A tokenizer receives a stream of characters, breaks it up into individual
tokens (usually individual words), and outputs a stream of tokens. For
instance, a 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.
The following tokenizers are usually used for tokenizing full text into individual words:
standard tokenizer 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 divides text into terms whenever it encounters a
character which is not a letter.
lowercase tokenizer, like the letter tokenizer, divides text into
terms whenever it encounters a character which is not a letter, but it also
lowercases all terms.
whitespace tokenizer divides text into terms whenever it encounters any
whitespace character.
uax_url_email tokenizer is like the standard tokenizer except that it
recognises URLs and email addresses as single tokens.
classic tokenizer is a grammar based tokenizer for the English Language.
thai tokenizer segments Thai text into words.
These tokenizers break up text or words into small fragments, for partial word matching:
ngram tokenizer 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. quick →
[qu, ui, ic, ck].
edge_ngram tokenizer 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. quick →
[q, qu, qui, quic, quick].
The following tokenizers are usually used with structured text like identifiers, email addresses, zip codes, and paths, rather than with full text:
keyword tokenizer 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 lowercase to
normalise the analysed terms.
pattern tokenizer 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 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 pattern tokenizer.
char_group tokenizer is configurable through sets of characters to split
on, which is usually less expensive than running regular expressions.
simple_pattern_split tokenizer uses the same restricted regular expression
subset as the simple_pattern tokenizer, but splits the input at matches rather
than returning the matches as terms.
path_hierarchy tokenizer 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/bar/baz → [/foo, /foo/bar, /foo/bar/baz ].