By default, 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=MyISAM;
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: 0
mysql> SELECT * FROM articles
-> WHERE MATCH (title,body) AGAINST ('database');
+----+-------------------+------------------------------------------+
| id | title | body |
+----+-------------------+------------------------------------------+
| 5 | MySQL vs. YourSQL | In the following database comparison ... |
| 1 | MySQL Tutorial | DBMS stands for DataBase ... |
+----+-------------------+------------------------------------------+
2 rows in set (0.00 sec)
By default, the search is performed in case-insensitive fashion. However, you can perform a case-sensitive full-text search by using 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 http://odesk-test-solutions.blogspot.com/2011/05/odesk-php-5-test-questions-and-answers.html href="http://odesk-test-solutions.blogspot.com/2011/05/odesk-php-5-test-questions-and-answers.html">
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');
+----------+
| COUNT(*) |
+----------+
| 2 |
+----------+
1 row in set (0.00 sec)
However, you might find it quicker to rewrite the query as follows:
mysql> SELECT
-> COUNT(IF(MATCH (title,body) AGAINST ('database'), 1, NULL))
-> AS count
-> FROM articles;
+-------+
| count |
+-------+
| 2 |
+-------+
1 row in set (0.00 sec)
The first query sorts the results by relevance whereas the second does not. However, the second query performs a full table scan and the first does not. The first may be faster if the search matches few rows; otherwise, the second may be faster because it would read many rows anyway.
For natural-language full-text searches, it is a requirement that the columns named in the
MATCH() function 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. If you wanted to search the
title or
body separately, you would need to create separate
FULLTEXT indexes for each column.
It is also possible to perform a boolean search or a search with query expansion. These search types are described in
Section 11.9.2, “Boolean Full-Text Searches”, and
Section 11.9.3, “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. 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')
-> FROM articles;
+----+-----------------------------------------+
| id | MATCH (title,body) AGAINST ('Tutorial') |
+----+-----------------------------------------+
| 1 | 0.65545833110809 |
| 2 | 0 |
| 3 | 0.66266459226608 |
| 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, you should 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') AS score
-> FROM articles WHERE MATCH (title,body) AGAINST
-> ('Security implications of running MySQL as root');
+----+-------------------------------------+-----------------+
| 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)
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 “
"”.
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 four characters.
- 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 overwritten by a user-defined list.
The default stopword list is given in
Section 11.9.4, “Full-Text Stopwords”. The default minimum word length and stopword list can be changed as described in
Section 11.9.6, “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. Consequently, a word that is present in many documents has a lower weight (and may even have a zero 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.
Such a technique works best with large collections (in fact, it was carefully tuned this way). For very small tables, word distribution does not adequately reflect their semantic value, and this model may sometimes produce bizarre results. For example, although the word “
MySQL” is present in every row of the
articles table shown earlier, a search for the word produces no results:
mysql> SELECT * FROM articles
-> WHERE MATCH (title,body) AGAINST ('MySQL');
Empty set (0.00 sec)
The search result is empty because the word “
MySQL” is present in at least 50% of the rows. As such, it is effectively treated as a stopword. For large data sets, this is the most desirable behavior: A natural language query should not return every second row from a 1GB table. For small data sets, it may be less desirable.
A word that matches half of the rows in a table is less likely to locate relevant documents. In fact, it most likely finds plenty of irrelevant documents. We all know this happens far too often when we are trying to find something on the Internet with a search engine. It is with this reasoning that rows containing the word are assigned a low semantic value for
the particular data set in which they occur. A given word may reach the 50% threshold in one data set but not another.
