263 lines
11 KiB
Plaintext
263 lines
11 KiB
Plaintext
[[release-highlights]]
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== What's new in {minor-version}
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coming[{minor-version}]
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Here are the highlights of what's new and improved in {es} {minor-version}!
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ifeval::["{release-state}"!="unreleased"]
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For detailed information about this release, see the
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<<release-notes-{elasticsearch_version}, Release notes >> and
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<<breaking-changes-{minor-version}, Breaking changes>>.
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endif::[]
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// Add previous release to the list
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Other versions:
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{ref-bare}/7.9/release-highlights.html[7.9]
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| {ref-bare}/7.8/release-highlights.html[7.8]
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| {ref-bare}/7.7/release-highlights.html[7.7]
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| {ref-bare}/7.6/release-highlights-7.6.0.html[7.6]
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| {ref-bare}/7.5/release-highlights-7.5.0.html[7.5]
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| {ref-bare}/7.4/release-highlights-7.4.0.html[7.4]
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| {ref-bare}/7.3/release-highlights-7.3.0.html[7.3]
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| {ref-bare}/7.2/release-highlights-7.2.0.html[7.2]
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| {ref-bare}/7.1/release-highlights-7.1.0.html[7.1]
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| {ref-bare}/7.0/release-highlights-7.0.0.html[7.0]
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// tag::notable-highlights[]
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[discrete]
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[[indexing-speed-improvement]]
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=== Indexing speed improvement
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{es} 7.10 improves indexing speed by up to 20%. We've reduced the coordination
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needed to add entries to the {ref}/index-modules-translog.html[transaction log].
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This reduction allows for more concurrency and increases the transaction
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log buffer size from `8KB` to `1MB`. However, performance gains are lower for
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full-text search and other analysis-intensive use cases. The heavier the
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indexing chain, the lower the gains, so indexing chains that involve many
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fields, ingest pipelines or full-text indexing will see lower gains.
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[discrete]
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[[more-space-efficient-indices]]
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=== More space-efficient indices
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{es} 7.10 depends on Apache Lucene 8.7, which introduces higher compression of
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stored fields, the part of the index that notably stores the
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{ref}/mapping-source-field.html[`_source`]. On the various data sets that we
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benchmark against, we noticed space reductions between 0% and 10%. This change
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especially helps on data sets that have lots of redundant data across documents,
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which is typically the case of the documents that are produced by our
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Observability solutions, which repeat metadata about the host that produced the
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data on every document.
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Elasticsearch offers the ability to configure the
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{ref}/index-modules.html#index-codec[`index.codec`] setting to tell
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{es} how aggressively to compress stored fields. Both supported values
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`default` and `best_compression` will get better compression with this change.
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[discrete]
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[[data-tier-formalization]]
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=== Data tiers
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7.10 introduces the concept of formalized data tiers within {es}.
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{ref}/data-tiers.html[Data tiers] are a simple, integrated approach that gives
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users control over optimizing for cost, performance, and breadth/depth of data.
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Prior to this formalization, many users configured their own tier topology using
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custom node attributes as well as using {ilm-init} to manage the lifecycle and
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location of data within a cluster.
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With this formalization, data tiers (content, hot, warm, and cold) can be
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explicitly configured using {ref}/modules-node.html#node-roles[node roles], and
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indices can be configured to be allocated within a specific tier using
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{ref}/data-tier-shard-filtering.html[index-level data tier allocation filtering].
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{ilm-init} will make use of these tiers to
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{ref}/ilm-migrate.html[automatically migrate] data between nodes as an index
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goes through the phases of its lifecycle.
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Newly created indices abstracted by a {ref}/data-streams.html[data stream] will
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be allocated to the `data_hot` tier automatically, while standalone indices will
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be allocated to the `data_content` tier automatically. Nodes with the
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pre-existing `data` role are considered to be part of all tiers.
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[discrete]
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[[auc-roc-eval-class]]
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=== AUC ROC evaluation metrics for classification analysis
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{ml-docs}/ml-dfanalytics-evaluate.html#ml-dfanalytics-class-aucroc[Area under the curve of receiver operating characteristic (AUC ROC)]
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is an evaluation metric that has been available for {oldetection} since 7.3 and
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now is available for {classification} analysis. AUC ROC represents the
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performance of the {classification} process at different predicted probability
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thresholds. The true positive rate for a specific class is compared against the
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rate of all the other classes combined at the different threshold levels to
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create the curve.
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[discrete]
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[[custom-feature-processor-dfa]]
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=== Custom feature processors in {dfanalytics}
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Feature processors enable you to extract process features from document fields.
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You can use these features in model training and model deployment. Custom
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feature processors provide a mechanism to create features that can be used at
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search and ingest time and they don’t take up space in the index.
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This process more tightly couples feature generation with the resulting model.
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The result is simplified model management as both the features and the model can
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easily follow the same life cycle.
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[discrete]
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[[points-in-time-for-search]]
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=== Points in time (PITs) for search
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In 7.10, we're introducing points in time (PITs), a lightweight way to preserve
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index state over searches. PITs improve end-user experience by making UIs more
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reactive.
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By default, a search request waits for complete results before returning a
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response. For example, a search that retrieves top hits and aggregations returns
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a response only after both top hits and aggregations are computed. However,
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aggregations are usually slower and more expensive to compute than top hits.
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Instead of sending a combined request, you can send two separate requests: one
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for top hits and another one for aggregations. With separate search requests, a
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UI can display top hits as soon as they're available and display aggregation
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data after the slower aggregation request completes. You can use a PIT to ensure
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both search requests run on the same data and index state.
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To use a PIT in a search, you must first explicitly create the PIT using the new
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{ref}/point-in-time-api.html[open PIT API]. PITs get automatically garbage-collected
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after `keep_alive` if no follow-up request extends their duration.
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[source,console]
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----
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POST /my-index-000001/_pit?keep_alive=1m
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----
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// TEST[setup:my_index]
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The API returns a PIT ID you can use in search requests. You can also
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configure by how long to extend your PIT's lifespan using the search request's
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`keep_alive` parameter.
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[source,console]
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----
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POST /_search
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{
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"size": 100,
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"query": {
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"match" : {
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"title" : "elasticsearch"
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}
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},
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"pit": {
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"id": "46ToAwMDaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQNpZHkFdXVpZDIrBm5vZGVfMwAAAAAAAAAAKgFjA2lkeQV1dWlkMioGbm9kZV8yAAAAAAAAAAAMAWICBXV1aWQyAAAFdXVpZDEAAQltYXRjaF9hbGw_gAAAAA==",
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"keep_alive": "1m"
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}
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}
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----
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// TEST[catch:missing]
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PITs automatically close when their `keep_alive` period ends. You can
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also manually close PITs you no longer need using the
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{ref}/point-in-time-api.html[close PIT API]. Closing a PIT releases the
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resources needed to maintain the PIT's index state.
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[source,console]
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----
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DELETE /_pit
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{
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"id" : "46ToAwMDaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQNpZHkFdXVpZDIrBm5vZGVfMwAAAAAAAAAAKgFjA2lkeQV1dWlkMioGbm9kZV8yAAAAAAAAAAAMAWIBBXV1aWQyAAA="
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}
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----
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// TEST[catch:missing]
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For more information about using PITs in search, see
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{ref}/paginate-search-results.html#search-after[Paginate search results with
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`search_after`] or the {ref}/point-in-time-api.html[PIT API documentation].
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[discrete]
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[[support-for-request-level-circuit-breakers]]
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=== Request-level circuit breakers on coordinating nodes
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You can now use a coordinating node to account for memory used to perform
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partial and final reduce of aggregations in the request circuit breaker. The
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search coordinator adds the memory that it used to save and reduce the results
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of shard aggregations in the request circuit breaker. Before any partial or
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final reduce, the memory needed to reduce the aggregations is estimated and a
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CircuitBreakingException is thrown if exceeds the maximum memory allowed in this
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breaker.
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This size is estimated as roughly 1.5 times the size of the serialized
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aggregations that need to be reduced. This estimation can be completely off for
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some aggregations but it is corrected with the real size after the reduce
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completes. If the reduce is successful, we update the circuit breaker to remove
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the size of the source aggregations and replace the estimation with the
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serialized size of the newly reduced result.
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[discrete]
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[[eql-case-sensitivity-operator]]
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=== EQL: Case-sensitivity and the `:` operator
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In 7.10, we made most EQL operators and functions case-sensitive by default.
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We've also added `:`, a new case-insensitive equal operator. Designed for
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security use cases, you can use the `:` operator to search for strings in
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Windows event logs and other event data containing a mix of letter cases.
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[source,console]
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----
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GET /my-index-000001/_eql/search
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{
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"query": """
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process where process.executable : "c:\\\\windows\\\\system32\\\\cmd.exe"
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"""
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}
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----
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// TEST[setup:sec_logs]
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For more information, see the {ref}/eql-syntax.html[EQL
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syntax documentation].
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[discrete]
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[[deprecate-rest-api-access-to-system-indices]]
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=== REST API access to system indices is deprecated
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We are deprecating REST API access to system indices. Most REST API
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requests that attempt to access system indices will return the following
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deprecation warning:
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[source,text]
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----
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this request accesses system indices: [.system_index_name], but in a future
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major version, direct access to system indices will be prevented by default
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----
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The following REST API endpoints access system indices as part of their
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implementation and will not return the deprecation warning:
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* `GET _cluster/health`
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* `GET {index}/_recovery`
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* `GET _cluster/allocation/explain`
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* `GET _cluster/state`
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* `POST _cluster/reroute`
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* `GET {index}/_stats`
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* `GET {index}/_segments`
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* `GET {index}/_shard_stores`
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* `GET _cat/[indices,aliases,health,recovery,shards,segments]`
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We are also adding a new metadata flag to track indices. {es} will automatically
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add this flag to any existing system indices during upgrade.
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[discrete]
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[[add-system-read-thread-pool]]
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=== New thread pools for system indices
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We've added two new thread pools for system indices: `system_read` and
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`system_write`. These thread pools ensure system indices critical to the Elastic
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Stack, such as those used by security or Kibana, remain responsive when
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a cluster is under heavy query or indexing load.
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`system_read` is a `fixed` thread pool used to manage resources for
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read operations targeting system indices. Similarly, `system_write` is a
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`fixed` thread pool used to manage resources for write operations targeting
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system indices. Both have a maximum number of threads equal to `5`
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or half of the available processors, whichever is smaller.
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// end::notable-highlights[]
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