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 MATCH() list.

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 (0.00 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 (0.00 sec)Records: 6  Duplicates: 0  Warnings: 0mysql> 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.

When 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, the number of unique words in that row, the total number of words in the collection, and the number of documents (rows) that contain a particular word.

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 (title and body) are the same as those named in the definition of the article table's FULLTEXT index. To search the title or 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 WHERE nor 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 | title                 | body                                     |+----+-----------------------+------------------------------------------+|  5 | MySQL vs. YourSQL     | In the following database comparison ... ||  1 | MySQL Tutorial        | DBMS stands for DataBase ...             ||  3 | Optimizing MySQL      | In this tutorial we will show ...        ||  6 | MySQL Security        | When configured properly, MySQL ...      ||  2 | How To Use MySQL Well | After you went through a ...             ||  4 | 1001 MySQL Tricks     | 1. Never run mysqld as root. 2. ...      |+----+-----------------------+------------------------------------------+6 rows in set (0.00 sec)

The MySQL 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 FULLTEXT parser; 'aaa'bbb' would be parsed as aaa'bbb.

The FULLTEXT parser determines where words start and end by looking for certain delimiter characters; for example, " " (space), "," (comma), and "." (period). If words are not separated by delimiters (as in, for example, Chinese), the 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, you must preprocess them so that they are separated by some arbitrary delimiter such as """.

In MySQL 5.6, it is possible to write a plugin that replaces the built-in full-text parser. For details, see Section 23.2, "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:

The default stopword lists are shown in Section 12.9.4, "Full-Text Stopwords". The default minimum word length and stopword list 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.

mysql> SELECT * FROM articles    WHERE MATCH (title,body)    AGAINST
            ('MySQL' IN NATURAL LANGUAGE MODE);Empty set (0.00 sec)