NAME
PCRE - Perl-compatible regular expressions
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.
COMPILED PATTERN MEMORY USAGE
Patterns are compiled by PCRE into a reasonably efficient byte 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}
is compiled as if it were
(abc|def)(abc|def)((abc|def)(abc|def)?)?
(Technical aside: It is done this way so that backtrack points within
each of the repetitions can be independently maintained.)
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}
uses 51K bytes when compiled. When PCRE is compiled with its default
internal pointer size of two bytes, the size limit on a compiled
pattern is 64K, and this is reached with the above pattern if the outer
repetition is increased from 3 to 4. PCRE can be compiled to use larger
internal pointers and thus handle larger compiled patterns, but it is
better to try to rewrite your pattern to use less memory if you can.
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}
reduces the memory requirements to 18K, and indeed it remains under 20K
even with the outer repetition increased to 100. However, this pattern
is not exactly equivalent, because the "subroutine" calls are treated
as atomic groups into which there can be no backtracking if there is a
subsequent matching failure. Therefore, PCRE cannot do this kind of
rewriting automatically. Furthermore, there is a noticeable loss of
speed when executing the modified pattern. Nevertheless, if the atomic
grouping is not a problem and the loss of speed is acceptable, this
kind of rewriting will allow you to process patterns that PCRE cannot
otherwise handle.
STACK USAGE AT RUN TIME
When pcre_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.
PROCESSING TIME
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 scan a structure that contains data for over
fifteen thousand characters 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.
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
matches the subject "first\nand second" (where \n stands for a newline
character), with the match starting at the seventh character. In order
to do this, PCRE has to retry the match starting after every newline in
the subject.
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+)*
This can match "aaaa" in 16 different ways, and this number increases
very rapidly as the string gets longer. (The * repeat can match 0, 1,
2, 3, or 4 times, and for each of those cases other than 0 or 4, the +
repeats can match different numbers of times.) When the remainder of
the pattern is such that the entire match is going to fail, PCRE has in
principle to try every possible variation, and this can take an
extremely long time, even for relatively short strings.
An optimization catches some of the more simple cases such as
(a+)*b
where a literal character follows. Before embarking on the standard
matching procedure, PCRE checks that there is a "b" later in the
subject string, and if there is not, it fails the match immediately.
However, when there is no following literal this optimization cannot be
used. You can see the difference by comparing the behaviour of
(a+)*\d
with the pattern above. The former gives a failure almost instantly
when applied to a whole line of "a" characters, whereas the latter
takes an appreciable time with strings longer than about 20 characters.
In many cases, the solution to this kind of performance issue is to use
an atomic group or a possessive quantifier.
AUTHOR
Philip Hazel
University Computing Service
Cambridge CB2 3QH, England.
REVISION
Last updated: 07 March 2010
Copyright (c) 1997-2010 University of Cambridge.