Enhancing Regex Performance

Enhancing Regex Performance: 3 Unrivaled Techniques You Must Know

Regex (regular expression or rational expression) is a sequence of characters specifying a search pattern in the text. Typically, these patterns are used by string-searching algorithms to find and replace or find operations on strings.

Google Analytics uses Regex in URL matching in supporting search and replace in Microsoft Word, Google Docs, Brackets, Notepad++, Sublime, and other popular editors. However, when writing regular expressions, one has to think about the asterisk symbol (*), repeaters symbols, the plus symbol, etc.

If you are a data analyst, developer, or marketer and need to speed up your work with Regex, you need to select a good tool. For instance, a sophisticated and handy Regex tester for Mac is Expressions. The tool is designed to save a lot of time when it comes to testing and crafting your code. The program also comes in handy when you need to debug your expressions and perform powerful searches. If you are working on Windows, you can check out Regex 101. These tools can make your work easier and augment productivity.

Now, if you seek to enhance Regex performance, a few techniques can do the trick. It would be best to try each of the techniques to find out which one offers the most performance boost and reduces processing time.

  • Possessive quantifiers and atomic groups

Atomic groups (denoted with ?>…) and possessive quantifiers (denoted with +) perform the same function. They don’t let go once they have consumed text. It is excellent for performance reasons because it cuts down on the backtracking that regular expressions wouldn’t do so much of.

Typically, you might find it challenging to get a use case where automatic groups might be a game-changer because the primary performance heavy hitter is the .* that results in backtracking.

You can eliminate all the backtracking by modifying .* to .*+ and making it possessive. However, it would be impossible to match anything else after because + doesn’t give back any text. Therefore, your regular expression must be reasonably accurate to use atomic groups, and hence, your performance boost will be incremental.

However, the possessive quantifier might surprisingly be useful to you. For instance, consider ^(\d{1,3}+\.\d{1,3}+\.\d{1,3}+\.\d{1,3}+).* and ^(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}).* to match an IPv4 address on 9.21.2015 non matching text that kind of matches and 107.21.20.1 – – [07/Dec/2012:18:55:53 -0500] “GET /” 200 2144.

Suppose you try to match the non-matching text, the regular expression devoid of the possessive quantifier results in consuming the first few characters. Since there’s no match, all the characters are backtracked in the hope of finding a match.

If there’s a possessive quantifier, the Regex stops looking and does not backtrack if it doesn’t find a match.

  • Character classes

Character classes are the most vital thing to remember when crafting performant regular expressions. Character classes determine what character you are not trying or trying to match. It would be better if you could be as accurate as possible. It would be best if you tried to replace . in your .*s with something more accurate because the .* will shoot at the end of the line and backtrack.

Using a specific character class will provide you with control over the characters the * will cause the regular expression engine to consume. Therefore, you will be able to prevent backtracking.

  • Order matters

The order of alternation matters. It refers to instances where a regular expression has more than two valid options, and the | character separates it. Furthermore, order matters when you have several lookbehinds or lookaheads. It is crucial to place each option so that it reduces the work required for the regex engine to do.

When it comes to alternations, you would want the standard option first and then the rarer options. If you end up placing the rarer options first, it will result in the regular expression engine wasting its time by checking those options before it moves on to check the standard options. However, if there are several lookbehinds and lookaheads, you would want the rarest options to be in the forefront. The Regex will fail if you start with the options that are not likely to match.

While this technique is similar to micro-optimization, it may provide you with a decent boost in productivity.

The bottom line

These are some of the techniques that you need to know when it comes to augmenting the performance of the regular expression. However, to find the technique that works best for you, it is imperative to experiment with all three. Also, don’t forget to use a good Regex tester to help save you time and accentuate processing. It will allow you to check for bugs and eliminate them quickly.

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