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Field datatypes

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Field datatypes

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

Core datatypes

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string: text and keyword
Numeric datatypes: long, integer, short, byte, double, float, half_float, scaled_float
Date datatype: date
Boolean datatype: boolean
Binary datatype: binary
Range datatypes: integer_range, float_range, long_range, double_range, date_range

Complex datatypes

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Object datatype: object for single JSON objects
Nested datatype: nested for arrays of JSON objects

Geo datatypes

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Geo-point datatype: geo_point for lat/lon points
Geo-Shape datatype: geo_shape for complex shapes like polygons

Specialised datatypes

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IP datatype: ip for IPv4 and IPv6 addresses
Completion datatype: completion to provide auto-complete suggestions
Token count datatype: token_count to count the number of tokens in a string
mapper-murmur3: murmur3 to compute hashes of values at index-time and store them in the index
mapper-annotated-text: annotated-text to index text containing special markup (typically used for identifying named entities)
Percolator type: Accepts queries from the query-dsl
join datatype: Defines parent/child relation for documents within the same index
Alias datatype: Defines an alias to an existing field.

Arrays

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In Elasticsearch, arrays do not require a dedicated field datatype. Any field can contain zero or more values by default, however, all values in the array must be of the same datatype. See Arrays.

Multi-fields

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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 standard analyzer, the english analyzer, and the french analyzer.

This is the purpose of multi-fields. Most datatypes support multi-fields via the fields parameter.

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