How to optimize the performance of Python regular expression?

Introduction

A regular expressions -specific built-in library named re exists in Python. You only need to import it to use its features (such as search, match, findall, etc.). They'll provide you back a Match object with helpful techniques for modifying your outcomes.

According to Wikipedia, regular expressions (also known as regexp) are collections of characters that specify a search pattern. It is a tool that enables you to filter, extract, or alter a series of characters. It has also been discovered that regular expressions function more quickly when the "in" operator is used.

Regular expressions have performance difficulties and are generally difficult to debug and maintain. To improve their performance, these problems must be resolved.

Example

import re
my_string = 'I like tortilla.'
# 'like (\w+)' is a regexp that matches 'like' followed by a space followed by
# any number of word characters ('\w' means 'word character' and '+' means '1 or more'),
# putting those word characters in a capture group (the parenthesis)
regex_result = re.search(r'like (\w+)', my_string)
print(regex_result.groups())

Output

('tortilla',)

The Soundex function first checks whether the input is a non-empty string of letters. What's the best way to do this?

If you answered “regular expressions,” go sit in the corner and contemplate your bad instincts. Regular expressions are rarely the right answer; they should be avoided whenever possible. Not only for performance reasons but simply because they're difficult to debug and maintain. Also, for performance reasons.

This code fragment from soundex/stage1/soundex1a.py checks whether the function argument source is a word made entirely of letters, with at least one letter (not the empty string) −

Why is regular expression efficiency important?

A poorly designed regular expression may take a long time to execute and severely slow down the system, even if a well-crafted regular expression might be quite effective. BMC Discovery has undergone several upgrades to make it more resistant to ineffective regular expressions than earlier iterations.

When applied to modestly big strings, it is quite conceivable to design a regular expression that will take hours, days or even the whole existence of the universe to complete. Additionally, it distributes the effort of executing TPL patterns among several processors so that the others may continue working even if one is occupied with a lengthy regular expression match.

Anatomy of an inefficient regular expression

So, how can you create a common phrase that is ineffective? One issue is when the regular expression backtracks too far; this might happen if the regular expression has several repetition operators. +, *, or n, m are examples of repetition operators. The regular expression must loop back and try any other potential partial matches in case any of them succeed if it makes a partial match but fails later.

Consider matching the string abc abc abc with the regular expression a.*b.*cd as an example. Since the string contains no d, the match will never be successful. However, before giving up, the regular expression must still exhaust all possibilities for the letter combinations a, b, and c.

"*abc* abc abc",

"*ab*c ab*c* abc",
"*ab*c abc ab*c*",
"*a*bc a*bc* abc",
"*a*bc a*b*c ab*c*",
"*a*bc abc a*bc*",
"abc *abc* abc",
"abc *ab*c ab*c*",
"abc *a*bc a*bc*",
"abc abc *abc*"

As a rough guide, the number of comparisons that the regular expression needs to perform is proportional to the length of the string times the number of possible intermediate matches.

In this example using the non-greedy operators, that is, a.*?b.*?cd, makes no difference to the number of matches it will make since the regular expression engine still needs to try every combination.>

Guidelines for writing efficient regular expressions

Think about potential failure situations

The issues arise when a regular expression fails to match entirely, yet there are several partial matches, as the preceding instances demonstrate. It is important to think about how a regular expression operates when it fails and what occurs when it succeeds while writing one.

Try to fail fast

If the regular expression reaches a point where it cannot possibly match the desired target, try to make the entire regular expression fail.

Profile - especially the failure cases

To ensure that your regular expression matches what you anticipate, it is crucial to verify it. However, it's also crucial to evaluate the efficiency of your regular expression against lengthy strings that only partially match it, such as a megabyte-long string of random letters.

Do not use groups unless necessary

When you use parenthesis to surround a portion of a regular expression, the regular expression engine has to work harder to preserve the text matched by the group in case it is required later. The matching process may be slowed down as a result, sometimes by a factor of four or more.

You can use the non-grouping variant of parentheses (?:) if you need to use parentheses but do not need to utilize the group's contents, such as when a portion of a regular expression is repeated.

Conclusion

Some would counter that Pandas are superior for these kinds of operations. However, I don't believe it would be as quick as the pure Python version we built because it would take much longer to load the dataset into a DataFrame.

Other options, like using the regex library or dividing the data into multiple parts and counting in parallel, could speed things up even more (a strategy related to map-reduce, a highly relevant algorithm in Big Data).