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WARNING: Version 5.1 of Elasticsearch has passed its EOL date.

This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.

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Elastic Docs ›Elasticsearch Guide [5.1] ›Analysis ›Analyzers

Custom Analyzer

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Custom Analyzer

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

Configuration

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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 configuration

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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>?"
}
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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?"
}
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The emoticon character filter, punctuation tokenizer and english_stop token filter are custom implementations which are defined in the same index settings.

The above example produces the following terms:

[ i'm, _happy_, person, you ]
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