(mysql.info) fulltext-search
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12.7 Full-Text Search Functions
===============================
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* fulltext-boolean Boolean Full-Text Searches
* fulltext-query-expansion Full-Text Searches with Query Expansion
* fulltext-stopwords Full-Text Stopwords
* fulltext-restrictions Full-Text Restrictions
* fulltext-fine-tuning Fine-Tuning MySQL Full-Text Search
MATCH (COL1,COL2,...) AGAINST (EXPR [SEARCH_MODIFIER])
SEARCH_MODIFIER: { IN BOOLEAN MODE | WITH QUERY EXPANSION }
MySQL has support for full-text indexing and searching:
* A full-text index in MySQL is an index of type `FULLTEXT'.
* Full-text indexes can be used only with `MyISAM' tables, and can
be created only for `CHAR', `VARCHAR', or `TEXT' columns.
* A `FULLTEXT' index definition can be given in the `CREATE TABLE'
statement when a table is created, or added later using `ALTER
TABLE' or `CREATE INDEX'.
* For large datasets, it is much faster to load your data into a
table that has no `FULLTEXT' index and then create the index after
that, than to load data into a table that has an existing
`FULLTEXT' index.
Full-text searching is performed using `MATCH() ... AGAINST' syntax.
`MATCH()' takes a comma-separated list that names the columns to be
searched. `AGAINST' takes a string to search for, and an optional
modifier that indicates what type of search to perform. The search
string must be a literal string, not a variable or a column name. There
are three types of full-text searches:
* A boolean search interprets the search string using the rules of a
special query language. The string contains the words to search
for. It can also contain operators that specify requirements such
that a word must be present or absent in matching rows, or that it
should be weighted higher or lower than usual. Common words such
as `some' or `then' are stopwords and do not match if present in
the search string. The `IN BOOLEAN MODE' modifier specifies a
boolean search. For more information, see fulltext-boolean.
* A natural language search interprets the search string as a phrase
in natural human language (a phrase in free text). There are no
special operators. The stopword list applies. In addition, words
that are present in more than 50% of the rows are considered
common and do not match. Full-text searches are natural language
searches if no modifier is given.
* A query expansion search is a modification of a natural language
search. The search string is used to perform a natural language
search. Then words from the most relevant rows returned by the
search are added to the search string and the search is done
again. The query returns the rows from the second search. The
`WITH QUERY EXPANSION' modifier specifies a query expansion
search. For more information, see fulltext-query-expansion.
Constraints on full-text searching are listed in
fulltext-restrictions.
mysql> CREATE TABLE articles (
-> id INT UNSIGNED AUTO_INCREMENT NOT NULL PRIMARY KEY,
-> title VARCHAR(200),
-> body TEXT,
-> FULLTEXT (title,body)
-> );
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)
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.
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 `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 non-negative 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.
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
fulltext-boolean, and fulltext-query-expansion.
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 fulltext-stopwords. The
default minimum word length and stopword list can be changed as
described in fulltext-fine-tuning.
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 datasets, this is the most desirable behavior: A
natural language query should not return every second row from a 1GB
table. For small datasets, 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 dataset in which they occur_. A
given word may exceed the 50% threshold in one dataset but not another.
The 50% threshold has a significant implication when you first try
full-text searching to see how it works: If you create a 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. Be sure to insert at least three rows, and preferably many
more. Users who need to bypass the 50% limitation can use the boolean
search mode; see fulltext-boolean.
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