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Elasticsearch Guide [7.7] » Text analysis » Configure text analysis » Create a custom analyzer
« Configuring built-in analyzers Specify an analyzer »

Create a custom analyzeredit

When the built-in analyzers do not fulfill your needs, you can create a custom analyzer which uses the appropriate combination of:

Configurationedit

The custom analyzer accepts the following parameters:

tokenizer

A built-in or customised tokenizer. (Required)

char_filter

An optional array of built-in or customised character filters.

filter

An optional array of built-in or customised token filters.

position_increment_gap

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 100. See position_increment_gap for more.

Example configurationedit

Here 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>?"
}

Setting type to custom tells Elasticsearch that we are defining a custom analyzer. Compare this to how built-in analyzers can be configured: type will be set to the name of the built-in analyzer, like standard or simple.

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
Tokenizer
Token Filters

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?"
}

Assigns the index a default custom analyzer, my_custom_analyzer. This analyzer uses a custom tokenizer, character filter, and token filter that are defined later in the request.

Defines the custom punctuation tokenizer.

Defines the custom emoticons character filter.

Defines the custom english_stop token filter.

The above example produces the following terms:

[ i'm, _happy_, person, you ]
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