---
title: N-gram tokenizer
description: The 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...
url: https://www.elastic.co/elastic/docs-builder/docs/3028/reference/text-analysis/analysis-ngram-tokenizer
products:
  - Elasticsearch
---

# N-gram tokenizer
The `ngram` tokenizer first breaks text down into words whenever it encounters one of a list of specified characters, then it emits [N-grams](https://en.wikipedia.org/wiki/N-gram) of each word of the specified length.
N-grams are like a sliding window that moves across the word - a continuous sequence of characters of the specified length. They are useful for querying languages that don’t use spaces or that have long compound words, like German.

## Example output

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

{
  "tokenizer": "ngram",
  "text": "Quick Fox"
}
```

The above sentence would produce the following terms:
```text
[ Q, Qu, u, ui, i, ic, c, ck, k, "k ", " ", " F", F, Fo, o, ox, x ]
```


## Configuration

The `ngram` tokenizer accepts the following parameters:
<definitions>
  <definition term="min_gram">
    Minimum length of characters in a gram. Defaults to `1`.
  </definition>
  <definition term="max_gram">
    Maximum length of characters in a gram. Defaults to `2`.
  </definition>
  <definition term="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 `√`
    - `custom` —      custom characters which need to be set using the `custom_token_chars` setting.
  </definition>
  <definition term="custom_token_chars">
    Custom characters that should be treated as part of a token. For example, setting this to `+-_` will make the tokenizer treat the plus, minus and underscore sign as part of a token.
  </definition>
</definitions>

<tip>
  It usually makes sense to set `min_gram` and `max_gram` to the same value. The smaller the length, the more documents will match but the lower the quality of the matches. The longer the length, the more specific the matches. A tri-gram (length `3`) is a good place to start.
</tip>

The index level setting `index.max_ngram_diff` controls the maximum allowed difference between `max_gram` and `min_gram`.

## Example configuration

In this example, we configure the `ngram` tokenizer to treat letters and digits as tokens, and to produce tri-grams (grams of length `3`):
```json

{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "tokenizer": "my_tokenizer"
        }
      },
      "tokenizer": {
        "my_tokenizer": {
          "type": "ngram",
          "min_gram": 3,
          "max_gram": 3,
          "token_chars": [
            "letter",
            "digit"
          ]
        }
      }
    }
  }
}


{
  "analyzer": "my_analyzer",
  "text": "2 Quick Foxes."
}
```

The above example produces the following terms:
```text
[ Qui, uic, ick, Fox, oxe, xes ]
```