Edge n-gram tokenizer

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.

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 ]

The edge_ngram tokenizer accepts the following parameters:

The edge_ngram tokenizer’s max_gram value limits the character length of tokens. When the edge_ngram tokenizer is used with an index analyzer, this means search terms longer than the max_gram length may not match any indexed terms.

For example, if the max_gram is 3, searches for apple won’t match the indexed term app.

To account for this, you can use the truncate token filter with a search analyzer to shorten search terms to the max_gram character length. However, this could return irrelevant results.

For example, if the max_gram is 3 and search terms are truncated to three characters, the search term apple is shortened to app. This means searches for apple return any indexed terms matching app, such as apply, snapped, and apple.

We recommend testing both approaches to see which best fits your use case and desired search experience.

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.

Note that the max_gram value for the index analyzer is 10, which limits indexed terms to 10 characters. Search terms are not truncated, meaning that search terms longer than 10 characters may not match any indexed terms.

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": {
    "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"
      }
    }
  }
}