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Merge remote-tracking branch 'origin/main' into per-project-s3-clients
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‎distribution/docker/src/docker/Dockerfile‎

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<% if (docker_base == 'iron_bank') { %>
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ARG BASE_REGISTRY=registry1.dso.mil
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ARG BASE_IMAGE=ironbank/redhat/ubi/ubi9
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ARG BASE_TAG=9.5
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ARG BASE_TAG=9.6
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<% } %>
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################################################################################

‎docs/changelog/124825.yaml‎

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pr: 124825
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summary: Check alias during update
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area: Transform
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type: bug
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issues: []

‎docs/changelog/127371.yaml‎

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pr: 127371
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summary: Add cancellation support in `TransportGetAllocationStatsAction`
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area: Allocation
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type: feature
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issues:
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- 123248

‎docs/changelog/127687.yaml‎

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pr: 127687
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summary: "ESQL: Fix alias removal in regex extraction with JOIN"
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area: ES|QL
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type: bug
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issues:
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- 127467

‎docs/changelog/127924.yaml‎

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pr: 127924
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summary: Limit Replace function memory usage
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area: ES|QL
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type: enhancement
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issues: []

‎docs/changelog/128043.yaml‎

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pr: 128043
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summary: Make S3 custom query parameter optional
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area: Snapshot/Restore
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type: breaking
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issues: []
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breaking:
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title: Make S3 custom query parameter optional
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area: Cluster and node setting
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details: >-
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Earlier versions of Elasticsearch would record the purpose of each S3 API
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call using the `?x-purpose=` custom query parameter. This isn't believed to
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be necessary outside of the ECH/ECE/ECK/... managed services, and it adds
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rather a lot to the request logs, so with this change we make the feature
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optional and disabled by default.
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impact: >-
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If you wish to reinstate the old behaviour on a S3 repository, set
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`s3.client.${CLIENT_NAME}.add_purpose_custom_query_parameter` to `true`
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for the relevant client.
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notable: false

‎docs/changelog/128111.yaml‎

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pr: 128111
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summary: Fix union types in CCS
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area: ES|QL
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type: bug
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issues: []

‎docs/changelog/128161.yaml‎

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pr: 128161
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summary: Fix system data streams incorrectly showing up in the list of template validation
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problems
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area: Data streams
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type: bug
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issues: []

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

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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]`.
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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.
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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.
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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)).
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‎docs/reference/aggregations/pipeline.md‎

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## Dealing with gaps in the data [gap-policy]
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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:
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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:
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* Documents falling into a bucket do not contain a required field
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* There are no documents matching the query for one or more buckets

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