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Elastic Docs ›Elasticsearch Guide [7.3] ›Aggregations ›Bucket Aggregations

Composite Aggregation

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Composite Aggregation

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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

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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

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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:

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

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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:

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

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The 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", "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:

GET /_search
{
    "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.

Mixing different values source

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The 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", "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.

GET /_search
{
    "aggs" : {
        "my_buckets": {
            "composite" : {
                "sources" : [
                    { "shop": { "terms": {"field": "shop" } } },
                    { "product": { "terms": { "field": "product" } } },
                    { "date": { "date_histogram": { "field": "timestamp", "calendar_interval": "1d" } } }
                ]
            }
        }
    }
}

Order

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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:

GET /_search
{
    "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

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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):

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

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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.

After

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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:

GET /_search
{
    "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
                }
            ]
        }
    }
}

The last composite bucket returned by the query.

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", "calendar_interval": "1d", "order": "desc" } } },
                    { "product": { "terms": {"field": "product", "order": "asc" } } }
                ],
                "after": { "date": 1494288000000, "product": "mad max" } 
            }
        }
    }
}

Should restrict the aggregation to buckets that sort after the provided values.

Sub-aggregations

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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:

GET /_search
{
    "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

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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.

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