Composite Aggregation
editComposite Aggregation
editA 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
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.
For instance the following document:
{ "keyword": ["foo", "bar"], "number": [23, 65, 76] }
... creates the following composite buckets when keyword
and number
are used as values source
for the aggregation:
{ "keyword": "foo", "number": 23 } { "keyword": "foo", "number": 65 } { "keyword": "foo", "number": 76 } { "keyword": "bar", "number": 23 } { "keyword": "bar", "number": 65 } { "keyword": "bar", "number": 76 }
Values source
editThe sources
parameter controls the sources that should be used to build the composite buckets.
There are three different types of values source:
Terms
editThe 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:
GET /_search { "aggs" : { "my_buckets": { "composite" : { "sources" : [ { "product": { "terms" : { "field": "product" } } } ] } } } }
Like the terms
aggregation it is also possible to use a script to create the values for the composite buckets:
GET /_search { "aggs" : { "my_buckets": { "composite" : { "sources" : [ { "product": { "terms" : { "script" : { "source": "doc['product'].value", "lang": "painless" } } } } ] } } } }
Histogram
editThe 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:
GET /_search { "aggs" : { "my_buckets": { "composite" : { "sources" : [ { "histo": { "histogram" : { "field": "price", "interval": 5 } } } ] } } } }
The values are built from a numeric field or a script that return numerical values:
GET /_search { "aggs" : { "my_buckets": { "composite" : { "sources" : [ { "histo": { "histogram" : { "interval": 5, "script" : { "source": "doc['price'].value", "lang": "painless" } } } } ] } } } }
Date Histogram
editThe date_histogram
is similar to the histogram
value source except that the interval
is specified by date/time expression:
GET /_search { "aggs" : { "my_buckets": { "composite" : { "sources" : [ { "date": { "date_histogram" : { "field": "timestamp", "interval": "1d" } } } ] } } } }
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 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
).
Format
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:
GET /_search { "aggs" : { "my_buckets": { "composite" : { "sources" : [ { "date": { "date_histogram" : { "field": "timestamp", "interval": "1d", "format": "yyyy-MM-dd" } } } ] } } } }
Supports expressive date format pattern |
Time Zone
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
editThe sources
parameter accepts an array of values source.
It is possible to mix different values source to create composite buckets.
For example:
GET /_search { "aggs" : { "my_buckets": { "composite" : { "sources" : [ { "date": { "date_histogram": { "field": "timestamp", "interval": "1d" } } }, { "product": { "terms": {"field": "product" } } } ] } } } }
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.
GET /_search { "aggs" : { "my_buckets": { "composite" : { "sources" : [ { "shop": { "terms": {"field": "shop" } } }, { "product": { "terms": { "field": "product" } } }, { "date": { "date_histogram": { "field": "timestamp", "interval": "1d" } } } ] } } } }
Order
editBy 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:
GET /_search { "aggs" : { "my_buckets": { "composite" : { "sources" : [ { "date": { "date_histogram": { "field": "timestamp", "interval": "1d", "order": "desc" } } }, { "product": { "terms": {"field": "product", "order": "asc" } } } ] } } } }
... 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.
Missing bucket
editBy 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
):
GET /_search { "aggs" : { "my_buckets": { "composite" : { "sources" : [ { "product_name": { "terms" : { "field": "product", "missing_bucket": true } } } ] } } } }
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
).
Size
editThe 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
first 10 composite buckets created from the values source.
The response contains the values for each composite bucket in an array containing the values extracted
from each value source.
After
editIf the number of composite buckets is too high (or unknown) to be returned in a single response
it is possible to split the retrieval in multiple requests.
Since the composite buckets are flat by nature, the requested size
is exactly the number of composite buckets
that will be returned in the response (assuming that they are at least size
composite buckets to return).
If all composite buckets should be retrieved it is preferable to use a small size (100
or 1000
for instance)
and then use the after
parameter to retrieve the next results.
For example:
GET /_search { "aggs" : { "my_buckets": { "composite" : { "size": 2, "sources" : [ { "date": { "date_histogram": { "field": "timestamp", "interval": "1d" } } }, { "product": { "terms": {"field": "product" } } } ] } } } }
... returns:
{ ... "aggregations": { "my_buckets": { "after_key": { "date": 1494288000000, "product": "mad max" }, "buckets": [ { "key": { "date": 1494201600000, "product": "rocky" }, "doc_count": 1 }, { "key": { "date": 1494288000000, "product": "mad max" }, "doc_count": 2 } ] } } }
The after_key
is equals to the last bucket returned in the response before
any filtering that could be done by Pipeline aggregations.
If all buckets are filtered/removed by a pipeline aggregation, the after_key
will contain
the last bucket before filtering.
The after
parameter can be used to retrieve the composite buckets that are after
the last composite buckets returned in a previous round.
For the example below the last bucket can be found in after_key
and the next
round of result can be retrieved with:
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" } } } } }
Sub-aggregations
editLike 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:
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" } } } } } }
... returns:
{ ... "aggregations": { "my_buckets": { "after_key": { "date": 1494201600000, "product": "rocky" }, "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 } } ] } } }