Merge remote-tracking branch 'elastic/master' into retention-lease-expiration

* elastic/master:
  Removing unused methods in Numbers (elastic#37186)
  Fix setting by time unit (elastic#37192)
  [DOCS] Cleans up xpackml attributes
  ML: fix delayed data annotations on secured cluster (elastic#37193)
  Update version in SearchRequest and related test
  [DOCS] Adds overview and API ref for cluster voting configurations (elastic#36954)
  ML: changing JobResultsProvider.getForecastRequestStats to support > 1 index (elastic#37157)
  Separate out validation of groups of settings (elastic#34184)

Author: jasontedor

docs/reference/cluster.asciidoc

@@ -104,3 +104,5 @@ include::cluster/tasks.asciidoc[]
 include::cluster/nodes-hot-threads.asciidoc[]
 
 include::cluster/allocation-explain.asciidoc[]
+
+include::cluster/voting-exclusions.asciidoc[]

docs/reference/cluster/voting-exclusions.asciidoc

@@ -0,0 +1,76 @@
+[[voting-config-exclusions]]
+== Voting configuration exclusions API
+++++
+<titleabbrev>Voting Configuration Exclusions</titleabbrev>
+++++
+
+Adds or removes master-eligible nodes from the
+<<modules-discovery-voting,voting configuration exclusion list>>.
+
+[float]
+=== Request
+
+`POST _cluster/voting_config_exclusions/<node_name>` +
+
+`DELETE _cluster/voting_config_exclusions`
+
+[float]
+=== Path parameters
+
+`node_name`::
+  A <<cluster-nodes,node filter>> that identifies {es} nodes.
+
+[float]
+=== Description
+  
+By default, if there are more than three master-eligible nodes in the cluster
+and you remove fewer than half of the master-eligible nodes in the cluster at
+once, the <<modules-discovery-voting,voting configuration>> automatically
+shrinks.
+
+If you want to shrink the voting configuration to contain fewer than three nodes
+or to remove half or more of the master-eligible nodes in the cluster at once,
+you must use this API to remove departed nodes from the voting configuration
+manually. It adds an entry for that node in the voting configuration exclusions
+list. The cluster then tries to reconfigure the voting configuration to remove
+that node and to prevent it from returning.
+
+If the API fails, you can safely retry it.  Only a successful response
+guarantees that the node has been removed from the voting configuration and will
+not be reinstated.
+
+NOTE: Voting exclusions are required only when you remove at least half of the
+master-eligible nodes from a cluster in a short time period. They are not
+required when removing master-ineligible nodes or fewer than half of the
+master-eligible nodes.
+
+The <<modules-discovery-settings,`cluster.max_voting_config_exclusions`
+setting>> limits the size of the voting configuration exclusion list. The
+default value is `10`. Since voting configuration exclusions are persistent and
+limited in number, you must clear the voting config exclusions list once the
+exclusions are no longer required.
+
+There is also a
+<<modules-discovery-settings,`cluster.auto_shrink_voting_configuration` setting>>,
+which is set to true by default. If it is set to false, you must use this API to
+maintain the voting configuration.
+
+For more information, see <<modules-discovery-removing-nodes>>.
+
+[float]
+=== Examples
+
+Add `nodeId1` to the voting configuration exclusions list:
+[source,js]
+-------------------------------------------------- 
+POST /_cluster/voting_config_exclusions/nodeId1
+--------------------------------------------------
+// CONSOLE
+// TEST[catch:bad_request]
+
+Remove all exclusions from the list:
+[source,js]
+--------------------------------------------------
+DELETE /_cluster/voting_config_exclusions
+--------------------------------------------------
+// CONSOLE

docs/reference/ml/aggregations.asciidoc

@@ -8,7 +8,7 @@ and to configure your jobs to analyze aggregated data.
 
 One of the benefits of aggregating data this way is that {es} automatically
 distributes these calculations across your cluster. You can then feed this
-aggregated data into {xpackml} instead of raw results, which
+aggregated data into the {ml-features} instead of raw results, which
 reduces the volume of data that must be considered while detecting anomalies.
 
 There are some limitations to using aggregations in {dfeeds}, however.

docs/reference/ml/apis/resultsresource.asciidoc

@@ -269,7 +269,7 @@ probability of this occurrence.
 
 There can be many anomaly records depending on the characteristics and size of
 the input data. In practice, there are often too many to be able to manually
-process them. The {xpackml} features therefore perform a sophisticated
+process them. The {ml-features} therefore perform a sophisticated
 aggregation of the anomaly records into buckets.
 
 The number of record results depends on the number of anomalies found in each

docs/reference/ml/configuring.asciidoc

@@ -2,12 +2,12 @@
 [[ml-configuring]]
 == Configuring machine learning
 
-If you want to use {xpackml} features, there must be at least one {ml} node in
+If you want to use {ml-features}, there must be at least one {ml} node in
 your cluster and all master-eligible nodes must have {ml} enabled. By default,
 all nodes are {ml} nodes. For more information about these settings, see 
 {ref}/modules-node.html#modules-node-xpack[{ml} nodes].
 
-To use the {xpackml} features to analyze your data, you must create a job and
+To use the {ml-features} to analyze your data, you must create a job and
 send your data to that job.
 
 * If your data is stored in {es}:

docs/reference/ml/functions.asciidoc

@@ -2,7 +2,7 @@
 [[ml-functions]]
 == Function reference
 
-The {xpackml} features include analysis functions that provide a wide variety of
+The {ml-features} include analysis functions that provide a wide variety of
 flexible ways to analyze data for anomalies.
 
 When you create jobs, you specify one or more detectors, which define the type of

docs/reference/ml/functions/count.asciidoc

@@ -14,7 +14,7 @@ in one field is unusual, as opposed to the total count.
 Use high-sided functions if you want to monitor unusually high event rates.
 Use low-sided functions if you want to look at drops in event rate.
 
-The {xpackml} features include the following count functions:
+The {ml-features} include the following count functions:
 
 * xref:ml-count[`count`, `high_count`, `low_count`]
 * xref:ml-nonzero-count[`non_zero_count`, `high_non_zero_count`, `low_non_zero_count`]

docs/reference/ml/functions/geo.asciidoc

@@ -5,7 +5,7 @@
 The geographic functions detect anomalies in the geographic location of the
 input data.
 
-The {xpackml} features include the following geographic function: `lat_long`.
+The {ml-features} include the following geographic function: `lat_long`.
 
 NOTE: You cannot create forecasts for jobs that contain geographic functions. 
 You also cannot add rules with conditions to detectors that use geographic 
@@ -72,7 +72,7 @@ For example, JSON data might contain the following transaction coordinates:
 
 In {es}, location data is likely to be stored in `geo_point` fields. For more
 information, see {ref}/geo-point.html[Geo-point datatype]. This data type is not
-supported natively in {xpackml} features. You can, however, use Painless scripts
+supported natively in {ml-features}. You can, however, use Painless scripts
 in `script_fields` in your {dfeed} to transform the data into an appropriate
 format. For example, the following Painless script transforms
 `"coords": {"lat" : 41.44, "lon":90.5}` into `"lat-lon": "41.44,90.5"`:

docs/reference/ml/functions/info.asciidoc

@@ -6,7 +6,7 @@ that is contained in strings within a bucket. These functions can be used as
 a more sophisticated method to identify incidences of data exfiltration or
 C2C activity, when analyzing the size in bytes of the data might not be sufficient.
 
-The {xpackml} features include the following information content functions:
+The {ml-features} include the following information content functions:
 
 * `info_content`, `high_info_content`, `low_info_content`
 

docs/reference/ml/functions/metric.asciidoc

@@ -6,7 +6,7 @@ The metric functions include functions such as mean, min and max. These values
 are calculated for each bucket. Field values that cannot be converted to
 double precision floating point numbers are ignored.
 
-The {xpackml} features include the following metric functions:
+The {ml-features} include the following metric functions:
 
 * <<ml-metric-min,`min`>>
 * <<ml-metric-max,`max`>>