Text field type
editText field type
editA 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 text 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-000001 { "mappings": { "properties": { "full_name": { "type": "text" } } } }
Use a field as both text and keyword
editSometimes 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
editThe following parameters are accepted by text
fields:
The analyzer which should be used for
the |
|
Mapping field-level query time boosting. Accepts a floating point number, defaults
to |
|
Should global ordinals be loaded eagerly on refresh? Accepts |
|
Can the field use in-memory fielddata for sorting, aggregations,
or scripting? Accepts |
|
Expert settings which allow to decide which values to load in memory when |
|
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. |
|
Should the field be searchable? Accepts |
|
What information should be stored in the index, for search and highlighting purposes.
Defaults to |
|
If enabled, term prefixes of between 2 and 5 characters are indexed into a separate field. This allows prefix searches to run more efficiently, at the expense of a larger index. |
|
If enabled, two-term word combinations (shingles) are indexed into a separate
field. This allows exact phrase queries (no slop) to run more efficiently, at the expense
of a larger index. Note that this works best when stopwords are not removed,
as phrases containing stopwords will not use the subsidiary field and will fall
back to a standard phrase query. Accepts |
|
Whether field-length should be taken into account when scoring queries.
Accepts |
|
The number of fake term position which should be inserted between each
element of an array of strings. Defaults to the |
|
Whether the field value should be stored and retrievable separately from
the |
|
The |
|
The |
|
Which scoring algorithm or similarity should be used. Defaults
to |
|
Whether term vectors should be stored for the field. Defaults to |
|
Metadata about the field. |
fielddata
mapping parameter
edittext
fields are searchable by default, but by default are not available for
aggregations, sorting, or scripting. If you try to sort, aggregate, or access
values from a script on a text
field, you will see this exception:
Fielddata is disabled on text fields by default. Set `fielddata=true` on [`your_field_name`] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory.
Field data is the only way to access the analyzed tokens from a full text field
in aggregations, sorting, or scripting. For example, a full text field like New York
would get analyzed as new
and york
. To aggregate on these tokens requires field data.
Before enabling fielddata
editIt usually doesn’t make sense to enable fielddata on text fields. Field data is stored in the heap with the field data cache because it is expensive to calculate. Calculating the field data can cause latency spikes, and increasing heap usage is a cause of cluster performance issues.
Most users who want to do more with text fields use multi-field mappings
by having both a text
field for full text searches, and an
unanalyzed keyword
field for aggregations, as follows:
Enabling fielddata on text
fields
editYou can enable fielddata on an existing text
field using the
PUT mapping API as follows:
fielddata_frequency_filter
mapping parameter
editFielddata filtering can be used to reduce the number of terms loaded into memory, and thus reduce memory usage. Terms can be filtered by frequency:
The frequency filter allows you to only load terms whose document frequency falls
between a min
and max
value, which can be expressed an absolute
number (when the number is bigger than 1.0) or as a percentage
(eg 0.01
is 1%
and 1.0
is 100%
). Frequency is calculated
per segment. Percentages are based on the number of docs which have a
value for the field, as opposed to all docs in the segment.
Small segments can be excluded completely by specifying the minimum
number of docs that the segment should contain with min_segment_size
:
PUT my-index-000001 { "mappings": { "properties": { "tag": { "type": "text", "fielddata": true, "fielddata_frequency_filter": { "min": 0.001, "max": 0.1, "min_segment_size": 500 } } } } }