Composite aggregation
editComposite aggregation
editThe composite aggregation is expensive. Load test your application before deploying a composite aggregation in production.
A multi-bucket aggregation that creates composite buckets from different sources.
Unlike the other multi-bucket aggregations, you can use the composite
aggregation to paginate all buckets from a multi-level aggregation
efficiently. This aggregation provides a way to stream all buckets of a
specific aggregation, similar 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 example, consider the following document:
{
"keyword": ["foo", "bar"],
"number": [23, 65, 76]
}
Using keyword and number as source fields for the aggregation results in
the following composite buckets:
{ "keyword": "foo", "number": 23 }
{ "keyword": "foo", "number": 65 }
{ "keyword": "foo", "number": 76 }
{ "keyword": "bar", "number": 23 }
{ "keyword": "bar", "number": 65 }
{ "keyword": "bar", "number": 76 }
Value sources
editThe sources parameter defines the source fields to use when building
composite buckets. The order that the sources are defined controls the order
that the keys are returned.
You must use a unique name when defining sources.
The sources parameter can be any of the following types:
Terms
editThe terms value source is similar to a simple terms aggregation.
The values are extracted from a field exactly like the terms aggregation.
Example:
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
product: {
terms: {
field: 'product'
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{ "product": { "terms": { "field": "product" } } }
]
}
}
}
}
Like the terms aggregation, it’s possible to use a
runtime field to create values for the composite buckets:
response = client.search(
body: {
runtime_mappings: {
day_of_week: {
type: 'keyword',
script: "\n emit(doc['timestamp'].value.dayOfWeekEnum\n .getDisplayName(TextStyle.FULL, Locale.ROOT))\n "
}
},
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
dow: {
terms: {
field: 'day_of_week'
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"runtime_mappings": {
"day_of_week": {
"type": "keyword",
"script": """
emit(doc['timestamp'].value.dayOfWeekEnum
.getDisplayName(TextStyle.FULL, Locale.ROOT))
"""
}
},
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{
"dow": {
"terms": { "field": "day_of_week" }
}
}
]
}
}
}
}
Although similar, the terms value source doesn’t support the same set of
parameters as the terms aggregation. For other supported value source
parameters, see:
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:
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
histo: {
histogram: {
field: 'price',
interval: 5
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{ "histo": { "histogram": { "field": "price", "interval": 5 } } }
]
}
}
}
}
Like the histogram aggregation it’s possible to use a
runtime field to create values for the composite buckets:
response = client.search(
body: {
runtime_mappings: {
"price.discounted": {
type: 'double',
script: "\n double price = doc['price'].value;\n if (doc['product'].value == 'mad max') {\n price *= 0.8;\n }\n emit(price);\n "
}
},
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
price: {
histogram: {
interval: 5,
field: 'price.discounted'
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"runtime_mappings": {
"price.discounted": {
"type": "double",
"script": """
double price = doc['price'].value;
if (doc['product'].value == 'mad max') {
price *= 0.8;
}
emit(price);
"""
}
},
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{
"price": {
"histogram": {
"interval": 5,
"field": "price.discounted"
}
}
}
]
}
}
}
}
Date histogram
editThe date_histogram is similar to the histogram value source except that the interval
is specified by date/time expression:
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
date: {
date_histogram: {
field: 'timestamp',
calendar_interval: '1d'
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{ "date": { "date_histogram": { "field": "timestamp", "calendar_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:
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
date: {
date_histogram: {
field: 'timestamp',
calendar_interval: '1d',
format: 'yyyy-MM-dd'
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{
"date": {
"date_histogram": {
"field": "timestamp",
"calendar_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.
Offset
Use the offset parameter to change the start value of each bucket by the
specified positive (+) or negative offset (-) duration, such as 1h for
an hour, or 1d for a day. See Time units for more possible time
duration options.
For example, when using an interval of day, each bucket runs from midnight
to midnight. Setting the offset parameter to +6h changes each bucket
to run from 6am to 6am:
PUT my-index-000001/_doc/1?refresh
{
"date": "2015-10-01T05:30:00Z"
}
PUT my-index-000001/_doc/2?refresh
{
"date": "2015-10-01T06:30:00Z"
}
GET my-index-000001/_search?size=0
{
"aggs": {
"my_buckets": {
"composite" : {
"sources" : [
{
"date": {
"date_histogram" : {
"field": "date",
"calendar_interval": "day",
"offset": "+6h",
"format": "iso8601"
}
}
}
]
}
}
}
}
Instead of a single bucket starting at midnight, the above request groups the documents into buckets starting at 6am:
{
...
"aggregations": {
"my_buckets": {
"after_key": { "date": "2015-10-01T06:00:00.000Z" },
"buckets": [
{
"key": { "date": "2015-09-30T06:00:00.000Z" },
"doc_count": 1
},
{
"key": { "date": "2015-10-01T06:00:00.000Z" },
"doc_count": 1
}
]
}
}
}
The start offset of each bucket is calculated after time_zone
adjustments have been made.
GeoTile grid
editThe geotile_grid value source works on geo_point fields and groups points into buckets that represent
cells in a grid. The resulting grid can be sparse and only contains cells
that have matching data. Each cell corresponds to a
map tile as used by many online map
sites. Each cell is labeled using a "{zoom}/{x}/{y}" format, where zoom is equal
to the user-specified precision.
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
tile: {
geotile_grid: {
field: 'location',
precision: 8
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{ "tile": { "geotile_grid": { "field": "location", "precision": 8 } } }
]
}
}
}
}
Precision
The highest-precision geotile of length 29 produces cells that cover less than 10cm by 10cm of land. This precision is uniquely suited for composite aggregations as each tile does not have to be generated and loaded in memory.
See Zoom level documentation on how precision (zoom) correlates to size on the ground. Precision for this aggregation can be between 0 and 29, inclusive.
Bounding box filtering
The geotile source can optionally be constrained to a specific geo bounding box, which reduces the range of tiles used. These bounds are useful when only a specific part of a geographical area needs high precision tiling.
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
tile: {
geotile_grid: {
field: 'location',
precision: 22,
bounds: {
top_left: 'POINT (4.9 52.4)',
bottom_right: 'POINT (5.0 52.3)'
}
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{
"tile": {
"geotile_grid": {
"field": "location",
"precision": 22,
"bounds": {
"top_left": "POINT (4.9 52.4)",
"bottom_right": "POINT (5.0 52.3)"
}
}
}
}
]
}
}
}
}
Mixing different value sources
editThe sources parameter accepts an array of value sources.
It is possible to mix different value sources to create composite buckets.
For example:
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
date: {
date_histogram: {
field: 'timestamp',
calendar_interval: '1d'
}
}
},
{
product: {
terms: {
field: 'product'
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{ "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d" } } },
{ "product": { "terms": { "field": "product" } } }
]
}
}
}
}
This will create composite buckets from the values created by two value sources, 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.
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
shop: {
terms: {
field: 'shop'
}
}
},
{
product: {
terms: {
field: 'product'
}
}
},
{
date: {
date_histogram: {
field: 'timestamp',
calendar_interval: '1d'
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{ "shop": { "terms": { "field": "shop" } } },
{ "product": { "terms": { "field": "product" } } },
{ "date": { "date_histogram": { "field": "timestamp", "calendar_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:
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
date: {
date_histogram: {
field: 'timestamp',
calendar_interval: '1d',
order: 'desc'
}
}
},
{
product: {
terms: {
field: 'product',
order: 'asc'
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{ "date": { "date_histogram": { "field": "timestamp", "calendar_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):
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
product_name: {
terms: {
field: 'product',
missing_bucket: true,
missing_order: 'last'
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [{
"product_name": {
"terms": {
"field": "product",
"missing_bucket": true,
"missing_order": "last"
}
}
}]
}
}
}
}
In the above example, the product_name source emits an explicit null bucket
for documents without a product value. This bucket is placed last.
You can control the position of the null bucket using the optional
missing_order parameter. If missing_order is first or last, the null
bucket is placed in the respective first or last position. If missing_order is
omitted or default, the source’s order determines the bucket’s position. If
order is asc (ascending), the bucket is in the first position. If order is
desc (descending), the bucket is in the last position.
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 value sources.
The response contains the values for each composite bucket in an array containing the values extracted
from each value source. Defaults to 10.
Pagination
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:
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
size: 2,
sources: [
{
date: {
date_histogram: {
field: 'timestamp',
calendar_interval: '1d'
}
}
},
{
product: {
terms: {
field: 'product'
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"size": 2,
"sources": [
{ "date": { "date_histogram": { "field": "timestamp", "calendar_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
}
]
}
}
}
To get the next set of buckets, resend the same aggregation with the after
parameter set to the after_key value returned in the response.
For example, this request uses the after_key value provided in the previous response:
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
size: 2,
sources: [
{
date: {
date_histogram: {
field: 'timestamp',
calendar_interval: '1d',
order: 'desc'
}
}
},
{
product: {
terms: {
field: 'product',
order: 'asc'
}
}
}
],
after: {
date: 1_494_288_000_000,
product: 'mad max'
}
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"size": 2,
"sources": [
{ "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d", "order": "desc" } } },
{ "product": { "terms": { "field": "product", "order": "asc" } } }
],
"after": { "date": 1494288000000, "product": "mad max" }
}
}
}
}
The after_key is usually the key to the last bucket returned in
the response, but that isn’t guaranteed. Always use the returned after_key instead
of deriving it from the buckets.
Early termination
editFor optimal performance the index sort should be set on the index so that it matches parts or fully the source order in the composite aggregation. For instance the following index sort:
PUT my-index-000001
{
"settings": {
"index": {
"sort.field": [ "username", "timestamp" ],
"sort.order": [ "asc", "desc" ]
}
},
"mappings": {
"properties": {
"username": {
"type": "keyword",
"doc_values": true
},
"timestamp": {
"type": "date"
}
}
}
}
|
This index is sorted by |
|
|
… in ascending order for the
|
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
user_name: {
terms: {
field: 'user_name'
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{ "user_name": { "terms": { "field": "user_name" } } }
]
}
}
}
}
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
user_name: {
terms: {
field: 'user_name'
}
}
},
{
date: {
date_histogram: {
field: 'timestamp',
calendar_interval: '1d',
order: 'desc'
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{ "user_name": { "terms": { "field": "user_name" } } },
{ "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d", "order": "desc" } } }
]
}
}
}
}
|
|
|
|
|
In order to optimize the early termination it is advised to set track_total_hits in the request
to false. The number of total hits that match the request can be retrieved on the first request
and it would be costly to compute this number on every page:
response = client.search(
body: {
size: 0,
track_total_hits: false,
aggregations: {
my_buckets: {
composite: {
sources: [
{
user_name: {
terms: {
field: 'user_name'
}
}
},
{
date: {
date_histogram: {
field: 'timestamp',
calendar_interval: '1d',
order: 'desc'
}
}
}
]
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"track_total_hits": false,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{ "user_name": { "terms": { "field": "user_name" } } },
{ "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d", "order": "desc" } } }
]
}
}
}
}
Note that the order of the source is important, in the example below switching the user_name with the timestamp
would deactivate the sort optimization since this configuration wouldn’t match the index sort specification.
If the order of sources do not matter for your use case you can follow these simple guidelines:
- Put the fields with the highest cardinality first.
- Make sure that the order of the field matches the order of the index sort.
- Put multi-valued fields last since they cannot be used for early termination.
index sort can slowdown indexing, it is very important to test index sorting
with your specific use case and dataset to ensure that it matches your requirement. If it doesn’t note that composite
aggregations will also try to early terminate on non-sorted indices if the query matches all document (match_all query).
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:
response = client.search(
body: {
size: 0,
aggregations: {
my_buckets: {
composite: {
sources: [
{
date: {
date_histogram: {
field: 'timestamp',
calendar_interval: '1d',
order: 'desc'
}
}
},
{
product: {
terms: {
field: 'product'
}
}
}
]
},
aggregations: {
the_avg: {
avg: {
field: 'price'
}
}
}
}
}
}
)
puts response
GET /_search
{
"size": 0,
"aggs": {
"my_buckets": {
"composite": {
"sources": [
{ "date": { "date_histogram": { "field": "timestamp", "calendar_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
}
}
]
}
}
}
Pipeline aggregations
editThe composite agg is not currently compatible with pipeline aggregations, nor does it make sense in most cases. E.g. due to the paging nature of composite aggs, a single logical partition (one day for example) might be spread over multiple pages. Since pipeline aggregations are purely post-processing on the final list of buckets, running something like a derivative on a composite page could lead to inaccurate results as it is only taking into account a "partial" result on that page.
Pipeline aggs that are self contained to a single bucket (such as bucket_selector) might be supported in the future.