Field datatypesedit

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

Core datatypesedit

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

Complex datatypesedit

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 datatypesedit

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

Specialised datatypesedit

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
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.
Percolator type
Accepts queries from the query-dsl


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.