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Elasticsearch Guide [7.7] » Text analysis » Built-in analyzer reference » Fingerprint Analyzer
« Built-in analyzer reference Keyword Analyzer »

Fingerprint Analyzeredit

The fingerprint analyzer implements a fingerprinting algorithm which is used by the OpenRefine project to assist in clustering.

Input text is lowercased, normalized to remove extended characters, sorted, deduplicated and concatenated into a single token. If a stopword list is configured, stop words will also be removed.

Example outputedit 

POST _analyze
{
  "analyzer": "fingerprint",
  "text": "Yes yes, Gödel said this sentence is consistent and."
}

The above sentence would produce the following single term:

[ and consistent godel is said sentence this yes ]

Configurationedit

The fingerprint analyzer accepts the following parameters:

separator

The character to use to concatenate the terms. Defaults to a space.

max_output_size

The maximum token size to emit. Defaults to 255. Tokens larger than this size will be discarded.

stopwords

A pre-defined stop words list like _english_ or an array containing a list of stop words. Defaults to _none_.

stopwords_path

The path to a file containing stop words.

See the Stop Token Filter for more information about stop word configuration.

Example configurationedit

In this example, we configure the fingerprint analyzer to use the pre-defined list of English stop words:

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_fingerprint_analyzer": {
          "type": "fingerprint",
          "stopwords": "_english_"
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_fingerprint_analyzer",
  "text": "Yes yes, Gödel said this sentence is consistent and."
}

The above example produces the following term:

[ consistent godel said sentence yes ]

Definitionedit

The fingerprint tokenizer consists of:

Tokenizer
Token Filters (in order)

If you need to customize the fingerprint analyzer beyond the configuration parameters then you need to recreate it as a custom analyzer and modify it, usually by adding token filters. This would recreate the built-in fingerprint analyzer and you can use it as a starting point for further customization:

PUT /fingerprint_example
{
  "settings": {
    "analysis": {
      "analyzer": {
        "rebuilt_fingerprint": {
          "tokenizer": "standard",
          "filter": [
            "lowercase",
            "asciifolding",
            "fingerprint"
          ]
        }
      }
    }
  }
}
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