Two aspects of performance are discussed below: memory usage and pro-
cessing time. The way you express your pattern as a regular expression
can affect both of them.
COMPILED PATTERN MEMORY USAGE
Patterns are compiled by PCRE2 into a reasonably efficient interpretive
code, so that most simple patterns do not use much memory for storing
the compiled version. However, there is one case where the memory usage
of a compiled pattern can be unexpectedly large. If a parenthesized
group has a quantifier with a minimum greater than 1 and/or a limited
maximum, the whole group is repeated in the compiled code. For example,
is compiled as if it were
(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 par-
ticularly if such repetitions are nested, the memory usage can become
an embarrassment. For example, the very simple pattern
uses over 50KiB when compiled using the 8-bit library. When PCRE2 is
compiled with its default internal pointer size of two bytes, the size
limit on a compiled pattern is 65535 code units in the 8-bit and 16-bit
libraries, and this is reached with the above pattern if the outer rep-
etition is increased from 3 to 4. PCRE2 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 PCRE2's "subroutine" facility. Re-writing the above pattern as
reduces the memory requirements to around 16KiB, and indeed it remains
under 20KiB even with the outer repetition increased to 100. However,
this kind of pattern is not always exactly equivalent, because any cap-
tures within subroutine calls are lost when the subroutine completes.
If this is not a problem, this kind of rewriting will allow you to
process patterns that PCRE2 cannot otherwise handle. The matching per-
formance of the two different versions of the pattern are roughly the
same. (This applies from release 10.30 - things were different in ear-
also reduce the memory requirements.
In contrast to pcre2_match(), pcre2_dfa_match() does use recursive
function calls, but only for processing atomic groups, lookaround
assertions, and recursion within the pattern. The original version of
the code used to allocate quite large internal workspace vectors on the
stack, which caused some problems for some patterns in environments
with small stacks. From release 10.32 the code for pcre2_dfa_match()
has been re-factored to use heap memory when necessary for internal
workspace when recursing, though recursive function calls are still
The "match depth" parameter can be used to limit the depth of function
recursion, and the "match heap" parameter to limit heap memory in
Certain items in regular expression patterns are processed more effi-
ciently 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 PCRE2.
Using Unicode character properties (the \p, \P, and \X escapes) is
slow, because PCRE2 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 the PCRE2_UCP option or start the pattern with
(*UCP) if you want Unicode character properties to be used. This can
double the matching time for items such as \d, when matched with
pcre2_match(); the performance loss is less with a DFA matching func-
tion, and in both cases there is not much difference for \b.
When a pattern begins with .* not in atomic parentheses, nor in paren-
theses that are the subject of a backreference, and the PCRE2_DOTALL
option is set, the pattern is implicitly anchored by PCRE2, since it
can match only at the start of a subject string. If the pattern has
multiple top-level branches, they must all be anchorable. The optimiza-
tion can be disabled by the PCRE2_NO_DOTSTAR_ANCHOR option, and is
automatically disabled if the pattern contains (*PRUNE) or (*SKIP).
If PCRE2_DOTALL is not set, PCRE2 cannot make this optimization,
because the dot metacharacter does not then match a newline, and if the
subject string contains newlines, the pattern may match from the char-
acter immediately following one of them instead of from the very start.
For example, the pattern
ject 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
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, PCRE2 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
where a literal character follows. Before embarking on the standard
matching procedure, PCRE2 checks that there is a "b" later in the sub-
ject string, and if there is not, it fails the match immediately. How-
ever, when there is no following literal this optimization cannot be
used. You can see the difference by comparing the behaviour of
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. This can often reduce mem-
ory requirements as well. As another example, consider this pattern:
It matches from wherever it starts until it encounters "<inet" or the
end of the data, and is the kind of pattern that might be used when
processing an XML file. Each iteration of the outer parentheses matches
either one character that is not "<" or a "<" that is not followed by
"inet". However, each time a parenthesis is processed, a backtracking
position is passed, so this formulation uses a memory frame for each
matched character. For a long string, a lot of memory is required. Con-
sider now this rewritten pattern, which matches exactly the same
This runs much faster, because sequences of characters that do not con-
tain "<" are "swallowed" in one item inside the parentheses, and a pos-
sessive quantifier is used to stop any backtracking into the runs of
non-"<" characters. This version also uses a lot less memory because
values of the limits are very large, and unlikely ever to operate. They
can be changed when PCRE2 is built, and they can also be set when
pcre2_match() or pcre2_dfa_match() is called. For details of these
interfaces, see the pcre2build documentation and the section entitled
"The match context" in the pcre2api documentation.
The pcre2test test program has a modifier called "find_limits" which,
if applied to a subject line, causes it to find the smallest limits
that allow a pattern to match. This is done by repeatedly matching with
University Computing Service
Last updated: 03 February 2019
Copyright (c) 1997-2019 University of Cambridge.
PCRE2 10.33 03 February 2019 PCRE2PERFORM(3)
Man Pages Copyright Respective Owners. Site Copyright (C) 1994 - 2020
All Rights Reserved.