2020-01-22 09:42:57 -05:00
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[[release-highlights-7.6.0]]
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== 7.6.0 release highlights
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++++
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<titleabbrev>7.6.0</titleabbrev>
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++++
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//NOTE: The notable-highlights tagged regions are re-used in the
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//Installation and Upgrade Guide
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// tag::notable-highlights[]
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[float]
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==== New histogram field type
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A new {ref}/histogram.html[histogram field type] has been added. The new `histogram` field accepts
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pre-aggregated histograms which can later be used directly in the
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{ref}/search-aggregations-metrics-percentile-aggregation.html[percentiles] and
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{ref}/search-aggregations-metrics-percentile-rank-aggregation.html[percentile_ranks] aggregations.
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This allows users to pre-aggregate histogram data locally and only send the final
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data structure, saving storage and network bandwidth while retaining the ability to
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aggregate it like any other data.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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==== Optimized sorting on long field types
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Sorting on a {ref}/number.html[`long`] field now internally rewrites into a Lucene `DistanceFeatureQuery`.
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This lets {es} skip non-competitive hits, which often improves query speed.
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In benchmarking tests, this sped up sorts on `long` fields by 10x.
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// end::notable-highlights[]
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2020-02-14 11:51:55 -05:00
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// tag::notable-highlights[]
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[float]
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==== Simplifying and operationalizing machine learning
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With the release of 7.6 the {stack} delivers an end-to-end {ml} pipeline
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providing the path from raw data to building, testing, and deploying {ml} models
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in production. Up to this point {ml} in the {stack} had primarily focused on
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unsupervised techniques by using sophisticated pattern recognition that builds
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time series models used for {anomaly-detect}. With the new {dfanalytics}, you
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can now use labelled data to train and test your own models, store those models
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as {es} indices, and use {ml-docs}/ml-inference.html[inference] to add predicted
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values to the indices based on your trained models.
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One packaged model that we are releasing in 7.6 is
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{ml-docs}/ml-lang-ident.html[{lang-ident}]. If you have documents or sources
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that come in a variety of languages, {lang-ident} can be used to determine the
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language of text so you can improve the overall search relevance.
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{lang-ident-cap} is a trained model that can provide a prediction of the
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language of any text field.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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==== {ccs-cap} in {transforms}
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{ref}/transforms.html[{transforms-cap}] can now use {ccs} (CCS) for the source
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index. Now you can have separate clusters (for example, project clusters) build
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entity-centric or feature indices against a primary cluster.
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// end::notable-highlights[]
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[float]
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=== Learn more
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Get more details on these features in the
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https://www.elastic.co/blog/elasticsearch-7-6-0-released[{es} 7.6 release blog].
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For a complete list of enhancements and other changes, check out the
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<<release-notes-7.6.0,{es} 7.6 release notes>>.
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