParseURLs([]string{
 				"http://node1:343/bbb",
-				"http://node2:343/bbb",
+				"http://srv+node2:343/bbb",
 			}),
 			TLSInsecureSkipVerify:  &insecureSkipVerifyFalse,
 			RetryStatusCodes:       []int{500, 501},
 			LoadBalancingPolicy:    "first_available",
 			DropSrcPathPrefixParts: intp(1),
+			DiscoverBackendIPs:     &discoverBackendIPsTrue,
 		},
 	})
 
@@ -384,32 +396,21 @@ users:
 			{
 				SrcHosts: getRegexs([]string{"foo\\.bar", "baz:1234"}),
 				SrcPaths: getRegexs([]string{"/api/v1/write"}),
-				SrcQueryArgs: []QueryArg{
-					{
-						Name:  "foo",
-						Value: "bar",
-					},
+				SrcQueryArgs: []*QueryArg{
+					mustNewQueryArg("foo=b.+ar"),
+					mustNewQueryArg("baz=~.*x=y.+"),
 				},
-				SrcHeaders: []Header{
-					{
-						Name:  "TenantID",
-						Value: "345",
-					},
+				SrcHeaders: []*Header{
+					mustNewHeader("'TenantID: 345'"),
 				},
 				URLPrefix: mustParseURLs([]string{
 					"http://vminsert1/insert/0/prometheus",
 					"http://vminsert2/insert/0/prometheus",
 				}),
 				HeadersConf: HeadersConf{
-					RequestHeaders: []Header{
-						{
-							Name:  "foo",
-							Value: "bar",
-						},
-						{
-							Name:  "xxx",
-							Value: "y",
-						},
+					RequestHeaders: []*Header{
+						mustNewHeader("'foo: bar'"),
+						mustNewHeader("'xxx: y'"),
 					},
 				},
 			},
@@ -423,7 +424,7 @@ users:
     url_prefix: http://vmselect/select/0/prometheus
   - src_paths: ["/api/v1/write"]
     src_hosts: ["foo\\.bar", "baz:1234"]
-    src_query_args: ['foo=bar']
+    src_query_args: ['foo=b.+ar', 'baz=~.*x=y.+']
     src_headers: ['TenantID: 345']
     url_prefix: ["http://vminsert1/insert/0/prometheus","http://vminsert2/insert/0/prometheus"]
     headers:
@@ -486,15 +487,9 @@ users:
 						"http://vminsert2/insert/0/prometheus",
 					}),
 					HeadersConf: HeadersConf{
-						RequestHeaders: []Header{
-							{
-								Name:  "foo",
-								Value: "bar",
-							},
-							{
-								Name:  "xxx",
-								Value: "y",
-							},
+						RequestHeaders: []*Header{
+							mustNewHeader("'foo: bar'"),
+							mustNewHeader("'xxx: y'"),
 						},
 					},
 				},
@@ -518,15 +513,9 @@ users:
 						"http://vminsert2/insert/0/prometheus",
 					}),
 					HeadersConf: HeadersConf{
-						RequestHeaders: []Header{
-							{
-								Name:  "foo",
-								Value: "bar",
-							},
-							{
-								Name:  "xxx",
-								Value: "y",
-							},
+						RequestHeaders: []*Header{
+							mustNewHeader("'foo: bar'"),
+							mustNewHeader("'xxx: y'"),
 						},
 					},
 				},
@@ -600,11 +589,11 @@ unauthorized_user:
 	}
 
 	ui := m[getHTTPAuthBasicToken("foo", "bar")]
-	if !isSetBool(ui.TLSInsecureSkipVerify, true) || !ui.httpTransport.TLSClientConfig.InsecureSkipVerify {
+	if !isSetBool(ui.TLSInsecureSkipVerify, true) {
 		t.Fatalf("unexpected TLSInsecureSkipVerify value for user foo")
 	}
 
-	if !isSetBool(ac.UnauthorizedUser.TLSInsecureSkipVerify, false) || ac.UnauthorizedUser.httpTransport.TLSClientConfig.InsecureSkipVerify {
+	if !isSetBool(ac.UnauthorizedUser.TLSInsecureSkipVerify, false) {
 		t.Fatalf("unexpected TLSInsecureSkipVerify value for unauthorized_user")
 	}
 }
@@ -699,10 +688,7 @@ func isSetBool(boolP *bool, expectedValue bool) bool {
 func getRegexs(paths []string) []*Regex {
 	var sps []*Regex
 	for _, path := range paths {
-		sps = append(sps, &Regex{
-			sOriginal: path,
-			re:        regexp.MustCompile("^(?:" + path + ")$"),
-		})
+		sps = append(sps, mustNewRegex(path))
 	}
 	return sps
 }
@@ -759,3 +745,27 @@ func mustParseURLs(us []string) *URLPrefix {
 func intp(n int) *int {
 	return &n
 }
+
+func mustNewRegex(s string) *Regex {
+	var re Regex
+	if err := yaml.Unmarshal([]byte(s), &re); err != nil {
+		logger.Panicf("cannot unmarshal regex %q: %s", s, err)
+	}
+	return &re
+}
+
+func mustNewQueryArg(s string) *QueryArg {
+	var qa QueryArg
+	if err := yaml.Unmarshal([]byte(s), &qa); err != nil {
+		logger.Panicf("cannot unmarshal query arg filter %q: %s", s, err)
+	}
+	return &qa
+}
+
+func mustNewHeader(s string) *Header {
+	var h Header
+	if err := yaml.Unmarshal([]byte(s), &h); err != nil {
+		logger.Panicf("cannot unmarshal header filter %q: %s", s, err)
+	}
+	return &h
+}

app/vmauth/main.go

@@ -2,8 +2,6 @@ package main
 
 import (
 	"context"
-	"crypto/tls"
-	"crypto/x509"
 	"errors"
 	"flag"
 	"fmt"
@@ -22,14 +20,13 @@ import (
 
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/buildinfo"
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/bytesutil"
-	"github.com/VictoriaMetrics/VictoriaMetrics/lib/encoding"
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/envflag"
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/flagutil"
-	"github.com/VictoriaMetrics/VictoriaMetrics/lib/fs/fscore"
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/httpserver"
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/logger"
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/netutil"
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/procutil"
+	"github.com/VictoriaMetrics/VictoriaMetrics/lib/promauth"
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/pushmetrics"
 )
 
@@ -53,9 +50,15 @@ var (
 	maxRequestBodySizeToRetry = flagutil.NewBytes("maxRequestBodySizeToRetry", 16*1024, "The maximum request body size, which can be cached and re-tried at other backends. "+
 		"Bigger values may require more memory")
 	backendTLSInsecureSkipVerify = flag.Bool("backend.tlsInsecureSkipVerify", false, "Whether to skip TLS verification when connecting to backends over HTTPS. "+
-		"See https://docs.victoriametrics.com/vmauth.html#backend-tls-setup")
+		"See https://docs.victoriametrics.com/vmauth/#backend-tls-setup")
 	backendTLSCAFile = flag.String("backend.TLSCAFile", "", "Optional path to TLS root CA file, which is used for TLS verification when connecting to backends over HTTPS. "+
-		"See https://docs.victoriametrics.com/vmauth.html#backend-tls-setup")
+		"See https://docs.victoriametrics.com/vmauth/#backend-tls-setup")
+	backendTLSCertFile = flag.String("backend.TLSCertFile", "", "Optional path to TLS client certificate file, which must be sent to HTTPS backend. "+
+		"See https://docs.victoriametrics.com/vmauth/#backend-tls-setup")
+	backendTLSKeyFile = flag.String("backend.TLSKeyFile", "", "Optional path to TLS client key file, which must be sent to HTTPS backend. "+
+		"See https://docs.victoriametrics.com/vmauth/#backend-tls-setup")
+	backendTLSServerName = flag.String("backend.TLSServerName", "", "Optional TLS ServerName, which must be sent to HTTPS backend. "+
+		"See https://docs.victoriametrics.com/vmauth/#backend-tls-setup")
 )
 
 func main() {
@@ -240,7 +243,7 @@ func tryProcessingRequest(w http.ResponseWriter, r *http.Request, targetURL *url
 		req.Host = targetURL.Host
 	}
 	updateHeadersByConfig(req.Header, hc.RequestHeaders)
-	res, err := ui.httpTransport.RoundTrip(req)
+	res, err := ui.rt.RoundTrip(req)
 	rtb, rtbOK := req.Body.(*readTrackingBody)
 	if err != nil {
 		if errors.Is(err, context.Canceled) || errors.Is(err, context.DeadlineExceeded) {
@@ -323,7 +326,7 @@ func copyHeader(dst, src http.Header) {
 	}
 }
 
-func updateHeadersByConfig(headers http.Header, config []Header) {
+func updateHeadersByConfig(headers http.Header, config []*Header) {
 	for _, h := range config {
 		if h.Value == "" {
 			headers.Del(h.Name)
@@ -392,50 +395,41 @@ var (
 	missingRouteRequests     = metrics.NewCounter(`vmauth_http_request_errors_total{reason="missing_route"}`)
 )
 
-func getTransport(insecureSkipVerifyP *bool, caFile string) (*http.Transport, error) {
-	if insecureSkipVerifyP == nil {
-		insecureSkipVerifyP = backendTLSInsecureSkipVerify
+func newRoundTripper(caFileOpt, certFileOpt, keyFileOpt, serverNameOpt string, insecureSkipVerifyP *bool) (http.RoundTripper, error) {
+	caFile := *backendTLSCAFile
+	if caFileOpt != "" {
+		caFile = caFileOpt
 	}
-	insecureSkipVerify := *insecureSkipVerifyP
-	if caFile == "" {
-		caFile = *backendTLSCAFile
+	certFile := *backendTLSCertFile
+	if certFileOpt != "" {
+		certFile = certFileOpt
+	}
+	keyFile := *backendTLSKeyFile
+	if keyFileOpt != "" {
+		keyFile = keyFileOpt
+	}
+	serverName := *backendTLSServerName
+	if serverNameOpt != "" {
+		serverName = serverNameOpt
+	}
+	insecureSkipVerify := *backendTLSInsecureSkipVerify
+	if p := insecureSkipVerifyP; p != nil {
+		insecureSkipVerify = *p
+	}
+	opts := &promauth.Options{
+		TLSConfig: &promauth.TLSConfig{
+			CAFile:             caFile,
+			CertFile:           certFile,
+			KeyFile:            keyFile,
+			ServerName:         serverName,
+			InsecureSkipVerify: insecureSkipVerify,
+		},
+	}
+	cfg, err := opts.NewConfig()
+	if err != nil {
+		return nil, fmt.Errorf("cannot initialize promauth.Config: %w", err)
 	}
 
-	bb := bbPool.Get()
-	defer bbPool.Put(bb)
-
-	bb.B = appendTransportKey(bb.B[:0], insecureSkipVerify, caFile)
-
-	transportMapLock.Lock()
-	defer transportMapLock.Unlock()
-
-	tr := transportMap[string(bb.B)]
-	if tr == nil {
-		trLocal, err := newTransport(insecureSkipVerify, caFile)
-		if err != nil {
-			return nil, err
-		}
-		transportMap[string(bb.B)] = trLocal
-		tr = trLocal
-	}
-
-	return tr, nil
-}
-
-var (
-	transportMap     = make(map[string]*http.Transport)
-	transportMapLock sync.Mutex
-)
-
-func appendTransportKey(dst []byte, insecureSkipVerify bool, caFile string) []byte {
-	dst = encoding.MarshalBool(dst, insecureSkipVerify)
-	dst = encoding.MarshalBytes(dst, bytesutil.ToUnsafeBytes(caFile))
-	return dst
-}
-
-var bbPool bytesutil.ByteBufferPool
-
-func newTransport(insecureSkipVerify bool, caFile string) (*http.Transport, error) {
 	tr := http.DefaultTransport.(*http.Transport).Clone()
 	tr.ResponseHeaderTimeout = *responseTimeout
 	// Automatic compression must be disabled in order to fix https://github.com/VictoriaMetrics/VictoriaMetrics/issues/535
@@ -444,27 +438,10 @@ func newTransport(insecureSkipVerify bool, caFile string) (*http.Transport, erro
 	if tr.MaxIdleConns != 0 && tr.MaxIdleConns < tr.MaxIdleConnsPerHost {
 		tr.MaxIdleConns = tr.MaxIdleConnsPerHost
 	}
-	tlsCfg := tr.TLSClientConfig
-	if tlsCfg == nil {
-		tlsCfg = &tls.Config{}
-		tr.TLSClientConfig = tlsCfg
-	}
-	if insecureSkipVerify || caFile != "" {
-		tlsCfg.ClientSessionCache = tls.NewLRUClientSessionCache(0)
-		tlsCfg.InsecureSkipVerify = insecureSkipVerify
-		if caFile != "" {
-			data, err := fscore.ReadFileOrHTTP(caFile)
-			if err != nil {
-				return nil, fmt.Errorf("cannot read tls_ca_file: %w", err)
-			}
-			rootCA := x509.NewCertPool()
-			if !rootCA.AppendCertsFromPEM(data) {
-				return nil, fmt.Errorf("cannot parse data read from tls_ca_file %q", caFile)
-			}
-			tlsCfg.RootCAs = rootCA
-		}
-	}
-	return tr, nil
+	tr.DialContext = netutil.DialMaybeSRV
+
+	rt := cfg.NewRoundTripper(tr)
+	return rt, nil
 }
 
 var (
@@ -488,7 +465,7 @@ func usage() {
 	const s = `
 vmauth authenticates and authorizes incoming requests and proxies them to VictoriaMetrics.
 
-See the docs at https://docs.victoriametrics.com/vmauth.html .
+See the docs at https://docs.victoriametrics.com/vmauth/ .
 `
 	flagutil.Usage(s)
 }

app/vmauth/target_url.go

@@ -86,19 +86,25 @@ func matchAnyRegex(rs []*Regex, s string) bool {
 	return false
 }
 
-func matchAnyQueryArg(qas []QueryArg, args url.Values) bool {
+func matchAnyQueryArg(qas []*QueryArg, args url.Values) bool {
 	if len(qas) == 0 {
 		return true
 	}
 	for _, qa := range qas {
-		if slices.Contains(args[qa.Name], qa.Value) {
-			return true
+		vs, ok := args[qa.Name]
+		if !ok {
+			continue
+		}
+		for _, v := range vs {
+			if qa.Value.match(v) {
+				return true
+			}
 		}
 	}
 	return false
 }
 
-func matchAnyHeader(headers []Header, h http.Header) bool {
+func matchAnyHeader(headers []*Header, h http.Header) bool {
 	if len(headers) == 0 {
 		return true
 	}

app/vmauth/target_url_test.go

@@ -97,8 +97,10 @@ func TestCreateTargetURLSuccess(t *testing.T) {
 		bu := up.getBackendURL()
 		target := mergeURLs(bu.url, u, up.dropSrcPathPrefixParts)
 		bu.put()
-		if target.String() != expectedTarget {
-			t.Fatalf("unexpected target; got %q; want %q", target, expectedTarget)
+
+		gotTarget := target.String()
+		if gotTarget != expectedTarget {
+			t.Fatalf("unexpected target; \ngot:\n%q;\nwant:\n%q", gotTarget, expectedTarget)
 		}
 		if s := headersToString(hc.RequestHeaders); s != expectedRequestHeaders {
 			t.Fatalf("unexpected request headers; got %q; want %q", s, expectedRequestHeaders)
@@ -123,17 +125,11 @@ func TestCreateTargetURLSuccess(t *testing.T) {
 	f(&UserInfo{
 		URLPrefix: mustParseURL("http://foo.bar"),
 		HeadersConf: HeadersConf{
-			RequestHeaders: []Header{
-				{
-					Name:  "bb",
-					Value: "aaa",
-				},
+			RequestHeaders: []*Header{
+				mustNewHeader("'bb: aaa'"),
 			},
-			ResponseHeaders: []Header{
-				{
-					Name:  "x",
-					Value: "y",
-				},
+			ResponseHeaders: []*Header{
+				mustNewHeader("'x: y'"),
 			},
 		},
 		RetryStatusCodes:       []int{503, 501},
@@ -162,29 +158,17 @@ func TestCreateTargetURLSuccess(t *testing.T) {
 			{
 				SrcHosts: getRegexs([]string{"host42"}),
 				SrcPaths: getRegexs([]string{"/vmsingle/api/v1/query"}),
-				SrcQueryArgs: []QueryArg{
-					{
-						Name:  "db",
-						Value: "foo",
-					},
+				SrcQueryArgs: []*QueryArg{
+					mustNewQueryArg("db=foo"),
 				},
 				URLPrefix: mustParseURL("http://vmselect/0/prometheus"),
 				HeadersConf: HeadersConf{
-					RequestHeaders: []Header{
-						{
-							Name:  "xx",
-							Value: "aa",
-						},
-						{
-							Name:  "yy",
-							Value: "asdf",
-						},
+					RequestHeaders: []*Header{
+						mustNewHeader("'xx: aa'"),
+						mustNewHeader("'yy: asdf'"),
 					},
-					ResponseHeaders: []Header{
-						{
-							Name:  "qwe",
-							Value: "rty",
-						},
+					ResponseHeaders: []*Header{
+						mustNewHeader("'qwe: rty'"),
 					},
 				},
 				RetryStatusCodes:       []int{503, 500, 501},
@@ -200,14 +184,12 @@ func TestCreateTargetURLSuccess(t *testing.T) {
 		},
 		URLPrefix: mustParseURL("http://default-server"),
 		HeadersConf: HeadersConf{
-			RequestHeaders: []Header{{
-				Name:  "bb",
-				Value: "aaa",
-			}},
-			ResponseHeaders: []Header{{
-				Name:  "x",
-				Value: "y",
-			}},
+			RequestHeaders: []*Header{
+				mustNewHeader("'bb: aaa'"),
+			},
+			ResponseHeaders: []*Header{
+				mustNewHeader("'x: y'"),
+			},
 		},
 		RetryStatusCodes:       []int{502},
 		DropSrcPathPrefixParts: intp(2),
@@ -250,6 +232,30 @@ func TestCreateTargetURLSuccess(t *testing.T) {
 	f(&UserInfo{
 		URLPrefix: mustParseURL("http://foo.bar?extra_label=team=mobile"),
 	}, "/api/v1/query?extra_label=team=dev", "http://foo.bar/api/v1/query?extra_label=team%3Dmobile", "", "", nil, "least_loaded", 0)
+
+	// Complex routing regexp query args in `url_map`
+	ui = &UserInfo{
+		URLMaps: []URLMap{
+			{
+				SrcPaths: getRegexs([]string{"/api/v1/query"}),
+				SrcQueryArgs: []*QueryArg{
+					mustNewQueryArg(`query=~.*{.*env="dev".*}*.`),
+				},
+				URLPrefix: mustParseURL("http://vmselect/0/prometheus"),
+			},
+			{
+				SrcPaths: getRegexs([]string{"/api/v1/query"}),
+				SrcQueryArgs: []*QueryArg{
+					mustNewQueryArg(`query=~.*{.*env="prod".*}.*`),
+				},
+				URLPrefix: mustParseURL("http://vmselect/1/prometheus"),
+			},
+		},
+		URLPrefix: mustParseURL("http://default-server"),
+	}
+	f(ui, `/api/v1/query?query=up{env="prod"}`, `http://vmselect/1/prometheus/api/v1/query?query=up%7Benv%3D%22prod%22%7D`, "", "", nil, "least_loaded", 0)
+	f(ui, `/api/v1/query?query=up{foo="bar",env="dev",pod!=""}`, `http://vmselect/0/prometheus/api/v1/query?query=up%7Bfoo%3D%22bar%22%2Cenv%3D%22dev%22%2Cpod%21%3D%22%22%7D`, "", "", nil, "least_loaded", 0)
+	f(ui, `/api/v1/query?query=up{foo="bar"}`, `http://default-server/api/v1/query?query=up%7Bfoo%3D%22bar%22%7D`, "", "", nil, "least_loaded", 0)
 }
 
 func TestCreateTargetURLFailure(t *testing.T) {
@@ -265,10 +271,10 @@ func TestCreateTargetURLFailure(t *testing.T) {
 			t.Fatalf("unexpected non-empty up=%#v", up)
 		}
 		if hc.RequestHeaders != nil {
-			t.Fatalf("unexpected non-empty request headers=%q", hc.RequestHeaders)
+			t.Fatalf("unexpected non-empty request headers: %s", headersToString(hc.RequestHeaders))
 		}
 		if hc.ResponseHeaders != nil {
-			t.Fatalf("unexpected non-empty response headers=%q", hc.ResponseHeaders)
+			t.Fatalf("unexpected non-empty response headers: %s", headersToString(hc.ResponseHeaders))
 		}
 	}
 	f(&UserInfo{}, "/foo/bar")
@@ -282,7 +288,7 @@ func TestCreateTargetURLFailure(t *testing.T) {
 	}, "/api/v1/write")
 }
 
-func headersToString(hs []Header) string {
+func headersToString(hs []*Header) string {
 	a := make([]string, len(hs))
 	for i, h := range hs {
 		a[i] = fmt.Sprintf("%s: %s", h.Name, h.Value)

app/vmbackup/README.md

@@ -1,3 +1,3 @@
-See vmbackup docs [here](https://docs.victoriametrics.com/vmbackup.html).
+See vmbackup docs [here](https://docs.victoriametrics.com/vmbackup/).
 
 vmbackup docs can be edited at [docs/vmbackup.md](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/docs/vmbackup.md).

app/vmbackup/main.go

@@ -26,7 +26,7 @@ import (
 var (
 	httpListenAddr    = flag.String("httpListenAddr", ":8420", "TCP address for exporting metrics at /metrics page")
 	storageDataPath   = flag.String("storageDataPath", "victoria-metrics-data", "Path to VictoriaMetrics data. Must match -storageDataPath from VictoriaMetrics or vmstorage")
-	snapshotName      = flag.String("snapshotName", "", "Name for the snapshot to backup. See https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-work-with-snapshots. There is no need in setting -snapshotName if -snapshot.createURL is set")
+	snapshotName      = flag.String("snapshotName", "", "Name for the snapshot to backup. See https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-work-with-snapshots. There is no need in setting -snapshotName if -snapshot.createURL is set")
 	snapshotCreateURL = flag.String("snapshot.createURL", "", "VictoriaMetrics create snapshot url. When this is given a snapshot will automatically be created during backup. "+
 		"Example: http://victoriametrics:8428/snapshot/create . There is no need in setting -snapshotName if -snapshot.createURL is set")
 	snapshotDeleteURL = flag.String("snapshot.deleteURL", "", "VictoriaMetrics delete snapshot url. Optional. Will be generated from -snapshot.createURL if not provided. "+
@@ -164,7 +164,7 @@ func usage() {
 vmbackup performs backups for VictoriaMetrics data from instant snapshots to gcs, s3, azblob
 or local filesystem. Backed up data can be restored with vmrestore.
 
-See the docs at https://docs.victoriametrics.com/vmbackup.html .
+See the docs at https://docs.victoriametrics.com/vmbackup/ .
 `
 	flagutil.Usage(s)
 }

app/vmbackupmanager/README.md

@@ -1,3 +1,3 @@
-See vmbackupmanager docs [here](https://docs.victoriametrics.com/vmbackupmanager.html).
+See vmbackupmanager docs [here](https://docs.victoriametrics.com/vmbackupmanager/).
 
 vmbackupmanager docs can be edited at [docs/vmbackupmanager.md](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/docs/vmbackupmanager.md).

app/vmctl/README.md

@@ -1,3 +1,3 @@
-See vmctl docs [here](https://docs.victoriametrics.com/vmctl.html).
+See vmctl docs [here](https://docs.victoriametrics.com/vmctl/).
 
 vmctl docs can be edited at [docs/vmctl.md](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/docs/vmctl.md).

app/vmctl/flags.go

@@ -442,12 +442,12 @@ var (
 		},
 		&cli.StringFlag{
 			Name:     vmNativeFilterTimeStart,
-			Usage:    "The time filter may contain different timestamp formats. See more details here https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#timestamp-formats",
+			Usage:    "The time filter may contain different timestamp formats. See more details here https://docs.victoriametrics.com/single-server-victoriametrics/#timestamp-formats",
 			Required: true,
 		},
 		&cli.StringFlag{
 			Name:  vmNativeFilterTimeEnd,
-			Usage: "The time filter may contain different timestamp formats. See more details here https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#timestamp-formats",
+			Usage: "The time filter may contain different timestamp formats. See more details here https://docs.victoriametrics.com/single-server-victoriametrics/#timestamp-formats",
 		},
 		&cli.StringFlag{
 			Name: vmNativeStepInterval,
@@ -469,7 +469,7 @@ var (
 			Name: vmNativeSrcAddr,
 			Usage: "VictoriaMetrics address to perform export from. \n" +
 				" Should be the same as --httpListenAddr value for single-node version or vmselect component." +
-				" If exporting from cluster version see https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#url-format",
+				" If exporting from cluster version see https://docs.victoriametrics.com/cluster-victoriametrics/#url-format",
 			Required: true,
 		},
 		&cli.StringFlag{
@@ -518,7 +518,7 @@ var (
 			Name: vmNativeDstAddr,
 			Usage: "VictoriaMetrics address to perform import to. \n" +
 				" Should be the same as --httpListenAddr value for single-node version or vminsert component." +
-				" If importing into cluster version see https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#url-format",
+				" If importing into cluster version see https://docs.victoriametrics.com/cluster-victoriametrics/#url-format",
 			Required: true,
 		},
 		&cli.StringFlag{

app/vmctl/vm/vm.go

@@ -123,7 +123,7 @@ func NewImporter(ctx context.Context, cfg Config) (*Importer, error) {
 	importPath := addr + "/api/v1/import"
 	if cfg.AccountID != "" {
 		// if cluster version
-		// see https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#url-format
+		// see https://docs.victoriametrics.com/cluster-victoriametrics/#url-format
 		importPath = fmt.Sprintf("%s/insert/%s/prometheus/api/v1/import", addr, cfg.AccountID)
 	}
 	importPath, err := AddExtraLabelsToImportPath(importPath, cfg.ExtraLabels)

app/vmgateway/README.md

@@ -1,3 +1,3 @@
-See vmgateway docs [here](https://docs.victoriametrics.com/vmgateway.html).
+See vmgateway docs [here](https://docs.victoriametrics.com/vmgateway/).
 
 vmgateway docs can be edited at [docs/vmgateway.md](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/docs/vmgateway.md).

app/vminsert/common/streamaggr.go

@@ -20,20 +20,20 @@ import (
 
 var (
 	streamAggrConfig = flag.String("streamAggr.config", "", "Optional path to file with stream aggregation config. "+
-		"See https://docs.victoriametrics.com/stream-aggregation.html . "+
+		"See https://docs.victoriametrics.com/stream-aggregation/ . "+
 		"See also -streamAggr.keepInput, -streamAggr.dropInput and -streamAggr.dedupInterval")
 	streamAggrKeepInput = flag.Bool("streamAggr.keepInput", false, "Whether to keep all the input samples after the aggregation with -streamAggr.config. "+
 		"By default, only aggregated samples are dropped, while the remaining samples are stored in the database. "+
-		"See also -streamAggr.dropInput and https://docs.victoriametrics.com/stream-aggregation.html")
+		"See also -streamAggr.dropInput and https://docs.victoriametrics.com/stream-aggregation/")
 	streamAggrDropInput = flag.Bool("streamAggr.dropInput", false, "Whether to drop all the input samples after the aggregation with -streamAggr.config. "+
 		"By default, only aggregated samples are dropped, while the remaining samples are stored in the database. "+
-		"See also -streamAggr.keepInput and https://docs.victoriametrics.com/stream-aggregation.html")
+		"See also -streamAggr.keepInput and https://docs.victoriametrics.com/stream-aggregation/")
 	streamAggrDedupInterval = flag.Duration("streamAggr.dedupInterval", 0, "Input samples are de-duplicated with this interval before optional aggregation with -streamAggr.config . "+
-		"See also -streamAggr.dropInputLabels and -dedup.minScrapeInterval and https://docs.victoriametrics.com/stream-aggregation.html#deduplication")
+		"See also -streamAggr.dropInputLabels and -dedup.minScrapeInterval and https://docs.victoriametrics.com/stream-aggregation/#deduplication")
 	streamAggrDropInputLabels = flagutil.NewArrayString("streamAggr.dropInputLabels", "An optional list of labels to drop from samples "+
-		"before stream de-duplication and aggregation . See https://docs.victoriametrics.com/stream-aggregation.html#dropping-unneeded-labels")
+		"before stream de-duplication and aggregation . See https://docs.victoriametrics.com/stream-aggregation/#dropping-unneeded-labels")
 	streamAggrIgnoreOldSamples = flag.Bool("streamAggr.ignoreOldSamples", false, "Whether to ignore input samples with old timestamps outside the current aggregation interval. "+
-		"See https://docs.victoriametrics.com/stream-aggregation.html#ignoring-old-samples")
+		"See https://docs.victoriametrics.com/stream-aggregation/#ignoring-old-samples")
 )
 
 var (

app/vmrestore/README.md

@@ -1,3 +1,3 @@
-See vmrestore docs [here](https://docs.victoriametrics.com/vmrestore.html).
+See vmrestore docs [here](https://docs.victoriametrics.com/vmrestore/).
 
 vmrestore docs can be edited at [docs/vmrestore.md](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/docs/vmrestore.md).

app/vmrestore/main.go

@@ -74,7 +74,7 @@ func usage() {
 	const s = `
 vmrestore restores VictoriaMetrics data from backups made by vmbackup.
 
-See the docs at https://docs.victoriametrics.com/vmrestore.html .
+See the docs at https://docs.victoriametrics.com/vmrestore/ .
 `
 	flagutil.Usage(s)
 }

app/vmselect/main.go

@@ -99,7 +99,7 @@ func RequestHandler(w http.ResponseWriter, r *http.Request) bool {
 
 	// Strip /prometheus and /graphite prefixes in order to provide path compatibility with cluster version
 	//
-	// See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#url-format
+	// See https://docs.victoriametrics.com/cluster-victoriametrics/#url-format
 	switch {
 	case strings.HasPrefix(path, "/prometheus/"):
 		path = path[len("/prometheus"):]

app/vmselect/prometheus/expand-with-exprs.qtpl

@@ -21,13 +21,13 @@ textarea { margin: 1em }
   <form method="get">
     <div>
       <p>
-        <a href="https://docs.victoriametrics.com/MetricsQL.html">MetricsQL</a> query with optional WITH expressions:
+        <a href="https://docs.victoriametrics.com/metricsql/">MetricsQL</a> query with optional WITH expressions:
       </p>
       <textarea name="query" style="height: 15em; width: 90%">{%s q %}</textarea><br/>
       <input type="submit" value="Expand" />
 
       <p>
-        <a href="https://docs.victoriametrics.com/MetricsQL.html">MetricsQL</a> query after expanding WITH expressions and applying other optimizations:
+        <a href="https://docs.victoriametrics.com/metricsql/">MetricsQL</a> query after expanding WITH expressions and applying other optimizations:
       </p>
       <textarea style="height: 5em; width: 90%" readonly="readonly">{%= expandWithExprs(q) %}</textarea>
     </div>
@@ -79,7 +79,7 @@ textarea { margin: 1em }
 {% endstripspace %}
 
 {% func withExprsTutorial() %}
-<h3>Tutorial for WITH expressions in <a href="https://docs.victoriametrics.com/MetricsQL.html">MetricsQL</a></h3>
+<h3>Tutorial for WITH expressions in <a href="https://docs.victoriametrics.com/metricsql/">MetricsQL</a></h3>
 
 <p>
     Let's look at the following real query from <a href="https://grafana.com/grafana/dashboards/1860">Node Exporter Full</a> dashboard:

app/vmselect/prometheus/expand-with-exprs.qtpl.go

@@ -28,11 +28,11 @@ var (
 //line app/vmselect/prometheus/expand-with-exprs.qtpl:9
 func StreamExpandWithExprsResponse(qw422016 *qt422016.Writer, q string) {
 //line app/vmselect/prometheus/expand-with-exprs.qtpl:9
-	qw422016.N().S(`<html><head><title>Expand WITH expressions</title><style>p { font-weight: bold }textarea { margin: 1em }</style></head><body><div><form method="get"><div><p><a href="https://docs.victoriametrics.com/MetricsQL.html">MetricsQL</a> query with optional WITH expressions:</p><textarea name="query" style="height: 15em; width: 90%">`)
+	qw422016.N().S(`<html><head><title>Expand WITH expressions</title><style>p { font-weight: bold }textarea { margin: 1em }</style></head><body><div><form method="get"><div><p><a href="https://docs.victoriametrics.com/metricsql/">MetricsQL</a> query with optional WITH expressions:</p><textarea name="query" style="height: 15em; width: 90%">`)
 //line app/vmselect/prometheus/expand-with-exprs.qtpl:26
 	qw422016.E().S(q)
 //line app/vmselect/prometheus/expand-with-exprs.qtpl:26
-	qw422016.N().S(`</textarea><br/><input type="submit" value="Expand" /><p><a href="https://docs.victoriametrics.com/MetricsQL.html">MetricsQL</a> query after expanding WITH expressions and applying other optimizations:</p><textarea style="height: 5em; width: 90%" readonly="readonly">`)
+	qw422016.N().S(`</textarea><br/><input type="submit" value="Expand" /><p><a href="https://docs.victoriametrics.com/metricsql/">MetricsQL</a> query after expanding WITH expressions and applying other optimizations:</p><textarea style="height: 5em; width: 90%" readonly="readonly">`)
 //line app/vmselect/prometheus/expand-with-exprs.qtpl:32
 	streamexpandWithExprs(qw422016, q)
 //line app/vmselect/prometheus/expand-with-exprs.qtpl:32
@@ -192,7 +192,7 @@ func ExpandWithExprsJSONResponse(q string) string {
 func streamwithExprsTutorial(qw422016 *qt422016.Writer) {
 //line app/vmselect/prometheus/expand-with-exprs.qtpl:81
 	qw422016.N().S(`
-<h3>Tutorial for WITH expressions in <a href="https://docs.victoriametrics.com/MetricsQL.html">MetricsQL</a></h3>
+<h3>Tutorial for WITH expressions in <a href="https://docs.victoriametrics.com/metricsql/">MetricsQL</a></h3>
 
 <p>
     Let's look at the following real query from <a href="https://grafana.com/grafana/dashboards/1860">Node Exporter Full</a> dashboard:

app/vmselect/promql/binary_op.go

@@ -327,7 +327,7 @@ func resetMetricGroupIfRequired(be *metricsql.BinaryOpExpr, ts *timeseries) {
 	}
 	if be.KeepMetricNames {
 		// Do not reset MetricGroup if it is explicitly requested via `a op b keep_metric_names`
-		// See https://docs.victoriametrics.com/MetricsQL.html#keep_metric_names
+		// See https://docs.victoriametrics.com/metricsql/#keep_metric_names
 		return
 	}
 

app/vmselect/vmui/asset-manifest.json

@@ -1,13 +1,13 @@
 {
   "files": {
-    "main.css": "./static/css/main.a2ad4674.css",
-    "main.js": "./static/js/main.f9cc1e6c.js",
+    "main.css": "./static/css/main.4ebf2874.css",
+    "main.js": "./static/js/main.202937c2.js",
     "static/js/685.bebe1265.chunk.js": "./static/js/685.bebe1265.chunk.js",
-    "static/media/MetricsQL.md": "./static/media/MetricsQL.10add6e7bdf0f1d98cf7.md",
+    "static/media/MetricsQL.md": "./static/media/MetricsQL.da86c2db4f0b05e286b0.md",
     "index.html": "./index.html"
   },
   "entrypoints": [
-    "static/css/main.a2ad4674.css",
-    "static/js/main.f9cc1e6c.js"
+    "static/css/main.4ebf2874.css",
+    "static/js/main.202937c2.js"
   ]
 }

app/vmselect/vmui/dashboards/README.md

@@ -1,3 +1,3 @@
 ## Predefined dashboards
 
-See [this docs](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/app/vmui#predefined-dashboards)
+See [this doc](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/app/vmui#predefined-dashboards)

app/vmselect/vmui/index.html

@@ -1 +1 @@
-<!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.f9cc1e6c.js"></script><link href="./static/css/main.a2ad4674.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div></body></html>
+<!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.202937c2.js"></script><link href="./static/css/main.4ebf2874.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div></body></html>

app/vmselect/vmui/static/media/MetricsQL.da86c2db4f0b05e286b0.md

@@ -22,7 +22,7 @@ However, there are some [intentional differences](https://medium.com/@romanhavro
 [Standalone MetricsQL package](https://godoc.org/github.com/VictoriaMetrics/metricsql) can be used for parsing MetricsQL in external apps.
 
 If you are unfamiliar with PromQL, then it is suggested reading [this tutorial for beginners](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085)
-and introduction into [basic querying via MetricsQL](https://docs.victoriametrics.com/keyConcepts.html#metricsql).
+and introduction into [basic querying via MetricsQL](https://docs.victoriametrics.com/keyconcepts/#metricsql).
 
 The following functionality is implemented differently in MetricsQL compared to PromQL. This improves user experience:
 
@@ -70,15 +70,15 @@ The list of MetricsQL features on top of PromQL:
   VictoriaMetrics can be used as Graphite datasource in Grafana. See [these docs](https://docs.victoriametrics.com/#graphite-api-usage) for details.
   See also [label_graphite_group](#label_graphite_group) function, which can be used for extracting the given groups from Graphite metric name.
 * Lookbehind window in square brackets for [rollup functions](#rollup-functions) may be omitted. VictoriaMetrics automatically selects the lookbehind window
-  depending on the `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query)
+  depending on the `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query)
   and the real interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) (aka `scrape_interval`).
   For instance, the following query is valid in VictoriaMetrics: `rate(node_network_receive_bytes_total)`.
   It is roughly equivalent to `rate(node_network_receive_bytes_total[$__interval])` when used in Grafana.
   The difference is documented in [rate() docs](#rate).
 * Numeric values can contain `_` delimiters for better readability. For example, `1_234_567_890` can be used in queries instead of `1234567890`.
-* [Series selectors](https://docs.victoriametrics.com/keyConcepts.html#filtering) accept multiple `or` filters. For example, `{env="prod",job="a" or env="dev",job="b"}`
+* [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.html#filtering-by-multiple-or-filters) for details.
+  See [these docs](https://docs.victoriametrics.com/keyconcepts/#filtering-by-multiple-or-filters) for details.
 * Support for `group_left(*)` and `group_right(*)` for copying all the labels from time series on the `one` side
   of [many-to-one operations](https://prometheus.io/docs/prometheus/latest/querying/operators/#many-to-one-and-one-to-many-vector-matches).
   The copied label names may clash with the existing label names, so MetricsQL provides an ability to add prefix to the copied metric names
@@ -153,26 +153,26 @@ MetricsQL provides the following functions:
 ### Rollup functions
 
 **Rollup functions** (aka range functions or window functions) calculate rollups over **raw samples**
-on the given lookbehind window for the [selected time series](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window for the [selected time series](https://docs.victoriametrics.com/keyconcepts/#filtering).
 For example, `avg_over_time(temperature[24h])` calculates the average temperature over raw samples for the last 24 hours.
 
 Additional details:
 
 * If rollup functions are used for building graphs in Grafana, then the rollup is calculated independently per each point on the graph.
   For example, every point for `avg_over_time(temperature[24h])` graph shows the average temperature for the last 24 hours ending at this point.
-  The interval between points is set as `step` query arg passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
-* If the given [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) returns multiple time series,
+  The interval between points is set as `step` query arg passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
+* If the given [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) returns multiple time series,
   then rollups are calculated individually per each returned series.
 * If lookbehind window in square brackets is missing, then it is automatically set to the following value:
-  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
+  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
     for all the [rollup functions](#rollup-functions) except of [default_rollup](#default_rollup) and [rate](#rate). This value is known as `$__interval` in Grafana or `1i` in MetricsQL.
     For example, `avg_over_time(temperature)` is automatically transformed to `avg_over_time(temperature[1i])`.
   - To the `max(step, scrape_interval)`, where `scrape_interval` is the interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
     for [default_rollup](#default_rollup) and [rate](#rate) functions. This allows avoiding unexpected gaps on the graph when `step` is smaller than `scrape_interval`.
-* Every [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) in MetricsQL must be wrapped into a rollup function.
+* Every [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) in MetricsQL must be wrapped into a rollup function.
   Otherwise, it is automatically wrapped into [default_rollup](#default_rollup). For example, `foo{bar="baz"}`
   is automatically converted to `default_rollup(foo{bar="baz"})` before performing the calculations.
-* If something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) is passed to rollup function,
+* If something other than [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) is passed to rollup function,
   then the inner arg is automatically converted to a [subquery](#subqueries).
 * All the rollup functions accept optional `keep_metric_names` modifier. If it is set, then the function keeps metric names in results.
   See [these docs](#keep_metric_names).
@@ -195,7 +195,7 @@ See also [present_over_time](#present_over_time).
 `aggr_over_time(("rollup_func1", "rollup_func2", ...), series_selector[d])` is a [rollup function](#rollup-functions),
 which calculates all the listed `rollup_func*` for raw samples on the given lookbehind window `d`.
 The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 `rollup_func*` can contain any rollup function. For instance, `aggr_over_time(("min_over_time", "max_over_time", "rate"), m[d])`
 would calculate [min_over_time](#min_over_time), [max_over_time](#max_over_time) and [rate](#rate) for `m[d]`.
@@ -204,7 +204,7 @@ would calculate [min_over_time](#min_over_time), [max_over_time](#max_over_time)
 
 `ascent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates
 ascent of raw sample values on the given lookbehind window `d`. The calculations are performed individually
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -216,7 +216,7 @@ See also [descent_over_time](#descent_over_time).
 
 `avg_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average value
 over raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is supported by PromQL.
 
@@ -226,7 +226,7 @@ See also [median_over_time](#median_over_time).
 
 `changes(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times
 the raw samples changed on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Unlike `changes()` in Prometheus it takes into account the change from the last sample before the given lookbehind window `d`.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -241,7 +241,7 @@ See also [changes_prometheus](#changes_prometheus).
 
 `changes_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times
 the raw samples changed on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 It doesn't take into account the change from the last sample before the given lookbehind window `d` in the same way as Prometheus does.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -256,7 +256,7 @@ See also [changes](#changes).
 
 `count_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -266,7 +266,7 @@ See also [count_over_time](#count_over_time), [share_eq_over_time](#share_eq_ove
 
 `count_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -276,7 +276,7 @@ See also [count_over_time](#count_over_time) and [share_gt_over_time](#share_gt_
 
 `count_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which don't exceed `le`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -286,7 +286,7 @@ See also [count_over_time](#count_over_time) and [share_le_over_time](#share_le_
 
 `count_ne_over_time(series_selector[d], ne)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which aren't equal to `ne`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -295,7 +295,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -307,7 +307,7 @@ See also [count_le_over_time](#count_le_over_time), [count_gt_over_time](#count_
 
 `count_values_over_time("label", series_selector[d])` is a [rollup function](#rollup-functions), which counts the number of raw samples
 with the same value over the given lookbehind window and stores the counts in a time series with an additional `label`, which contains each initial value.
-The results are calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The results are calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -316,7 +316,7 @@ 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.html#filtering).
+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.
 
@@ -325,7 +325,7 @@ See also [increases_over_time](#increases_over_time).
 #### default_rollup
 
 `default_rollup(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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),
@@ -336,7 +336,7 @@ This allows avoiding unexpected gaps on the graph when `step` is smaller than th
 
 `delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between
 the last sample before the given lookbehind window `d` and the last sample at the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 The behaviour of `delta()` function in MetricsQL is slightly different to the behaviour of `delta()` function in Prometheus.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -351,7 +351,7 @@ See also [increase](#increase) and [delta_prometheus](#delta_prometheus).
 
 `delta_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between
 the first and the last samples at the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 The behaviour of `delta_prometheus()` is close to the behaviour of `delta()` function in Prometheus.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -363,7 +363,7 @@ See also [delta](#delta).
 #### deriv
 
 `deriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivative over the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 The derivative is calculated using linear regression.
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -376,7 +376,7 @@ See also [deriv_fast](#deriv_fast) and [ideriv](#ideriv).
 
 `deriv_fast(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivative
 using the first and the last raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -386,7 +386,7 @@ See also [deriv](#deriv) and [ideriv](#ideriv).
 
 `descent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates descent of raw sample values
 on the given lookbehind window `d`. The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is useful for tracking height loss in GPS tracking.
 
@@ -397,7 +397,7 @@ See also [ascent_over_time](#ascent_over_time).
 #### distinct_over_time
 
 `distinct_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number of distinct raw sample values
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -406,7 +406,7 @@ See also [count_values_over_time](#count_values_over_time).
 #### duration_over_time
 
 `duration_over_time(series_selector[d], max_interval)` is a [rollup function](#rollup-functions), which returns the duration in seconds
-when time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) were present
+when time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering) were present
 over the given lookbehind window `d`. It is expected that intervals between adjacent samples per each series don't exceed the `max_interval`.
 Otherwise, such intervals are considered as gaps and aren't counted.
 
@@ -417,7 +417,7 @@ See also [lifetime](#lifetime) and [lag](#lag).
 #### first_over_time
 
 `first_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the first raw sample value
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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).
 
@@ -425,7 +425,7 @@ See also [last_over_time](#last_over_time) and [tfirst_over_time](#tfirst_over_t
 
 `geomean_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [geometric mean](https://en.wikipedia.org/wiki/Geometric_mean)
 over raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -433,9 +433,9 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 
 `histogram_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates
 [VictoriaMetrics histogram](https://godoc.org/github.com/VictoriaMetrics/metrics#Histogram) over raw samples on the given lookbehind window `d`.
-It is calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.html#gauge).
+over multiple [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge).
 For example, the following query calculates median temperature by country over the last 24 hours:
 
 `histogram_quantile(0.5, sum(histogram_over_time(temperature[24h])) by (vmrange,country))`.
@@ -459,8 +459,8 @@ See also [hoeffding_bound_lower](#hoeffding_bound_lower).
 `holt_winters(series_selector[d], sf, tf)` is a [rollup function](#rollup-functions), which calculates Holt-Winters value
 (aka [double exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing#Double_exponential_smoothing)) for raw samples
 over the given lookbehind window `d` using the given smoothing factor `sf` and the given trend factor `tf`.
-Both `sf` and `tf` must be in the range `[0...1]`. It is expected that the [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering)
-returns time series of [gauge type](https://docs.victoriametrics.com/keyConcepts.html#gauge).
+Both `sf` and `tf` must be in the range `[0...1]`. It is expected that the [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering)
+returns time series of [gauge type](https://docs.victoriametrics.com/keyconcepts/#gauge).
 
 This function is supported by PromQL.
 
@@ -469,7 +469,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -481,7 +481,7 @@ See also [delta](#delta).
 
 `ideriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the per-second derivative based on the last two raw samples
 over the given lookbehind window `d`. The derivative is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -490,8 +490,8 @@ See also [deriv](#deriv).
 #### increase
 
 `increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase over the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 Unlike Prometheus, it takes into account the last sample before the given lookbehind window `d` when calculating the result.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -505,8 +505,8 @@ See also [increase_pure](#increase_pure), [increase_prometheus](#increase_promet
 #### increase_prometheus
 
 `increase_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase
-over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 It doesn't take into account the last sample before the given lookbehind window `d` when calculating the result in the same way as Prometheus does.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
 
@@ -517,13 +517,13 @@ See also [increase_pure](#increase_pure) and [increase](#increase).
 #### increase_pure
 
 `increase_pure(series_selector[d])` is a [rollup function](#rollup-functions), which works the same as [increase](#increase) except
-of the following corner case - it assumes that [counters](https://docs.victoriametrics.com/keyConcepts.html#counter) always start from 0,
+of the following corner case - it assumes that [counters](https://docs.victoriametrics.com/keyconcepts/#counter) always start from 0,
 while [increase](#increase) ignores the first value in a series if it is too big.
 
 #### increases_over_time
 
 `increases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of raw sample value increases
-over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -532,15 +532,15 @@ 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.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### irate
 
 `irate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the "instant" per-second increase rate over the last two raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -552,7 +552,7 @@ See also [rate](#rate) and [rollup_rate](#rollup_rate).
 
 `lag(series_selector[d])` is a [rollup function](#rollup-functions), which returns the duration in seconds between the last sample
 on the given lookbehind window `d` and the timestamp of the current point. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -561,7 +561,7 @@ See also [lifetime](#lifetime) and [duration_over_time](#duration_over_time).
 #### last_over_time
 
 `last_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is supported by PromQL.
 
@@ -570,7 +570,7 @@ See also [first_over_time](#first_over_time) and [tlast_over_time](#tlast_over_t
 #### lifetime
 
 `lifetime(series_selector[d])` is a [rollup function](#rollup-functions), which returns the duration in seconds between the last and the first sample
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -579,14 +579,14 @@ 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.html#filtering).
+over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 See also [mad](#mad), [range_mad](#range_mad) and [outlier_iqr_over_time](#outlier_iqr_over_time).
 
 #### max_over_time
 
 `max_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the maximum value over raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -596,14 +596,14 @@ See also [tmax_over_time](#tmax_over_time).
 
 `median_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates median value over raw samples
 on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 See also [avg_over_time](#avg_over_time).
 
 #### min_over_time
 
 `min_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the minimum value over raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -613,7 +613,7 @@ See also [tmin_over_time](#tmin_over_time).
 
 `mode_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [mode](https://en.wikipedia.org/wiki/Mode_(statistics))
 for raw samples on the given lookbehind window `d`. It is calculated individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). It is expected that raw sample values are discrete.
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). It is expected that raw sample values are discrete.
 
 #### outlier_iqr_over_time
 
@@ -631,7 +631,7 @@ See also [outliers_iqr](#outliers_iqr).
 
 `predict_linear(series_selector[d], t)` is a [rollup function](#rollup-functions), which calculates the value `t` seconds in the future using
 linear interpolation over raw samples on the given lookbehind window `d`. The predicted value is calculated individually per each time series
-returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is supported by PromQL.
 
@@ -649,7 +649,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 The `phi` value must be in the range `[0...1]`.
 
 This function is supported by PromQL.
@@ -660,7 +660,7 @@ See also [quantiles_over_time](#quantiles_over_time).
 
 `quantiles_over_time("phiLabel", phi1, ..., phiN, series_selector[d])` is a [rollup function](#rollup-functions), which calculates `phi*`-quantiles
 over raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 The function returns individual series per each `phi*` with `{phiLabel="phi*"}` label. `phi*` values must be in the range `[0...1]`.
 
 See also [quantile_over_time](#quantile_over_time).
@@ -668,7 +668,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 E.g. it calculates `max_over_time(series_selector[d]) - min_over_time(series_selector[d])`.
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -676,8 +676,8 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 #### rate
 
 `rate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average per-second increase rate
-over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 If the lookbehind window is skipped in square brackets, then it is automatically calculated as `max(step, scrape_interval)`, where `step` is the query arg value
 passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query),
@@ -694,16 +694,16 @@ See also [irate](#irate) and [rollup_rate](#rollup_rate).
 
 `rate_over_sum(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second rate over the sum of raw samples
 on the given lookbehind window `d`. The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### resets
 
 `resets(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number
-of [counter](https://docs.victoriametrics.com/keyConcepts.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).
+of [counter](https://docs.victoriametrics.com/keyconcepts/#counter) resets over the given lookbehind window `d`
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -713,7 +713,7 @@ This function is supported by PromQL.
 
 `rollup(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `min`, `max` and `avg` values for raw samples
 on the given lookbehind window `d` and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-These values are calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+These values are calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -723,7 +723,7 @@ See also [label_match](#label_match).
 `rollup_candlestick(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `open`, `high`, `low` and `close` values (aka OHLC)
 over raw samples on the given lookbehind window `d` and returns them in time series with `rollup="open"`, `rollup="high"`, `rollup="low"` and `rollup="close"` additional labels.
 The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is useful for financial applications.
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). This function is useful for financial applications.
 
 Optional 2nd argument `"open"`, `"high"` or `"low"` or `"close"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -733,7 +733,7 @@ See also [label_match](#label_match).
 `rollup_delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates differences between adjacent raw samples
 on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated differences
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -747,7 +747,7 @@ See also [rollup_increase](#rollup_increase).
 `rollup_deriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivatives
 for adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second derivatives
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -759,7 +759,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 `rollup_increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates increases for adjacent raw samples
 on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated increases
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -778,7 +778,7 @@ when to use `rollup_rate()`.
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
 
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -787,7 +787,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 `rollup_scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the interval in seconds between
 adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated interval
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -797,7 +797,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -807,7 +807,7 @@ See also [rollup_scrape_interval](#rollup_scrape_interval).
 
 `share_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
 on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is useful for calculating SLI and SLO. Example: `share_gt_over_time(up[24h], 0)` - returns service availability for the last 24 hours.
 
@@ -819,7 +819,7 @@ See also [share_le_over_time](#share_le_over_time) and [count_gt_over_time](#cou
 
 `share_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
 on the given lookbehind window `d`, which are smaller or equal to `le`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is useful for calculating SLI and SLO. Example: `share_le_over_time(memory_usage_bytes[24h], 100*1024*1024)` returns
 the share of time series values for the last 24 hours when memory usage was below or equal to 100MB.
@@ -832,7 +832,7 @@ See also [share_gt_over_time](#share_gt_over_time) and [count_le_over_time](#cou
 
 `share_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
 on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -841,15 +841,15 @@ See also [count_eq_over_time](#count_eq_over_time).
 #### stale_samples_over_time
 
 `stale_samples_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number
-of [staleness markers](https://docs.victoriametrics.com/vmagent.html#prometheus-staleness-markers) on the given lookbehind window `d`
-per each time series matching the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+of [staleness markers](https://docs.victoriametrics.com/vmagent/#prometheus-staleness-markers) on the given lookbehind window `d`
+per each time series matching the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### stddev_over_time
 
 `stddev_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard deviation over raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -860,7 +860,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -871,7 +871,7 @@ See also [stddev_over_time](#stddev_over_time).
 #### sum_eq_over_time
 
 `sum_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-function), which calculates the sum of raw sample values equal to `eq`
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -880,7 +880,7 @@ 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.html#filtering).
+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.
 
@@ -889,7 +889,7 @@ 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.html#filtering).
+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.
 
@@ -898,7 +898,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -907,14 +907,14 @@ This function is supported by PromQL.
 #### sum2_over_time
 
 `sum2_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of squares for raw sample values
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### timestamp
 
 `timestamp(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last raw sample
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -925,7 +925,7 @@ See also [time](#time) and [now](#now).
 #### timestamp_with_name
 
 `timestamp_with_name(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last raw sample
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -934,7 +934,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -943,7 +943,7 @@ See also [first_over_time](#first_over_time).
 #### tlast_change_over_time
 
 `tlast_change_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last change
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) on the given lookbehind window `d`.
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering) on the given lookbehind window `d`.
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -959,7 +959,7 @@ See also [tlast_change_over_time](#tlast_change_over_time).
 
 `tmax_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the raw sample
 with the maximum value on the given lookbehind window `d`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -969,7 +969,7 @@ See also [max_over_time](#max_over_time).
 
 `tmin_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the raw sample
 with the minimum value on the given lookbehind window `d`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -979,7 +979,7 @@ See also [min_over_time](#min_over_time).
 
 `zscore_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns [z-score](https://en.wikipedia.org/wiki/Standard_score)
 for raw samples on the given lookbehind window `d`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -994,7 +994,7 @@ returned from the rollup `delta(temperature[24h])`.
 
 Additional details:
 
-* If transform function is applied directly to a [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* If transform function is applied directly to a [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering),
   then the [default_rollup()](#default_rollup) function is automatically applied before calculating the transformations.
   For example, `abs(temperature)` is implicitly transformed to `abs(default_rollup(temperature))`.
 * All the transform functions accept optional `keep_metric_names` modifier. If it is set,
@@ -1230,7 +1230,7 @@ by replacing all the values bigger or equal to 30 with 40.
 #### end
 
 `end()` is a [transform function](#transform-functions), which returns the unix timestamp in seconds for the last point.
-It is known as `end` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+It is known as `end` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 See also [start](#start), [time](#time) and [now](#now).
 
@@ -1653,14 +1653,14 @@ This function is supported by PromQL.
 
 `start()` is a [transform function](#transform-functions), which returns unix timestamp in seconds for the first point.
 
-It is known as `start` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+It is known as `start` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 See also [end](#end), [time](#time) and [now](#now).
 
 #### step
 
 `step()` is a [transform function](#transform-functions), which returns the step in seconds (aka interval) between the returned points.
-It is known as `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+It is known as `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 See also [start](#start) and [end](#end).
 
@@ -1717,7 +1717,7 @@ This function is supported by PromQL.
 
 Additional details:
 
-* If label manipulation function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* If label manipulation function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering),
   then the [default_rollup()](#default_rollup) function is automatically applied before performing the label transformation.
   For example, `alias(temperature, "foo")` is implicitly transformed to `alias(default_rollup(temperature), "foo")`.
 
@@ -1894,7 +1894,7 @@ Additional details:
   and calculate the [count](#count) aggregate function independently per each group, while `count(up) without (instance)`
   would group [rollup results](#rollup-functions) by all the labels except `instance` before calculating [count](#count) aggregate function independently per each group.
   Multiple labels can be put in `by` and `without` modifiers.
-* If the aggregate function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* If the aggregate function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering),
   then the [default_rollup()](#default_rollup) function is automatically applied before calculating the aggregate.
   For example, `count(up)` is implicitly transformed to `count(default_rollup(up))`.
 * Aggregate functions accept arbitrary number of args. For example, `avg(q1, q2, q3)` would return the average values for every point
@@ -2104,7 +2104,7 @@ See also [quantile](#quantile).
 `share(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns shares in the range `[0..1]`
 for every non-negative points returned by `q` per each timestamp, so the sum of shares per each `group_labels` equals 1.
 
-This function is useful for normalizing [histogram bucket](https://docs.victoriametrics.com/keyConcepts.html#histogram) shares
+This function is useful for normalizing [histogram bucket](https://docs.victoriametrics.com/keyconcepts/#histogram) shares
 into `[0..1]` range:
 
 ```metricsql
@@ -2208,7 +2208,7 @@ See also [zscore_over_time](#zscore_over_time), [range_trim_zscore](#range_trim_
 ## Subqueries
 
 MetricsQL supports and extends PromQL subqueries. See [this article](https://valyala.medium.com/prometheus-subqueries-in-victoriametrics-9b1492b720b3) for details.
-Any [rollup function](#rollup-functions) for something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) form a subquery.
+Any [rollup function](#rollup-functions) for something other than [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) form a subquery.
 Nested rollup functions can be implicit thanks to the [implicit query conversions](#implicit-query-conversions).
 For example, `delta(sum(m))` is implicitly converted to `delta(sum(default_rollup(m))[1i:1i])`, so it becomes a subquery,
 since it contains [default_rollup](#default_rollup) nested into [delta](#delta).
@@ -2219,19 +2219,19 @@ VictoriaMetrics performs subqueries in the following way:
   For example, for expression `max_over_time(rate(http_requests_total[5m])[1h:30s])` the inner function `rate(http_requests_total[5m])`
   is calculated with `step=30s`. The resulting data points are aligned by the `step`.
 * It calculates the outer rollup function over the results of the inner rollup function using the `step` value
-  passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+  passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 ## Implicit query conversions
 
 VictoriaMetrics performs the following implicit conversions for incoming queries before starting the calculations:
 
 * If lookbehind window in square brackets is missing inside [rollup function](#rollup-functions), then it is automatically set to the following value:
-  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
+  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
     for all the [rollup functions](#rollup-functions) except of [default_rollup](#default_rollup) and [rate](#rate). This value is known as `$__interval` in Grafana or `1i` in MetricsQL.
     For example, `avg_over_time(temperature)` is automatically transformed to `avg_over_time(temperature[1i])`.
   - To the `max(step, scrape_interval)`, where `scrape_interval` is the interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
     for [default_rollup](#default_rollup) and [rate](#rate) functions. This allows avoiding unexpected gaps on the graph when `step` is smaller than `scrape_interval`.
-* All the [series selectors](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* All the [series selectors](https://docs.victoriametrics.com/keyconcepts/#filtering),
   which aren't wrapped into [rollup functions](#rollup-functions), are automatically wrapped into [default_rollup](#default_rollup) function.
   Examples:
   * `foo` is transformed to `default_rollup(foo)`
@@ -2242,6 +2242,6 @@ VictoriaMetrics performs the following implicit conversions for incoming queries
     it is [transform function](#transform-functions)
 * If `step` in square brackets is missing inside [subquery](#subqueries), then `1i` step is automatically added there.
   For example, `avg_over_time(rate(http_requests_total[5m])[1h])` is automatically converted to `avg_over_time(rate(http_requests_total[5m])[1h:1i])`.
-* If something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering)
+* 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])`.

app/vmstorage/main.go

@@ -61,13 +61,13 @@ var (
 	minFreeDiskSpaceBytes = flagutil.NewBytes("storage.minFreeDiskSpaceBytes", 10e6, "The minimum free disk space at -storageDataPath after which the storage stops accepting new data")
 
 	cacheSizeStorageTSID = flagutil.NewBytes("storage.cacheSizeStorageTSID", 0, "Overrides max size for storage/tsid cache. "+
-		"See https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#cache-tuning")
+		"See https://docs.victoriametrics.com/single-server-victoriametrics/#cache-tuning")
 	cacheSizeIndexDBIndexBlocks = flagutil.NewBytes("storage.cacheSizeIndexDBIndexBlocks", 0, "Overrides max size for indexdb/indexBlocks cache. "+
-		"See https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#cache-tuning")
+		"See https://docs.victoriametrics.com/single-server-victoriametrics/#cache-tuning")
 	cacheSizeIndexDBDataBlocks = flagutil.NewBytes("storage.cacheSizeIndexDBDataBlocks", 0, "Overrides max size for indexdb/dataBlocks cache. "+
-		"See https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#cache-tuning")
+		"See https://docs.victoriametrics.com/single-server-victoriametrics/#cache-tuning")
 	cacheSizeIndexDBTagFilters = flagutil.NewBytes("storage.cacheSizeIndexDBTagFilters", 0, "Overrides max size for indexdb/tagFiltersToMetricIDs cache. "+
-		"See https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#cache-tuning")
+		"See https://docs.victoriametrics.com/single-server-victoriametrics/#cache-tuning")
 )
 
 // CheckTimeRange returns true if the given tr is denied for querying.

app/vmui/packages/vmui/src/assets/MetricsQL.md

@@ -22,7 +22,7 @@ However, there are some [intentional differences](https://medium.com/@romanhavro
 [Standalone MetricsQL package](https://godoc.org/github.com/VictoriaMetrics/metricsql) can be used for parsing MetricsQL in external apps.
 
 If you are unfamiliar with PromQL, then it is suggested reading [this tutorial for beginners](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085)
-and introduction into [basic querying via MetricsQL](https://docs.victoriametrics.com/keyConcepts.html#metricsql).
+and introduction into [basic querying via MetricsQL](https://docs.victoriametrics.com/keyconcepts/#metricsql).
 
 The following functionality is implemented differently in MetricsQL compared to PromQL. This improves user experience:
 
@@ -70,15 +70,15 @@ The list of MetricsQL features on top of PromQL:
   VictoriaMetrics can be used as Graphite datasource in Grafana. See [these docs](https://docs.victoriametrics.com/#graphite-api-usage) for details.
   See also [label_graphite_group](#label_graphite_group) function, which can be used for extracting the given groups from Graphite metric name.
 * Lookbehind window in square brackets for [rollup functions](#rollup-functions) may be omitted. VictoriaMetrics automatically selects the lookbehind window
-  depending on the `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query)
+  depending on the `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query)
   and the real interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) (aka `scrape_interval`).
   For instance, the following query is valid in VictoriaMetrics: `rate(node_network_receive_bytes_total)`.
   It is roughly equivalent to `rate(node_network_receive_bytes_total[$__interval])` when used in Grafana.
   The difference is documented in [rate() docs](#rate).
 * Numeric values can contain `_` delimiters for better readability. For example, `1_234_567_890` can be used in queries instead of `1234567890`.
-* [Series selectors](https://docs.victoriametrics.com/keyConcepts.html#filtering) accept multiple `or` filters. For example, `{env="prod",job="a" or env="dev",job="b"}`
+* [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.html#filtering-by-multiple-or-filters) for details.
+  See [these docs](https://docs.victoriametrics.com/keyconcepts/#filtering-by-multiple-or-filters) for details.
 * Support for `group_left(*)` and `group_right(*)` for copying all the labels from time series on the `one` side
   of [many-to-one operations](https://prometheus.io/docs/prometheus/latest/querying/operators/#many-to-one-and-one-to-many-vector-matches).
   The copied label names may clash with the existing label names, so MetricsQL provides an ability to add prefix to the copied metric names
@@ -153,26 +153,26 @@ MetricsQL provides the following functions:
 ### Rollup functions
 
 **Rollup functions** (aka range functions or window functions) calculate rollups over **raw samples**
-on the given lookbehind window for the [selected time series](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window for the [selected time series](https://docs.victoriametrics.com/keyconcepts/#filtering).
 For example, `avg_over_time(temperature[24h])` calculates the average temperature over raw samples for the last 24 hours.
 
 Additional details:
 
 * If rollup functions are used for building graphs in Grafana, then the rollup is calculated independently per each point on the graph.
   For example, every point for `avg_over_time(temperature[24h])` graph shows the average temperature for the last 24 hours ending at this point.
-  The interval between points is set as `step` query arg passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
-* If the given [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) returns multiple time series,
+  The interval between points is set as `step` query arg passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
+* If the given [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) returns multiple time series,
   then rollups are calculated individually per each returned series.
 * If lookbehind window in square brackets is missing, then it is automatically set to the following value:
-  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
+  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
     for all the [rollup functions](#rollup-functions) except of [default_rollup](#default_rollup) and [rate](#rate). This value is known as `$__interval` in Grafana or `1i` in MetricsQL.
     For example, `avg_over_time(temperature)` is automatically transformed to `avg_over_time(temperature[1i])`.
   - To the `max(step, scrape_interval)`, where `scrape_interval` is the interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
     for [default_rollup](#default_rollup) and [rate](#rate) functions. This allows avoiding unexpected gaps on the graph when `step` is smaller than `scrape_interval`.
-* Every [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) in MetricsQL must be wrapped into a rollup function.
+* Every [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) in MetricsQL must be wrapped into a rollup function.
   Otherwise, it is automatically wrapped into [default_rollup](#default_rollup). For example, `foo{bar="baz"}`
   is automatically converted to `default_rollup(foo{bar="baz"})` before performing the calculations.
-* If something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) is passed to rollup function,
+* If something other than [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) is passed to rollup function,
   then the inner arg is automatically converted to a [subquery](#subqueries).
 * All the rollup functions accept optional `keep_metric_names` modifier. If it is set, then the function keeps metric names in results.
   See [these docs](#keep_metric_names).
@@ -195,7 +195,7 @@ See also [present_over_time](#present_over_time).
 `aggr_over_time(("rollup_func1", "rollup_func2", ...), series_selector[d])` is a [rollup function](#rollup-functions),
 which calculates all the listed `rollup_func*` for raw samples on the given lookbehind window `d`.
 The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 `rollup_func*` can contain any rollup function. For instance, `aggr_over_time(("min_over_time", "max_over_time", "rate"), m[d])`
 would calculate [min_over_time](#min_over_time), [max_over_time](#max_over_time) and [rate](#rate) for `m[d]`.
@@ -204,7 +204,7 @@ would calculate [min_over_time](#min_over_time), [max_over_time](#max_over_time)
 
 `ascent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates
 ascent of raw sample values on the given lookbehind window `d`. The calculations are performed individually
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -216,7 +216,7 @@ See also [descent_over_time](#descent_over_time).
 
 `avg_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average value
 over raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is supported by PromQL.
 
@@ -226,7 +226,7 @@ See also [median_over_time](#median_over_time).
 
 `changes(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times
 the raw samples changed on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Unlike `changes()` in Prometheus it takes into account the change from the last sample before the given lookbehind window `d`.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -241,7 +241,7 @@ See also [changes_prometheus](#changes_prometheus).
 
 `changes_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times
 the raw samples changed on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 It doesn't take into account the change from the last sample before the given lookbehind window `d` in the same way as Prometheus does.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -256,7 +256,7 @@ See also [changes](#changes).
 
 `count_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -266,7 +266,7 @@ See also [count_over_time](#count_over_time), [share_eq_over_time](#share_eq_ove
 
 `count_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -276,7 +276,7 @@ See also [count_over_time](#count_over_time) and [share_gt_over_time](#share_gt_
 
 `count_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which don't exceed `le`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -286,7 +286,7 @@ See also [count_over_time](#count_over_time) and [share_le_over_time](#share_le_
 
 `count_ne_over_time(series_selector[d], ne)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which aren't equal to `ne`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -295,7 +295,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -307,7 +307,7 @@ See also [count_le_over_time](#count_le_over_time), [count_gt_over_time](#count_
 
 `count_values_over_time("label", series_selector[d])` is a [rollup function](#rollup-functions), which counts the number of raw samples
 with the same value over the given lookbehind window and stores the counts in a time series with an additional `label`, which contains each initial value.
-The results are calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The results are calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -316,7 +316,7 @@ 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.html#filtering).
+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.
 
@@ -325,7 +325,7 @@ See also [increases_over_time](#increases_over_time).
 #### default_rollup
 
 `default_rollup(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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),
@@ -336,7 +336,7 @@ This allows avoiding unexpected gaps on the graph when `step` is smaller than th
 
 `delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between
 the last sample before the given lookbehind window `d` and the last sample at the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 The behaviour of `delta()` function in MetricsQL is slightly different to the behaviour of `delta()` function in Prometheus.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -351,7 +351,7 @@ See also [increase](#increase) and [delta_prometheus](#delta_prometheus).
 
 `delta_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between
 the first and the last samples at the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 The behaviour of `delta_prometheus()` is close to the behaviour of `delta()` function in Prometheus.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -363,7 +363,7 @@ See also [delta](#delta).
 #### deriv
 
 `deriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivative over the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 The derivative is calculated using linear regression.
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -376,7 +376,7 @@ See also [deriv_fast](#deriv_fast) and [ideriv](#ideriv).
 
 `deriv_fast(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivative
 using the first and the last raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -386,7 +386,7 @@ See also [deriv](#deriv) and [ideriv](#ideriv).
 
 `descent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates descent of raw sample values
 on the given lookbehind window `d`. The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is useful for tracking height loss in GPS tracking.
 
@@ -397,7 +397,7 @@ See also [ascent_over_time](#ascent_over_time).
 #### distinct_over_time
 
 `distinct_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number of distinct raw sample values
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -406,7 +406,7 @@ See also [count_values_over_time](#count_values_over_time).
 #### duration_over_time
 
 `duration_over_time(series_selector[d], max_interval)` is a [rollup function](#rollup-functions), which returns the duration in seconds
-when time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) were present
+when time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering) were present
 over the given lookbehind window `d`. It is expected that intervals between adjacent samples per each series don't exceed the `max_interval`.
 Otherwise, such intervals are considered as gaps and aren't counted.
 
@@ -417,7 +417,7 @@ See also [lifetime](#lifetime) and [lag](#lag).
 #### first_over_time
 
 `first_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the first raw sample value
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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).
 
@@ -425,7 +425,7 @@ See also [last_over_time](#last_over_time) and [tfirst_over_time](#tfirst_over_t
 
 `geomean_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [geometric mean](https://en.wikipedia.org/wiki/Geometric_mean)
 over raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -433,9 +433,9 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 
 `histogram_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates
 [VictoriaMetrics histogram](https://godoc.org/github.com/VictoriaMetrics/metrics#Histogram) over raw samples on the given lookbehind window `d`.
-It is calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.html#gauge).
+over multiple [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge).
 For example, the following query calculates median temperature by country over the last 24 hours:
 
 `histogram_quantile(0.5, sum(histogram_over_time(temperature[24h])) by (vmrange,country))`.
@@ -459,8 +459,8 @@ See also [hoeffding_bound_lower](#hoeffding_bound_lower).
 `holt_winters(series_selector[d], sf, tf)` is a [rollup function](#rollup-functions), which calculates Holt-Winters value
 (aka [double exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing#Double_exponential_smoothing)) for raw samples
 over the given lookbehind window `d` using the given smoothing factor `sf` and the given trend factor `tf`.
-Both `sf` and `tf` must be in the range `[0...1]`. It is expected that the [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering)
-returns time series of [gauge type](https://docs.victoriametrics.com/keyConcepts.html#gauge).
+Both `sf` and `tf` must be in the range `[0...1]`. It is expected that the [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering)
+returns time series of [gauge type](https://docs.victoriametrics.com/keyconcepts/#gauge).
 
 This function is supported by PromQL.
 
@@ -469,7 +469,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -481,7 +481,7 @@ See also [delta](#delta).
 
 `ideriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the per-second derivative based on the last two raw samples
 over the given lookbehind window `d`. The derivative is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -490,8 +490,8 @@ See also [deriv](#deriv).
 #### increase
 
 `increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase over the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 Unlike Prometheus, it takes into account the last sample before the given lookbehind window `d` when calculating the result.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -505,8 +505,8 @@ See also [increase_pure](#increase_pure), [increase_prometheus](#increase_promet
 #### increase_prometheus
 
 `increase_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase
-over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 It doesn't take into account the last sample before the given lookbehind window `d` when calculating the result in the same way as Prometheus does.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
 
@@ -517,13 +517,13 @@ See also [increase_pure](#increase_pure) and [increase](#increase).
 #### increase_pure
 
 `increase_pure(series_selector[d])` is a [rollup function](#rollup-functions), which works the same as [increase](#increase) except
-of the following corner case - it assumes that [counters](https://docs.victoriametrics.com/keyConcepts.html#counter) always start from 0,
+of the following corner case - it assumes that [counters](https://docs.victoriametrics.com/keyconcepts/#counter) always start from 0,
 while [increase](#increase) ignores the first value in a series if it is too big.
 
 #### increases_over_time
 
 `increases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of raw sample value increases
-over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -532,15 +532,15 @@ 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.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### irate
 
 `irate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the "instant" per-second increase rate over the last two raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -552,7 +552,7 @@ See also [rate](#rate) and [rollup_rate](#rollup_rate).
 
 `lag(series_selector[d])` is a [rollup function](#rollup-functions), which returns the duration in seconds between the last sample
 on the given lookbehind window `d` and the timestamp of the current point. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -561,7 +561,7 @@ See also [lifetime](#lifetime) and [duration_over_time](#duration_over_time).
 #### last_over_time
 
 `last_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is supported by PromQL.
 
@@ -570,7 +570,7 @@ See also [first_over_time](#first_over_time) and [tlast_over_time](#tlast_over_t
 #### lifetime
 
 `lifetime(series_selector[d])` is a [rollup function](#rollup-functions), which returns the duration in seconds between the last and the first sample
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -579,14 +579,14 @@ 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.html#filtering).
+over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 See also [mad](#mad), [range_mad](#range_mad) and [outlier_iqr_over_time](#outlier_iqr_over_time).
 
 #### max_over_time
 
 `max_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the maximum value over raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -596,14 +596,14 @@ See also [tmax_over_time](#tmax_over_time).
 
 `median_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates median value over raw samples
 on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 See also [avg_over_time](#avg_over_time).
 
 #### min_over_time
 
 `min_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the minimum value over raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -613,7 +613,7 @@ See also [tmin_over_time](#tmin_over_time).
 
 `mode_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [mode](https://en.wikipedia.org/wiki/Mode_(statistics))
 for raw samples on the given lookbehind window `d`. It is calculated individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). It is expected that raw sample values are discrete.
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). It is expected that raw sample values are discrete.
 
 #### outlier_iqr_over_time
 
@@ -631,7 +631,7 @@ See also [outliers_iqr](#outliers_iqr).
 
 `predict_linear(series_selector[d], t)` is a [rollup function](#rollup-functions), which calculates the value `t` seconds in the future using
 linear interpolation over raw samples on the given lookbehind window `d`. The predicted value is calculated individually per each time series
-returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is supported by PromQL.
 
@@ -649,7 +649,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 The `phi` value must be in the range `[0...1]`.
 
 This function is supported by PromQL.
@@ -660,7 +660,7 @@ See also [quantiles_over_time](#quantiles_over_time).
 
 `quantiles_over_time("phiLabel", phi1, ..., phiN, series_selector[d])` is a [rollup function](#rollup-functions), which calculates `phi*`-quantiles
 over raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 The function returns individual series per each `phi*` with `{phiLabel="phi*"}` label. `phi*` values must be in the range `[0...1]`.
 
 See also [quantile_over_time](#quantile_over_time).
@@ -668,7 +668,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 E.g. it calculates `max_over_time(series_selector[d]) - min_over_time(series_selector[d])`.
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -676,8 +676,8 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 #### rate
 
 `rate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average per-second increase rate
-over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 If the lookbehind window is skipped in square brackets, then it is automatically calculated as `max(step, scrape_interval)`, where `step` is the query arg value
 passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query),
@@ -694,16 +694,16 @@ See also [irate](#irate) and [rollup_rate](#rollup_rate).
 
 `rate_over_sum(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second rate over the sum of raw samples
 on the given lookbehind window `d`. The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### resets
 
 `resets(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number
-of [counter](https://docs.victoriametrics.com/keyConcepts.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).
+of [counter](https://docs.victoriametrics.com/keyconcepts/#counter) resets over the given lookbehind window `d`
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -713,7 +713,7 @@ This function is supported by PromQL.
 
 `rollup(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `min`, `max` and `avg` values for raw samples
 on the given lookbehind window `d` and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-These values are calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+These values are calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -723,7 +723,7 @@ See also [label_match](#label_match).
 `rollup_candlestick(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `open`, `high`, `low` and `close` values (aka OHLC)
 over raw samples on the given lookbehind window `d` and returns them in time series with `rollup="open"`, `rollup="high"`, `rollup="low"` and `rollup="close"` additional labels.
 The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is useful for financial applications.
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). This function is useful for financial applications.
 
 Optional 2nd argument `"open"`, `"high"` or `"low"` or `"close"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -733,7 +733,7 @@ See also [label_match](#label_match).
 `rollup_delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates differences between adjacent raw samples
 on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated differences
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -747,7 +747,7 @@ See also [rollup_increase](#rollup_increase).
 `rollup_deriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivatives
 for adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second derivatives
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -759,7 +759,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 `rollup_increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates increases for adjacent raw samples
 on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated increases
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -778,7 +778,7 @@ when to use `rollup_rate()`.
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
 
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -787,7 +787,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 `rollup_scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the interval in seconds between
 adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated interval
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -797,7 +797,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -807,7 +807,7 @@ See also [rollup_scrape_interval](#rollup_scrape_interval).
 
 `share_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
 on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is useful for calculating SLI and SLO. Example: `share_gt_over_time(up[24h], 0)` - returns service availability for the last 24 hours.
 
@@ -819,7 +819,7 @@ See also [share_le_over_time](#share_le_over_time) and [count_gt_over_time](#cou
 
 `share_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
 on the given lookbehind window `d`, which are smaller or equal to `le`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is useful for calculating SLI and SLO. Example: `share_le_over_time(memory_usage_bytes[24h], 100*1024*1024)` returns
 the share of time series values for the last 24 hours when memory usage was below or equal to 100MB.
@@ -832,7 +832,7 @@ See also [share_gt_over_time](#share_gt_over_time) and [count_le_over_time](#cou
 
 `share_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
 on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -841,15 +841,15 @@ See also [count_eq_over_time](#count_eq_over_time).
 #### stale_samples_over_time
 
 `stale_samples_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number
-of [staleness markers](https://docs.victoriametrics.com/vmagent.html#prometheus-staleness-markers) on the given lookbehind window `d`
-per each time series matching the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+of [staleness markers](https://docs.victoriametrics.com/vmagent/#prometheus-staleness-markers) on the given lookbehind window `d`
+per each time series matching the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### stddev_over_time
 
 `stddev_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard deviation over raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -860,7 +860,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -871,7 +871,7 @@ See also [stddev_over_time](#stddev_over_time).
 #### sum_eq_over_time
 
 `sum_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-function), which calculates the sum of raw sample values equal to `eq`
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -880,7 +880,7 @@ 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.html#filtering).
+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.
 
@@ -889,7 +889,7 @@ 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.html#filtering).
+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.
 
@@ -898,7 +898,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -907,14 +907,14 @@ This function is supported by PromQL.
 #### sum2_over_time
 
 `sum2_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of squares for raw sample values
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### timestamp
 
 `timestamp(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last raw sample
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -925,7 +925,7 @@ See also [time](#time) and [now](#now).
 #### timestamp_with_name
 
 `timestamp_with_name(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last raw sample
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -934,7 +934,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -943,7 +943,7 @@ See also [first_over_time](#first_over_time).
 #### tlast_change_over_time
 
 `tlast_change_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last change
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) on the given lookbehind window `d`.
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering) on the given lookbehind window `d`.
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -959,7 +959,7 @@ See also [tlast_change_over_time](#tlast_change_over_time).
 
 `tmax_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the raw sample
 with the maximum value on the given lookbehind window `d`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -969,7 +969,7 @@ See also [max_over_time](#max_over_time).
 
 `tmin_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the raw sample
 with the minimum value on the given lookbehind window `d`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -979,7 +979,7 @@ See also [min_over_time](#min_over_time).
 
 `zscore_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns [z-score](https://en.wikipedia.org/wiki/Standard_score)
 for raw samples on the given lookbehind window `d`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -994,7 +994,7 @@ returned from the rollup `delta(temperature[24h])`.
 
 Additional details:
 
-* If transform function is applied directly to a [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* If transform function is applied directly to a [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering),
   then the [default_rollup()](#default_rollup) function is automatically applied before calculating the transformations.
   For example, `abs(temperature)` is implicitly transformed to `abs(default_rollup(temperature))`.
 * All the transform functions accept optional `keep_metric_names` modifier. If it is set,
@@ -1230,7 +1230,7 @@ by replacing all the values bigger or equal to 30 with 40.
 #### end
 
 `end()` is a [transform function](#transform-functions), which returns the unix timestamp in seconds for the last point.
-It is known as `end` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+It is known as `end` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 See also [start](#start), [time](#time) and [now](#now).
 
@@ -1653,14 +1653,14 @@ This function is supported by PromQL.
 
 `start()` is a [transform function](#transform-functions), which returns unix timestamp in seconds for the first point.
 
-It is known as `start` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+It is known as `start` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 See also [end](#end), [time](#time) and [now](#now).
 
 #### step
 
 `step()` is a [transform function](#transform-functions), which returns the step in seconds (aka interval) between the returned points.
-It is known as `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+It is known as `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 See also [start](#start) and [end](#end).
 
@@ -1717,7 +1717,7 @@ This function is supported by PromQL.
 
 Additional details:
 
-* If label manipulation function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* If label manipulation function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering),
   then the [default_rollup()](#default_rollup) function is automatically applied before performing the label transformation.
   For example, `alias(temperature, "foo")` is implicitly transformed to `alias(default_rollup(temperature), "foo")`.
 
@@ -1894,7 +1894,7 @@ Additional details:
   and calculate the [count](#count) aggregate function independently per each group, while `count(up) without (instance)`
   would group [rollup results](#rollup-functions) by all the labels except `instance` before calculating [count](#count) aggregate function independently per each group.
   Multiple labels can be put in `by` and `without` modifiers.
-* If the aggregate function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* If the aggregate function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering),
   then the [default_rollup()](#default_rollup) function is automatically applied before calculating the aggregate.
   For example, `count(up)` is implicitly transformed to `count(default_rollup(up))`.
 * Aggregate functions accept arbitrary number of args. For example, `avg(q1, q2, q3)` would return the average values for every point
@@ -2104,7 +2104,7 @@ See also [quantile](#quantile).
 `share(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns shares in the range `[0..1]`
 for every non-negative points returned by `q` per each timestamp, so the sum of shares per each `group_labels` equals 1.
 
-This function is useful for normalizing [histogram bucket](https://docs.victoriametrics.com/keyConcepts.html#histogram) shares
+This function is useful for normalizing [histogram bucket](https://docs.victoriametrics.com/keyconcepts/#histogram) shares
 into `[0..1]` range:
 
 ```metricsql
@@ -2208,7 +2208,7 @@ See also [zscore_over_time](#zscore_over_time), [range_trim_zscore](#range_trim_
 ## Subqueries
 
 MetricsQL supports and extends PromQL subqueries. See [this article](https://valyala.medium.com/prometheus-subqueries-in-victoriametrics-9b1492b720b3) for details.
-Any [rollup function](#rollup-functions) for something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) form a subquery.
+Any [rollup function](#rollup-functions) for something other than [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) form a subquery.
 Nested rollup functions can be implicit thanks to the [implicit query conversions](#implicit-query-conversions).
 For example, `delta(sum(m))` is implicitly converted to `delta(sum(default_rollup(m))[1i:1i])`, so it becomes a subquery,
 since it contains [default_rollup](#default_rollup) nested into [delta](#delta).
@@ -2219,19 +2219,19 @@ VictoriaMetrics performs subqueries in the following way:
   For example, for expression `max_over_time(rate(http_requests_total[5m])[1h:30s])` the inner function `rate(http_requests_total[5m])`
   is calculated with `step=30s`. The resulting data points are aligned by the `step`.
 * It calculates the outer rollup function over the results of the inner rollup function using the `step` value
-  passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+  passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 ## Implicit query conversions
 
 VictoriaMetrics performs the following implicit conversions for incoming queries before starting the calculations:
 
 * If lookbehind window in square brackets is missing inside [rollup function](#rollup-functions), then it is automatically set to the following value:
-  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
+  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
     for all the [rollup functions](#rollup-functions) except of [default_rollup](#default_rollup) and [rate](#rate). This value is known as `$__interval` in Grafana or `1i` in MetricsQL.
     For example, `avg_over_time(temperature)` is automatically transformed to `avg_over_time(temperature[1i])`.
   - To the `max(step, scrape_interval)`, where `scrape_interval` is the interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
     for [default_rollup](#default_rollup) and [rate](#rate) functions. This allows avoiding unexpected gaps on the graph when `step` is smaller than `scrape_interval`.
-* All the [series selectors](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* All the [series selectors](https://docs.victoriametrics.com/keyconcepts/#filtering),
   which aren't wrapped into [rollup functions](#rollup-functions), are automatically wrapped into [default_rollup](#default_rollup) function.
   Examples:
   * `foo` is transformed to `default_rollup(foo)`
@@ -2242,6 +2242,6 @@ VictoriaMetrics performs the following implicit conversions for incoming queries
     it is [transform function](#transform-functions)
 * If `step` in square brackets is missing inside [subquery](#subqueries), then `1i` step is automatically added there.
   For example, `avg_over_time(rate(http_requests_total[5m])[1h])` is automatically converted to `avg_over_time(rate(http_requests_total[5m])[1h:1i])`.
-* If something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering)
+* 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])`.

app/vmui/packages/vmui/src/components/Main/Select/Select.tsx

@@ -72,6 +72,10 @@ const Select: FC<SelectProps> = ({
     setOpenList(true);
   };
 
+  const handleBlur = () => {
+    list.includes(search) && onChange(search);
+  };
+
   const handleToggleList = (e: MouseEvent<HTMLDivElement>) => {
     if (e.target instanceof HTMLInputElement || disabled) return;
     setOpenList(prev => !prev);
@@ -142,6 +146,7 @@ const Select: FC<SelectProps> = ({
               placeholder={placeholder}
               onInput={handleChange}
               onFocus={handleFocus}
+              onBlur={handleBlur}
               ref={inputRef}
               readOnly={isMobile || !searchable}
             />

app/vmui/packages/vmui/src/pages/ExploreLogs/hooks/useFetchLogs.ts

@@ -13,13 +13,21 @@ export const useFetchLogs = (server: string, query: string, limit: number) => {
 
   const url = useMemo(() => getLogsUrl(server), [server]);
 
+  const prepareQuery = (query: string) => {
+    const limitRegex = /\|\s*(limit|head)\s*:?[\s-]*\d+/gm;
+    const matchHeadLimit = query.match(limitRegex);
+    if (!matchHeadLimit) return `(${query})`;
+    const cleanedQuery = query.replace(limitRegex, "").trim();
+    return `(${cleanedQuery}) ${matchHeadLimit.join(" ")}`;
+  };
+
   // include time range in query if not already present
   const queryWithTime = useMemo(() => {
     if (!/_time/.test(query)) {
       const start = dayjs(period.start * 1000).tz().toISOString();
       const end = dayjs(period.end * 1000).tz().toISOString();
       const timerange = `_time:[${start}, ${end}]`;
-      return `${timerange} AND (${query})`;
+      return `${timerange} AND ${prepareQuery(query)}`;
     }
     return query;
   }, [query, period]);

dashboards/victoriametrics-cluster.json

@@ -2973,7 +2973,7 @@
             "type": "prometheus",
             "uid": "$ds"
           },
-          "description": "Shows the rate and total number of new series created over last 24h.\n\nHigh [churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate) tightly connected with database performance and may result in unexpected OOM's or slow queries. It is recommended to always keep an eye on this metric to avoid unexpected [cardinality](https://docs.victoriametrics.com/keyconcepts/#cardinality) \"explosions\".\n\nThe higher churn rate is, the more resources required to handle it. Consider to keep the churn rate as low as possible.\n\nTo investigate stats about most expensive series use `api/v1/status/tsdb` handler. More details here https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#url-format\n\nGood references to read:\n* https://www.robustperception.io/cardinality-is-key\n* https://valyala.medium.com/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b",
+          "description": "Shows the rate and total number of new series created over last 24h.\n\nHigh [churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate) tightly connected with database performance and may result in unexpected OOM's or slow queries. It is recommended to always keep an eye on this metric to avoid unexpected [cardinality](https://docs.victoriametrics.com/keyconcepts/#cardinality) \"explosions\".\n\nThe higher churn rate is, the more resources required to handle it. Consider to keep the churn rate as low as possible.\n\nTo investigate stats about most expensive series use `api/v1/status/tsdb` handler. More details here https://docs.victoriametrics.com/cluster-victoriametrics/#url-format\n\nGood references to read:\n* https://www.robustperception.io/cardinality-is-key\n* https://valyala.medium.com/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b",
           "fieldConfig": {
             "defaults": {
               "color": {
@@ -3271,7 +3271,7 @@
             {
               "targetBlank": true,
               "title": "Readonly mode",
-              "url": "https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#readonly-mode"
+              "url": "https://docs.victoriametrics.com/cluster-victoriametrics/#readonly-mode"
             }
           ],
           "options": {
@@ -5343,7 +5343,7 @@
             {
               "targetBlank": true,
               "title": "Readonly mode",
-              "url": "https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#readonly-mode"
+              "url": "https://docs.victoriametrics.com/cluster-victoriametrics/#readonly-mode"
             }
           ],
           "options": {
@@ -7230,7 +7230,7 @@
             "type": "prometheus",
             "uid": "$ds"
           },
-          "description": "99th percentile of number of [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) scanner per query.\n\nThis number can exceed number of DatapointsReadPerQuery if `step` query arg passed to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries) is smaller than the lookbehind window set in square brackets of [rollup function](https://docs.victoriametrics.com/MetricsQL.html#rollup-functions). For example, if `increase(some_metric[1h])` is executed with the `step=5m`, then the same [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on a hour time range are scanned `1h/5m=12` times. See [this article](https://valyala.medium.com/how-to-optimize-promql-and-metricsql-queries-85a1b75bf986) for details.",
+          "description": "99th percentile of number of [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) scanner per query.\n\nThis number can exceed number of DatapointsReadPerQuery if `step` query arg passed to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries) is smaller than the lookbehind window set in square brackets of [rollup function](https://docs.victoriametrics.com/metricsql/#rollup-functions). For example, if `increase(some_metric[1h])` is executed with the `step=5m`, then the same [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on a hour time range are scanned `1h/5m=12` times. See [this article](https://valyala.medium.com/how-to-optimize-promql-and-metricsql-queries-85a1b75bf986) for details.",
           "fieldConfig": {
             "defaults": {
               "color": {
@@ -9681,4 +9681,4 @@
   "uid": "oS7Bi_0Wz",
   "version": 1,
   "weekStart": ""
-}
+}

dashboards/victoriametrics.json

@@ -5490,7 +5490,7 @@
             "type": "prometheus",
             "uid": "$ds"
           },
-          "description": "99th percentile of number of [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) scanner per query.\n\nThis number can exceed number of RowsReadPerQuery if `step` query arg passed to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries) is smaller than the lookbehind window set in square brackets of [rollup function](https://docs.victoriametrics.com/MetricsQL.html#rollup-functions). For example, if `increase(some_metric[1h])` is executed with the `step=5m`, then the same [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on a hour time range are scanned `1h/5m=12` times. See [this article](https://valyala.medium.com/how-to-optimize-promql-and-metricsql-queries-85a1b75bf986) for details.",
+          "description": "99th percentile of number of [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) scanner per query.\n\nThis number can exceed number of RowsReadPerQuery if `step` query arg passed to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries) is smaller than the lookbehind window set in square brackets of [rollup function](https://docs.victoriametrics.com/metricsql/#rollup-functions). For example, if `increase(some_metric[1h])` is executed with the `step=5m`, then the same [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on a hour time range are scanned `1h/5m=12` times. See [this article](https://valyala.medium.com/how-to-optimize-promql-and-metricsql-queries-85a1b75bf986) for details.",
           "fieldConfig": {
             "defaults": {
               "color": {
@@ -5753,4 +5753,4 @@
   "uid": "wNf0q_kZk",
   "version": 1,
   "weekStart": ""
-}
+}

dashboards/vm/victoriametrics-cluster.json

@@ -2974,7 +2974,7 @@
             "type": "victoriametrics-datasource",
             "uid": "$ds"
           },
-          "description": "Shows the rate and total number of new series created over last 24h.\n\nHigh [churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate) tightly connected with database performance and may result in unexpected OOM's or slow queries. It is recommended to always keep an eye on this metric to avoid unexpected [cardinality](https://docs.victoriametrics.com/keyconcepts/#cardinality) \"explosions\".\n\nThe higher churn rate is, the more resources required to handle it. Consider to keep the churn rate as low as possible.\n\nTo investigate stats about most expensive series use `api/v1/status/tsdb` handler. More details here https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#url-format\n\nGood references to read:\n* https://www.robustperception.io/cardinality-is-key\n* https://valyala.medium.com/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b",
+          "description": "Shows the rate and total number of new series created over last 24h.\n\nHigh [churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate) tightly connected with database performance and may result in unexpected OOM's or slow queries. It is recommended to always keep an eye on this metric to avoid unexpected [cardinality](https://docs.victoriametrics.com/keyconcepts/#cardinality) \"explosions\".\n\nThe higher churn rate is, the more resources required to handle it. Consider to keep the churn rate as low as possible.\n\nTo investigate stats about most expensive series use `api/v1/status/tsdb` handler. More details here https://docs.victoriametrics.com/cluster-victoriametrics/#url-format\n\nGood references to read:\n* https://www.robustperception.io/cardinality-is-key\n* https://valyala.medium.com/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b",
           "fieldConfig": {
             "defaults": {
               "color": {
@@ -3272,7 +3272,7 @@
             {
               "targetBlank": true,
               "title": "Readonly mode",
-              "url": "https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#readonly-mode"
+              "url": "https://docs.victoriametrics.com/cluster-victoriametrics/#readonly-mode"
             }
           ],
           "options": {
@@ -5344,7 +5344,7 @@
             {
               "targetBlank": true,
               "title": "Readonly mode",
-              "url": "https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#readonly-mode"
+              "url": "https://docs.victoriametrics.com/cluster-victoriametrics/#readonly-mode"
             }
           ],
           "options": {
@@ -7231,7 +7231,7 @@
             "type": "victoriametrics-datasource",
             "uid": "$ds"
           },
-          "description": "99th percentile of number of [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) scanner per query.\n\nThis number can exceed number of DatapointsReadPerQuery if `step` query arg passed to [/api/v1/query_range](https://victoriametrics-datasource.io/docs/victoriametrics-datasource/latest/querying/api/#range-queries) is smaller than the lookbehind window set in square brackets of [rollup function](https://docs.victoriametrics.com/MetricsQL.html#rollup-functions). For example, if `increase(some_metric[1h])` is executed with the `step=5m`, then the same [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on a hour time range are scanned `1h/5m=12` times. See [this article](https://valyala.medium.com/how-to-optimize-promql-and-metricsql-queries-85a1b75bf986) for details.",
+          "description": "99th percentile of number of [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) scanner per query.\n\nThis number can exceed number of DatapointsReadPerQuery if `step` query arg passed to [/api/v1/query_range](https://victoriametrics-datasource.io/docs/victoriametrics-datasource/latest/querying/api/#range-queries) is smaller than the lookbehind window set in square brackets of [rollup function](https://docs.victoriametrics.com/metricsql/#rollup-functions). For example, if `increase(some_metric[1h])` is executed with the `step=5m`, then the same [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on a hour time range are scanned `1h/5m=12` times. See [this article](https://valyala.medium.com/how-to-optimize-promql-and-metricsql-queries-85a1b75bf986) for details.",
           "fieldConfig": {
             "defaults": {
               "color": {
@@ -9682,4 +9682,4 @@
   "uid": "oS7Bi_0Wz_vm",
   "version": 1,
   "weekStart": ""
-}
+}

dashboards/vm/victoriametrics.json

@@ -5491,7 +5491,7 @@
             "type": "victoriametrics-datasource",
             "uid": "$ds"
           },
-          "description": "99th percentile of number of [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) scanner per query.\n\nThis number can exceed number of RowsReadPerQuery if `step` query arg passed to [/api/v1/query_range](https://victoriametrics-datasource.io/docs/victoriametrics-datasource/latest/querying/api/#range-queries) is smaller than the lookbehind window set in square brackets of [rollup function](https://docs.victoriametrics.com/MetricsQL.html#rollup-functions). For example, if `increase(some_metric[1h])` is executed with the `step=5m`, then the same [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on a hour time range are scanned `1h/5m=12` times. See [this article](https://valyala.medium.com/how-to-optimize-promql-and-metricsql-queries-85a1b75bf986) for details.",
+          "description": "99th percentile of number of [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) scanner per query.\n\nThis number can exceed number of RowsReadPerQuery if `step` query arg passed to [/api/v1/query_range](https://victoriametrics-datasource.io/docs/victoriametrics-datasource/latest/querying/api/#range-queries) is smaller than the lookbehind window set in square brackets of [rollup function](https://docs.victoriametrics.com/metricsql/#rollup-functions). For example, if `increase(some_metric[1h])` is executed with the `step=5m`, then the same [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on a hour time range are scanned `1h/5m=12` times. See [this article](https://valyala.medium.com/how-to-optimize-promql-and-metricsql-queries-85a1b75bf986) for details.",
           "fieldConfig": {
             "defaults": {
               "color": {
@@ -5754,4 +5754,4 @@
   "uid": "wNf0q_kZk_vm",
   "version": 1,
   "weekStart": ""
-}
+}

dashboards/vm/vmagent.json

@@ -100,7 +100,7 @@
       "title": "vmagent wiki",
       "tooltip": "",
       "type": "link",
-      "url": "https://docs.victoriametrics.com/vmagent.html"
+      "url": "https://docs.victoriametrics.com/vmagent/"
     },
     {
       "icon": "external link",
@@ -166,7 +166,7 @@
       "links": [
         {
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -236,7 +236,7 @@
       "links": [
         {
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -397,7 +397,7 @@
       "links": [
         {
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -487,7 +487,7 @@
         {
           "targetBlank": true,
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -1037,7 +1037,7 @@
       "links": [
         {
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -1264,7 +1264,7 @@
         {
           "targetBlank": true,
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -1482,7 +1482,7 @@
         {
           "targetBlank": true,
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -3138,7 +3138,7 @@
             {
               "targetBlank": true,
               "title": "Relabeling",
-              "url": "https://docs.victoriametrics.com/vmagent.html#relabeling"
+              "url": "https://docs.victoriametrics.com/vmagent/#relabeling"
             }
           ],
           "options": {
@@ -5960,7 +5960,7 @@
           "links": [
             {
               "title": "Troubleshooting",
-              "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+              "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
             }
           ],
           "options": {
@@ -6402,4 +6402,4 @@
   "uid": "G7Z9GzMGz_vm",
   "version": 1,
   "weekStart": ""
-}
+}

dashboards/vm/vmalert.json

@@ -97,7 +97,7 @@
       "title": "vmalert docs",
       "tooltip": "",
       "type": "link",
-      "url": "https://docs.victoriametrics.com/vmalert.html"
+      "url": "https://docs.victoriametrics.com/vmalert/"
     },
     {
       "asDropdown": false,
@@ -1271,7 +1271,7 @@
         "type": "victoriametrics-datasource",
         "uid": "$ds"
       },
-      "description": "Missed evaluation means that group evaluation time takes longer than the configured evaluation interval. \nThis may result in missed alerting notifications or recording rules samples. Try increasing evaluation interval or concurrency for such groups. See https://docs.victoriametrics.com/vmalert.html#groups\n\nIf rule expressions are taking longer than expected, please see https://docs.victoriametrics.com/Troubleshooting.html#slow-queries.\"",
+      "description": "Missed evaluation means that group evaluation time takes longer than the configured evaluation interval. \nThis may result in missed alerting notifications or recording rules samples. Try increasing evaluation interval or concurrency for such groups. See https://docs.victoriametrics.com/vmalert/#groups\n\nIf rule expressions are taking longer than expected, please see https://docs.victoriametrics.com/troubleshooting/#slow-queries.\"",
       "fieldConfig": {
         "defaults": {
           "color": {
@@ -1455,7 +1455,7 @@
             {
               "targetBlank": true,
               "title": "Profiling",
-              "url": "https://docs.victoriametrics.com/vmagent.html#profiling"
+              "url": "https://docs.victoriametrics.com/vmagent/#profiling"
             }
           ],
           "options": {
@@ -1570,7 +1570,7 @@
             {
               "targetBlank": true,
               "title": "Profiling",
-              "url": "https://docs.victoriametrics.com/vmagent.html#profiling"
+              "url": "https://docs.victoriametrics.com/vmagent/#profiling"
             }
           ],
           "options": {
@@ -1685,7 +1685,7 @@
             {
               "targetBlank": true,
               "title": "Profiling",
-              "url": "https://docs.victoriametrics.com/vmagent.html#profiling"
+              "url": "https://docs.victoriametrics.com/vmagent/#profiling"
             }
           ],
           "options": {
@@ -1802,7 +1802,7 @@
             {
               "targetBlank": true,
               "title": "Profiling",
-              "url": "https://docs.victoriametrics.com/vmagent.html#profiling"
+              "url": "https://docs.victoriametrics.com/vmagent/#profiling"
             }
           ],
           "options": {
@@ -3425,4 +3425,4 @@
   "uid": "LzldHAVnz_vm",
   "version": 1,
   "weekStart": ""
-}
+}

dashboards/vmagent.json

@@ -99,7 +99,7 @@
       "title": "vmagent wiki",
       "tooltip": "",
       "type": "link",
-      "url": "https://docs.victoriametrics.com/vmagent.html"
+      "url": "https://docs.victoriametrics.com/vmagent/"
     },
     {
       "icon": "external link",
@@ -165,7 +165,7 @@
       "links": [
         {
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -235,7 +235,7 @@
       "links": [
         {
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -396,7 +396,7 @@
       "links": [
         {
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -486,7 +486,7 @@
         {
           "targetBlank": true,
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -1036,7 +1036,7 @@
       "links": [
         {
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -1263,7 +1263,7 @@
         {
           "targetBlank": true,
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -1481,7 +1481,7 @@
         {
           "targetBlank": true,
           "title": "Troubleshooting",
-          "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+          "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
         }
       ],
       "options": {
@@ -3137,7 +3137,7 @@
             {
               "targetBlank": true,
               "title": "Relabeling",
-              "url": "https://docs.victoriametrics.com/vmagent.html#relabeling"
+              "url": "https://docs.victoriametrics.com/vmagent/#relabeling"
             }
           ],
           "options": {
@@ -5959,7 +5959,7 @@
           "links": [
             {
               "title": "Troubleshooting",
-              "url": "https://docs.victoriametrics.com/vmagent.html#troubleshooting"
+              "url": "https://docs.victoriametrics.com/vmagent/#troubleshooting"
             }
           ],
           "options": {
@@ -6401,4 +6401,4 @@
   "uid": "G7Z9GzMGz",
   "version": 1,
   "weekStart": ""
-}
+}

dashboards/vmalert.json

@@ -96,7 +96,7 @@
       "title": "vmalert docs",
       "tooltip": "",
       "type": "link",
-      "url": "https://docs.victoriametrics.com/vmalert.html"
+      "url": "https://docs.victoriametrics.com/vmalert/"
     },
     {
       "asDropdown": false,
@@ -1270,7 +1270,7 @@
         "type": "prometheus",
         "uid": "$ds"
       },
-      "description": "Missed evaluation means that group evaluation time takes longer than the configured evaluation interval. \nThis may result in missed alerting notifications or recording rules samples. Try increasing evaluation interval or concurrency for such groups. See https://docs.victoriametrics.com/vmalert.html#groups\n\nIf rule expressions are taking longer than expected, please see https://docs.victoriametrics.com/Troubleshooting.html#slow-queries.\"",
+      "description": "Missed evaluation means that group evaluation time takes longer than the configured evaluation interval. \nThis may result in missed alerting notifications or recording rules samples. Try increasing evaluation interval or concurrency for such groups. See https://docs.victoriametrics.com/vmalert/#groups\n\nIf rule expressions are taking longer than expected, please see https://docs.victoriametrics.com/troubleshooting/#slow-queries.\"",
       "fieldConfig": {
         "defaults": {
           "color": {
@@ -1454,7 +1454,7 @@
             {
               "targetBlank": true,
               "title": "Profiling",
-              "url": "https://docs.victoriametrics.com/vmagent.html#profiling"
+              "url": "https://docs.victoriametrics.com/vmagent/#profiling"
             }
           ],
           "options": {
@@ -1569,7 +1569,7 @@
             {
               "targetBlank": true,
               "title": "Profiling",
-              "url": "https://docs.victoriametrics.com/vmagent.html#profiling"
+              "url": "https://docs.victoriametrics.com/vmagent/#profiling"
             }
           ],
           "options": {
@@ -1684,7 +1684,7 @@
             {
               "targetBlank": true,
               "title": "Profiling",
-              "url": "https://docs.victoriametrics.com/vmagent.html#profiling"
+              "url": "https://docs.victoriametrics.com/vmagent/#profiling"
             }
           ],
           "options": {
@@ -1801,7 +1801,7 @@
             {
               "targetBlank": true,
               "title": "Profiling",
-              "url": "https://docs.victoriametrics.com/vmagent.html#profiling"
+              "url": "https://docs.victoriametrics.com/vmagent/#profiling"
             }
           ],
           "options": {
@@ -3424,4 +3424,4 @@
   "uid": "LzldHAVnz",
   "version": 1,
   "weekStart": ""
-}
+}

deployment/docker/README.md

@@ -42,7 +42,7 @@ The communication scheme between components is the following:
 
 To access Grafana use link [http://localhost:3000](http://localhost:3000).
 
-To access [vmui](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#vmui)
+To access [vmui](https://docs.victoriametrics.com/single-server-victoriametrics/#vmui)
 use link [http://localhost:8428/vmui](http://localhost:8428/vmui).
 
 To access `vmalert` use link [http://localhost:8428/vmalert](http://localhost:8428/vmalert/).
@@ -78,7 +78,7 @@ The communication scheme between components is the following:
 
 To access Grafana use link [http://localhost:3000](http://localhost:3000).
 
-To access [vmui](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#vmui)
+To access [vmui](https://docs.victoriametrics.com/single-server-victoriametrics/#vmui)
 use link [http://localhost:8427/select/0/prometheus/vmui/](http://localhost:8427/select/0/prometheus/vmui/).
 
 To access `vmalert` use link [http://localhost:8427/select/0/prometheus/vmalert/](http://localhost:8427/select/0/prometheus/vmalert/).
@@ -98,7 +98,7 @@ with listed targets for scraping.
 
 ## vmauth
 
-[vmauth](https://docs.victoriametrics.com/vmauth.html) acts as a [balancer](https://docs.victoriametrics.com/vmauth.html#load-balancing)
+[vmauth](https://docs.victoriametrics.com/vmauth/) acts as a [balancer](https://docs.victoriametrics.com/vmauth/#load-balancing)
 to spread the load across `vmselect`'s. [Grafana](#grafana) and [vmalert](#vmalert) use vmauth for read queries.
 vmauth config is available [here](ttps://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/deployment/docker/auth-cluster.yml)
 
@@ -156,15 +156,15 @@ of alerting rules is the following:
 * [alerts-health.yml](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/deployment/docker/alerts-health.yml):
   alerting rules related to all VictoriaMetrics components for tracking their "health" state; 
 * [alerts.yml](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/deployment/docker/alerts.yml):
-  alerting rules related to [single-server VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html) installation;
+  alerting rules related to [single-server VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/) installation;
 * [alerts-cluster.yml](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/deployment/docker/alerts-cluster.yml):
-  alerting rules related to [cluster version of VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html);
+  alerting rules related to [cluster version of VictoriaMetrics](https://docs.victoriametrics.com/cluster-victoriametrics/);
 * [alerts-vmagent.yml](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/deployment/docker/alerts-vmagent.yml):
-  alerting rules related to [vmagent](https://docs.victoriametrics.com/vmagent.html) component;
+  alerting rules related to [vmagent](https://docs.victoriametrics.com/vmagent/) component;
 * [alerts-vmalert.yml](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/deployment/docker/alerts-vmalert.yml):
-  alerting rules related to [vmalert](https://docs.victoriametrics.com/vmalert.html) component;
+  alerting rules related to [vmalert](https://docs.victoriametrics.com/vmalert/) component;
 
-Please, also see [how to monitor](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#monitoring) 
+Please, also see [how to monitor](https://docs.victoriametrics.com/single-server-victoriametrics/#monitoring) 
 VictoriaMetrics installations.
 
 ## VictoriaLogs server
@@ -199,4 +199,4 @@ Please see more examples on integration of VictoriaLogs with other log shippers
 * [fluentbit-docker](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/victorialogs/fluentbit-docker) 
 * [logstash](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/victorialogs/logstash) 
 * [promtail](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/victorialogs/promtail) 
-* [vector-docker](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/victorialogs/vector-docker) 
+* [vector-docker](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/victorialogs/vector-docker)

deployment/docker/alerts-vmalert.yml

@@ -61,8 +61,8 @@ groups:
           description: "vmalert instance {{ $labels.instance }} is missing rules evaluations for group \"{{ $labels.group }}\".
             The group evaluation time takes longer than the configured evaluation interval. This may result in missed 
             alerting notifications or recording rules samples. Try increasing evaluation interval or concurrency of
-            group \"{{ $labels.group }}\". See https://docs.victoriametrics.com/vmalert.html#groups. 
-            If rule expressions are taking longer than expected, please see https://docs.victoriametrics.com/Troubleshooting.html#slow-queries."
+            group \"{{ $labels.group }}\". See https://docs.victoriametrics.com/vmalert/#groups. 
+            If rule expressions are taking longer than expected, please see https://docs.victoriametrics.com/troubleshooting/#slow-queries."
 
       - alert: RemoteWriteErrors
         expr: increase(vmalert_remotewrite_errors_total[5m]) > 0

deployment/docker/auth-cluster.yml

@@ -1,6 +1,6 @@
 # balance load among vmselects
-# see https://docs.victoriametrics.com/vmauth.html#load-balancing
+# see https://docs.victoriametrics.com/vmauth/#load-balancing
 unauthorized_user:
   url_prefix:
     - http://vmselect-1:8481
-    - http://vmselect-2:8481
+    - http://vmselect-2:8481

deployment/docker/vmanomaly/vmanomaly-integration/README.md

@@ -20,4 +20,4 @@ docker compose up -d
 open http://127.0.0.1:3000/
 ```
 
-If you don't see any data, please wait a few minutes.
+If you don't see any data, please wait a few minutes.

deployment/marketplace/digitialocean/one-click-droplet/README.md

@@ -6,11 +6,11 @@ VictoriaMetrics is a fast and scalable open source time series database and moni
 
 VictoriaMetrics is a free [open source time series database](https://en.wikipedia.org/wiki/Time_series_database) (TSDB) and monitoring solution, designed to collect, store and process real-time metrics.
 
-It supports the [Prometheus](https://en.wikipedia.org/wiki/Prometheus_(software)) pull model and various push protocols ([Graphite](https://en.wikipedia.org/wiki/Graphite_(software)), [InfluxDB](https://en.wikipedia.org/wiki/InfluxDB), OpenTSDB) for data ingestion. It is optimized for storage with high-latency IO, low IOPS and time series with [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
+It supports the [Prometheus](https://en.wikipedia.org/wiki/Prometheus_(software)) pull model and various push protocols ([Graphite](https://en.wikipedia.org/wiki/Graphite_(software)), [InfluxDB](https://en.wikipedia.org/wiki/InfluxDB), OpenTSDB) for data ingestion. It is optimized for storage with high-latency IO, low IOPS and time series with [high churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate).
 
-For reading the data and evaluating alerting rules, VictoriaMetrics supports the PromQL, [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) and Graphite query languages. VictoriaMetrics Single is fully autonomous and can be used as a long-term storage for time series.
+For reading the data and evaluating alerting rules, VictoriaMetrics supports the PromQL, [MetricsQL](https://docs.victoriametrics.com/metricsql/) and Graphite query languages. VictoriaMetrics Single is fully autonomous and can be used as a long-term storage for time series.
 
-[VictoriaMetrics Single](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html) = Hassle-free monitoring solution. Easily handles 10M+ of active time series on a single instance. Perfect for small and medium environments.
+[VictoriaMetrics Single](https://docs.victoriametrics.com/single-server-victoriametrics/) = Hassle-free monitoring solution. Easily handles 10M+ of active time series on a single instance. Perfect for small and medium environments.
 
 ## Getting started after deploying VictoriaMetrics Single
 
@@ -21,22 +21,22 @@ This One Click app uses 8428, 2003, 4242 and 8089 ports to accept metrics from d
 
 ### Scraping metrics
 
-VictoriaMetrics supports metrics scraping in the same way as Prometheus does. Check the configuration file to edit scraping targets. See more details about scraping at [How to scrape Prometheus exporters](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-scrape-prometheus-exporters-such-as-node-exporter).
+VictoriaMetrics supports metrics scraping in the same way as Prometheus does. Check the configuration file to edit scraping targets. See more details about scraping at [How to scrape Prometheus exporters](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-scrape-prometheus-exporters-such-as-node-exporter).
 
 ### Sending metrics
 
 Besides scraping, VictoriaMetrics accepts write requests for various ingestion protocols. This One Click app supports the following protocols:
 
-- [Datadog](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-send-data-from-datadog-agent), [Influx (telegraph)](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-send-data-from-influxdb-compatible-agents-such-as-telegraf), [JSON](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-import-data-in-json-line-format), [CSV](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-import-csv-data), [Prometheus](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-import-data-in-prometheus-exposition-format)  on port :8428
-- [Graphite (statsd)](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-send-data-from-graphite-compatible-agents-such-as-statsd) on port :2003 tcp/udp
-- [OpenTSDB](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-send-data-from-opentsdb-compatible-agents) on port :4242
+- [Datadog](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-send-data-from-datadog-agent), [Influx (telegraph)](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-send-data-from-influxdb-compatible-agents-such-as-telegraf), [JSON](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-import-data-in-json-line-format), [CSV](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-import-csv-data), [Prometheus](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-import-data-in-prometheus-exposition-format)  on port :8428
+- [Graphite (statsd)](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-send-data-from-graphite-compatible-agents-such-as-statsd) on port :2003 tcp/udp
+- [OpenTSDB](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-send-data-from-opentsdb-compatible-agents) on port :4242
 - Influx (telegraph) on port :8089 tcp/udp
 
-See more details and examples in [official documentation](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html).
+See more details and examples in [official documentation](https://docs.victoriametrics.com/single-server-victoriametrics/).
 
 ### UI
 
-VictoriaMetrics provides a [User Interface (UI)](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#vmui) for query troubleshooting and exploration. The UI is available at `http://your_droplet_public_ipv4:8428/vmui`. It lets users explore query results via graphs and tables.
+VictoriaMetrics provides a [User Interface (UI)](https://docs.victoriametrics.com/single-server-victoriametrics/#vmui) for query troubleshooting and exploration. The UI is available at `http://your_droplet_public_ipv4:8428/vmui`. It lets users explore query results via graphs and tables.
 
 To check it, open the following in your browser `http://your_droplet_public_ipv4:8428/vmui` and then enter `vm_app_uptime_seconds` to the Query Field to Execute the Query.
 

deployment/marketplace/digitialocean/one-click-droplet/files/etc/systemd/system/vmsingle.service

@@ -16,7 +16,7 @@ EnvironmentFile=-/etc/victoriametrics/single/victoriametrics.conf
 ExecStart=/usr/bin/victoria-metrics-prod $ARGS
 ExecStop=/bin/kill -s SIGTERM $MAINPID
 ExecReload=/bin/kill -HUP $MAINPID
-# See docs https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#tuning
+# See docs https://docs.victoriametrics.com/single-server-victoriametrics/#tuning
 ProtectSystem=full
 LimitNOFILE=1048576
 LimitNPROC=1048576
@@ -26,4 +26,4 @@ StandardError=syslog
 SyslogIdentifier=vmsingle
 
 [Install]
-WantedBy=multi-user.target
+WantedBy=multi-user.target

deployment/marketplace/vultr/README.md

@@ -6,11 +6,11 @@ VictoriaMetrics is a fast and scalable open source time series database and moni
 
 VictoriaMetrics is a free [open source time series database](https://en.wikipedia.org/wiki/Time_series_database) (TSDB) and monitoring solution, designed to collect, store and process real-time metrics.
 
-It supports the [Prometheus](https://en.wikipedia.org/wiki/Prometheus_(software)) pull model and various push protocols ([Graphite](https://en.wikipedia.org/wiki/Graphite_(software)), [InfluxDB](https://en.wikipedia.org/wiki/InfluxDB), OpenTSDB) for data ingestion. It is optimized for storage with high-latency IO, low IOPS and time series with [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
+It supports the [Prometheus](https://en.wikipedia.org/wiki/Prometheus_(software)) pull model and various push protocols ([Graphite](https://en.wikipedia.org/wiki/Graphite_(software)), [InfluxDB](https://en.wikipedia.org/wiki/InfluxDB), OpenTSDB) for data ingestion. It is optimized for storage with high-latency IO, low IOPS and time series with [high churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate).
 
-For reading the data and evaluating alerting rules, VictoriaMetrics supports the PromQL, [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) and Graphite query languages. VictoriaMetrics Single is fully autonomous and can be used as a long-term storage for time series.
+For reading the data and evaluating alerting rules, VictoriaMetrics supports the PromQL, [MetricsQL](https://docs.victoriametrics.com/metricsql/) and Graphite query languages. VictoriaMetrics Single is fully autonomous and can be used as a long-term storage for time series.
 
-[VictoriaMetrics Single](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html) = Hassle-free monitoring solution. Easily handles 10M+ of active time series on a single instance. Perfect for small and medium environments.
+[VictoriaMetrics Single](https://docs.victoriametrics.com/single-server-victoriametrics/) = Hassle-free monitoring solution. Easily handles 10M+ of active time series on a single instance. Perfect for small and medium environments.
 
 ## Getting started after deploying VictoriaMetrics Single
 
@@ -21,22 +21,22 @@ This One Click app uses 8428, 2003, 4242 and 8089 ports to accept metrics from d
 
 ### Scraping metrics
 
-VictoriaMetrics supports metrics scraping in the same way as Prometheus does. Check the configuration file to edit scraping targets. See more details about scraping at [How to scrape Prometheus exporters](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-scrape-prometheus-exporters-such-as-node-exporter).
+VictoriaMetrics supports metrics scraping in the same way as Prometheus does. Check the configuration file to edit scraping targets. See more details about scraping at [How to scrape Prometheus exporters](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-scrape-prometheus-exporters-such-as-node-exporter).
 
 ### Sending metrics
 
 Besides scraping, VictoriaMetrics accepts write requests for various ingestion protocols. This One Click app supports the following protocols:
 
-- [Datadog](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-send-data-from-datadog-agent), [Influx (telegraph)](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-send-data-from-influxdb-compatible-agents-such-as-telegraf), [JSON](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-import-data-in-json-line-format), [CSV](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-import-csv-data), [Prometheus](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-import-data-in-prometheus-exposition-format)  on port :8428
-- [Graphite (statsd)](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-send-data-from-graphite-compatible-agents-such-as-statsd) on port :2003 tcp/udp
-- [OpenTSDB](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-send-data-from-opentsdb-compatible-agents) on port :4242
+- [Datadog](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-send-data-from-datadog-agent), [Influx (telegraph)](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-send-data-from-influxdb-compatible-agents-such-as-telegraf), [JSON](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-import-data-in-json-line-format), [CSV](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-import-csv-data), [Prometheus](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-import-data-in-prometheus-exposition-format)  on port :8428
+- [Graphite (statsd)](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-send-data-from-graphite-compatible-agents-such-as-statsd) on port :2003 tcp/udp
+- [OpenTSDB](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-send-data-from-opentsdb-compatible-agents) on port :4242
 - Influx (telegraph) on port :8089 tcp/udp
 
-See more details and examples in [official documentation](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html).
+See more details and examples in [official documentation](https://docs.victoriametrics.com/single-server-victoriametrics/).
 
 ### UI
 
-VictoriaMetrics provides a [User Interface (UI)](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#vmui) for query troubleshooting and exploration. The UI is available at `http://your_server_public_ipv4:8428/vmui`. It lets users explore query results via graphs and tables.
+VictoriaMetrics provides a [User Interface (UI)](https://docs.victoriametrics.com/single-server-victoriametrics/#vmui) for query troubleshooting and exploration. The UI is available at `http://your_server_public_ipv4:8428/vmui`. It lets users explore query results via graphs and tables.
 
 To check it, open the following in your browser `http://your_server_public_ipv4:8428/vmui` and then enter `vm_app_uptime_seconds` to the Query Field to Execute the Query.
 

deployment/marketplace/vultr/victoriametrics-single/etc/systemd/system/vmsingle.service

@@ -16,7 +16,7 @@ EnvironmentFile=-/etc/victoriametrics/single/victoriametrics.conf
 ExecStart=/usr/bin/victoria-metrics-prod $ARGS
 ExecStop=/bin/kill -s SIGTERM $MAINPID
 ExecReload=/bin/kill -HUP $MAINPID
-# See docs https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#tuning
+# See docs https://docs.victoriametrics.com/single-server-victoriametrics/#tuning
 ProtectSystem=full
 LimitNOFILE=1048576
 LimitNPROC=1048576
@@ -26,4 +26,4 @@ StandardError=syslog
 SyslogIdentifier=vmsingle
 
 [Install]
-WantedBy=multi-user.target
+WantedBy=multi-user.target

docs/Articles.md

@@ -12,7 +12,7 @@ aliases:
 
 # Articles
 
-See also [case studies](https://docs.victoriametrics.com/CaseStudies.html).
+See also [case studies](https://docs.victoriametrics.com/casestudies/).
 
 ## Third-party articles and slides about VictoriaMetrics
 

docs/BestPractices.md

@@ -67,7 +67,7 @@ The following steps must be performed during the upgrade / downgrade procedure:
 
 ## Backup Recommendations
 
-VictoriaMetrics supports backups via [vmbackup](https://docs.victoriametrics.com/vmbackup.html) and [vmrestore](https://docs.victoriametrics.com/vmrestore.html) tools. There is also [vmbackupmanager](https://docs.victoriametrics.com/vmbackupmanager.html), which simplifies backup automation.
+VictoriaMetrics supports backups via [vmbackup](https://docs.victoriametrics.com/vmbackup/) and [vmrestore](https://docs.victoriametrics.com/vmrestore/) tools. There is also [vmbackupmanager](https://docs.victoriametrics.com/vmbackupmanager/), which simplifies backup automation.
 
 ## Technical Support and Services
 

docs/CHANGELOG_2020.md

@@ -29,7 +29,7 @@ Released at 2020-12-27
 
 Released at 2020-12-19
 
-* FEATURE: do not publish duplicate Docker images with `-cluster` tag suffix for [vmagent](https://docs.victoriametrics.com/vmagent.html), [vmalert](https://docs.victoriametrics.com/vmalert.html), [vmauth](https://docs.victoriametrics.com/vmauth.html), [vmbackup](https://docs.victoriametrics.com/vmbackup.html) and [vmrestore](https://docs.victoriametrics.com/vmrestore.html), since they are identical to images without `-cluster` tag suffix.
+* FEATURE: do not publish duplicate Docker images with `-cluster` tag suffix for [vmagent](https://docs.victoriametrics.com/vmagent/), [vmalert](https://docs.victoriametrics.com/vmalert/), [vmauth](https://docs.victoriametrics.com/vmauth/), [vmbackup](https://docs.victoriametrics.com/vmbackup/) and [vmrestore](https://docs.victoriametrics.com/vmrestore/), since they are identical to images without `-cluster` tag suffix.
 
 * BUGFIX: vmalert: properly populate template variables. This has been broken in v1.50.0. See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/974>
 * BUGFIX: properly parse negative combined duration in MetricsQL such as `-1h3m4s`. It must be parsed as `-(1h + 3m + 4s)`. Prevsiously it was parsed as `-1h + 3m + 4s`.
@@ -74,7 +74,7 @@ Released at 2020-12-15
 Released at 2020-12-05
 
 * FEATURE: optimize Consul service discovery speed when discovering big number of services. See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/574>
-* FEATURE: add `label_uppercase(q, label1, ... labelN)` and `label_lowercase(q, label1, ... labelN)` function to [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html)
+* FEATURE: add `label_uppercase(q, label1, ... labelN)` and `label_lowercase(q, label1, ... labelN)` function to [MetricsQL](https://docs.victoriametrics.com/metricsql/)
   for uppercasing and lowercasing values for the given labels. See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/936>
 * FEATURE: add `count_eq_over_time(m[d], N)` and `count_ne_over_time(m[d], N)` for counting the number of samples for `m` over `d` that (equal / not equal) to `N`.
 * FEATURE: do not print usage info for all the command-line flags when incorrect command-line flag is passed. Previously it could be hard reading the error message
@@ -103,7 +103,7 @@ Released at 2020-11-26
 * FEATURE: vmselect: add `-replicationFactor` command-line flag for reducing query duration when replication is enabled and a part of vmstorage nodes
   are temporarily slow and/or temporarily unavailable. See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/711>
 * FEATURE: vminsert: export `vm_rpc_vmstorage_is_reachable` metric, which can be used for monitoring reachability of vmstorage nodes from vminsert nodes.
-* FEATURE: vmagent: add [Netflix Eureka](https://github.com/Netflix/eureka) service discovery (aka [eureka_sd_config](https://docs.victoriametrics.com/sd_configs.html#eureka_sd_configs)). See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/851>
+* FEATURE: vmagent: add [Netflix Eureka](https://github.com/Netflix/eureka) service discovery (aka [eureka_sd_config](https://docs.victoriametrics.com/sd_configs/#eureka_sd_configs)). See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/851>
 * FEATURE: add `filters` option to `dockerswarm_sd_config` like Prometheus did in v2.23.0 - see <https://github.com/prometheus/prometheus/pull/8074>
 * FEATURE: expose `__meta_ec2_ipv6_addresses` label for `ec2_sd_config` like Prometheus will do in the next release.
 * FEATURE: add `-loggerWarnsPerSecondLimit` command-line flag for rate limiting of WARN messages in logs. See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/905>
@@ -166,7 +166,7 @@ Released at 2020-11-07
 
 * BUGFIX: vmagent: eliminate data race when `-promscrape.streamParse` command-line is set. Previously this mode could result in scraped metrics with garbage labels.
   See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/825#issuecomment-723198247> for details.
-* BUGFIX: properly calculate `topk_*` and `bottomk_*` functions from [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) for time series with gaps.
+* BUGFIX: properly calculate `topk_*` and `bottomk_*` functions from [MetricsQL](https://docs.victoriametrics.com/metricsql/) for time series with gaps.
   See <https://github.com/VictoriaMetrics/VictoriaMetrics/pull/883>
 
 ## [v1.45.0](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.45.0)
@@ -184,9 +184,9 @@ Released at 2020-11-02
 * FEATURE: vmagent: add `stream parse` mode. This mode allows reducing memory usage when individual scrape targets expose tens of millions of metrics.
   For example, during scraping Prometheus in [federation](https://prometheus.io/docs/prometheus/latest/federation/) mode.
   See `-promscrape.streamParse` command-line option and `stream_parse: true` config option for `scrape_config` section in `-promscrape.config`.
-  See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/825> and [troubleshooting docs for vmagent](https://docs.victoriametrics.com/vmagent.html#troubleshooting).
+  See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/825> and [troubleshooting docs for vmagent](https://docs.victoriametrics.com/vmagent/#troubleshooting).
 * FEATURE: vmalert: add `-dryRun` command-line option for validating the provided config files without the need to start `vmalert` service.
-* FEATURE: accept optional third argument of string type at `topk_*` and `bottomk_*` functions. This is label name for additional time series to return with the sum of time series outside top/bottom K. See [MetricsQL docs](https://docs.victoriametrics.com/MetricsQL.html) for more details.
+* FEATURE: accept optional third argument of string type at `topk_*` and `bottomk_*` functions. This is label name for additional time series to return with the sum of time series outside top/bottom K. See [MetricsQL docs](https://docs.victoriametrics.com/metricsql/) for more details.
 * FEATURE: vmagent: expose `/api/v1/targets` page according to [the corresponding Prometheus API](https://prometheus.io/docs/prometheus/latest/querying/api/#targets).
   See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/643>
 
@@ -216,11 +216,11 @@ Released at 2020-10-13
      node_filesystem_files{ host="$host", mountpoint="/" } - node_filesystem_files_free
   ```
 
-* FEATURE: vmagent: add Docker Swarm service discovery (aka [dockerswarm_sd_config](https://docs.victoriametrics.com/sd_configs.html#dockerswarm_sd_configs)).
+* FEATURE: vmagent: add Docker Swarm service discovery (aka [dockerswarm_sd_config](https://docs.victoriametrics.com/sd_configs/#dockerswarm_sd_configs)).
   See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/656>
 * FEATURE: add ability to export data in CSV format. See [these docs](https://docs.victoriametrics.com/#how-to-export-csv-data) for details.
 * FEATURE: vmagent: add `-promscrape.suppressDuplicateScrapeTargetErrors` command-line flag for suppressing `duplicate scrape target` errors.
-  See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/651> and <https://docs.victoriametrics.com/vmagent.html#troubleshooting> .
+  See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/651> and <https://docs.victoriametrics.com/vmagent/#troubleshooting> .
 * FEATURE: vmagent: show original labels before relabeling is applied on `duplicate scrape target` errors. This should simplify debugging for incorrect relabeling.
   See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/651>
 * FEATURE: vmagent: `/targets` page now accepts optional `show_original_labels=1` query arg for displaying original labels for each target before relabeling is applied.
@@ -230,8 +230,8 @@ Released at 2020-10-13
 * FEATURE: add `vm_rows_added_to_storage_total` metric, which shows the total number of rows added to storage since app start.
   The `sum(rate(vm_rows_added_to_storage_total))` can be smaller than `sum(rate(vm_rows_inserted_total))` if certain metrics are dropped
   due to [relabeling](https://docs.victoriametrics.com/#relabeling). The `sum(rate(vm_rows_added_to_storage_total))` can be bigger
-  than `sum(rate(vm_rows_inserted_total))` if [replication](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#replication-and-data-safety) is enabled.
-* FEATURE: keep metric name after applying [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) functions, which don't change time series meaning.
+  than `sum(rate(vm_rows_inserted_total))` if [replication](https://docs.victoriametrics.com/cluster-victoriametrics/#replication-and-data-safety) is enabled.
+* FEATURE: keep metric name after applying [MetricsQL](https://docs.victoriametrics.com/metricsql/) functions, which don't change time series meaning.
   The list of such functions:
   * `keep_last_value`
   * `keep_next_value`
@@ -272,8 +272,8 @@ Released at 2020-10-13
 Released at 2020-10-06
 
 * FEATURE: reduce CPU usage for repeated queries over sliding time window when no new time series are added to the database.
-  Typical use cases: repeated evaluation of alerting rules in [vmalert](https://docs.victoriametrics.com/vmalert.html) or dashboard auto-refresh in Grafana.
-* FEATURE: vmagent: add OpenStack service discovery aka [openstack_sd_config](https://docs.victoriametrics.com/sd_configs.html#openstack_sd_configs).
+  Typical use cases: repeated evaluation of alerting rules in [vmalert](https://docs.victoriametrics.com/vmalert/) or dashboard auto-refresh in Grafana.
+* FEATURE: vmagent: add OpenStack service discovery aka [openstack_sd_config](https://docs.victoriametrics.com/sd_configs/#openstack_sd_configs).
   See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/728> .
 * FEATURE: vmalert: make `-maxIdleConnections` configurable for datasource HTTP client. This option can be used for minimizing connection churn.
   See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/795> .
@@ -295,7 +295,7 @@ Released at 2020-09-30
   for [all the supported protocols](https://docs.victoriametrics.com/#how-to-import-time-series-data).
   Previously data ingested via a single TCP connection could use only a single CPU core. This could limit data ingestion performance.
   The main benefit of this feature is that data can be imported at max speed via a single connection - there is no need to open multiple concurrent
-  connections to VictoriaMetrics or [vmagent](https://docs.victoriametrics.com/vmagent.html) in order to achieve the maximum data ingestion speed.
+  connections to VictoriaMetrics or [vmagent](https://docs.victoriametrics.com/vmagent/) in order to achieve the maximum data ingestion speed.
 * FEATURE: cluster: improve performance for data ingestion path from `vminsert` to `vmstorage` nodes. The maximum data ingestion performance
   for a single connection between `vminsert` and `vmstorage` node scales with the number of available CPU cores on `vmstorage` side.
   This should help with <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/791> .

docs/CHANGELOG_2021.md

@@ -26,51 +26,51 @@ We recommend updating in "off-peak" time when load on the VictoriaMetrics is on
 * FEATURE: [VictoriaMetrics enterprise](https://docs.victoriametrics.com/enterprise/): add multi-level downsampling support. See [these docs](https://docs.victoriametrics.com/#downsampling) and [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/36).
 * FEATURE: [vmui](https://docs.victoriametrics.com/#vmui): add ability to analyze the correlation between two queries on a single graph. Just click `+Query` button, enter the second query in the newly appeared input field and press `Ctrl+Enter`. Results for both queries should be displayed simultaneously on the same graph. Every query has its own vertical scale, which is displayed on the left and the right side of the graph. Lines for the second query are dashed. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1916).
 * FEATURE: [vmui](https://docs.victoriametrics.com/#vmui): add ability to override the interval between returned datapoints. By default it is automatically calculated depending on the selected time range and horizontal resolution of the graph. Now it is possible to override it with custom values. This may be useful during data exploration and debugging.
-* FEATURE: accept optional `extra_filters[]=series_selector` query args at Prometheus query APIs additionally to `extra_label` query args. This allows enforcing additional filters for all the Prometheus query APIs by using [vmgateway](https://docs.victoriametrics.com/vmgateway.html) or [vmauth](https://docs.victoriametrics.com/vmauth.html). See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1863).
-* FEATURE: [vmauth](https://docs.victoriametrics.com/vmauth.html): allow specifying `http` and `https` urls in `-auth.config` command-line flag. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1898). Thanks for @TFM93 .
-* FEATURE: [vmagent](https://docs.victoriametrics.com/vmagent.html): allow specifying `http` and `https` urls in the following command-line flags: `-promscrape.config`, `-remoteWrite.relabelConfig` and `-remoteWrite.urlRelabelConfig`.
+* FEATURE: accept optional `extra_filters[]=series_selector` query args at Prometheus query APIs additionally to `extra_label` query args. This allows enforcing additional filters for all the Prometheus query APIs by using [vmgateway](https://docs.victoriametrics.com/vmgateway/) or [vmauth](https://docs.victoriametrics.com/vmauth/). See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1863).
+* FEATURE: [vmauth](https://docs.victoriametrics.com/vmauth/): allow specifying `http` and `https` urls in `-auth.config` command-line flag. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1898). Thanks for @TFM93 .
+* FEATURE: [vmagent](https://docs.victoriametrics.com/vmagent/): allow specifying `http` and `https` urls in the following command-line flags: `-promscrape.config`, `-remoteWrite.relabelConfig` and `-remoteWrite.urlRelabelConfig`.
 * FEATURE: vminsert: allow specifying `http` and `https` urls in `-relabelConfig` command-line flag.
 * FEATURE: vminsert: add `-maxLabelValueLen` command-line flag for the ability to configure the maximum length of label value. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1908).
-* FEATURE: preserve the order of time series passed to [limit_offset](https://docs.victoriametrics.com/MetricsQL.html#limit_offset) function. This allows implementing series paging via `limit_offset(limit, offset, sort_by_label(...))`. See [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1920) and [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/951) issues.
+* FEATURE: preserve the order of time series passed to [limit_offset](https://docs.victoriametrics.com/metricsql/#limit_offset) function. This allows implementing series paging via `limit_offset(limit, offset, sort_by_label(...))`. See [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1920) and [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/951) issues.
 * FEATURE: automaticall convert `(value1|...|valueN)` into `{value1,...,valueN}` inside `__graphite__` pseudo-label. This allows using [Grafana multi-value template variables](https://grafana.com/docs/grafana/latest/variables/formatting-multi-value-variables/) inside `__graphite__` pseudo-label. For example, `{__graphite__=~"foo.($bar)"}` is expanded to `{__graphite__=~"foo.{x,y}"}` if both `x` and `y` are selected for `$bar` template variable. See [these docs](https://docs.victoriametrics.com/#selecting-graphite-metrics) for details.
-* FEATURE: add [timestamp_with_name](https://docs.victoriametrics.com/MetricsQL.html#timestamp_with_name) function. It works the same as [timestamp](https://docs.victoriametrics.com/MetricsQL.html#timestamp), but leaves the original time series names, so it can be used in queries, which match multiple time series names: `timestamp_with_name({foo="bar"}[1h])`. See [this comment](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/949#issuecomment-995222388) for more context.
-* FEATURE: add [changes_prometheus](https://docs.victoriametrics.com/MetricsQL.html#changes_prometheus), [increase_prometheus](https://docs.victoriametrics.com/MetricsQL.html#increase_prometheus) and [delta_prometheus](https://docs.victoriametrics.com/MetricsQL.html#delta_prometheus) functions, which don't take into account the previous sample before the given lookbehind window specified in square brackets. These functions may be used when the Prometheus behaviour for `changes()`, `increase()` and `delta()` functions is needed to be preserved. VictoriaMetrics uses slightly different behaviour for `changes()`, `increase()` and `delta()` functions by default - see [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1962).
+* FEATURE: add [timestamp_with_name](https://docs.victoriametrics.com/metricsql/#timestamp_with_name) function. It works the same as [timestamp](https://docs.victoriametrics.com/metricsql/#timestamp), but leaves the original time series names, so it can be used in queries, which match multiple time series names: `timestamp_with_name({foo="bar"}[1h])`. See [this comment](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/949#issuecomment-995222388) for more context.
+* FEATURE: add [changes_prometheus](https://docs.victoriametrics.com/metricsql/#changes_prometheus), [increase_prometheus](https://docs.victoriametrics.com/metricsql/#increase_prometheus) and [delta_prometheus](https://docs.victoriametrics.com/metricsql/#delta_prometheus) functions, which don't take into account the previous sample before the given lookbehind window specified in square brackets. These functions may be used when the Prometheus behaviour for `changes()`, `increase()` and `delta()` functions is needed to be preserved. VictoriaMetrics uses slightly different behaviour for `changes()`, `increase()` and `delta()` functions by default - see [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1962).
 
 * BUGFIX: fix `unaligned 64-bit atomic operation` panic on 32-bit architectures, which has been introduced in v1.70.0. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1944).
-* BUGFIX: [vmalert](https://docs.victoriametrics.com/vmalert.html): restore the ability to use `$labels.alertname` in labels templating. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1921).
+* BUGFIX: [vmalert](https://docs.victoriametrics.com/vmalert/): restore the ability to use `$labels.alertname` in labels templating. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1921).
 * BUGFIX: [vmui](https://docs.victoriametrics.com/#vmui): add missing `query` caption to the input field for the query. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1900).
 * BUGFIX: [vmui](https://docs.victoriametrics.com/#vmui): fix navigation over query history with `Ctrl+up/down` and fix zoom relatively to the cursor position. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1936).
 * BUGFIX: deduplicate samples more thoroughly if [deduplication](https://docs.victoriametrics.com/#deduplication) is enabled. Previously some duplicate samples may be left on disk for time series with high churn rate. This may result in bigger storage space requirements.
-* BUGFIX: [vmagent](https://docs.victoriametrics.com/vmagent.html): follow up to 5 redirects when `follow_redirects: true` is set for a particular scrape config. Previously only a single redirect was performed in this case. It is expected these redirects are performed to the original hostname. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1945).
+* BUGFIX: [vmagent](https://docs.victoriametrics.com/vmagent/): follow up to 5 redirects when `follow_redirects: true` is set for a particular scrape config. Previously only a single redirect was performed in this case. It is expected these redirects are performed to the original hostname. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1945).
 * BUGFIX: de-duplicate data exported via [/api/v1/export/csv](https://docs.victoriametrics.com/#how-to-export-csv-data) by default if [deduplication](https://docs.victoriametrics.com/#deduplication) is enabled. The de-duplication can be disabled by passing `reduce_mem_usage=1` query arg to `/api/v1/export/csv`. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1837).
-* BUGFIX: [vmalert](https://docs.victoriametrics.com/vmalert.html): properly store [historical data](https://docs.victoriametrics.com/vmalert.html#rules-backfilling) to old Prometheus versions. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1943).
+* BUGFIX: [vmalert](https://docs.victoriametrics.com/vmalert/): properly store [historical data](https://docs.victoriametrics.com/vmalert/#rules-backfilling) to old Prometheus versions. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1943).
 
 ## [v1.70.0](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.70.0)
 
 Released at 2021-12-02
 
-* FEATURE: [vmalert](https://docs.victoriametrics.com/vmalert.html): add ability to pass arbitrary query args to `-datasource.url` on a per-group basis via `params` option. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1892).
-* FEATURE: add `now()` function to MetricsQL. This function returns the current timestamp in seconds. See [these docs](https://docs.victoriametrics.com/MetricsQL.html#now).
+* FEATURE: [vmalert](https://docs.victoriametrics.com/vmalert/): add ability to pass arbitrary query args to `-datasource.url` on a per-group basis via `params` option. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1892).
+* FEATURE: add `now()` function to MetricsQL. This function returns the current timestamp in seconds. See [these docs](https://docs.victoriametrics.com/metricsql/#now).
 * FEATURE: vmauth: allow using optional `name` field in configs. This field is then used as `username` label value for `vmauth_user_requests_total` metric. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1805).
 * FEATURE: vmagent: export `vm_persistentqueue_read_duration_seconds_total` and `vm_persistentqueue_write_duration_seconds_total` metrics, which can be used for detecting persistent queue saturation with `rate(vm_persistentqueue_write_duration_seconds_total) > 0.9` alerting rule.
 * FEATURE: export `vm_filestream_read_duration_seconds_total` and `vm_filestream_write_duration_seconds_total` metrics, which can be used for detecting persistent disk saturation with `rate(vm_filestream_read_duration_seconds_total) > 0.9` alerting rule.
 * FEATURE: export `vm_cache_size_max_bytes` metrics, which show capacity for various caches. These metrics can be used for determining caches with reach its capacity with `vm_cache_size_bytes / vm_cache_size_max_bytes > 0.9` query.
-* FEATURE: [vmbackup](https://docs.victoriametrics.com/vmbackup.html), [vmrestore](https://docs.victoriametrics.com/vmrestore.html): add `-s3ForcePathStyle` command-line flag, which can be used for making backups to [Aliyun OSS](https://www.aliyun.com/product/oss). See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1802).
-* FEATURE: [vmctl](https://docs.victoriametrics.com/vmctl.html): improve data migration from OpenTSDB. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1809). Thanks to @johnseekins .
+* FEATURE: [vmbackup](https://docs.victoriametrics.com/vmbackup/), [vmrestore](https://docs.victoriametrics.com/vmrestore/): add `-s3ForcePathStyle` command-line flag, which can be used for making backups to [Aliyun OSS](https://www.aliyun.com/product/oss). See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1802).
+* FEATURE: [vmctl](https://docs.victoriametrics.com/vmctl/): improve data migration from OpenTSDB. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1809). Thanks to @johnseekins .
 * FEATURE: suppress `connection reset by peer` errors when remote client resets TCP connection to VictoriaMetrics / vmagent while ingesting the data via InfluxDB line protocol, Graphite protocol or OpenTSDB protocol. This error is expected, so there is no need in logging it.
 * FEATURE: [vmui](https://docs.victoriametrics.com/#vmui): store the display type in URL, so it isn't lost when copy-pasting the URL. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1804).
 * FEATURE: vmalert: make `-notifier.url` command-line flag optional. This flag can be omitted if `vmalert` is used solely for recording rules and doesn't evaluate alerting rules. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1870).
-* FEATURE: [vmbackup](https://docs.victoriametrics.com/vmbackup.html), [vmrestore](https://docs.victoriametrics.com/vmrestore.html): export internal metrics at `http://vmbackup:8420/metrics` and `http://vmrestore:8421/metrics` for better visibility of the backup/restore process.
+* FEATURE: [vmbackup](https://docs.victoriametrics.com/vmbackup/), [vmrestore](https://docs.victoriametrics.com/vmrestore/): export internal metrics at `http://vmbackup:8420/metrics` and `http://vmrestore:8421/metrics` for better visibility of the backup/restore process.
 * FEATURE: allow trailing whitespace after the timestamp when [parsing Graphite plaintext lines](https://docs.victoriametrics.com/#how-to-send-data-from-graphite-compatible-agents-such-as-statsd). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1865).
 * FEATURE: expose `/-/healthy` and `/-/ready` endpoints as Prometheus does. This is needed for improving integration with third-party solutions, which rely on these endpoints. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1833).
 
 * BUGFIX: vmagent: prevent from scraping duplicate targets if `-promscrape.dropOriginalLabels` command-line flag is set. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1830). Thanks to @guidao for the fix.
-* BUGFIX: vmstorage [enterprise](https://docs.victoriametrics.com/enterprise/): added missing `vm_tenant_used_tenant_bytes` metric, which shows the approximate per-tenant disk usage. See [these docs](https://docs.victoriametrics.com/PerTenantStatistic.html) and [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1605).
+* BUGFIX: vmstorage [enterprise](https://docs.victoriametrics.com/enterprise/): added missing `vm_tenant_used_tenant_bytes` metric, which shows the approximate per-tenant disk usage. See [these docs](https://docs.victoriametrics.com/pertenantstatistic/) and [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1605).
 * BUGFIX: vmauth: properly take into account the value passed to `-maxIdleConnsPerBackend` command-line flag. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1300).
-* BUGFIX: vmagent: fix [reading data from Kafka](https://docs.victoriametrics.com/vmagent.html#reading-metrics-from-kafka).
-* BUGFIX: vmalert: fix [replay mode](https://docs.victoriametrics.com/vmalert.html#rules-backfilling) in enterprise version.
-* BUGFIX: consistently return zero from [deriv()](https://docs.victoriametrics.com/MetricsQL.html#deriv) function applied to a constant time series. Previously it could return small non-zero values in this case.
-* BUGFIX: [vmrestore](https://docs.victoriametrics.com/vmrestore.html): properly resume downloading for partially downloaded big files. Previously such files were re-downloaded from the beginning after the interrupt. Now only the remaining parts of the file are downloaded. This allows saving network bandwidth. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/487).
+* BUGFIX: vmagent: fix [reading data from Kafka](https://docs.victoriametrics.com/vmagent/#reading-metrics-from-kafka).
+* BUGFIX: vmalert: fix [replay mode](https://docs.victoriametrics.com/vmalert/#rules-backfilling) in enterprise version.
+* BUGFIX: consistently return zero from [deriv()](https://docs.victoriametrics.com/metricsql/#deriv) function applied to a constant time series. Previously it could return small non-zero values in this case.
+* BUGFIX: [vmrestore](https://docs.victoriametrics.com/vmrestore/): properly resume downloading for partially downloaded big files. Previously such files were re-downloaded from the beginning after the interrupt. Now only the remaining parts of the file are downloaded. This allows saving network bandwidth. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/487).
 * BUGFIX: [vmui](https://docs.victoriametrics.com/#vmui): do not store the last query across vmui page reloads. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1694).
 * BUGFIX: [vmui](https://docs.victoriametrics.com/#vmui): fix `Cannot read properties of undefined` error at table view. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1797).
 
@@ -79,7 +79,7 @@ Released at 2021-12-02
 Released at 2021-11-08
 
 * FEATURE: vmalert: allow groups with empty rules list like Prometheus does. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1742).
-* FEATURE: vmalert: allow groups with default `tenant` in `-clusterMode`. Default `tenant` values can be specified via `-defaultTenant.prometheus` and `-defaultTenant.graphite`. See [these docs](https://docs.victoriametrics.com/vmalert.html#multitenancy).
+* FEATURE: vmalert: allow groups with default `tenant` in `-clusterMode`. Default `tenant` values can be specified via `-defaultTenant.prometheus` and `-defaultTenant.graphite`. See [these docs](https://docs.victoriametrics.com/vmalert/#multitenancy).
 * FEATURE: vmagent: add `collapse` and `expand` buttons per each group of targets with the same `job_name` at `http://vmagent:8429/targets` page.
 * FEATURE: automatically detect timestamp precision (ns, us, ms or s) for the data ingested into VictoriaMetrics via [InfluxDB line protocol](https://docs.victoriametrics.com/#how-to-send-data-from-influxdb-compatible-agents-such-as-telegraf).
 * FEATURE: vmagent: add ability to protect `/config` page with auth key via `-configAuthKey` command-line flag. This page may contain sensitive config information, so it may be good to restrict access to this page. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1764).
@@ -87,12 +87,12 @@ Released at 2021-11-08
 * FEATURE: vmagent: add `-promscrape.maxResponseHeadersSize` command-line flag for tuning the maximum HTTP response headers size for Prometheus scrape targets.
 * FEATURE: vmagent: send data to multiple configured remote storage systems in parallel (e.g. when multiple `-remoteWrite.url` flag values are specified). This should improve data ingestion speed.
 * FEATURE: vmagent: add `-remoteWrite.maxRowsPerBlock` command-line flag for tuning the number of samples to send to remote storage per each block. Bigger values may improve data ingestion performance at the cost of higher memory usage.
-* FEATURE: vmagent: distribute Kafka messages among all the partitions when [writing data to Kafka](https://docs.victoriametrics.com/vmagent.html#writing-metrics-to-kafka).
-* FEATURE: add [label_graphite_group](https://docs.victoriametrics.com/MetricsQL.html#label_graphite_group) function for extracting the given groups from Graphite metric names.
-* FEATURE: add [duration_over_time](https://docs.victoriametrics.com/MetricsQL.html#duration_over_time) function for calculating the actual lifetime of the time series with possible gaps. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1780).
-* FEATURE: add [limit_offset](https://docs.victoriametrics.com/MetricsQL.html#limit_offset) function, which can be used for implementing simple paging over big number of time series. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1778).
+* FEATURE: vmagent: distribute Kafka messages among all the partitions when [writing data to Kafka](https://docs.victoriametrics.com/vmagent/#writing-metrics-to-kafka).
+* FEATURE: add [label_graphite_group](https://docs.victoriametrics.com/metricsql/#label_graphite_group) function for extracting the given groups from Graphite metric names.
+* FEATURE: add [duration_over_time](https://docs.victoriametrics.com/metricsql/#duration_over_time) function for calculating the actual lifetime of the time series with possible gaps. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1780).
+* FEATURE: add [limit_offset](https://docs.victoriametrics.com/metricsql/#limit_offset) function, which can be used for implementing simple paging over big number of time series. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1778).
 
-* BUGFIX: vmagent: reduce the increased memory usage when scraping targets with big number of metrics which periodically change. The memory usage has been increased in v1.68.0 after vmagent started generating staleness markers in [stream parse mode](https://docs.victoriametrics.com/vmagent.html#stream-parsing-mode). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1745).
+* BUGFIX: vmagent: reduce the increased memory usage when scraping targets with big number of metrics which periodically change. The memory usage has been increased in v1.68.0 after vmagent started generating staleness markers in [stream parse mode](https://docs.victoriametrics.com/vmagent/#stream-parsing-mode). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1745).
 * BUGFIX: vmagent: properly display `proxy_url` config option at `http://vmagent:8429/config` page. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1755).
 * BUGFIX: fix tests for Apple M1. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1653).
 
@@ -103,43 +103,43 @@ Released at 2021-10-22
 * FEATURE: vmagent: expose `-promscrape.config` contents at `/config` page as Prometheus does. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1695).
 * FEATURE: vmagent: add `show original labels` button per each scrape target displayed at `http://vmagent:8429/targets` page. This should improve debuggability for service discovery and relabeling issues similar to [this one](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1664). See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1698).
 * FEATURE: vmagent: shard targets among cluster nodes after the relabeling is applied. This should guarantee that targets with the same set of labels go to the same `vmagent` node in the cluster. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1687).
-* FEATURE: vmagent: atomatically switch to [stream parsing mode](https://docs.victoriametrics.com/vmagent.html#stream-parsing-mode) if the response from the given target exceeds the command-line flag value `-promscrape.minResponseSizeForStreamParse`. This should reduce memory usage when `vmagent` scrapes targets with non-uniform response sizes (this is the case in Kubernetes monitoring).
-* FEATURE: vmagent: send Prometheus-like staleness marks in [stream parsing mode](https://docs.victoriametrics.com/vmagent.html#stream-parsing-mode). Previously staleness marks wern't sent in stream parsing mode. See [these docs](https://docs.victoriametrics.com/vmagent.html#prometheus-staleness-markers) for details.
-* FEATURE: vmagent: properly calculate `scrape_series_added` metric for targets in [stream parsing mode](https://docs.victoriametrics.com/vmagent.html#stream-parsing-mode). Previously it was set to 0 in stream parsing mode. See [more details about this metric](https://prometheus.io/docs/concepts/jobs_instances/#automatically-generated-labels-and-time-series).
-* FEATURE: vmagent: expose `promscrape_series_limit_max_series` and `promscrape_series_limit_current_series` metrics at `http://vmagent:8429/metrics` for scrape targets with the [enabled series limiter](https://docs.victoriametrics.com/vmagent.html#cardinality-limiter).
-* FEATURE: vmagent: return error if `sample_limit` or `series_limit` options are set when [stream parsing mode](https://docs.victoriametrics.com/vmagent.html#stream-parsing-mode) is enabled, since these limits cannot be applied in stream parsing mode.
+* FEATURE: vmagent: atomatically switch to [stream parsing mode](https://docs.victoriametrics.com/vmagent/#stream-parsing-mode) if the response from the given target exceeds the command-line flag value `-promscrape.minResponseSizeForStreamParse`. This should reduce memory usage when `vmagent` scrapes targets with non-uniform response sizes (this is the case in Kubernetes monitoring).
+* FEATURE: vmagent: send Prometheus-like staleness marks in [stream parsing mode](https://docs.victoriametrics.com/vmagent/#stream-parsing-mode). Previously staleness marks wern't sent in stream parsing mode. See [these docs](https://docs.victoriametrics.com/vmagent/#prometheus-staleness-markers) for details.
+* FEATURE: vmagent: properly calculate `scrape_series_added` metric for targets in [stream parsing mode](https://docs.victoriametrics.com/vmagent/#stream-parsing-mode). Previously it was set to 0 in stream parsing mode. See [more details about this metric](https://prometheus.io/docs/concepts/jobs_instances/#automatically-generated-labels-and-time-series).
+* FEATURE: vmagent: expose `promscrape_series_limit_max_series` and `promscrape_series_limit_current_series` metrics at `http://vmagent:8429/metrics` for scrape targets with the [enabled series limiter](https://docs.victoriametrics.com/vmagent/#cardinality-limiter).
+* FEATURE: vmagent: return error if `sample_limit` or `series_limit` options are set when [stream parsing mode](https://docs.victoriametrics.com/vmagent/#stream-parsing-mode) is enabled, since these limits cannot be applied in stream parsing mode.
 * FEATURE: vmalert: add `-remoteRead.disablePathAppend` command-line flag, which allows specifying the full `-remoteRead.url`. If `-remoteRead.disablePathAppend` is set, then `vmalert` doesn't add `/api/v1/query` suffix to `-remoteRead.url`.
-* FEATURE: add trigonometric functions, which are going to be added in [Prometheus 2.31](https://github.com/prometheus/prometheus/pull/9239): [acosh](https://docs.victoriametrics.com/MetricsQL.html#acosh), [asinh](https://docs.victoriametrics.com/MetricsQL.html#asinh), [atan](https://docs.victoriametrics.com/MetricsQL.html#atan), [atanh](https://docs.victoriametrics.com/MetricsQL.html#atanh), [cosh](https://docs.victoriametrics.com/MetricsQL.html#cosh), [deg](https://docs.victoriametrics.com/MetricsQL.html#deg), [rad](https://docs.victoriametrics.com/MetricsQL.html#rad), [sinh](https://docs.victoriametrics.com/MetricsQL.html#sinh), [tan](https://docs.victoriametrics.com/MetricsQL.html#tan), [tanh](https://docs.victoriametrics.com/MetricsQL.html#tanh). Also add `atan2` binary operator. See [this pull request](https://github.com/prometheus/prometheus/pull/9248).
-* FEATURE: consistently return the same set of time series from [limitk](https://docs.victoriametrics.com/MetricsQL.html#limitk) function. This improves the usability of periodically refreshed graphs.
+* FEATURE: add trigonometric functions, which are going to be added in [Prometheus 2.31](https://github.com/prometheus/prometheus/pull/9239): [acosh](https://docs.victoriametrics.com/metricsql/#acosh), [asinh](https://docs.victoriametrics.com/metricsql/#asinh), [atan](https://docs.victoriametrics.com/metricsql/#atan), [atanh](https://docs.victoriametrics.com/metricsql/#atanh), [cosh](https://docs.victoriametrics.com/metricsql/#cosh), [deg](https://docs.victoriametrics.com/metricsql/#deg), [rad](https://docs.victoriametrics.com/metricsql/#rad), [sinh](https://docs.victoriametrics.com/metricsql/#sinh), [tan](https://docs.victoriametrics.com/metricsql/#tan), [tanh](https://docs.victoriametrics.com/metricsql/#tanh). Also add `atan2` binary operator. See [this pull request](https://github.com/prometheus/prometheus/pull/9248).
+* FEATURE: consistently return the same set of time series from [limitk](https://docs.victoriametrics.com/metricsql/#limitk) function. This improves the usability of periodically refreshed graphs.
 * FEATURE: [vmui](https://docs.victoriametrics.com/#vmui): varios UX improvements. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1711) and [these docs](https://docs.victoriametrics.com/#vmui).
-* FEATURE: [vmauth](https://docs.victoriametrics.com/vmauth.html): add ability to specify HTTP headers, which will be sent in requests to backends. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1736).
+* FEATURE: [vmauth](https://docs.victoriametrics.com/vmauth/): add ability to specify HTTP headers, which will be sent in requests to backends. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1736).
 * FEATURE: add `/flags` page to all the VictoriaMetrics components. This page contains command-line flags passed to the component.
 * FEATURE: allow using tab separators additionally to whitespace separators when [ingesting data in Graphite plaintext protocol](https://docs.victoriametrics.com/#how-to-send-data-from-graphite-compatible-agents-such-as-statsd). Such separators are [supported by Carbon-c-relay](https://github.com/grobian/carbon-c-relay/commit/f3ffe6cc2b52b07d14acbda649ad3fd6babdd528).
 
 * BUGFIX: vmstorage: fix `unaligned 64-bit atomic operation` panic on 32-bit architectures (arm and 386). The panic has been introduced in v1.67.0.
 * BUGFIX: vmalert, vmauth: prevent from frequent closing of TCP connections established to backends under high load. This should reduce the number of TCP sockets in `TIME_WAIT` state at `vmalert` and `vmauth` under high load. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1704).
 * BUGFIX: vmalert: correctly calculate alert ID including extra labels. Previously, ID for alert entity was generated without alertname or groupname. This led to collision, when multiple alerting rules within the same group producing same labelsets. E.g. expr: `sum(metric1) by (job) > 0` and expr: `sum(metric2) by (job) > 0` could result into same labelset `job: "job"`. The bugfix adds all extra labels right after receiving response from the datasource. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1734).
-* BUGFIX: vmalert: fix links in [Web UI](https://docs.victoriametrics.com/vmalert.html#web). See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1717).
-* BUGFIX: vmagent: set `honor_timestamps: true` by default in [scrape configs](https://docs.victoriametrics.com/sd_configs.html#scrape_configs) if this options isn't set explicitly. This aligns the behaviour with Prometheus.
+* BUGFIX: vmalert: fix links in [Web UI](https://docs.victoriametrics.com/vmalert/#web). See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1717).
+* BUGFIX: vmagent: set `honor_timestamps: true` by default in [scrape configs](https://docs.victoriametrics.com/sd_configs/#scrape_configs) if this options isn't set explicitly. This aligns the behaviour with Prometheus.
 * BUGFIX: vmagent: group scrape targets by the original job names at `http://vmagent:8429/targets` page like Prometheus does. Previously they were grouped by the job name after relabeling, which may result in unexpected empty target groups. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1707).
-* BUGFIX: [vmctl](https://docs.victoriametrics.com/vmctl.html): fix importing boolean fields from InfluxDB line protocol. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1709).
+* BUGFIX: [vmctl](https://docs.victoriametrics.com/vmctl/): fix importing boolean fields from InfluxDB line protocol. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1709).
 
 ## [v1.67.0](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.67.0)
 
 Released at 2021-10-08
 
-* FEATURE: add ability to accept metrics from [DataDog agent](https://docs.datadoghq.com/agent/) and [DogStatsD](https://docs.datadoghq.com/developers/dogstatsd/). See [these docs](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-send-data-from-datadog-agent). This option simplifies the migration path from DataDog to VictoriaMetrics. See also [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/206).
-* FEATURE: vmagent [enterprise](https://docs.victoriametrics.com/enterprise/): add support for data reading and writing from/to [Apache Kafka](https://kafka.apache.org/). See [these docs](https://docs.victoriametrics.com/vmagent.html#kafka-integration).
+* FEATURE: add ability to accept metrics from [DataDog agent](https://docs.datadoghq.com/agent/) and [DogStatsD](https://docs.datadoghq.com/developers/dogstatsd/). See [these docs](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-send-data-from-datadog-agent). This option simplifies the migration path from DataDog to VictoriaMetrics. See also [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/206).
+* FEATURE: vmagent [enterprise](https://docs.victoriametrics.com/enterprise/): add support for data reading and writing from/to [Apache Kafka](https://kafka.apache.org/). See [these docs](https://docs.victoriametrics.com/vmagent/#kafka-integration).
 * FEATURE: vmui: switch to [μPlot](https://github.com/leeoniya/uPlot) and add ability to naturally scroll and zoom graphs. See [these docs](https://docs.victoriametrics.com/#vmui). Thanks to @Loori-R.
 * FEATURE: vmstorage: stop accepting new data if `-storageDataPath` directory contains less than `-storage.minFreeDiskSpaceBytes` of free space. This should prevent from `out of disk space` crashes. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/269).
-* FEATURE: calculate quantiles in the same way as Prometheus does in such functions as [quantile_over_time](https://docs.victoriametrics.com/MetricsQL.html#quantile_over_time) and [quantile](https://docs.victoriametrics.com/MetricsQL.html#quantile). Previously results from VictoriaMetrics could be slightly different than results from Prometheus. See [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1625) and [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1612) issues.
-* FEATURE: add `rollup_scrape_interval(m[d])` function to [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html), which returns `min`, `max` and `avg` values for the interval between samples for `m` on the given lookbehind window `d`.
-* FEATURE: add `topk_last(k, q)` and `bottomk_last(k, q)` functions to [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html), which return up to `k` time series from `q` with the maximum / minimum last value on the graph.
+* FEATURE: calculate quantiles in the same way as Prometheus does in such functions as [quantile_over_time](https://docs.victoriametrics.com/metricsql/#quantile_over_time) and [quantile](https://docs.victoriametrics.com/metricsql/#quantile). Previously results from VictoriaMetrics could be slightly different than results from Prometheus. See [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1625) and [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1612) issues.
+* FEATURE: add `rollup_scrape_interval(m[d])` function to [MetricsQL](https://docs.victoriametrics.com/metricsql/), which returns `min`, `max` and `avg` values for the interval between samples for `m` on the given lookbehind window `d`.
+* FEATURE: add `topk_last(k, q)` and `bottomk_last(k, q)` functions to [MetricsQL](https://docs.victoriametrics.com/metricsql/), which return up to `k` time series from `q` with the maximum / minimum last value on the graph.
 
 * BUGFIX: align behavior of the queries `a or on (labels) b`, `a and on (labels) b` and `a unless on (labels) b` where `b` has multiple time series with the given `labels` to Prometheus behavior. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1643).
 * BUGFIX: vmagent: fix `openstack_sd_config` service discovery when both `domain_name` and `project_id` config options are set. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1655).
-* BUGFIX: return proper values (zeroes) from [stddev_over_time](https://docs.victoriametrics.com/MetricsQL.html#stddev_over_time) and [stdvar_over_time](https://docs.victoriametrics.com/MetricsQL.html#stdvar_over_time) functions when the lookbehind window in square brackets contains only a single sample. Previously the sample value was incorrectly returned in this case.
-* BUGFIX: vminsert: fix uneven distribution of time series among storage nodes in [multi-level cluster setup](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#multi-level-cluster-setup). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1672).
+* BUGFIX: return proper values (zeroes) from [stddev_over_time](https://docs.victoriametrics.com/metricsql/#stddev_over_time) and [stdvar_over_time](https://docs.victoriametrics.com/metricsql/#stdvar_over_time) functions when the lookbehind window in square brackets contains only a single sample. Previously the sample value was incorrectly returned in this case.
+* BUGFIX: vminsert: fix uneven distribution of time series among storage nodes in [multi-level cluster setup](https://docs.victoriametrics.com/cluster-victoriametrics/#multi-level-cluster-setup). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1672).
 
 ## [v1.66.2](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.66.2)
 
@@ -166,65 +166,65 @@ Released at 2021-09-20
 * FEATURE: vmalert: add web UI with the list of alerting groups, alerts and alert statuses. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1602).
 * FEATURE: vmalert: add `-rule.maxResolveDuration` command-line flag, which could be used for limiting the auto-resolve duration for the alerting rule. By default it is limited to 3x evaluation interval. This could be too high for big evaluation intervals. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1586).
 * FEATURE: vmalert: add support for Bearer token authorization for `-datasource.url`, `-remoteRead.url` and `-remoteWrite.url`. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1608).
-* FEATURE: vmagent: send stale markers for disappeared metrics like Prometheus does. Previously stale markers were sent only when the scrape target disappears or when it becomes temporarily unavailable. See [these docs](https://docs.victoriametrics.com/vmagent.html#prometheus-staleness-markers) for details.
-* FEATURE: vmagent: add ability to set `series_limit` option for a particular scrape target via `__series_limit__` label. This allows setting the limit on the number of time series on a per-target basis. See [these docs](https://docs.victoriametrics.com/vmagent.html#cardinality-limiter) for details.
-* FEATURE: vmagent: add ability to set `stream_parse` option for a particular scrape target via `__stream_parse__` label. This allows managing the stream parsing mode on a per-target basis. See [these docs](https://docs.victoriametrics.com/vmagent.html#stream-parsing-mode) for details.
+* FEATURE: vmagent: send stale markers for disappeared metrics like Prometheus does. Previously stale markers were sent only when the scrape target disappears or when it becomes temporarily unavailable. See [these docs](https://docs.victoriametrics.com/vmagent/#prometheus-staleness-markers) for details.
+* FEATURE: vmagent: add ability to set `series_limit` option for a particular scrape target via `__series_limit__` label. This allows setting the limit on the number of time series on a per-target basis. See [these docs](https://docs.victoriametrics.com/vmagent/#cardinality-limiter) for details.
+* FEATURE: vmagent: add ability to set `stream_parse` option for a particular scrape target via `__stream_parse__` label. This allows managing the stream parsing mode on a per-target basis. See [these docs](https://docs.victoriametrics.com/vmagent/#stream-parsing-mode) for details.
 * FEATURE: vmagent: add ability to set `scrape_interval` and `scrape_timeout` options for a particular target via `__scrape_interval__` and `__scrape_timeout__` labels in the same way as Prometheus 2.30 does. See [this pull request](https://github.com/prometheus/prometheus/pull/8911).
 * FEATURE: vmagent: generate `scrape_timeout_seconds` metric per each scrape target, so the target saturation could be calculated with `scrape_duration_seconds / scrape_timeout_seconds`. See the corresponding [pull request from Prometheus 2.30](https://github.com/prometheus/prometheus/pull/9247).
 * FEATURE: vmagent: reduce CPU usage when calculating the number of newly added series per scrape (this number is sent to remote storage in `scrape_series_added` metric).
-* FEATURE: vmagent: reduce CPU usage when applying `series_limit` to scrape targets with constant set of metrics. See more information about `series_limit` [here](https://docs.victoriametrics.com/vmagent.html#cardinality-limiter).
-* FEATURE: vminsert: disable rerouting by default when a few of `vmstorage` nodes start accepting data at lower speed than the rest of `vmstorage` nodes. This should improve VictoriaMetrics cluster stability during rolling restarts and during spikes in [time series churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate). The rerouting can be enabled by passing `-disableRerouting=false` command-line flag to `vminsert`.
+* FEATURE: vmagent: reduce CPU usage when applying `series_limit` to scrape targets with constant set of metrics. See more information about `series_limit` [here](https://docs.victoriametrics.com/vmagent/#cardinality-limiter).
+* FEATURE: vminsert: disable rerouting by default when a few of `vmstorage` nodes start accepting data at lower speed than the rest of `vmstorage` nodes. This should improve VictoriaMetrics cluster stability during rolling restarts and during spikes in [time series churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate). The rerouting can be enabled by passing `-disableRerouting=false` command-line flag to `vminsert`.
 * FEATURE: vmauth: do not put invalid auth tokens into log by default due to security reasons. The logging can be returned back by passing `-logInvalidAuthTokens` command-line flag to `vmauth`. Requests with invalid auth tokens are counted at `vmagent_http_request_errors_total{reason="invalid_auth_token"}` metric exposed by `vmauth` at `/metrics` page.
-* FEATURE: add new relabeling actions: `keep_metrics` and `drop_metrics`. This simplifies metrics filtering by metric names. See [these docs](https://docs.victoriametrics.com/vmagent.html#relabeling) for more details.
-* FEATURE: allow splitting long `regex` in relabeling filters into an array of shorter regexps, which can be put into multiple lines for better readability and maintainability. See [these docs](https://docs.victoriametrics.com/vmagent.html#relabeling) for more details.
+* FEATURE: add new relabeling actions: `keep_metrics` and `drop_metrics`. This simplifies metrics filtering by metric names. See [these docs](https://docs.victoriametrics.com/vmagent/#relabeling) for more details.
+* FEATURE: allow splitting long `regex` in relabeling filters into an array of shorter regexps, which can be put into multiple lines for better readability and maintainability. See [these docs](https://docs.victoriametrics.com/vmagent/#relabeling) for more details.
 * FEATURE: optimize performance for queries with regexp filters on metric name like `{__name__=~"metric1|...|metricN"}`. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1610) from @faceair.
 * FEATURE: vmui: use Prometheus-compatible query args, so `vmui` could be accessed from graph editor in Grafana. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1619). Thanks to @Loori-R.
-* FEATURE: vmselect: automatically add missing port to `-storageNode` hostnames. For example, `-storageNode=vmstorage1,vmstorage2` is automatically translated to `-storageNode=vmstorage1:8401,vmstorage2:8401`. This simplifies [manual setup of VictoriaMetrics cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#cluster-setup).
-* FEATURE: vminsert: automatically add missing port to `-storageNode` hostnames. For example, `-storageNode=vmstorage1,vmstorage2` is automatically translated to `-storageNode=vmstorage1:8400,vmstorage2:8400`. This simplifies [manual setup of VictoriaMetrics cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#cluster-setup).
-* FEATURE: add [mad(q)](https://docs.victoriametrics.com/MetricsQL.html#mad) function to [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html). It calculates [Median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation) for groups of points with identical timestamps across multiple time series.
-* FEATURE: add [outliers_mad(tolerance, q)](https://docs.victoriametrics.com/MetricsQL.html#outliers_mad) function to [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html). It returns time series with peaks outside the [Median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation) multiplied by `tolerance`.
-* FEATURE: add `histogram_quantiles("phiLabel", phi1, ..., phiN, buckets)` function to [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html). It calculates the given `phi*`-quantiles over the given `buckets` and returns time series per each quantile with the corresponding `{phiLabel="phi*"}` label.
-* FEATURE: add `quantiles_over_time("phiLabel", phi1, ..., phiN, series_selector[d])` function to [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html). It calculates the given `phi*`-quantiles over raw samples selected by `series_selector` on the given lookbehind window `d`. It returns time series per each quantile with the corresponding `{phiLabel="phi*"}` label.
+* FEATURE: vmselect: automatically add missing port to `-storageNode` hostnames. For example, `-storageNode=vmstorage1,vmstorage2` is automatically translated to `-storageNode=vmstorage1:8401,vmstorage2:8401`. This simplifies [manual setup of VictoriaMetrics cluster](https://docs.victoriametrics.com/cluster-victoriametrics/#cluster-setup).
+* FEATURE: vminsert: automatically add missing port to `-storageNode` hostnames. For example, `-storageNode=vmstorage1,vmstorage2` is automatically translated to `-storageNode=vmstorage1:8400,vmstorage2:8400`. This simplifies [manual setup of VictoriaMetrics cluster](https://docs.victoriametrics.com/cluster-victoriametrics/#cluster-setup).
+* FEATURE: add [mad(q)](https://docs.victoriametrics.com/metricsql/#mad) function to [MetricsQL](https://docs.victoriametrics.com/metricsql/). It calculates [Median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation) for groups of points with identical timestamps across multiple time series.
+* FEATURE: add [outliers_mad(tolerance, q)](https://docs.victoriametrics.com/metricsql/#outliers_mad) function to [MetricsQL](https://docs.victoriametrics.com/metricsql/). It returns time series with peaks outside the [Median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation) multiplied by `tolerance`.
+* FEATURE: add `histogram_quantiles("phiLabel", phi1, ..., phiN, buckets)` function to [MetricsQL](https://docs.victoriametrics.com/metricsql/). It calculates the given `phi*`-quantiles over the given `buckets` and returns time series per each quantile with the corresponding `{phiLabel="phi*"}` label.
+* FEATURE: add `quantiles_over_time("phiLabel", phi1, ..., phiN, series_selector[d])` function to [MetricsQL](https://docs.victoriametrics.com/metricsql/). It calculates the given `phi*`-quantiles over raw samples selected by `series_selector` on the given lookbehind window `d`. It returns time series per each quantile with the corresponding `{phiLabel="phi*"}` label.
 * FEATURE: [enterprise](https://docs.victoriametrics.com/enterprise/): do not ask for `-eula` flag if `-version` flag is passed to enteprise app. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1621).
 
 * BUGFIX: properly handle queries with multiple filters matching empty labels such as `metric{label1=~"foo|",label2="bar|"}`. This filter must match the following series: `metric`, `metric{label1="foo"}`, `metric{label2="bar"}` and `metric{label1="foo",label2="bar"}`. Previously it was matching only `metric{label1="foo",label2="bar"}`. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1601).
 * BUGFIX: vmselect: reset connection timeouts after each request to `vmstorage`. This should prevent from `cannot read data in 0.000 seconds: unexpected EOF` warning in logs. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1562). Thanks to @mxlxm .
-* BUGFIX: keep metric name for time series returned from [rollup_candlestick](https://docs.victoriametrics.com/MetricsQL.html#rollup_candlestick) function, since the returned series don't change the meaning of the original series. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1600).
-* BUGFIX: use Prometheus-compatible label value formatting for [count_values](https://docs.victoriametrics.com/MetricsQL.html#count_values) function. Previously big values could be improperly formatted, which could break query results, which rely on label value such as `... on(label) count_values("label", ...)`.
+* BUGFIX: keep metric name for time series returned from [rollup_candlestick](https://docs.victoriametrics.com/metricsql/#rollup_candlestick) function, since the returned series don't change the meaning of the original series. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1600).
+* BUGFIX: use Prometheus-compatible label value formatting for [count_values](https://docs.victoriametrics.com/metricsql/#count_values) function. Previously big values could be improperly formatted, which could break query results, which rely on label value such as `... on(label) count_values("label", ...)`.
 * BUGFIX: vmagent: properly use `https` scheme for wildcard TLS certificates for `role: ingress` targets in Kubernetes service discovery. See [this issue](https://github.com/prometheus/prometheus/issues/8902).
 * BUGFIX: vmagent: support host networking mode for `docker_sd_config`. See [this issue](https://github.com/prometheus/prometheus/issues/9116).
 * BUGFIX: fix non-repeatable results from `quantile_over_time()` function when the number of input samples exceeds 1000. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1612).
-* BUGFIX: vmagent: fix EC2 zone discovery when `filters` are specified in [ec2_sc_config](https://docs.victoriametrics.com/sd_configs.html#ec2_sd_configs). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1626).
+* BUGFIX: vmagent: fix EC2 zone discovery when `filters` are specified in [ec2_sc_config](https://docs.victoriametrics.com/sd_configs/#ec2_sd_configs). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1626).
 
 ## [v1.65.0](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.65.0)
 
 Released at 2021-09-01
 
 * FEATURE: vmagent: add ability to read scrape configs from multiple files specified in `scrape_config_files` section. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1559).
-* FEATURE: vmagent: reduce memory usage and CPU usage when [Prometheus staleness tracking](https://docs.victoriametrics.com/vmagent.html#prometheus-staleness-markers) is enabled for metrics exported from the deleted or disappeared scrape targets.
-* FEATURE: vmagent: add the ability to limit the number of unique time series scraped per each target. This can be done either globally via `-promscrape.seriesLimitPerTarget` command-line option or on per-target basis via `series_limit` option at `scrape_config` section. See [the updated docs on cardinality limiter](https://docs.victoriametrics.com/vmagent.html#cardinality-limiter) and [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1561).
-* FEATURE: vmagent: discover `role: ingress` and `role: endpointslice` in [kubernetes_sd_config](https://docs.victoriametrics.com/sd_configs.html#kubernetes_sd_configs) via v1 API instead of v1beta1 API if Kubernetes supports it. This fixes service discovery in Kubernetes v1.22 and newer versions. See [these docs](https://kubernetes.io/docs/reference/using-api/deprecation-guide/#ingress-v122).
+* FEATURE: vmagent: reduce memory usage and CPU usage when [Prometheus staleness tracking](https://docs.victoriametrics.com/vmagent/#prometheus-staleness-markers) is enabled for metrics exported from the deleted or disappeared scrape targets.
+* FEATURE: vmagent: add the ability to limit the number of unique time series scraped per each target. This can be done either globally via `-promscrape.seriesLimitPerTarget` command-line option or on per-target basis via `series_limit` option at `scrape_config` section. See [the updated docs on cardinality limiter](https://docs.victoriametrics.com/vmagent/#cardinality-limiter) and [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1561).
+* FEATURE: vmagent: discover `role: ingress` and `role: endpointslice` in [kubernetes_sd_config](https://docs.victoriametrics.com/sd_configs/#kubernetes_sd_configs) via v1 API instead of v1beta1 API if Kubernetes supports it. This fixes service discovery in Kubernetes v1.22 and newer versions. See [these docs](https://kubernetes.io/docs/reference/using-api/deprecation-guide/#ingress-v122).
 * FEATURE: take into account failed queries in `vm_request_duration_seconds` summary at `/metrics`. Previously only successful queries were taken into account. This could result in skewed summary. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1537).
-* FEATURE: vmalert: add an official dashboard for vmalert. See [these docs](https://docs.victoriametrics.com/vmalert.html#monitoring).
+* FEATURE: vmalert: add an official dashboard for vmalert. See [these docs](https://docs.victoriametrics.com/vmalert/#monitoring).
 * FEATURE: vmalert: add ability to set additional labels per group via `labels` config section. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1471).
 * FEATURE: vmalert: add `-disableAlertgroupLabel` command-line flag for disabling the label with alert group name. This may be needed for proper deduplication in Alertmanager. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1532).
 * FEATURE: update Go builder from v1.16.7 to v1.17.0. This improves data ingestion and query performance by up to 5% according to benchmarks. See [the release post for Go1.17](https://go.dev/blog/go1.17).
 * FEATURE: vmagent: expose `promscrape_discovery_http_errors_total` metric, which can be used for monitoring the number of failed discovery attempts per each `http_sd` config.
 * FEATURE: do not reset response cache when a sample with old timestamp is ingested into VictoriaMetrics if `-search.disableAutoCacheReset` command-line option is set. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1570).
-* FEATURE: add `quantiles("phiLabel", phi1, ..., phiN, q)` aggregate function to [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html), which calculates the given `phi*` quantiles over time series returned by `q`. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1573).
+* FEATURE: add `quantiles("phiLabel", phi1, ..., phiN, q)` aggregate function to [MetricsQL](https://docs.victoriametrics.com/metricsql/), which calculates the given `phi*` quantiles over time series returned by `q`. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1573).
 
 * BUGFIX: rename `sign` function to `sgn` in order to be consistent with PromQL. See [this pull request from Prometheus](https://github.com/prometheus/prometheus/pull/8457).
-* BUGFIX: vmagent: add `role: endpointslice` in [kubernetes_sd_config](https://docs.victoriametrics.com/sd_configs.html#kubernetes_sd_configs) in order to be consistent with Prometheus. Previously this role was supported with incorrect name: `role: endpointslices`. Now both `endpointslice` and `endpointslices` are supported. See [the corresponding code in Prometheus](https://github.com/prometheus/prometheus/blob/2ec6c7dbb82b72834021e01f1773eb90a67a371f/discovery/kubernetes/kubernetes.go#L99).
+* BUGFIX: vmagent: add `role: endpointslice` in [kubernetes_sd_config](https://docs.victoriametrics.com/sd_configs/#kubernetes_sd_configs) in order to be consistent with Prometheus. Previously this role was supported with incorrect name: `role: endpointslices`. Now both `endpointslice` and `endpointslices` are supported. See [the corresponding code in Prometheus](https://github.com/prometheus/prometheus/blob/2ec6c7dbb82b72834021e01f1773eb90a67a371f/discovery/kubernetes/kubernetes.go#L99).
 * BUGFIX: improve the detection of the needed free space for background merge operation. This should prevent from possible out of disk space crashes during big merges. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1560).
 * BUGFIX: vmauth: remove trailing slash from the full url before requesting it from the backend. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1554).
-* BUGFIX: [vmbackupmanager](https://docs.victoriametrics.com/vmbackupmanager.html): fix timeout error when snapshot takes longer than 10 seconds. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1571).
+* BUGFIX: [vmbackupmanager](https://docs.victoriametrics.com/vmbackupmanager/): fix timeout error when snapshot takes longer than 10 seconds. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1571).
 * BUGFIX: properly parse OpenTSDB `put` messages with multiple spaces between message elements. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1574). Thanks to @envzhu for the fix.
 
 ## [v1.64.1](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.64.1)
 
 Released at 2021-08-19
 
-* FEATURE: add `bitmap_and(q, mask)`, `bitmap_or(q, mask)` and `bitmak_xor(q, mask)` functions to [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html). These functions allow performing bitwise operations over data points in time series. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1541).
+* FEATURE: add `bitmap_and(q, mask)`, `bitmap_or(q, mask)` and `bitmak_xor(q, mask)` functions to [MetricsQL](https://docs.victoriametrics.com/metricsql/). These functions allow performing bitwise operations over data points in time series. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1541).
 * FEATURE: vmalert: add `-remoteWrite.disablePathAppend` command-line flag, which can be used when custom `-remoteWrite.url` must be specified. For example, `./vmalert -disablePathAppend -remoteWrite.url='http://foo.bar/a/b/c?d=e'` would write data to `http://foo.bar/a/b/c?d=e` instead of `http://foo.bar/a/b/c?d=e/api/v1/write`. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1536).
 * FEATURE: vmagent: add `-promscrape.noStaleMarkers` command-line flag for disabling sending Prometheus stale markers for metrics from disappeared scrape targets. This option may be used for reducing memory usage when scraping big number of metrics with big number of labels and when stale markers aren't needed.
 * FEATURE: vmselect: add `-search.noStaleMarkers` command-line flag for disabling stale markers handling in queries. This may save some CPU time when the queried data doesn't contain stale markers.
@@ -241,7 +241,7 @@ Released at 2021-08-15
 * FEATURE: add support for Prometheus staleness markers. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1526).
 * FEATURE: vmagent: automatically generate Prometheus staleness markers for the scraped metrics when scrape targets disappear in the same way as Prometheus does. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1526).
 * FEATURE: add `present_over_time(m[d])` function, which returns 1 if `m` has a least a single sample over the previous duration `d`. This function has been added also to [Prometheus 2.29](https://github.com/prometheus/prometheus/releases/tag/v2.29.0).
-* FEATURE: vmagent: support multitenant writes according to [these docs](https://docs.victoriametrics.com/vmagent.html#multitenancy). This allows using a single `vmagent` instance in front of VictoriaMetrics cluster for all the tenants. Thanks to @omarghader for [the pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1505). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1491).
+* FEATURE: vmagent: support multitenant writes according to [these docs](https://docs.victoriametrics.com/vmagent/#multitenancy). This allows using a single `vmagent` instance in front of VictoriaMetrics cluster for all the tenants. Thanks to @omarghader for [the pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1505). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1491).
 * FEATURE: vmagent: add `__meta_ec2_availability_zone_id` label to discovered Amazon EC2 targets. This label is available in Prometheus [starting from v2.29](https://github.com/prometheus/prometheus/releases/tag/v2.29.0).
 * FAETURE: vmagent: add `__meta_gce_interface_ipv4_<name>` labels to discovered GCE targets. These labels are available in Prometheus [starting from v2.29](https://github.com/prometheus/prometheus/releases/tag/v2.29.0).
 * FEATURE: add `-search.maxSamplesPerSeries` command-line flag for limiting the number of raw samples a single query can process per each time series. This option can protect from out of memory errors when a query processes tens of millions of raw samples per series. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1067).
@@ -268,8 +268,8 @@ Released at 2021-07-15
 * FEATURE: vmselect: embed [vmui](https://github.com/VictoriaMetrics/vmui) into a single-node VictoriaMetrics and into `vmselect` component of cluster version. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1413). The web interface is available at the following paths:
   * `/vmui/` for a single-node VictoriaMetrics
   * `/select/<accountID>/vmui/` for `vmselect` at cluster version of VictoriaMetrics
-* FEATURE: support durations anywhere in [MetricsQL queries](https://docs.victoriametrics.com/MetricsQL.html). For example, `sum_over_time(m[1h]) / 1h` is a valid query, which is equivalent to `sum_over_time(m[1h]) / 3600`.
-* FEATURE: support durations without suffixes in [MetricsQL queries](https://docs.victoriametrics.com/MetricsQL.html). For example, `rate(m[3600])` is a valid query, which is equivalent to `rate(m[1h])`.
+* FEATURE: support durations anywhere in [MetricsQL queries](https://docs.victoriametrics.com/metricsql/). For example, `sum_over_time(m[1h]) / 1h` is a valid query, which is equivalent to `sum_over_time(m[1h]) / 3600`.
+* FEATURE: support durations without suffixes in [MetricsQL queries](https://docs.victoriametrics.com/metricsql/). For example, `rate(m[3600])` is a valid query, which is equivalent to `rate(m[1h])`.
 * FEATURE: export `vmselect_request_duration_seconds` and `vminsert_request_duration_seconds` [VictoriaMetrics histograms](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350) at `/metrics` page. These histograms can be used for determining latency distribution and SLI/SLO for the served requests. For example, the following query would return the percent of queries that took less than 500ms during the last hour: `histogram_share(500ms, sum(rate(vmselect_request_duration_seconds_bucket[1h])) by (vmrange))`.
 * FEATURE: vmagent: dynamically reload client TLS certificates from disk on every [mTLS connection](https://developers.cloudflare.com/cloudflare-one/identity/devices/mutual-tls-authentication). This should allow using `vmagent` with [Istio service mesh](https://istio.io/latest/about/service-mesh/). See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1420).
 * FEATURE: log http request path plus all the query args on errors during request processing. Previously only http request path was logged without query args, so it could be hard debugging such errors.
@@ -285,15 +285,15 @@ Released at 2021-07-15
 
 Released at 2021-06-25
 
-* FEATURE: vmagent: add service discovery for Docker (aka [docker_sd_config](https://docs.victoriametrics.com/sd_configs.html#docker_sd_configs)). See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1402).
-* FEATURE: vmagent: add service discovery for DigitalOcean (aka [digitalocean_sd_config](https://docs.victoriametrics.com/sd_configs.html#digitalocean_sd_configs)). See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1367).
+* FEATURE: vmagent: add service discovery for Docker (aka [docker_sd_config](https://docs.victoriametrics.com/sd_configs/#docker_sd_configs)). See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1402).
+* FEATURE: vmagent: add service discovery for DigitalOcean (aka [digitalocean_sd_config](https://docs.victoriametrics.com/sd_configs/#digitalocean_sd_configs)). See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1367).
 * FEATURE: vmagent: change the default value for `-remoteWrite.queues` from 4 to `2 * numCPUs`. This should reduce scrape duration for highly loaded vmagent, which scrapes tens of thousands of targets. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1385).
 * FEATURE: vmagent: show the number of samples the target returns during the last scrape on `/targets` and `/api/v1/targets` pages. This should simplify debugging targets, which may return too big or too low number of samples. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1377).
 * FEATURE: vmagent: show jobs with zero discovered targets on `/targets` page. This should help debugging improperly configured scrape configs.
-* FEATURE: vmagent: support for http-based service discovery (aka [http_sd_config](https://docs.victoriametrics.com/sd_configs.html#http_sd_configs)), which has been added since Prometheus 2.28. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1392).
+* FEATURE: vmagent: support for http-based service discovery (aka [http_sd_config](https://docs.victoriametrics.com/sd_configs/#http_sd_configs)), which has been added since Prometheus 2.28. See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1392).
 * FEATURE: vmagent: support namespace in Consul serive discovery in the same way as Prometheus 2.28 does. See [this issue](https://github.com/prometheus/prometheus/issues/8894) for details.
 * FEATURE: vmagent: support generic auth configs in `consul_sd_configs` in the same way as Prometheus 2.28 does. See [this issue](https://github.com/prometheus/prometheus/issues/8924) for details.
-* FEATURE: [vmctl](https://docs.victoriametrics.com/vmctl.html): limit the number of samples per each imported JSON line. This should limit the memory usage at VictoriaMetrics side when importing time series with big number of samples.
+* FEATURE: [vmctl](https://docs.victoriametrics.com/vmctl/): limit the number of samples per each imported JSON line. This should limit the memory usage at VictoriaMetrics side when importing time series with big number of samples.
 * FEATURE: vmselect: log slow queries across all the `/api/v1/*` handlers (aka [Prometheus query API](https://prometheus.io/docs/prometheus/latest/querying/api)) if their execution duration exceeds `-search.logSlowQueryDuration`. This should simplify debugging slow requests to such handlers as `/api/v1/labels` or `/api/v1/series` additionally to `/api/v1/query` and `/api/v1/query_range`, which were logged in the previous releases.
 * FEATURE: vminsert: sort the `-storageNode` list in order to guarantee the identical `series -> vmstorage` mapping across all the `vminsert` nodes. This should reduce resource usage (RAM, CPU and disk IO) at `vmstorage` nodes if `vmstorage` addresses are passed in random order to `vminsert` nodes.
 * FEATURE: vmstorage: reduce memory usage on a system with many CPU cores under high ingestion rate.
@@ -301,8 +301,8 @@ Released at 2021-06-25
 * BUGFIX: prevent from adding new samples to deleted time series after the rotation of the inverted index (the rotation is performed once per `-retentionPeriod`). See [this comment](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1347#issuecomment-861232136) for details.
 * BUGFIX: vmstorage: reduce high disk write IO usage on systems with big number of CPU cores. The issue has been introduced in the release [v1.59.0](#v1590). See [this commit](https://github.com/VictoriaMetrics/VictoriaMetrics/commit/aa9b56a046b6ae8083fa659df35dd5e994bf9115) and [this comment](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1338#issuecomment-863046999) for details.
 * BUGFIX: vmstorage: prevent from incorrect stats collection when multiple concurrent queries execute the same tag filter. This may help reducing CPU usage under certain workloads. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1338).
-* BUGFIX: vmselect: return the last timestamp for the max / min value from `tmax_over_time(m[d])` and `tmin_over_time(m[d])` [MetricsQL functions](https://docs.victoriametrics.com/MetricsQL.html) as most users expect. See also [this issue](https://github.com/prometheus/prometheus/issues/8966).
-* BUGFIX: vmselect: return the expected value for `increase_pure()` [MetricsQL function](https://docs.victoriametrics.com/MetricsQL.html) after a gap in a time series. Previously incorrect too big value could be returned after the gap from `increase_pure()`.
+* BUGFIX: vmselect: return the last timestamp for the max / min value from `tmax_over_time(m[d])` and `tmin_over_time(m[d])` [MetricsQL functions](https://docs.victoriametrics.com/metricsql/) as most users expect. See also [this issue](https://github.com/prometheus/prometheus/issues/8966).
+* BUGFIX: vmselect: return the expected value for `increase_pure()` [MetricsQL function](https://docs.victoriametrics.com/metricsql/) after a gap in a time series. Previously incorrect too big value could be returned after the gap from `increase_pure()`.
 
 ## [v1.61.1](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.61.1)
 
@@ -315,14 +315,14 @@ Released at 2021-06-11
 
 Released at 2021-06-09
 
-* FEATURE: vmalert: add support for backfilling (aka replay) of recording and alerting rules. See [these docs](https://docs.victoriametrics.com/vmalert.html#rules-backfilling) and [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/836).
+* FEATURE: vmalert: add support for backfilling (aka replay) of recording and alerting rules. See [these docs](https://docs.victoriametrics.com/vmalert/#rules-backfilling) and [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/836).
 * FEATURE: vmalert: add a command-line flag `-rule.configCheckInterval` for automatic re-reading of `-rule` files without the need to send SIGHUP signal. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/512).
-* FEATURE: vmagent: respect the `sample_limit` and `-promscrape.maxScrapeSize` values when scraping targets in [stream parsing mode](https://docs.victoriametrics.com/vmagent.html#stream-parsing-mode). See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1331).
-* FEATURE: vmauth: add ability to specify mutliple `url_prefix` entries for balancing the load among multiple `vmselect` and/or `vminsert` nodes in a cluster. See [these docs](https://docs.victoriametrics.com/vmauth.html#load-balancing).
+* FEATURE: vmagent: respect the `sample_limit` and `-promscrape.maxScrapeSize` values when scraping targets in [stream parsing mode](https://docs.victoriametrics.com/vmagent/#stream-parsing-mode). See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1331).
+* FEATURE: vmauth: add ability to specify mutliple `url_prefix` entries for balancing the load among multiple `vmselect` and/or `vminsert` nodes in a cluster. See [these docs](https://docs.victoriametrics.com/vmauth/#load-balancing).
 * FEATURE: vminsert: add `-disableRerouting` command-line flag for forcibly disabling the rerouting. This should help resolving [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/791) and [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1054) issues.
 * FEATURE: vminsert: reduce the probability of global re-routing storm if all the vmstorage nodes cannot keep up with the given ingestion rate for some time. This should improve cluster stability in such cases. See [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/791) and [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1054) issues.
 * FEATURE: allow building VictoriaMetrics components for Solaris / SmartOS. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1322).
-* FEATURE: vmagent: add ability to debug relabeling rules. See [these docs](https://docs.victoriametrics.com/vmagent.html#relabeling) and [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1343).
+* FEATURE: vmagent: add ability to debug relabeling rules. See [these docs](https://docs.victoriametrics.com/vmagent/#relabeling) and [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1343).
 
 * BUGFIX: reduce CPU usage by up to 2x during querying a database with big number of active daily time series. The issue has been introduced in `v1.59.0`.
 * BUGFIX: vmagent: properly apply auth and tls configs in `eureka_sd_configs`. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1350).
@@ -334,21 +334,21 @@ Released at 2021-06-09
 Released at 2021-05-24
 
 * FEATURE: add ability to limit the number of unique time series, which can be added to storage per hour and per day. This can help dealing with high cardinality and high churn rate issues. See [these docs](https://docs.victoriametrics.com/#cardinality-limiter).
-* FEATURE: vmagent: add ability to limit the number of unique time series, which can be sent to remote storage systems per hour and per day. This can help dealing with high cardinality and high churn rate issues. See [these docs](https://docs.victoriametrics.com/vmagent.html#cardinality-limiter).
-* FEATURE: vmalert: add ability to run alerting and recording rules for multiple tenants. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/740) and [these docs](https://docs.victoriametrics.com/vmalert.html#multitenancy).
-* FEATURE: vminsert: add support for data ingestion via other `vminsert` nodes. This allows building multi-level data ingestion paths in VictoriaMetrics cluster by writing data from one level of `vminsert` nodes to another level of `vminsert` nodes. See [these docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#multi-level-cluster-setup) and [this comment](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/541#issuecomment-835487858) for details.
+* FEATURE: vmagent: add ability to limit the number of unique time series, which can be sent to remote storage systems per hour and per day. This can help dealing with high cardinality and high churn rate issues. See [these docs](https://docs.victoriametrics.com/vmagent/#cardinality-limiter).
+* FEATURE: vmalert: add ability to run alerting and recording rules for multiple tenants. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/740) and [these docs](https://docs.victoriametrics.com/vmalert/#multitenancy).
+* FEATURE: vminsert: add support for data ingestion via other `vminsert` nodes. This allows building multi-level data ingestion paths in VictoriaMetrics cluster by writing data from one level of `vminsert` nodes to another level of `vminsert` nodes. See [these docs](https://docs.victoriametrics.com/cluster-victoriametrics/#multi-level-cluster-setup) and [this comment](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/541#issuecomment-835487858) for details.
 * FEATURE: vmagent: reload `bearer_token_file`, `credentials_file` and `password_file` contents every second. This allows dynamically changing the contents of these files during target scraping and service discovery without the need to restart `vmagent`. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1297).
 * FEATURE: vmalert: add a flag to control behaviour on startup for state restore errors. Such errors were returned and logged before as well. Now user can specify whether to just log these errors (`-remoteRead.ignoreRestoreErrors=true`) or to stop the process (`-remoteRead.ignoreRestoreErrors=false`). The latter is important when VM isn't ready yet to serve queries from vmalert and it needs to wait. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1252).
 * FEATURE: vmalert: add ability to pass `round_digits` query arg to datasource via `-datasource.roundDigits` command-line flag. This can be used for limiting the number of decimal digits after the point in recording rule results. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/525).
 * FEATURE: return `X-Server-Hostname` header in http responses of all the VictoriaMetrics components. This should simplify tracing the origin server behind a load balancer or behind auth proxy during troubleshooting.
-* FEATURE: vmselect: allow to use 2x more memory for query processing at `vmselect` nodes in [VictoriaMetrics cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html). This should allow processing heavy queries without the need to increase RAM size at `vmselect` nodes.
+* FEATURE: vmselect: allow to use 2x more memory for query processing at `vmselect` nodes in [VictoriaMetrics cluster](https://docs.victoriametrics.com/cluster-victoriametrics/). This should allow processing heavy queries without the need to increase RAM size at `vmselect` nodes.
 * FEATURE: add ability to filter `/api/v1/status/tsdb` output with arbitrary [time series selectors](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) passed via `match[]` query args. See [these docs](https://docs.victoriametrics.com/#tsdb-stats) and [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1168) for details.
 * FEATURE: automatically detect memory and cpu limits for VictoriaMetrics components running under [cgroup v2](https://www.kernel.org/doc/html/latest/admin-guide/cgroup-v2.html) environments such as [HashiCorp Nomad](https://www.nomadproject.io/). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1269).
 * FEATURE: vmauth: allow `-auth.config` reloading via `/-/reload` http endpoint. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1194).
-* FEATURE: add `timezone_offset(tz)` function. It returns offset in seconds for the given timezone `tz` relative to UTC. This can be useful when combining with datetime-related functions. For example, `day_of_week(time()+timezone_offset("America/Los_Angeles"))` would return weekdays for `America/Los_Angeles` time zone. Special `Local` time zone can be used for returning an offset for the time zone set on the host where VictoriaMetrics runs. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1306) and [MetricsQL docs](https://docs.victoriametrics.com/MetricsQL.html) for more details.
+* FEATURE: add `timezone_offset(tz)` function. It returns offset in seconds for the given timezone `tz` relative to UTC. This can be useful when combining with datetime-related functions. For example, `day_of_week(time()+timezone_offset("America/Los_Angeles"))` would return weekdays for `America/Los_Angeles` time zone. Special `Local` time zone can be used for returning an offset for the time zone set on the host where VictoriaMetrics runs. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1306) and [MetricsQL docs](https://docs.victoriametrics.com/metricsql/) for more details.
 * FEATURE: vmagent: add support for OAuth2 authorization for scrape targets and service discovery in the same way as Prometheus does. See [these docs](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#oauth2).
 * FEATURE: vmagent: add support for OAuth2 authorization when writing data to `-remoteWrite.url`. See `-remoteWrite.oauth2.*` config params in `/path/to/vmagent -help` output.
-* FEATURE: vmalert: add ability to set `extra_filter_labels` at alerting and recording group configs. See [these docs](https://docs.victoriametrics.com/vmalert.html#groups).
+* FEATURE: vmalert: add ability to set `extra_filter_labels` at alerting and recording group configs. See [these docs](https://docs.victoriametrics.com/vmalert/#groups).
 * FEATURE: vmstorage: reduce memory usage by up to 30% when ingesting big number of active time series.
 
 * BUGFIX: vmagent: do not retry scraping targets, which don't support HTTP. This should reduce CPU load and network usage at `vmagent` and at scrape target. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1289).
@@ -371,7 +371,7 @@ Released at 2021-05-01
 
 * FEATURE: improved new time series registration speed on systems with many CPU cores. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1244). Thanks to @waldoweng for the idea and [draft implementation](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1243).
 * FEATURE: vmalert: use the same technique as Grafana for determining evaluation timestamps for recording rules. This should make consistent graphs for series generated by recording rules compared to graphs generated for queries from recording rules in Grafana. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1232).
-* FEATURE: vmauth: add ability to set madatory query args in `url_prefix`. For example, `url_prefix: http://vm:8428/?extra_label=team=dev` would add `extra_label=team=dev` query arg to all the incoming requests. See [the example](https://docs.victoriametrics.com/vmauth.html#auth-config) for more details.
+* FEATURE: vmauth: add ability to set madatory query args in `url_prefix`. For example, `url_prefix: http://vm:8428/?extra_label=team=dev` would add `extra_label=team=dev` query arg to all the incoming requests. See [the example](https://docs.victoriametrics.com/vmauth/#auth-config) for more details.
 * FEATURE: vmctl: add OpenTSDB migration option. See more details [here](https://docs.victoriametrics.com/vmctl#migrating-data-from-opentsdb).
 Thanks to @johnseekins!
 * FEATURE: log metrics with dropped labels if the number of labels in the ingested metric exceeds `-maxLabelsPerTimeseries`. This should simplify debugging for this case.
@@ -387,8 +387,8 @@ Thanks to @johnseekins!
 Released at 2021-04-08
 
 * FEATURE: vminsert and vmagent: add `-sortLabels` command-line flag for sorting metric labels before pushing them to `vmstorage`. This should reduce the size of `MetricName -> internal_series_id` cache (aka `vm_cache_size_bytes{type="storage/tsid"}`) when ingesting samples for the same time series with distinct order of labels. For example, `foo{k1="v1",k2="v2"}` and `foo{k2="v2",k1="v1"}` represent a single time series. Labels sorting is disabled by default, since the majority of established exporters preserve the order of labels for the exported metrics.
-* FEATURE: allow specifying label value alongside label name for the `others sum` time series returned from `topk_*` and `bottomk_*` functions from [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html). For example, `topk_avg(3, max(process_resident_memory_bytes) by (instance), "instance=other_sum")` would return top 3 series from `max(process_resident_memory_bytes) by (instance)` plus a series containing the sum of other series. The `others sum` series will have `{instance="other_sum"}` label.
-* FEATURE: do not delete `dst_label` when applying `label_copy(q, "src_label", "dst_label")` and `label_move(q, "src_label", "dst_label")` to series without `src_label` and with non-empty `dst_label`. See more details at [MetricsQL docs](https://docs.victoriametrics.com/MetricsQL.html).
+* FEATURE: allow specifying label value alongside label name for the `others sum` time series returned from `topk_*` and `bottomk_*` functions from [MetricsQL](https://docs.victoriametrics.com/metricsql/). For example, `topk_avg(3, max(process_resident_memory_bytes) by (instance), "instance=other_sum")` would return top 3 series from `max(process_resident_memory_bytes) by (instance)` plus a series containing the sum of other series. The `others sum` series will have `{instance="other_sum"}` label.
+* FEATURE: do not delete `dst_label` when applying `label_copy(q, "src_label", "dst_label")` and `label_move(q, "src_label", "dst_label")` to series without `src_label` and with non-empty `dst_label`. See more details at [MetricsQL docs](https://docs.victoriametrics.com/metricsql/).
 * FEATURE: update Go builder from `v1.16.2` to `v1.16.3`. This should fix [these issues](https://github.com/golang/go/issues?q=milestone%3AGo1.16.3+label%3ACherryPickApproved).
 * FEATURE: vmagent: add support for `follow_redirects` option to `scrape_configs` section in the same way as [Prometheus 2.26 does](https://github.com/prometheus/prometheus/pull/8546).
 * FEATURE: vmagent: add support for `authorization` section in `-promscrape.config` in the same way as [Prometheus 2.26 does](https://github.com/prometheus/prometheus/pull/8512).
@@ -399,11 +399,11 @@ Released at 2021-04-08
 * FEATURE: vmagent: add support for `proxy_tls_config`, `proxy_authorization`, `proxy_basic_auth`, `proxy_bearer_token` and `proxy_bearer_token_file` options in `consul_sd_config`, `dockerswarm_sd_config` and `eureka_sd_config` sections.
 * FEATURE: vmagent: pass `X-Prometheus-Scrape-Timeout-Seconds` header to scrape targets as Prometheus does. In this case scrape targets can limit the time needed for performing the scrape. See [this comment](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1179#issuecomment-813118733) for details.
 * FEATURE: vmagent: drop corrupted persistent queue files at `-remoteWrite.tmpDataPath` instead of throwing a fatal error. Corrupted files can appear after unclean shutdown of `vmagent` such as OOM kill or hardware reset. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1030).
-* FEATURE: vmauth: add support for authorization via [bearer token](https://swagger.io/docs/specification/authentication/bearer-authentication/). See [the docs](https://docs.victoriametrics.com/vmauth.html#auth-config) for details.
+* FEATURE: vmauth: add support for authorization via [bearer token](https://swagger.io/docs/specification/authentication/bearer-authentication/). See [the docs](https://docs.victoriametrics.com/vmauth/#auth-config) for details.
 * FEATURE: publish `arm64` and `amd64` binaries for cluster version of VictoriaMetrics at [releases page](https://github.com/VictoriaMetrics/VictoriaMetrics/releases).
 
 * BUGFIX: properly handle `/api/v1/labels` and `/api/v1/label/<label_name>/values` queries on big `start ... end` time range. This should fix big resource usage when VictoriaMetrics is queried with [Promxy](https://github.com/jacksontj/promxy) v0.0.62 or newer versions.
-* BUGFIX: do not break sort order for series returned from `topk*`, `bottomk*` and `outliersk` [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) functions. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1189).
+* BUGFIX: do not break sort order for series returned from `topk*`, `bottomk*` and `outliersk` [MetricsQL](https://docs.victoriametrics.com/metricsql/) functions. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1189).
 * BUGFIX: vmagent: properly work with simple HTTP proxies which don't support `CONNECT` method. For example, [PushProx](https://github.com/prometheus-community/PushProx). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1179).
 * BUGFIX: vmagent: properly discover targets if multiple namespace selectors are put inside `kubernetes_sd_config`. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1170).
 * BUGFIX: vmagent: properly discover `role: endpoints` and `role: endpointslices` targets in `kubernetes_sd_config`. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1182).
@@ -447,20 +447,20 @@ Released at 2021-03-29
 
 Released at 2021-03-17
 
-* FEATURE: add the following functions to [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html):
+* FEATURE: add the following functions to [MetricsQL](https://docs.victoriametrics.com/metricsql/):
   * `histogram_avg(buckets)` - returns the average value for the given buckets.
   * `histogram_stdvar(buckets)` - returns standard variance for the given buckets.
   * `histogram_stddev(buckets)` - returns standard deviation for the given buckets.
 * FEATURE: export `vm_available_memory_bytes` and `vm_available_cpu_cores` metrics, which show the number of available RAM and available CPU cores for VictoriaMetrics apps.
 * FEATURE: export `vm_index_search_duration_seconds` histogram, which can be used for troubleshooting time series search performance.
-* FEATURE: vmagent: add ability to replicate scrape targets among `vmagent` instances in the cluster with `-promscrape.cluster.replicationFactor` command-line flag. See [these docs](https://docs.victoriametrics.com/vmagent.html#scraping-big-number-of-targets).
-* FEATURE: vmagent: accept `scrape_offset` option at `scrape_config`. This option may be useful when scrapes must start at the specified offset of every scrape interval. See [these docs](https://docs.victoriametrics.com/vmagent.html#troubleshooting) for details.
-* FEATURE: vmagent: support `proxy_tls_config`, `proxy_basic_auth`, `proxy_bearer_token` and `proxy_bearer_token_file` options at `scrape_config` section for configuring proxies specified via `proxy_url`. See [these docs](https://docs.victoriametrics.com/vmagent.html#scraping-targets-via-a-proxy).
+* FEATURE: vmagent: add ability to replicate scrape targets among `vmagent` instances in the cluster with `-promscrape.cluster.replicationFactor` command-line flag. See [these docs](https://docs.victoriametrics.com/vmagent/#scraping-big-number-of-targets).
+* FEATURE: vmagent: accept `scrape_offset` option at `scrape_config`. This option may be useful when scrapes must start at the specified offset of every scrape interval. See [these docs](https://docs.victoriametrics.com/vmagent/#troubleshooting) for details.
+* FEATURE: vmagent: support `proxy_tls_config`, `proxy_basic_auth`, `proxy_bearer_token` and `proxy_bearer_token_file` options at `scrape_config` section for configuring proxies specified via `proxy_url`. See [these docs](https://docs.victoriametrics.com/vmagent/#scraping-targets-via-a-proxy).
 * FEATURE: vmauth: allow using regexp paths in `url_map`. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1112) for details.
 * FEATURE: accept `round_digits` query arg at `/api/v1/query` and `/api/v1/query_range` handlers. This option can be set at Prometheus datasource in Grafana for limiting the number of digits after the decimal point in response values.
 * FEATURE: add `-influx.databaseNames` command-line flag, which can be used for accepting data from some Telegraf plugins such as [fluentd plugin](https://github.com/fangli/fluent-plugin-influxdb). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1124).
 * FEATURE: add `-logNewSeries` command-line flag, which can be used for debugging the source of time series churn rate.
-* FEATURE: publish Windows builds for [vmagent](https://docs.victoriametrics.com/vmagent.html), [vmalert](https://docs.victoriametrics.com/vmalert.html), [vmauth](https://docs.victoriametrics.com/vmauth.html) and [vmctl](https://docs.victoriametrics.com/vmctl.html) at `vmutils-windows-*.zip` archives at [releases page](https://github.com/VictoriaMetrics/VictoriaMetrics/releases).
+* FEATURE: publish Windows builds for [vmagent](https://docs.victoriametrics.com/vmagent/), [vmalert](https://docs.victoriametrics.com/vmalert/), [vmauth](https://docs.victoriametrics.com/vmauth/) and [vmctl](https://docs.victoriametrics.com/vmctl/) at `vmutils-windows-*.zip` archives at [releases page](https://github.com/VictoriaMetrics/VictoriaMetrics/releases).
 * FEATURE: listen for IPv6 UDP if `-enableTCP6` command-line flag is passed to VictoriaMetrics. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1131).
 
 * BUGFIX: vmagent: prevent from high CPU usage bug during failing scrapes with small `scrape_timeout` (less than a few seconds).
@@ -484,8 +484,8 @@ Released at 2021-03-03
 
 Released at 2021-03-02
 
-* FEATURE: add `sign(q)` and `clamp(q, min, max)` functions, which are planned to be added in [the upcoming Prometheus release](https://twitter.com/roidelapluie/status/1363428376162295811) . The `last_over_time(m[d])` function is already supported in [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html).
-* FEATURE: vmagent: add `scrape_align_interval` config option, which can be used for aligning scrapes to the beginning of the configured interval. See [these docs](https://docs.victoriametrics.com/vmagent.html#troubleshooting) for details.
+* FEATURE: add `sign(q)` and `clamp(q, min, max)` functions, which are planned to be added in [the upcoming Prometheus release](https://twitter.com/roidelapluie/status/1363428376162295811) . The `last_over_time(m[d])` function is already supported in [MetricsQL](https://docs.victoriametrics.com/metricsql/).
+* FEATURE: vmagent: add `scrape_align_interval` config option, which can be used for aligning scrapes to the beginning of the configured interval. See [these docs](https://docs.victoriametrics.com/vmagent/#troubleshooting) for details.
 * FEATURE: expose io-related metrics at `/metrics` page for every VictoriaMetrics component:
   * `process_io_read_bytes_total` - the number of bytes read via io syscalls such as read and pread
   * `process_io_written_bytes_total` - the number of bytes written via io syscalls such as write and pwrite
@@ -493,12 +493,12 @@ Released at 2021-03-02
   * `process_io_write_syscalls_total` - the number of write syscalls such as write and pwrite
   * `process_io_storage_read_bytes_total` - the number of bytes read from storage layer
   * `process_io_storage_written_bytes_total` - the number of bytes written to storage layer
-* FEATURE: vmagent: add ability to spread scrape targets among multiple `vmagent` instances. See [these docs](https://docs.victoriametrics.com/vmagent.html#scraping-big-number-of-targets) and [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1084) for details.
+* FEATURE: vmagent: add ability to spread scrape targets among multiple `vmagent` instances. See [these docs](https://docs.victoriametrics.com/vmagent/#scraping-big-number-of-targets) and [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1084) for details.
 * FEATURE: vmagent: use watch API for Kubernetes service discovery. This should reduce load on Kubernetes API server when it tracks big number of objects (for example, 10K pods). This should also reduce the time needed for k8s targets discovery. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1057) for details.
 * FEATURE: vmagent: export `vm_promscrape_target_relabel_duration_seconds` metric, which can be used for monitoring the time spend on relabeling for discovered targets.
-* FEATURE: vmagent: optimize [relabeling](https://docs.victoriametrics.com/vmagent.html#relabeling) performance for common cases.
+* FEATURE: vmagent: optimize [relabeling](https://docs.victoriametrics.com/vmagent/#relabeling) performance for common cases.
 * FEATURE: add `increase_pure(m[d])` function to MetricsQL. It works the same as `increase(m[d])` except of various edge cases. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/962) for details.
-* FEATURE: increase accuracy for `buckets_limit(limit, buckets)` results for small `limit` values. See [MetricsQL docs](https://docs.victoriametrics.com/MetricsQL.html) for details.
+* FEATURE: increase accuracy for `buckets_limit(limit, buckets)` results for small `limit` values. See [MetricsQL docs](https://docs.victoriametrics.com/metricsql/) for details.
 * FEATURE: vmagent: initial support for Windows build with `CGO_ENABLED=0 GOOS=windows go build ./app/vmagent`. See [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70) and [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1036).
 * FEATURE: vmagent: support WebIdentityToken auth in EC2 service discovery. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1080) for details.
 * FEATURE: vmalert: properly process query params in `-datasource.url` and `-remoteRead.url` command-line flags. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1087) for details.
@@ -529,9 +529,9 @@ Released at 2021-02-18
 * FEATURE: optimize searching for matching metrics for `metric{<label_filters>}` queries if `<label_filters>` contains at least a single filter. For example, the query `up{job="foobar"}` should find the matching time series much faster than previously.
 * FEATURE: reduce execution times for `q1 <binary_op> q2` queries by executing `q1` and `q2` in parallel.
 * FEATURE: switch from Go1.15 to [Go1.16](https://golang.org/doc/go1.16) for building prod binaries.
-* FEATURE: single-node VictoriaMetrics now accepts requests to handlers with `/prometheus` and `/graphite` prefixes such as `/prometheus/api/v1/query`. This improves compatibility with [handlers from VictoriaMetrics cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#url-format).
+* FEATURE: single-node VictoriaMetrics now accepts requests to handlers with `/prometheus` and `/graphite` prefixes such as `/prometheus/api/v1/query`. This improves compatibility with [handlers from VictoriaMetrics cluster](https://docs.victoriametrics.com/cluster-victoriametrics/#url-format).
 * FEATURE: expose `process_open_fds` and `process_max_fds` metrics. These metrics can be used for alerting when `process_open_fds` reaches `process_max_fds`. See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/402> and <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1037>
-* FEATURE: vmalert: add `-datasource.appendTypePrefix` command-line option for querying both Prometheus and Graphite datasource in cluster version of VictoriaMetrics. See [these docs](https://docs.victoriametrics.com/vmalert.html#graphite) for details.
+* FEATURE: vmalert: add `-datasource.appendTypePrefix` command-line option for querying both Prometheus and Graphite datasource in cluster version of VictoriaMetrics. See [these docs](https://docs.victoriametrics.com/vmalert/#graphite) for details.
 * FEATURE: vmauth: add ability to route requests from a single user to multiple destinations depending on the requested paths. See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1064>
 * FEATURE: remove dependency on external programs such as `cat`, `grep` and `cut` when detecting cpu and memory limits inside Docker or LXC container.
 * FEATURE: vmagent: add `__meta_kubernetes_endpoints_label_*`, `__meta_kubernetes_endpoints_labelpresent_*`, `__meta_kubernetes_endpoints_annotation_*` and `__meta_kubernetes_endpoints_annotationpresent_*` labels for `role: endpoints` in Kubernetes service discovery. These labels where added in Prometheus 2.25.
@@ -554,14 +554,14 @@ Released at 2021-02-03
 
 Released at 2021-02-03
 
-* FEATURE: added [vmctl tool](https://docs.victoriametrics.com/vmctl.html) to VictoriaMetrics release process. Now it is packaged in `vmutils-*.tar.gz` archive on [the releases page](https://github.com/VictoriaMetrics/VictoriaMetrics/releases). Source code for `vmctl` tool has been moved from [github.com/VictoriaMetrics/vmctl](https://github.com/VictoriaMetrics/vmctl) to [github.com/VictoriaMetrics/VictoriaMetrics/app/vmctl](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/app/vmctl).
+* FEATURE: added [vmctl tool](https://docs.victoriametrics.com/vmctl/) to VictoriaMetrics release process. Now it is packaged in `vmutils-*.tar.gz` archive on [the releases page](https://github.com/VictoriaMetrics/VictoriaMetrics/releases). Source code for `vmctl` tool has been moved from [github.com/VictoriaMetrics/vmctl](https://github.com/VictoriaMetrics/vmctl) to [github.com/VictoriaMetrics/VictoriaMetrics/app/vmctl](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/app/vmctl).
 * FEATURE: added `-loggerTimezone` command-line flag for adjusting time zone for timestamps in log messages. By default UTC is used.
 * FEATURE: added `-search.maxStepForPointsAdjustment` command-line flag, which can be used for disabling adjustment for points returned by `/api/v1/query_range` handler if such points have timestamps closer than `-search.latencyOffset` to the current time. Such points may contain incomplete data, so they are substituted by the previous values for `step` query args smaller than one minute by default.
 * FEATURE: vmselect: added ability to use Graphite-compatible filters in MetricsQL via `{__graphite__="foo.*.bar"}` syntax. This expression is equivalent to `{__name__=~"foo[.][^.]*[.]bar"}`, but it works faster and it is easier to use when migrating from Graphite to VictoriaMetrics. This feature deprecates the usage of `-search.treatDotsAsIsInRegexps` command-line flag.
 * FEATURE: vmselect: added ability to set additional label filters, which must be applied during queries. Such label filters can be set via optional `extra_label` query arg, which is accepted by [querying API](https://docs.victoriametrics.com/#prometheus-querying-api-usage) handlers. For example, the request to `/api/v1/query_range?extra_label=tenant_id=123&query=<query>` adds `{tenant_id="123"}` label filter to the given `<query>`. It is expected that the `extra_label` query arg is automatically set by auth proxy sitting
 in front of VictoriaMetrics. [Contact us](mailto:sales@victoriametrics.com) if you need assistance with such a proxy. See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1021> .
 * FEATURE: vmalert: added `-datasource.queryStep` command-line flag for passing optional `step` query arg to `/api/v1/query` endpoint. See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1025>
-* FEATURE: vmalert: added ability to query Graphite datasource when evaluating alerting and recording rules. See [these docs](https://docs.victoriametrics.com/vmalert.html#graphite) for details.
+* FEATURE: vmalert: added ability to query Graphite datasource when evaluating alerting and recording rules. See [these docs](https://docs.victoriametrics.com/vmalert/#graphite) for details.
 * FEATURE: vmagent: added `-remoteWrite.roundDigits` command-line option for rounding metric values to the given number of decimal digits after the point before sending the metric to the corresponding `-remoteWrite.url`. This option can be used for improving data compression on the remote storage, because values with lower number of decimal digits can be compressed better than values with bigger number of decimal digits.
 * FEATURE: vmagent: added `-remoteWrite.rateLimit` command-line flag for limiting data transfer rate to `-remoteWrite.url`. This may be useful when big amounts of buffered data is sent after temporarily unavailability of the remote storage. See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1035>
 * FEATURE: vmagent: export the following additional metrics, which may be useful during troubleshooting:
@@ -582,7 +582,7 @@ Released at 2021-01-13
 
 * FEATURE: provide a sample list of alerting rules for VictoriaMetrics components. It is available [here](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker#alerts).
 * FEATURE: disable final merge for data for the previous month at the beginning of new month, since it may result in high disk IO and CPU usage. Final merge can be enabled by setting `-finalMergeDelay` command-line flag to positive duration.
-* FEATURE: add `tfirst_over_time(m[d])` and `tlast_over_time(m[d])` functions to [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) for returning timestamps for the first and the last data point in `m` over `d` duration.
+* FEATURE: add `tfirst_over_time(m[d])` and `tlast_over_time(m[d])` functions to [MetricsQL](https://docs.victoriametrics.com/metricsql/) for returning timestamps for the first and the last data point in `m` over `d` duration.
 * FEATURE: add ability to pass multiple labels to `sort_by_label()` and `sort_by_label_desc()` functions. See <https://github.com/VictoriaMetrics/VictoriaMetrics/issues/992> .
 * FEATURE: enforce at least TLS v1.2 when accepting HTTPS requests if `-tls`, `-tlsCertFile` and `-tlsKeyFile` command-line flags are set, because older TLS protocols such as v1.0 and v1.1 have been deprecated due to security vulnerabilities.
 * FEATURE: support `extra_label` query arg for all HTTP-based [data ingestion protocols](https://docs.victoriametrics.com/#how-to-import-time-series-data). This query arg can be used for specifying extra labels which should be added for the ingested data.
@@ -599,4 +599,4 @@ Released at 2021-01-13
 
 ## Previous releases
 
-See changes for older releases [here](https://docs.victoriametrics.com/CHANGELOG_2020.html).
+See changes for older releases [here](https://docs.victoriametrics.com/changelog_2020/).

docs/CaseStudies.md

@@ -46,7 +46,7 @@ where you can chat with VictoriaMetrics users to get additional references, revi
   - [Zerodha](#zerodha)
   - [zhihu](#zhihu)
 
-You can also read [articles about VictoriaMetrics from our users](https://docs.victoriametrics.com/Articles.html#third-party-articles-and-slides-about-victoriametrics).
+You can also read [articles about VictoriaMetrics from our users](https://docs.victoriametrics.com/articles/#third-party-articles-and-slides-about-victoriametrics).
 
 ## AbiosGaming
 
@@ -89,12 +89,12 @@ We ended up with the following configuration:
 
 We learned that remote write protocol generated too much traffic and connections so after 8 months we started looking for alternatives.
 
-Around the same time, VictoriaMetrics released [vmagent](https://docs.victoriametrics.com/vmagent.html).
+Around the same time, VictoriaMetrics released [vmagent](https://docs.victoriametrics.com/vmagent/).
 We tried to scrape all the metrics via a single instance of vmagent but it that didn't work because vmagent wasn't able to catch up with writes
 into VictoriaMetrics. We tested different options and end up with the following scheme:
 
 - We removed Prometheus from our setup.
-- VictoriaMetrics [can scrape targets](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-scrape-prometheus-exporters-such-as-node-exporter) as well
+- VictoriaMetrics [can scrape targets](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-scrape-prometheus-exporters-such-as-node-exporter) as well
 so we removed vmagent. Now, VictoriaMetrics scrapes all the metrics from 110 jobs and 5531 targets.
 - We use [Promxy](https://github.com/jacksontj/promxy) for alerting.
 
@@ -105,7 +105,7 @@ Such a scheme has generated the following benefits compared with Prometheus:
 
 Cons are the following:
 
-- VictoriaMetrics didn't support replication (it [supports replication now](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#replication-and-data-safety)) - we run an extra instance of VictoriaMetrics and Promxy in front of a VictoriaMetrics pair for high availability.
+- VictoriaMetrics didn't support replication (it [supports replication now](https://docs.victoriametrics.com/cluster-victoriametrics/#replication-and-data-safety)) - we run an extra instance of VictoriaMetrics and Promxy in front of a VictoriaMetrics pair for high availability.
 - VictoriaMetrics stores 1 extra month for defined retention (if retention is set to N months, then VM stores N+1 months of data), but this is still better than other solutions.
 
 Here are some numbers from our single-node VictoriaMetrics setup:
@@ -572,7 +572,7 @@ Numbers:
 
 ## Wedos.com
 
-> [Wedos](https://www.wedos.com/) is the biggest hosting provider in the Czech Republic. We have two our own private data centers that hold our servers and technologies, such as cooling the servers in bath oils. We started using [cluster VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html) to store Prometheus metrics from all our infrastructure after receiving positive references from people who had successfully used VictoriaMetrics. We're using it throughout our services, including the new WEDOS Global Protection.
+> [Wedos](https://www.wedos.com/) is the biggest hosting provider in the Czech Republic. We have two our own private data centers that hold our servers and technologies, such as cooling the servers in bath oils. We started using [cluster VictoriaMetrics](https://docs.victoriametrics.com/cluster-victoriametrics/) to store Prometheus metrics from all our infrastructure after receiving positive references from people who had successfully used VictoriaMetrics. We're using it throughout our services, including the new WEDOS Global Protection.
 
 Numbers:
 
@@ -587,7 +587,7 @@ Numbers:
 
 [Wix.com](https://en.wikipedia.org/wiki/Wix.com) is the leading web development platform.
 
-> We needed to redesign our metrics infrastructure from the ground up after the move to Kubernetes. We had tried out a few different options before landing on this solution which is working great. We have a Prometheus instance in every datacenter with 2 hours retention for local storage and remote write into [HA pair of single-node VictoriaMetrics instances](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#high-availability).
+> We needed to redesign our metrics infrastructure from the ground up after the move to Kubernetes. We had tried out a few different options before landing on this solution which is working great. We have a Prometheus instance in every datacenter with 2 hours retention for local storage and remote write into [HA pair of single-node VictoriaMetrics instances](https://docs.victoriametrics.com/single-server-victoriametrics/#high-availability).
 
 Numbers:
 
@@ -610,7 +610,7 @@ Numbers:
 - Enough headroom/scaling capacity for future growth which is planned to be up to 100M active time series.
 - Ability to split DB replicas per workload. Alert queries go to one replica and user queries go to another (speed for users, effective cache).
 
-> Optimizing for those points and our specific workload, VictoriaMetrics proved to be the best option. As icing on the cake we’ve got [PromQL extensions](https://docs.victoriametrics.com/MetricsQL.html) - `default 0` and `histogram` are my favorite ones. We really like having a lot of tsdb params easily available via config options which makes tsdb easy to tune for each specific use case. We've also found a great community in [Slack channel](https://slack.victoriametrics.com/) and responsive and helpful maintainer support.
+> Optimizing for those points and our specific workload, VictoriaMetrics proved to be the best option. As icing on the cake we’ve got [PromQL extensions](https://docs.victoriametrics.com/metricsql/) - `default 0` and `histogram` are my favorite ones. We really like having a lot of tsdb params easily available via config options which makes tsdb easy to tune for each specific use case. We've also found a great community in [Slack channel](https://slack.victoriametrics.com/) and responsive and helpful maintainer support.
 
 Alex Ulstein, Head of Monitoring, Wix.com
 
@@ -647,7 +647,7 @@ Thanos, Cortex and VictoriaMetrics were evaluated as a long-term storage for Pro
 - Blazingly fast benchmarks for a single node setup.
 - Single binary mode. Easy to scale vertically with far fewer operational headaches.
 - Considerable [improvements on creating Histograms](https://medium.com/@valyala/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350).
-- [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) gives us the ability to extend PromQL with more aggregation operators.
+- [MetricsQL](https://docs.victoriametrics.com/metricsql/) gives us the ability to extend PromQL with more aggregation operators.
 - The API is compatible with Prometheus and nearly all standard PromQL queries work well out of the box.
 - Handles storage well, with periodic compaction which makes it easy to take snapshots.
 

docs/Cluster-VictoriaMetrics.md

@@ -20,7 +20,7 @@ for ingestion rates lower than a million data points per second.
 The single-node version [scales perfectly](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae)
 with the number of CPU cores, RAM and available storage space.
 The single-node version is easier to configure and operate compared to the cluster version, so think twice before choosing the cluster version.
-See [this question](https://docs.victoriametrics.com/FAQ.html#which-victoriametrics-type-is-recommended-for-use-in-production---single-node-or-cluster) for more details.
+See [this question](https://docs.victoriametrics.com/faq/#which-victoriametrics-type-is-recommended-for-use-in-production---single-node-or-cluster) for more details.
 
 There is also user-friendly database for logs - [VictoriaLogs](https://docs.victoriametrics.com/VictoriaLogs/).
 
@@ -33,7 +33,7 @@ Enterprise binaries can be downloaded and evaluated for free
 from [the releases page](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/latest).
 See how to request a free trial license [here](https://victoriametrics.com/products/enterprise/trial/).
 
-VictoriaMetrics is developed at a fast pace, so it is recommended periodically checking the [CHANGELOG](https://docs.victoriametrics.com/CHANGELOG.html) and performing [regular upgrades](#how-to-upgrade-victoriametrics).
+VictoriaMetrics is developed at a fast pace, so it is recommended periodically checking the [CHANGELOG](https://docs.victoriametrics.com/changelog/) and performing [regular upgrades](#how-to-upgrade-victoriametrics).
 
 VictoriaMetrics has achieved security certifications for Database Software Development and Software-Based Monitoring Services. We apply strict security measures in everything we do. See our [Security page](https://victoriametrics.com/security/) for more details.
 
@@ -70,8 +70,8 @@ Some facts about tenants in VictoriaMetrics:
 - Each `accountID` and `projectID` is identified by an arbitrary 32-bit integer in the range `[0 .. 2^32)`.
 If `projectID` is missing, then it is automatically assigned to `0`. It is expected that other information about tenants
 such as auth tokens, tenant names, limits, accounting, etc. is stored in a separate relational database. This database must be managed
-by a separate service sitting in front of VictoriaMetrics cluster such as [vmauth](https://docs.victoriametrics.com/vmauth.html)
-or [vmgateway](https://docs.victoriametrics.com/vmgateway.html). [Contact us](mailto:info@victoriametrics.com) if you need assistance with such service.
+by a separate service sitting in front of VictoriaMetrics cluster such as [vmauth](https://docs.victoriametrics.com/vmauth/)
+or [vmgateway](https://docs.victoriametrics.com/vmgateway/). [Contact us](mailto:info@victoriametrics.com) if you need assistance with such service.
 
 - Tenants are automatically created when the first data point is written into the given tenant.
 
@@ -84,7 +84,7 @@ when different tenants have different amounts of data and different query load.
 
 - The list of registered tenants can be obtained via `http://<vmselect>:8481/admin/tenants` url. See [these docs](#url-format).
 
-- VictoriaMetrics exposes various per-tenant statistics via metrics - see [these docs](https://docs.victoriametrics.com/PerTenantStatistic.html).
+- VictoriaMetrics exposes various per-tenant statistics via metrics - see [these docs](https://docs.victoriametrics.com/pertenantstatistic/).
 
 See also [multitenancy via labels](#multitenancy-via-labels).
 
@@ -106,7 +106,7 @@ http_requests_total{path="/bar",vm_account_id="7",vm_project_id="9"} 34
 Then the `http_requests_total{path="/foo"} 12` would be stored in the tenant `accountID=42, projectID=0`,
 while the `http_requests_total{path="/bar"} 34` would be stored in the tenant `accountID=7, projectID=9`.
 
-The `vm_account_id` and `vm_project_id` labels are extracted after applying the [relabeling](https://docs.victoriametrics.com/relabeling.html)
+The `vm_account_id` and `vm_project_id` labels are extracted after applying the [relabeling](https://docs.victoriametrics.com/relabeling/)
 set via `-relabelConfig` command-line flag, so these labels can be set at this stage.
 
 The `vm_account_id` and `vm_project_id` labels are also taken into account when ingesting data via non-http-based protocols
@@ -196,7 +196,7 @@ It is recommended to run at least two nodes for each service for high availabili
 
 It is preferred to run many small `vmstorage` nodes over a few big `vmstorage` nodes, since this reduces the workload increase on the remaining `vmstorage` nodes when some of `vmstorage` nodes become temporarily unavailable.
 
-An http load balancer such as [vmauth](https://docs.victoriametrics.com/vmauth.html) or `nginx` must be put in front of `vminsert` and `vmselect` nodes.
+An http load balancer such as [vmauth](https://docs.victoriametrics.com/vmauth/) or `nginx` must be put in front of `vminsert` and `vmselect` nodes.
 It must contain the following routing configs according to [the url format](#url-format):
 
 - requests starting with `/insert` must be routed to port `8480` on `vminsert` nodes.
@@ -245,18 +245,18 @@ the following approaches for automatic discovery of `vmstorage` nodes:
   The list of discovered `vmstorage` nodes is automatically updated when the file contents changes.
   The update frequency can be controlled with `-storageNode.discoveryInterval` command-line flag.
 
-- [dns+srv](https://en.wikipedia.org/wiki/SRV_record) - pass `dns+srv:some-name` value to `-storageNode` command-line flag.
-  In this case the provided `dns+srv` names are resolved into tcp addresses of `vmstorage` nodes.
+- [DNS SRV](https://en.wikipedia.org/wiki/SRV_record) - pass `srv+some-name` value to `-storageNode` command-line flag.
+  In this case the provided `some-name` is resolved into tcp addresses of `vmstorage` nodes.
   The list of discovered `vmstorage` nodes is automatically updated at `vminsert` and `vmselect`
-  when it changes behind the corresponding `dns+srv` names.
+  when it changes behind the `some-name` DNS SRV record.
   The update frequency can be controlled with `-storageNode.discoveryInterval` command-line flag.
 
-It is possible passing multiple `file` and `dns+srv` names to `-storageNode` command-line flag. In this case all these names
+It is possible passing multiple `file` and DNS SRV names to `-storageNode` command-line flag. In this case all these names
 are resolved to tcp addresses of `vmstorage` nodes to connect to.
-For example, `-storageNode=file:/path/to/local-vmstorage-list -storageNode='dns+srv:vmstorage-hot' -storageNode='dns+srv:vmstorage-cold'`.
+For example, `-storageNode=file:/path/to/local-vmstorage-list -storageNode='srv+vmstorage-hot' -storageNode='srv+vmstorage-cold'`.
 
-It is OK to pass regular static `vmstorage` addresses together with `file` and `dns+srv` addresses at `-storageNode`. For example,
-`-storageNode=vmstorage1,vmstorage2 -storageNode='dns+srv:vmstorage-autodiscovery'`.
+It is OK to pass regular static `vmstorage` addresses together with `file` and DNS SRV addresses at `-storageNode`. For example,
+`-storageNode=vmstorage1,vmstorage2 -storageNode='srv+vmstorage-autodiscovery'`.
 
 The discovered addresses can be filtered with optional `-storageNode.filter` command-line flag, which can contain arbitrary regular expression filter.
 For example, `-storageNode.filter='^[^:]+:8400$'` would leave discovered addresses ending with `8400` port only, e.g. the default port used
@@ -269,8 +269,8 @@ The currently discovered `vmstorage` nodes can be [monitored](#monitoring) with
 General security recommendations:
 
 - All the VictoriaMetrics cluster components must run in protected private network without direct access from untrusted networks such as Internet.
-- External clients must access `vminsert` and `vmselect` via auth proxy such as [vmauth](https://docs.victoriametrics.com/vmauth.html)
-  or [vmgateway](https://docs.victoriametrics.com/vmgateway.html).
+- External clients must access `vminsert` and `vmselect` via auth proxy such as [vmauth](https://docs.victoriametrics.com/vmauth/)
+  or [vmgateway](https://docs.victoriametrics.com/vmgateway/).
 - The auth proxy must accept auth tokens from untrusted networks only via https in order to protect the auth tokens from MitM attacks.
 - It is recommended using distinct auth tokens for distinct [tenants](#multitenancy) in order to reduce potential damage in case of compromised auth token for some tenants.
 - Prefer using lists of allowed [API endpoints](#url-format), while disallowing access to other endpoints when configuring auth proxy in front of `vminsert` and `vmselect`.
@@ -283,7 +283,7 @@ and [the general security page at VictoriaMetrics website](https://victoriametri
 ## mTLS protection
 
 By default `vminsert` and `vmselect` nodes accept http requests at `8480` and `8481` ports accordingly (these ports can be changed via `-httpListenAddr` command-line flags),
-since it is expected that [vmauth](https://docs.victoriametrics.com/vmauth.html) is used for authorization and [TLS termination](https://en.wikipedia.org/wiki/TLS_termination_proxy)
+since it is expected that [vmauth](https://docs.victoriametrics.com/vmauth/) is used for authorization and [TLS termination](https://en.wikipedia.org/wiki/TLS_termination_proxy)
 in front of `vminsert` and `vmselect`.
 [Enterprise version of VictoriaMetrics](https://docs.victoriametrics.com/enterprise/) supports the ability to accept [mTLS](https://en.wikipedia.org/wiki/Mutual_authentication)
 requests at `8480` and `8481` ports for `vminsert` and `vmselect` nodes, by specifying `-tls` and `-mtls` command-line flags.
@@ -320,7 +320,7 @@ By default, the following TCP ports are used:
 - `vmselect` - 8481
 - `vmstorage` - 8482
 
-It is recommended setting up [vmagent](https://docs.victoriametrics.com/vmagent.html)
+It is recommended setting up [vmagent](https://docs.victoriametrics.com/vmagent/)
 or Prometheus to scrape `/metrics` pages from all the cluster components, so they can be monitored and analyzed
 with [the official Grafana dashboard for VictoriaMetrics cluster](https://grafana.com/grafana/dashboards/11176)
 or [an alternative dashboard for VictoriaMetrics cluster](https://grafana.com/grafana/dashboards/11831).
@@ -330,15 +330,15 @@ If you use Google Cloud Managed Prometheus for scraping metrics from VictoriaMet
 command-line to them, so they add `TYPE` and `HELP` comments per each exposed metric at `/metrics` page.
 See [these docs](https://cloud.google.com/stackdriver/docs/managed-prometheus/troubleshooting#missing-metric-type) for details.
 
-It is recommended setting up alerts in [vmalert](https://docs.victoriametrics.com/vmalert.html) or in Prometheus from [this list](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker#alerts).
+It is recommended setting up alerts in [vmalert](https://docs.victoriametrics.com/vmalert/) or in Prometheus from [this list](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker#alerts).
 See more details in the article [VictoriaMetrics Monitoring](https://victoriametrics.com/blog/victoriametrics-monitoring/).
 
 ## Cardinality limiter
 
 `vmstorage` nodes can be configured with limits on the number of unique time series across all the tenants with the following command-line flags:
 
-- `-storage.maxHourlySeries` is the limit on the number of [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series) during the last hour.
-- `-storage.maxDailySeries` is the limit on the number of unique time series during the day. This limit can be used for limiting daily [time series churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
+- `-storage.maxHourlySeries` is the limit on the number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series) during the last hour.
+- `-storage.maxDailySeries` is the limit on the number of unique time series during the day. This limit can be used for limiting daily [time series churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate).
 
 Note that these limits are set and applied individually per each `vmstorage` node in the cluster. So, if the cluster has `N` `vmstorage` nodes, then the cluster-level limits will be `N` times bigger than the per-`vmstorage` limits.
 
@@ -347,10 +347,10 @@ See more details about cardinality limiter in [these docs](https://docs.victoria
 ## Troubleshooting
 
 - If your VictoriaMetrics cluster experiences data ingestion delays during
-  [rolling restarts and configuration updates](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#updating--reconfiguring-cluster-nodes),
+  [rolling restarts and configuration updates](https://docs.victoriametrics.com/cluster-victoriametrics/#updating--reconfiguring-cluster-nodes),
   then see [these docs](#improving-re-routing-performance-during-restart).
 
-[Troubleshooting docs for single-node VictoriaMetrics](https://docs.victoriametrics.com/Troubleshooting.html) apply to VictoriaMetrics cluster as well.
+[Troubleshooting docs for single-node VictoriaMetrics](https://docs.victoriametrics.com/troubleshooting/) apply to VictoriaMetrics cluster as well.
 
 ## Readonly mode
 
@@ -364,10 +364,10 @@ The metric is set to `0` when the `vmstorage` isn't in read-only mode.
 
 ## URL format
 
-The main differences between URL formats of cluster and [Single server](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html)
+The main differences between URL formats of cluster and [Single server](https://docs.victoriametrics.com/single-server-victoriametrics/)
 versions are that cluster has separate components for read and ingestion path, and because of multi-tenancy support.
 Also in the cluster version the `/prometheus/api/v1` endpoint ingests  `jsonl`, `csv`, `native` and `prometheus` data formats **not** only `prometheus` data.
-Check practical examples of VictoriaMetrics API [here](https://docs.victoriametrics.com/url-examples.html).
+Check practical examples of VictoriaMetrics API [here](https://docs.victoriametrics.com/url-examples/).
 
 - URLs for data ingestion: `http://<vminsert>:8480/insert/<accountID>/<suffix>`, where:
   - `<accountID>` is an arbitrary 32-bit integer identifying namespace for data ingestion (aka tenant). It is possible to set it as `accountID:projectID`,
@@ -378,21 +378,21 @@ Check practical examples of VictoriaMetrics API [here](https://docs.victoriametr
     - `prometheus` and `prometheus/api/v1/write` - for ingesting data with [Prometheus remote write API](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#remote_write).
     - `prometheus/api/v1/import` - for importing data obtained via `api/v1/export` at `vmselect` (see below), JSON line format.
     - `prometheus/api/v1/import/native` - for importing data obtained via `api/v1/export/native` on `vmselect` (see below).
-    - `prometheus/api/v1/import/csv` - for importing arbitrary CSV data. See [these docs](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-import-csv-data) for details.
-    - `prometheus/api/v1/import/prometheus` - for importing data in [Prometheus text exposition format](https://github.com/prometheus/docs/blob/master/content/docs/instrumenting/exposition_formats.md#text-based-format) and in [OpenMetrics format](https://github.com/OpenObservability/OpenMetrics/blob/master/specification/OpenMetrics.md). This endpoint also supports [Pushgateway protocol](https://github.com/prometheus/pushgateway#url). See [these docs](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-import-data-in-prometheus-exposition-format) for details.
-    - `opentelemetry/v1/metrics` - for ingesting data via [OpenTelemetry protocol for metrics](https://github.com/open-telemetry/opentelemetry-specification/blob/ffddc289462dfe0c2041e3ca42a7b1df805706de/specification/metrics/data-model.md). See [these docs](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#sending-data-via-opentelemetry).
-    - `datadog/api/v1/series` - for ingesting data with DataDog submit metrics API v1. See [these docs](https://docs.victoriametrics.com/url-examples.html#datadogapiv1series) for details.
-    - `datadog/api/v2/series` - for ingesting data with [DataDog submit metrics API](https://docs.datadoghq.com/api/latest/metrics/#submit-metrics). See [these docs](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-send-data-from-datadog-agent) for details.
+    - `prometheus/api/v1/import/csv` - for importing arbitrary CSV data. See [these docs](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-import-csv-data) for details.
+    - `prometheus/api/v1/import/prometheus` - for importing data in [Prometheus text exposition format](https://github.com/prometheus/docs/blob/master/content/docs/instrumenting/exposition_formats.md#text-based-format) and in [OpenMetrics format](https://github.com/OpenObservability/OpenMetrics/blob/master/specification/OpenMetrics.md). This endpoint also supports [Pushgateway protocol](https://github.com/prometheus/pushgateway#url). See [these docs](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-import-data-in-prometheus-exposition-format) for details.
+    - `opentelemetry/v1/metrics` - for ingesting data via [OpenTelemetry protocol for metrics](https://github.com/open-telemetry/opentelemetry-specification/blob/ffddc289462dfe0c2041e3ca42a7b1df805706de/specification/metrics/data-model.md). See [these docs](https://docs.victoriametrics.com/single-server-victoriametrics/#sending-data-via-opentelemetry).
+    - `datadog/api/v1/series` - for ingesting data with DataDog submit metrics API v1. See [these docs](https://docs.victoriametrics.com/url-examples/#datadogapiv1series) for details.
+    - `datadog/api/v2/series` - for ingesting data with [DataDog submit metrics API](https://docs.datadoghq.com/api/latest/metrics/#submit-metrics). See [these docs](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-send-data-from-datadog-agent) for details.
     - `datadog/api/beta/sketches` - for ingesting data with [DataDog lambda extension](https://docs.datadoghq.com/serverless/libraries_integrations/extension/).
-    - `influx/write` and `influx/api/v2/write` - for ingesting data with [InfluxDB line protocol](https://docs.influxdata.com/influxdb/v1.7/write_protocols/line_protocol_tutorial/). See [these docs](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-send-data-from-influxdb-compatible-agents-such-as-telegraf) for details.
+    - `influx/write` and `influx/api/v2/write` - for ingesting data with [InfluxDB line protocol](https://docs.influxdata.com/influxdb/v1.7/write_protocols/line_protocol_tutorial/). See [these docs](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-send-data-from-influxdb-compatible-agents-such-as-telegraf) for details.
     - `newrelic/infra/v2/metrics/events/bulk` - for accepting data from [NewRelic infrastructure agent](https://docs.newrelic.com/docs/infrastructure/install-infrastructure-agent). See [these docs](https://docs.victoriametrics.com/#how-to-send-data-from-newrelic-agent) for details.
-    - `opentsdb/api/put` - for accepting [OpenTSDB HTTP /api/put requests](http://opentsdb.net/docs/build/html/api_http/put.html). This handler is disabled by default. It is exposed on a distinct TCP address set via `-opentsdbHTTPListenAddr` command-line flag. See [these docs](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#sending-opentsdb-data-via-http-apiput-requests) for details.
+    - `opentsdb/api/put` - for accepting [OpenTSDB HTTP /api/put requests](http://opentsdb.net/docs/build/html/api_http/put.html). This handler is disabled by default. It is exposed on a distinct TCP address set via `-opentsdbHTTPListenAddr` command-line flag. See [these docs](https://docs.victoriametrics.com/single-server-victoriametrics/#sending-opentsdb-data-via-http-apiput-requests) for details.
 
 - URLs for [Prometheus querying API](https://prometheus.io/docs/prometheus/latest/querying/api/): `http://<vmselect>:8481/select/<accountID>/prometheus/<suffix>`, where:
   - `<accountID>` is an arbitrary number identifying data namespace for the query (aka tenant)
   - `<suffix>` may have the following values:
-    - `api/v1/query` - performs [PromQL instant query](https://docs.victoriametrics.com/keyConcepts.html#instant-query).
-    - `api/v1/query_range` - performs [PromQL range query](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+    - `api/v1/query` - performs [PromQL instant query](https://docs.victoriametrics.com/keyconcepts/#instant-query).
+    - `api/v1/query_range` - performs [PromQL range query](https://docs.victoriametrics.com/keyconcepts/#range-query).
     - `api/v1/series` - performs [series query](https://docs.victoriametrics.com/url-examples/#apiv1series).
     - `api/v1/labels` - returns a [list of label names](https://docs.victoriametrics.com/url-examples/#apiv1labels).
     - `api/v1/label/<label_name>/values` - returns values for the given `<label_name>` according [to the API](https://docs.victoriametrics.com/url-examples/#apiv1labelvalues).
@@ -405,7 +405,7 @@ Check practical examples of VictoriaMetrics API [here](https://docs.victoriametr
     - `api/v1/status/active_queries` - for currently executed active queries. Note that every `vmselect` maintains an independent list of active queries,
       which is returned in the response.
     - `api/v1/status/top_queries` - for listing the most frequently executed queries and queries taking the most duration.
-    - `metric-relabel-debug` - for debugging [relabeling rules](https://docs.victoriametrics.com/relabeling.html).
+    - `metric-relabel-debug` - for debugging [relabeling rules](https://docs.victoriametrics.com/relabeling/).
 
 - URLs for [Graphite Metrics API](https://graphite-api.readthedocs.io/en/latest/api.html#the-metrics-api): `http://<vmselect>:8481/select/<accountID>/graphite/<suffix>`, where:
   - `<accountID>` is an arbitrary number identifying data namespace for query (aka tenant)
@@ -434,7 +434,7 @@ Check practical examples of VictoriaMetrics API [here](https://docs.victoriametr
 - URL for listing [tenants](#multitenancy) with the ingested data on the given time range: `http://<vmselect>:8481/admin/tenants?start=...&end=...` .
 The `start` and `end` query args are optional. If they are missing, then all the tenants with at least one sample stored in VictoriaMetrics are returned.
 
-- URL for accessing [vmalerts](https://docs.victoriametrics.com/vmalert.html) UI: `http://<vmselect>:8481/select/<accountID>/prometheus/vmalert/`.
+- URL for accessing [vmalerts](https://docs.victoriametrics.com/vmalert/) UI: `http://<vmselect>:8481/select/<accountID>/prometheus/vmalert/`.
   This URL works only when `-vmalert.proxyURL` flag is set. See more about vmalert [here](#vmalert). 
 
 - `vmstorage` nodes provide the following HTTP endpoints on `8482` port:
@@ -458,10 +458,25 @@ Cluster performance and capacity can be scaled up in two ways:
 
 General recommendations for cluster scalability:
 
-- Adding more CPU and RAM to existing `vmselect` nodes improves the performance for heavy queries, which process big number of time series with big number of raw samples. See [this article on how to detect and optimize heavy queries](https://valyala.medium.com/how-to-optimize-promql-and-metricsql-queries-85a1b75bf986).
-- Adding more `vmstorage` nodes (aka horizontal scaling) increases the number of [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series) the cluster can handle. This also increases query performance over time series with [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate). The cluster stability is also improved with the number of `vmstorage` nodes, since active `vmstorage` nodes need to handle lower additional workload when some of `vmstorage` nodes become unavailable. For example, during a node outage the rest of the nodes will pick up the load designated to unavailable node. So in case one node out of 5 is unavailable 20% of the load will be re-distributed across 4 remaining nodes which means each node will take 5% of the load. With 10 nodes in case of outage 10% of the load will be distributed across 9 remaining node, so around ~1% of load will be distributed across other nodes.
-- Adding more CPU and RAM to existing `vmstorage` nodes (aka vertical scaling) increases the number of [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series) the cluster can handle. It is preferred to add more `vmstorage` nodes over adding more CPU and RAM to existing `vmstorage` nodes, since higher number of `vmstorage` nodes increases cluster stability and improves query performance over time series with [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
+- Adding more CPU and RAM to existing `vmselect` nodes improves the performance for heavy queries, which process big number of time series with big number of raw samples.
+  See [this article on how to detect and optimize heavy queries](https://valyala.medium.com/how-to-optimize-promql-and-metricsql-queries-85a1b75bf986).
+
+- Adding more `vmstorage` nodes (aka horizontal scaling) increases the number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series)
+  the cluster can handle. This also increases query performance over time series with [high churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate),
+  since every `vmstorage` node contains lower number of time series when the number of `vmstorage` nodes increases.
+
+  The cluster stability is also improved with the number of `vmstorage` nodes, since active `vmstorage` nodes need to handle lower additional workload
+  when some of `vmstorage` nodes become unavailable. For example, if one node out of 3 nodes is unavailable, then `1/3=33%` of the load is re-distributed across 2 remaining nodes,
+  so per-node workload increase is `(1/3/2)/(1/3) = 1/2 = 50%`.
+  If one node out of 10 nodes is unavailable, then `1/10=10%` of the load is re-distributed across 9 remaining nodes, so per-node workload increase is `(1/10/9)/(1/10) = 1/9 =~ 11%`.
+
+- Adding more CPU and RAM to existing `vmstorage` nodes (aka vertical scaling) increases the number
+  of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series) the cluster can handle.
+  It is preferred to add more `vmstorage` nodes over adding more CPU and RAM to existing `vmstorage` nodes, since higher number of `vmstorage` nodes
+  increases cluster stability and improves query performance over time series with [high churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate).
+
 - Adding more `vminsert` nodes increases the maximum possible data ingestion speed, since the ingested data may be split among bigger number of `vminsert` nodes.
+
 - Adding more `vmselect` nodes increases the maximum possible queries rate, since the incoming concurrent requests may be split among bigger number of `vmselect` nodes.
 
 Steps to add `vmstorage` node:
@@ -499,7 +514,7 @@ This strategy allows upgrading the cluster without downtime if the following con
   the current workload when a single node of any type (`vminsert`, `vmselect` or `vmstorage`)
   is temporarily unavailable during its restart.
 - The updated config / upgraded binary is compatible with the remaining components in the cluster.
-  See the [CHANGELOG](https://docs.victoriametrics.com/CHANGELOG.html) for compatibility notes between different releases.
+  See the [CHANGELOG](https://docs.victoriametrics.com/changelog/) for compatibility notes between different releases.
 
 If at least a single condition isn't met, then the rolling restart may result in cluster unavailability
 during the config update / version upgrade. In this case the following strategy is recommended.
@@ -525,7 +540,7 @@ The `minimum downtime` strategy has the following benefits comparing to `no down
 ## Improving re-routing performance during restart
 
 `vmstorage` nodes may experience increased usage for CPU, RAM and disk IO during
-[rolling restarts](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#no-downtime-strategy),
+[rolling restarts](https://docs.victoriametrics.com/cluster-victoriametrics/#no-downtime-strategy),
 since they need to process higher load when some of `vmstorage` nodes are temporarily unavailable in the cluster.
 
 The following approaches can be used for reducing resource usage at `vmstorage` nodes during rolling restart:
@@ -535,7 +550,7 @@ The following approaches can be used for reducing resource usage at `vmstorage`
   Note that the `-disableReroutingOnUnavailable` flag may pause data ingestion for long time when some `vmstorage` nodes are unavailable
   for long time.
 
-- To pass bigger values to `-storage.vminsertConnsShutdownDuration` (available from [v1.95.0](https://docs.victoriametrics.com/CHANGELOG.html#v1950))
+- To pass bigger values to `-storage.vminsertConnsShutdownDuration` (available from [v1.95.0](https://docs.victoriametrics.com/changelog/#v1950))
   command-line flag at `vmstorage` nodes.In this case `vmstorage` increases the interval between gradual closing of `vminsert` connections during graceful shutdown.
   This reduces data ingestion slowdown during rollout restarts.
 
@@ -554,7 +569,7 @@ if some of its components are temporarily unavailable.
 VictoriaMetrics cluster remains available if the following conditions are met:
 
 - HTTP load balancer must stop routing requests to unavailable `vminsert` and `vmselect` nodes
-  ([vmauth](https://docs.victoriametrics.com/vmauth.html) stops routing requests to unavailable nodes).
+  ([vmauth](https://docs.victoriametrics.com/vmauth/) stops routing requests to unavailable nodes).
 
 - At least a single `vminsert` node must remain available in the cluster for processing data ingestion workload.
   The remaining active `vminsert` nodes must have enough compute capacity (CPU, RAM, network bandwidth)
@@ -584,7 +599,7 @@ The cluster works in the following way when some of `vmstorage` nodes are unavai
   for processing the increased data ingestion workload.
   `vminsert` spreads evenly the additional data among the healthy `vmstorage` nodes in order to spread evenly
   the increased load on these nodes. During re-routing, healthy `vmstorage` nodes will experience higher resource usage
-  and increase in number of [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series).
+  and increase in number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series).
 
 - `vmselect` continues serving queries if at least a single `vmstorage` nodes is available.
   It marks responses as partial for queries served from the remaining healthy `vmstorage` nodes,
@@ -614,9 +629,9 @@ Data replication can be used for increasing storage durability. See [these docs]
 
 ## Capacity planning
 
-VictoriaMetrics uses lower amounts of CPU, RAM and storage space on production workloads compared to competing solutions (Prometheus, Thanos, Cortex, TimescaleDB, InfluxDB, QuestDB, M3DB) according to [our case studies](https://docs.victoriametrics.com/CaseStudies.html).
+VictoriaMetrics uses lower amounts of CPU, RAM and storage space on production workloads compared to competing solutions (Prometheus, Thanos, Cortex, TimescaleDB, InfluxDB, QuestDB, M3DB) according to [our case studies](https://docs.victoriametrics.com/casestudies/).
 
-Each node type - `vminsert`, `vmselect` and `vmstorage` - can run on the most suitable hardware. Cluster capacity scales linearly with the available resources. The needed amounts of CPU and RAM per each node type highly depends on the workload - the number of [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series), [series churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate), query types, query qps, etc. It is recommended setting up a test VictoriaMetrics cluster for your production workload and iteratively scaling per-node resources and the number of nodes per node type until the cluster becomes stable. It is recommended setting up [monitoring for the cluster](#monitoring). It helps to determine bottlenecks in cluster setup. It is also recommended following [the troubleshooting docs](https://docs.victoriametrics.com/#troubleshooting).
+Each node type - `vminsert`, `vmselect` and `vmstorage` - can run on the most suitable hardware. Cluster capacity scales linearly with the available resources. The needed amounts of CPU and RAM per each node type highly depends on the workload - the number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series), [series churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate), query types, query qps, etc. It is recommended setting up a test VictoriaMetrics cluster for your production workload and iteratively scaling per-node resources and the number of nodes per node type until the cluster becomes stable. It is recommended setting up [monitoring for the cluster](#monitoring). It helps to determine bottlenecks in cluster setup. It is also recommended following [the troubleshooting docs](https://docs.victoriametrics.com/#troubleshooting).
 
 The needed storage space for the given retention (the retention is set via `-retentionPeriod` command-line flag at `vmstorage`) can be extrapolated from disk space usage in a test run. For example, if the storage space usage is 10GB after a day-long test run on a production workload, then it will need at least `10GB*100=1TB` of disk space for `-retentionPeriod=100d` (100-days retention period). Storage space usage can be monitored with [the official Grafana dashboard for VictoriaMetrics cluster](#monitoring).
 
@@ -632,7 +647,7 @@ Some capacity planning tips for VictoriaMetrics cluster:
 
 - The [replication](#replication-and-data-safety) increases the amounts of needed resources for the cluster by up to `N` times where `N` is replication factor. This is because `vminsert` stores `N` copies of every ingested sample on distinct `vmstorage` nodes. These copies are de-duplicated by `vmselect` during querying. The most cost-efficient and performant solution for data durability is to rely on replicated durable persistent disks such as [Google Compute persistent disks](https://cloud.google.com/compute/docs/disks#pdspecs) instead of using the [replication at VictoriaMetrics level](#replication-and-data-safety).
 - It is recommended to run a cluster with big number of small `vmstorage` nodes instead of a cluster with small number of big `vmstorage` nodes. This increases chances that the cluster remains available and stable when some of `vmstorage` nodes are temporarily unavailable during maintenance events such as upgrades, configuration changes or migrations. For example, when a cluster contains 10 `vmstorage` nodes and a single node becomes temporarily unavailable, then the workload on the remaining 9 nodes increases by `1/9=11%`. When a cluster contains 3 `vmstorage` nodes and a single node becomes temporarily unavailable, then the workload on the remaining 2 nodes increases by `1/2=50%`. The remaining `vmstorage` nodes may have no enough free capacity for handling the increased workload. In this case the cluster may become overloaded, which may result to decreased availability and stability.
-- Cluster capacity for [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series) can be increased by increasing RAM and CPU resources per each `vmstorage` node or by adding new `vmstorage` nodes.
+- Cluster capacity for [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series) can be increased by increasing RAM and CPU resources per each `vmstorage` node or by adding new `vmstorage` nodes.
 - Query latency can be reduced by increasing CPU resources per each `vmselect` node, since each incoming query is processed by a single `vmselect` node. Performance for heavy queries scales with the number of available CPU cores at `vmselect` node, since `vmselect` processes time series referred by the query on all the available CPU cores.
 - If the cluster needs to process incoming queries at a high rate, then its capacity can be increased by adding more `vmselect` nodes, so incoming queries could be spread among bigger number of `vmselect` nodes.
 - By default `vminsert` compresses the data it sends to `vmstorage` in order to reduce network bandwidth usage. The compression takes additional CPU resources at `vminsert`. If `vminsert` nodes have limited CPU, then the compression can be disabled by passing `-rpc.disableCompression` command-line flag at `vminsert` nodes.
@@ -675,33 +690,33 @@ Some workloads may need fine-grained resource usage limits. In these cases the f
   The downside is that the endpoints can return labels and series, which do not match the provided extra filters.
 - `-search.maxSamplesPerSeries` at `vmselect` limits the number of raw samples the query can process per each time series.
   `vmselect` processes raw samples sequentially per each found time series during the query. It unpacks raw samples on the selected time range
-  per each time series into memory and then applies the given [rollup function](https://docs.victoriametrics.com/MetricsQL.html#rollup-functions).
+  per each time series into memory and then applies the given [rollup function](https://docs.victoriametrics.com/metricsql/#rollup-functions).
   The `-search.maxSamplesPerSeries` command-line flag allows limiting memory usage at `vmselect` in the case when the query is executed on a time range,
   which contains hundreds of millions of raw samples per each located time series.
 - `-search.maxSamplesPerQuery` at `vmselect` limits the number of raw samples a single query can process. This allows limiting CPU usage at `vmselect` for heavy queries.
 - `-search.maxResponseSeries` at `vmselect` limits the number of time series a single query can return
-  from [`/api/v1/query`](https://docs.victoriametrics.com/keyConcepts.html#instant-query) and [`/api/v1/query_range`](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+  from [`/api/v1/query`](https://docs.victoriametrics.com/keyconcepts/#instant-query) and [`/api/v1/query_range`](https://docs.victoriametrics.com/keyconcepts/#range-query).
 - `-search.maxPointsPerTimeseries` limits the number of calculated points, which can be returned per each
-  matching time series from [range query](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+  matching time series from [range query](https://docs.victoriametrics.com/keyconcepts/#range-query).
 - `-search.maxPointsSubqueryPerTimeseries` limits the number of calculated points, which can be generated
-  per each matching time series during [subquery](https://docs.victoriametrics.com/MetricsQL.html#subqueries) evaluation.
-- `-search.maxSeriesPerAggrFunc` limits the number of time series, which can be generated by [MetricsQL aggregate functions](https://docs.victoriametrics.com/MetricsQL.html#aggregate-functions) in a single query.
+  per each matching time series during [subquery](https://docs.victoriametrics.com/metricsql/#subqueries) evaluation.
+- `-search.maxSeriesPerAggrFunc` limits the number of time series, which can be generated by [MetricsQL aggregate functions](https://docs.victoriametrics.com/metricsql/#aggregate-functions) in a single query.
 - `-search.maxSeries` at `vmselect` limits the number of time series, which may be returned from
   [/api/v1/series](https://docs.victoriametrics.com/url-examples/#apiv1series).
   This endpoint is used mostly by Grafana for auto-completion of metric names, label names and label values.
   Queries to this endpoint may take big amounts of CPU time and memory at `vmstorage` and `vmselect` when the database contains
-  big number of unique time series because of [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
+  big number of unique time series because of [high churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate).
   In this case it might be useful to set the `-search.maxSeries` to quite low value in order limit CPU and memory usage.
 - `-search.maxTagKeys` at `vmstorage` limits the number of items, which may be returned from
   [/api/v1/labels](https://docs.victoriametrics.com/url-examples/#apiv1labels). This endpoint is used mostly by Grafana
   for auto-completion of label names. Queries to this endpoint may take big amounts of CPU time and memory at `vmstorage` and `vmselect`
-  when the database contains big number of unique time series because of [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
+  when the database contains big number of unique time series because of [high churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate).
   In this case it might be useful to set the `-search.maxTagKeys` to quite low value in order to limit CPU and memory usage.
   See also `-search.maxLabelsAPIDuration` and `-search.maxLabelsAPISeries`.
 - `-search.maxTagValues` at `vmstorage` limits the number of items, which may be returned from
   [/api/v1/label/.../values](https://docs.victoriametrics.com/url-examples/#apiv1labelvalues). This endpoint is used mostly by Grafana
   for auto-completion of label values. Queries to this endpoint may take big amounts of CPU time and memory at `vmstorage` and `vmselect`
-  when the database contains big number of unique time series because of [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
+  when the database contains big number of unique time series because of [high churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate).
   In this case it might be useful to set the `-search.maxTagValues` to quite low value in order to limit CPU and memory usage.
   See also `-search.maxLabelsAPIDuration` and `-search.maxLabelsAPISeries`.
 - `-search.maxLabelsAPISeries` at `vmselect` limits the number of time series, which can be scanned
@@ -709,7 +724,7 @@ Some workloads may need fine-grained resource usage limits. In these cases the f
   [/api/v1/label/.../values](https://docs.victoriametrics.com/url-examples/#apiv1labelvalues)
   or [/api/v1/series](https://docs.victoriametrics.com/url-examples/#apiv1series) requests.
   These endpoints are used mostly by Grafana for auto-completion of label names and label values. Queries to these endpoints may take big amounts of CPU time and memory
-  when the database contains big number of unique time series because of [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
+  when the database contains big number of unique time series because of [high churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate).
   In this case it might be useful to set the `-search.maxLabelsAPISeries` to quite low value in order to limit CPU and memory usage.
   See also `-search.maxLabelsAPIDuration` and `-search.ignoreExtraFiltersAtLabelsAPI`.
 - `-search.maxLabelsAPIDuration` at `vmselect` limits the duration for requests to [/api/v1/labels](https://docs.victoriametrics.com/url-examples/#apiv1labels),
@@ -717,15 +732,15 @@ Some workloads may need fine-grained resource usage limits. In these cases the f
   or [/api/v1/series](https://docs.victoriametrics.com/url-examples/#apiv1series).
   The limit can be altered for each query by passing `timeout` GET parameter, but can't exceed the limit specified via cmd-line flag.
   These endpoints are used mostly by Grafana for auto-completion of label names and label values. Queries to these endpoints may take big amounts of CPU time and memory
-  when the database contains big number of unique time series because of [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
+  when the database contains big number of unique time series because of [high churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate).
   In this case it might be useful to set the `-search.maxLabelsAPIDuration` to quite low value in order to limit CPU and memory usage.
   See also `-search.maxLabelsAPISeries` and `-search.ignoreExtraFiltersAtLabelsAPI`.
 - `-storage.maxDailySeries` at `vmstorage` can be used for limiting the number of time series seen per day aka
-  [time series churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate). See [cardinality limiter docs](#cardinality-limiter).
-- `-storage.maxHourlySeries` at `vmstorage` can be used for limiting the number of [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series).
+  [time series churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate). See [cardinality limiter docs](#cardinality-limiter).
+- `-storage.maxHourlySeries` at `vmstorage` can be used for limiting the number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series).
   See [cardinality limiter docs](#cardinality-limiter).
 
-See also [capacity planning docs](#capacity-planning) and [cardinality limiter in vmagent](https://docs.victoriametrics.com/vmagent.html#cardinality-limiter).
+See also [capacity planning docs](#capacity-planning) and [cardinality limiter in vmagent](https://docs.victoriametrics.com/vmagent/#cardinality-limiter).
 
 ## High availability
 
@@ -738,8 +753,8 @@ across multiple availability zones, since cross-AZ network usually has lower ban
 error rates comparing the network inside a single AZ.
 
 If you need multi-AZ setup, then it is recommended running independent clusters in each AZ and setting up
-[vmagent](https://docs.victoriametrics.com/vmagent.html) in front of these clusters, so it could replicate incoming data
-into all the cluster - see [these docs](https://docs.victoriametrics.com/vmagent.html#multitenancy) for details.
+[vmagent](https://docs.victoriametrics.com/vmagent/) in front of these clusters, so it could replicate incoming data
+into all the cluster - see [these docs](https://docs.victoriametrics.com/vmagent/#multitenancy) for details.
 Then an additional `vmselect` nodes can be configured for reading the data from multiple clusters according to [these docs](#multi-level-cluster-setup).
 
 
@@ -762,45 +777,67 @@ The multi-level cluster setup for `vminsert` nodes has the following shortcoming
 * Data ingestion speed is limited by the slowest link to AZ.
 * `vminsert` nodes at top level re-route incoming data to the remaining AZs when some AZs are temporarily unavailable. This results in data gaps at AZs which were temporarily unavailable.
 
-These issues are addressed by [vmagent](https://docs.victoriametrics.com/vmagent.html) when it runs in [multitenancy mode](https://docs.victoriametrics.com/vmagent.html#multitenancy).
+These issues are addressed by [vmagent](https://docs.victoriametrics.com/vmagent/) when it runs in [multitenancy mode](https://docs.victoriametrics.com/vmagent/#multitenancy).
 `vmagent` buffers data, which must be sent to a particular AZ, when this AZ is temporarily unavailable. The buffer is stored on disk. The buffered data is sent to AZ as soon as it becomes available.
 
 ## vmstorage groups at vmselect
 
 `vmselect` can be configured to query multiple distinct groups of `vmstorage` nodes with individual `-replicationFactor` per each group.
 The following format for `-storageNode` command-line flag value should be used for assigning a particular `addr` of `vmstorage` to a particular `groupName` -
-`-storageNode=groupName/addr`. For example, the following command runs `vmselect`, which continues returning full responses if up to one node per each group is temporarily unavailable
+`-storageNode=groupName/addr`. The `groupName` can contain arbitrary value. The only rule is that every `vmstorage` group must have an unique name.
+
+For example, the following command runs `vmselect`, which continues returning full responses if up to one node per each group is temporarily unavailable
 because the given `-replicationFactor=2` is applied individually per each group:
 
 ```
 /path/to/vmselect \
  -replicationFactor=2 \
- -storageNode=group1/host1 \
- -storageNode=group1/host2 \
- -storageNode=group1/host3 \
- -storageNode=group2/host4 \
- -storageNode=group2/host5 \
- -storageNode=group2/host6 \
- -storageNode=group3/host7 \
- -storageNode=group3/host8 \
- -storageNode=group3/host9
+ -storageNode=g1/host1,g1/host2,g1/host3 \
+ -storageNode=g2/host4,g2/host5,g2/host6 \
+ -storageNode=g3/host7,g3/host8,g3/host9
 ```
 
-It is possible to specify distinct `-replicationFactor` per each group via the following format - `-replicationFactor=groupName:rf`.
-For example, the following command runs `vmselect`, which uses `-replicationFactor=3` for the `group1`, while it uses `-replicationFactor=1` for the `group2`:
+It is possible specifying distinct `-replicationFactor` per each group via the following format - `-replicationFactor=groupName:rf`.
+For example, the following command runs `vmselect`, which uses `-replicationFactor=3` for the group `g1`, `-replicationFactor=2` for the group `g2`
+and `-replicationFactor=1` for the group `g3`:
 
 ```
 /path/to/vmselect \
- -replicationFactor=group1:3 \
- -storageNode=group1/host1 \
- -storageNode=group1/host2 \
- -storageNode=group1/host3 \
- -replicationFactor=group2:1 \
- -storageNode=group2/host4 \
- -storageNode=group2/host5 \
- -storageNode=group2/host6
+ -replicationFactor=g1:3 \
+ -storageNode=g1/host1,g1/host2,g1/host3 \
+ -replicationFactor=g2:2 \
+ -storageNode=g2/host4,g2/host5,g2/host6 \
+ -replicationFactor=g3:1 \
+ -storageNode=g3/host4,g3/host5,g3/host6
 ```
 
+If every ingested sample is replicated across multiple `vmstorage` groups, then pass `-globalReplicationFactor=N` command-line flag to `vmselect`,
+so it could continue returning full responses if up to `N-1` `vmstorage` groups are temporarily unavailable.
+For example, the following command runs `vmselect`, which continues returning full responses if any number of `vmstorage` nodes
+in a single `vmstorage` group are temporarily unavailable:
+
+```
+/path/to/vmselect \
+ -globalReplicationFactor=2 \
+ -storageNode=g1/host1,g1/host2,g1/host3 \
+ -storageNode=g2/host4,g2/host5,g2/host6 \
+ -storageNode=g3/host7,g3/host8,g3/host9
+```
+
+It is OK to mix `-replicationFactor` and `-globalReplicationFactor`. For example, the following command runs `vmselect`, which continues returning full responses
+if any number of `vmstorage` nodes in a single `vmstorage` group are temporarily unavailable and the remaining groups contain up to two unavailable `vmstorage` node:
+
+```
+/path/to/vmselect \
+ -globalReplicationFactor=2 \
+ -replicationFactor=3 \
+ -storageNode=g1/host1,g1/host2,g1/host3 \
+ -storageNode=g2/host4,g2/host5,g2/host6 \
+ -storageNode=g3/host7,g3/host8,g3/host9
+```
+
+See also [multi-level cluster setup](#multi-level-cluster-setup).
+
 ## Helm
 
 Helm chart simplifies managing cluster version of VictoriaMetrics in Kubernetes.
@@ -826,7 +863,7 @@ for newly ingested data when `N-1` of storage nodes are unavailable.
 
 VictoriaMetrics stores timestamps with millisecond precision, so `-dedup.minScrapeInterval=1ms` command-line flag must be passed to `vmselect` nodes when the replication is enabled,
 so they could de-duplicate replicated samples obtained from distinct `vmstorage` nodes during querying. If duplicate data is pushed to VictoriaMetrics
-from identically configured [vmagent](https://docs.victoriametrics.com/vmagent.html) instances or Prometheus instances, then the `-dedup.minScrapeInterval` must be set
+from identically configured [vmagent](https://docs.victoriametrics.com/vmagent/) instances or Prometheus instances, then the `-dedup.minScrapeInterval` must be set
 to `scrape_interval` from scrape configs according to [deduplication docs](#deduplication).
 
 Note that [replication doesn't save from disaster](https://medium.com/@valyala/speeding-up-backups-for-big-time-series-databases-533c1a927883),
@@ -857,7 +894,7 @@ for protecting from user errors such as accidental data deletion.
 The following steps must be performed for each `vmstorage` node for creating a backup:
 
 1. Create an instant snapshot by navigating to `/snapshot/create` HTTP handler. It will create snapshot and return its name.
-1. Archive the created snapshot from `<-storageDataPath>/snapshots/<snapshot_name>` folder using [vmbackup](https://docs.victoriametrics.com/vmbackup.html).
+1. Archive the created snapshot from `<-storageDataPath>/snapshots/<snapshot_name>` folder using [vmbackup](https://docs.victoriametrics.com/vmbackup/).
    The archival process doesn't interfere with `vmstorage` work, so it may be performed at any suitable time.
 1. Delete unused snapshots via `/snapshot/delete?snapshot=<snapshot_name>` or `/snapshot/delete_all` in order to free up occupied storage space.
 
@@ -866,7 +903,7 @@ There is no need in synchronizing backups among all the `vmstorage` nodes.
 Restoring from backup:
 
 1. Stop `vmstorage` node with `kill -INT`.
-1. Restore data from backup using [vmrestore](https://docs.victoriametrics.com/vmrestore.html) into `-storageDataPath` directory.
+1. Restore data from backup using [vmrestore](https://docs.victoriametrics.com/vmrestore/) into `-storageDataPath` directory.
 1. Start `vmstorage` node.
 
 ## Retention filters
@@ -904,7 +941,7 @@ See how to request a free trial license [here](https://victoriametrics.com/produ
 Downsampling is available in [enterprise version of VictoriaMetrics](https://docs.victoriametrics.com/enterprise/).
 It is configured with `-downsampling.period` command-line flag according to [these docs](https://docs.victoriametrics.com/#downsampling).
 
-It is possible to downsample series, which belong to a particular [tenant](#multitenancy) by using [filters](https://docs.victoriametrics.com/keyConcepts.html#filtering)
+It is possible to downsample series, which belong to a particular [tenant](#multitenancy) by using [filters](https://docs.victoriametrics.com/keyconcepts/#filtering)
 on `vm_account_id` or `vm_project_id` pseudo-labels in `-downsampling.period` command-line flag. For example, the following config leaves the last sample per each minute for samples
 older than one hour only for [tenants](#multitenancy) with accountID equal to 12 and 42, while series for other tenants are dropped:
 
@@ -923,7 +960,7 @@ The same flag value must be passed to both `vmstorage` and `vmselect` nodes. Con
 command-line flag makes query results more consistent, because `vmselect` uses the maximum configured downsampling interval
 on the requested time range if this time range covers multiple downsampling levels.
 For example, if `-downsampling.period=30d:5m` and the query requests the last 60 days of data, then `vmselect`
-downsamples all the [raw samples](https://docs.victoriametrics.com/keyConcepts.html#raw-samples) on the requested time range
+downsamples all the [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the requested time range
 using 5 minute interval. If `-downsampling.period` command-line flag isn't set at `vmselect`,
 then query results can be less consistent because of mixing raw and downsampled data.
 
@@ -960,7 +997,7 @@ It is safe sharing the collected profiles from security point of view, since the
 
 ## vmalert
 
-vmselect is capable of proxying requests to [vmalert](https://docs.victoriametrics.com/vmalert.html)
+vmselect is capable of proxying requests to [vmalert](https://docs.victoriametrics.com/vmalert/)
 when `-vmalert.proxyURL` flag is set. Use this feature for the following cases:
 * for proxying requests from [Grafana Alerting UI](https://grafana.com/docs/grafana/latest/alerting/);
 * for accessing vmalerts UI through vmselects Web interface.
@@ -1023,19 +1060,19 @@ Below is the output for `/path/to/vminsert -help`:
   -cacheExpireDuration duration
      Items are removed from in-memory caches after they aren't accessed for this duration. Lower values may reduce memory usage at the cost of higher CPU usage. See also -prevCacheRemovalPercent (default 30m0s)
   -cluster.tls
-     Whether to use TLS for connections to -storageNode. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Whether to use TLS for connections to -storageNode. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -cluster.tlsCAFile string
-     Path to TLS CA file to use for verifying certificates provided by -storageNode if -cluster.tls flag is set. By default system CA is used. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Path to TLS CA file to use for verifying certificates provided by -storageNode if -cluster.tls flag is set. By default system CA is used. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -cluster.tlsCertFile string
-     Path to client-side TLS certificate file to use when connecting to -storageNode if -cluster.tls flag is set. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Path to client-side TLS certificate file to use when connecting to -storageNode if -cluster.tls flag is set. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -cluster.tlsInsecureSkipVerify
      Whether to skip verification of TLS certificates provided by -storageNode nodes if -cluster.tls flag is set. Note that disabled TLS certificate verification breaks security. This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -cluster.tlsKeyFile string
-     Path to client-side TLS key file to use when connecting to -storageNode if -cluster.tls flag is set. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Path to client-side TLS key file to use when connecting to -storageNode if -cluster.tls flag is set. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -clusternative.vminsertConnsShutdownDuration duration
-     The time needed for gradual closing of upstream vminsert connections during graceful shutdown. Bigger duration reduces spikes in CPU, RAM and disk IO load on the remaining lower-level clusters during rolling restart. Smaller duration reduces the time needed to close all the upstream vminsert connections, thus reducing the time for graceful shutdown. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#improving-re-routing-performance-during-restart (default 25s)
+     The time needed for gradual closing of upstream vminsert connections during graceful shutdown. Bigger duration reduces spikes in CPU, RAM and disk IO load on the remaining lower-level clusters during rolling restart. Smaller duration reduces the time needed to close all the upstream vminsert connections, thus reducing the time for graceful shutdown. See https://docs.victoriametrics.com/cluster-victoriametrics/#improving-re-routing-performance-during-restart (default 25s)
   -clusternativeListenAddr string
-     TCP address to listen for data from other vminsert nodes in multi-level cluster setup. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#multi-level-cluster-setup . Usually :8400 should be set to match default vmstorage port for vminsert. Disabled work if empty
+     TCP address to listen for data from other vminsert nodes in multi-level cluster setup. See https://docs.victoriametrics.com/cluster-victoriametrics/#multi-level-cluster-setup . Usually :8400 should be set to match default vmstorage port for vminsert. Disabled work if empty
   -csvTrimTimestamp duration
      Trim timestamps when importing csv data to this duration. Minimum practical duration is 1ms. Higher duration (i.e. 1s) may be used for reducing disk space usage for timestamp data (default 1ms)
   -datadog.maxInsertRequestSize size
@@ -1160,7 +1197,7 @@ Below is the output for `/path/to/vminsert -help`:
   -loggerWarnsPerSecondLimit int
      Per-second limit on the number of WARN messages. If more than the given number of warns are emitted per second, then the remaining warns are suppressed. Zero values disable the rate limit
   -maxConcurrentInserts int
-     The maximum number of concurrent insert requests. Default value depends on the number of CPU cores and should work for most cases since it minimizes the memory usage. The default value can be increased when clients send data over slow networks. See also -insert.maxQueueDuration
+     The maximum number of concurrent insert requests. Set higher value when clients send data over slow networks. Default value depends on the number of available CPU cores. It should work fine in most cases since it minimizes resource usage. See also -insert.maxQueueDuration (default 32)
   -maxInsertRequestSize size
      The maximum size in bytes of a single Prometheus remote_write API request
      Supports the following optional suffixes for size values: KB, MB, GB, TB, KiB, MiB, GiB, TiB (default 33554432)
@@ -1189,6 +1226,8 @@ Below is the output for `/path/to/vminsert -help`:
   -newrelic.maxInsertRequestSize size
      The maximum size in bytes of a single NewRelic request to /newrelic/infra/v2/metrics/events/bulk
      Supports the following optional suffixes for size values: KB, MB, GB, TB, KiB, MiB, GiB, TiB (default 67108864)
+  -opentelemetry.usePrometheusNaming
+     Whether to convert metric names and labels into Prometheus-compatible format for the metrics ingested via OpenTelemetry protocol; see https://docs.victoriametrics.com/#sending-data-via-opentelemetry
   -opentsdbHTTPListenAddr string
      TCP address to listen for OpenTSDB HTTP put requests. Usually :4242 must be set. Doesn't work if empty. See also -opentsdbHTTPListenAddr.useProxyProtocol
   -opentsdbHTTPListenAddr.useProxyProtocol
@@ -1234,19 +1273,27 @@ Below is the output for `/path/to/vminsert -help`:
   -sortLabels
      Whether to sort labels for incoming samples before writing them to storage. This may be needed for reducing memory usage at storage when the order of labels in incoming samples is random. For example, if m{k1="v1",k2="v2"} may be sent as m{k2="v2",k1="v1"}. Enabled sorting for labels can slow down ingestion performance a bit
   -storageNode array
-     Comma-separated addresses of vmstorage nodes; usage: -storageNode=vmstorage-host1,...,vmstorage-hostN . Enterprise version of VictoriaMetrics supports automatic discovery of vmstorage addresses via dns+srv records. For example, -storageNode=dns+srv:vmstorage.addrs . See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#automatic-vmstorage-discovery
+     Comma-separated addresses of vmstorage nodes; usage: -storageNode=vmstorage-host1,...,vmstorage-hostN . Enterprise version of VictoriaMetrics supports automatic discovery of vmstorage addresses via DNS SRV records. For example, -storageNode=srv+vmstorage.addrs . See https://docs.victoriametrics.com/cluster-victoriametrics/#automatic-vmstorage-discovery
      Supports an array of values separated by comma or specified via multiple flags.
      Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -storageNode.discoveryInterval duration
-     Interval for refreshing -storageNode list behind dns+srv records. The minimum supported interval is 1s. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#automatic-vmstorage-discovery . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/ (default 2s)
+     Interval for refreshing -storageNode list behind DNS SRV records. The minimum supported interval is 1s. See https://docs.victoriametrics.com/cluster-victoriametrics/#automatic-vmstorage-discovery . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/ (default 2s)
   -storageNode.filter string
-     An optional regexp filter for discovered -storageNode addresses according to https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#automatic-vmstorage-discovery. Discovered addresses matching the filter are retained, while other addresses are ignored. This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     An optional regexp filter for discovered -storageNode addresses according to https://docs.victoriametrics.com/cluster-victoriametrics/#automatic-vmstorage-discovery. Discovered addresses matching the filter are retained, while other addresses are ignored. This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -tls array
      Whether to enable TLS for incoming HTTP requests at the given -httpListenAddr (aka https). -tlsCertFile and -tlsKeyFile must be set if -tls is set. See also -mtls
      Supports array of values separated by comma or specified via multiple flags.
      Empty values are set to false.
+  -tlsAutocertCacheDir string
+     Directory to store TLS certificates issued via Let's Encrypt. Certificates are lost on restarts if this flag isn't set. This flag is available only in Enterprise binaries. See https://docs.victoriametrics.com/enterprise/
+  -tlsAutocertEmail string
+     Contact email for the issued Let's Encrypt TLS certificates. See also -tlsAutocertHosts and -tlsAutocertCacheDir .This flag is available only in Enterprise binaries. See https://docs.victoriametrics.com/enterprise/
+  -tlsAutocertHosts array
+     Optional hostnames for automatic issuing of Let's Encrypt TLS certificates. These hostnames must be reachable at -httpListenAddr . The -httpListenAddr must listen tcp port 443 . The -tlsAutocertHosts overrides -tlsCertFile and -tlsKeyFile . See also -tlsAutocertEmail and -tlsAutocertCacheDir . This flag is available only in Enterprise binaries. See https://docs.victoriametrics.com/enterprise/
+     Supports an array of values separated by comma or specified via multiple flags.
+     Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -tlsCertFile array
-     Path to file with TLS certificate for the corresponding -httpListenAddr if -tls is set. Prefer ECDSA certs instead of RSA certs as RSA certs are slower. The provided certificate file is automatically re-read every second, so it can be dynamically updated
+     Path to file with TLS certificate for the corresponding -httpListenAddr if -tls is set. Prefer ECDSA certs instead of RSA certs as RSA certs are slower. The provided certificate file is automatically re-read every second, so it can be dynamically updated. See also -tlsAutocertHosts
      Supports an array of values separated by comma or specified via multiple flags.
      Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -tlsCipherSuites array
@@ -1254,7 +1301,7 @@ Below is the output for `/path/to/vminsert -help`:
      Supports an array of values separated by comma or specified via multiple flags.
      Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -tlsKeyFile array
-     Path to file with TLS key for the corresponding -httpListenAddr if -tls is set. The provided key file is automatically re-read every second, so it can be dynamically updated
+     Path to file with TLS key for the corresponding -httpListenAddr if -tls is set. The provided key file is automatically re-read every second, so it can be dynamically updated. See also -tlsAutocertHosts
      Supports an array of values separated by comma or specified via multiple flags.
      Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -tlsMinVersion array
@@ -1283,15 +1330,15 @@ Below is the output for `/path/to/vmselect -help`:
   -cacheExpireDuration duration
      Items are removed from in-memory caches after they aren't accessed for this duration. Lower values may reduce memory usage at the cost of higher CPU usage. See also -prevCacheRemovalPercent (default 30m0s)
   -cluster.tls
-     Whether to use TLS for connections to -storageNode. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Whether to use TLS for connections to -storageNode. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -cluster.tlsCAFile string
-     Path to TLS CA file to use for verifying certificates provided by -storageNode if -cluster.tls flag is set. By default system CA is used. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Path to TLS CA file to use for verifying certificates provided by -storageNode if -cluster.tls flag is set. By default system CA is used. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -cluster.tlsCertFile string
-     Path to client-side TLS certificate file to use when connecting to -storageNode if -cluster.tls flag is set. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Path to client-side TLS certificate file to use when connecting to -storageNode if -cluster.tls flag is set. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -cluster.tlsInsecureSkipVerify
      Whether to skip verification of TLS certificates provided by -storageNode nodes if -cluster.tls flag is set. Note that disabled TLS certificate verification breaks security. This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -cluster.tlsKeyFile string
-     Path to client-side TLS key file to use when connecting to -storageNode if -cluster.tls flag is set. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Path to client-side TLS key file to use when connecting to -storageNode if -cluster.tls flag is set. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -clusternative.disableCompression
      Whether to disable compression of the data sent to vmselect via -clusternativeListenAddr. This reduces CPU usage at the cost of higher network bandwidth usage
   -clusternative.maxConcurrentRequests int
@@ -1305,11 +1352,11 @@ Below is the output for `/path/to/vmselect -help`:
   -clusternative.maxTagValues int
      The maximum number of tag values returned per search at -clusternativeListenAddr (default 100000)
   -clusternative.tls
-     Whether to use TLS when accepting connections at -clusternativeListenAddr. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Whether to use TLS when accepting connections at -clusternativeListenAddr. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -clusternative.tlsCAFile string
-     Path to TLS CA file to use for verifying certificates provided by vmselect, which connects at -clusternativeListenAddr if -clusternative.tls flag is set. By default system CA is used. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Path to TLS CA file to use for verifying certificates provided by vmselect, which connects at -clusternativeListenAddr if -clusternative.tls flag is set. By default system CA is used. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -clusternative.tlsCertFile string
-     Path to server-side TLS certificate file to use when accepting connections at -clusternativeListenAddr if -clusternative.tls flag is set. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Path to server-side TLS certificate file to use when accepting connections at -clusternativeListenAddr if -clusternative.tls flag is set. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -clusternative.tlsCipherSuites array
      Optional list of TLS cipher suites used for connections at -clusternativeListenAddr if -clusternative.tls flag is set. See the list of supported cipher suites at https://pkg.go.dev/crypto/tls#pkg-constants . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
      Supports an array of values separated by comma or specified via multiple flags.
@@ -1317,9 +1364,9 @@ Below is the output for `/path/to/vmselect -help`:
   -clusternative.tlsInsecureSkipVerify
      Whether to skip verification of TLS certificates provided by vmselect, which connects to -clusternativeListenAddr if -clusternative.tls flag is set. Note that disabled TLS certificate verification breaks security. This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -clusternative.tlsKeyFile string
-     Path to server-side TLS key file to use when accepting connections at -clusternativeListenAddr if -clusternative.tls flag is set. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Path to server-side TLS key file to use when accepting connections at -clusternativeListenAddr if -clusternative.tls flag is set. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -clusternativeListenAddr string
-     TCP address to listen for requests from other vmselect nodes in multi-level cluster setup. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#multi-level-cluster-setup . Usually :8401 should be set to match default vmstorage port for vmselect. Disabled work if empty
+     TCP address to listen for requests from other vmselect nodes in multi-level cluster setup. See https://docs.victoriametrics.com/cluster-victoriametrics/#multi-level-cluster-setup . Usually :8401 should be set to match default vmstorage port for vmselect. Disabled work if empty
   -dedup.minScrapeInterval duration
      Leave only the last sample in every time series per each discrete interval equal to -dedup.minScrapeInterval > 0. See https://docs.victoriametrics.com/#deduplication for details
   -deleteAuthKey value
@@ -1346,6 +1393,8 @@ Below is the output for `/path/to/vmselect -help`:
      Flag value can be read from the given file when using -flagsAuthKey=file:///abs/path/to/file or -flagsAuthKey=file://./relative/path/to/file . Flag value can be read from the given http/https url when using -flagsAuthKey=http://host/path or -flagsAuthKey=https://host/path
   -fs.disableMmap
      Whether to use pread() instead of mmap() for reading data files. By default, mmap() is used for 64-bit arches and pread() is used for 32-bit arches, since they cannot read data files bigger than 2^32 bytes in memory. mmap() is usually faster for reading small data chunks than pread()
+  -globalReplicationFactor int
+     How many copies of every ingested sample is available across vmstorage groups. vmselect continues returning full responses when up to globalReplicationFactor-1 vmstorage groups are temporarily unavailable. See https://docs.victoriametrics.com/cluster-victoriametrics/#vmstorage-groups-at-vmselect . See also -replicationFactor (default 1)
   -http.connTimeout duration
      Incoming connections to -httpListenAddr are closed after the configured timeout. This may help evenly spreading load among a cluster of services behind TCP-level load balancer. Zero value disables closing of incoming connections (default 2m0s)
   -http.disableResponseCompression
@@ -1447,7 +1496,7 @@ Below is the output for `/path/to/vmselect -help`:
      Supports an array of values separated by comma or specified via multiple flags.
      Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -replicationFactor array
-     How many copies of every time series is available on the provided -storageNode nodes. vmselect continues returning full responses when up to replicationFactor-1 vmstorage nodes are temporarily unavailable during querying. See also -search.skipSlowReplicas (default 1)
+     How many copies of every ingested sample is available across -storageNode nodes. vmselect continues returning full responses when up to replicationFactor-1 vmstorage nodes are temporarily unavailable. See also -globalReplicationFactor and -search.skipSlowReplicas (default 1)
      Supports an array of `key:value` entries separated by comma or specified via multiple flags.
   -search.cacheTimestampOffset duration
      The maximum duration since the current time for response data, which is always queried from the original raw data, without using the response cache. Increase this value if you see gaps in responses due to time synchronization issues between VictoriaMetrics and data sources (default 5m0s)
@@ -1553,19 +1602,27 @@ Below is the output for `/path/to/vmselect -help`:
      Supports an array of values separated by comma or specified via multiple flags.
      Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -storageNode array
-     Comma-separated addresses of vmstorage nodes; usage: -storageNode=vmstorage-host1,...,vmstorage-hostN . Enterprise version of VictoriaMetrics supports automatic discovery of vmstorage addresses via dns+srv records. For example, -storageNode=dns+srv:vmstorage.addrs . See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#automatic-vmstorage-discovery
+     Comma-separated addresses of vmstorage nodes; usage: -storageNode=vmstorage-host1,...,vmstorage-hostN . Enterprise version of VictoriaMetrics supports automatic discovery of vmstorage addresses via DNS SRV records. For example, -storageNode=srv+vmstorage.addrs . See https://docs.victoriametrics.com/cluster-victoriametrics/#automatic-vmstorage-discovery
      Supports an array of values separated by comma or specified via multiple flags.
      Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -storageNode.discoveryInterval duration
-     Interval for refreshing -storageNode list behind dns+srv records. The minimum supported interval is 1s. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#automatic-vmstorage-discovery . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/ (default 2s)
+     Interval for refreshing -storageNode list behind DNS SRV records. The minimum supported interval is 1s. See https://docs.victoriametrics.com/cluster-victoriametrics/#automatic-vmstorage-discovery . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/ (default 2s)
   -storageNode.filter string
-     An optional regexp filter for discovered -storageNode addresses according to https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#automatic-vmstorage-discovery. Discovered addresses matching the filter are retained, while other addresses are ignored. This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     An optional regexp filter for discovered -storageNode addresses according to https://docs.victoriametrics.com/cluster-victoriametrics/#automatic-vmstorage-discovery. Discovered addresses matching the filter are retained, while other addresses are ignored. This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -tls array
      Whether to enable TLS for incoming HTTP requests at the given -httpListenAddr (aka https). -tlsCertFile and -tlsKeyFile must be set if -tls is set. See also -mtls
      Supports array of values separated by comma or specified via multiple flags.
      Empty values are set to false.
+  -tlsAutocertCacheDir string
+     Directory to store TLS certificates issued via Let's Encrypt. Certificates are lost on restarts if this flag isn't set. This flag is available only in Enterprise binaries. See https://docs.victoriametrics.com/enterprise/
+  -tlsAutocertEmail string
+     Contact email for the issued Let's Encrypt TLS certificates. See also -tlsAutocertHosts and -tlsAutocertCacheDir .This flag is available only in Enterprise binaries. See https://docs.victoriametrics.com/enterprise/
+  -tlsAutocertHosts array
+     Optional hostnames for automatic issuing of Let's Encrypt TLS certificates. These hostnames must be reachable at -httpListenAddr . The -httpListenAddr must listen tcp port 443 . The -tlsAutocertHosts overrides -tlsCertFile and -tlsKeyFile . See also -tlsAutocertEmail and -tlsAutocertCacheDir . This flag is available only in Enterprise binaries. See https://docs.victoriametrics.com/enterprise/
+     Supports an array of values separated by comma or specified via multiple flags.
+     Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -tlsCertFile array
-     Path to file with TLS certificate for the corresponding -httpListenAddr if -tls is set. Prefer ECDSA certs instead of RSA certs as RSA certs are slower. The provided certificate file is automatically re-read every second, so it can be dynamically updated
+     Path to file with TLS certificate for the corresponding -httpListenAddr if -tls is set. Prefer ECDSA certs instead of RSA certs as RSA certs are slower. The provided certificate file is automatically re-read every second, so it can be dynamically updated. See also -tlsAutocertHosts
      Supports an array of values separated by comma or specified via multiple flags.
      Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -tlsCipherSuites array
@@ -1573,7 +1630,7 @@ Below is the output for `/path/to/vmselect -help`:
      Supports an array of values separated by comma or specified via multiple flags.
      Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -tlsKeyFile array
-     Path to file with TLS key for the corresponding -httpListenAddr if -tls is set. The provided key file is automatically re-read every second, so it can be dynamically updated
+     Path to file with TLS key for the corresponding -httpListenAddr if -tls is set. The provided key file is automatically re-read every second, so it can be dynamically updated. See also -tlsAutocertHosts
      Supports an array of values separated by comma or specified via multiple flags.
      Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -tlsMinVersion array
@@ -1606,11 +1663,11 @@ Below is the output for `/path/to/vmstorage -help`:
   -cacheExpireDuration duration
      Items are removed from in-memory caches after they aren't accessed for this duration. Lower values may reduce memory usage at the cost of higher CPU usage. See also -prevCacheRemovalPercent (default 30m0s)
   -cluster.tls
-     Whether to use TLS when accepting connections from vminsert and vmselect. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Whether to use TLS when accepting connections from vminsert and vmselect. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -cluster.tlsCAFile string
-     Path to TLS CA file to use for verifying certificates provided by vminsert and vmselect if -cluster.tls flag is set. By default system CA is used. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Path to TLS CA file to use for verifying certificates provided by vminsert and vmselect if -cluster.tls flag is set. By default system CA is used. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -cluster.tlsCertFile string
-     Path to server-side TLS certificate file to use when accepting connections from vminsert and vmselect if -cluster.tls flag is set. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Path to server-side TLS certificate file to use when accepting connections from vminsert and vmselect if -cluster.tls flag is set. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -cluster.tlsCipherSuites array
      Optional list of TLS cipher suites used for connections from vminsert and vmselect if -cluster.tls flag is set. See the list of supported cipher suites at https://pkg.go.dev/crypto/tls#pkg-constants . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
      Supports an array of values separated by comma or specified via multiple flags.
@@ -1618,7 +1675,7 @@ Below is the output for `/path/to/vmstorage -help`:
   -cluster.tlsInsecureSkipVerify
      Whether to skip verification of TLS certificates provided by vminsert and vmselect if -cluster.tls flag is set. Note that disabled TLS certificate verification breaks security. This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -cluster.tlsKeyFile string
-     Path to server-side TLS key file to use when accepting connections from vminsert and vmselect if -cluster.tls flag is set. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
+     Path to server-side TLS key file to use when accepting connections from vminsert and vmselect if -cluster.tls flag is set. See https://docs.victoriametrics.com/cluster-victoriametrics/#mtls-protection . This flag is available only in VictoriaMetrics enterprise. See https://docs.victoriametrics.com/enterprise/
   -dedup.minScrapeInterval duration
      Leave only the last sample in every time series per each discrete interval equal to -dedup.minScrapeInterval > 0. See https://docs.victoriametrics.com/#deduplication for details
   -denyQueriesOutsideRetention
@@ -1720,7 +1777,7 @@ Below is the output for `/path/to/vmstorage -help`:
   -loggerWarnsPerSecondLimit int
      Per-second limit on the number of WARN messages. If more than the given number of warns are emitted per second, then the remaining warns are suppressed. Zero values disable the rate limit
   -maxConcurrentInserts int
-     The maximum number of concurrent insert requests. Default value depends on the number of CPU cores and should work for most cases since it minimizes the memory usage. The default value can be increased when clients send data over slow networks. See also -insert.maxQueueDuration
+     The maximum number of concurrent insert requests. Set higher value when clients send data over slow networks. Default value depends on the number of available CPU cores. It should work fine in most cases since it minimizes resource usage. See also -insert.maxQueueDuration (default 32)
   -memory.allowedBytes size
      Allowed size of system memory VictoriaMetrics caches may occupy. This option overrides -memory.allowedPercent if set to a non-zero value. Too low a value may increase the cache miss rate usually resulting in higher CPU and disk IO usage. Too high a value may evict too much data from the OS page cache resulting in higher disk IO usage
      Supports the following optional suffixes for size values: KB, MB, GB, TB, KiB, MiB, GiB, TiB (default 0)
@@ -1796,16 +1853,16 @@ Below is the output for `/path/to/vmstorage -help`:
      Automatically delete snapshots older than -snapshotsMaxAge if it is set to non-zero duration. Make sure that backup process has enough time to finish the backup before the corresponding snapshot is automatically deleted
      The following optional suffixes are supported: s (second), m (minute), h (hour), d (day), w (week), y (year). If suffix isn't set, then the duration is counted in months (default 0)
   -storage.cacheSizeIndexDBDataBlocks size
-     Overrides max size for indexdb/dataBlocks cache. See https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#cache-tuning
+     Overrides max size for indexdb/dataBlocks cache. See https://docs.victoriametrics.com/single-server-victoriametrics/#cache-tuning
      Supports the following optional suffixes for size values: KB, MB, GB, TB, KiB, MiB, GiB, TiB (default 0)
   -storage.cacheSizeIndexDBIndexBlocks size
-     Overrides max size for indexdb/indexBlocks cache. See https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#cache-tuning
+     Overrides max size for indexdb/indexBlocks cache. See https://docs.victoriametrics.com/single-server-victoriametrics/#cache-tuning
      Supports the following optional suffixes for size values: KB, MB, GB, TB, KiB, MiB, GiB, TiB (default 0)
   -storage.cacheSizeIndexDBTagFilters size
-     Overrides max size for indexdb/tagFiltersToMetricIDs cache. See https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#cache-tuning
+     Overrides max size for indexdb/tagFiltersToMetricIDs cache. See https://docs.victoriametrics.com/single-server-victoriametrics/#cache-tuning
      Supports the following optional suffixes for size values: KB, MB, GB, TB, KiB, MiB, GiB, TiB (default 0)
   -storage.cacheSizeStorageTSID size
-     Overrides max size for storage/tsid cache. See https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#cache-tuning
+     Overrides max size for storage/tsid cache. See https://docs.victoriametrics.com/single-server-victoriametrics/#cache-tuning
      Supports the following optional suffixes for size values: KB, MB, GB, TB, KiB, MiB, GiB, TiB (default 0)
   -storage.maxDailySeries int
      The maximum number of unique series can be added to the storage during the last 24 hours. Excess series are logged and dropped. This can be useful for limiting series churn rate. See https://docs.victoriametrics.com/#cardinality-limiter . See also -storage.maxHourlySeries
@@ -1815,15 +1872,23 @@ Below is the output for `/path/to/vmstorage -help`:
      The minimum free disk space at -storageDataPath after which the storage stops accepting new data
      Supports the following optional suffixes for size values: KB, MB, GB, TB, KiB, MiB, GiB, TiB (default 10000000)
   -storage.vminsertConnsShutdownDuration duration
-     The time needed for gradual closing of vminsert connections during graceful shutdown. Bigger duration reduces spikes in CPU, RAM and disk IO load on the remaining vmstorage nodes during rolling restart. Smaller duration reduces the time needed to close all the vminsert connections, thus reducing the time for graceful shutdown. See https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#improving-re-routing-performance-during-restart (default 25s)
+     The time needed for gradual closing of vminsert connections during graceful shutdown. Bigger duration reduces spikes in CPU, RAM and disk IO load on the remaining vmstorage nodes during rolling restart. Smaller duration reduces the time needed to close all the vminsert connections, thus reducing the time for graceful shutdown. See https://docs.victoriametrics.com/cluster-victoriametrics/#improving-re-routing-performance-during-restart (default 25s)
   -storageDataPath string
      Path to storage data (default "vmstorage-data")
   -tls array
      Whether to enable TLS for incoming HTTP requests at the given -httpListenAddr (aka https). -tlsCertFile and -tlsKeyFile must be set if -tls is set. See also -mtls
      Supports array of values separated by comma or specified via multiple flags.
      Empty values are set to false.
+  -tlsAutocertCacheDir string
+     Directory to store TLS certificates issued via Let's Encrypt. Certificates are lost on restarts if this flag isn't set. This flag is available only in Enterprise binaries. See https://docs.victoriametrics.com/enterprise/
+  -tlsAutocertEmail string
+     Contact email for the issued Let's Encrypt TLS certificates. See also -tlsAutocertHosts and -tlsAutocertCacheDir .This flag is available only in Enterprise binaries. See https://docs.victoriametrics.com/enterprise/
+  -tlsAutocertHosts array
+     Optional hostnames for automatic issuing of Let's Encrypt TLS certificates. These hostnames must be reachable at -httpListenAddr . The -httpListenAddr must listen tcp port 443 . The -tlsAutocertHosts overrides -tlsCertFile and -tlsKeyFile . See also -tlsAutocertEmail and -tlsAutocertCacheDir . This flag is available only in Enterprise binaries. See https://docs.victoriametrics.com/enterprise/
+     Supports an array of values separated by comma or specified via multiple flags.
+     Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -tlsCertFile array
-     Path to file with TLS certificate for the corresponding -httpListenAddr if -tls is set. Prefer ECDSA certs instead of RSA certs as RSA certs are slower. The provided certificate file is automatically re-read every second, so it can be dynamically updated
+     Path to file with TLS certificate for the corresponding -httpListenAddr if -tls is set. Prefer ECDSA certs instead of RSA certs as RSA certs are slower. The provided certificate file is automatically re-read every second, so it can be dynamically updated. See also -tlsAutocertHosts
      Supports an array of values separated by comma or specified via multiple flags.
      Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -tlsCipherSuites array
@@ -1831,7 +1896,7 @@ Below is the output for `/path/to/vmstorage -help`:
      Supports an array of values separated by comma or specified via multiple flags.
      Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -tlsKeyFile array
-     Path to file with TLS key for the corresponding -httpListenAddr if -tls is set. The provided key file is automatically re-read every second, so it can be dynamically updated
+     Path to file with TLS key for the corresponding -httpListenAddr if -tls is set. The provided key file is automatically re-read every second, so it can be dynamically updated. See also -tlsAutocertHosts
      Supports an array of values separated by comma or specified via multiple flags.
      Value can contain comma inside single-quoted or double-quoted string, {}, [] and () braces.
   -tlsMinVersion array

docs/ExtendedPromQL.md

@@ -5,4 +5,4 @@ weight: 100
 
 # MetricsQL old page
 
-The page has been moved to [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html).
+The page has been moved to [MetricsQL](https://docs.victoriametrics.com/metricsql/).

docs/FAQ.md

@@ -19,36 +19,36 @@ To provide the best monitoring solution.
 
 ## Who uses VictoriaMetrics?
 
-See [case studies](https://docs.victoriametrics.com/CaseStudies.html).
+See [case studies](https://docs.victoriametrics.com/casestudies/).
 
 ## Which features does VictoriaMetrics have?
 
-See [these docs](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#prominent-features).
+See [these docs](https://docs.victoriametrics.com/single-server-victoriametrics/#prominent-features).
 
 ## Are there performance comparisons with other solutions?
 
-Yes. See [these benchmarks](https://docs.victoriametrics.com/Articles.html#benchmarks).
+Yes. See [these benchmarks](https://docs.victoriametrics.com/articles/#benchmarks).
 
 ## How to start using VictoriaMetrics?
 
-See [these docs](https://docs.victoriametrics.com/Quick-Start.html).
+See [these docs](https://docs.victoriametrics.com/quick-start/).
 
 ## Does VictoriaMetrics support replication?
 
-Yes. See [these docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#replication-and-data-safety) for details.
+Yes. See [these docs](https://docs.victoriametrics.com/cluster-victoriametrics/#replication-and-data-safety) for details.
 
 ## Can I use VictoriaMetrics instead of Prometheus?
 
 Yes in most cases. VictoriaMetrics can substitute Prometheus in the following aspects:
 
-* Prometheus-compatible service discovery and target scraping can be done with [vmagent](https://docs.victoriametrics.com/vmagent.html) and with single-node VictoriaMetrics. See [these docs](https://docs.victoriametrics.com/#how-to-scrape-prometheus-exporters-such-as-node-exporter).
-* Prometheus-compatible alerting rules and recording rules can be processed with [vmalert](https://docs.victoriametrics.com/vmalert.html).
+* Prometheus-compatible service discovery and target scraping can be done with [vmagent](https://docs.victoriametrics.com/vmagent/) and with single-node VictoriaMetrics. See [these docs](https://docs.victoriametrics.com/#how-to-scrape-prometheus-exporters-such-as-node-exporter).
+* Prometheus-compatible alerting rules and recording rules can be processed with [vmalert](https://docs.victoriametrics.com/vmalert/).
 * Prometheus-compatible querying in Grafana is supported by VictoriaMetrics. See [these docs](https://docs.victoriametrics.com/#grafana-setup).
 
 ## What is the difference between vmagent and Prometheus?
 
-While both [vmagent](https://docs.victoriametrics.com/vmagent.html) and Prometheus may scrape Prometheus targets (aka `/metrics` pages)
-according to the provided Prometheus-compatible [scrape configs](https://docs.victoriametrics.com/sd_configs.html#scrape_configs)
+While both [vmagent](https://docs.victoriametrics.com/vmagent/) and Prometheus may scrape Prometheus targets (aka `/metrics` pages)
+according to the provided Prometheus-compatible [scrape configs](https://docs.victoriametrics.com/sd_configs/#scrape_configs)
 and send data to multiple remote storage systems, vmagent has the following additional features:
 
 * vmagent usually requires lower amounts of CPU, RAM and disk IO compared to Prometheus when scraping an enormous number of targets (more than 1000)
@@ -58,30 +58,30 @@ and send data to multiple remote storage systems, vmagent has the following addi
   with a hardcoded retention of 2 hours.
 * vmagent may accept, relabel and filter data obtained via multiple data ingestion protocols in addition to data scraped from Prometheus targets.
   That means it supports both `pull` and `push` protocols for data ingestion.
-  See [these docs](https://docs.victoriametrics.com/vmagent.html#features) for details.
-* vmagent may be used in different [use cases](https://docs.victoriametrics.com/vmagent.html#use-cases):
-  * [IoT and edge monitoring](https://docs.victoriametrics.com/vmagent.html#iot-and-edge-monitoring)
-  * [Drop-in replacement for Prometheus](https://docs.victoriametrics.com/vmagent.html#drop-in-replacement-for-prometheus)
-  * [Statsd alternative](https://docs.victoriametrics.com/vmagent.html#statsd-alternative)
-  * [Flexible metrics relay](https://docs.victoriametrics.com/vmagent.html#flexible-metrics-relay)
-  * [Replication and high availability](https://docs.victoriametrics.com/vmagent.html#replication-and-high-availability)
-  * [Sharding among remote storages](https://docs.victoriametrics.com/vmagent.html#sharding-among-remote-storages)
-  * [Relabeling and filtering](https://docs.victoriametrics.com/vmagent.html#relabeling-and-filtering)
-  * [Splitting data streams among multiple systems](https://docs.victoriametrics.com/vmagent.html#splitting-data-streams-among-multiple-systems)
-  * [Prometheus remote_write proxy](https://docs.victoriametrics.com/vmagent.html#prometheus-remote_write-proxy)
-  * [remote_write for clustered version](https://docs.victoriametrics.com/vmagent.html#remote_write-for-clustered-version)
+  See [these docs](https://docs.victoriametrics.com/vmagent/#features) for details.
+* vmagent may be used in different [use cases](https://docs.victoriametrics.com/vmagent/#use-cases):
+  * [IoT and edge monitoring](https://docs.victoriametrics.com/vmagent/#iot-and-edge-monitoring)
+  * [Drop-in replacement for Prometheus](https://docs.victoriametrics.com/vmagent/#drop-in-replacement-for-prometheus)
+  * [Statsd alternative](https://docs.victoriametrics.com/vmagent/#statsd-alternative)
+  * [Flexible metrics relay](https://docs.victoriametrics.com/vmagent/#flexible-metrics-relay)
+  * [Replication and high availability](https://docs.victoriametrics.com/vmagent/#replication-and-high-availability)
+  * [Sharding among remote storages](https://docs.victoriametrics.com/vmagent/#sharding-among-remote-storages)
+  * [Relabeling and filtering](https://docs.victoriametrics.com/vmagent/#relabeling-and-filtering)
+  * [Splitting data streams among multiple systems](https://docs.victoriametrics.com/vmagent/#splitting-data-streams-among-multiple-systems)
+  * [Prometheus remote_write proxy](https://docs.victoriametrics.com/vmagent/#prometheus-remote_write-proxy)
+  * [remote_write for clustered version](https://docs.victoriametrics.com/vmagent/#remote_write-for-clustered-version)
 
 ## What is the difference between vmagent and Prometheus agent?
 
-Both [vmagent](https://docs.victoriametrics.com/vmagent.html) and [Prometheus agent](https://prometheus.io/blog/2021/11/16/agent/) serve the same purpose – to efficiently scrape Prometheus-compatible targets at the edge. They have the following differences:
+Both [vmagent](https://docs.victoriametrics.com/vmagent/) and [Prometheus agent](https://prometheus.io/blog/2021/11/16/agent/) serve the same purpose – to efficiently scrape Prometheus-compatible targets at the edge. They have the following differences:
 
 * vmagent usually requires lower amounts of CPU, RAM and disk IO compared to the Prometheus agent.
-* Prometheus agent supports only pull-based data collection (e.g. it can scrape Prometheus-compatible targets), while vmagent supports both pull and push data collection – it can accept data via many popular data ingestion protocols such as InfluxDB line protocol, Graphite protocol, OpenTSDB protocol, DataDog protocol, Prometheus protocol, CSV and JSON – see [these docs](https://docs.victoriametrics.com/vmagent.html#features).
-* vmagent can easily scale horizontally to multiple instances for scraping a big number of targets – see [these docs](https://docs.victoriametrics.com/vmagent.html#scraping-big-number-of-targets).
-* vmagent supports [improved relabeling](https://docs.victoriametrics.com/vmagent.html#relabeling).
-* vmagent can limit the number of scraped metrics per target – see [these docs](https://docs.victoriametrics.com/vmagent.html#cardinality-limiter).
-* vmagent supports loading scrape configs from multiple files – see [these docs](https://docs.victoriametrics.com/vmagent.html#loading-scrape-configs-from-multiple-files).
-* vmagent supports data reading and data writing from/to Kafka – see [these docs](https://docs.victoriametrics.com/vmagent.html#kafka-integration).
+* Prometheus agent supports only pull-based data collection (e.g. it can scrape Prometheus-compatible targets), while vmagent supports both pull and push data collection – it can accept data via many popular data ingestion protocols such as InfluxDB line protocol, Graphite protocol, OpenTSDB protocol, DataDog protocol, Prometheus protocol, CSV and JSON – see [these docs](https://docs.victoriametrics.com/vmagent/#features).
+* vmagent can easily scale horizontally to multiple instances for scraping a big number of targets – see [these docs](https://docs.victoriametrics.com/vmagent/#scraping-big-number-of-targets).
+* vmagent supports [improved relabeling](https://docs.victoriametrics.com/vmagent/#relabeling).
+* vmagent can limit the number of scraped metrics per target – see [these docs](https://docs.victoriametrics.com/vmagent/#cardinality-limiter).
+* vmagent supports loading scrape configs from multiple files – see [these docs](https://docs.victoriametrics.com/vmagent/#loading-scrape-configs-from-multiple-files).
+* vmagent supports data reading and data writing from/to Kafka – see [these docs](https://docs.victoriametrics.com/vmagent/#kafka-integration).
 * vmagent can read and update scrape configs from http and https URLs, while the Prometheus agent can read them only from the local file system.
 
 ## Is it safe to enable [remote write](https://prometheus.io/docs/operating/integrations/#remote-endpoints-and-storage) in Prometheus?
@@ -89,7 +89,7 @@ Both [vmagent](https://docs.victoriametrics.com/vmagent.html) and [Prometheus ag
 Yes. Prometheus continues writing data to local storage after enabling remote write, so all the existing local storage data
 and new data is available for querying via Prometheus as usual.
 
-It is recommended using [vmagent](https://docs.victoriametrics.com/vmagent.html) for scraping Prometheus targets
+It is recommended using [vmagent](https://docs.victoriametrics.com/vmagent/) for scraping Prometheus targets
 and writing data to VictoriaMetrics.
 
 ## How does VictoriaMetrics compare to other remote storage solutions for Prometheus such as [M3DB](https://github.com/m3db/m3), [Thanos](https://github.com/thanos-io/thanos), [Cortex](https://github.com/cortexproject/cortex), [Mimir](https://github.com/grafana/mimir), etc.?
@@ -113,10 +113,10 @@ VictoriaMetrics also [uses less RAM than Thanos components](https://github.com/t
 
 * QuestDB needs more than 20x storage space than VictoriaMetrics. This translates to higher storage costs and slower queries over historical data, which must be read from the disk.
 * QuestDB is much harder to set up and operate than VictoriaMetrics. Compare [setup instructions for QuestDB](https://questdb.io/docs/get-started/binaries) to [setup instructions for VictoriaMetrics](https://docs.victoriametrics.com/#how-to-start-victoriametrics).
-* VictoriaMetrics provides the [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) query language, which is better suited for typical queries over time series data than the SQL-like query language provided by QuestDB. See [this article](https://valyala.medium.com/promql-tutorial-for-beginners-9ab455142085) for details.
+* VictoriaMetrics provides the [MetricsQL](https://docs.victoriametrics.com/metricsql/) query language, which is better suited for typical queries over time series data than the SQL-like query language provided by QuestDB. See [this article](https://valyala.medium.com/promql-tutorial-for-beginners-9ab455142085) for details.
 * VictoriaMetrics can be queried via the [Prometheus querying API](https://docs.victoriametrics.com/#prometheus-querying-api-usage) and via [Graphite's API](https://docs.victoriametrics.com/#graphite-api-usage).
 * Thanks to PromQL support, VictoriaMetrics [can be used as a drop-in replacement for Prometheus in Grafana](https://docs.victoriametrics.com/#grafana-setup), while QuestDB needs a full rewrite of existing dashboards in Grafana.
-* Thanks to Prometheus' remote_write API support, VictoriaMetrics can be used as a long-term storage for Prometheus or for [vmagent](https://docs.victoriametrics.com/vmagent.html), while QuestDB has no integration with Prometheus.
+* Thanks to Prometheus' remote_write API support, VictoriaMetrics can be used as a long-term storage for Prometheus or for [vmagent](https://docs.victoriametrics.com/vmagent/), while QuestDB has no integration with Prometheus.
 * QuestDB [supports a smaller range of popular data ingestion protocols](https://questdb.io/docs/develop/insert-data) compared to VictoriaMetrics (compare to [the list of supported data ingestion protocols for VictoriaMetrics](https://docs.victoriametrics.com/#how-to-import-time-series-data)).
 * [VictoriaMetrics supports backfilling (e.g. storing historical data) out of the box](https://docs.victoriametrics.com/#backfilling), while QuestDB provides [very limited support for backfilling](https://questdb.io/blog/2021/05/10/questdb-release-6-0-tsbs-benchmark#the-problem-with-out-of-order-data).
 
@@ -131,13 +131,13 @@ See also [Grafana Mimir vs VictoriaMetrics benchmark](https://victoriametrics.co
 
 VictoriaMetrics is similar to Cortex in the following aspects:
 
-* Both systems accept data from [vmagent](https://docs.victoriametrics.com/vmagent.html) or Prometheus
+* Both systems accept data from [vmagent](https://docs.victoriametrics.com/vmagent/) or Prometheus
   via the standard [remote_write API](https://prometheus.io/docs/practices/remote_write/), so there is no need for running sidecars
   unlike in [Thanos](https://github.com/thanos-io/thanos)' case.
-* Both systems support multi-tenancy out of the box. See [the corresponding docs for VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#multitenancy).
-* Both systems support data replication. See [replication in Cortex](https://github.com/cortexproject/cortex/blob/fe56f1420099aa1bf1ce09316c186e05bddee879/docs/architecture.md#hashing) and [replication in VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#replication-and-data-safety).
-* Both systems scale horizontally to multiple nodes. See [these docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#cluster-resizing-and-scalability) for details.
-* Both systems support alerting and recording rules via the corresponding tools such as [vmalert](https://docs.victoriametrics.com/vmalert.html).
+* Both systems support multi-tenancy out of the box. See [the corresponding docs for VictoriaMetrics](https://docs.victoriametrics.com/cluster-victoriametrics/#multitenancy).
+* Both systems support data replication. See [replication in Cortex](https://github.com/cortexproject/cortex/blob/fe56f1420099aa1bf1ce09316c186e05bddee879/docs/architecture.md#hashing) and [replication in VictoriaMetrics](https://docs.victoriametrics.com/cluster-victoriametrics/#replication-and-data-safety).
+* Both systems scale horizontally to multiple nodes. See [these docs](https://docs.victoriametrics.com/cluster-victoriametrics/#cluster-resizing-and-scalability) for details.
+* Both systems support alerting and recording rules via the corresponding tools such as [vmalert](https://docs.victoriametrics.com/vmalert/).
 * Both systems can be queried via the [Prometheus querying API](https://prometheus.io/docs/prometheus/latest/querying/api/) and integrate perfectly with Grafana.
 
 The main differences between Cortex and VictoriaMetrics:
@@ -146,16 +146,16 @@ The main differences between Cortex and VictoriaMetrics:
 * Cortex heavily relies on third-party services such as Consul, Memcache, DynamoDB, BigTable, Cassandra, etc.
   This may increase operational complexity and reduce system reliability compared to VictoriaMetrics' case,
   which doesn't use any external services. Compare [Cortex' Architecture](https://github.com/cortexproject/cortex/blob/master/docs/architecture.md)
-  to [VictoriaMetrics' architecture](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#architecture-overview).
-* VictoriaMetrics provides [production-ready single-node solution](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html),
+  to [VictoriaMetrics' architecture](https://docs.victoriametrics.com/cluster-victoriametrics/#architecture-overview).
+* VictoriaMetrics provides [production-ready single-node solution](https://docs.victoriametrics.com/single-server-victoriametrics/),
   which is much easier to set up and operate than a Cortex cluster.
 * Cortex may lose up to 12 hours of recent data on Ingestor failure – see [the corresponding docs](https://github.com/cortexproject/cortex/blob/fe56f1420099aa1bf1ce09316c186e05bddee879/docs/architecture.md#ingesters-failure-and-data-loss).
   VictoriaMetrics may lose only a few seconds of recent data, which isn't synced to persistent storage yet.
   See [this article for details](https://medium.com/@valyala/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704).
-* Cortex is usually slower and requires more CPU and RAM than VictoriaMetrics. See [this talk from adidas at PromCon 2019](https://promcon.io/2019-munich/talks/remote-write-storage-wars/) and [other case studies](https://docs.victoriametrics.com/CaseStudies.html).
+* Cortex is usually slower and requires more CPU and RAM than VictoriaMetrics. See [this talk from adidas at PromCon 2019](https://promcon.io/2019-munich/talks/remote-write-storage-wars/) and [other case studies](https://docs.victoriametrics.com/casestudies/).
 * VictoriaMetrics accepts data in multiple popular data ingestion protocols additionally to Prometheus remote_write protocol – InfluxDB, OpenTSDB, Graphite, CSV, JSON, native binary.
-  See [these docs](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-import-time-series-data) for details.
-* VictoriaMetrics provides the [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) query language, while Cortex provides the [PromQL](https://prometheus.io/docs/prometheus/latest/querying/basics/) query language.
+  See [these docs](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-import-time-series-data) for details.
+* VictoriaMetrics provides the [MetricsQL](https://docs.victoriametrics.com/metricsql/) query language, while Cortex provides the [PromQL](https://prometheus.io/docs/prometheus/latest/querying/basics/) query language.
 * VictoriaMetrics can be queried via [Graphite's API](https://docs.victoriametrics.com/#graphite-api-usage).
 
 ## What is the difference between VictoriaMetrics and [Thanos](https://github.com/thanos-io/thanos)?
@@ -170,23 +170,23 @@ The main differences between Cortex and VictoriaMetrics:
   VictoriaMetrics works perfectly with HDD-based block storage – there is no need for using more expensive SSD or NVMe disks in most cases.
 * Thanos may lose up to 2 hours of recent data, which wasn't uploaded yet to object storage. VictoriaMetrics may lose only a few seconds of recent data,
   which hasn't been synced to persistent storage yet. See [this article for details](https://medium.com/@valyala/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704).
-* VictoriaMetrics provides a [production-ready single-node solution](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html),
+* VictoriaMetrics provides a [production-ready single-node solution](https://docs.victoriametrics.com/single-server-victoriametrics/),
   which is much easier to set up and operate than Thanos components.
 * Thanos may be harder to set up and operate compared to VictoriaMetrics, since it has more moving parts, which can be connected with fewer reliable networks.
   See [this article for details](https://medium.com/faun/comparing-thanos-to-victoriametrics-cluster-b193bea1683).
 * Thanos is usually slower and requires more CPU and RAM than VictoriaMetrics. See [this talk from adidas at PromCon 2019](https://promcon.io/2019-munich/talks/remote-write-storage-wars/).
 * VictoriaMetrics accepts data via multiple popular data ingestion protocols in addition to the Prometheus remote_write protocol – InfluxDB, OpenTSDB, Graphite, CSV, JSON, native binary.
-  See [these docs](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-import-time-series-data) for details.
-* VictoriaMetrics provides the [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) query language, while Thanos provides the [PromQL](https://prometheus.io/docs/prometheus/latest/querying/basics/) query language.
+  See [these docs](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-import-time-series-data) for details.
+* VictoriaMetrics provides the [MetricsQL](https://docs.victoriametrics.com/metricsql/) query language, while Thanos provides the [PromQL](https://prometheus.io/docs/prometheus/latest/querying/basics/) query language.
 * VictoriaMetrics can be queried via [Graphite's API](https://docs.victoriametrics.com/#graphite-api-usage).
 
 ## How does VictoriaMetrics compare to [InfluxDB](https://www.influxdata.com/time-series-platform/influxdb/)?
 
 * VictoriaMetrics requires [10x less RAM](https://medium.com/@valyala/insert-benchmarks-with-inch-influxdb-vs-victoriametrics-e31a41ae2893) and it [works faster](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae).
 * VictoriaMetrics needs lower amounts of storage space than InfluxDB for production data.
-* VictoriaMetrics doesn't support InfluxQL or Flux but provides a better query language – [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html). See [this tutorial](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085) for details.
+* VictoriaMetrics doesn't support InfluxQL or Flux but provides a better query language – [MetricsQL](https://docs.victoriametrics.com/metricsql/). See [this tutorial](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085) for details.
 * VictoriaMetrics accepts data in multiple popular data ingestion protocols in addition to InfluxDB – Prometheus remote_write, OpenTSDB, Graphite, CSV, JSON, native binary.
-  See [these docs](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-import-time-series-data) for details.
+  See [these docs](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-import-time-series-data) for details.
 * VictoriaMetrics can be queried via [Graphite's API](https://docs.victoriametrics.com/#graphite-api-usage).
 
 ## How does VictoriaMetrics compare to [TimescaleDB](https://www.timescale.com/)?
@@ -207,7 +207,7 @@ The architecture is [optimized for storing and querying large amounts of time se
 
 The following versions are open source and free:
 
-* [Single-node version](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html).
+* [Single-node version](https://docs.victoriametrics.com/single-server-victoriametrics/).
 * [Cluster version](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/cluster).
 
 We provide commercial support for both versions. [Contact us](mailto:info@victoriametrics.com) for the pricing.
@@ -235,7 +235,7 @@ or via [Prometheus datasource in Grafana](https://docs.victoriametrics.com/#graf
 
 ## Does VictoriaMetrics deduplicate data from Prometheus instances scraping the same targets (aka `HA pairs`)?
 
-Yes. See [these docs](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#deduplication) for details.
+Yes. See [these docs](https://docs.victoriametrics.com/single-server-victoriametrics/#deduplication) for details.
 
 ## Where is the source code of VictoriaMetrics?
 
@@ -253,21 +253,21 @@ and scales horizontally to multiple nodes.
 
 ## What is the difference between single-node and cluster versions of VictoriaMetrics?
 
-Both [single-node](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html) and
-[cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html) versions of VictoriaMetrics
+Both [single-node](https://docs.victoriametrics.com/single-server-victoriametrics/) and
+[cluster](https://docs.victoriametrics.com/cluster-victoriametrics/) versions of VictoriaMetrics
 share the core source code, so they have many common features. They have the following differences though:
 
-* [Single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html) runs on a single host,
-  while [cluster version of VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html) can scale to many hosts.
+* [Single-node VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/) runs on a single host,
+  while [cluster version of VictoriaMetrics](https://docs.victoriametrics.com/cluster-victoriametrics/) can scale to many hosts.
   Single-node VictoriaMetrics scales vertically though, e.g. its capacity and performance scales almost linearly when increasing
   available CPU, RAM, disk IO and disk space. See [an article about vertical scalability of a single-node VictoriaMetrics](https://valyala.medium.com/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae).
 
-* Cluster version of VictoriaMetrics supports [multitenancy](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#multitenancy),
+* Cluster version of VictoriaMetrics supports [multitenancy](https://docs.victoriametrics.com/cluster-victoriametrics/#multitenancy),
   while single-node VictoriaMetrics doesn't support it.
 
 * Cluster version of VictoriaMetrics supports data replication, while single-node VictoriaMetrics relies on the durability
   of the persistent storage pointed by `-storageDataPath` command-line flag.
-  See [these docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#replication-and-data-safety) for details.
+  See [these docs](https://docs.victoriametrics.com/cluster-victoriametrics/#replication-and-data-safety) for details.
 
 * Single-node VictoriaMetrics provides higher capacity and performance comparing to cluster version of VictoriaMetrics
   when running on the same hardware with the same amounts of CPU and RAM, since it has no overhead on data transfer
@@ -289,7 +289,7 @@ File bugs and feature requests [here](https://github.com/VictoriaMetrics/Victori
 
 ## Where can I find information about multi-tenancy?
 
-See [these docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#multitenancy). Multitenancy is supported only by the [cluster version](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html) of VictoriaMetrics.
+See [these docs](https://docs.victoriametrics.com/cluster-victoriametrics/#multitenancy). Multitenancy is supported only by the [cluster version](https://docs.victoriametrics.com/cluster-victoriametrics/) of VictoriaMetrics.
 
 ## How to set a memory limit for VictoriaMetrics components?
 
@@ -298,8 +298,8 @@ All the VictoriaMetrics components provide command-line flags to control the siz
 Memory usage for VictoriaMetrics components can be tuned according to the following docs:
 
 * [Resource usage limits for single-node VictoriaMetrics](https://docs.victoriametrics.com/#resource-usage-limits)
-* [Resource usage limits for cluster VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#resource-usage-limits)
-* [Troubleshooting for vmagent](https://docs.victoriametrics.com/vmagent.html#troubleshooting)
+* [Resource usage limits for cluster VictoriaMetrics](https://docs.victoriametrics.com/cluster-victoriametrics/#resource-usage-limits)
+* [Troubleshooting for vmagent](https://docs.victoriametrics.com/vmagent/#troubleshooting)
 * [Troubleshooting for single-node VictoriaMetrics](https://docs.victoriametrics.com/#troubleshooting)
 
 ## How can I run VictoriaMetrics on FreeBSD/OpenBSD?
@@ -373,20 +373,20 @@ See [this article](https://valyala.medium.com/how-to-optimize-promql-and-metrics
 VictoriaMetrics also provides [query tracer](https://docs.victoriametrics.com/#query-tracing) and [cardinality explorer](https://docs.victoriametrics.com/#cardinality-explorer),
 which can help during query optimization.
 
-See also [troubleshooting slow queries](https://docs.victoriametrics.com/Troubleshooting.html#slow-queries).
+See also [troubleshooting slow queries](https://docs.victoriametrics.com/troubleshooting/#slow-queries).
 
 ## Which VictoriaMetrics type is recommended for use in production - single-node or cluster?
 
-Both [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html) and
-[VictoriaMetrics cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html) are production-ready.
+Both [single-node VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/) and
+[VictoriaMetrics cluster](https://docs.victoriametrics.com/cluster-victoriametrics/) are production-ready.
 
 Single-node VictoriaMetrics is able to handle quite big workloads in production
-with tens of millions of [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series)
-at the ingestion rate of million of samples per second. See [this case study](https://docs.victoriametrics.com/CaseStudies.html#wixcom).
+with tens of millions of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series)
+at the ingestion rate of million of samples per second. See [this case study](https://docs.victoriametrics.com/casestudies/#wixcom).
 
 Single-node VictoriaMetrics requires lower amounts of CPU and RAM for handling the same workload comparing
 to cluster version of VictoriaMetrics, since it doesn't need to pass the encoded data over the network
-between [cluster components](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#architecture-overview).
+between [cluster components](https://docs.victoriametrics.com/cluster-victoriametrics/#architecture-overview).
 
 The performance of a single-node VictoriaMetrics scales almost perfectly with the available CPU, RAM and disk IO resources on the host where it runs -
 see [this article](https://valyala.medium.com/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae).
@@ -397,36 +397,36 @@ Given the facts above **it is recommended to use single-node VictoriaMetrics in
 
 Cluster version of VictoriaMetrics may be preferred over single-node VictoriaMetrics in the following relatively rare cases:
 
-- If [multitenancy support](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#multitenancy) is needed,
+- If [multitenancy support](https://docs.victoriametrics.com/cluster-victoriametrics/#multitenancy) is needed,
   since single-node VictoriaMetrics doesn't support multitenancy. Though it is possible to run multiple single-node VictoriaMetrics
   instances - one per each tenant - and route incoming requests from particular tenant to the needed VictoriaMetrics instance
-  via [vmauth](https://docs.victoriametrics.com/vmauth.html).
+  via [vmauth](https://docs.victoriametrics.com/vmauth/).
 
 - If the current workload cannot be handled by a single-node VictoriaMetrics. For example, if you are going to ingest hundreds of millions of active time series
   at ingestion rates exceeding a million samples per second, then it is better to use cluster version of VictoriaMetrics,
-  since its capacity can [scale horizontally with the number of nodes in the cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#cluster-resizing-and-scalability).
+  since its capacity can [scale horizontally with the number of nodes in the cluster](https://docs.victoriametrics.com/cluster-victoriametrics/#cluster-resizing-and-scalability).
 
 ## How to migrate data from single-node VictoriaMetrics to cluster version?
 
 Single-node VictoriaMetrics stores data on disk in slightly different format comparing to cluster version of VictoriaMetrics.
 So it is impossible to just copy the on-disk data from `-storageDataPath` directory from single-node VictoriaMetrics to a `vmstorage` node in VictoriaMetrics cluster.
-If you need migrating data from single-node VictoriaMetrics to cluster version, then [follow these instructions](https://docs.victoriametrics.com/vmctl.html#migrating-data-from-victoriametrics).
+If you need migrating data from single-node VictoriaMetrics to cluster version, then [follow these instructions](https://docs.victoriametrics.com/vmctl/#migrating-data-from-victoriametrics).
 
 ## Why isn't MetricsQL 100% compatible with PromQL?
 
-[MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) provides better user experience than PromQL. It fixes a few annoying issues in PromQL. This prevents MetricsQL to be 100% compatible with PromQL. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
+[MetricsQL](https://docs.victoriametrics.com/metricsql/) provides better user experience than PromQL. It fixes a few annoying issues in PromQL. This prevents MetricsQL to be 100% compatible with PromQL. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
 
 ## How to migrate data from Prometheus to VictoriaMetrics?
 
-Please see [these docs](https://docs.victoriametrics.com/vmctl.html#migrating-data-from-prometheus).
+Please see [these docs](https://docs.victoriametrics.com/vmctl/#migrating-data-from-prometheus).
 
 ## How to migrate data from InfluxDB to VictoriaMetrics?
 
-Please see [these docs](https://docs.victoriametrics.com/vmctl.html#migrating-data-from-influxdb-1x).
+Please see [these docs](https://docs.victoriametrics.com/vmctl/#migrating-data-from-influxdb-1x).
 
 ## How to migrate data from OpenTSDB to VictoriaMetrics?
 
-Please see [these docs](https://docs.victoriametrics.com/vmctl.html#migrating-data-from-opentsdb).
+Please see [these docs](https://docs.victoriametrics.com/vmctl/#migrating-data-from-opentsdb).
 
 ## How to migrate data from Graphite to VictoriaMetrics?
 
@@ -446,7 +446,7 @@ The query engine may behave differently for some functions. Please see [this art
 
 Single-node VictoriaMetrics cannot be restarted / upgraded or downgraded without downtime, since it needs to be gracefully shut down and then started again. See [how to upgrade VictoriaMetrics](https://docs.victoriametrics.com/#how-to-upgrade-victoriametrics).
 
-[Cluster version of VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html) can be restarted / upgraded / downgraded without downtime according to [these instructions](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#updating--reconfiguring-cluster-nodes).
+[Cluster version of VictoriaMetrics](https://docs.victoriametrics.com/cluster-victoriametrics/) can be restarted / upgraded / downgraded without downtime according to [these instructions](https://docs.victoriametrics.com/cluster-victoriametrics/#updating--reconfiguring-cluster-nodes).
 
 ## Why VictoriaMetrics misses automatic data re-balancing between vmstorage nodes?
 
@@ -466,17 +466,17 @@ after historical data is removed from the old `vmstorage` nodes because it goes
 The data ingestion load becomes even between old `vmstorage` nodes and new `vmstorage` nodes almost immediately
 after adding new `vmstorage` nodes to the cluster, since `vminsert` nodes evenly distribute incoming time series
 among the nodes specified in `-storageNode` command-line flag. The newly added `vmstorage` nodes may experience
-increased load during the first couple of minutes because they need to register [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series).
+increased load during the first couple of minutes because they need to register [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series).
 
 The query load becomes even between old `vmstorage` nodes and new `vmstorage` nodes after most of queries are executed
 over time ranges with data covered by new `vmstorage` nodes. Usually the most of queries are received
-from [alerting and recording rules](https://docs.victoriametrics.com/vmalert.html), which query data on limited time ranges
+from [alerting and recording rules](https://docs.victoriametrics.com/vmalert/), which query data on limited time ranges
 such as a few hours or few days at max. This means that the query load between old `vmstorage` nodes and new `vmstorage` nodes
 should become even in a few hours / days after adding new `vmstorage` nodes.
 
 ## Why VictoriaMetrics misses automatic recovery of replication factor?
 
-VictoriaMetrics doesn't restore [replication factor](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#replication-and-data-safety)
+VictoriaMetrics doesn't restore [replication factor](https://docs.victoriametrics.com/cluster-victoriametrics/#replication-and-data-safety)
 when some of `vmstorage` nodes are removed from the cluster because of the following reasons:
 
 - Automatic replication factor recovery needs copying non-trivial amounts of data between the remaining `vmstorage` nodes.
@@ -486,5 +486,5 @@ when some of `vmstorage` nodes are removed from the cluster because of the follo
 - It is unclear when the automatic replication factor recovery must be started. How to distiguinsh the expected temporary
   `vmstorage` node unavailability because of maintenance, upgrade or config changes from permanent loss of data at the `vmstorage` node?
 
-It is recommended reading [replication and data safety docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#replication-and-data-safety)
+It is recommended reading [replication and data safety docs](https://docs.victoriametrics.com/cluster-victoriametrics/#replication-and-data-safety)
 for more details.

docs/LTS-releases.md

@@ -19,9 +19,9 @@ for the migration to new LTS lines for [VictoriaMetrics Enterprise](https://docs
 
 All the bugfixes and security fixes, which are included in LTS releases, are also available in [the latest release](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/latest),
 so non-enterprise users are advised to regularly [upgrade](https://docs.victoriametrics.com/#how-to-upgrade-victoriametrics) VictoriaMetrics products
-to [the latest available releases](https://docs.victoriametrics.com/CHANGELOG.html).
+to [the latest available releases](https://docs.victoriametrics.com/changelog/).
 
 ## Currently supported LTS release lines
 
-- 1.97.x - the latest one is [v1.97.3 LTS release](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.97.3)
-- 1.93.x - the latest one is [v1.93.13 LTS release](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.93.13)
+- 1.97.x - the latest one is [v1.97.4 LTS release](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.97.4)
+- 1.93.x - the latest one is [v1.93.14 LTS release](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.93.14)

docs/Makefile

@@ -11,7 +11,7 @@ docs-up:
 		jekyll/jekyll:3.8 jekyll serve --livereload --incremental
 
 # Converts images at docs folder to webp format
-# See https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#images-in-documentation
+# See https://docs.victoriametrics.com/single-server-victoriametrics/#images-in-documentation
 docs-images-to-webp:
 	IMAGES_EXTENSION=jpg $(MAKE) docs-images-to-webp-by-extension
 	IMAGES_EXTENSION=jpeg $(MAKE) docs-images-to-webp-by-extension

docs/MetricsQL.md

@@ -22,7 +22,7 @@ However, there are some [intentional differences](https://medium.com/@romanhavro
 [Standalone MetricsQL package](https://godoc.org/github.com/VictoriaMetrics/metricsql) can be used for parsing MetricsQL in external apps.
 
 If you are unfamiliar with PromQL, then it is suggested reading [this tutorial for beginners](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085)
-and introduction into [basic querying via MetricsQL](https://docs.victoriametrics.com/keyConcepts.html#metricsql).
+and introduction into [basic querying via MetricsQL](https://docs.victoriametrics.com/keyconcepts/#metricsql).
 
 The following functionality is implemented differently in MetricsQL compared to PromQL. This improves user experience:
 
@@ -70,15 +70,15 @@ The list of MetricsQL features on top of PromQL:
   VictoriaMetrics can be used as Graphite datasource in Grafana. See [these docs](https://docs.victoriametrics.com/#graphite-api-usage) for details.
   See also [label_graphite_group](#label_graphite_group) function, which can be used for extracting the given groups from Graphite metric name.
 * Lookbehind window in square brackets for [rollup functions](#rollup-functions) may be omitted. VictoriaMetrics automatically selects the lookbehind window
-  depending on the `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query)
+  depending on the `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query)
   and the real interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) (aka `scrape_interval`).
   For instance, the following query is valid in VictoriaMetrics: `rate(node_network_receive_bytes_total)`.
   It is roughly equivalent to `rate(node_network_receive_bytes_total[$__interval])` when used in Grafana.
   The difference is documented in [rate() docs](#rate).
 * Numeric values can contain `_` delimiters for better readability. For example, `1_234_567_890` can be used in queries instead of `1234567890`.
-* [Series selectors](https://docs.victoriametrics.com/keyConcepts.html#filtering) accept multiple `or` filters. For example, `{env="prod",job="a" or env="dev",job="b"}`
+* [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.html#filtering-by-multiple-or-filters) for details.
+  See [these docs](https://docs.victoriametrics.com/keyconcepts/#filtering-by-multiple-or-filters) for details.
 * Support for `group_left(*)` and `group_right(*)` for copying all the labels from time series on the `one` side
   of [many-to-one operations](https://prometheus.io/docs/prometheus/latest/querying/operators/#many-to-one-and-one-to-many-vector-matches).
   The copied label names may clash with the existing label names, so MetricsQL provides an ability to add prefix to the copied metric names
@@ -153,26 +153,26 @@ MetricsQL provides the following functions:
 ### Rollup functions
 
 **Rollup functions** (aka range functions or window functions) calculate rollups over **raw samples**
-on the given lookbehind window for the [selected time series](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window for the [selected time series](https://docs.victoriametrics.com/keyconcepts/#filtering).
 For example, `avg_over_time(temperature[24h])` calculates the average temperature over raw samples for the last 24 hours.
 
 Additional details:
 
 * If rollup functions are used for building graphs in Grafana, then the rollup is calculated independently per each point on the graph.
   For example, every point for `avg_over_time(temperature[24h])` graph shows the average temperature for the last 24 hours ending at this point.
-  The interval between points is set as `step` query arg passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
-* If the given [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) returns multiple time series,
+  The interval between points is set as `step` query arg passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
+* If the given [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) returns multiple time series,
   then rollups are calculated individually per each returned series.
 * If lookbehind window in square brackets is missing, then it is automatically set to the following value:
-  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
+  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
     for all the [rollup functions](#rollup-functions) except of [default_rollup](#default_rollup) and [rate](#rate). This value is known as `$__interval` in Grafana or `1i` in MetricsQL.
     For example, `avg_over_time(temperature)` is automatically transformed to `avg_over_time(temperature[1i])`.
   - To the `max(step, scrape_interval)`, where `scrape_interval` is the interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
     for [default_rollup](#default_rollup) and [rate](#rate) functions. This allows avoiding unexpected gaps on the graph when `step` is smaller than `scrape_interval`.
-* Every [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) in MetricsQL must be wrapped into a rollup function.
+* Every [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) in MetricsQL must be wrapped into a rollup function.
   Otherwise, it is automatically wrapped into [default_rollup](#default_rollup). For example, `foo{bar="baz"}`
   is automatically converted to `default_rollup(foo{bar="baz"})` before performing the calculations.
-* If something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) is passed to rollup function,
+* If something other than [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) is passed to rollup function,
   then the inner arg is automatically converted to a [subquery](#subqueries).
 * All the rollup functions accept optional `keep_metric_names` modifier. If it is set, then the function keeps metric names in results.
   See [these docs](#keep_metric_names).
@@ -195,7 +195,7 @@ See also [present_over_time](#present_over_time).
 `aggr_over_time(("rollup_func1", "rollup_func2", ...), series_selector[d])` is a [rollup function](#rollup-functions),
 which calculates all the listed `rollup_func*` for raw samples on the given lookbehind window `d`.
 The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 `rollup_func*` can contain any rollup function. For instance, `aggr_over_time(("min_over_time", "max_over_time", "rate"), m[d])`
 would calculate [min_over_time](#min_over_time), [max_over_time](#max_over_time) and [rate](#rate) for `m[d]`.
@@ -204,7 +204,7 @@ would calculate [min_over_time](#min_over_time), [max_over_time](#max_over_time)
 
 `ascent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates
 ascent of raw sample values on the given lookbehind window `d`. The calculations are performed individually
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -216,7 +216,7 @@ See also [descent_over_time](#descent_over_time).
 
 `avg_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average value
 over raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is supported by PromQL.
 
@@ -226,7 +226,7 @@ See also [median_over_time](#median_over_time).
 
 `changes(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times
 the raw samples changed on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Unlike `changes()` in Prometheus it takes into account the change from the last sample before the given lookbehind window `d`.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -241,7 +241,7 @@ See also [changes_prometheus](#changes_prometheus).
 
 `changes_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of times
 the raw samples changed on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 It doesn't take into account the change from the last sample before the given lookbehind window `d` in the same way as Prometheus does.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -256,7 +256,7 @@ See also [changes](#changes).
 
 `count_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -266,7 +266,7 @@ See also [count_over_time](#count_over_time), [share_eq_over_time](#share_eq_ove
 
 `count_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -276,7 +276,7 @@ See also [count_over_time](#count_over_time) and [share_gt_over_time](#share_gt_
 
 `count_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which don't exceed `le`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -286,7 +286,7 @@ See also [count_over_time](#count_over_time) and [share_le_over_time](#share_le_
 
 `count_ne_over_time(series_selector[d], ne)` is a [rollup function](#rollup-functions), which calculates the number of raw samples
 on the given lookbehind window `d`, which aren't equal to `ne`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -295,7 +295,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -307,7 +307,7 @@ See also [count_le_over_time](#count_le_over_time), [count_gt_over_time](#count_
 
 `count_values_over_time("label", series_selector[d])` is a [rollup function](#rollup-functions), which counts the number of raw samples
 with the same value over the given lookbehind window and stores the counts in a time series with an additional `label`, which contains each initial value.
-The results are calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The results are calculated independently per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -316,7 +316,7 @@ 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.html#filtering).
+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.
 
@@ -325,7 +325,7 @@ See also [increases_over_time](#increases_over_time).
 #### default_rollup
 
 `default_rollup(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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),
@@ -336,7 +336,7 @@ This allows avoiding unexpected gaps on the graph when `step` is smaller than th
 
 `delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between
 the last sample before the given lookbehind window `d` and the last sample at the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 The behaviour of `delta()` function in MetricsQL is slightly different to the behaviour of `delta()` function in Prometheus.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -351,7 +351,7 @@ See also [increase](#increase) and [delta_prometheus](#delta_prometheus).
 
 `delta_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the difference between
 the first and the last samples at the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 The behaviour of `delta_prometheus()` is close to the behaviour of `delta()` function in Prometheus.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -363,7 +363,7 @@ See also [delta](#delta).
 #### deriv
 
 `deriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivative over the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 The derivative is calculated using linear regression.
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -376,7 +376,7 @@ See also [deriv_fast](#deriv_fast) and [ideriv](#ideriv).
 
 `deriv_fast(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivative
 using the first and the last raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -386,7 +386,7 @@ See also [deriv](#deriv) and [ideriv](#ideriv).
 
 `descent_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates descent of raw sample values
 on the given lookbehind window `d`. The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is useful for tracking height loss in GPS tracking.
 
@@ -397,7 +397,7 @@ See also [ascent_over_time](#ascent_over_time).
 #### distinct_over_time
 
 `distinct_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number of distinct raw sample values
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -406,7 +406,7 @@ See also [count_values_over_time](#count_values_over_time).
 #### duration_over_time
 
 `duration_over_time(series_selector[d], max_interval)` is a [rollup function](#rollup-functions), which returns the duration in seconds
-when time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) were present
+when time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering) were present
 over the given lookbehind window `d`. It is expected that intervals between adjacent samples per each series don't exceed the `max_interval`.
 Otherwise, such intervals are considered as gaps and aren't counted.
 
@@ -417,7 +417,7 @@ See also [lifetime](#lifetime) and [lag](#lag).
 #### first_over_time
 
 `first_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the first raw sample value
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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).
 
@@ -425,7 +425,7 @@ See also [last_over_time](#last_over_time) and [tfirst_over_time](#tfirst_over_t
 
 `geomean_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [geometric mean](https://en.wikipedia.org/wiki/Geometric_mean)
 over raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -433,9 +433,9 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 
 `histogram_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates
 [VictoriaMetrics histogram](https://godoc.org/github.com/VictoriaMetrics/metrics#Histogram) over raw samples on the given lookbehind window `d`.
-It is calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.html#gauge).
+over multiple [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge).
 For example, the following query calculates median temperature by country over the last 24 hours:
 
 `histogram_quantile(0.5, sum(histogram_over_time(temperature[24h])) by (vmrange,country))`.
@@ -459,8 +459,8 @@ See also [hoeffding_bound_lower](#hoeffding_bound_lower).
 `holt_winters(series_selector[d], sf, tf)` is a [rollup function](#rollup-functions), which calculates Holt-Winters value
 (aka [double exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing#Double_exponential_smoothing)) for raw samples
 over the given lookbehind window `d` using the given smoothing factor `sf` and the given trend factor `tf`.
-Both `sf` and `tf` must be in the range `[0...1]`. It is expected that the [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering)
-returns time series of [gauge type](https://docs.victoriametrics.com/keyConcepts.html#gauge).
+Both `sf` and `tf` must be in the range `[0...1]`. It is expected that the [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering)
+returns time series of [gauge type](https://docs.victoriametrics.com/keyconcepts/#gauge).
 
 This function is supported by PromQL.
 
@@ -469,7 +469,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -481,7 +481,7 @@ See also [delta](#delta).
 
 `ideriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the per-second derivative based on the last two raw samples
 over the given lookbehind window `d`. The derivative is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -490,8 +490,8 @@ See also [deriv](#deriv).
 #### increase
 
 `increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase over the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 Unlike Prometheus, it takes into account the last sample before the given lookbehind window `d` when calculating the result.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
@@ -505,8 +505,8 @@ See also [increase_pure](#increase_pure), [increase_prometheus](#increase_promet
 #### increase_prometheus
 
 `increase_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase
-over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 It doesn't take into account the last sample before the given lookbehind window `d` when calculating the result in the same way as Prometheus does.
 See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
 
@@ -517,13 +517,13 @@ See also [increase_pure](#increase_pure) and [increase](#increase).
 #### increase_pure
 
 `increase_pure(series_selector[d])` is a [rollup function](#rollup-functions), which works the same as [increase](#increase) except
-of the following corner case - it assumes that [counters](https://docs.victoriametrics.com/keyConcepts.html#counter) always start from 0,
+of the following corner case - it assumes that [counters](https://docs.victoriametrics.com/keyconcepts/#counter) always start from 0,
 while [increase](#increase) ignores the first value in a series if it is too big.
 
 #### increases_over_time
 
 `increases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of raw sample value increases
-over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -532,15 +532,15 @@ 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.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### irate
 
 `irate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the "instant" per-second increase rate over the last two raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -552,7 +552,7 @@ See also [rate](#rate) and [rollup_rate](#rollup_rate).
 
 `lag(series_selector[d])` is a [rollup function](#rollup-functions), which returns the duration in seconds between the last sample
 on the given lookbehind window `d` and the timestamp of the current point. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -561,7 +561,7 @@ See also [lifetime](#lifetime) and [duration_over_time](#duration_over_time).
 #### last_over_time
 
 `last_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last raw sample value on the given lookbehind window `d`
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is supported by PromQL.
 
@@ -570,7 +570,7 @@ See also [first_over_time](#first_over_time) and [tlast_over_time](#tlast_over_t
 #### lifetime
 
 `lifetime(series_selector[d])` is a [rollup function](#rollup-functions), which returns the duration in seconds between the last and the first sample
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -579,14 +579,14 @@ 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.html#filtering).
+over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 See also [mad](#mad), [range_mad](#range_mad) and [outlier_iqr_over_time](#outlier_iqr_over_time).
 
 #### max_over_time
 
 `max_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the maximum value over raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -596,14 +596,14 @@ See also [tmax_over_time](#tmax_over_time).
 
 `median_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates median value over raw samples
 on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 See also [avg_over_time](#avg_over_time).
 
 #### min_over_time
 
 `min_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the minimum value over raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -613,7 +613,7 @@ See also [tmin_over_time](#tmin_over_time).
 
 `mode_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [mode](https://en.wikipedia.org/wiki/Mode_(statistics))
 for raw samples on the given lookbehind window `d`. It is calculated individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). It is expected that raw sample values are discrete.
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). It is expected that raw sample values are discrete.
 
 #### outlier_iqr_over_time
 
@@ -631,7 +631,7 @@ See also [outliers_iqr](#outliers_iqr).
 
 `predict_linear(series_selector[d], t)` is a [rollup function](#rollup-functions), which calculates the value `t` seconds in the future using
 linear interpolation over raw samples on the given lookbehind window `d`. The predicted value is calculated individually per each time series
-returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is supported by PromQL.
 
@@ -649,7 +649,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 The `phi` value must be in the range `[0...1]`.
 
 This function is supported by PromQL.
@@ -660,7 +660,7 @@ See also [quantiles_over_time](#quantiles_over_time).
 
 `quantiles_over_time("phiLabel", phi1, ..., phiN, series_selector[d])` is a [rollup function](#rollup-functions), which calculates `phi*`-quantiles
 over raw samples on the given lookbehind window `d` per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 The function returns individual series per each `phi*` with `{phiLabel="phi*"}` label. `phi*` values must be in the range `[0...1]`.
 
 See also [quantile_over_time](#quantile_over_time).
@@ -668,7 +668,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 E.g. it calculates `max_over_time(series_selector[d]) - min_over_time(series_selector[d])`.
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
@@ -676,8 +676,8 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 #### rate
 
 `rate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average per-second increase rate
-over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
-It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyConcepts.html#counter).
+over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 If the lookbehind window is skipped in square brackets, then it is automatically calculated as `max(step, scrape_interval)`, where `step` is the query arg value
 passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query),
@@ -694,16 +694,16 @@ See also [irate](#irate) and [rollup_rate](#rollup_rate).
 
 `rate_over_sum(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second rate over the sum of raw samples
 on the given lookbehind window `d`. The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### resets
 
 `resets(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number
-of [counter](https://docs.victoriametrics.com/keyConcepts.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).
+of [counter](https://docs.victoriametrics.com/keyconcepts/#counter) resets over the given lookbehind window `d`
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
+It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -713,7 +713,7 @@ This function is supported by PromQL.
 
 `rollup(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `min`, `max` and `avg` values for raw samples
 on the given lookbehind window `d` and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-These values are calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+These values are calculated individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -723,7 +723,7 @@ See also [label_match](#label_match).
 `rollup_candlestick(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `open`, `high`, `low` and `close` values (aka OHLC)
 over raw samples on the given lookbehind window `d` and returns them in time series with `rollup="open"`, `rollup="high"`, `rollup="low"` and `rollup="close"` additional labels.
 The calculations are performed individually per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). This function is useful for financial applications.
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). This function is useful for financial applications.
 
 Optional 2nd argument `"open"`, `"high"` or `"low"` or `"close"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -733,7 +733,7 @@ See also [label_match](#label_match).
 `rollup_delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates differences between adjacent raw samples
 on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated differences
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -747,7 +747,7 @@ See also [rollup_increase](#rollup_increase).
 `rollup_deriv(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second derivatives
 for adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second derivatives
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -759,7 +759,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 `rollup_increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates increases for adjacent raw samples
 on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated increases
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -778,7 +778,7 @@ when to use `rollup_rate()`.
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
 
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -787,7 +787,7 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k
 `rollup_scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the interval in seconds between
 adjacent raw samples on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated interval
 and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels.
-The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label.
 See also [label_match](#label_match).
@@ -797,7 +797,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -807,7 +807,7 @@ See also [rollup_scrape_interval](#rollup_scrape_interval).
 
 `share_gt_over_time(series_selector[d], gt)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
 on the given lookbehind window `d`, which are bigger than `gt`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is useful for calculating SLI and SLO. Example: `share_gt_over_time(up[24h], 0)` - returns service availability for the last 24 hours.
 
@@ -819,7 +819,7 @@ See also [share_le_over_time](#share_le_over_time) and [count_gt_over_time](#cou
 
 `share_le_over_time(series_selector[d], le)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
 on the given lookbehind window `d`, which are smaller or equal to `le`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 This function is useful for calculating SLI and SLO. Example: `share_le_over_time(memory_usage_bytes[24h], 100*1024*1024)` returns
 the share of time series values for the last 24 hours when memory usage was below or equal to 100MB.
@@ -832,7 +832,7 @@ See also [share_gt_over_time](#share_gt_over_time) and [count_le_over_time](#cou
 
 `share_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-functions), which returns share (in the range `[0...1]`) of raw samples
 on the given lookbehind window `d`, which are equal to `eq`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -841,15 +841,15 @@ See also [count_eq_over_time](#count_eq_over_time).
 #### stale_samples_over_time
 
 `stale_samples_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number
-of [staleness markers](https://docs.victoriametrics.com/vmagent.html#prometheus-staleness-markers) on the given lookbehind window `d`
-per each time series matching the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+of [staleness markers](https://docs.victoriametrics.com/vmagent/#prometheus-staleness-markers) on the given lookbehind window `d`
+per each time series matching the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### stddev_over_time
 
 `stddev_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates standard deviation over raw samples
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -860,7 +860,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -871,7 +871,7 @@ See also [stddev_over_time](#stddev_over_time).
 #### sum_eq_over_time
 
 `sum_eq_over_time(series_selector[d], eq)` is a [rollup function](#rollup-function), which calculates the sum of raw sample values equal to `eq`
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -880,7 +880,7 @@ 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.html#filtering).
+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.
 
@@ -889,7 +889,7 @@ 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.html#filtering).
+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.
 
@@ -898,7 +898,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -907,14 +907,14 @@ This function is supported by PromQL.
 #### sum2_over_time
 
 `sum2_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the sum of squares for raw sample values
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering).
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
 #### timestamp
 
 `timestamp(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last raw sample
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -925,7 +925,7 @@ See also [time](#time) and [now](#now).
 #### timestamp_with_name
 
 `timestamp_with_name(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last raw sample
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -934,7 +934,7 @@ 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
-on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -943,7 +943,7 @@ See also [first_over_time](#first_over_time).
 #### tlast_change_over_time
 
 `tlast_change_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the last change
-per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) on the given lookbehind window `d`.
+per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering) on the given lookbehind window `d`.
 
 Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names.
 
@@ -959,7 +959,7 @@ See also [tlast_change_over_time](#tlast_change_over_time).
 
 `tmax_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the raw sample
 with the maximum value on the given lookbehind window `d`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -969,7 +969,7 @@ See also [max_over_time](#max_over_time).
 
 `tmin_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision for the raw sample
 with the minimum value on the given lookbehind window `d`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -979,7 +979,7 @@ See also [min_over_time](#min_over_time).
 
 `zscore_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns [z-score](https://en.wikipedia.org/wiki/Standard_score)
 for raw samples on the given lookbehind window `d`. It is calculated independently per each time series returned
-from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering).
+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.
 
@@ -994,7 +994,7 @@ returned from the rollup `delta(temperature[24h])`.
 
 Additional details:
 
-* If transform function is applied directly to a [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* If transform function is applied directly to a [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering),
   then the [default_rollup()](#default_rollup) function is automatically applied before calculating the transformations.
   For example, `abs(temperature)` is implicitly transformed to `abs(default_rollup(temperature))`.
 * All the transform functions accept optional `keep_metric_names` modifier. If it is set,
@@ -1230,7 +1230,7 @@ by replacing all the values bigger or equal to 30 with 40.
 #### end
 
 `end()` is a [transform function](#transform-functions), which returns the unix timestamp in seconds for the last point.
-It is known as `end` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+It is known as `end` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 See also [start](#start), [time](#time) and [now](#now).
 
@@ -1653,14 +1653,14 @@ This function is supported by PromQL.
 
 `start()` is a [transform function](#transform-functions), which returns unix timestamp in seconds for the first point.
 
-It is known as `start` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+It is known as `start` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 See also [end](#end), [time](#time) and [now](#now).
 
 #### step
 
 `step()` is a [transform function](#transform-functions), which returns the step in seconds (aka interval) between the returned points.
-It is known as `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+It is known as `step` query arg passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 See also [start](#start) and [end](#end).
 
@@ -1717,7 +1717,7 @@ This function is supported by PromQL.
 
 Additional details:
 
-* If label manipulation function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* If label manipulation function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering),
   then the [default_rollup()](#default_rollup) function is automatically applied before performing the label transformation.
   For example, `alias(temperature, "foo")` is implicitly transformed to `alias(default_rollup(temperature), "foo")`.
 
@@ -1894,7 +1894,7 @@ Additional details:
   and calculate the [count](#count) aggregate function independently per each group, while `count(up) without (instance)`
   would group [rollup results](#rollup-functions) by all the labels except `instance` before calculating [count](#count) aggregate function independently per each group.
   Multiple labels can be put in `by` and `without` modifiers.
-* If the aggregate function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* If the aggregate function is applied directly to a [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering),
   then the [default_rollup()](#default_rollup) function is automatically applied before calculating the aggregate.
   For example, `count(up)` is implicitly transformed to `count(default_rollup(up))`.
 * Aggregate functions accept arbitrary number of args. For example, `avg(q1, q2, q3)` would return the average values for every point
@@ -2104,7 +2104,7 @@ See also [quantile](#quantile).
 `share(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns shares in the range `[0..1]`
 for every non-negative points returned by `q` per each timestamp, so the sum of shares per each `group_labels` equals 1.
 
-This function is useful for normalizing [histogram bucket](https://docs.victoriametrics.com/keyConcepts.html#histogram) shares
+This function is useful for normalizing [histogram bucket](https://docs.victoriametrics.com/keyconcepts/#histogram) shares
 into `[0..1]` range:
 
 ```metricsql
@@ -2208,7 +2208,7 @@ See also [zscore_over_time](#zscore_over_time), [range_trim_zscore](#range_trim_
 ## Subqueries
 
 MetricsQL supports and extends PromQL subqueries. See [this article](https://valyala.medium.com/prometheus-subqueries-in-victoriametrics-9b1492b720b3) for details.
-Any [rollup function](#rollup-functions) for something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering) form a subquery.
+Any [rollup function](#rollup-functions) for something other than [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) form a subquery.
 Nested rollup functions can be implicit thanks to the [implicit query conversions](#implicit-query-conversions).
 For example, `delta(sum(m))` is implicitly converted to `delta(sum(default_rollup(m))[1i:1i])`, so it becomes a subquery,
 since it contains [default_rollup](#default_rollup) nested into [delta](#delta).
@@ -2219,19 +2219,19 @@ VictoriaMetrics performs subqueries in the following way:
   For example, for expression `max_over_time(rate(http_requests_total[5m])[1h:30s])` the inner function `rate(http_requests_total[5m])`
   is calculated with `step=30s`. The resulting data points are aligned by the `step`.
 * It calculates the outer rollup function over the results of the inner rollup function using the `step` value
-  passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query).
+  passed by Grafana to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
 
 ## Implicit query conversions
 
 VictoriaMetrics performs the following implicit conversions for incoming queries before starting the calculations:
 
 * If lookbehind window in square brackets is missing inside [rollup function](#rollup-functions), then it is automatically set to the following value:
-  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
+  - To `step` value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
     for all the [rollup functions](#rollup-functions) except of [default_rollup](#default_rollup) and [rate](#rate). This value is known as `$__interval` in Grafana or `1i` in MetricsQL.
     For example, `avg_over_time(temperature)` is automatically transformed to `avg_over_time(temperature[1i])`.
   - To the `max(step, scrape_interval)`, where `scrape_interval` is the interval between [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
     for [default_rollup](#default_rollup) and [rate](#rate) functions. This allows avoiding unexpected gaps on the graph when `step` is smaller than `scrape_interval`.
-* All the [series selectors](https://docs.victoriametrics.com/keyConcepts.html#filtering),
+* All the [series selectors](https://docs.victoriametrics.com/keyconcepts/#filtering),
   which aren't wrapped into [rollup functions](#rollup-functions), are automatically wrapped into [default_rollup](#default_rollup) function.
   Examples:
   * `foo` is transformed to `default_rollup(foo)`
@@ -2242,6 +2242,6 @@ VictoriaMetrics performs the following implicit conversions for incoming queries
     it is [transform function](#transform-functions)
 * If `step` in square brackets is missing inside [subquery](#subqueries), then `1i` step is automatically added there.
   For example, `avg_over_time(rate(http_requests_total[5m])[1h])` is automatically converted to `avg_over_time(rate(http_requests_total[5m])[1h:1i])`.
-* If something other than [series selector](https://docs.victoriametrics.com/keyConcepts.html#filtering)
+* 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])`.

docs/PerTenantStatistic.md

@@ -80,7 +80,7 @@ Check the Billing section of [Grafana Dashboard](#visualization), it contains bi
 ## Integration with vmgateway
 
 `vmgateway` supports integration with Per Tenant Statistics data for rate limiting purposes.
-More information can be found [here](https://docs.victoriametrics.com/vmgateway.html)
+More information can be found [here](https://docs.victoriametrics.com/vmgateway/)
 
 ## Integration with vmalert
 

docs/Quick-Start.md

@@ -15,10 +15,10 @@ aliases:
 ## How to install
 
 VictoriaMetrics is distributed in two forms:
-* [Single-server-VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html) - all-in-one
+* [Single-server-VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/) - all-in-one
   binary, which is very easy to use and maintain.
   Single-server-VictoriaMetrics perfectly scales vertically and easily handles millions of metrics/s;
-* [VictoriaMetrics Cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html) - set of components
+* [VictoriaMetrics Cluster](https://docs.victoriametrics.com/cluster-victoriametrics/) - set of components
   for building horizontally scalable clusters.
 
 Single-server-VictoriaMetrics VictoriaMetrics is available as:
@@ -29,16 +29,16 @@ Single-server-VictoriaMetrics VictoriaMetrics is available as:
 * [Helm Charts](https://github.com/VictoriaMetrics/helm-charts#list-of-charts)
 * [Binary releases](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/latest)
 * [Source code](https://github.com/VictoriaMetrics/VictoriaMetrics).
-  See [How to build from sources](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-build-from-sources)
+  See [How to build from sources](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-build-from-sources)
 * [VictoriaMetrics on Linode](https://www.linode.com/marketplace/apps/victoriametrics/victoriametrics/)
 * [VictoriaMetrics on DigitalOcean](https://marketplace.digitalocean.com/apps/victoriametrics-single)
 
 Just download VictoriaMetrics and follow
-[these instructions](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-start-victoriametrics).
-Then read [Prometheus setup](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#prometheus-setup)
-and [Grafana setup](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#grafana-setup) docs.
+[these instructions](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-start-victoriametrics).
+Then read [Prometheus setup](https://docs.victoriametrics.com/single-server-victoriametrics/#prometheus-setup)
+and [Grafana setup](https://docs.victoriametrics.com/single-server-victoriametrics/#grafana-setup) docs.
 
-VictoriaMetrics is developed at a fast pace, so it is recommended periodically checking the [CHANGELOG](https://docs.victoriametrics.com/CHANGELOG.html) and performing [regular upgrades](https://docs.victoriametrics.com/#how-to-upgrade-victoriametrics).
+VictoriaMetrics is developed at a fast pace, so it is recommended periodically checking the [CHANGELOG](https://docs.victoriametrics.com/changelog/) and performing [regular upgrades](https://docs.victoriametrics.com/#how-to-upgrade-victoriametrics).
 
 
 ### Starting VM-Single via Docker
@@ -58,7 +58,7 @@ docker run -it --rm -v `pwd`/victoria-metrics-data:/victoria-metrics-data -p 842
 Open <a href="http://localhost:8428">http://localhost:8428</a> in web browser
 and read [these docs](https://docs.victoriametrics.com/#operation).
 
-There is also [VictoriaMetrics cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html)
+There is also [VictoriaMetrics cluster](https://docs.victoriametrics.com/cluster-victoriametrics/)
 - horizontally scalable installation, which scales to multiple nodes.
 
 ### Starting VM-Cluster via Docker
@@ -78,53 +78,53 @@ make docker-cluster-up
 
 See more details [here](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker#readme).
 
-* [Cluster setup](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#cluster-setup)
+* [Cluster setup](https://docs.victoriametrics.com/cluster-victoriametrics/#cluster-setup)
 
 ## Write data
 
 There are two main models in monitoring for data collection: 
-[push](https://docs.victoriametrics.com/keyConcepts.html#push-model) 
-and [pull](https://docs.victoriametrics.com/keyConcepts.html#pull-model). 
+[push](https://docs.victoriametrics.com/keyconcepts/#push-model) 
+and [pull](https://docs.victoriametrics.com/keyconcepts/#pull-model). 
 Both are used in modern monitoring and both are supported by VictoriaMetrics.
 
-See more details on [writing data here](https://docs.victoriametrics.com/keyConcepts.html#write-data).
+See more details on [writing data here](https://docs.victoriametrics.com/keyconcepts/#write-data).
 
 
 ## Query data
 
 VictoriaMetrics provides an 
-[HTTP API](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#prometheus-querying-api-usage)
+[HTTP API](https://docs.victoriametrics.com/single-server-victoriametrics/#prometheus-querying-api-usage)
 for serving read queries. The API is used in various integrations such as
-[Grafana](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#grafana-setup).
+[Grafana](https://docs.victoriametrics.com/single-server-victoriametrics/#grafana-setup).
 The same API is also used by
-[VMUI](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#vmui) - graphical User Interface
+[VMUI](https://docs.victoriametrics.com/single-server-victoriametrics/#vmui) - graphical User Interface
 for querying and visualizing metrics.
 
-[MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) - is the query language for executing read queries
+[MetricsQL](https://docs.victoriametrics.com/metricsql/) - is the query language for executing read queries
 in VictoriaMetrics. MetricsQL is a [PromQL](https://prometheus.io/docs/prometheus/latest/querying/basics) 
 -like query language with a powerful set of functions and features for working specifically with time series data.
 
-See more details on [querying data here](https://docs.victoriametrics.com/keyConcepts.html#query-data)
+See more details on [querying data here](https://docs.victoriametrics.com/keyconcepts/#query-data)
 
 
 ## Alerting
 
 It is not possible to physically trace all changes on graphs all the time, that is why alerting exists.
-In [vmalert](https://docs.victoriametrics.com/vmalert.html) it is possible to create a set of conditions
+In [vmalert](https://docs.victoriametrics.com/vmalert/) it is possible to create a set of conditions
 based on PromQL and MetricsQL queries that will send a notification when such conditions are met.
 
 ## Data migration
 
 Migrating data from other TSDBs to VictoriaMetrics is as simple as importing data via any of
-[supported formats](https://docs.victoriametrics.com/keyConcepts.html#push-model).
+[supported formats](https://docs.victoriametrics.com/keyconcepts/#push-model).
 
-The migration might get easier when using [vmctl](https://docs.victoriametrics.com/vmctl.html) - VictoriaMetrics
+The migration might get easier when using [vmctl](https://docs.victoriametrics.com/vmctl/) - VictoriaMetrics
 command line tool. It supports the following databases for migration to VictoriaMetrics:
-* [Prometheus using snapshot API](https://docs.victoriametrics.com/vmctl.html#migrating-data-from-prometheus);
-* [Thanos](https://docs.victoriametrics.com/vmctl.html#migrating-data-from-thanos);
-* [InfluxDB](https://docs.victoriametrics.com/vmctl.html#migrating-data-from-influxdb-1x);
-* [OpenTSDB](https://docs.victoriametrics.com/vmctl.html#migrating-data-from-opentsdb);
-* [Migrate data between VictoriaMetrics single and cluster versions](https://docs.victoriametrics.com/vmctl.html#migrating-data-from-victoriametrics).
+* [Prometheus using snapshot API](https://docs.victoriametrics.com/vmctl/#migrating-data-from-prometheus);
+* [Thanos](https://docs.victoriametrics.com/vmctl/#migrating-data-from-thanos);
+* [InfluxDB](https://docs.victoriametrics.com/vmctl/#migrating-data-from-influxdb-1x);
+* [OpenTSDB](https://docs.victoriametrics.com/vmctl/#migrating-data-from-opentsdb);
+* [Migrate data between VictoriaMetrics single and cluster versions](https://docs.victoriametrics.com/vmctl/#migrating-data-from-victoriametrics).
 
 ## Productionisation
 
@@ -135,7 +135,7 @@ When going to production with VictoriaMetrics we recommend following the recomme
 Each VictoriaMetrics component emits its own metrics with various details regarding performance
 and health state. Docs for the components also contain a `Monitoring` section with an explanation
 of what and how should be monitored. For example,
-[Single-server-VictoriaMetrics Monitoring](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#monitoring).
+[Single-server-VictoriaMetrics Monitoring](https://docs.victoriametrics.com/cluster-victoriametrics/#monitoring).
 
 VictoriaMetric team prepared a list of [Grafana dashboards](https://grafana.com/orgs/victoriametrics/dashboards)
 for the main components. Each dashboard contains a lot of useful information and tips. It is recommended
@@ -153,32 +153,32 @@ See more details in the article [VictoriaMetrics Monitoring](https://victoriamet
 ### Capacity planning
 
 See capacity planning sections in [docs](https://docs.victoriametrics.com) for
-[Single-server-VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#capacity-planning)
-and [VictoriaMetrics Cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#capacity-planning).
+[Single-server-VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/#capacity-planning)
+and [VictoriaMetrics Cluster](https://docs.victoriametrics.com/cluster-victoriametrics/#capacity-planning).
 
 Capacity planning isn't possible without [monitoring](#monitoring), so consider configuring it first.
 Understanding resource usage and performance of VictoriaMetrics also requires knowing the tech terms
-[active series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series),
-[churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate),
-[cardinality](https://docs.victoriametrics.com/FAQ.html#what-is-high-cardinality),
-[slow inserts](https://docs.victoriametrics.com/FAQ.html#what-is-a-slow-insert).
+[active series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series),
+[churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate),
+[cardinality](https://docs.victoriametrics.com/faq/#what-is-high-cardinality),
+[slow inserts](https://docs.victoriametrics.com/faq/#what-is-a-slow-insert).
 All of them are present in [Grafana dashboards](https://grafana.com/orgs/victoriametrics/dashboards).
 
 
 ### Data safety
 
-It is recommended to read [Replication and data safety](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#replication-and-data-safety),
+It is recommended to read [Replication and data safety](https://docs.victoriametrics.com/cluster-victoriametrics/#replication-and-data-safety),
 [Why replication doesn’t save from disaster?](https://valyala.medium.com/speeding-up-backups-for-big-time-series-databases-533c1a927883)
-and [backups](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#backups).
+and [backups](https://docs.victoriametrics.com/single-server-victoriametrics/#backups).
 
 
 ### Configuring limits
 
 To avoid excessive resource usage or performance degradation limits must be in place:
-* [Resource usage limits](https://docs.victoriametrics.com/FAQ.html#how-to-set-a-memory-limit-for-victoriametrics-components);
-* [Cardinality limiter](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#cardinality-limiter).
+* [Resource usage limits](https://docs.victoriametrics.com/faq/#how-to-set-a-memory-limit-for-victoriametrics-components);
+* [Cardinality limiter](https://docs.victoriametrics.com/single-server-victoriametrics/#cardinality-limiter).
 
 ### Security recommendations
 
 * [Security recommendations for single-node VictoriaMetrics](https://docs.victoriametrics.com/#security)
-* [Security recommendations for cluster version of VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#security)
+* [Security recommendations for cluster version of VictoriaMetrics](https://docs.victoriametrics.com/cluster-victoriametrics/#security)

docs/Troubleshooting.md

@@ -29,21 +29,21 @@ If you hit some issue or have some question about VictoriaMetrics components,
 then please follow the following steps in order to quickly find the solution:
 
 1. Check the version of VictoriaMetrics component, which needs to be troubleshot and compare
-   it to [the latest available version](https://docs.victoriametrics.com/CHANGELOG.html).
+   it to [the latest available version](https://docs.victoriametrics.com/changelog/).
    If the used version is lower than the latest available version, then there are high chances
-   that the issue is already resolved in newer versions. Carefully read [the changelog](https://docs.victoriametrics.com/CHANGELOG.html)
+   that the issue is already resolved in newer versions. Carefully read [the changelog](https://docs.victoriametrics.com/changelog/)
    between your version and the latest version and check whether the issue is already fixed there.
 
    If the issue is already fixed in newer versions, then upgrade to the newer version and verify whether the issue is fixed:
 
    - [How to upgrade single-node VictoriaMetrics](https://docs.victoriametrics.com/#how-to-upgrade-victoriametrics)
-   - [How to upgrade VictoriaMetrics cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#updating--reconfiguring-cluster-nodes)
+   - [How to upgrade VictoriaMetrics cluster](https://docs.victoriametrics.com/cluster-victoriametrics/#updating--reconfiguring-cluster-nodes)
 
    Upgrade procedure for other VictoriaMetrics components is as simple as gracefully stopping the component
    by sending `SIGINT` signal to it and starting the new version of the component.
 
    There may be breaking changes between different versions of VictoriaMetrics components in rare cases.
-   These cases are documented in [the changelog](https://docs.victoriametrics.com/CHANGELOG.html).
+   These cases are documented in [the changelog](https://docs.victoriametrics.com/changelog/).
    So please read the changelog before the upgrade.
 
 1. Inspect command-line flags passed to VictoriaMetrics components and remove flags which unclear outcomes for your workload.
@@ -124,9 +124,9 @@ If you see unexpected or unreliable query results from VictoriaMetrics, then try
      to reduce the number of returned series.
 
    Sometimes the query may be improperly constructed, so it returns unexpected results.
-   It is recommended reading and understanding [MetricsQL docs](https://docs.victoriametrics.com/MetricsQL.html),
-   especially [subqueries](https://docs.victoriametrics.com/MetricsQL.html#subqueries)
-   and [rollup functions](https://docs.victoriametrics.com/MetricsQL.html#rollup-functions) sections.
+   It is recommended reading and understanding [MetricsQL docs](https://docs.victoriametrics.com/metricsql/),
+   especially [subqueries](https://docs.victoriametrics.com/metricsql/#subqueries)
+   and [rollup functions](https://docs.victoriametrics.com/metricsql/#rollup-functions) sections.
 
 1. If the simplest query continues returning unexpected / unreliable results, then try verifying correctness
    of raw unprocessed samples for this query via [/api/v1/export](https://docs.victoriametrics.com/#how-to-export-data-in-json-line-format)
@@ -137,8 +137,8 @@ If you see unexpected or unreliable query results from VictoriaMetrics, then try
    
    cluster: curl http://<vmselect>:8481/select/<tenantID>/prometheus/api/v1/export -d 'match[]=http_requests_total' -d 'start=...' -d 'end=...'
    ```
-   Note that responses returned from [/api/v1/query](https://docs.victoriametrics.com/keyConcepts.html#instant-query)
-   and from [/api/v1/query_range](https://docs.victoriametrics.com/keyConcepts.html#range-query) contain **evaluated** data
+   Note that responses returned from [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query)
+   and from [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) contain **evaluated** data
    instead of raw samples stored in VictoriaMetrics. See [these docs](https://prometheus.io/docs/prometheus/latest/querying/basics/#staleness)
    for details.
 
@@ -156,10 +156,10 @@ If you see unexpected or unreliable query results from VictoriaMetrics, then try
      If you use Grafana, then this query arg can be specified in `Custom Query Parameters` field
      at Prometheus datasource settings - see [these docs](https://grafana.com/docs/grafana/latest/datasources/prometheus/) for details.
     
-    If the problem was in the cache, try resetting it via [resetRollupCache handler](https://docs.victoriametrics.com/url-examples.html#internalresetrollupresultcache).
+    If the problem was in the cache, try resetting it via [resetRollupCache handler](https://docs.victoriametrics.com/url-examples/#internalresetrollupresultcache).
 
 1. If you use cluster version of VictoriaMetrics, then it may return partial responses by default
-   when some of `vmstorage` nodes are temporarily unavailable - see [cluster availability docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#cluster-availability)
+   when some of `vmstorage` nodes are temporarily unavailable - see [cluster availability docs](https://docs.victoriametrics.com/cluster-victoriametrics/#cluster-availability)
    for details. If you want to prioritize query consistency over cluster availability,
    then you can pass `-search.denyPartialResponse` command-line flag to all the `vmselect` nodes.
    In this case VictoriaMetrics returns an error during querying if at least a single `vmstorage` node is unavailable.
@@ -174,14 +174,14 @@ If you see unexpected or unreliable query results from VictoriaMetrics, then try
 1. If you observe gaps when plotting time series try simplifying your query according to p2 and follow the list.
    If problem still remains, then it is likely caused by irregular intervals for metrics collection (network delays
    or targets unavailability on scrapes, irregular pushes, irregular timestamps).
-   VictoriaMetrics automatically [fills the gaps](https://docs.victoriametrics.com/keyConcepts.html#range-query)
-   based on median interval between [data samples](https://docs.victoriametrics.com/keyConcepts.html#raw-samples).
+   VictoriaMetrics automatically [fills the gaps](https://docs.victoriametrics.com/keyconcepts/#range-query)
+   based on median interval between [data samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples).
    This might work incorrect for irregular data as median will be skewed. In this case it is recommended to switch
    to the static interval for gaps filling by setting `-search.minStalenessInterval=5m` cmd-line flag (`5m` is
    the static interval used by Prometheus).
 
 1. If you observe recently written data is not immediately visible/queryable, then read more about 
-   [query latency](https://docs.victoriametrics.com/keyConcepts.html#query-latency) behavior.
+   [query latency](https://docs.victoriametrics.com/keyconcepts/#query-latency) behavior.
 
 1. Try upgrading to the [latest available version of VictoriaMetrics](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/latest)
    and verifying whether the issue is fixed there.
@@ -204,7 +204,7 @@ If you see unexpected or unreliable query results from VictoriaMetrics, then try
 
 There are the following most commons reasons for slow data ingestion in VictoriaMetrics:
 
-1. Memory shortage for the given amounts of [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series).
+1. Memory shortage for the given amounts of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series).
 
    VictoriaMetrics (or `vmstorage` in cluster version of VictoriaMetrics) maintains an in-memory cache
    for quick search for internal series ids per each incoming metric.
@@ -216,7 +216,7 @@ There are the following most commons reasons for slow data ingestion in Victoria
    The [official Grafana dashboards for VictoriaMetrics](https://docs.victoriametrics.com/#monitoring)
    contain `Slow inserts` graph, which shows the cache miss percentage for `storage/tsid` cache
    during data ingestion. If `slow inserts` graph shows values greater than 5% for more than 10 minutes,
-   then it is likely the current number of [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series)
+   then it is likely the current number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series)
    cannot fit the `storage/tsid` cache.
 
    There are the following solutions exist for this issue:
@@ -229,20 +229,20 @@ There are the following most commons reasons for slow data ingestion in Victoria
    - To reduce the number of active time series. The [official Grafana dashboards for VictoriaMetrics](https://docs.victoriametrics.com/#monitoring)
      contain a graph showing the number of active time series. Recent versions of VictoriaMetrics
      provide [cardinality explorer](https://docs.victoriametrics.com/#cardinality-explorer),
-     which can help determining and fixing the source of [high cardinality](https://docs.victoriametrics.com/FAQ.html#what-is-high-cardinality).
+     which can help determining and fixing the source of [high cardinality](https://docs.victoriametrics.com/faq/#what-is-high-cardinality).
 
-1. [High churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate),
+1. [High churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate),
    e.g. when old time series are substituted with new time series at a high rate.
    When VictoriaMetrics encounters a sample for new time series, it needs to register the time series
    in the internal index (aka `indexdb`), so it can be quickly located on subsequent select queries.
    The process of registering new time series in the internal index is an order of magnitude slower
    than the process of adding new sample to already registered time series.
-   So VictoriaMetrics may work slower than expected under [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
+   So VictoriaMetrics may work slower than expected under [high churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate).
 
    The [official Grafana dashboards for VictoriaMetrics](https://docs.victoriametrics.com/#monitoring)
    provides `Churn rate` graph, which shows the average number of new time series registered
-   during the last 24 hours. If this number exceeds the number of [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series),
-   then you need to identify and fix the source of [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
+   during the last 24 hours. If this number exceeds the number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series),
+   then you need to identify and fix the source of [high churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate).
    The most commons source of high churn rate is a label, which frequently changes its value. Try avoiding such labels.
    The [cardinality explorer](https://docs.victoriametrics.com/#cardinality-explorer) can help identifying
    such labels.
@@ -282,7 +282,7 @@ There are the following most commons reasons for slow data ingestion in Victoria
    If the network latency between `vminsert` and `vmstorage` is high (for example, if they run in different datacenters),
    then this may become limiting factor for data ingestion speed.
 
-   The [official Grafana dashboard for cluster version of VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#monitoring)
+   The [official Grafana dashboard for cluster version of VictoriaMetrics](https://docs.victoriametrics.com/cluster-victoriametrics/#monitoring)
    contain `connection saturation` graph for `vminsert` components. If this graph reaches 100% (1s),
    then it is likely you have issues with network latency between `vminsert` and `vmstorage`.
    Another possible issue for 100% connection saturation between `vminsert` and `vmstorage`
@@ -291,7 +291,7 @@ There are the following most commons reasons for slow data ingestion in Victoria
 
 1. Noisy neighbor. Make sure VictoriaMetrics components run in an environments without other resource-hungry apps.
    Such apps may steal RAM, CPU, disk IO and network bandwidth, which is needed for VictoriaMetrics components.
-   Issues like this are very hard to catch via [official Grafana dashboard for cluster version of VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#monitoring)
+   Issues like this are very hard to catch via [official Grafana dashboard for cluster version of VictoriaMetrics](https://docs.victoriametrics.com/cluster-victoriametrics/#monitoring)
    and proper diagnosis would require checking resource usage on the instances where VictoriaMetrics runs.
 
 1. If you see `TooHighSlowInsertsRate` [alert](https://docs.victoriametrics.com/#monitoring) when single-node VictoriaMetrics or `vmstorage` has enough
@@ -321,33 +321,33 @@ There are the following solutions exist for improving performance of slow querie
   with more CPU and RAM should help improving speed for slow queries. Query performance
   is always limited by resources of one `vmselect` which processes the query. For example, if 2vCPU cores on `vmselect`
   isn't enough to process query fast enough, then migrating `vmselect` to a machine with 4vCPU cores should increase heavy query performance by up to 2x.
-  If the line on `concurrent select` graph form the [official Grafana dashboard for VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#monitoring)
+  If the line on `concurrent select` graph form the [official Grafana dashboard for VictoriaMetrics](https://docs.victoriametrics.com/cluster-victoriametrics/#monitoring)
   is close to the limit, then prefer adding more `vmselect` nodes to the cluster.
   Sometimes adding more `vmstorage` nodes also can help improving the speed for slow queries.
 
 - Rewriting slow queries, so they become faster. Unfortunately it is hard determining
   whether the given query is slow by just looking at it.
 
-  The main source of slow queries in practice is [alerting and recording rules](https://docs.victoriametrics.com/vmalert.html#rules)
+  The main source of slow queries in practice is [alerting and recording rules](https://docs.victoriametrics.com/vmalert/#rules)
   with long lookbehind windows in square brackets. These queries are frequently used in SLI/SLO calculations such as [Sloth](https://github.com/slok/sloth).
 
   For example, `avg_over_time(up[30d]) > 0.99` needs to read and process
-  all the [raw samples](https://docs.victoriametrics.com/keyConcepts.html#raw-samples)
-  for `up` [time series](https://docs.victoriametrics.com/keyConcepts.html#time-series) over the last 30 days
+  all the [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
+  for `up` [time series](https://docs.victoriametrics.com/keyconcepts/#time-series) over the last 30 days
   each time it executes. If this query is executed frequently, then it can take significant share of CPU, disk read IO, network bandwidth and RAM.
   Such queries can be optimized in the following ways:
 
   - To reduce the lookbehind window in square brackets. For example, `avg_over_time(up[10d])` takes up to 3x less compute resources
     than `avg_over_time(up[30d])` at VictoriaMetrics.
   - To increase evaluation interval for alerting and recording rules, so they are executed less frequently.
-    For example, increasing `-evaluationInterval` command-line flag value at [vmalert](https://docs.victoriametrics.com/vmalert.html)
+    For example, increasing `-evaluationInterval` command-line flag value at [vmalert](https://docs.victoriametrics.com/vmalert/)
     from `1m` to `2m` should reduce compute resource usage at VictoriaMetrics by 2x.
 
-  Another source of slow queries is improper use of [subqueries](https://docs.victoriametrics.com/MetricsQL.html#subqueries).
+  Another source of slow queries is improper use of [subqueries](https://docs.victoriametrics.com/metricsql/#subqueries).
   It is recommended avoiding subqueries if you don't understand clearly how they work.
   It is easy to create a subquery without knowing about it.
   For example, `rate(sum(some_metric))` is implicitly transformed into the following subquery
-  according to [implicit conversion rules for MetricsQL queries](https://docs.victoriametrics.com/MetricsQL.html#implicit-query-conversions):
+  according to [implicit conversion rules for MetricsQL queries](https://docs.victoriametrics.com/metricsql/#implicit-query-conversions):
 
   ```metricsql
   rate(
@@ -394,7 +394,7 @@ There are the following most common sources of out of memory (aka OOM) crashes i
    This would protect from possible OOM crashes on workload spikes. It is recommended to have at least 50%
    of free memory for graceful handling of possible workload spikes.
    See [capacity planning for single-node VictoriaMetrics](https://docs.victoriametrics.com/#capacity-planning)
-   and [capacity planning for cluster version of VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#capacity-planning).
+   and [capacity planning for cluster version of VictoriaMetrics](https://docs.victoriametrics.com/cluster-victoriametrics/#capacity-planning).
 
 
 ## Cluster instability
@@ -408,7 +408,7 @@ The most common sources of cluster instability are:
   the cluster has no enough free resources for processing the increased workload,
   then it may become unstable.
   VictoriaMetrics provides various configuration settings, which can be used for limiting unexpected workload spikes.
-  See [these docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#resource-usage-limits) for details.
+  See [these docs](https://docs.victoriametrics.com/cluster-victoriametrics/#resource-usage-limits) for details.
 
 - Various maintenance tasks such as rolling upgrades or rolling restarts during configuration changes.
   For example, if a cluster contains `N=3` `vmstorage` nodes and they are restarted one-by-one (aka rolling restart),
@@ -426,10 +426,10 @@ The most common sources of cluster instability are:
   `N=11` `vmstorage` nodes, then the workload increase during rolling restart of `vmstorage` nodes
   would be `100%/(N-1)=10%`. It is recommended to have at least 8 `vmstorage` nodes in the cluster.
   The recommended number of `vmstorage` nodes should be multiplied by `-replicationFactor` if replication is enabled -
-  see [replication and data safety docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#replication-and-data-safety)
+  see [replication and data safety docs](https://docs.victoriametrics.com/cluster-victoriametrics/#replication-and-data-safety)
   for details.
 
-- Time series sharding. Received time series [are consistently sharded](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#architecture-overview)
+- Time series sharding. Received time series [are consistently sharded](https://docs.victoriametrics.com/cluster-victoriametrics/#architecture-overview)
   by `vminsert` between configured `vmstorage` nodes. As a sharding key `vminsert` is using time series name and labels,
   respecting their order. If the order of labels in time series is constantly changing, this could cause wrong sharding
   calculation and result in un-even and sub-optimal time series distribution across available vmstorages. It is expected
@@ -439,15 +439,15 @@ The most common sources of cluster instability are:
 
 The obvious solution against VictoriaMetrics cluster instability is to make sure cluster components
 have enough free resources for graceful processing of the increased workload.
-See [capacity planning docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#capacity-planning)
-and [cluster resizing and scalability docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#cluster-resizing-and-scalability)
+See [capacity planning docs](https://docs.victoriametrics.com/cluster-victoriametrics/#capacity-planning)
+and [cluster resizing and scalability docs](https://docs.victoriametrics.com/cluster-victoriametrics/#cluster-resizing-and-scalability)
 for details.
 
 
 ## Too much disk space used
 
 If too much disk space is used by a [single-node VictoriaMetrics](https://docs.victoriametrics.com/) or by `vmstorage` component
-at [VictoriaMetrics cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html), then please check the following:
+at [VictoriaMetrics cluster](https://docs.victoriametrics.com/cluster-victoriametrics/), then please check the following:
 
 - Make sure that there are no old snapsots, since they can occupy disk space. See [how to work with snapshots](https://docs.victoriametrics.com/#how-to-work-with-snapshots)
   and [snapshot troubleshooting](https://docs.victoriametrics.com/#snapshot-troubleshooting).
@@ -466,7 +466,7 @@ at [VictoriaMetrics cluster](https://docs.victoriametrics.com/Cluster-VictoriaMe
 
 ## Monitoring
 
-Having proper [monitoring](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#monitoring)
+Having proper [monitoring](https://docs.victoriametrics.com/single-server-victoriametrics/#monitoring)
 would help identify and prevent most of the issues listed above.
 
 [Grafana dashboards](https://grafana.com/orgs/victoriametrics/dashboards) contain panels reflecting the

docs/VictoriaLogs/CHANGELOG.md

@@ -19,6 +19,8 @@ according to [these docs](https://docs.victoriametrics.com/VictoriaLogs/QuickSta
 
 ## tip
 
+* FEATURE: [web UI](https://docs.victoriametrics.com/VictoriaLogs/querying/#web-ui): add support for head|limit in expressions. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6154).
+
 ## [v0.5.2](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v0.5.2-victorialogs)
 
 Released at 2024-04-11

docs/VictoriaLogs/LogsQL.md

@@ -305,7 +305,7 @@ See also:
 
 VictoriaLogs provides an optimized way to select log entries, which belong to particular [log streams](https://docs.victoriametrics.com/VictoriaLogs/keyConcepts.html#stream-fields).
 This can be done via `_stream:{...}` filter. The `{...}` may contain arbitrary
-[Prometheus-compatible label selector](https://docs.victoriametrics.com/keyConcepts.html#filtering)
+[Prometheus-compatible label selector](https://docs.victoriametrics.com/keyconcepts/#filtering)
 over fields associated with [log streams](https://docs.victoriametrics.com/VictoriaLogs/keyConcepts.html#stream-fields).
 For example, the following query selects [log entries](https://docs.victoriametrics.com/VictoriaLogs/keyConcepts.html#data-model)
 with `app` field equal to `nginx`:

docs/VictoriaLogs/README.md

@@ -45,7 +45,7 @@ See [Quick start docs](https://docs.victoriametrics.com/VictoriaLogs/QuickStart.
 VictoriaLogs exposes internal metrics in Prometheus exposition format at `http://localhost:9428/metrics` page.
 It is recommended to set up monitoring of these metrics via VictoriaMetrics
 (see [these docs](https://docs.victoriametrics.com/#how-to-scrape-prometheus-exporters-such-as-node-exporter)),
-vmagent (see [these docs](https://docs.victoriametrics.com/vmagent.html#how-to-collect-metrics-in-prometheus-format)) or via Prometheus.
+vmagent (see [these docs](https://docs.victoriametrics.com/vmagent/#how-to-collect-metrics-in-prometheus-format)) or via Prometheus.
 
 VictoriaLogs emits its own logs to stdout. It is recommended to investigate these logs during troubleshooting.
 
@@ -83,7 +83,7 @@ It automatically drops partition directories outside the configured retention.
 VictoriaLogs automatically drops logs at [data ingestion](https://docs.victoriametrics.com/VictoriaLogs/data-ingestion/) stage
 if they have timestamps outside the configured retention. A sample of dropped logs is logged with `WARN` message in order to simplify troubleshooting.
 The `vl_rows_dropped_total` [metric](#monitoring) is incremented each time an ingested log entry is dropped because of timestamp outside the retention.
-It is recommended to set up the following alerting rule at [vmalert](https://docs.victoriametrics.com/vmalert.html) in order to be notified
+It is recommended to set up the following alerting rule at [vmalert](https://docs.victoriametrics.com/vmalert/) in order to be notified
 when logs with wrong timestamps are ingested into VictoriaLogs:
 
 ```metricsql
@@ -122,7 +122,7 @@ If `AccountID` and/or `ProjectID` request headers aren't set, then the default `
 
 VictoriaLogs has very low overhead for per-tenant management, so it is OK to have thousands of tenants in a single VictoriaLogs instance.
 
-VictoriaLogs doesn't perform per-tenant authorization. Use [vmauth](https://docs.victoriametrics.com/vmauth.html) or similar tools for per-tenant authorization.
+VictoriaLogs doesn't perform per-tenant authorization. Use [vmauth](https://docs.victoriametrics.com/vmauth/) or similar tools for per-tenant authorization.
 
 ## Benchmarks
 

docs/anomaly-detection/FAQ.md

@@ -34,12 +34,12 @@ The decision to set the changepoint at `1.0` is made to ensure consistency acros
 2. To view a diagram illustrating the interaction of components, please explore the [components section](/anomaly-detection/components/).
 
 ## What data does vmanomaly operate on?
-`vmanomaly` operates on data fetched from VictoriaMetrics, where you can leverage full power of [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) for data selection, sampling, and processing. Users can also [apply global filters](https://docs.victoriametrics.com/#prometheus-querying-api-enhancements) for more targeted data analysis, enhancing scope limitation and tenant visibility.
+`vmanomaly` operates on data fetched from VictoriaMetrics, where you can leverage full power of [MetricsQL](https://docs.victoriametrics.com/metricsql/) for data selection, sampling, and processing. Users can also [apply global filters](https://docs.victoriametrics.com/#prometheus-querying-api-enhancements) for more targeted data analysis, enhancing scope limitation and tenant visibility.
 
 Respective config is defined in a [`reader`](/anomaly-detection/components/reader.html#vm-reader) section.
 
 ## Handling noisy input data
-`vmanomaly` operates on data fetched from VictoriaMetrics using [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) queries, so the initial data quality can be fine-tuned with aggregation, grouping, and filtering to reduce noise and improve anomaly detection accuracy.
+`vmanomaly` operates on data fetched from VictoriaMetrics using [MetricsQL](https://docs.victoriametrics.com/metricsql/) queries, so the initial data quality can be fine-tuned with aggregation, grouping, and filtering to reduce noise and improve anomaly detection accuracy.
 
 ## Output produced by vmanomaly
 `vmanomaly` models generate [metrics](/anomaly-detection/components/models.html#vmanomaly-output) like `anomaly_score`, `yhat`, `yhat_lower`, `yhat_upper`, and `y`. These metrics provide a comprehensive view of the detected anomalies. The service also produces [health check metrics](/anomaly-detection/components/monitoring.html#metrics-generated-by-vmanomaly) for monitoring its performance.
@@ -55,10 +55,10 @@ Still not 100% sure what to use? We are [here to help](/anomaly-detection/#get-i
 While `vmanomaly` detects anomalies and produces scores, it *does not directly generate alerts*. The anomaly scores are written back to VictoriaMetrics, where an external alerting tool, like [`vmalert`](/vmalert.html), can be used to create alerts based on these scores for integrating it with your alerting management system.
 
 ## Preventing alert fatigue
-Produced anomaly scores are designed in such a way that values from 0.0 to 1.0 indicate non-anomalous data, while a value greater than 1.0 is generally classified as an anomaly. However, there are no perfect models for anomaly detection, that's why reasonable defaults expressions like `anomaly_score > 1` may not work 100% of the time. However, anomaly scores, produced by `vmanomaly` are written back as metrics to VictoriaMetrics, where tools like [`vmalert`](/vmalert.html) can use [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) expressions to fine-tune alerting thresholds and conditions, balancing between avoiding [false negatives](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#false-negative) and reducing [false positives](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#false-positive).
+Produced anomaly scores are designed in such a way that values from 0.0 to 1.0 indicate non-anomalous data, while a value greater than 1.0 is generally classified as an anomaly. However, there are no perfect models for anomaly detection, that's why reasonable defaults expressions like `anomaly_score > 1` may not work 100% of the time. However, anomaly scores, produced by `vmanomaly` are written back as metrics to VictoriaMetrics, where tools like [`vmalert`](/vmalert.html) can use [MetricsQL](https://docs.victoriametrics.com/metricsql/) expressions to fine-tune alerting thresholds and conditions, balancing between avoiding [false negatives](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#false-negative) and reducing [false positives](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#false-positive).
 
 ## Resource consumption of vmanomaly
 `vmanomaly` itself is a lightweight service, resource usage is primarily dependent on [scheduling](/anomaly-detection/components/scheduler.html) (how often and on what data to fit/infer your models), [# and size of timeseries returned by your queries](/anomaly-detection/components/reader.html#vm-reader), and the complexity of the employed [models](anomaly-detection/components/models.html). Its resource usage is directly related to these factors, making it adaptable to various operational scales.
 
 ## Scaling vmanomaly
-`vmanomaly` can be scaled horizontally by launching multiple independent instances, each with its own [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) queries and [configurations](/anomaly-detection/components/). This flexibility allows it to handle varying data volumes and throughput demands efficiently.
+`vmanomaly` can be scaled horizontally by launching multiple independent instances, each with its own [MetricsQL](https://docs.victoriametrics.com/metricsql/) queries and [configurations](/anomaly-detection/components/). This flexibility allows it to handle varying data volumes and throughput demands efficiently.

docs/anomaly-detection/Overview.md

@@ -203,7 +203,7 @@ monitoring:
 #### Pull approach
 
 *vmanomaly* can export internal metrics in Prometheus exposition format at `/metrics` page.
-These metrics can be scraped via [vmagent](https://docs.victoriametrics.com/vmagent.html) or Prometheus.
+These metrics can be scraped via [vmagent](https://docs.victoriametrics.com/vmagent/) or Prometheus.
 
 In order to enable pull approach, specify `pull` section in config file:
 
@@ -274,7 +274,7 @@ vm_license_expires_at 1.6963776e+09
 vm_license_expires_in_seconds 4.886608e+06
 ```
 
-Example alerts for [vmalert](https://docs.victoriametrics.com/vmalert.html):
+Example alerts for [vmalert](https://docs.victoriametrics.com/vmalert/):
 
 ```yaml
 groups:

docs/anomaly-detection/components/monitoring.md

@@ -13,7 +13,7 @@ aliases:
 
 # Monitoring
 
-There are 2 models to monitor VictoriaMetrics Anomaly Detection behavior - [push](https://docs.victoriametrics.com/keyConcepts.html#push-model) and [pull](https://docs.victoriametrics.com/keyConcepts.html#pull-model). Parameters for each of them should be specified in the config file, `monitoring` section.
+There are 2 models to monitor VictoriaMetrics Anomaly Detection behavior - [push](https://docs.victoriametrics.com/keyconcepts/#push-model) and [pull](https://docs.victoriametrics.com/keyconcepts/#pull-model). Parameters for each of them should be specified in the config file, `monitoring` section.
 
 ## Pull Model Config parameters