Here are the highlights of features that were added in Elasticsearch 6.3.0.
Refer to the Elasticsearch 6.3.0 release notes for the full list of changes in this release.
We want your feedback on the experimental features in this release! Let us know what you’d like to see next and any problems you encounter.
This experimental feature enables users who are familiar with SQL to use SQL statements to query Elasticsearch indices. In addition to querying through the SQL API, you can use the Translate API to see how SQL queries are translated to native Elasticsearch queries.
The included SQL CLI provides a simple way to submit SQL queries to Elasticsearch. Similarly, the Elasticsearch JDBC driver enables you to connect your favorite JDBC-compatible tool to Elasticsearch.
For more information, see SQL Access.
This experimental feature enables you to summarize and store historical data so that is still available for analysis, but consumes significantly less storage space. This is particularly useful when you’re working with monitoring and metrics data where it’s not feasible to retain the raw data indefinitely.
When you ask Elasticsearch to store a rollup of data, it also stores the underlying statistics. For example, if you roll up an average, the sum and count are also stored so that the average can be recomputed at query time. This enables you to query both rolled up data and “live” data simultaneously using the standard query DSL.
For more information, see Rolling up historical data.
Java 10 Supportedit
Java 9 was a short-term release that reached EOL in March 2018. 6.3.0 introduces support for Java 10, which is scheduled to reach EOL in September 2018.
If you are not comfortable with the rapid release cycle of Java short term versions (and EOL dates), you can continue to use Java 8. See the support matrix for all of the JVM options for Elasticsearch.