What’s new in 7.13

Here are the highlights of what’s new and improved in Elasticsearch 7.13!

For detailed information about this release, see the Release notes and Migration guide.

Other versions: 7.12 | 7.11 | 7.10 | 7.9 | 7.8 | 7.7 | 7.6 | 7.5 | 7.4 | 7.3 | 7.2 | 7.1 | 7.0

The frozen tier and partially mounted indices, formerly known as shared cache searchable snapshots, are now generally available. We these features as technical previews in 7.12. See the 7.12 announcement.

Runtime fields let you dynamically create fields from other fields and document properties at query time. These query-time runtime fields optimize for flexibility over speed, letting you change them at any time.

With 7.13, you can now also create index-time runtime fields, trading flexibility for better query performance. See Index a runtime field.

Painless expressions can now match IPv4 and IPv6 addresses against CIDR ranges. This is useful for classifying and grouping IP addresses in security and monitoring use cases. This feature is available in all contexts, including runtime fields, ingest, search, and aggregations.

For example:

CIDR myCidrRange = new CIDR('10.1.1.0/25');
myCidrRange.contains('10.1.1.127'); // true
myCidrRange.contains('10.1.1.129'); // false

myCidrRange = new CIDR('2001:0db8:85a3::/64');
myCidrRange.contains('2001:0db8:85a3:0000:0000:8a2e:0370:7334'); // true
myCidrRange.contains('2001:0db8:85a3:0001:0000:8a2e:0370:7334'); // false

7.13 introduces the combined_fields query, a new DSL query type for searching multiple text fields as a combined field. You can use the combined_fields query as a simpler alternative to the multi_match query’s cross_fields type option. See the combined_fields query documentation.

We’ve significantly improved the speed of the terms aggregation when:

Many time series use cases are likely to meet these criteria, particularly for data in the cold or frozen tier.

The ability to train outlier detection, regression, and classification models and then use those models to infer against incoming data becomes generally available in 7.13.

To simplify the deployment and upgrade of trained models, the concept of model aliases is introduced in 7.13. When using a model_alias in an ingest pipeline, it is now possible to make changes to the underlying referenced model without having to update the pipeline. The underlying referenced model changes in place for all ingest pipelines automatically when the new model is loaded into cache.