* Replace custom type names with _doc in REST examples.
* Avoid using two mapping types in the percolator docs.
* Rename doc -> _doc in the main repository README.
* Also replace some custom type names in the HLRC docs.
We generate tests from our documentation, including assertions about the
responses returned by a particular API. But sometimes we *can't* assert
that the response is correct because of some defficiency in our tooling.
Previously we marked the response `// NOTCONSOLE` to skip it, but this
is kind of odd because `// NOTCONSOLE` is really to mark snippets that
are json but aren't requests or responses. This introduces a new
construct to skip response assertions:
```
// TESTRESPONSE[skip:reason we skipped this]
```
This commit switches the joda time backcompat in scripting to use
augmentation over ZonedDateTime. The augmentation methods provide
compatibility with the missing methods between joda's DateTime and
java's ZonedDateTime. Due to getDayOfWeek returning an enum in the java
API, ZonedDateTime is wrapped so that the method can return int like the
joda time does. The java time api version is renamed to
getDayOfWeekEnum, which will be kept through 7.x for compatibility while
users switch back to getDayOfWeek once joda compatibility is removed.
We used to set `maxScore` to `0` within `TopDocs` in situations where there is really no score as the size was set to `0` and scores were not even tracked. In such scenarios, `Float.Nan` is more appropriate, which gets converted to `max_score: null` on the REST layer. That's also more consistent with lucene which set `maxScore` to `Float.Nan` when merging empty `TopDocs` (see `TopDocs#merge`).
This commit adds a boolean system property, `es.scripting.use_java_time`,
which controls the concrete return type used by doc values within
scripts. The return type of accessing doc values for a date field is
changed to Object, essentially duck typing the type to allow
co-existence during the transition from joda time to java time.
* Adds a new auto-interval date histogram
This change adds a new type of histogram aggregation called `auto_date_histogram` where you can specify the target number of buckets you require and it will find an appropriate interval for the returned buckets. The aggregation works by first collecting documents in buckets at second interval, when it has created more than the target number of buckets it merges these buckets into minute interval bucket and continues collecting until it reaches the target number of buckets again. It will keep merging buckets when it exceeds the target until either collection is finished or the highest interval (currently years) is reached. A similar process happens at reduce time.
This aggregation intentionally does not support min_doc_count, offest and extended_bounds to keep the already complex logic from becoming more complex. The aggregation accepts sub-aggregations but will always operate in `breadth_first` mode deferring the computation of sub-aggregations until the final buckets from the shard are known. min_doc_count is effectively hard-coded to zero meaning that we will insert empty buckets where necessary.
Closes#9572
* Adds documentation
* Added sub aggregator test
* Fixes failing docs test
* Brings branch up to date with master changes
* trying to get tests to pass again
* Fixes multiBucketConsumer accounting
* Collects more buckets than needed on shards
This gives us more options at reduce time in terms of how we do the
final merge of the buckeets to produce the final result
* Revert "Collects more buckets than needed on shards"
This reverts commit 993c782d117892af9a3c86a51921cdee630a3ac5.
* Adds ability to merge within a rounding
* Fixes nonn-timezone doc test failure
* Fix time zone tests
* iterates on tests
* Adds test case and documentation changes
Added some notes in the documentation about the intervals that can bbe
returned.
Also added a test case that utilises the merging of conseecutive buckets
* Fixes performance bug
The bug meant that getAppropriate rounding look a huge amount of time
if the range of the data was large but also sparsely populated. In
these situations the rounding would be very low so iterating through
the rounding values from the min key to the max keey look a long time
(~120 seconds in one test).
The solution is to add a rough estimate first which chooses the
rounding based just on the long values of the min and max keeys alone
but selects the rounding one lower than the one it thinks is
appropriate so the accurate method can choose the final rounding taking
into account the fact that intervals are not always fixed length.
Thee commit also adds more tests
* Changes to only do complex reduction on final reduce
* merge latest with master
* correct tests and add a new test case for 10k buckets
* refactor to perform bucket number check in innerBuild
* correctly derive bucket setting, update tests to increase bucket threshold
* fix checkstyle
* address code review comments
* add documentation for default buckets
* fix typo
This change adds a new option to the composite aggregation named `missing_bucket`.
This option can be set by source and dictates whether documents without a value for the
source should be ignored. When set to true, documents without a value for a field emits
an explicit `null` value which is then added in the composite bucket.
The `missing` option that allows to set an explicit value (instead of `null`) is deprecated in this change and will be removed in a follow up (only in 7.x).
This commit also changes how the big arrays are allocated, instead of reserving
the provided `size` for all sources they are created with a small intial size and they grow
depending on the number of buckets created by the aggregation:
Closes#29380
This commit changes the default out-of-the-box configuration for the
number of shards from five to one. We think this will help address a
common problem of oversharding. For users with time-based indices that
need a different default, this can be managed with index templates. For
users with non-time-based indices that find they need to re-shard with
the split API in place they no longer need to resort only to
reindexing.
Since this has the impact of changing the default number of shards used
in REST tests, we want to ensure that we still have coverage for issues
that could arise from multiple shards. As such, we randomize (rarely)
the default number of shards in REST tests to two. This is managed via a
global index template. However, some tests check the templates that are
in the cluster state during the test. Since this template is randomly
there, we need a way for tests to skip adding the template used to set
the number of shards to two. For this we add the default_shards feature
skip. To avoid having to write our docs in a complicated way because
sometimes they might be behind one shard, and sometimes they might be
behind two shards we apply the default_shards feature skip to all docs
tests. That is, these tests will always run with the default number of
shards (one).
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 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
Julie Tibshirani
markharwood
Jim Ferenczi
Nik Everett
Serge Populov
Ryan Ernst
Christoph Büscher
Paul Sanwald
lipsill
Luca Cavanna
Colm O'Shea
Colin Goodheart-Smithe
Jimi Ford
Sue Gallagher
Piotr Prądzyński
Jason Tedor
Adrien Grand
Paul Sanwald
Menno Oudshoorn
Tim Roes
Clinton Gormley
Alex Moros Marco
Jim Ferenczi
Shaunak Kashyap
Christoph Büscher
Takumasa Ochi
shaulzorea
Tanguy Leroux
Christoph Büscher
Zachary Tong