[[release-highlights-7.3.0]] == 7.3.0 release highlights ++++ 7.3.0 ++++ coming[7.3.0] //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 <> 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 <> 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 <> 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 <> 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 <> and the <> of a query vector and either values of a <> or <> 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 <> now support querying by <> or <>. // end::notable-highlights[] // tag::notable-highlights[] [float] ==== Rare terms aggregation A new <> allows to find the least frequent values in a field. It is intended to replace the `"order" : { "_count" : "asc" }` option of the <>. // end::notable-highlights[] // tag::notable-highlights[] [float] ==== Aliases are replicated via {ccr} Read aliases are now replicated via <>. 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 <> via the new <> keyword. // end::notable-highlights[] // tag::notable-highlights[] [float] ==== Fixed memory leak when using templates in document-level security {xpack-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 <> generally need to build <> 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[] {stack-ov}/ml-dataframes.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 {stack-ov}/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[]