12.9.8 ngram Full-Text Parser
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 12.9.9, “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
n characters. For example, you can tokenize “abcd” for different values of
n using the ngram full-text parser.
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 12.9, “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 applicable.
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
ngram_token_size configuration option, which has a minimum value of 1 and maximum value of 10.
ngram_token_size is 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
ngram_token_size accordingly. For example, “Happy Birthday” is “生日快乐” in simplified Chinese, where “生日” is “birthday”, and “快乐” translates as “happy”. To search on two-character words such as these, set
ngram_token_size 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
FULLTEXT indexes that use the ngram parser:
The following example demonstrates creating a table with an
FULLTEXT index, inserting sample data (Simplified Chinese text), and viewing tokenized data in the
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;
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;
The ngram parser eliminates spaces when parsing. For example:
“ab cd” is parsed to “ab”, “cd”
“a bc” is parsed to “bc”
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 12.9.4, “Full-Text Stopwords”.
Stopwords greater in length than
ngram_token_size are ignored.
For natural language mode search, the search term is converted to a union of ngram terms. For example, the string “abc” (assuming
ngram_token_size=2) is converted to “ab bc”. Given two documents, one containing “ab” and the other containing “abc”, the search term “ab bc” matches both documents.
For boolean mode search, the search term is converted to an ngram phrase search. For example, the string 'abc' (assuming
ngram_token_size=2) is converted to '“ab bc”'. Given two documents, one containing 'ab' and the other containing 'abc', the search phrase '“ab bc”' only matches the document containing 'abc'.
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”.
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.