A tokenizer receives a stream of characters, breaks it up into individual
tokens (usually individual words), and outputs a stream of tokens. For
whitespace tokenizer breaks
text into tokens whenever it sees any whitespace. It would convert the text
"Quick brown fox!" into the terms
[Quick, brown, fox!].
The tokenizer is also responsible for recording the order or position of each term (used for phrase and word proximity queries) and the start and end character offsets of the original word which the term represents (used for highlighting search snippets).
Elasticsearch has a number of built in tokenizers which can be used to build custom analyzers.
Word Oriented Tokenizersedit
The following tokenizers are usually used for tokenizing full text into individual words:
- Standard Tokenizer
standardtokenizer divides text into terms on word boundaries, as defined by the Unicode Text Segmentation algorithm. It removes most punctuation symbols. It is the best choice for most languages.
- Letter Tokenizer
lettertokenizer divides text into terms whenever it encounters a character which is not a letter.
- Lowercase Tokenizer
lowercasetokenizer, like the
lettertokenizer, divides text into terms whenever it encounters a character which is not a letter, but it also lowercases all terms.
- Whitespace Tokenizer
whitespacetokenizer divides text into terms whenever it encounters any whitespace character.
- UAX URL Email Tokenizer
uax_url_emailtokenizer is like the
standardtokenizer except that it recognises URLs and email addresses as single tokens.
- Classic Tokenizer
classictokenizer is a grammar based tokenizer for the English Language.
- Thai Tokenizer
thaitokenizer segments Thai text into words.
Partial Word Tokenizersedit
These tokenizers break up text or words into small fragments, for partial word matching:
- N-Gram Tokenizer
ngramtokenizer can break up text into words when it encounters any of a list of specified characters (e.g. whitespace or punctuation), then it returns n-grams of each word: a sliding window of continuous letters, e.g.
[qu, ui, ic, ck].
- Edge N-Gram Tokenizer
edge_ngramtokenizer can break up text into words when it encounters any of a list of specified characters (e.g. whitespace or punctuation), then it returns n-grams of each word which are anchored to the start of the word, e.g.
[q, qu, qui, quic, quick].
Structured Text Tokenizersedit
The following tokenizers are usually used with structured text like identifiers, email addresses, zip codes, and paths, rather than with full text:
- Keyword Tokenizer
keywordtokenizer is a “noop” tokenizer that accepts whatever text it is given and outputs the exact same text as a single term. It can be combined with token filters like
lowercaseto normalise the analysed terms.
- Pattern Tokenizer
patterntokenizer uses a regular expression to either split text into terms whenever it matches a word separator, or to capture matching text as terms.
- Simple Pattern Tokenizer
simple_patterntokenizer uses a regular expression to capture matching text as terms. It uses a restricted subset of regular expression features and is generally faster than the
- Char Group Tokenizer
char_grouptokenizer is configurable through sets of characters to split on, which is usually less expensive than running regular expressions.
- Simple Pattern Split Tokenizer
simple_pattern_splittokenizer uses the same restricted regular expression subset as the
simple_patterntokenizer, but splits the input at matches rather than returning the matches as terms.
- Path Tokenizer
path_hierarchytokenizer takes a hierarchical value like a filesystem path, splits on the path separator, and emits a term for each component in the tree, e.g.
[/foo, /foo/bar, /foo/bar/baz ].