This list summarizes the most important enhancements in Elasticsearch 7.8. For the complete list, go to Elasticsearch release highlights.
We have made several improvements to geo support in Elasticsearch 7.8.
- You can now run an aggregation that finds the bounding box (top left point and bottom right point) that contains all shapes matching a query. A shape is anything that is defined by multiple points. See Geo Bounds Aggregations.
- GeoHash grid aggregations and map tile grid aggregations allow you to group geo_points into buckets.
- Geo centroid aggregations allow you to compute the weighted centroid from all coordinate values for a geo_point field.
Add support for t-test aggregationsedit
Elasticsearch now supports a
aggregation, which performs a statistical hypothesis test in which the test
statistic follows a
t-distribution under the null hypothesis on numeric values extracted from
the aggregated documents or generated by provided scripts. In practice,
this will tell you if the difference between two population means are
statistically significant and did not occur by chance alone. See
Expose aggregation usage in feature usage APIedit
It is now possible to fetch a count of aggregations that have been executed via the node features API. This is broken down per combination of aggregation and data type, per shard on each node, from the last restart until the time when the counts are fetched. When trying to analyze how Elasticsearch is being used in practice, it is useful to know the usage distribution across aggregations and field types. For example, you might be able to conclude that a certain part of an index is not used a lot and could perhaps can be eliminated.
avg aggregations over histogram fieldsedit
Elasticsearch now implements
avg aggregations over histogram
value_count aggregation is computed on histogram
fields, the result of the aggregation is the sum of all numbers in the
counts array of the histogram.
When the average is computed on histogram fields, the result of the
aggregation is the weighted average of all elements in the
taking into consideration the number in the same position in the
Reduce aggregation memory consumptionedit
Elasticsearch now attempts to save memory on the coordinating node by delaying deserialization of the shard results for an aggregation until the last second. This is helpful as it makes the shard-aggregations results "short lived" garbage. It also should shrink the memory usage of aggregations when they are waiting to be merged.
Additionally, when the search is in batched reduce mode, Elasticsearch will force the results to be serialized between batch reduces in an attempt to keep the memory usage as low as possible between reductions.
Scalar functions now supported in SQL aggregationsedit
When querying Elasticsearch using SQL, it is now possible to use scalar functions
inside aggregations. This allows for more complex expressions, including
GROUP BY or
HAVING clauses. For example:
SELECT MAX(CASE WHEN a IS NULL then -1 ELSE abs(a * 10) + 1 END) AS max, b FROM test GROUP BY b HAVING MAX(CASE WHEN a IS NULL then -1 ELSE abs(a * 10) + 1 END) > 5
Increase the performance and scalability of transforms with throttlingedit
Transforms achieved GA status in 7.7 and now in 7.8 they are even better
with the introduction of
throttling. You can spread
out the impact of the transforms on your cluster by defining the rate at which
they perform search and index requests. Set the
docs_per_second limit when you
create or update your transform.
Better estimates for machine learning model memory usageedit
For 7.8, we introduce dynamic estimation of the model memory limit for jobs in ML solution modules. The estimate is generated during the job creation. It uses a calculation based on the specific detectors of the job and the cardinality of the partitioning and influencer fields. It means the job setup has better default values depending on the size of the data being analyzed.
Additional loss functions for regressionedit
Loss functions measure how well a machine learning model fits a specific data set. In 7.8, we added two new loss functions for regression analysis. In addition to the existing mean squared error function, there are now mean squared logarithmic error and Pseudo-Huber loss functions. These additions enable you to choose the loss function that fits best with your data set.
Extended upload limit and explanations for Data Visualizeredit
You can now upload files up to 1 GB in Data Visualizer. The file structure finder functionality of the Data Visualizer provides more detailed explanations after both successful and unsuccessful analysis which makes it easier to diagnose issues with file upload.
Fixed out-of-memory error when using cross-cluster replication with large documentsedit
A bug caused cross-cluster replication to use more memory than configured with large documents, which could cause memory pressure or even out-of-memory errors in some cases.