elasticsearch/docs/reference/mapping/params/multi-fields.asciidoc

[[multi-fields]]
=== `fields`

It is often useful to index the same field in different ways for different
purposes.  This is the purpose of _multi-fields_. For instance, a `string`
field could be <<mapping-index,indexed>> as an `analyzed` field for full-text
search, and as a `not_analyzed` field for sorting or aggregations:

[source,js]
--------------------------------------------------
PUT /my_index
{
  "mappings": {
    "my_type": {
      "properties": {
        "city": {
          "type": "string",
          "fields": {
            "raw": { <1>
              "type":  "string",
              "index": "not_analyzed"
            }
          }
        }
      }
    }
  }
}

PUT /my_index/my_type/1
{
  "city": "New York"
}

PUT /my_index/my_type/2
{
  "city": "York"
}

GET /my_index/_search
{
  "query": {
    "match": {
      "city": "york" <2>
    }
  },
  "sort": {
    "city.raw": "asc" <3>
  },
  "aggs": {
    "Cities": {
      "terms": {
        "field": "city.raw" <3>
      }
    }
  }
}
--------------------------------------------------
// AUTOSENSE
<1> The `city.raw` field is a `not_analyzed` version of the `city` field.
<2> The analyzed `city` field can be used for full text search.
<3> The `city.raw` field can be used for sorting and aggregations

NOTE: Multi-fields do not change the original `_source` field.

TIP: The `fields` setting is allowed to have different settings for fields of
the same name in the same index.  New multi-fields can be added to existing
fields using the <<indices-put-mapping,PUT mapping API>>.

==== Multi-fields with multiple analyzers

Another use case of multi-fields is to analyze the same field in different
ways for better relevance. For instance we could index a field with the
<<analysis-standard-analyzer,`standard` analyzer>> which breaks text up into
words, and again with the <<english-analyzer,`english` analyzer>>
which stems words into their root form:

[source,js]
--------------------------------------------------
PUT my_index
{
  "mappings": {
    "my_type": {
      "properties": {
        "text": { <1>
          "type": "string"
        },
        "fields": {
          "english": { <2>
            "type":     "string",
            "analyzer": "english"
          }
        }
      }
    }
  }
}

PUT my_index/my_type/1
{ "text": "quick brown fox" } <3>

PUT my_index/my_type/2
{ "text": "quick brown foxes" } <3>

GET my_index/_search
{
  "query": {
    "multi_match": {
      "query": "quick brown foxes",
      "fields": [ <4>
        "text",
        "text.english"
      ],
      "type": "most_fields" <4>
    }
  }
}
--------------------------------------------------
// AUTOSENSE

<1> The `text` field uses the `standard` analyzer.
<2> The `text.english` field uses the `english` analyzer.
<3> Index two documents, one with `fox` and the other with `foxes`.
<4> Query both the `text` and `text.english` fields and combine the scores.

The `text` field contains the term `fox` in the first document and `foxes` in
the second document.  The `text.english` field contains `fox` for both
documents, because `foxes` is stemmed to `fox`.

The query string is also analyzed by the `standard` analyzer for the `text`
field, and by the `english` analyzer` for the `text.english` field.  The
stemmed field allows a query for `foxes` to also match the document containing
just `fox`.  This allows us to match as many documents as possible.  By also
querying the unstemmed `text` field, we improve the relevance score of the
document which matches `foxes` exactly.