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Elastic Docs ›Elasticsearch Reference [6.0] ›Analysis ›Tokenizers

Edge NGram Tokenizer

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Edge NGram Tokenizer

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The edge_ngram tokenizer first breaks text down into words whenever it encounters one of a list of specified characters, then it emits N-grams of each word where the start of the N-gram is anchored to the beginning of the word.

Edge N-Grams are useful for search-as-you-type queries.

When you need search-as-you-type for text which has a widely known order, such as movie or song titles, the completion suggester is a much more efficient choice than edge N-grams. Edge N-grams have the advantage when trying to autocomplete words that can appear in any order.

Example output

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With the default settings, the edge_ngram tokenizer treats the initial text as a single token and produces N-grams with minimum length 1 and maximum length 2:

POST _analyze
{
  "tokenizer": "edge_ngram",
  "text": "Quick Fox"
}

The above sentence would produce the following terms:

[ Q, Qu ]

These default gram lengths are almost entirely useless. You need to configure the edge_ngram before using it.

Configuration

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The edge_ngram tokenizer accepts the following parameters:

min_gram

Minimum length of characters in a gram. Defaults to 1.

max_gram

Maximum length of characters in a gram. Defaults to 2.

token_chars

Character classes that should be included in a token. Elasticsearch will split on characters that don’t belong to the classes specified. Defaults to [] (keep all characters).

Character classes may be any of the following:

  • letter —  for example a, b, ï or 京
  • digit —  for example 3 or 7
  • whitespace —  for example " " or "\n"
  • punctuation — for example ! or "
  • symbol —  for example $ or √

Example configuration

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In this example, we configure the edge_ngram tokenizer to treat letters and digits as tokens, and to produce grams with minimum length 2 and maximum length 10:

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "tokenizer": "my_tokenizer"
        }
      },
      "tokenizer": {
        "my_tokenizer": {
          "type": "edge_ngram",
          "min_gram": 2,
          "max_gram": 10,
          "token_chars": [
            "letter",
            "digit"
          ]
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_analyzer",
  "text": "2 Quick Foxes."
}

The above example produces the following terms:

[ Qu, Qui, Quic, Quick, Fo, Fox, Foxe, Foxes ]

Usually we recommend using the same analyzer at index time and at search time. In the case of the edge_ngram tokenizer, the advice is different. It only makes sense to use the edge_ngram tokenizer at index time, to ensure that partial words are available for matching in the index. At search time, just search for the terms the user has typed in, for instance: Quick Fo.

Below is an example of how to set up a field for search-as-you-type:

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "autocomplete": {
          "tokenizer": "autocomplete",
          "filter": [
            "lowercase"
          ]
        },
        "autocomplete_search": {
          "tokenizer": "lowercase"
        }
      },
      "tokenizer": {
        "autocomplete": {
          "type": "edge_ngram",
          "min_gram": 2,
          "max_gram": 10,
          "token_chars": [
            "letter"
          ]
        }
      }
    }
  },
  "mappings": {
    "doc": {
      "properties": {
        "title": {
          "type": "text",
          "analyzer": "autocomplete",
          "search_analyzer": "autocomplete_search"
        }
      }
    }
  }
}

PUT my_index/doc/1
{
  "title": "Quick Foxes" 
}

POST my_index/_refresh

GET my_index/_search
{
  "query": {
    "match": {
      "title": {
        "query": "Quick Fo", 
        "operator": "and"
      }
    }
  }
}

The autocomplete analyzer indexes the terms [qu, qui, quic, quick, fo, fox, foxe, foxes].

The autocomplete_search analyzer searches for the terms [quick, fo], both of which appear in the index.
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