This commit adds back "id" as the key within a script to specify a
stored script (which with file scripts now gone is no longer ambiguous).
It also adds "source" as a replacement for "code". This is in an attempt
to normalize how scripts are specified across both put stored scripts and script usages, including search template requests. This also deprecates the old inline/stored keys.
This commit adds a new bg_count field to the REST response of
SignificantTerms aggregations. Similarly to the bg_count that already
exists in significant terms buckets, this new bg_count field is set at
the aggregation level and is populated with the superset size value.
This commit adds a `doc_count` field to the response body of Matrix
Stats aggregation. It exposes the number of documents involved in
the computation of statistics, a value that can already be retrieved using
the method MatrixStats.getDocCount() in the Java API.
* SignificantText aggregation - like significant_terms but doesn’t require fielddata=true, recommended used with `sampler` agg to limit expense of tokenizing docs and takes optional `filter_duplicate_text`:true setting to avoid stats skew from repeated sections of text in search results.
Closes#23674
This commit adds support for histogram and date_histogram agg compound order by refactoring and reusing terms agg order code. The major change is that the Terms.Order and Histogram.Order classes have been replaced/refactored into a new class BucketOrder. This is a breaking change for the Java Transport API. For backward compatibility with previous ES versions the (date)histogram compound order will use the first order. Also the _term and _time aggregation order keys have been deprecated; replaced by _key.
Relates to #20003: now that all these aggregations use the same order code, it should be easier to move validation to parse time (as a follow up PR).
Relates to #14771: histogram and date_histogram aggregation order will now be validated at reduce time.
Closes#23613: if a single BucketOrder that is not a tie-breaker is added with the Java Transport API, it will be converted into a CompoundOrder with a tie-breaker.
Currently we don't write the count value to the geo_centroid aggregation rest response,
but it is provided via the java api and the count() method in the GeoCentroid interface.
We should add this parameter to the rest output and also provide it via the getProperty()
method.
This adds the `index.mapping.single_type` setting, which enforces that indices
have at most one type when it is true. The default value is true for 6.0+ indices
and false for old indices.
Relates #15613
We want to upgrade to Lucene 7 ahead of time in order to be able to check whether it causes any trouble to Elasticsearch before Lucene 7.0 gets released. From a user perspective, the main benefit of this upgrade is the enhanced support for sparse fields, whose resource consumption is now function of the number of docs that have a value rather than the total number of docs in the index.
Some notes about the change:
- it includes the deprecation of the `disable_coord` parameter of the `bool` and `common_terms` queries: Lucene has removed support for coord factors
- it includes the deprecation of the `index.similarity.base` expert setting, since it was only useful to configure coords and query norms, which have both been removed
- two tests have been marked with `@AwaitsFix` because of #23966, which we intend to address after the merge
Turns the top example in each of the geo aggregation docs into a working
example that can be opened in CONSOLE. Subsequent examples can all also
be opened in console and will work after you've run the first example.
All examples are tested as part of the build.
This pull request reuses the typed_keys parameter added in #22965, but this time it applies it to suggesters. When set to true, the suggester names in the search response will be prefixed with a prefix that reflects their type.
This pull request adds a new parameter to the REST Search API named `typed_keys`. When set to true, the aggregation names in the search response will be prefixed with a prefix that reflects the internal type of the aggregation.
Here is a simple example:
```
GET /_search?typed_keys
{
"aggs": {
"tweets_per_user": {
"terms": {
"field": "user"
}
}
},
"size": 0
}
```
And the response:
```
{
"aggs": {
"sterms:tweets_per_user": {
...
}
}
}
```
This parameter is intended to make life easier for REST clients that could parse back the prefix and could detect the type of the aggregation to parse. It could also be implemented for suggesters.
This adds the `COPY AS CURL` and `VIEW IN CONSOLE` links to the docs
and causes the snippets to be tested during Elasticsearch's build.
Relates to #18160
This adds the `COPY AS CURL` and `VIEW IN CONSOLE` buttons to the
docs and makes the build execute the snippets as part of `docs:check`.
Relates to #18160
GeoDistance query, sort, and scripts make use of a crazy GeoDistance enum for handling 4 different ways of computing geo distance: SLOPPY_ARC, ARC, FACTOR, and PLANE. Only two of these are necessary: ARC, PLANE. This commit removes SLOPPY_ARC, and FACTOR and cleans up the way Geo distance is computed.
Added missing CONSOLE scripts to documentation for sampler and diversified_sampler aggs.
Includes new StackOverflow index setup in build.gradle
Closes#22746
* Formatting tweaks
Add unit tests for `TopHitsAggregator` and convert some snippets in
docs for `top_hits` aggregation to `// CONSOLE`.
Relates to #22278
Relates to #18160
Adds the `VIEW IN CONSOLE` and `COPY AS CURL` links to the snippets
in the `value_count` docs and causes the build to execute the snippets
for testing.
Release #18160
This adds the `VIEW IN CONSOLE` and `COPY AS CURL` links to the
snippets in the docs for the `date_range` aggregation and tests
those snippets as part of the build.
Relates to #18160
This adds the `VIEW IN SENSE` and `COPY AS CURL` links and has
the build automatically execute the snippets and verify that they
work.
Relates to #18160
Adds the `VIEW IN CONSOLE` and `COPY AS CURL` links to the example
`global` aggregation. Also improves the example by adding a
non-`global` aggregation to compare it to.
Relates to #18160
Similar to the Filters aggregation but only supports "keyed" filter buckets and automatically "ANDs" pairs of filters to produce a form of adjacency matrix.
The intersection of buckets "A" and "B" is named "A&B" (the choice of separator is configurable). Empty intersection buckets are removed from the final results.
Closes#22169
* Promote longs to doubles when a terms agg mixes decimal and non-decimal number
This change makes the terms aggregation work when the buckets coming from different indices are a mix of decimal numbers and non-decimal numbers. In this case non-decimal number (longs) are promoted to decimal (double) which can result in a loss of precision for big numbers.
Fixes#22232
The use of the avg aggregation for sorting the terms aggregation is not encouraged since it has unbounded error. This changes the examples to use the max aggregation which does not suffer the same issues
and be much more stingy about what we consider a console candidate.
* Add `// CONSOLE` to check-running
* Fix version in some snippets
* Mark groovy snippets as groovy
* Fix versions in plugins
* Fix language marker errors
* Fix language parsing in snippets
This adds support for snippets who's language is written like
`[source, txt]` and `["source","js",subs="attributes,callouts"]`.
This also makes language required for snippets which is nice because
then we can be sure we can grep for snippets in a particular language.
This change adds a special field named _none_ that allows to disable the retrieval of the stored fields in a search request or in a TopHitsAggregation.
To completely disable stored fields retrieval (including disabling metadata fields retrieval such as _id or _type) use _none_ like this:
````
POST _search
{
"stored_fields": "_none_"
}
````
Most of the examples in the pipeline aggregation docs use a small
"sales" test data set and I converted all of the examples that use
it to `// CONSOLE`. There are still a bunch of snippets in the pipeline
aggregation docs that aren't `// CONSOLE` so they aren't tested. Most
of them are "this is the most basic form of this aggregation" so they
are more immune to errors and bit rot then the examples that I converted.
I'd like to do something with them as well but I'm not sure what.
Also, the moving average docs and serial diff docs didn't get a lot of
love from this pass because they don't use the test data set or follow
the same general layout.
Relates to #18160
Currently both aggregations really share the same implementation. This commit
splits the implementations so that regular histograms can support decimal
intervals/offsets and compute correct buckets for negative decimal values.
However the response API is still the same. So for intance both regular
histograms and date histograms will produce an
`org.elasticsearch.search.aggregations.bucket.histogram.Histogram`
aggregation.
The optimization to compute an identifier of the rounded value and the
rounded value itself has been removed since it was only used by regular
histograms, which now do the rounding themselves instead of relying on the
Rounding abstraction.
Closes#8082Closes#4847
The current heuristic to compute a default shard size is pretty aggressive,
it returns `max(10, number_of_shards * size)` as a value for the shard size.
I think making it less aggressive has the benefit that it would reduce the
likelyness of running into OOME when there are many shards (yearly
aggregations with time-based indices can make numbers of shards in the
thousands) and make the use of breadth-first more likely/efficient.
This commit replaces the heuristic with `size * 1.5 + 10`, which is enough
to have good accuracy on zipfian distributions.
This change adds a new special path to the buckets_path syntax
`_bucket_count`. This new option will return the number of buckets for a
multi-bucket aggregation, which can then be used in pipeline
aggregations.
Closes#19553
Today the default precision for the cardinality aggregation depends on how many
parent bucket aggregations it had. The reasoning was that the more parent bucket
aggregations, the more buckets the cardinality had to be computed on. And this
number could be huge depending on what the parent aggregations actually are.
However now that we run terms aggregations in breadth-first mode by default when
there are sub aggregations, it is less likely that we have to run the cardinality
aggregation on kagilions of buckets. So we could use a static default, which will
be less confusing to users.
Rename `fields` to `stored_fields` and add `docvalue_fields`
`stored_fields` parameter will no longer try to retrieve fields from the _source but will only return stored fields.
`fields` will throw an exception if the user uses it.
Add `docvalue_fields` as an adjunct to `fielddata_fields` which is deprecated. `docvalue_fields` will try to load the value from the docvalue and fallback to fielddata cache if docvalues are not enabled on that field.
Closes#18943
`stored_fields` parameter will no longer try to retrieve fields from the _source but will only return stored fields.
`fields` will throw an exception if the user uses it.
Add `docvalue_fields` as an adjunct to `fielddata_fields` which is deprecated. `docvalue_fields` will try to load the value from the docvalue and fallback to fielddata cache if docvalues are not enabled on that field.
Closes#18943
This commit adds a new metric aggregator for computing the geo_centroid over a set of geo_point fields. This can be combined with other aggregators (e.g., geohash_grid, significant_terms) for computing the geospatial centroid based on the document sets from other aggregation results.
This pipeline will calculate percentiles over a set of sibling buckets. This is an exact
implementation, meaning it needs to cache a copy of the series in memory and sort it to determine
the percentiles.
This comes with a few limitations: to prevent serializing data around, only the requested percentiles
are calculated (unlike the TDigest version, which allows the java API to ask for any percentile).
It also needs to store the data in-memory, resulting in some overhead if the requested series is
very large.
Alexander Kazakov
Ryan Ernst
Colin Goodheart-Smithe
Tanguy Leroux
markharwood
Zachary Tong
Vlad Holubiev
qwerty4030
Suhas Karanth
Zachary Tong
Christoph Büscher
Adrien Grand
Andrew Selden
Ulugbek Baymuradov
Nik Everett
msancho
Randall Britten
Jun Ohtani
Nicholas Knize
Mathieu Berube
Jim Ferenczi
Johannes Kanavin
Florian Hopf
Adrin Jalali
Carney Wu
Chris Fritz
Sumit Gupta
Clinton Gormley
Robin Clarke
Pascal Borreli
Jack Conradson
Thomas Decaux
Deb Adair
Ryan Biesemeyer
Jared McQueen
Leon Weidauer
Jason Tedor
Robert Muir
Robert Muir
Jerry Liu
Glen Smith
Sergii Golubev
ericamick
Martijn van Groningen
Dmitrii Izgurskii
evanfreed
Robert
Dongjoon Hyun
Kevin Adams
Eugene Pirogov
omiend
KangYongKyun
Alex
Taehee Kim
Zachary Tong
Lee Hinman
Shane Connelly
Murilo Pereira
Ian Truslove