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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 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, 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 PCRE2's "subroutine" facility. Re-writing the above pattern as
((ab)(?2){0,999}c)(?1){0,2}
From release 10.30, the interpretive (non-JIT) version of pcre2_match() uses very little system stack at run time. In earlier releases recursive function calls could use a great deal of stack, and this could cause problems, but this usage has been eliminated. Backtracking positions are now explicitly remembered in memory frames controlled by the code. An initial 20KiB vector of frames is allocated on the system stack (enough for about 100 frames for small patterns), but if this is insufficient, heap memory is used. The amount of heap memory can be limited; if the limit is set to zero, only the initial stack vector is used. Rewriting patterns to be time-efficient, as described below, may 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 used.
The "match depth" parameter can be used to limit the depth of function recursion, and the "match heap" parameter to limit heap memory in pcre2_dfa_match().
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 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 function, and in both cases there is not much difference for \b.
When a pattern begins with .* not in atomic parentheses, nor in parentheses 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 optimization 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 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 PCRE2_DOTALL, or starting the pattern with ^.* or ^.*? to indicate explicit anchoring. That saves PCRE2 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. This can often reduce memory requirements as well. As another example, consider this pattern:
([^<]|<(?!inet))+
([^<]++|<(?!inet))+
This example shows that one way of optimizing performance when matching long subject strings is to write repeated parenthesized subpatterns to match more than one character whenever possible.
You can set limits on the amount of processing that takes place when matching, and on the amount of heap memory that is used. The default 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 different limits.
Philip Hazel University Computing Service Cambridge, England.
Last updated: 03 February 2019 Copyright © 1997-2019 University of Cambridge.