opensearch-docs-cn/_analyzers/tokenizers/index.md

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title: Tokenizers
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# Tokenizers

A tokenizer receives a stream of characters and splits the text into individual _tokens_. A token consists of a term (usually, a word) and metadata about this term. For example, a tokenizer can split text on white space so that the text `Actions speak louder than words.` becomes [`Actions`, `speak`, `louder`, `than`, `words.`]. 

The output of a tokenizer is a stream of tokens. Tokenizers also maintain the following metadata about tokens:

- The **order** or **position** of each token: This information is used for word and phrase proximity queries. 
- The starting and ending positions (**offsets**) of the tokens in the text: This information is used for highlighting search terms. 
- The token **type**: Some tokenizers (for example, `standard`) classify tokens by type, for example, `<ALPHANUM>` or `<NUM>`. Simpler tokenizers (for example, `letter`) only classify tokens as type `word`.

You can use tokenizers to define custom analyzers. 

## Built-in tokenizers

The following tables list the built-in tokenizers that OpenSearch provides. 

### Word tokenizers

Word tokenizers parse full text into words.

Tokenizer | Description | Example
:--- | :--- | :---
`standard` | - Parses strings into tokens at word boundaries <br> - Removes most punctuation | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!` <br>becomes<br> [`It’s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `PR`, `or`, `2`, `to`, `OpenSearch`] 
`letter` | - Parses strings into tokens on any non-letter character <br> - Removes non-letter characters | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!` <br>becomes<br> [`It`, `s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `PR`, `or`, `to`, `OpenSearch`]
`lowercase` | - Parses strings into tokens on any non-letter character <br> - Removes non-letter characters <br> - Converts terms to lowercase | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!` <br>becomes<br> [`it`, `s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `pr`, `or`, `to`, `opensearch`]
`whitespace` | - Parses strings into tokens at white space characters | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!` <br>becomes<br> [`It’s`, `fun`, `to`, `contribute`, `a`,`brand-new`, `PR`, `or`, `2`, `to`, `OpenSearch!`] 
`uax_url_email` | - Similar to the standard tokenizer <br> - Unlike the standard tokenizer, leaves URLs and email addresses as single terms | `It’s fun to contribute a brand-new PR or 2 to OpenSearch opensearch-project@github.com!` <br>becomes<br> [`It’s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `PR`, `or`, `2`, `to`, `OpenSearch`, `opensearch-project@github.com`] 
`classic` | - Parses strings into tokens on: <br> &emsp; - Punctuation characters that are followed by a white space character <br> &emsp; - Hyphens if the term does not contain numbers <br> - Removes punctuation <br>  - Leaves URLs and email addresses as single terms | `Part number PA-35234, single-use product (128.32)` <br>becomes<br> [`Part`, `number`, `PA-35234`, `single`, `use`, `product`, `128.32`]
`thai` | - Parses Thai text into terms | `สวัสดีและยินดีต` <br>becomes<br> [`สวัสด`, `และ`, `ยินดี`, `ต`] 

### Partial word tokenizers

Partial word tokenizers parse text into words and generate fragments of those words for partial word matching.

Tokenizer | Description | Example
:--- | :--- | :---
`ngram`| - Parses strings into words on specified characters (for example, punctuation or white space characters) and generates n-grams of each word | `My repo` <br>becomes<br> [`M`, `My`, `y`, `y `, <code>&nbsp;</code>, <code>&nbsp;r</code>, `r`, `re`, `e`, `ep`, `p`, `po`, `o`] <br> because the default n-gram length is 1--2 characters 
`edge_ngram` | - Parses strings into words on specified characters (for example, punctuation or white space characters) and generates edge n-grams of each word (n-grams that start at the beginning of the word) | `My repo` <br>becomes<br> [`M`, `My`] <br> because the default n-gram length is 1--2 characters 

### Structured text tokenizers

Structured text tokenizers parse structured text, such as identifiers, email addresses, paths, or ZIP Codes.

Tokenizer | Description | Example
:--- | :--- | :---
`keyword` | - No-op tokenizer <br> - Outputs the entire string unchanged <br> - Can be combined with token filters, like lowercase, to normalize terms | `My repo` <br>becomes<br> `My repo`
`pattern` | - Uses a regular expression pattern to parse text into terms on a word separator or to capture matching text as terms <br> - Uses [Java regular expressions](https://docs.oracle.com/javase/8/docs/api/java/util/regex/Pattern.html) | `https://opensearch.org/forum` <br>becomes<br> [`https`, `opensearch`, `org`, `forum`] because by default the tokenizer splits terms at word boundaries (`\W+`)<br>  Can be configured with a regex pattern
`simple_pattern` | - Uses a regular expression pattern to return matching text as terms <br>  - Uses [Lucene regular expressions](https://lucene.apache.org/core/8_7_0/core/org/apache/lucene/util/automaton/RegExp.html)  <br> - Faster than the `pattern` tokenizer because it uses a subset of the `pattern` tokenizer regular expressions |  Returns an empty array by default <br> Must be configured with a pattern because the pattern defaults to an empty string
`simple_pattern_split` | - Uses a regular expression pattern to split the text at matches rather than returning the matches as terms  <br>  - Uses [Lucene regular expressions](https://lucene.apache.org/core/8_7_0/core/org/apache/lucene/util/automaton/RegExp.html)  <br> - Faster than the `pattern` tokenizer because it uses a subset of the `pattern` tokenizer regular expressions | No-op by default<br> Must be configured with a pattern
`char_group` | - Parses on a set of configurable characters <br> - Faster than tokenizers that run regular expressions | No-op by default<br> Must be configured with a list of characters
`path_hierarchy` | - Parses text on the path separator (by default, `/`) and returns a full path to each component in the tree hierarchy | `one/two/three` <br>becomes<br> [`one`, `one/two`, `one/two/three`]