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

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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 - Removes most punctuation	It’s fun to contribute a brand-new PR or 2 to OpenSearch! becomes [It’s, fun, to, contribute, a,brand, new, PR, or, 2, to, OpenSearch]
letter	- Parses strings into tokens on any non-letter character - Removes non-letter characters	It’s fun to contribute a brand-new PR or 2 to OpenSearch! becomes [It, s, fun, to, contribute, a,brand, new, PR, or, to, OpenSearch]
lowercase	- Parses strings into tokens on any non-letter character - Removes non-letter characters - Converts terms to lowercase	It’s fun to contribute a brand-new PR or 2 to OpenSearch! becomes [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! becomes [It’s, fun, to, contribute, a,brand-new, PR, or, 2, to, OpenSearch!]
uax_url_email	- Similar to the standard tokenizer - 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! becomes [It’s, fun, to, contribute, a,brand, new, PR, or, 2, to, OpenSearch, opensearch-project@github.com]
classic	- Parses strings into tokens on:   - Punctuation characters that are followed by a white space character   - Hyphens if the term does not contain numbers - Removes punctuation - Leaves URLs and email addresses as single terms	Part number PA-35234, single-use product (128.32) becomes [Part, number, PA-35234, single, use, product, 128.32]
thai	- Parses Thai text into terms	สวัสดีและยินดีต becomes [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 becomes [M, My, y, y ,  ,  r, r, re, e, ep, p, po, o] 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 becomes [M, My] 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 - Outputs the entire string unchanged - Can be combined with token filters, like lowercase, to normalize terms	My repo becomes My repo
pattern	- Uses a regular expression pattern to parse text into terms on a word separator or to capture matching text as terms - Uses Java regular expressions	https://opensearch.org/forum becomes [https, opensearch, org, forum] because by default the tokenizer splits terms at word boundaries (\W+) Can be configured with a regex pattern
simple_pattern	- Uses a regular expression pattern to return matching text as terms - Uses Lucene regular expressions - Faster than the pattern tokenizer because it uses a subset of the pattern tokenizer regular expressions	Returns an empty array by default 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 - Uses Lucene regular expressions - Faster than the pattern tokenizer because it uses a subset of the pattern tokenizer regular expressions	No-op by default Must be configured with a pattern
char_group	- Parses on a set of configurable characters - Faster than tokenizers that run regular expressions	No-op by default 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 becomes [one, one/two, one/two/three]