- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 8.10
- Set up Elasticsearch
- Installing Elasticsearch
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- EQL
- SQL
- Overview
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- System Functions
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- ILM: Manage the index lifecycle
- Tutorial: Customize built-in policies
- Tutorial: Automate rollover
- Index management in Kibana
- Overview
- Concepts
- Index lifecycle actions
- Configure a lifecycle policy
- Migrate index allocation filters to node roles
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- Elasticsearch security principles
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- Active Directory user authentication
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- Integrating with other authentication systems
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- Configuring SAML single-sign-on on the Elastic Stack
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- User authorization
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- Some settings are not returned via the nodes settings API
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- Limitations
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- elasticsearch-certgen
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- elasticsearch-shard
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- How to
- Troubleshooting
- Fix common cluster issues
- Diagnose unassigned shards
- Add a missing tier to the system
- Allow Elasticsearch to allocate the data in the system
- Allow Elasticsearch to allocate the index
- Indices mix index allocation filters with data tiers node roles to move through data tiers
- Not enough nodes to allocate all shard replicas
- Total number of shards for an index on a single node exceeded
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- Troubleshooting corruption
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- Fix master nodes out of disk
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- Start index lifecycle management
- Start Snapshot Lifecycle Management
- Restore from snapshot
- Multiple deployments writing to the same snapshot repository
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- REST APIs
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- Compact and aligned text (CAT) APIs
- cat aliases
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- Cluster APIs
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- Definitions
- Migration guide
- Release notes
- Elasticsearch version 8.10.4
- Elasticsearch version 8.10.3
- Elasticsearch version 8.10.2
- Elasticsearch version 8.10.1
- Elasticsearch version 8.10.0
- Elasticsearch version 8.9.2
- Elasticsearch version 8.9.1
- Elasticsearch version 8.9.0
- Elasticsearch version 8.8.2
- Elasticsearch version 8.8.1
- Elasticsearch version 8.8.0
- Elasticsearch version 8.7.1
- Elasticsearch version 8.7.0
- Elasticsearch version 8.6.2
- Elasticsearch version 8.6.1
- Elasticsearch version 8.6.0
- Elasticsearch version 8.5.3
- Elasticsearch version 8.5.2
- Elasticsearch version 8.5.1
- Elasticsearch version 8.5.0
- Elasticsearch version 8.4.3
- Elasticsearch version 8.4.2
- Elasticsearch version 8.4.1
- Elasticsearch version 8.4.0
- Elasticsearch version 8.3.3
- Elasticsearch version 8.3.2
- Elasticsearch version 8.3.1
- Elasticsearch version 8.3.0
- Elasticsearch version 8.2.3
- Elasticsearch version 8.2.2
- Elasticsearch version 8.2.1
- Elasticsearch version 8.2.0
- Elasticsearch version 8.1.3
- Elasticsearch version 8.1.2
- Elasticsearch version 8.1.1
- Elasticsearch version 8.1.0
- Elasticsearch version 8.0.1
- Elasticsearch version 8.0.0
- Elasticsearch version 8.0.0-rc2
- Elasticsearch version 8.0.0-rc1
- Elasticsearch version 8.0.0-beta1
- Elasticsearch version 8.0.0-alpha2
- Elasticsearch version 8.0.0-alpha1
- Dependencies and versions
Text type family
editText type family
editThe 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.
Text 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).
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:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { properties: { full_name: { type: 'text' } } } } ) puts response
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 |
|
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. |
Synthetic _source
editSynthetic _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:
response = client.indices.create( index: 'idx', body: { mappings: { _source: { mode: 'synthetic' }, properties: { text: { type: 'text', fields: { raw: { type: 'keyword' } } } } } } ) puts response response = client.index( index: 'idx', id: 1, body: { text: [ 'the quick brown fox', 'the quick brown fox', 'jumped over the lazy dog' ] } ) puts response
PUT idx { "mappings": { "_source": { "mode": "synthetic" }, "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.
response = client.indices.create( index: 'idx', body: { mappings: { _source: { mode: 'synthetic' }, properties: { text: { type: 'text', store: true } } } } ) puts response response = client.index( index: 'idx', id: 1, body: { text: [ 'the quick brown fox', 'the quick brown fox', 'jumped over the lazy dog' ] } ) puts response
PUT idx { "mappings": { "_source": { "mode": "synthetic" }, "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" ] }
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 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.
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:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { properties: { my_field: { type: 'text', fields: { keyword: { type: 'keyword' } } } } } } ) puts response
res, err := es.Indices.Create( "my-index-000001", es.Indices.Create.WithBody(strings.NewReader(`{ "mappings": { "properties": { "my_field": { "type": "text", "fields": { "keyword": { "type": "keyword" } } } } } }`)), ) fmt.Println(res, err)
Enabling fielddata on text
fields
editYou can enable fielddata on an existing text
field using the
update mapping API as follows:
response = client.indices.put_mapping( index: 'my-index-000001', body: { properties: { my_field: { type: 'text', fielddata: true } } } ) puts response
res, err := es.Indices.PutMapping( []string{"my-index-000001"}, strings.NewReader(`{ "properties": { "my_field": { "type": "text", "fielddata": true } } }`), ) fmt.Println(res, err)
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
:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { properties: { tag: { type: 'text', fielddata: true, fielddata_frequency_filter: { min: 0.001, max: 0.1, min_segment_size: 500 } } } } } ) puts response
res, err := es.Indices.Create( "my-index-000001", es.Indices.Create.WithBody(strings.NewReader(`{ "mappings": { "properties": { "tag": { "type": "text", "fielddata": true, "fielddata_frequency_filter": { "min": 0.001, "max": 0.1, "min_segment_size": 500 } } } } }`)), ) fmt.Println(res, err)
PUT my-index-000001 { "mappings": { "properties": { "tag": { "type": "text", "fielddata": true, "fielddata_frequency_filter": { "min": 0.001, "max": 0.1, "min_segment_size": 500 } } } } }
Match-only text field type
editA 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.
response = client.indices.create( index: 'logs', body: { mappings: { properties: { "@timestamp": { type: 'date' }, message: { type: 'match_only_text' } } } } ) puts response
PUT logs { "mappings": { "properties": { "@timestamp": { "type": "date" }, "message": { "type": "match_only_text" } } } }
Parameters for match-only text fields
editThe following mapping parameters are accepted:
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