Elasticsearch highlights

This list summarizes the most important enhancements in Elasticsearch 7.2.1. For the complete list, go to Elasticsearch release highlights.

[beta] This functionality is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features. You can now transform your data with data frames. There is a new Kibana wizard that guides you through the process of creating a transform to pivot and summarize your data and store it in a new index. Alternatively, you can use data frame APIs to preview, create, and manage the transforms.

Elasticsearch 7.2.0 brings better support for closed indices by allowing shards of closed indices to be replicated. As soon as an index is closed, Elasticsearch takes care of safely tearing down the "opened" shards before reinitializing them as "closed" shards, which require fewer resources. Closed shards can later be promoted to primary shards or automatically recovered during peer recovery The data is also automatically replicated by the cluster to ensure that enough shard copies are safely kept around at all times (configurable with index.number_of_replicas).

In addition to that, it is now possible to snapshot closed indices using the Snapshot/Restore API. To include a closed index when creating a snapshot on Elasticsearch 7.2+, the expand_wildcards parameter must be explicitly set to either all or closed .

Note that only indices closed in Elasticsearch 7.2+ are automatically replicated. Indices closed on previous versions of Elasticsearch will remain non replicated unless they are opened and closed again in 7.2+.

[beta] This functionality is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features. This release introduces the first set of geo features to SQL. The implementation is based on the OpenGIS® Implementation Standard for Geographic information - "Simple feature access". This is the current de-facto standard for GIS system implementation. This release includes a small subset of SQL option AKA ISO 19125-2.

For this initial beta release, we added support for returning geo_shapes and geo_points as results, added support for a few geo functions (ST_AsText, ST_Distance, ST_GeometryType, ST_GeometryFromText, ST_X, ST_Y, and ST_Z) , and added a limited support for using geo_points in distance queries. For example: SELECT * FROM my_index WHERE ST_Distance(point, ST_WKTToSQL(point (10 20))) < 20.

This release introduces OpenId Connect as an authentication realm. Elasticsearch (with the assistance of Kibana or another web component) can now serve as an OpenID Connect Relying Party (RP). Elasticsearch supports the Authorization Code Grant and Implicit flows as described in http://ela.st/oidc-spec. It also supports consuming and verifying signed ID Tokens , RP initiated single sign on (SSO), 3rd party initiated SSO, and RP initiated single logout.

The search_as_you_type field type is a text-like field optimized to provide out-of-the-box support for queries that serve an as-you-type completion use case. It creates a series of subfields that are analyzed to index terms that can be efficiently matched by a query that partially matches the entire indexed text value. Both prefix completion (i.e matching terms starting at the beginning of the input) and infix completion (i.e. matching terms at any position within the input) are supported.

The distance_feature query is a specialized query that only works on date, date_nanos, or geo_point fields. The query boosts documents scores based on proximity to some given origin. For example, you can use this query to give higher scores to documents with dates closer to a certain date or locations closer to a certain location.