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Custom Analyzer
editCustom Analyzer
editWhen the built-in analyzers do not fulfill your needs, you can create a
custom
analyzer which uses the appropriate combination of:
- zero or more character filters
- a tokenizer
- zero or more token filters.
Configuration
editThe custom
analyzer accepts the following parameters:
|
A built-in or customised tokenizer. (Required) |
|
An optional array of built-in or customised character filters. |
|
An optional array of built-in or customised token filters. |
|
When indexing an array of text values, Elasticsearch inserts a fake "gap"
between the last term of one value and the first term of the next value to
ensure that a phrase query doesn’t match two terms from different array
elements. Defaults to |
Example configuration
editHere is an example that combines the following:
- Character Filter
- Tokenizer
- Token Filters
PUT my_index { "settings": { "analysis": { "analyzer": { "my_custom_analyzer": { "type": "custom", "tokenizer": "standard", "char_filter": [ "html_strip" ], "filter": [ "lowercase", "asciifolding" ] } } } } } POST my_index/_analyze { "analyzer": "my_custom_analyzer", "text": "Is this <b>déjà vu</b>?" }
The above example produces the following terms:
[ is, this, deja, vu ]
The previous example used tokenizer, token filters, and character filters with their default configurations, but it is possible to create configured versions of each and to use them in a custom analyzer.
Here is a more complicated example that combines the following:
- Character Filter
-
-
Mapping Character Filter, configured to replace
:)
with_happy_
and:(
with_sad_
-
Mapping Character Filter, configured to replace
- Tokenizer
-
- Pattern Tokenizer, configured to split on punctuation characters
- Token Filters
-
- Lowercase Token Filter
- Stop Token Filter, configured to use the pre-defined list of English stop words
Here is an example:
PUT my_index { "settings": { "analysis": { "analyzer": { "my_custom_analyzer": { "type": "custom", "char_filter": [ "emoticons" ], "tokenizer": "punctuation", "filter": [ "lowercase", "english_stop" ] } }, "tokenizer": { "punctuation": { "type": "pattern", "pattern": "[ .,!?]" } }, "char_filter": { "emoticons": { "type": "mapping", "mappings": [ ":) => _happy_", ":( => _sad_" ] } }, "filter": { "english_stop": { "type": "stop", "stopwords": "_english_" } } } } } POST my_index/_analyze { "analyzer": "my_custom_analyzer", "text": "I'm a :) person, and you?" }
The |
The above example produces the following terms:
[ i'm, _happy_, person, you ]