Text type family
The text family includes the following field types:
text
, the traditional field type for full-text content such as the body of an email or the description of a product.match_only_text
, a space-optimized variant oftext
that disables scoring and performs slower on queries that need positions. It is best suited for indexing log messages.
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 text aggregation is a notable exception).
text
fields are best suited for unstructured but human-readable content. If you need to index unstructured machine-generated content, see Mapping unstructured content.
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"
}
}
}
}
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.
The following parameters are accepted by text
fields:
analyzer
- The analyzer which should be used for the
text
field, both at index-time and at search-time (unless overridden by thesearch_analyzer
). Defaults to the default index analyzer, or thestandard
analyzer. eager_global_ordinals
- Should global ordinals be loaded eagerly on refresh? Accepts
true
orfalse
(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
orfalse
(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.
index
- Should the field be searchable? Accepts
true
(default) orfalse
. index_options
- What information should be stored in the index, for search and highlighting purposes. Defaults to
positions
. index_prefixes
- 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.
index_phrases
- 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
true
orfalse
(default). norms
- Whether field-length should be taken into account when scoring queries. Accepts
true
(default) orfalse
. 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 to100
.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. Acceptstrue
orfalse
(default). search_analyzer
- The
analyzer
that should be used at search time on thetext
field. Defaults to theanalyzer
setting. search_quote_analyzer
- The
analyzer
that should be used at search time when a phrase is encountered. Defaults to thesearch_analyzer
setting. similarity
- Which scoring algorithm or similarity should be used. Defaults to
BM25
. term_vector
- Whether term vectors should be stored for the field. Defaults to
no
. meta
- Metadata about the field.
Synthetic _source
is Generally Available only for TSDB indices (indices that have index.mode
set to time_series
). For other indices synthetic _source
is in technical preview. Features in technical preview may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
text
fields support synthetic _source
if they have a keyword
sub-field that supports synthetic _source
or if the text
field sets store
to true
. Either way, it may not have copy_to
.
If using a sub-keyword
field, then the values are sorted in the same way as a keyword
field’s values are sorted. By default, that means sorted with duplicates removed. So:
PUT idx
{
"settings": {
"index": {
"mapping": {
"source": {
"mode": "synthetic"
}
}
}
},
"mappings": {
"properties": {
"text": {
"type": "text",
"fields": {
"raw": {
"type": "keyword"
}
}
}
}
}
}
PUT idx/_doc/1
{
"text": [
"the quick brown fox",
"the quick brown fox",
"jumped over the lazy dog"
]
}
Will become:
{
"text": [
"jumped over the lazy dog",
"the quick brown fox"
]
}
Reordering text fields can have an effect on phrase and span queries. See the discussion about position_increment_gap
for more detail. You can avoid this by making sure the slop
parameter on the phrase queries is lower than the position_increment_gap
. This is the default.
If the text
field sets store
to true then order and duplicates are preserved.
PUT idx
{
"settings": {
"index": {
"mapping": {
"source": {
"mode": "synthetic"
}
}
}
},
"mappings": {
"properties": {
"text": { "type": "text", "store": true }
}
}
}
PUT idx/_doc/1
{
"text": [
"the quick brown fox",
"the quick brown fox",
"jumped over the lazy dog"
]
}
Will become:
{
"text": [
"the quick brown fox",
"the quick brown fox",
"jumped over the lazy dog"
]
}
text
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 text
field using a script, you’ll see an exception indicating that field data is disabled by default on text fields. To load field data in memory, set fielddata=true
on your field.
Loading field data in memory can consume 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.
It 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:
PUT my-index-000001
{
"mappings": {
"properties": {
"my_field": { 1
"type": "text",
"fields": {
"keyword": { 2
"type": "keyword"
}
}
}
}
}
}
- Use the
my_field
field for searches. - Use the
my_field.keyword
field for aggregations, sorting, or in scripts.
You can enable fielddata on an existing text
field using the update mapping API as follows:
PUT my-index-000001/_mapping
{
"properties": {
"my_field": { 1
"type": "text",
"fielddata": true
}
}
}
- The mapping that you specify for
my_field
should consist of the existing mapping for that field, plus thefielddata
parameter.
Fielddata 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
}
}
}
}
}
A variant of text
that trades scoring and efficiency of positional queries for space efficiency. This field effectively stores data the same way as a text
field that only indexes documents (index_options: docs
) and disables norms (norms: false
). Term queries perform as fast if not faster as on text
fields, however queries that need positions such as the match_phrase
query perform slower as they need to look at the _source
document to verify whether a phrase matches. All queries return constant scores that are equal to 1.0.
Analysis is not configurable: text is always analyzed with the default analyzer (standard
by default).
span queries are not supported with this field, use interval queries instead, or the text
field type if you absolutely need span queries.
Other than that, match_only_text
supports the same queries as text
. And like text
, it does not support sorting and has only limited support for aggregations.
PUT logs
{
"mappings": {
"properties": {
"@timestamp": {
"type": "date"
},
"message": {
"type": "match_only_text"
}
}
}
}
The following mapping parameters are accepted: