Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
[[search-aggregations-bucket-composite-aggregation]]
|
|
|
|
=== Composite Aggregation
|
|
|
|
|
2018-02-02 03:24:10 -05:00
|
|
|
beta[]
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
|
|
|
|
A multi-bucket aggregation that creates composite buckets from different sources.
|
|
|
|
|
|
|
|
Unlike the other `multi-bucket` aggregation the `composite` aggregation can be used
|
|
|
|
to paginate **all** buckets from a multi-level aggregation efficiently. This aggregation
|
|
|
|
provides a way to stream **all** buckets of a specific aggregation similarly to what
|
|
|
|
<<search-request-scroll, scroll>> does for documents.
|
|
|
|
|
|
|
|
The composite buckets are built from the combinations of the
|
|
|
|
values extracted/created for each document and each combination is considered as
|
|
|
|
a composite bucket.
|
|
|
|
|
|
|
|
//////////////////////////
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
PUT /sales
|
|
|
|
{
|
|
|
|
"mappings": {
|
|
|
|
"docs": {
|
|
|
|
"properties": {
|
|
|
|
"product": {
|
|
|
|
"type": "keyword"
|
|
|
|
},
|
|
|
|
"timestamp": {
|
|
|
|
"type": "date"
|
|
|
|
},
|
|
|
|
"price": {
|
|
|
|
"type": "long"
|
|
|
|
},
|
|
|
|
"shop": {
|
|
|
|
"type": "keyword"
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
POST /sales/docs/_bulk?refresh
|
|
|
|
{"index":{"_id":0}}
|
|
|
|
{"product": "mad max", "price": "20", "timestamp": "2017-05-09T14:35"}
|
|
|
|
{"index":{"_id":1}}
|
|
|
|
{"product": "mad max", "price": "25", "timestamp": "2017-05-09T12:35"}
|
|
|
|
{"index":{"_id":2}}
|
|
|
|
{"product": "rocky", "price": "10", "timestamp": "2017-05-08T09:10"}
|
|
|
|
{"index":{"_id":3}}
|
|
|
|
{"product": "mad max", "price": "27", "timestamp": "2017-05-10T07:07"}
|
|
|
|
{"index":{"_id":4}}
|
|
|
|
{"product": "apocalypse now", "price": "10", "timestamp": "2017-05-11T08:35"}
|
|
|
|
-------------------------------------------------
|
|
|
|
// NOTCONSOLE
|
|
|
|
// TESTSETUP
|
|
|
|
|
|
|
|
//////////////////////////
|
|
|
|
|
|
|
|
For instance the following document:
|
|
|
|
|
2017-11-20 02:41:02 -05:00
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
{
|
|
|
|
"keyword": ["foo", "bar"],
|
|
|
|
"number": [23, 65, 76]
|
|
|
|
}
|
2017-11-20 02:41:02 -05:00
|
|
|
--------------------------------------------------
|
|
|
|
// NOTCONSOLE
|
|
|
|
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
\... creates the following composite buckets when `keyword` and `number` are used as values source
|
|
|
|
for the aggregation:
|
|
|
|
|
2017-11-20 02:41:02 -05:00
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
{ "keyword": "foo", "number": 23 }
|
|
|
|
{ "keyword": "foo", "number": 65 }
|
|
|
|
{ "keyword": "foo", "number": 76 }
|
|
|
|
{ "keyword": "bar", "number": 23 }
|
|
|
|
{ "keyword": "bar", "number": 65 }
|
|
|
|
{ "keyword": "bar", "number": 76 }
|
2017-11-20 02:41:02 -05:00
|
|
|
--------------------------------------------------
|
|
|
|
// NOTCONSOLE
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
|
|
|
|
==== Values source
|
|
|
|
|
2017-12-28 12:18:30 -05:00
|
|
|
The `sources` parameter controls the sources that should be used to build the composite buckets.
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
There are three different types of values source:
|
|
|
|
|
|
|
|
===== Terms
|
|
|
|
|
|
|
|
The `terms` value source is equivalent to a simple `terms` aggregation.
|
|
|
|
The values are extracted from a field or a script exactly like the `terms` aggregation.
|
|
|
|
|
|
|
|
Example:
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
GET /_search
|
|
|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"my_buckets": {
|
|
|
|
"composite" : {
|
|
|
|
"sources" : [
|
|
|
|
{ "product": { "terms" : { "field": "product" } } }
|
|
|
|
]
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
|
|
|
// CONSOLE
|
|
|
|
|
|
|
|
Like the `terms` aggregation it is also possible to use a script to create the values for the composite buckets:
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
GET /_search
|
|
|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"my_buckets": {
|
|
|
|
"composite" : {
|
|
|
|
"sources" : [
|
|
|
|
{
|
|
|
|
"product": {
|
|
|
|
"terms" : {
|
|
|
|
"script" : {
|
|
|
|
"source": "doc['product'].value",
|
|
|
|
"lang": "painless"
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
]
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
|
|
|
// CONSOLE
|
|
|
|
|
|
|
|
===== Histogram
|
|
|
|
|
|
|
|
The `histogram` value source can be applied on numeric values to build fixed size
|
|
|
|
interval over the values. The `interval` parameter defines how the numeric values should be
|
|
|
|
transformed. For instance an `interval` set to 5 will translate any numeric values to its closest interval,
|
|
|
|
a value of `101` would be translated to `100` which is the key for the interval between 100 and 105.
|
|
|
|
|
|
|
|
Example:
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
GET /_search
|
|
|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"my_buckets": {
|
|
|
|
"composite" : {
|
|
|
|
"sources" : [
|
|
|
|
{ "histo": { "histogram" : { "field": "price", "interval": 5 } } }
|
|
|
|
]
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
|
|
|
// CONSOLE
|
|
|
|
|
|
|
|
The values are built from a numeric field or a script that return numerical values:
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
GET /_search
|
|
|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"my_buckets": {
|
|
|
|
"composite" : {
|
|
|
|
"sources" : [
|
|
|
|
{
|
|
|
|
"histo": {
|
|
|
|
"histogram" : {
|
|
|
|
"interval": 5,
|
|
|
|
"script" : {
|
|
|
|
"source": "doc['price'].value",
|
|
|
|
"lang": "painless"
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
]
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
|
|
|
// CONSOLE
|
|
|
|
|
|
|
|
|
|
|
|
===== Date Histogram
|
|
|
|
|
|
|
|
The `date_histogram` is similar to the `histogram` value source except that the interval
|
|
|
|
is specified by date/time expression:
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
GET /_search
|
|
|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"my_buckets": {
|
|
|
|
"composite" : {
|
|
|
|
"sources" : [
|
|
|
|
{ "date": { "date_histogram" : { "field": "timestamp", "interval": "1d" } } }
|
|
|
|
]
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
|
|
|
// CONSOLE
|
|
|
|
|
|
|
|
The example above creates an interval per day and translates all `timestamp` values to the start of its closest intervals.
|
|
|
|
Available expressions for interval: `year`, `quarter`, `month`, `week`, `day`, `hour`, `minute`, `second`
|
|
|
|
|
|
|
|
Time values can also be specified via abbreviations supported by <<time-units,time units>> parsing.
|
|
|
|
Note that fractional time values are not supported, but you can address this by shifting to another
|
|
|
|
time unit (e.g., `1.5h` could instead be specified as `90m`).
|
|
|
|
|
2018-06-25 20:25:32 -04:00
|
|
|
*Format*
|
2018-01-23 09:14:49 -05:00
|
|
|
|
|
|
|
Internally, a date is represented as a 64 bit number representing a timestamp in milliseconds-since-the-epoch.
|
|
|
|
These timestamps are returned as the bucket keys. It is possible to return a formatted date string instead using
|
|
|
|
the format specified with the format parameter:
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
GET /_search
|
|
|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"my_buckets": {
|
|
|
|
"composite" : {
|
|
|
|
"sources" : [
|
|
|
|
{
|
|
|
|
"date": {
|
|
|
|
"date_histogram" : {
|
|
|
|
"field": "timestamp",
|
|
|
|
"interval": "1d",
|
|
|
|
"format": "yyyy-MM-dd" <1>
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
]
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
|
|
|
// CONSOLE
|
|
|
|
|
|
|
|
<1> Supports expressive date <<date-format-pattern,format pattern>>
|
|
|
|
|
2018-06-25 20:25:32 -04:00
|
|
|
*Time Zone*
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
|
|
|
|
Date-times are stored in Elasticsearch in UTC. By default, all bucketing and
|
|
|
|
rounding is also done in UTC. The `time_zone` parameter can be used to indicate
|
|
|
|
that bucketing should use a different time zone.
|
|
|
|
|
|
|
|
Time zones may either be specified as an ISO 8601 UTC offset (e.g. `+01:00` or
|
|
|
|
`-08:00`) or as a timezone id, an identifier used in the TZ database like
|
|
|
|
`America/Los_Angeles`.
|
|
|
|
|
|
|
|
===== Mixing different values source
|
|
|
|
|
|
|
|
The `sources` parameter accepts an array of values source.
|
|
|
|
It is possible to mix different values source to create composite buckets.
|
|
|
|
For example:
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
GET /_search
|
|
|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"my_buckets": {
|
|
|
|
"composite" : {
|
|
|
|
"sources" : [
|
|
|
|
{ "date": { "date_histogram": { "field": "timestamp", "interval": "1d" } } },
|
|
|
|
{ "product": { "terms": {"field": "product" } } }
|
|
|
|
]
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
|
|
|
// CONSOLE
|
|
|
|
|
|
|
|
|
|
|
|
This will create composite buckets from the values created by two values source, a `date_histogram` and a `terms`.
|
|
|
|
Each bucket is composed of two values, one for each value source defined in the aggregation.
|
|
|
|
Any type of combinations is allowed and the order in the array is preserved
|
|
|
|
in the composite buckets.
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
GET /_search
|
|
|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"my_buckets": {
|
|
|
|
"composite" : {
|
|
|
|
"sources" : [
|
|
|
|
{ "shop": { "terms": {"field": "shop" } } },
|
|
|
|
{ "product": { "terms": { "field": "product" } } },
|
|
|
|
{ "date": { "date_histogram": { "field": "timestamp", "interval": "1d" } } }
|
|
|
|
]
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
|
|
|
// CONSOLE
|
|
|
|
|
|
|
|
==== Order
|
|
|
|
|
|
|
|
By default the composite buckets are sorted by their natural ordering. Values are sorted
|
|
|
|
in ascending order of their values. When multiple value sources are requested, the ordering is done per value
|
|
|
|
source, the first value of the composite bucket is compared to the first value of the other composite bucket and if they are equals the
|
|
|
|
next values in the composite bucket are used for tie-breaking. This means that the composite bucket
|
|
|
|
`[foo, 100]` is considered smaller than `[foobar, 0]` because `foo` is considered smaller than `foobar`.
|
|
|
|
It is possible to define the direction of the sort for each value source by setting `order` to `asc` (default value)
|
|
|
|
or `desc` (descending order) directly in the value source definition.
|
|
|
|
For example:
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
GET /_search
|
|
|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"my_buckets": {
|
|
|
|
"composite" : {
|
|
|
|
"sources" : [
|
|
|
|
{ "date": { "date_histogram": { "field": "timestamp", "interval": "1d", "order": "desc" } } },
|
|
|
|
{ "product": { "terms": {"field": "product", "order": "asc" } } }
|
|
|
|
]
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
|
|
|
// CONSOLE
|
|
|
|
|
|
|
|
\... will sort the composite bucket in descending order when comparing values from the `date_histogram` source
|
|
|
|
and in ascending order when comparing values from the `terms` source.
|
|
|
|
|
2018-09-12 10:56:03 -04:00
|
|
|
==== Missing bucket
|
2018-05-30 03:48:40 -04:00
|
|
|
|
|
|
|
By default documents without a value for a given source are ignored.
|
|
|
|
It is possible to include them in the response by setting `missing_bucket` to
|
|
|
|
`true` (defaults to `false`):
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
GET /_search
|
|
|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"my_buckets": {
|
|
|
|
"composite" : {
|
|
|
|
"sources" : [
|
|
|
|
{ "product_name": { "terms" : { "field": "product", "missing_bucket": true } } }
|
|
|
|
]
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
|
|
|
// CONSOLE
|
|
|
|
|
|
|
|
In the example above the source `product_name` will emit an explicit `null` value
|
|
|
|
for documents without a value for the field `product`.
|
|
|
|
The `order` specified in the source dictates whether the `null` values should rank
|
|
|
|
first (ascending order, `asc`) or last (descending order, `desc`).
|
|
|
|
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
==== Size
|
|
|
|
|
|
|
|
The `size` parameter can be set to define how many composite buckets should be returned.
|
|
|
|
Each composite bucket is considered as a single bucket so setting a size of 10 will return the
|
2018-03-04 14:47:24 -05:00
|
|
|
first 10 composite buckets created from the values source.
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
The response contains the values for each composite bucket in an array containing the values extracted
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|
from each value source.
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==== After
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|
If the number of composite buckets is too high (or unknown) to be returned in a single response
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|
it is possible to split the retrieval in multiple requests.
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|
Since the composite buckets are flat by nature, the requested `size` is exactly the number of composite buckets
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|
that will be returned in the response (assuming that they are at least `size` composite buckets to return).
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If all composite buckets should be retrieved it is preferable to use a small size (`100` or `1000` for instance)
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and then use the `after` parameter to retrieve the next results.
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For example:
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|
[source,js]
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|
--------------------------------------------------
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|
GET /_search
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|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"my_buckets": {
|
|
|
|
"composite" : {
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|
"size": 2,
|
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|
|
"sources" : [
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|
{ "date": { "date_histogram": { "field": "timestamp", "interval": "1d" } } },
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|
{ "product": { "terms": {"field": "product" } } }
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|
|
|
]
|
|
|
|
}
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|
|
|
}
|
|
|
|
}
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|
|
|
}
|
|
|
|
--------------------------------------------------
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|
|
|
// CONSOLE
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|
|
// TEST[s/_search/_search\?filter_path=aggregations/]
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|
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|
\... returns:
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|
|
|
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|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
{
|
|
|
|
...
|
|
|
|
"aggregations": {
|
|
|
|
"my_buckets": {
|
2018-01-25 03:15:27 -05:00
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|
"after_key": { <1>
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|
|
"date": 1494288000000,
|
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|
|
"product": "mad max"
|
|
|
|
},
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
"buckets": [
|
|
|
|
{
|
|
|
|
"key": {
|
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|
|
"date": 1494201600000,
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|
|
"product": "rocky"
|
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|
|
},
|
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|
|
"doc_count": 1
|
|
|
|
},
|
|
|
|
{
|
2018-01-25 03:15:27 -05:00
|
|
|
"key": {
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
"date": 1494288000000,
|
|
|
|
"product": "mad max"
|
|
|
|
},
|
|
|
|
"doc_count": 2
|
|
|
|
}
|
|
|
|
]
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
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|
|
// TESTRESPONSE[s/\.\.\.//]
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<1> The last composite bucket returned by the query.
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|
|
2018-01-25 03:15:27 -05:00
|
|
|
NOTE: The `after_key` is equals to the last bucket returned in the response before
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|
|
any filtering that could be done by <<search-aggregations-pipeline, Pipeline aggregations>>.
|
|
|
|
If all buckets are filtered/removed by a pipeline aggregation, the `after_key` will contain
|
|
|
|
the last bucket before filtering.
|
|
|
|
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
The `after` parameter can be used to retrieve the composite buckets that are **after**
|
|
|
|
the last composite buckets returned in a previous round.
|
2018-01-25 03:15:27 -05:00
|
|
|
For the example below the last bucket can be found in `after_key` and the next
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
round of result can be retrieved with:
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
GET /_search
|
|
|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"my_buckets": {
|
|
|
|
"composite" : {
|
|
|
|
"size": 2,
|
|
|
|
"sources" : [
|
|
|
|
{ "date": { "date_histogram": { "field": "timestamp", "interval": "1d", "order": "desc" } } },
|
|
|
|
{ "product": { "terms": {"field": "product", "order": "asc" } } }
|
|
|
|
],
|
|
|
|
"after": { "date": 1494288000000, "product": "mad max" } <1>
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
|
|
|
// CONSOLE
|
|
|
|
|
|
|
|
<1> Should restrict the aggregation to buckets that sort **after** the provided values.
|
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|
|
|
|
|
|
==== Sub-aggregations
|
|
|
|
|
|
|
|
Like any `multi-bucket` aggregations the `composite` aggregation can hold sub-aggregations.
|
|
|
|
These sub-aggregations can be used to compute other buckets or statistics on each composite bucket created by this
|
|
|
|
parent aggregation.
|
|
|
|
For instance the following example computes the average value of a field
|
|
|
|
per composite bucket:
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
GET /_search
|
|
|
|
{
|
|
|
|
"aggs" : {
|
|
|
|
"my_buckets": {
|
|
|
|
"composite" : {
|
|
|
|
"sources" : [
|
|
|
|
{ "date": { "date_histogram": { "field": "timestamp", "interval": "1d", "order": "desc" } } },
|
|
|
|
{ "product": { "terms": {"field": "product" } } }
|
|
|
|
]
|
|
|
|
},
|
|
|
|
"aggregations": {
|
|
|
|
"the_avg": {
|
|
|
|
"avg": { "field": "price" }
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
|
|
|
// CONSOLE
|
|
|
|
// TEST[s/_search/_search\?filter_path=aggregations/]
|
|
|
|
|
|
|
|
\... returns:
|
|
|
|
|
|
|
|
[source,js]
|
|
|
|
--------------------------------------------------
|
|
|
|
{
|
|
|
|
...
|
|
|
|
"aggregations": {
|
|
|
|
"my_buckets": {
|
2018-01-25 03:15:27 -05:00
|
|
|
"after_key": {
|
|
|
|
"date": 1494201600000,
|
|
|
|
"product": "rocky"
|
|
|
|
},
|
Add composite aggregator (#26800)
* 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.
2017-11-16 09:13:36 -05:00
|
|
|
"buckets": [
|
|
|
|
{
|
|
|
|
"key": {
|
|
|
|
"date": 1494460800000,
|
|
|
|
"product": "apocalypse now"
|
|
|
|
},
|
|
|
|
"doc_count": 1,
|
|
|
|
"the_avg": {
|
|
|
|
"value": 10.0
|
|
|
|
}
|
|
|
|
},
|
|
|
|
{
|
|
|
|
"key": {
|
|
|
|
"date": 1494374400000,
|
|
|
|
"product": "mad max"
|
|
|
|
},
|
|
|
|
"doc_count": 1,
|
|
|
|
"the_avg": {
|
|
|
|
"value": 27.0
|
|
|
|
}
|
|
|
|
},
|
|
|
|
{
|
|
|
|
"key": {
|
|
|
|
"date": 1494288000000,
|
|
|
|
"product" : "mad max"
|
|
|
|
},
|
|
|
|
"doc_count": 2,
|
|
|
|
"the_avg": {
|
|
|
|
"value": 22.5
|
|
|
|
}
|
|
|
|
},
|
|
|
|
{
|
|
|
|
"key": {
|
|
|
|
"date": 1494201600000,
|
|
|
|
"product": "rocky"
|
|
|
|
},
|
|
|
|
"doc_count": 1,
|
|
|
|
"the_avg": {
|
|
|
|
"value": 10.0
|
|
|
|
}
|
|
|
|
}
|
|
|
|
]
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
--------------------------------------------------
|
|
|
|
// TESTRESPONSE[s/\.\.\.//]
|