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Composite Aggregation
editComposite Aggregation
editThis functionality is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features.
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
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).
Time Zone
editDate-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.
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": {
"buckets": [
{
"key": {
"date": 1494201600000,
"product": "rocky"
},
"doc_count": 1
},
{
"key": {
"date": 1494288000000,
"product": "mad max"
},
"doc_count": 2
}
]
}
}
}
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 is "key": [1494288000000, "mad max"] so 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": {
"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
}
}
]
}
}
}
Index sorting
editBy default this aggregation runs on every document that match the query. Though if the index sort matches the composite sort this aggregation can optimize the execution and can skip documents that contain composite buckets that would not be part of the response.
For instance the following aggregations:
GET /_search
{
"aggs" : {
"my_buckets": {
"composite" : {
"size": 2,
"sources" : [
{ "date": { "date_histogram": { "field": "timestamp", "interval": "1d", "order": "asc" } } },
{ "product": { "terms": { "field": "product", "order": "asc" } } }
]
}
}
}
}
... is much faster on an index that uses the following sort:
PUT twitter
{
"settings" : {
"index" : {
"sort.field" : ["timestamp", "product"],
"sort.order" : ["asc", "asc"]
}
},
"mappings": {
"sales": {
"properties": {
"timestamp": {
"type": "date"
},
"product": {
"type": "keyword"
}
}
}
}
}
The optimization takes effect only if the fields used for sorting are single-valued and follow
the same order as the aggregation (desc or asc).
If only the aggregation results are needed it is also better to set the size of the query to 0
and track_total_hits to false in order to remove other slowing factors:
GET /_search
{
"size": 0,
"track_total_hits": false,
"aggs" : {
"my_buckets": {
"composite" : {
"size": 2,
"sources" : [
{ "date": { "date_histogram": { "field": "timestamp", "interval": "1d" } } },
{ "product": { "terms": { "field": "product" } } }
]
}
}
}
}
See index sorting for more details.