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README.md

pgpcre

Build Status

This is a module for PostgreSQL that exposes Perl-compatible regular expressions (PCRE) functionality as functions and operators. It is based on the popular PCRE library.

Installation

You need to have libpcre installed. pkg-config will be used to find it.

To build and install this module:

make
make install

or selecting a specific PostgreSQL installation:

make PG_CONFIG=/some/where/bin/pg_config
make PG_CONFIG=/some/where/bin/pg_config install

And finally inside the database:

CREATE EXTENSION pgpcre;

Using

A regular expression is a separate data type, named pcre. (This is different from how the built-in regular expressions in PostgreSQL work, which are simply values of type text.)

The supported regular expressions are documented on the pcrepattern(3) man page.

Basic matching

Boolean operators are available for checking whether a pattern matches a string. These operators return true or false, respectively. They only return null when one of the operands is null.

Examples:

SELECT 'foo' ~ pcre 'fo+';
SELECT 'bar' !~ pcre 'fo+';

You can also write it the other way around:

SELECT pcre 'fo+' ~ 'foo';
SELECT pcre 'fo+' !~ 'bar';

This can be handy for writing things like

SELECT pcre 'fo+' ~ ANY(ARRAY['foo', 'bar']);

For Perl nostalgia, you can also use this operator:

SELECT 'foo' =~ pcre 'fo+';

And if this operator is unique (which it should be, unless you have something else installed that uses it), you can also write:

SELECT 'foo' =~ 'fo+';

(The ~ operator, by contrast, is not unique, of course, because it is used by the built-in regular expressions.)

To get case-insensitive matching, set the appropriate option in the pattern, for example:

SELECT 'FOO' ~ pcre '(?i)fo+';

Extracting the matched string

To extract the substring that was matched by the pattern, use the function pcre_match. It returns either a value of type text, or null if the pattern did not match. Examples:

SELECT pcre_match('fo+', 'foobar');  --> 'foo'
SELECT pcre_match('fo+', 'barbar');  --> NULL

There is no support for extracting multiple matches of a pattern in a string, because PCRE does not (easily) support that.

Extracting captured substrings

Captured substrings (parenthesized subexpressions) are extracted using the function pcre_captured_substrings. It returns either an array of text, or null if the pattern did not match. Examples:

SELECT pcre_captured_substrings('(fo+)(b..)', 'foobar');  --> ARRAY['foo','bar']
SELECT pcre_captured_substrings('(fo+)(b..)', 'abcdef');  --> NULL

Note that elements of the array can be null if a substring was not used, for example:

SELECT pcre_captured_substrings('(a|(z))(bc)', 'abc');  --> ARRAY['a',NULL,'bc']

Storing regular expressions

You can store regular expression values of type pcre in tables, like any other data. Note, however, that the binary representation of the pcre values contains the compiled regular expression, which is tied to the version of the PCRE library. If you upgrade the PCRE library and use a compiled value created by a different version, things might not work or even crash (according to the PCRE documentation; I don't know how likely that is). pgpcre will warn if you attempt to use a value that was compiled by a different version of the library. If that happens, it is advisable to recompile and rewrite all stored pcre values by doing something like

UPDATE ... SET pcre_col = pcre_col::text::pcre

(To be clear, storing regular expressions in tables is not a typical use. Normally, you store text in tables and match it against regular expressions provided by your application.)

Discussion

Some possible advantages over the regular expression support built into PostgreSQL:

  • richer pattern language, more familiar to Perl and Python programmers
  • complete Unicode support
  • saner operators and functions

Some disadvantages:

  • no repeated matching ('g' flag)
  • no index optimization

You can workaround the lack of index optimization by manually augmenting queries like

column =~ '^foo'

with

AND column ~>=~ 'foo' AND column ~<~ 'fop'

and creating the appropriate text_pattern_ops index as you would for the built-in pattern matching.

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