The built-in MySQL full-text parser uses the white space between
words as a delimiter to determine where words begin and end,
which is a limitation when working with ideographic languages
that do not use word delimiters. To address this limitation,
MySQL provides an ngram full-text parser that supports Chinese,
Japanese, and Korean (CJK). The ngram full-text parser is
supported for use with
MySQL also provides a MeCab full-text parser plugin for Japanese, which tokenizes documents into meaningful words. For more information, see Section 12.10.9, “MeCab Full-Text Parser Plugin”.
An ngram is a contiguous sequence of
n characters from a given sequence of
text. The ngram parser tokenizes a sequence of text into a
contiguous sequence of
For example, you can tokenize “abcd” for different
n using the ngram full-text
n=1: 'a', 'b', 'c', 'd' n=2: 'ab', 'bc', 'cd' n=3: 'abc', 'bcd' n=4: 'abcd'
The ngram full-text parser is a built-in server plugin. As with other built-in server plugins, it is automatically loaded when the server is started.
The full-text search syntax described in
Section 12.10, “Full-Text Search Functions” applies to the ngram parser
plugin. Differences in parsing behavior are described in this
section. Full-text-related configuration options, except for
minimum and maximum word length options
ft_max_word_len) are also
Configuring ngram Token Size
The ngram parser has a default ngram token size of 2 (bigram). For example, with a token size of 2, the ngram parser parses the string “abc def” into four tokens: “ab”, “bc”, “de” and “ef”.
ngram token size is configurable using the
option, which has a minimum value of 1 and maximum value of 10.
set to the size of the largest token that you want to search
for. If you only intend to search for single characters, set
ngram_token_size to 1. A
smaller token size produces a smaller full-text search index,
and faster searches. If you need to search for words comprised
of more than one character, set
For example, “Happy Birthday” is
simplified Chinese, where
as “happy”. To search on two-character words such
as these, set
to a value of 2 or higher.
As a read-only variable,
ngram_token_size may only be
set as part of a startup string or in a configuration file:
The following minimum and maximum word length configuration
options are ignored for
that use the ngram parser:
Creating a FULLTEXT Index that Uses the ngram Parser
To create a
FULLTEXT index that uses the
ngram parser, specify
WITH PARSER ngram with
ALTER TABLE, or
The following example demonstrates creating a table with an
inserting sample data (Simplified Chinese text), and viewing
tokenized data in the Information Schema
mysql> USE test; mysql> CREATE TABLE articles ( id INT UNSIGNED AUTO_INCREMENT NOT NULL PRIMARY KEY, title VARCHAR(200), body TEXT, FULLTEXT (title,body) WITH PARSER ngram ) ENGINE=InnoDB CHARACTER SET utf8mb4; mysql> SET NAMES utf8mb4; INSERT INTO articles (title,body) VALUES ('数据库管理','在本教程中我将向你展示如何管理数据库'), ('数据库应用开发','学习开发数据库应用程序'); mysql> SET GLOBAL innodb_ft_aux_table="test/articles"; mysql> SELECT * FROM INFORMATION_SCHEMA.INNODB_FT_INDEX_CACHE ORDER BY doc_id, position;
To add a
FULLTEXT index to an existing table,
you can use
ALTER TABLE or
CREATE INDEX. For example:
CREATE TABLE articles ( id INT UNSIGNED AUTO_INCREMENT NOT NULL PRIMARY KEY, title VARCHAR(200), body TEXT ) ENGINE=InnoDB CHARACTER SET utf8; ALTER TABLE articles ADD FULLTEXT INDEX ft_index (title,body) WITH PARSER ngram; # Or: CREATE FULLTEXT INDEX ft_index ON articles (title,body) WITH PARSER ngram;
ngram Parser Space Handling
The ngram parser eliminates spaces when parsing. For example:
“ab cd” is parsed to “ab”, “cd”
“a bc” is parsed to “bc”
ngram Parser Stopword Handling
The built-in MySQL full-text parser compares words to entries in
the stopword list. If a word is equal to an entry in the
stopword list, the word is excluded from the index. For the
ngram parser, stopword handling is performed differently.
Instead of excluding tokens that are equal to entries in the
stopword list, the ngram parser excludes tokens that
contain stopwords. For example, assuming
ngram_token_size=2, a document
that contains “a,b” is parsed to “a,”
and “,b”. If a comma (“,”) is defined
as a stopword, both “a,” and “,b” are
excluded from the index because they contain a comma.
By default, the ngram parser uses the default stopword list, which contains a list of English stopwords. For a stopword list applicable to Chinese, Japanese, or Korean, you must create your own. For information about creating a stopword list, see Section 12.10.4, “Full-Text Stopwords”.
Stopwords greater in length than
ngram_token_size are ignored.
ngram Parser Term Search
For natural language mode search, the
search term is converted to a union of ngram terms. For example,
the string “abc” (assuming
converted to “ab bc”. Given two documents, one
containing “ab” and the other containing
“abc”, the search term “ab bc” matches
For boolean mode search, the search term is
converted to an ngram phrase search. For example, the string
converted to '“ab bc”'. Given two documents, one
containing 'ab' and the other containing 'abc', the search
phrase '“ab bc”' only matches the document
ngram Parser Wildcard Search
Because an ngram
FULLTEXT index contains only
ngrams, and does not contain information about the beginning of
terms, wildcard searches may return unexpected results. The
following behaviors apply to wildcard searches using ngram
FULLTEXT search indexes:
If the prefix term of a wildcard search is shorter than ngram token size, the query returns all indexed rows that contain ngram tokens starting with the prefix term. For example, assuming
ngram_token_size=2, a search on “a*” returns all rows starting with “a”.
If the prefix term of a wildcard search is longer than ngram token size, the prefix term is converted to an ngram phrase and the wildcard operator is ignored. For example, assuming
ngram_token_size=2, an “abc*” wildcard search is converted to “ab bc”.
ngram Parser Phrase Search
Phrase searches are converted to ngram phrase searches. For example, The search phrase “abc” is converted to “ab bc”, which returns documents containing “abc” and “ab bc”.
The search phrase “abc def” is converted to “ab bc de ef”, which returns documents containing “abc def” and “ab bc de ef”. A document that contains “abcdef” is not returned.