157 lines
4.8 KiB
Plaintext
157 lines
4.8 KiB
Plaintext
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[[release-highlights-7.4.0]]
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== 7.4.0 release highlights
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++++
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<titleabbrev>7.4.0</titleabbrev>
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++++
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//NOTE: The notable-highlights tagged regions are re-used in the
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//Installation and Upgrade Guide
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// tag::notable-highlights[]
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[float]
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==== Results pinning
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You can use the new <<query-dsl-pinned-query,pinned query>>
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to define the first records
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(and the order in which they are returned)
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in a result set directly within {es}.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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==== New `shape` field type
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A new <<shape,`shape`>> field type has been added,
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which allows you to position and query shapes
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in a geometry of your choosing.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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==== Circle ingest processor
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A new <<ingest-circle-processor, circle ingest processor>> has been added,
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which translates circles into regular polygons (bounded by the circles).
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This makes ingesting, indexing, searching, and aggregating circles both easy and efficient.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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==== Aggregations on range fields
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The <<search-aggregations-bucket-histogram-aggregation,histogram>>
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and <<search-aggregations-bucket-datehistogram-aggregation,Date Histogram>>
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aggregations now support the <<range,`range`>> field type.
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Range aggregations are useful
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when counting ranges that overlap with specific buckets
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(e.g. the number of phone calls that took place during a specific minute).
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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==== Cumulative cardinality aggregation
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A new <<search-aggregations-pipeline-cumulative-cardinality-aggregation,cumulative cardinality aggregation>>
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has been added
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as part of our ongoing effort to provide advanced aggregations.
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You can use this new pipeline aggregation
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to calculate a net-new total of document occurrences
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within a given time range.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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==== Snapshot lifecycle management
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We’re introducing <<getting-started-snapshot-lifecycle-management,snapshot lifecycle management (SLM)>>,
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which allows an administrator to define policies,
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via API or {kibana-ref}/index-lifecycle-policies.html[{kib} UI],
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that manage when and how often snapshots are taken.
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You can use SLM
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to ensure that appropriate, recent backups are ready
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if disaster strikes
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or you need to restore {es} data.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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==== API key management
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New {stack-ov}/security-privileges.html[cluster privileges] to manage API keys have been added,
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allowing cluster administrators to manage everything,
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and regular users to manage their own keys.
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Users can create API keys
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and use them to provide long-term credentials
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while interacting with {es}.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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==== TLS settings for email notifications
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Notifications may contain sensitive information that must be protected over the wire. This requires that communication with the mail server is encrypted and authenticated properly.
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{es} now supports custom <<ssl-notification-smtp-settings,TLS settings>> for email notifications,
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allowing secure connections to servers with custom security configuration.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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==== Automatic query cancellation
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{es} now automatically terminates queries
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sent through the `_search` endpoint
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when the initiating connection is closed.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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==== Support for AdoptOpenJDK
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AdoptOpenJDK 13 is now supported and shipped with {es} as the pre-bundled JDK.
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If you want to use your own JDK,
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you can still do so by setting `JAVA_HOME` before starting Elasticsearch.
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The availability of a notarized AdoptOpenJDK package
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(per the new requirements for software running on macOS Catalina)
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facilitates notarization of {es} for continued support on macOS.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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==== Regression analysis - Experimental
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{stack-ov}/dfa-regression.html[Regression analysis] is an experimental machine learning process
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for estimating the relationships among a number of feature variables and a dependent variable,
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then making further predictions based on the described relationship.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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==== New vector distance functions for document script scoring - Experimental
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Two experimential similarity measurements—
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Manhattan distance (L1 norm)
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and Euclidean distance (L2 norm)—
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have been added.
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Like the dot product and cosine similarity,
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the Euclidean and Manhattan distances are provided as <<vector-functions,predefined Painless functions>>
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so that they may be incorporated with other query elements
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as part of a <<query-dsl-script-score-query,script_score>> query.
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// end::notable-highlights[]
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