- Elasticsearch Guide: other versions:
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- Index management in Kibana
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- Definitions
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- 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
Dynamic templates
editDynamic templates
editDynamic templates allow you greater control of how Elasticsearch maps your data beyond
the default dynamic field mapping rules. You enable
dynamic mapping by setting the dynamic parameter to true
or runtime
. You
can then use dynamic templates to define custom mappings that can be applied to
dynamically added fields based on the matching condition:
-
match_mapping_type
operates on the data type that Elasticsearch detects -
match
andunmatch
use a pattern to match on the field name -
path_match
andpath_unmatch
operate on the full dotted path to the field -
If a dynamic template doesn’t define
match_mapping_type
,match
, orpath_match
, it won’t match any field. You can still refer to the template by name indynamic_templates
section of a bulk request.
Use the {name}
and {dynamic_type}
template variables
in the mapping specification as placeholders.
Dynamic field mappings are only added when a field contains a
concrete value. Elasticsearch doesn’t add a dynamic field mapping when the field contains
null
or an empty array. If the null_value
option is used in a
dynamic_template
, it will only be applied after the first document with a
concrete value for the field has been
indexed.
Dynamic templates are specified as an array of named objects:
"dynamic_templates": [ { "my_template_name": { ... match conditions ... "mapping": { ... } } }, ... ]
The template name can be any string value. |
|
The match conditions can include any of : |
|
The mapping that the matched field should use. |
Validating dynamic templates
editIf a provided mapping contains an invalid mapping snippet, a validation error is returned. Validation occurs when applying the dynamic template at index time, and, in most cases, when the dynamic template is updated. Providing an invalid mapping snippet may cause the update or validation of a dynamic template to fail under certain conditions:
-
If no
match_mapping_type
has been specified but the template is valid for at least one predefined mapping type, the mapping snippet is considered valid. However, a validation error is returned at index time if a field matching the template is indexed as a different type. For example, configuring a dynamic template with nomatch_mapping_type
is considered valid as string type, but if a field matching the dynamic template is indexed as a long, a validation error is returned at index time. It is recommended to configure thematch_mapping_type
to the expected JSON type or configure the desiredtype
in the mapping snippet. -
If the
{name}
placeholder is used in the mapping snippet, validation is skipped when updating the dynamic template. This is because the field name is unknown at that time. Instead, validation occurs when the template is applied at index time.
Templates are processed in order — the first matching template wins. When putting new dynamic templates through the update mapping API, all existing templates are overwritten. This allows for dynamic templates to be reordered or deleted after they were initially added.
Mapping runtime fields in a dynamic template
editIf you want Elasticsearch to dynamically map new fields of a certain type as runtime
fields, set "dynamic":"runtime"
in the index mappings. These fields are not
indexed, and are loaded from _source
at query time.
Alternatively, you can use the default dynamic mapping rules and then create
dynamic templates to map specific fields as runtime fields. You set
"dynamic":"true"
in your index mapping, and then create a dynamic template to map
new fields of a certain type as runtime fields.
Let’s say you have data where each of the fields start with ip_
. Based on the
dynamic mapping rules, Elasticsearch maps any string
that passes
numeric
detection as a float
or long
. However, you can create a dynamic
template that maps new strings as runtime fields of type ip
.
The following request defines a dynamic template named strings_as_ip
. When
Elasticsearch detects new string
fields matching the ip*
pattern, it maps those
fields as runtime fields of type ip
. Because ip
fields aren’t mapped
dynamically, you can use this template with either "dynamic":"true"
or
"dynamic":"runtime"
.
response = client.indices.create( index: 'my-index-000001', body: { mappings: { dynamic_templates: [ { strings_as_ip: { match_mapping_type: 'string', match: 'ip*', runtime: { type: 'ip' } } } ] } } ) puts response
PUT my-index-000001/ { "mappings": { "dynamic_templates": [ { "strings_as_ip": { "match_mapping_type": "string", "match": "ip*", "runtime": { "type": "ip" } } } ] } }
See this example for how to use dynamic templates
to map string
fields as either indexed fields or runtime fields.
match_mapping_type
editThe match_mapping_type
is the data type detected by the JSON parser. Because
JSON doesn’t distinguish a long
from an integer
or a double
from
a float
, any parsed floating point number is considered a double
JSON data
type, while any parsed integer
number is considered a long
.
With dynamic mappings, Elasticsearch will always choose the wider data type. The
one exception is float
, which requires less storage space than double
and
is precise enough for most applications. Runtime fields do not support float
,
which is why "dynamic":"runtime"
uses double
.
Elasticsearch automatically detects the following data types:
Elasticsearch data type |
||
JSON data type |
|
|
|
No field added |
No field added |
|
|
|
|
|
|
|
|
|
|
|
No field added |
|
Depends on the first non- |
Depends on the first non- |
|
|
|
|
|
|
|
|
|
Use a wildcard (*
) to match all data types.
For example, if we wanted to map all integer fields as integer
instead of
long
, and all string
fields as both text
and keyword
, we
could use the following template:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { dynamic_templates: [ { integers: { match_mapping_type: 'long', mapping: { type: 'integer' } } }, { strings: { match_mapping_type: 'string', mapping: { type: 'text', fields: { raw: { type: 'keyword', ignore_above: 256 } } } } } ] } } ) puts response response = client.index( index: 'my-index-000001', id: 1, body: { my_integer: 5, my_string: 'Some string' } ) puts response
PUT my-index-000001 { "mappings": { "dynamic_templates": [ { "integers": { "match_mapping_type": "long", "mapping": { "type": "integer" } } }, { "strings": { "match_mapping_type": "string", "mapping": { "type": "text", "fields": { "raw": { "type": "keyword", "ignore_above": 256 } } } } } ] } } PUT my-index-000001/_doc/1 { "my_integer": 5, "my_string": "Some string" }
The |
|
The |
match
and unmatch
editThe match
parameter uses one or more patterns to match on the field name, while
unmatch
uses one or more patterns to exclude fields matched by match
.
The match_pattern
parameter adjusts the behavior of the match
parameter
to support full Java regular expressions matching on the field name
instead of simple wildcards. For example:
"match_pattern": "regex", "match": "^profit_\d+$"
The following example matches all string
fields whose name starts with
long_
(except for those which end with _text
) and maps them as long
fields:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { dynamic_templates: [ { longs_as_strings: { match_mapping_type: 'string', match: 'long_*', unmatch: '*_text', mapping: { type: 'long' } } } ] } } ) puts response response = client.index( index: 'my-index-000001', id: 1, body: { long_num: '5', long_text: 'foo' } ) puts response
PUT my-index-000001 { "mappings": { "dynamic_templates": [ { "longs_as_strings": { "match_mapping_type": "string", "match": "long_*", "unmatch": "*_text", "mapping": { "type": "long" } } } ] } } PUT my-index-000001/_doc/1 { "long_num": "5", "long_text": "foo" }
You can specify a list of patterns using a JSON array for either the
match
or unmatch
fields.
The next example matches all fields whose name starts with ip_
or ends with _ip
,
except for fields which start with one
or end with two
and maps them
as ip
fields:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { dynamic_templates: [ { ip_fields: { match: [ 'ip_*', '*_ip' ], unmatch: [ 'one*', '*two' ], mapping: { type: 'ip' } } } ] } } ) puts response response = client.index( index: 'my-index', id: 1, body: { one_ip: 'will not match', ip_two: 'will not match', three_ip: '12.12.12.12', ip_four: '13.13.13.13' } ) puts response
PUT my-index-000001 { "mappings": { "dynamic_templates": [ { "ip_fields": { "match": ["ip_*", "*_ip"], "unmatch": ["one*", "*two"], "mapping": { "type": "ip" } } } ] } } PUT my-index/_doc/1 { "one_ip": "will not match", "ip_two": "will not match", "three_ip": "12.12.12.12", "ip_four": "13.13.13.13" }
path_match
and path_unmatch
editThe path_match
and path_unmatch
parameters work in the same way as match
and unmatch
, but operate on the full dotted path to the field, not just the
final name, e.g. some_object.*.some_field
.
This example copies the values of any fields in the name
object to the
top-level full_name
field, except for the middle
field:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { dynamic_templates: [ { full_name: { path_match: 'name.*', path_unmatch: '*.middle', mapping: { type: 'text', copy_to: 'full_name' } } } ] } } ) puts response response = client.index( index: 'my-index-000001', id: 1, body: { name: { first: 'John', middle: 'Winston', last: 'Lennon' } } ) puts response
PUT my-index-000001 { "mappings": { "dynamic_templates": [ { "full_name": { "path_match": "name.*", "path_unmatch": "*.middle", "mapping": { "type": "text", "copy_to": "full_name" } } } ] } } PUT my-index-000001/_doc/1 { "name": { "first": "John", "middle": "Winston", "last": "Lennon" } }
And the following example uses an array of patterns for both path_match
and path_unmatch
.
The values of any fields in the name
object or the user.name
object
are copied to the top-level full_name
field, except for the middle
and midinitial
fields:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { dynamic_templates: [ { full_name: { path_match: [ 'name.*', 'user.name.*' ], path_unmatch: [ '*.middle', '*.midinitial' ], mapping: { type: 'text', copy_to: 'full_name' } } } ] } } ) puts response response = client.index( index: 'my-index-000001', id: 1, body: { name: { first: 'John', middle: 'Winston', last: 'Lennon' } } ) puts response response = client.index( index: 'my-index-000001', id: 2, body: { user: { name: { first: 'Jane', midinitial: 'M', last: 'Salazar' } } } ) puts response
PUT my-index-000001 { "mappings": { "dynamic_templates": [ { "full_name": { "path_match": ["name.*", "user.name.*"], "path_unmatch": ["*.middle", "*.midinitial"], "mapping": { "type": "text", "copy_to": "full_name" } } } ] } } PUT my-index-000001/_doc/1 { "name": { "first": "John", "middle": "Winston", "last": "Lennon" } } PUT my-index-000001/_doc/2 { "user": { "name": { "first": "Jane", "midinitial": "M", "last": "Salazar" } } }
Note that the path_match
and path_unmatch
parameters match on object paths
in addition to leaf fields. As an example, indexing the following document will
result in an error because the path_match
setting also matches the object
field name.title
, which can’t be mapped as text:
response = client.index( index: 'my-index-000001', id: 2, body: { name: { first: 'Paul', last: 'McCartney', title: { value: 'Sir', category: 'order of chivalry' } } } ) puts response
PUT my-index-000001/_doc/2 { "name": { "first": "Paul", "last": "McCartney", "title": { "value": "Sir", "category": "order of chivalry" } } }
Template variables
editThe {name}
and {dynamic_type}
placeholders are replaced in the mapping
with the field name and detected dynamic type. The following example sets all
string fields to use an analyzer
with the same name as the
field, and disables doc_values
for all non-string fields:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { dynamic_templates: [ { named_analyzers: { match_mapping_type: 'string', match: '*', mapping: { type: 'text', analyzer: '{name}' } } }, { no_doc_values: { match_mapping_type: '*', mapping: { type: '{dynamic_type}', doc_values: false } } } ] } } ) puts response response = client.index( index: 'my-index-000001', id: 1, body: { english: 'Some English text', count: 5 } ) puts response
PUT my-index-000001 { "mappings": { "dynamic_templates": [ { "named_analyzers": { "match_mapping_type": "string", "match": "*", "mapping": { "type": "text", "analyzer": "{name}" } } }, { "no_doc_values": { "match_mapping_type":"*", "mapping": { "type": "{dynamic_type}", "doc_values": false } } } ] } } PUT my-index-000001/_doc/1 { "english": "Some English text", "count": 5 }
Dynamic template examples
editHere are some examples of potentially useful dynamic templates:
Structured search
editWhen you set "dynamic":"true"
, Elasticsearch will map string fields as a text
field with
a keyword
subfield. If you are only indexing structured content and not
interested in full text search, you can make Elasticsearch map your fields
only as keyword
fields. However, you must search on the exact same value that
was indexed to search those fields.
response = client.indices.create( index: 'my-index-000001', body: { mappings: { dynamic_templates: [ { strings_as_keywords: { match_mapping_type: 'string', mapping: { type: 'keyword' } } } ] } } ) puts response
PUT my-index-000001 { "mappings": { "dynamic_templates": [ { "strings_as_keywords": { "match_mapping_type": "string", "mapping": { "type": "keyword" } } } ] } }
text
-only mappings for strings
editContrary to the previous example, if you only care about full-text search on
string fields and don’t plan on running aggregations, sorting, or exact
searches, you could tell instruct Elasticsearch to map strings as text
:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { dynamic_templates: [ { strings_as_text: { match_mapping_type: 'string', mapping: { type: 'text' } } } ] } } ) puts response
PUT my-index-000001 { "mappings": { "dynamic_templates": [ { "strings_as_text": { "match_mapping_type": "string", "mapping": { "type": "text" } } } ] } }
Alternatively, you can create a dynamic template to map your string fields as
keyword
fields in the runtime section of the mapping. When Elasticsearch detects new
fields of type string
, those fields will be created as runtime fields of
type keyword
.
Although your string
fields won’t be indexed, their values are stored in
_source
and can be used in search requests, aggregations, filtering, and
sorting.
For example, the following request creates a dynamic template to map string
fields as runtime fields of type keyword
. Although the runtime
definition
is blank, new string
fields will be mapped as keyword
runtime fields based
on the dynamic mapping rules that Elasticsearch uses for
adding field types to the mapping. Any string
that doesn’t pass date
detection or numeric detection is automatically mapped as a keyword
:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { dynamic_templates: [ { strings_as_keywords: { match_mapping_type: 'string', runtime: {} } } ] } } ) puts response
PUT my-index-000001 { "mappings": { "dynamic_templates": [ { "strings_as_keywords": { "match_mapping_type": "string", "runtime": {} } } ] } }
You index a simple document:
response = client.index( index: 'my-index-000001', id: 1, body: { english: 'Some English text', count: 5 } ) puts response
PUT my-index-000001/_doc/1 { "english": "Some English text", "count": 5 }
When you view the mapping, you’ll see that the english
field is a runtime
field of type keyword
:
response = client.indices.get_mapping( index: 'my-index-000001' ) puts response
GET my-index-000001/_mapping
{ "my-index-000001" : { "mappings" : { "dynamic_templates" : [ { "strings_as_keywords" : { "match_mapping_type" : "string", "runtime" : { } } } ], "runtime" : { "english" : { "type" : "keyword" } }, "properties" : { "count" : { "type" : "long" } } } } }
Disabled norms
editNorms are index-time scoring factors. If you do not care about scoring, which would be the case for instance if you never sort documents by score, you could disable the storage of these scoring factors in the index and save some space.
response = client.indices.create( index: 'my-index-000001', body: { mappings: { dynamic_templates: [ { strings_as_keywords: { match_mapping_type: 'string', mapping: { type: 'text', norms: false, fields: { keyword: { type: 'keyword', ignore_above: 256 } } } } } ] } } ) puts response
PUT my-index-000001 { "mappings": { "dynamic_templates": [ { "strings_as_keywords": { "match_mapping_type": "string", "mapping": { "type": "text", "norms": false, "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } } } } ] } }
The sub keyword
field appears in this template to be consistent with the
default rules of dynamic mappings. Of course if you do not need them because
you don’t need to perform exact search or aggregate on this field, you could
remove it as described in the previous section.
Time series
editWhen doing time series analysis with Elasticsearch, it is common to have many numeric fields that you will often aggregate on but never filter on. In such a case, you could disable indexing on those fields to save disk space and also maybe gain some indexing speed:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { dynamic_templates: [ { unindexed_longs: { match_mapping_type: 'long', mapping: { type: 'long', index: false } } }, { unindexed_doubles: { match_mapping_type: 'double', mapping: { type: 'float', index: false } } } ] } } ) puts response
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