12.9.1 Natural Language Full-Text Searches
By default or with the
IN NATURAL LANGUAGE MODE modifier, the
MATCH() function performs a natural language search for a string against a text collection. A collection is a set of one or more columns included in a
FULLTEXT index. The search string is given as the argument to
AGAINST(). For each row in the table,
MATCH() returns a relevance value; that is, a similarity measure between the search string and the text in that row in the columns named in the
mysql> CREATE TABLE articles ( id INT UNSIGNED AUTO_INCREMENT NOT NULL PRIMARY KEY, title VARCHAR(200), body TEXT, FULLTEXT (title,body) ) ENGINE=InnoDB; Query OK, 0 rows affected (0.08 sec) mysql> INSERT INTO articles (title,body) VALUES ('MySQL Tutorial','DBMS stands for DataBase ...'), ('How To Use MySQL Well','After you went through a ...'), ('Optimizing MySQL','In this tutorial we will show ...'), ('1001 MySQL Tricks','1. Never run mysqld as root. 2. ...'), ('MySQL vs. YourSQL','In the following database comparison ...'), ('MySQL Security','When configured properly, MySQL ...'); Query OK, 6 rows affected (0.01 sec) Records: 6 Duplicates: 0 Warnings: 0 mysql> SELECT * FROM articles WHERE MATCH (title,body) AGAINST ('database' IN NATURAL LANGUAGE MODE); +----+-------------------+------------------------------------------+ | id | title | body | +----+-------------------+------------------------------------------+ | 1 | MySQL Tutorial | DBMS stands for DataBase ... | | 5 | MySQL vs. YourSQL | In the following database comparison ... | +----+-------------------+------------------------------------------+ 2 rows in set (0.00 sec)
By default, the search is performed in case-insensitive fashion. To perform a case-sensitive full-text search, use a binary collation for the indexed columns. For example, a column that uses the
latin1 character set of can be assigned a collation of
latin1_bin to make it case-sensitive for full-text searches.
MATCH() is used in a
WHERE clause, as in the example shown earlier, the rows returned are automatically sorted with the highest relevance first. Relevance values are nonnegative floating-point numbers. Zero relevance means no similarity. Relevance is computed based on the number of words in the row (document), the number of unique words in the row, the total number of words in the collection, and the number of rows that contain a particular word.
The term “document” may be used interchangeably with the term “row”, and both terms refer to the indexed part of the row. The term “collection” refers to the indexed columns and encompasses all rows.
To simply count matches, you could use a query like this:
mysql> SELECT COUNT(*) FROM articles WHERE MATCH (title,body) AGAINST ('database' IN NATURAL LANGUAGE MODE); +----------+ | COUNT(*) | +----------+ | 2 | +----------+ 1 row in set (0.00 sec)
You might find it quicker to rewrite the query as follows:
mysql> SELECT COUNT(IF(MATCH (title,body) AGAINST ('database' IN NATURAL LANGUAGE MODE), 1, NULL)) AS count FROM articles; +-------+ | count | +-------+ | 2 | +-------+ 1 row in set (0.03 sec)
The first query does some extra work (sorting the results by relevance) but also can use an index lookup based on the
WHERE clause. The index lookup might make the first query faster if the search matches few rows. The second query performs a full table scan, which might be faster than the index lookup if the search term was present in most rows.
For natural-language full-text searches, the columns named in the
MATCH() function must be the same columns included in some
FULLTEXT index in your table. For the preceding query, note that the columns named in the
MATCH() function (
body) are the same as those named in the definition of the
FULLTEXT index. To search the
body separately, you would create separate
FULLTEXT indexes for each column.
You can also perform a boolean search or a search with query expansion. These search types are described in Section 12.9.2, “Boolean Full-Text Searches”, and Section 12.9.3, “Full-Text Searches with Query Expansion”.
A full-text search that uses an index can name columns only from a single table in the
MATCH() clause because an index cannot span multiple tables. For
MyISAM tables, a boolean search can be done in the absence of an index (albeit more slowly), in which case it is possible to name columns from multiple tables.
The preceding example is a basic illustration that shows how to use the
MATCH() function where rows are returned in order of decreasing relevance. The next example shows how to retrieve the relevance values explicitly. Returned rows are not ordered because the
SELECT statement includes neither
ORDER BY clauses:
mysql> SELECT id, MATCH (title,body) AGAINST ('Tutorial' IN NATURAL LANGUAGE MODE) AS score FROM articles; +----+---------------------+ | id | score | +----+---------------------+ | 1 | 0.22764469683170319 | | 2 | 0 | | 3 | 0.22764469683170319 | | 4 | 0 | | 5 | 0 | | 6 | 0 | +----+---------------------+ 6 rows in set (0.00 sec)
The following example is more complex. The query returns the relevance values and it also sorts the rows in order of decreasing relevance. To achieve this result, specify
MATCH() twice: once in the
SELECT list and once in the
WHERE clause. This causes no additional overhead, because the MySQL optimizer notices that the two
MATCH() calls are identical and invokes the full-text search code only once.
mysql> SELECT id, body, MATCH (title,body) AGAINST ('Security implications of running MySQL as root' IN NATURAL LANGUAGE MODE) AS score FROM articles WHERE MATCH (title,body) AGAINST ('Security implications of running MySQL as root' IN NATURAL LANGUAGE MODE); +----+-------------------------------------+-----------------+ | id | body | score | +----+-------------------------------------+-----------------+ | 4 | 1. Never run mysqld as root. 2. ... | 1.5219271183014 | | 6 | When configured properly, MySQL ... | 1.3114095926285 | +----+-------------------------------------+-----------------+ 2 rows in set (0.00 sec)
A phrase that is enclosed within double quote (
") characters matches only rows that contain the phrase literally, as it was typed. The full-text engine splits the phrase into words and performs a search in the
FULLTEXT index for the words. Nonword characters need not be matched exactly: Phrase searching requires only that matches contain exactly the same words as the phrase and in the same order. For example,
"test phrase" matches
"test, phrase". If the phrase contains no words that are in the index, the result is empty. For example, if all words are either stopwords or shorter than the minimum length of indexed words, the result is empty.
FULLTEXT implementation regards any sequence of true word characters (letters, digits, and underscores) as a word. That sequence may also contain apostrophes (
'), but not more than one in a row. This means that
aaa'bbb is regarded as one word, but
aaa''bbb is regarded as two words. Apostrophes at the beginning or the end of a word are stripped by the
'aaa'bbb' would be parsed as
FULLTEXT parser determines where words start and end by looking for certain delimiter characters; for example,
, (comma), and
. (period). If words are not separated by delimiters (as in, for example, Chinese), the built-in
FULLTEXT parser cannot determine where a word begins or ends. To be able to add words or other indexed terms in such languages to a
FULLTEXT index that uses the built-in
FULLTEXT parser, you must preprocess them so that they are separated by some arbitrary delimiter. Alternatively, you can create
FULLTEXT indexes using the ngram parser plugin (for Chinese, Japanese, or Korean) or the MeCab parser plugin (for Japanese).
It is possible to write a plugin that replaces the built-in full-text parser. For details, see Section 28.2, “The MySQL Plugin API”. For example parser plugin source code, see the
plugin/fulltext directory of a MySQL source distribution.
Some words are ignored in full-text searches:
Any word that is too short is ignored. The default minimum length of words that are found by full-text searches is three characters for
InnoDBsearch indexes, or four characters for
MyISAM. You can control the cutoff by setting a configuration option before creating the index:
innodb_ft_min_token_sizeconfiguration option for
InnoDBsearch indexes, or
This behavior does not apply to
FULLTEXTindexes that use the ngram parser. For the ngram parser, token length is defined by the
Words in the stopword list are ignored. A stopword is a word such as “the” or “some” that is so common that it is considered to have zero semantic value. There is a built-in stopword list, but it can be overridden by a user-defined list. The stopword lists and related configuration options are different for
InnoDBsearch indexes and
MyISAMones. Stopword processing is controlled by the configuration options
InnoDBsearch indexes, and
See Section 12.9.4, “Full-Text Stopwords” to view default stopword lists and how to change them. The default minimum word length can be changed as described in Section 12.9.6, “Fine-Tuning MySQL Full-Text Search”.
Every correct word in the collection and in the query is weighted according to its significance in the collection or query. Thus, a word that is present in many documents has a lower weight, because it has lower semantic value in this particular collection. Conversely, if the word is rare, it receives a higher weight. The weights of the words are combined to compute the relevance of the row. This technique works best with large collections.
For very small tables, word distribution does not adequately reflect their semantic value, and this model may sometimes produce bizarre results for search indexes on
MyISAM tables. For example, although the word “MySQL” is present in every row of the
articles table shown earlier, a search for the word in a
MyISAM search index produces no results:
mysql> SELECT * FROM articles WHERE MATCH (title,body) AGAINST ('MySQL' IN NATURAL LANGUAGE MODE); Empty set (0.00 sec)
The search result is empty because the word “MySQL” is present in at least 50% of the rows, and so is effectively treated as a stopword. This filtering technique is more suitable for large data sets, where you might not want the result set to return every second row from a 1GB table, than for small data sets where it might cause poor results for popular terms.
The 50% threshold can surprise you when you first try full-text searching to see how it works, and makes
InnoDB tables more suited to experimentation with full-text searches. If you create a
MyISAM table and insert only one or two rows of text into it, every word in the text occurs in at least 50% of the rows. As a result, no search returns any results until the table contains more rows. Users who need to bypass the 50% limitation can build search indexes on
InnoDB tables, or use the boolean search mode explained in Section 12.9.2, “Boolean Full-Text Searches”.