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This page is part of the PCRE HTML documentation. It was generated automatically from the original man page. If there is any nonsense in it, please consult the man page, in case the conversion went wrong. PCRE PERFORMANCE
Two aspects of performance are discussed below: memory usage and processing time. The way you express your pattern as a regular expression can affect both of them.
Patterns are compiled by PCRE into a reasonably efficient interpretive code, so that most simple patterns do not use much memory. However, there is one case where the memory usage of a compiled pattern can be unexpectedly large. If a parenthesized subpattern has a quantifier with a minimum greater than 1 and/or a limited maximum, the whole subpattern is repeated in the compiled code. For example, the pattern
(abc|def){2,4}
(abc|def)(abc|def)((abc|def)(abc|def)?)?
For regular expressions whose quantifiers use only small numbers, this is not usually a problem. However, if the numbers are large, and particularly if such repetitions are nested, the memory usage can become an embarrassment. For example, the very simple pattern
((ab){1,1000}c){1,3}
One way of reducing the memory usage for such patterns is to make use of PCRE's "subroutine" facility. Re-writing the above pattern as
((ab)(?2){0,999}c)(?1){0,2}
When pcre_exec() or pcre[16|32]_exec() is used for matching, certain kinds of pattern can cause it to use large amounts of the process stack. In some environments the default process stack is quite small, and if it runs out the result is often SIGSEGV. This issue is probably the most frequently raised problem with PCRE. Rewriting your pattern can often help. The pcrestack documentation discusses this issue in detail.
Certain items in regular expression patterns are processed more efficiently than others. It is more efficient to use a character class like [aeiou] than a set of single-character alternatives such as (a|e|i|o|u). In general, the simplest construction that provides the required behaviour is usually the most efficient. Jeffrey Friedl's book contains a lot of useful general discussion about optimizing regular expressions for efficient performance. This document contains a few observations about PCRE.
Using Unicode character properties (the \p, \P, and \X escapes) is slow, because PCRE has to use a multi-stage table lookup whenever it needs a character's property. If you can find an alternative pattern that does not use character properties, it will probably be faster.
By default, the escape sequences \b, \d, \s, and \w, and the POSIX character classes such as [:alpha:] do not use Unicode properties, partly for backwards compatibility, and partly for performance reasons. However, you can set PCRE_UCP if you want Unicode character properties to be used. This can double the matching time for items such as \d, when matched with a traditional matching function; the performance loss is less with a DFA matching function, and in both cases there is not much difference for \b.
When a pattern begins with .* not in parentheses, or in parentheses that are not the subject of a backreference, and the PCRE_DOTALL option is set, the pattern is implicitly anchored by PCRE, since it can match only at the start of a subject string. However, if PCRE_DOTALL is not set, PCRE cannot make this optimization, because the . metacharacter does not then match a newline, and if the subject string contains newlines, the pattern may match from the character immediately following one of them instead of from the very start. For example, the pattern
.*second
If you are using such a pattern with subject strings that do not contain newlines, the best performance is obtained by setting PCRE_DOTALL, or starting the pattern with ^.* or ^.*? to indicate explicit anchoring. That saves PCRE from having to scan along the subject looking for a newline to restart at.
Beware of patterns that contain nested indefinite repeats. These can take a long time to run when applied to a string that does not match. Consider the pattern fragment
^(a+)*
An optimization catches some of the more simple cases such as
(a+)*b
(a+)*\d
In many cases, the solution to this kind of performance issue is to use an atomic group or a possessive quantifier.
Philip Hazel University Computing Service Cambridge CB2 3QH, England.
Last updated: 25 August 2012 Copyright © 1997-2012 University of Cambridge.