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
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
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 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
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 as long as the following conditions are
There must be no explicit
The search must be performed using a full-text index scan rather than a table scan.
If the query joins tables, the full-text index scan must be the leftmost non-constant table in the join.
Given the conditions just listed, it is usually less effort to
ORDER BY an explicit sort order
when one is necessary or desired.
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
MATCH() function must be the
same columns included in some
in your table. For the preceding query, the columns named in the
body) are the
same as those named in the definition of the
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 12.10.2, “Boolean Full-Text Searches”, and Section 12.10.3, “Full-Text Searches with Query Expansion”.
A full-text search that uses an index can name columns only from
a single table in the
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
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
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". 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
FULLTEXT implementation regards any
sequence of true word characters (letters, digits, and
underscores) as a word. That sequence may also contain
'), 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;
, (comma), and
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
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 The MySQL Plugin API.
For example parser plugin source code, see the
plugin/fulltext directory of a MySQL source
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 12.10.4, “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 12.10.6, “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
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 12.10.2, “Boolean Full-Text Searches”.