7.4.0 release highlights

You can use the new pinned query to define the first records (and the order in which they are returned) in a result set directly within Elasticsearch.

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

A new 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.

The histogram and date histogram aggregations now support the 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).

A new 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.

We’re introducing snapshot lifecycle management (SLM), which allows an administrator to define policies, via API or Kibana 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 Elasticsearch data.

New 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 Elasticsearch.

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. Elasticsearch now supports custom TLS settings for email notifications, allowing secure connections to servers with custom security configuration.

Elasticsearch now automatically terminates queries sent through the _search endpoint when the initiating connection is closed.

AdoptOpenJDK 13 is now supported and shipped with Elasticsearch 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 Elasticsearch for continued support on macOS.

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.

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 predefined Painless functions so that they may be incorporated with other query elements as part of a script_score query.