[[search-aggregations-bucket-composite-aggregation]]
=== Composite aggregation

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-search-results, 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": {
    "properties": {
      "product": {
          "type": "keyword"
      },
      "timestamp": {
          "type": "date"
      },
      "price": {
          "type": "long"
      },
      "shop": {
          "type": "keyword"
      },
      "location": {
          "type": "geo_point"
      },
      "nested": {
          "type": "nested",
          "properties": {
            "product": {
                "type": "keyword"
            },
            "timestamp": {
                "type": "date"
            },
            "price": {
                "type": "long"
            },
            "shop": {
                "type": "keyword"
            }
          }
       }
    }
  }
}

POST /sales/_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:

[source,js]
--------------------------------------------------
{
  "keyword": ["foo", "bar"],
  "number": [23, 65, 76]
}
--------------------------------------------------
// NOTCONSOLE

\... creates the following composite buckets when `keyword` and `number` are used as values source
for the aggregation:

[source,js]
--------------------------------------------------
{ "keyword": "foo", "number": 23 }
{ "keyword": "foo", "number": 65 }
{ "keyword": "foo", "number": 76 }
{ "keyword": "bar", "number": 23 }
{ "keyword": "bar", "number": 65 }
{ "keyword": "bar", "number": 76 }
--------------------------------------------------
// NOTCONSOLE

==== Values source

The `sources` parameter controls the sources that should be used to build the composite buckets.
The order that the `sources` are defined is important because it also controls the order
the keys are returned.

The name given to each sources must be unique.

There are three different types of values source:

[[_terms]]
===== 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,console]
--------------------------------------------------
GET /_search
{
  "size": 0,
  "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:

[source,console]
--------------------------------------------------
GET /_search
{
  "size": 0,
  "aggs": {
    "my_buckets": {
      "composite": {
        "sources": [
          {
            "product": {
              "terms": {
                "script": {
                  "source": "doc['product'].value",
                  "lang": "painless"
                }
              }
            }
          }
        ]
      }
    }
  }
}
--------------------------------------------------

[[_histogram]]
===== 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,console]
--------------------------------------------------
GET /_search
{
  "size": 0,
  "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:

[source,console]
--------------------------------------------------
GET /_search
{
  "size": 0,
  "aggs": {
    "my_buckets": {
      "composite": {
        "sources": [
          {
            "histo": {
              "histogram": {
                "interval": 5,
                "script": {
                  "source": "doc['price'].value",
                  "lang": "painless"
                }
              }
            }
          }
        ]
      }
    }
  }
}
--------------------------------------------------

[[_date_histogram]]
===== Date histogram

The `date_histogram` is similar to the `histogram` value source except that the interval
is specified by date/time expression:

[source,console]
--------------------------------------------------
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,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:

[source,console]
--------------------------------------------------
GET /_search
{
  "size": 0,
  "aggs": {
    "my_buckets": {
      "composite": {
        "sources": [
          {
            "date": {
              "date_histogram": {
                "field": "timestamp",
                "calendar_interval": "1d",
                "format": "yyyy-MM-dd"         <1>
              }
            }
          }
        ]
      }
    }
  }
}
--------------------------------------------------

<1> Supports expressive date <<date-format-pattern,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*

include::datehistogram-aggregation.asciidoc[tag=offset-explanation]

[source,console,id=composite-aggregation-datehistogram-offset-example]
----
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"
              }
            }
          }
        ]
      }
    }
  }
}
----

include::datehistogram-aggregation.asciidoc[tag=offset-result-intro]

[source,console-result]
----
{
  ...
  "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
        }
      ]
    }
  }
}
----
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]

include::datehistogram-aggregation.asciidoc[tag=offset-note]

[[_geotile_grid]]
===== GeoTile grid

The `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
{wikipedia}/Tiled_web_map[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.

[source,console,id=composite-aggregation-geotilegrid-example]
--------------------------------------------------
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 https://wiki.openstreetmap.org/wiki/Zoom_levels[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.

[source,console,id=composite-aggregation-geotilegrid-boundingbox-example]
--------------------------------------------------
GET /_search
{
  "size": 0,
  "aggs": {
    "my_buckets": {
      "composite": {
        "sources": [
          {
            "tile": {
              "geotile_grid": {
                "field": "location",
                "precision": 22,
                "bounds": {
                  "top_left": "52.4, 4.9",
                  "bottom_right": "52.3, 5.0"
                }
              }
            }
          }
        ]
      }
    }
  }
}
--------------------------------------------------

===== 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,console]
--------------------------------------------------
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 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,console]
--------------------------------------------------
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

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,console]
--------------------------------------------------
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

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,console]
--------------------------------------------------
GET /_search
{
  "size": 0,
  "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

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
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. Defaults to `10`.

==== Pagination

If 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:

[source,console]
--------------------------------------------------
GET /_search
{
  "size": 0,
  "aggs": {
    "my_buckets": {
      "composite": {
        "size": 2,
        "sources": [
          { "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d" } } },
          { "product": { "terms": { "field": "product" } } }
        ]
      }
    }
  }
}
--------------------------------------------------
// TEST[s/_search/_search\?filter_path=aggregations/]

\... returns:

[source,console-result]
--------------------------------------------------
{
  ...
  "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
        }
      ]
    }
  }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\.//]

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:

[source,console]
--------------------------------------------------
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" } <1>
      }
    }
  }
}
--------------------------------------------------

<1> Should restrict the aggregation to buckets that sort **after** the provided values.

NOTE: 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 derriving it from the buckets.

==== Early termination

For optimal performance the <<index-modules-index-sorting,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:

[source,console]
--------------------------------------------------
PUT my-index-000001
{
  "settings": {
    "index": {
      "sort.field": [ "username", "timestamp" ],   <1>
      "sort.order": [ "asc", "desc" ]              <2>
    }
  },
  "mappings": {
    "properties": {
      "username": {
        "type": "keyword",
        "doc_values": true
      },
      "timestamp": {
        "type": "date"
      }
    }
  }
}
--------------------------------------------------

<1> This index is sorted by `username` first then by `timestamp`.
<2> ... in ascending order for the `username` field and in descending order for the `timestamp` field.

.. could be used to optimize these composite aggregations:

[source,console]
--------------------------------------------------
GET /_search
{
  "size": 0,
  "aggs": {
    "my_buckets": {
      "composite": {
        "sources": [
          { "user_name": { "terms": { "field": "user_name" } } }     <1>
        ]
      }
    }
  }
}
--------------------------------------------------

<1> `user_name` is a prefix of the index sort and the order matches (`asc`).

[source,console]
--------------------------------------------------
GET /_search
{
  "size": 0,
  "aggs": {
    "my_buckets": {
      "composite": {
        "sources": [
          { "user_name": { "terms": { "field": "user_name" } } }, <1>
          { "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d", "order": "desc" } } } <2>
        ]
      }
    }
  }
}
--------------------------------------------------

<1> `user_name` is a prefix of the index sort and the order matches (`asc`).
<2> `timestamp` matches also the prefix and the order matches (`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:

[source,console]
--------------------------------------------------
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.

WARNING: <<index-modules-index-sorting,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

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,console]
--------------------------------------------------
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" }
        }
      }
    }
  }
}
--------------------------------------------------
// TEST[s/_search/_search\?filter_path=aggregations/]

\... returns:

[source,console-result]
--------------------------------------------------
{
  ...
  "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
          }
        }
      ]
    }
  }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\.//]

==== Pipeline aggregations

The 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.
