[[search-facets]] == Facets include::facets/deprecated.asciidoc[] The usual purpose of a full-text search engine is to return a small number of documents matching your query. _Facets_ provide aggregated data based on a search query. In the simplest case, a <> can return _facet counts_ for various _facet values_ for a specific _field_. Elasticsearch supports more facet implementations, such as <> or <> facets. The field used for facet calculations _must_ be of type numeric, date/time or be analyzed as a single token — see the <> guide for details on the analysis process. You can give the facet a custom _name_ and return multiple facets in one request. Let's try it out with a simple example. Suppose we have a number of articles with a field called `tags`, preferably analyzed with the <> analyzer. The facet aggregation will return counts for the most popular tags across the documents matching your query — or across all documents in the index. We will store some example data first: [source,js] -------------------------------------------------- curl -X DELETE "http://localhost:9200/articles" curl -X POST "http://localhost:9200/articles/article" -d '{"title" : "One", "tags" : ["foo"]}' curl -X POST "http://localhost:9200/articles/article" -d '{"title" : "Two", "tags" : ["foo", "bar"]}' curl -X POST "http://localhost:9200/articles/article" -d '{"title" : "Three", "tags" : ["foo", "bar", "baz"]}' -------------------------------------------------- Now, let's query the index for articles beginning with letter `T` and retrieve a <> for the `tags` field. We will name the facet simply: _tags_. [source,js] -------------------------------------------------- curl -X POST "http://localhost:9200/articles/_search?pretty=true" -d ' { "query" : { "query_string" : {"query" : "T*"} }, "facets" : { "tags" : { "terms" : {"field" : "tags"} } } } ' -------------------------------------------------- This request will return articles `Two` and `Three` (because they match our query), as well as the `tags` facet: [source,js] -------------------------------------------------- "facets" : { "tags" : { "_type" : "terms", "missing" : 0, "total": 5, "other": 0, "terms" : [ { "term" : "foo", "count" : 2 }, { "term" : "bar", "count" : 2 }, { "term" : "baz", "count" : 1 } ] } } -------------------------------------------------- In the `terms` array, relevant _terms_ and _counts_ are returned. You'll probably want to display these to your users. The facet returns several important counts: * `missing` : The number of documents which have no value for the faceted field + * `total` : The total number of terms in the facet + * `other` : The number of terms not included in the returned facet (effectively `other` = `total` - `terms` ) Notice, that the counts are scoped to the current query: _foo_ is counted only twice (not three times), _bar_ is counted twice and _baz_ once. Also note that terms are counted once per document, even if the occur more frequently in that document. That's because the primary purpose of facets is to enable http://en.wikipedia.org/wiki/Faceted_search[_faceted navigation_], allowing the user to refine her query based on the insight from the facet, i.e. restrict the search to a specific category, price or date range. Facets can be used, however, for other purposes: computing histograms, statistical aggregations, and more. See the blog about link:/blog/data-visualization-with-elasticsearch-and-protovis/[data visualization].for inspiration. [float] === Scope As we have already mentioned, facet computation is restricted to the scope of the current query, called `main`, by default. Facets can be computed within the `global` scope as well, in which case it will return values computed across all documents in the index: [source,js] -------------------------------------------------- { "facets" : { "my_facets" : { "terms" : { ... }, "global" : true <1> } } } -------------------------------------------------- <1> The `global` keyword can be used with any facet type. There's one *important distinction* to keep in mind. While search _queries_ restrict both the returned documents and facet counts, search _filters_ restrict only returned documents — but _not_ facet counts. If you need to restrict both the documents and facets, and you're not willing or able to use a query, you may use a _facet filter_. [float] === Facet Filter All facets can be configured with an additional filter (explained in the <> section), which _will_ reduce the documents they use for computing results. An example with a _term_ filter: [source,js] -------------------------------------------------- { "facets" : { "" : { "" : { ... }, "facet_filter" : { "term" : { "user" : "kimchy"} } } } } -------------------------------------------------- Note that this is different from a facet of the <> type. [float] === Facets with the _nested_ types <> mapping allows for better support for "inner" documents faceting, especially when it comes to multi valued key and value facets (like histograms, or term stats). What is it good for? First of all, this is the only way to use facets on nested documents once they are used (possibly for other reasons). But, there is also facet specific reason why nested documents can be used, and that's the fact that facets working on different key and value field (like term_stats, or histogram) can now support cases where both are multi valued properly. For example, let's use the following mapping: [source,js] -------------------------------------------------- { "type1" : { "properties" : { "obj1" : { "type" : "nested" } } } } -------------------------------------------------- And, here is a sample data: [source,js] -------------------------------------------------- { "obj1" : [ { "name" : "blue", "count" : 4 }, { "name" : "green", "count" : 6 } ] } -------------------------------------------------- [float] ==== All Nested Matching Root Documents Another option is to run the facet on all the nested documents matching the root objects that the main query will end up producing. For example: [source,js] -------------------------------------------------- { "query": { "match_all": {} }, "facets": { "facet1": { "terms_stats": { "key_field" : "name", "value_field": "count" }, "nested": "obj1" } } } -------------------------------------------------- The `nested` element provides the path to the nested document (can be a multi level nested docs) that will be used. Facet filter allows you to filter your facet on the nested object level. It is important that these filters match on the nested object level and not on the root document level. In the following example the `terms_stats` only applies on nested objects with the name 'blue'. [source,js] -------------------------------------------------- { "query": { "match_all": {} }, "facets": { "facet1": { "terms_stats": { "key_field" : "name", "value_field": "count" }, "nested": "obj1", "facet_filter" : { "term" : {"name" : "blue"} } } } } -------------------------------------------------- include::facets/terms-facet.asciidoc[] include::facets/range-facet.asciidoc[] include::facets/histogram-facet.asciidoc[] include::facets/date-histogram-facet.asciidoc[] include::facets/filter-facet.asciidoc[] include::facets/query-facet.asciidoc[] include::facets/statistical-facet.asciidoc[] include::facets/terms-stats-facet.asciidoc[] include::facets/geo-distance-facet.asciidoc[]