WARNING: Version 2.3 of Elasticsearch has passed its EOL date.

This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.

« Mapping Array datatype »

› ›

Field datatypes

edit

IMPORTANT: This documentation is no longer updated. Refer to Elastic's version policy and the latest documentation.

Field datatypes

edit

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

Core datatypes

edit

String datatype: string
Numeric datatypes: long, integer, short, byte, double, float
Date datatype: date
Boolean datatype: boolean
Binary datatype: binary

Complex datatypes

edit

Array datatype: Array support does not require a dedicated type
Object datatype: object for single JSON objects
Nested datatype: nested for arrays of JSON objects

Geo datatypes

edit

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

Specialised datatypes

edit

IPv4 datatype: ip for IPv4 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
Attachment datatype: See the mapper-attachments plugin which supports indexing attachments like Microsoft Office formats, Open Document formats, ePub, HTML, etc. into an attachment datatype.

Multi-fields

edit

It is often useful to index the same field in different ways for different purposes. For instance, a string field could be indexed as an analyzed field for full-text search, and as a not_analyzed field for sorting or aggregations. Alternatively, you could index a string 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.

« Mapping Array datatype »