elasticsearch/docs/reference/mapping/types.asciidoc

[[mapping-types]]
== Field datatypes

Elasticsearch supports a number of different datatypes for the fields in a
document:

[float]
=== Core datatypes

string::         <<text,`text`>> and <<keyword,`keyword`>>
<<number>>::     `long`, `integer`, `short`, `byte`, `double`, `float`, `half_float`, `scaled_float`
<<date>>::       `date`
<<date_nanos>>:: `date_nanos`
<<boolean>>::    `boolean`
<<binary>>::     `binary`
<<range>>::      `integer_range`, `float_range`, `long_range`, `double_range`, `date_range`

[float]
=== Complex datatypes

<<array>>::     Array support does not require a dedicated `type`
<<object>>::    `object` for single JSON objects
<<nested>>::    `nested` for arrays of JSON objects

[float]
=== Geo datatypes

<<geo-point>>::     `geo_point` for lat/lon points
<<geo-shape>>::     `geo_shape` for complex shapes like polygons

[float]
=== Specialised datatypes

<<ip>>::            `ip` for IPv4 and IPv6 addresses
<<search-suggesters-completion,Completion datatype>>::
                    `completion` to provide auto-complete suggestions
<<token-count>>::   `token_count` to count the number of tokens in a string
{plugins}/mapper-murmur3.html[`mapper-murmur3`]:: `murmur3` to compute hashes of values at index-time and store them in the index
{plugins}/mapper-annotated-text.html[`mapper-annotated-text`]:: `annotated-text` to index text containing special markup (typically used for identifying named entities)

<<percolator>>::    Accepts queries from the query-dsl

<<parent-join>>::   Defines parent/child relation for documents within the same index

<<alias>>::         Defines an alias to an existing field.

<<rank-feature>>::  Record numeric feature to boost hits at query time.

<<rank-features>>:: Record numeric features to boost hits at query time.

<<dense-vector>>::   Record dense vectors of float values.

<<sparse-vector>>::   Record sparse vectors of float values.


[float]
=== Multi-fields

It is often useful to index the same field in different ways for different
purposes. For instance, a `string` field could be mapped as
a `text` field for full-text search, and as a `keyword` field for
sorting or aggregations.  Alternatively, you could index a text field with
the <<analysis-standard-analyzer,`standard` analyzer>>, the
<<english-analyzer,`english`>> analyzer, and the
<<french-analyzer,`french` analyzer>>.

This is the purpose of _multi-fields_.  Most datatypes support multi-fields
via the <<multi-fields>> parameter.

include::types/alias.asciidoc[]

include::types/array.asciidoc[]

include::types/binary.asciidoc[]

include::types/range.asciidoc[]

include::types/boolean.asciidoc[]

include::types/date.asciidoc[]

include::types/date_nanos.asciidoc[]

include::types/geo-point.asciidoc[]

include::types/geo-shape.asciidoc[]

include::types/ip.asciidoc[]

include::types/keyword.asciidoc[]

include::types/nested.asciidoc[]

include::types/numeric.asciidoc[]

include::types/object.asciidoc[]

include::types/text.asciidoc[]

include::types/token-count.asciidoc[]

include::types/percolator.asciidoc[]

include::types/parent-join.asciidoc[]

include::types/rank-feature.asciidoc[]

include::types/rank-features.asciidoc[]

include::types/dense-vector.asciidoc[]

include::types/sparse-vector.asciidoc[]