Pattern Matching with SQL Patterns

4.7.1 Problem

You want to perform a pattern match rather than a literal comparison.

4.7.2 Solution

Use the LIKE operator and a SQL pattern, described in this section. Or use a regular expression pattern match, described in Recipe 4.8.

4.7.3 Discussion

Patterns are strings that contain special characters. These are known as metacharacters because they stand for something other than themselves. MySQL provides two kinds of pattern matching. One is based on SQL patterns and the other on regular expressions. SQL patterns are more standard among different database systems, but regular expressions are more powerful. The two kinds of pattern match uses different operators and different sets of metacharacters. This section describes SQL patterns; Recipe 4.8 describes regular expressions.

SQL pattern matching uses the LIKE and NOT LIKE operators rather than = and != to perform matching against a pattern string. Patterns may contain two special metacharacters: _ matches any single character, and % matches any sequence of characters, including the empty string. You can use these characters to create patterns that match a wide variety of values:

  • Strings that begin with a particular substring:

    mysql> SELECT name FROM metal WHERE name LIKE 'co%';
    +--------+
    | name |
    +--------+
    | copper |
    +--------+
  • Strings that end with a particular substring:

    mysql> SELECT name FROM metal WHERE name LIKE '%er';
    +--------+
    | name |
    +--------+
    | copper |
    | silver |
    +--------+
  • Strings that contain a particular substring anywhere:

    mysql> SELECT name FROM metal WHERE name LIKE '%er%';
    +---------+
    | name |
    +---------+
    | copper |
    | mercury |
    | silver |
    +---------+
  • Strings that contain a substring at a specific position (the pattern matches only if pp occurs at the third position of the name column):

    mysql> SELECT name FROM metal where name LIKE '_ _pp%';
    +--------+
    | name |
    +--------+
    | copper |
    +--------+

A SQL pattern matches successfully only if it matches the entire comparison value. Thus, of the following two pattern matches, only the second succeeds:

'abc' LIKE 'b'
'abc' LIKE '%b%'

To reverse the sense of a pattern match, use NOT LIKE. The following query finds strings that contain no i characters:

mysql> SELECT name FROM metal WHERE name NOT LIKE '%i%';
+---------+
| name |
+---------+
| copper |
| gold |
| lead |
| mercury |
+---------+

SQL patterns do not match NULL values. This is true both for LIKE and NOT LIKE:

mysql> SELECT NULL LIKE '%', NULL NOT LIKE '%';
+---------------+-------------------+
| NULL LIKE '%' | NULL NOT LIKE '%' |
+---------------+-------------------+
| NULL | NULL |
+---------------+-------------------+

In some cases, pattern matches are equivalent to substring comparisons. For example, using patterns to find strings at one end or the other of a string is like using LEFT( ) or RIGHT( ):

Pattern match

Substring comparison

str LIKE 'abc%'

LEFT(str,3) = 'abc'

str LIKE '%abc'

RIGHT(str,3) = 'abc'

If you're matching against a column that is indexed and you have a choice of using a pattern or an equivalent LEFT( ) expression, you'll likely find that the pattern match is faster. MySQL can use the index to narrow the search for a pattern that begins with a literal string; with LEFT( ), it cannot.

Using Patterns with Non String Values

Unlike some other databases, MySQL allows pattern matches to be applied to numeric or date values, which can sometimes be useful. The following table shows some ways to test a DATE value d using function calls that extract date parts and using the equivalent pattern matches. The pairs of expressions are true for dates occurring in the year 1976, in the month of April, or on the first day of the month:

Function value test

Pattern match test

YEAR(d) = 1976

d LIKE '1976-%'

MONTH(d) = 4

d LIKE '%-04-%'

DAYOFMONTH(d) = 1

d LIKE '%-01'

Using the mysql Client Program

Writing MySQL-Based Programs

Record Selection Techniques

Working with Strings

Working with Dates and Times

Sorting Query Results

Generating Summaries

Modifying Tables with ALTER TABLE

Obtaining and Using Metadata

Importing and Exporting Data

Generating and Using Sequences

Using Multiple Tables

Statistical Techniques

Handling Duplicates

Performing Transactions

Introduction to MySQL on the Web

Incorporating Query Resultsinto Web Pages

Processing Web Input with MySQL

Using MySQL-Based Web Session Management

Appendix A. Obtaining MySQL Software

Appendix B. JSP and Tomcat Primer

Appendix C. References



MySQL Cookbook
MySQL Cookbook
ISBN: 059652708X
EAN: 2147483647
Year: 2005
Pages: 412
Authors: Paul DuBois

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