• string - text and keyword
• [number] - long, integer, short, byte, double, float, half_float, scaled_float
• [date] - date
• [boolean] - boolean
• [binary] - binary
• [range] - integer_range, float_range, long_range, double_range, date_range

text - Эти поля предварительно обрабатываются анализатором для преобразования строки в список отдельных терминов.
keyword - Поля ключевых слов доступны только по их точному значению.

• [array] - Array support does not require a dedicated type
• [object] - object for single JSON objects
• [nested] - nested for arrays of JSON objects

• [geo-point] - geo_point for lat/lon points
• [geo-shape] - geo_shape for complex shapes like polygons

• [ip] - ip for IPv4 and IPv6 addresses
• Completion datatype - completion to provide auto-complete suggestions
• [token-count] - token_count to count the number of tokens in a string
• {plugins}/mapper-murmur3.html[mapper-murmur3]
• murmur3 to compute hashes of values at index-time and store them in the index
• [percolator] - Accepts queries from the query-dsl
• [parent-join] - Defines parent/child relation for documents within the same index

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 Multi-fields parameter.

types/array.asciidoc

types/binary.asciidoc

types/range.asciidoc

types/boolean.asciidoc

types/date.asciidoc

types/geo-point.asciidoc

types/geo-shape.asciidoc

types/ip.asciidoc

types/keyword.asciidoc

types/nested.asciidoc

types/numeric.asciidoc

types/object.asciidoc

types/text.asciidoc

types/token-count.asciidoc

types/percolator.asciidoc

types/parent-join.asciidoc