[[release-highlights-7.4.0]]
== 7.4.0 release highlights
++++
<titleabbrev>7.4.0</titleabbrev>
++++

//NOTE: The notable-highlights tagged regions are re-used in the
//Installation and Upgrade Guide

// tag::notable-highlights[]
[float]
==== Results pinning

You can use the new <<query-dsl-pinned-query,pinned query>>
to define the first records
(and the order in which they are returned)
in a result set directly within {es}.

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== New `shape` field type

A new <<shape,`shape`>> field type has been added,
which allows you to position and query shapes
in a geometry of your choosing.

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== Circle ingest processor

A new <<ingest-circle-processor, circle ingest processor>> has been added,
which translates circles into regular polygons (bounded by the circles).
This makes ingesting, indexing, searching, and aggregating circles both easy and efficient.

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== Aggregations on range fields

The <<search-aggregations-bucket-histogram-aggregation,histogram>>
and <<search-aggregations-bucket-datehistogram-aggregation,Date Histogram>>
aggregations now support the <<range,`range`>> field type.

Range aggregations are useful
when counting ranges that overlap with specific buckets
(e.g. the number of phone calls that took place during a specific minute).

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== Cumulative cardinality aggregation

A new <<search-aggregations-pipeline-cumulative-cardinality-aggregation,cumulative cardinality aggregation>>
has been added
as part of our ongoing effort to provide advanced aggregations.

You can use this new pipeline aggregation
to calculate a net-new total of document occurrences
within a given time range.

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== Snapshot lifecycle management

We’re introducing <<getting-started-snapshot-lifecycle-management,snapshot lifecycle management (SLM)>>,
which allows an administrator to define policies, 
via API or {kibana-ref}/index-lifecycle-policies.html[{kib} UI],
that manage when and how often snapshots are taken.
You can use SLM
to ensure that appropriate, recent backups are ready
if disaster strikes
or you need to restore {es} data. 

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== API key management

New {stack-ov}/security-privileges.html[cluster privileges] to manage API keys have been added,
allowing cluster administrators to manage everything,
and regular users to manage their own keys.
Users can create API keys
and use them to provide long-term credentials
while interacting with {es}. 

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== TLS settings for email notifications

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.
{es} now supports custom <<ssl-notification-smtp-settings,TLS settings>> for email notifications,
allowing secure connections to servers with custom security configuration.

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== Automatic query cancellation

{es} now automatically terminates queries
sent through the `_search` endpoint
when the initiating connection is closed.

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== Support for AdoptOpenJDK

AdoptOpenJDK 13 is now supported and shipped with {es} as the pre-bundled JDK.

If you want to use your own JDK,
you can still do so by setting `JAVA_HOME` before starting Elasticsearch.

The availability of a notarized AdoptOpenJDK package
(per the new requirements for software running on macOS Catalina)
facilitates notarization of {es} for continued support on macOS. 

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== Regression analysis - Experimental

{stack-ov}/dfa-regression.html[Regression analysis] is an experimental machine learning process
for estimating the relationships among a number of feature variables and a dependent variable,
then making further predictions based on the described relationship.

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== New vector distance functions for document script scoring - Experimental

Two experimential similarity measurements—
Manhattan distance (L1 norm)
and Euclidean distance (L2 norm)—
have been added.
Like the dot product and cosine similarity,
the Euclidean and Manhattan distances are provided as <<vector-functions,predefined Painless functions>>
so that they may be incorporated with other query elements
as part of a <<query-dsl-script-score-query,script_score>> query.

// end::notable-highlights[]