* This change adds a module called `aggs-composite` that defines a new aggregation named `composite`.
The `composite` aggregation is a multi-buckets aggregation that creates composite buckets made of multiple sources.
The sources for each bucket can be defined as:
* A `terms` source, values are extracted from a field or a script.
* A `date_histogram` source, values are extracted from a date field and rounded to the provided interval.
This aggregation can be used to retrieve all buckets of a deeply nested aggregation by flattening the nested aggregation in composite buckets.
A composite buckets is composed of one value per source and is built for each document as the combinations of values in the provided sources.
For instance the following aggregation:
````
"test_agg": {
"terms": {
"field": "field1"
},
"aggs": {
"nested_test_agg":
"terms": {
"field": "field2"
}
}
}
````
... which retrieves the top N terms for `field1` and for each top term in `field1` the top N terms for `field2`, can be replaced by a `composite` aggregation in order to retrieve **all** the combinations of `field1`, `field2` in the matching documents:
````
"composite_agg": {
"composite": {
"sources": [
{
"field1": {
"terms": {
"field": "field1"
}
}
},
{
"field2": {
"terms": {
"field": "field2"
}
}
},
}
}
````
The response of the aggregation looks like this:
````
"aggregations": {
"composite_agg": {
"buckets": [
{
"key": {
"field1": "alabama",
"field2": "almanach"
},
"doc_count": 100
},
{
"key": {
"field1": "alabama",
"field2": "calendar"
},
"doc_count": 1
},
{
"key": {
"field1": "arizona",
"field2": "calendar"
},
"doc_count": 1
}
]
}
}
````
By default this aggregation returns 10 buckets sorted in ascending order of the composite key.
Pagination can be achieved by providing `after` values, the values of the composite key to aggregate after.
For instance the following aggregation will aggregate all composite keys that sorts after `arizona, calendar`:
````
"composite_agg": {
"composite": {
"after": {"field1": "alabama", "field2": "calendar"},
"size": 100,
"sources": [
{
"field1": {
"terms": {
"field": "field1"
}
}
},
{
"field2": {
"terms": {
"field": "field2"
}
}
}
}
}
````
This aggregation is optimized for indices that set an index sorting that match the composite source definition.
For instance the aggregation above could run faster on indices that defines an index sorting like this:
````
"settings": {
"index.sort.field": ["field1", "field2"]
}
````
In this case the `composite` aggregation can early terminate on each segment.
This aggregation also accepts multi-valued field but disables early termination for these fields even if index sorting matches the sources definition.
This is mandatory because index sorting picks only one value per document to perform the sort.
This commit adds a parent pipeline aggregation that allows
sorting the buckets of a parent multi-bucket aggregation.
The aggregation also offers [from] and [size] parameters
in order to truncate the result as desired.
Closes#14928
Due to a change happened via #26102 to make the nested source consistent
with or without source filtering, the _source of a nested inner hit was
always wrapped in the parent path. This turned out to be not ideal for
users relying on the nested source, as it would require additional parsing
on the client side. This change fixes this, the _source of nested inner hits
is now no longer wrapped by parent json objects, irregardless of whether
the _source is included as is or source filtering is used.
Internally source filtering and highlighting relies on the fact that the
_source of nested inner hits are accessible by its full field path, so
in order to now break this, the conversion of the _source into its binary
form is performed in FetchSourceSubPhase, after any potential source filtering
is performed to make sure the structure of _source of the nested inner hit
is consistent irregardless if source filtering is performed.
PR for #26944Closes#26944
* Deprecate global_ordinals_hash and global_ordinals_low_cardinality
This change deprecates the `global_ordinals_hash` and `global_ordinals_low_cardinality` and
makes the `global_ordinals` execution hint choose internally if global ords should be remapped or use the segment ord directly.
These hints are too sensitive and expert to be exposed and we should be able to take the right decision internally based on the agg tree.
Currently the `precision` parameter must be a precision level
in the range of [1,12]. In #5042 it was suggested also supporting
distance units like "1km" to automatically approcimate the needed
precision level. This change adds this support to the Rest API by
making use of GeoUtils#geoHashLevelsForPrecision.
Plain integer values without a unit are still treated as precision
levels like before. Distance values that are too small to be represented
by a precision level of 12 (values approx. less than 0.056m) are
rejected.
Closes#5042
All of the snippets in our docs marked with `// TESTRESPONSE` are
checked against the response from Elasticsearch but, due to the
way they are implemented they are actually parsed as YAML instead
of JSON. Luckilly, all valid JSON is valid YAML! Unfurtunately
that means that invalid JSON has snuck into the exmples!
This adds a step during the build to parse them as JSON and fail
the build if they don't parse.
But no! It isn't quite that simple. The displayed text of some of
these responses looks like:
```
{
...
"aggregations": {
"range": {
"buckets": [
{
"to": 1.4436576E12,
"to_as_string": "10-2015",
"doc_count": 7,
"key": "*-10-2015"
},
{
"from": 1.4436576E12,
"from_as_string": "10-2015",
"doc_count": 0,
"key": "10-2015-*"
}
]
}
}
}
```
Note the `...` which isn't valid json but we like it anyway and want
it in the output. We use substitution rules to convert the `...`
into the response we expect. That yields a response that looks like:
```
{
"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,
"aggregations": {
"range": {
"buckets": [
{
"to": 1.4436576E12,
"to_as_string": "10-2015",
"doc_count": 7,
"key": "*-10-2015"
},
{
"from": 1.4436576E12,
"from_as_string": "10-2015",
"doc_count": 0,
"key": "10-2015-*"
}
]
}
}
}
```
That is what the tests consume but it isn't valid JSON! Oh no! We don't
want to go update all the substitution rules because that'd be huge and,
ultimately, wouldn't buy much. So we quote the `$body.took` bits before
parsing the JSON.
Note the responses that we use for the `_cat` APIs are all converted into
regexes and there is no expectation that they are valid JSON.
Closes#26233
Today if we search across a large amount of shards we hit every shard. Yet, it's quite
common to search across an index pattern for time based indices but filtering will exclude
all results outside a certain time range ie. `now-3d`. While the search can potentially hit
hundreds of shards the majority of the shards might yield 0 results since there is not document
that is within this date range. Kibana for instance does this regularly but used `_field_stats`
to optimize the indexes they need to query. Now with the deprecation of `_field_stats` and it's upcoming removal a single dashboard in kibana can potentially turn into searches hitting hundreds or thousands of shards and that can easily cause search rejections even though the most of the requests are very likely super cheap and only need a query rewriting to early terminate with 0 results.
This change adds a pre-filter phase for searches that can, if the number of shards are higher than a the `pre_filter_shard_size` threshold (defaults to 128 shards), fan out to the shards
and check if the query can potentially match any documents at all. While false positives are possible, a negative response means that no matches are possible. These requests are not subject to rejection and can greatly reduce the number of shards a request needs to hit. The approach here is preferable to the kibana approach with field stats since it correctly handles aliases and uses the correct threadpools to execute these requests. Further it's completely transparent to the user and improves scalability of elasticsearch in general on large clusters.
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
Takumasa Ochi
Jim Ferenczi
Jim Ferenczi
Dimitris Athanasiou
Martijn van Groningen
shaulzorea
Ryan Ernst
Tanguy Leroux
Christoph Büscher
Nik Everett
Zachary Tong
Clinton Gormley
Simon Willnauer
matarrese
Alexander Kazakov
Colin Goodheart-Smithe
markharwood
Vlad Holubiev
qwerty4030
Suhas Karanth
Zachary Tong
Adrien Grand
Andrew Selden
Ulugbek Baymuradov
msancho
Randall Britten
Jun Ohtani
Nicholas Knize
Mathieu Berube
Johannes Kanavin
Florian Hopf
Adrin Jalali
Carney Wu
Chris Fritz
Sumit Gupta
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
Dmitrii Izgurskii
evanfreed
Robert
Dongjoon Hyun
Kevin Adams