[Elasticsearch] 데이터타입

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, 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 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 englishanalyzer, and the french analyzer.

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

참고사이트

https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping-types.html