168 lines
6.2 KiB
Plaintext
168 lines
6.2 KiB
Plaintext
[[release-highlights-7.8.0]]
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== 7.8.0 release highlights
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++++
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<titleabbrev>7.8.0</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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=== Geo improvements
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We have made several improvements to geo support in {es} 7.8.
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- You can now run an aggregation that finds the bounding box (top left point and
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bottom right point) that contains all shapes matching a query. A shape is
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anything that is defined by multiple points. See
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{ref}/search-aggregations-metrics-geobounds-aggregation.html[Geo Bounds Aggregations].
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- {ref}/search-aggregations-bucket-geohashgrid-aggregation.html[GeoHash grid aggregations]
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and {ref}/search-aggregations-bucket-geotilegrid-aggregation.html[map tile grid aggregations]
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allow you to group geo_points into buckets.
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- {ref}/search-aggregations-metrics-geocentroid-aggregation.html[Geo centroid aggregations]
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allow you to compute the weighted https://en.wikipedia.org/wiki/Centroid[centroid]
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from all coordinate values for a geo_point field.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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=== Add support for t-test aggregations
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{es} now supports a `t_test` metrics
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aggregation, which performs a statistical hypothesis test in which the test
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statistic follows a
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https://en.wikipedia.org/wiki/Student%27s_t-distribution[Student’s
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t-distribution] under the null hypothesis on numeric values extracted from
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the aggregated documents or generated by provided scripts. In practice,
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this will tell you if the difference between two population means are
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statistically significant and did not occur by chance alone. See
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{ref}/search-aggregations-metrics-ttest-aggregation.html[T-Test Aggregation].
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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=== Expose aggregation usage in feature usage API
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It is now possible to fetch a count of aggregations that have been executed
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via the {ref}/cluster-nodes-usage.html[node features API]. This is broken down per
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combination of aggregation and data type, per shard on each node, from the
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last restart until the time when the counts are fetched. When trying to
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analyze how {es} is being used in practice, it is useful to know
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the usage distribution across aggregations and field types. For example,
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you might be able to conclude that a certain part of an index is not used a
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lot and could perhaps can be eliminated.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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=== Support `value_count` and `avg` aggregations over histogram fields
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{es} now implements `value_count` and `avg` aggregations over histogram
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fields.
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When the `value_count` aggregation is computed on {ref}/histogram.html[histogram
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fields], the result of the aggregation is the sum of all numbers in the
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`counts` array of the histogram.
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When the average is computed on histogram fields, the result of the
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aggregation is the weighted average of all elements in the `values` array
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taking into consideration the number in the same position in the `counts`
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array.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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=== Reduce aggregation memory consumption
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{es} now attempts to save memory on the coordinating node by delaying
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deserialization of the shard results for an aggregation until the last
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second. This is helpful as it makes the shard-aggregations results "short
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lived" garbage. It also should shrink the memory usage of aggregations when
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they are waiting to be merged.
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Additionally, when the search is in batched reduce mode, {es} will force
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the results to be serialized between batch reduces in an attempt to keep
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the memory usage as low as possible between reductions.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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=== Scalar functions now supported in SQL aggregations
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When querying {es} using SQL, it is now possible to use scalar functions
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inside aggregations. This allows for more complex expressions, including
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within `GROUP BY` or `HAVING` clauses. For example:
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[source, sql]
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----
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SELECT
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MAX(CASE WHEN a IS NULL then -1 ELSE abs(a * 10) + 1 END) AS max,
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b
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FROM test
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GROUP BY b
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HAVING
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MAX(CASE WHEN a IS NULL then -1 ELSE abs(a * 10) + 1 END) > 5
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----
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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[[release-highlights-7.8.0-throttling]]
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=== Increase the performance and scalability of {transforms} with throttling
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{transforms-cap} achieved GA status in 7.7 and now in 7.8 they are even better
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with the introduction of
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{ref}/transform-overview.html#transform-performance[throttling]. You can spread
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out the impact of the {transforms} on your cluster by defining the rate at which
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they perform search and index requests. Set the `docs_per_second` limit when you
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create or update your {transform}.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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[[release-highlights-7.8.0-mml]]
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=== Better estimates for {ml} model memory usage
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For 7.8, we introduce dynamic estimation of the model memory limit for jobs in
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{ml-docs}/ootb-ml-jobs.html[ML solution modules]. The estimate is generated
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during the job creation. It uses a calculation based on the specific detectors
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of the job and the cardinality of the partitioning and influencer fields. It
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means the job setup has better default values depending on the size of the data
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being analyzed.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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[[release-highlights-7.8.0-loss-functions]]
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=== Additional loss functions for {regression}
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{ml-docs}/dfa-regression.html#dfa-regression-lossfunction[Loss functions]
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measure how well a {ml} model fits a specific data set. In 7.8, we added two new
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loss functions for {regression} analysis. In addition to the existing mean
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squared error function, there are now mean squared logarithmic error and
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Pseudo-Huber loss functions. These additions enable you to choose the
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loss function that fits best with your data set.
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// end::notable-highlights[]
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// tag::notable-highlights[]
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[float]
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[[release-highlights-7.8.0-data-visualizer]]
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=== Extended upload limit and explanations for Data Visualizer
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You can now upload files up to 1 GB in Data Visualizer. The file structure
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finder functionality of the Data Visualizer provides more detailed explanations
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after both successful and unsuccessful analysis which makes it easier to
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diagnose issues with file upload.
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// end::notable-highlights[]
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