OpenSearch/docs/reference/release-notes/highlights-7.3.0.asciidoc

[[release-highlights-7.3.0]]
== 7.3.0 release highlights
++++
<titleabbrev>7.3.0</titleabbrev>
++++

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

// tag::notable-highlights[]
[float]
==== Voting-only master nodes

A new {ref}/modules-node.html#voting-only-node[`node.voting-only`] role has been
introduced that allows nodes to participate in elections even though they are
not eligible to become the master.
The benefit is that these nodes still help with high availability while
requiring less CPU and heap than master nodes.

NOTE: The `node.voting-only` role is only available with the default
distribution of {es}.

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== Reloading of search-time synonyms

A new {ref}/indices-reload-analyzers.html[Analyzer reload API] allows to reload
the definition of search-time analyzers and their associated resources. A common
use-case for this API is the reloading of search-time synonyms. In earlier
versions of Elasticsearch, users could force synonyms to be reloaded by closing
the index and then opening it again. With this new API, synonyms can be updated
without closing the index.

NOTE: The Analyzer reload API is only available with the default distribution
of {es}.

// end::notable-highlights[]

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

A new {ref}/flattened.html[`flattened`] field type has been added, which can index
arbitrary `json` objects into a single field. This helps avoid hitting issues
due to many fields in mappings, at the cost of more limited search
functionality.

NOTE: The `flattened` field type is only available with the
default distribution of {es}.

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== Functions on vector fields

Painless now support computing the
{ref}/query-dsl-script-score-query.html#vector-functions[cosine similarity] and
the {ref}/query-dsl-script-score-query.html#vector-functions[dot product] of a
query vector and either values of a
{ref}/sparse-vector.html[`sparse_vector`] or
{ref}/dense-vector.html[`dense_vector`] field.

NOTE: These functions are only available with the default distribution of {es}.

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== Prefix and wildcard support for intervals

{ref}/query-dsl-intervals-query.html[Intervals] now support querying by
{ref}/query-dsl-intervals-query.html#intervals-prefix[prefix] or
{ref}/query-dsl-intervals-query.html#intervals-wildcard[wildcard].

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== Rare terms aggregation

A new
{ref}/search-aggregations-bucket-rare-terms-aggregation.html[Rare Terms aggregation]
allows to find the least frequent values in a field. It is intended to replace
the `"order" : { "_count" : "asc" }` option of the
{ref}/search-aggregations-bucket-terms-aggregation.html[terms aggregations].

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== Aliases are replicated via {ccr}

Read aliases are now replicated via {ref}/ccr-put-follow.html[{ccr}]. Note that
write aliases are still not replicated since they only make sense for indices that
are being written to while follower indices do not receive direct writes.

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== SQL supports frozen indices

{es-sql} now supports querying {ref}/frozen-indices.html[frozen indices] via the
new {ref}/sql-index-frozen.html[`FROZEN`] keyword.

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== Fixed memory leak when using templates in document-level security

{ref}/document-level-security.html[Document-level security] was using an
unbounded cache for the set of visible documents. This could lead to a memory
leak when using a templated query as a role query. The cache has been fixed to
evict based on memory usage and has a limit of 50MB.

// end::notable-highlights[]

// tag::notable-highlights[]
[float]
==== More memory-efficient aggregations on `keyword` fields

{ref}/search-aggregations-bucket-terms-aggregation.html[Terms aggregations]
generally need to build
{ref}/search-aggregations-bucket-terms-aggregation.html#search-aggregations-bucket-terms-aggregation-execution-hint[global ordinals]
in order to run. Unfortunately this operation became more memory-intensive in
6.0 due to the move to doc-value iterators in order to improve handling of
sparse fields. Memory pressure of global ordinals now goes back to a more
similar level as what you could have on pre-6.0 releases.

// end::notable-highlights[]

// tag::notable-highlights[]
[discrete]
[[release-highlights-7.3.0-transforms]]
==== {dataframes-cap}: transform and pivot your streaming data

beta[] {ref}/transforms.html[{dataframe-transforms-cap}] are a core new
feature in {es} that enable you to transform an existing index to a secondary,
summarized index. {dataframe-transforms-cap} enable you to pivot your data and
create entity-centric indices that can summarize the behavior of an entity. This
organizes the data into an analysis-friendly format.

{dataframe-transforms-cap} were originally available in 7.2. With 7.3 they can
now run either as a single batch transform or continuously incorporating new
data as it is ingested. 

{dataframes-cap} enable new possibilities for {ml} analysis (such as
_outlier detection_), but they can also be useful for other types of
visualizations and custom types of analysis. 

// end::notable-highlights[]

// tag::notable-highlights[]
[discrete]
[[release-highlights-7.3.0-outlier-detection]]
==== Discover your most unusual data using {oldetection}

The goal of {ml-docs}/dfa-outlier-detection.html[{oldetection}] is to find 
the most unusual data points in an index. We analyse the numerical fields of 
each data point (document in an index) and annotate them with how unusual they 
are.
 
We use unsupervised {oldetection} which means there is no need to provide a 
training data set to teach {oldetection} to recognize outliers. In practice, 
this is achieved by using an ensemble of distance based and density based 
techniques to identify those data points which are the most different from the 
bulk of the data in the index. We assign to each analysed data point an 
{olscore}, which captures how different the entity is from other entities in the 
index.
 
In addition to new {oldetection} functionality, we are introducing the 
{ref}/evaluate-dfanalytics.html[evaluate {dfanalytics} API], which enables you
to compute a range of performance metrics such 
as confusion matrices, precision, recall, the 
https://en.wikipedia.org/wiki/Receiver_operating_characteristic[receiver-operating characteristics (ROC) curve] 
and the area under the ROC curve. If you are running {oldetection} on a source 
index that has already been labeled to indicate which points are truly outliers 
and which are normal, you can use the 
evaluate {dfanalytics} API to assess the performance of the 
{oldetection} analytics on your dataset.

// end::notable-highlights[]