Create a custom analyzer

Serverless Stack

When 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.

The custom analyzer accepts the following parameters:

type

Analyzer type. Accepts built-in analyzer types. For custom analyzers, use custom or omit this parameter.

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.

Here is an example that combines the following:

Character Filter

• HTML Strip Character Filter

Tokenizer

• Standard Tokenizer

Token Filters

• Lowercase Token Filter
• ASCII-Folding Token Filter

				PUT my-index-000001
					{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_custom_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "char_filter": [
            "html_strip"
          ],
          "filter": [
            "lowercase",
            "asciifolding"
          ]
        }
      }
    }
  }
}
				POST my-index-000001/_analyze
					{
  "analyzer": "my_custom_analyzer",
  "text": "Is this <b>déjà vu</b>?"
}
		

1. For custom analyzers, use a type of custom or omit the type parameter.

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_

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-000001
					{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_custom_analyzer": {
          "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-000001/_analyze
					{
  "analyzer": "my_custom_analyzer",
  "text": "I'm a :) person, and you?"
}
		

1. 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. This analyzer also omits the type parameter.
2. Defines the custom punctuation tokenizer.
3. Defines the custom emoticons character filter.
4. Defines the custom english_stop token filter.

The above example produces the following terms:

[ i'm, _happy_, person, you ]