Merge branch 'main' into cleanup-docker-files

Author: breskeby

docs/changelog/124825.yaml

@@ -0,0 +1,5 @@
+pr: 124825
+summary: Check alias during update
+area: Transform
+type: bug
+issues: []

docs/changelog/127371.yaml

@@ -0,0 +1,6 @@
+pr: 127371
+summary: Add cancellation support in `TransportGetAllocationStatsAction`
+area: Allocation
+type: feature
+issues:
+ - 123248

docs/changelog/127563.yaml

@@ -0,0 +1,6 @@
+pr: 127563
+summary: "ESQL: Avoid unintended attribute removal"
+area: ES|QL
+type: bug
+issues:
+  - 127468

docs/changelog/127687.yaml

@@ -0,0 +1,6 @@
+pr: 127687
+summary: "ESQL: Fix alias removal in regex extraction with JOIN"
+area: ES|QL
+type: bug
+issues:
+  - 127467

docs/changelog/127924.yaml

@@ -0,0 +1,5 @@
+pr: 127924
+summary: Limit Replace function memory usage
+area: ES|QL
+type: enhancement
+issues: []

docs/changelog/128161.yaml

@@ -0,0 +1,6 @@
+pr: 128161
+summary: Fix system data streams incorrectly showing up in the list of template validation
+  problems
+area: Data streams
+type: bug
+issues: []

docs/reference/aggregations/_snippets/search-aggregations-metrics-percentile-aggregation-approximate.md

@@ -1,6 +1,6 @@
 There are many different algorithms to calculate percentiles. The naive implementation simply stores all the values in a sorted array. To find the 50th percentile, you simply find the value that is at `my_array[count(my_array) * 0.5]`.
 
-Clearly, the naive implementation does not scale — the sorted array grows linearly with the number of values in your dataset. To calculate percentiles across potentially billions of values in an Elasticsearch cluster, *approximate* percentiles are calculated.
+Clearly, the naive implementation does not scale — the sorted array grows linearly with the number of values in your dataset. To calculate percentiles across potentially billions of values in an Elasticsearch cluster, *approximate* percentiles are calculated.
 
 The algorithm used by the `percentile` metric is called TDigest (introduced by Ted Dunning in [Computing Accurate Quantiles using T-Digests](https://github.com/tdunning/t-digest/blob/master/docs/t-digest-paper/histo.pdf)).
 

docs/reference/aggregations/pipeline.md

@@ -230,7 +230,7 @@ An alternate syntax is supported to cope with aggregations or metrics which have
 
 ## Dealing with gaps in the data [gap-policy]
 
-Data in the real world is often noisy and sometimes contains **gaps** — places where data simply doesn’t exist. This can occur for a variety of reasons, the most common being:
+Data in the real world is often noisy and sometimes contains **gaps** — places where data simply doesn’t exist. This can occur for a variety of reasons, the most common being:
 
 * Documents falling into a bucket do not contain a required field
 * There are no documents matching the query for one or more buckets

docs/reference/aggregations/search-aggregations-bucket-composite-aggregation.md

@@ -606,7 +606,7 @@ PUT my-index-000001
 ```
 
 1. This index is sorted by `username` first then by `timestamp`.
-2. …​ in ascending order for the `username` field and in descending order for the `timestamp` field.1. could be used to optimize these composite aggregations:
+2. …  in ascending order for the `username` field and in descending order for the `timestamp` field.1. could be used to optimize these composite aggregations:
 
 
 

docs/reference/aggregations/search-aggregations-bucket-datehistogram-aggregation.md

@@ -679,7 +679,7 @@ Response:
 }
 ```
 
-The response will contain all the buckets having the relative day of the week as key : 1 for Monday, 2 for Tuesday…​ 7 for Sunday.
+The response will contain all the buckets having the relative day of the week as key : 1 for Monday, 2 for Tuesday…  7 for Sunday.