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WARNING: Version 5.4 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.

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Elastic Docs ›Elasticsearch Reference [5.4] ›Mapping ›Field datatypes

Text datatype

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Text datatype

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A field to index full-text values, such as the body of an email or the description of a product. These fields are analyzed, that is they are passed through an analyzer to convert the string into a list of individual terms before being indexed. The analysis process allows Elasticsearch to search for individual words within each full text field. Text fields are not used for sorting and seldom used for aggregations (although the significant terms aggregation is a notable exception).

If you need to index structured content such as email addresses, hostnames, status codes, or tags, it is likely that you should rather use a keyword field.

Below is an example of a mapping for a text field:

PUT my_index
{
  "mappings": {
    "my_type": {
      "properties": {
        "full_name": {
          "type":  "text"
        }
      }
    }
  }
}
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Sometimes it is useful to have both a full text (text) and a keyword (keyword) version of the same field: one for full text search and the other for aggregations and sorting. This can be achieved with multi-fields.

Parameters for text fields

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The following parameters are accepted by text fields:

analyzer

The analyzer which should be used for analyzed string fields, both at index-time and at search-time (unless overridden by the search_analyzer). Defaults to the default index analyzer, or the standard analyzer.

boost

Mapping field-level query time boosting. Accepts a floating point number, defaults to 1.0.

eager_global_ordinals

Should global ordinals be loaded eagerly on refresh? Accepts true or false (default). Enabling this is a good idea on fields that are frequently used for (significant) terms aggregations.

fielddata

Can the field use in-memory fielddata for sorting, aggregations, or scripting? Accepts true or false (default).

fielddata_frequency_filter

Expert settings which allow to decide which values to load in memory when fielddata is enabled. By default all values are loaded.

fields

Multi-fields allow the same string value to be indexed in multiple ways for different purposes, such as one field for search and a multi-field for sorting and aggregations, or the same string value analyzed by different analyzers.

include_in_all

Whether or not the field value should be included in the _all field? Accepts true or false. Defaults to false if index is set to no, or if a parent object field sets include_in_all to false. Otherwise defaults to true.

index

Should the field be searchable? Accepts true (default) or false.

index_options

What information should be stored in the index, for search and highlighting purposes. Defaults to positions.

norms

Whether field-length should be taken into account when scoring queries. Accepts true (default) or false.

position_increment_gap

The number of fake term position which should be inserted between each element of an array of strings. Defaults to the position_increment_gap configured on the analyzer which defaults to 100. 100 was chosen because it prevents phrase queries with reasonably large slops (less than 100) from matching terms across field values.

store

Whether the field value should be stored and retrievable separately from the _source field. Accepts true or false (default).

search_analyzer

The analyzer that should be used at search time on analyzed fields. Defaults to the analyzer setting.

search_quote_analyzer

The analyzer that should be used at search time when a phrase is encountered. Defaults to the search_analyzer setting.

similarity

Which scoring algorithm or similarity should be used. Defaults to BM25.

term_vector

Whether term vectors should be stored for an analyzed field. Defaults to no.

Indexes imported from 2.x do not support text. Instead they will attempt to downgrade text into string. This allows you to merge modern mappings with legacy mappings. Long lived indexes will have to be recreated before upgrading to 6.x but mapping downgrade gives you the opportunity to do the recreation on your own schedule.

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