Field datatypesedit

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

Core datatypesedit

string
text and keyword
Numeric datatypes
long, integer, short, byte, double, float
Date datatype
date
Boolean datatype
boolean
Binary datatype
binary

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
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.
Percolator type
Accepts queries from the query-dsl

Multi-fieldsedit

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.