* 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.
* 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.
* 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.
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
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 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
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
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.
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.
The filters aggregation now has an option to add an 'other' bucket which will, when turned on, contain all documents which do not match any of the defined filters. There is also an option to change the name of the 'other' bucket from the default of '_other_'
Closes#11289
Replace the previous example which leveraged a range filter, which causes unnecessary confusion about when to use a range filter to create a single bucket or a range aggregation with exactly one member in ranges.
Closes#11704
This change unifies the way scripts and templates are specified for all instances in the codebase. It builds on the Script class added previously and adds request building and parsing support as well as the ability to transfer script objects between nodes. It also adds a Template class which aims to provide the same functionality for template APIs
Closes#11091
Jim Ferenczi
shaulzorea
Tanguy Leroux
Christoph Büscher
Nik Everett
Zachary Tong
Clinton Gormley
Simon Willnauer
matarrese
Ryan Ernst
markharwood
qwerty4030
Adrien Grand
Suhas Karanth
Ulugbek Baymuradov
Randall Britten
Jun Ohtani
Nicholas Knize
Mathieu Berube
Colin Goodheart-Smithe
Chris Fritz
Sumit Gupta
Robin Clarke
Pascal Borreli
Jared McQueen
Leon Weidauer
Jason Tedor
Robert Muir
Glen Smith
Sergii Golubev
Dongjoon Hyun
Kevin Adams
Eugene Pirogov
Alex
Taehee Kim
Sylvain Zimmer
William Li
caldwecr
Colin Goodheart-Smithe