Adding some allowed abbreviated values for intervals in date histograms
as well as documenting the limitations of intervals larger than days.
Closes#23294
This change refactors the composite aggregation to add an execution mode that visits documents in the order of the values
present in the leading source of the composite definition. This mode does not need to visit all documents since it can early terminate
the collection when the leading source value is greater than the lowest value in the queue.
Instead of collecting the documents in the order of their doc_id, this mode uses the inverted lists (or the bkd tree for numerics) to collect documents
in the order of the values present in the leading source.
For instance the following aggregation:
```
"composite" : {
"sources" : [
{ "value1": { "terms" : { "field": "timestamp", "order": "asc" } } }
],
"size": 10
}
```
... can use the field `timestamp` to collect the documents with the 10 lowest values for the field instead of visiting all documents.
For composite aggregation with more than one source the execution can early terminate as soon as one of the 10 lowest values produces enough
composite buckets. For instance if visiting the first two lowest timestamp created 10 composite buckets we can early terminate the collection since it
is guaranteed that the third lowest timestamp cannot create a composite key that compares lower than the one already visited.
This mode can execute iff:
* The leading source in the composite definition uses an indexed field of type `date` (works also with `date_histogram` source), `integer`, `long` or `keyword`.
* The query is a match_all query or a range query over the field that is used as the leading source in the composite definition.
* The sort order of the leading source is the natural order (ascending since postings and numerics are sorted in ascending order only).
If these conditions are not met this aggregation visits each document like any other agg.
* Add a REST integration test that documents date_range support
Add a test case that exercises date_range aggregations using the missing
option.
Addresses #17597
* Test cleanup and correction
Adding a document with a null date to exercise `missing` option, update
test name to something reasonable.
* Update documentation to explain how the "missing" parameter works for
date_range aggregations.
* Wrap lines at 80 chars in docs.
* Change format of test to YAML for readability.
Adds a usage example of the JLH score used in significant terms aggregation.
All other methods to calculate significance score have such an example
Closes#28513
This change adds the `after_key` of a composite aggregation directly in the response.
It is redundant when all buckets are not filtered/removed by a pipeline aggregation since in this case the `after_key` is always the last bucket
in the response. Though when using a pipeline aggregation to filter composite buckets, the `after_key` can be lost if the last bucket is filtered.
This commit fixes this situation by always returning the `after_key` in a dedicated section.
This change adds a note in the `terms` aggregation that explains how to retrieve **all**
terms (or all combinations of terms in a nested agg) using the `composite` aggregation.
This commit adds the ability to specify a date format on the `date_histogram` composite source.
If the format is defined, the key for the source is returned as a formatted date.
Closes#27923
Allowing `_doc` as a type will enable users to make the transition to 7.0
smoother since the index APIs will be `PUT index/_doc/id` and `POST index/_doc`.
This also moves most of the documentation to `_doc` as a type name.
Closes#27750Closes#27751
This commit adds a new dynamic cluster setting named `search.max_buckets` that can be used to limit the number of buckets created per shard or by the reduce phase. Each multi bucket aggregator can consume buckets during the final build of the aggregation at the shard level or during the reduce phase (final or not) in the coordinating node. When an aggregator consumes a bucket, a global count for the request is incremented and if this number is greater than the limit an exception is thrown (TooManyBuckets exception).
This change adds the ability for multi bucket aggregator to "consume" buckets in the global limit, the default is 10,000. It's an opt-in consumer so each multi-bucket aggregator must explicitly call the consumer when a bucket is added in the response.
Closes#27452#26012
Also include _type and _id for parent/child hits inside inner hits.
In the case of top_hits aggregation the nested search hits are
directly returned and are not grouped by a root or parent document, so
it is important to include the _id and _index attributes in order to know
to what documents these nested search hits belong to.
Closes#27053
Today we require users to prepare their indices for split operations.
Yet, we can do this automatically when an index is created which would
make the split feature a much more appealing option since it doesn't have
any 3rd party prerequisites anymore.
This change automatically sets the number of routinng shards such that
an index is guaranteed to be able to split once into twice as many shards.
The number of routing shards is scaled towards the default shard limit per index
such that indices with a smaller amount of shards can be split more often than
larger ones. For instance an index with 1 or 2 shards can be split 10x
(until it approaches 1024 shards) while an index created with 128 shards can only
be split 3x by a factor of 2. Please note this is just a default value and users
can still prepare their indices with `index.number_of_routing_shards` for custom
splitting.
NOTE: this change has an impact on the document distribution since we are changing
the hash space. Documents are still uniformly distributed across all shards but since
we are artificually changing the number of buckets in the consistent hashign space
document might be hashed into different shards compared to previous versions.
This is a 7.0 only change.
* 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.
Christoph Büscher
Adrien Grand
D Pinto
Jim Ferenczi
Paul Sanwald
Craig van Tonder
Menno Oudshoorn
Tim Roes
Ke Li
Islam Heggo
Clinton Gormley
Alex Moros Marco
Jim Ferenczi
Jin Liang
David Shimon
akadko
Andrew Banchich
Shaunak Kashyap
Christoph Büscher
Martijn van Groningen
Simon Willnauer
Takumasa Ochi
Dimitris Athanasiou
shaulzorea
Ryan Ernst
Tanguy Leroux
Christoph Büscher
Nik Everett
Zachary Tong