Percentile Rank Aggregation is the reverse of the Percetiles aggregation. It determines the percentile rank (the proportion of values less than a given value) of the provided array of values.
Closes#6386
A new "breadth_first" results collection mode allows upper branches of aggregation tree to be calculated and then pruned
to a smaller selection before advancing into executing collection on child branches.
Closes#6128
The existing Note about the shorthand suggest syntax was poorly worded and confusing. Please check whether the way I've phrased it now is still correct as to what the shorthand form actually does and doesn't do: the original wording did not provide me enough information to be sure.
Thanks!
The GeoBounds Aggregation is a new single bucket aggregation which outputs the coordinates of a bounding box containing all the points from all the documents passed to the aggregation as well as the doc count. Geobound Aggregation also use a wrap_logitude parameter which specifies whether the resulting bounding box is permitted to overlap the international date line. This option defaults to true.
This aggregation introduces the idea of MetricsAggregation which do not return double values and cannot be used for sorting. The existing MetricsAggregation has been renamed to NumericMetricsAggregation and is a subclass of MetricsAggregation. MetricsAggregations do not store doc counts and do not support child aggregations.
Closes#5634
Because json objects are unordered this also adds an explicit order syntax
that looks like
"highlight": {
"fields": [
{"title":{ /*params*/ }},
{"text":{ /*params*/ }}
]
}
This is not useful for any of the builtin highlighters but will be useful
in plugins.
Closes#4649
Our improvements to t-digest have been pushed upstream and t-digest also got
some additional nice improvements around memory usage and speedups of quantile
estimation. So it makes sense to use it as a dependency now.
This also allows to remove the test dependency on Apache Mahout.
Close#6142
By default More Like This API excludes the queried document from the response.
However, when debugging or when comparing scores across different queries, it
could be useful to have the best possible matched hit. So this option lets users
explicitly specify the desired behavior.
Closes#6067
In the Google Groups forum there appears to be some confusion as to what mlt
does. This documentation update should hopefully help demystifying this
feature, and provide some understanding as to how to use its parameters.
Closes#6092
- Randomized integration tests for the benchmark API.
- Negative tests for cases where the cluster cannot run benchmarks.
- Return 404 on missing benchmark name.
- Allow to specify 'types' as an array in the JSON syntax when describing a benchmark competition.
- Don't record slowest for single-request competitions.
Closes#6003, #5906, #5903, #5904
Significant terms internally maintain a priority queue per shard with a size potentially
lower than the number of terms. This queue uses the score as criterion to determine if
a bucket is kept or not. If many terms with low subsetDF score very high
but the `min_doc_count` is set high, this might result in no terms being
returned because the pq is filled with low frequent terms which are all sorted
out in the end.
This can be avoided by increasing the `shard_size` parameter to a higher value.
However, it is not immediately clear to which value this parameter must be set
because we can not know how many terms with low frequency are scored higher that
the high frequent terms that we are actually interested in.
On the other hand, if there is no routing of docs to shards involved, we can maybe
assume that the documents of classes and also the terms therein are distributed evenly
across shards. In that case it might be easier to not add documents to the pq that have
subsetDF <= `shard_min_doc_count` which can be set to something like
`min_doc_count`/number of shards because we would assume that even when summing up
the subsetDF across shards `min_doc_count` will not be reached.
closes#5998closes#6041
The default precision was way too exact and could lead people to
think that geo context suggestions are not working. This patch now
requires you to set the precision in the mapping, as elasticsearch itself
can never tell exactly, what the required precision for the users
suggestions are.
Closes#5621
Duncan Angus Wilkie
Clinton Gormley
Chris
Colin Goodheart-Smithe
stephlag
Adrien Grand
Luke Fender
Martijn van Groningen
markharwood
fransflippo
Jad Naous
javanna
leonardo menezes
Nik Everett
Simon Willnauer
Britta Weber
David Pilato
Tiago Alves Macambira
Rashid Khan
Alex Ksikes
Andrew Selden
uboness
gabriel-tessier
Audrey
Lee Hinman
Scott Wilkerson
Malte Schirnacher
Andrew O'Brien
Karl Meisterheim
Alexander Reelsen