OpenSearch/docs/reference/search/aggregations.asciidoc

[[search-aggregations]]
== Aggregations

Aggregations grew out of the <<search-facets, facets>> module and the long expirience of how users use it (and would like to use it) for real-time data analytics purposes. As such, it serves as the next generation replacement for the functionality we currently refer to as "faceting".

<<search-facets, Facets>> provide a great way to aggregate data within a document set context. This context is defined by the executed query in combination with the different levels of filters that can be defined (filtered queries, top level filters, and facet level filters). While powerful, their implementation is not designed from ground up to support complex aggregations and thus limited.

.Are facets deprecated?
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As the functionality facets offer is a subset of the the one offered by aggregations, over time, we would like to see users move to aggregations for all realtime data analytics. That said, we are well aware that such transitions/migrations take time, and for this reason we are keeping the facets around for the time being. Nonetheless, facets are and should be considered deprecated and will likely be removed in one of the future major releases.
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The aggregations module breaks the barriers the current facet implementation put in place. The new name ("Aggregations") also indicate the intention here - a generic yet extremely powerful framework for building aggregations - any types of aggregations.

An aggregation can be seen as a _unit-of-work_ that builds analytic information over a set of documents. The context of the execution defines what this document set is (e.g. a top level aggregation executes within the context of the executed query/filters of the search request). 

There are many different types of aggregations, each with its own purpose and output. To better understand these types, it is often easier to break them into two main families:

_Bucketing_::
				A family of aggregations that build buckets, where each bucket is associated with a _key_ and a document criteria. When the aggregations is executed, the buckets criterias are evaluated on every document in the context and when matches, the document is considered to "fall in" the relevant bucket. By the end of the aggreagation process, we'll end up with a list of buckets - each one with a set of documents that "belong" to it. 

_Metric_::
				Aggregations that keep track and compute metrics over a set of documents  

The interesting part comes next, since each bucket effectively defines a document set (all documents belonging to the bucket), one can potentially associated aggregations on the bucket level, and those will execute within the context of that bucket. This is where the real power of aggregations kicks in: *aggregations can be nested!*

NOTE:	Bucketing aggregations can have sub-aggregations (bucketing or metric). The sub aggregations will be computed for 
		each of the buckets their parent aggregation generates. There is not hard limit on the level/depth of nested aggregations (one can nest an aggregation under a "parent" aggregation which is itself a sub-aggregation of another highter aggregations)

=== Structuring Aggregations

The following snippet captures the basic structure of aggregations:

[source,js]
--------------------------------------------------
"aggregations" : {
    "<aggregation_name>" : {
        "<aggregation_type>" : {
            <aggregation_body>
        }
        [,"aggregations" : { [<sub_aggregation>]+ } ]?
    }
    [,"<aggregation_name_2>" : { ... } ]*
}
--------------------------------------------------

The `aggregations` object (a.k.a `aggs` for short) in the json holds the aggregations to be computed. Each aggregation is associated with a logical name that the user defines (e.g. if the aggregation computes the average price, then it'll make sense to name it `avg_price`). These logical names will also be used to uniquely identify the aggregations in the response. Each aggregation has a specific type (`<aggregation_type>` in the above snippet) and is typically the first key within the named aggregation body. Each type of aggregation define its own body, depending on the nature of the aggregation (eg. an `avg` aggregation on a specific field will define the field on which the avg will be calculated). At the same level of the aggregation type definition, one can optionally define a set of additional aggregations, though this only makes sense if the aggregation you defined is of a bucketing nature. In this scenario, the sub-aggregations you define on the bucketing aggregation level will be computed for all the buckets built by the bucketing aggregation. For example, if the you define a set of aggregations under the `range` aggregation, the sub-aggregations will be computed for each of the range buckets that are defined.

==== Values Source

Some aggregations work on values extracted from the aggregated documents. Typically, the values will be extracted from a sepcific document field which is set under the `field` settings for the aggrations. It is also possible to define a `<<modules-scripting,script>>` that will generate the values (per document).

When both `field` and `script` settings are configured for the aggregation, the script will be treated as a `value script`.  While normal scripts are evaluated on a document level (i.e. the script has access to all the data associated with the document), value scripts are evaluated on the *value* level. In this mode, the values are extracted from the configured `field` and the `script` is used to apply a "transformation" over these value/s 

["NOTE",id="metrics-script-note"]
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When working with scripts, the `script_lang` and `params` settings can also be defined. The former defines the scripting language that is used (assuming the proper language is available in es either by default or as a plugin). The latter enables defining all the "dynamic" expressions in the script as parameters, and by that keep the script itself static between calls (this will ensure the use of the cached compiled scripts in elasticsearch). 
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Scripts can generate a single value or multiple values per documents. When generating multiple values, once can use the `script_values_sorted` settings to indicate whether these values are sorted or not. Internally, elasticsearch can perform optimizations when dealing with sorted values (for example, with the `min` aggregations, knowing the values are sorted, elasticsearch will skip the iterations over all the values and rely on the first value in the list to be the minimum value among all other values associated with the same document).

include::aggregations/metrics.asciidoc[] 

include::aggregations/bucket.asciidoc[]