Table of Contents for
Regular Expressions Cookbook, 2nd Edition

Version ebook / Retour

Cover image for bash Cookbook, 2nd Edition Regular Expressions Cookbook, 2nd Edition by Steven Levithan Published by O'Reilly Media, Inc., 2012
  1. Cover
  2. Regular Expressions Cookbook
  3. Preface
  4. Caught in the Snarls of Different Versions
  5. Intended Audience
  6. Technology Covered
  7. Organization of This Book
  8. Conventions Used in This Book
  9. Using Code Examples
  10. Safari® Books Online
  11. How to Contact Us
  12. Acknowledgments
  13. 1. Introduction to Regular Expressions
  14. Regular Expressions Defined
  15. Search and Replace with Regular Expressions
  16. Tools for Working with Regular Expressions
  17. 2. Basic Regular Expression Skills
  18. 2.1. Match Literal Text
  19. 2.2. Match Nonprintable Characters
  20. 2.3. Match One of Many Characters
  21. 2.4. Match Any Character
  22. 2.5. Match Something at the Start and/or the End of a Line
  23. 2.6. Match Whole Words
  24. 2.7. Unicode Code Points, Categories, Blocks, and Scripts
  25. 2.8. Match One of Several Alternatives
  26. 2.9. Group and Capture Parts of the Match
  27. 2.10. Match Previously Matched Text Again
  28. 2.11. Capture and Name Parts of the Match
  29. 2.12. Repeat Part of the Regex a Certain Number of Times
  30. 2.13. Choose Minimal or Maximal Repetition
  31. 2.14. Eliminate Needless Backtracking
  32. 2.15. Prevent Runaway Repetition
  33. 2.16. Test for a Match Without Adding It to the Overall Match
  34. 2.17. Match One of Two Alternatives Based on a Condition
  35. 2.18. Add Comments to a Regular Expression
  36. 2.19. Insert Literal Text into the Replacement Text
  37. 2.20. Insert the Regex Match into the Replacement Text
  38. 2.21. Insert Part of the Regex Match into the Replacement Text
  39. 2.22. Insert Match Context into the Replacement Text
  40. 3. Programming with Regular Expressions
  41. Programming Languages and Regex Flavors
  42. 3.1. Literal Regular Expressions in Source Code
  43. 3.2. Import the Regular Expression Library
  44. 3.3. Create Regular Expression Objects
  45. 3.4. Set Regular Expression Options
  46. 3.5. Test If a Match Can Be Found Within a Subject String
  47. 3.6. Test Whether a Regex Matches the Subject String Entirely
  48. 3.7. Retrieve the Matched Text
  49. 3.8. Determine the Position and Length of the Match
  50. 3.9. Retrieve Part of the Matched Text
  51. 3.10. Retrieve a List of All Matches
  52. 3.11. Iterate over All Matches
  53. 3.12. Validate Matches in Procedural Code
  54. 3.13. Find a Match Within Another Match
  55. 3.14. Replace All Matches
  56. 3.15. Replace Matches Reusing Parts of the Match
  57. 3.16. Replace Matches with Replacements Generated in Code
  58. 3.17. Replace All Matches Within the Matches of Another Regex
  59. 3.18. Replace All Matches Between the Matches of Another Regex
  60. 3.19. Split a String
  61. 3.20. Split a String, Keeping the Regex Matches
  62. 3.21. Search Line by Line
  63. Construct a Parser
  64. 4. Validation and Formatting
  65. 4.1. Validate Email Addresses
  66. 4.2. Validate and Format North American Phone Numbers
  67. 4.3. Validate International Phone Numbers
  68. 4.4. Validate Traditional Date Formats
  69. 4.5. Validate Traditional Date Formats, Excluding Invalid Dates
  70. 4.6. Validate Traditional Time Formats
  71. 4.7. Validate ISO 8601 Dates and Times
  72. 4.8. Limit Input to Alphanumeric Characters
  73. 4.9. Limit the Length of Text
  74. 4.10. Limit the Number of Lines in Text
  75. 4.11. Validate Affirmative Responses
  76. 4.12. Validate Social Security Numbers
  77. 4.13. Validate ISBNs
  78. 4.14. Validate ZIP Codes
  79. 4.15. Validate Canadian Postal Codes
  80. 4.16. Validate U.K. Postcodes
  81. 4.17. Find Addresses with Post Office Boxes
  82. 4.18. Reformat Names From “FirstName LastName” to “LastName, FirstName”
  83. 4.19. Validate Password Complexity
  84. 4.20. Validate Credit Card Numbers
  85. 4.21. European VAT Numbers
  86. 5. Words, Lines, and Special Characters
  87. 5.1. Find a Specific Word
  88. 5.2. Find Any of Multiple Words
  89. 5.3. Find Similar Words
  90. 5.4. Find All Except a Specific Word
  91. 5.5. Find Any Word Not Followed by a Specific Word
  92. 5.6. Find Any Word Not Preceded by a Specific Word
  93. 5.7. Find Words Near Each Other
  94. 5.8. Find Repeated Words
  95. 5.9. Remove Duplicate Lines
  96. 5.10. Match Complete Lines That Contain a Word
  97. 5.11. Match Complete Lines That Do Not Contain a Word
  98. 5.12. Trim Leading and Trailing Whitespace
  99. 5.13. Replace Repeated Whitespace with a Single Space
  100. 5.14. Escape Regular Expression Metacharacters
  101. 6. Numbers
  102. 6.1. Integer Numbers
  103. 6.2. Hexadecimal Numbers
  104. 6.3. Binary Numbers
  105. 6.4. Octal Numbers
  106. 6.5. Decimal Numbers
  107. 6.6. Strip Leading Zeros
  108. 6.7. Numbers Within a Certain Range
  109. 6.8. Hexadecimal Numbers Within a Certain Range
  110. 6.9. Integer Numbers with Separators
  111. 6.10. Floating-Point Numbers
  112. 6.11. Numbers with Thousand Separators
  113. 6.12. Add Thousand Separators to Numbers
  114. 6.13. Roman Numerals
  115. 7. Source Code and Log Files
  116. Keywords
  117. Identifiers
  118. Numeric Constants
  119. Operators
  120. Single-Line Comments
  121. Multiline Comments
  122. All Comments
  123. Strings
  124. Strings with Escapes
  125. Regex Literals
  126. Here Documents
  127. Common Log Format
  128. Combined Log Format
  129. Broken Links Reported in Web Logs
  130. 8. URLs, Paths, and Internet Addresses
  131. 8.1. Validating URLs
  132. 8.2. Finding URLs Within Full Text
  133. 8.3. Finding Quoted URLs in Full Text
  134. 8.4. Finding URLs with Parentheses in Full Text
  135. 8.5. Turn URLs into Links
  136. 8.6. Validating URNs
  137. 8.7. Validating Generic URLs
  138. 8.8. Extracting the Scheme from a URL
  139. 8.9. Extracting the User from a URL
  140. 8.10. Extracting the Host from a URL
  141. 8.11. Extracting the Port from a URL
  142. 8.12. Extracting the Path from a URL
  143. 8.13. Extracting the Query from a URL
  144. 8.14. Extracting the Fragment from a URL
  145. 8.15. Validating Domain Names
  146. 8.16. Matching IPv4 Addresses
  147. 8.17. Matching IPv6 Addresses
  148. 8.18. Validate Windows Paths
  149. 8.19. Split Windows Paths into Their Parts
  150. 8.20. Extract the Drive Letter from a Windows Path
  151. 8.21. Extract the Server and Share from a UNC Path
  152. 8.22. Extract the Folder from a Windows Path
  153. 8.23. Extract the Filename from a Windows Path
  154. 8.24. Extract the File Extension from a Windows Path
  155. 8.25. Strip Invalid Characters from Filenames
  156. 9. Markup and Data Formats
  157. Processing Markup and Data Formats with Regular Expressions
  158. 9.1. Find XML-Style Tags
  159. 9.2. Replace Tags with
  160. 9.3. Remove All XML-Style Tags Except and
  161. 9.4. Match XML Names
  162. 9.5. Convert Plain Text to HTML by Adding

    and
    Tags

  163. 9.6. Decode XML Entities
  164. 9.7. Find a Specific Attribute in XML-Style Tags
  165. 9.8. Add a cellspacing Attribute to Tags That Do Not Already Include It
  166. 9.9. Remove XML-Style Comments
  167. 9.10. Find Words Within XML-Style Comments
  168. 9.11. Change the Delimiter Used in CSV Files
  169. 9.12. Extract CSV Fields from a Specific Column
  170. 9.13. Match INI Section Headers
  171. 9.14. Match INI Section Blocks
  172. 9.15. Match INI Name-Value Pairs
  173. Index
  174. Index
  175. Index
  176. Index
  177. Index
  178. Index
  179. Index
  180. Index
  181. Index
  182. Index
  183. Index
  184. Index
  185. Index
  186. Index
  187. Index
  188. Index
  189. Index
  190. Index
  191. Index
  192. Index
  193. Index
  194. Index
  195. Index
  196. Index
  197. Index
  198. Index
  199. About the Authors
  200. Colophon
  201. Copyright
  202. 2.14. Eliminate Needless Backtracking

    Problem

    The previous recipe explains the difference between greedy and lazy quantifiers, and how they backtrack. In some situations, this backtracking is unnecessary.

    \b\d+\b uses a greedy quantifier, and \b\d+?\b uses a lazy quantifier. They both match the same thing: an integer. Given the same subject text, both will find the exact same matches. Any backtracking that is done is unnecessary. Rewrite this regular expression to explicitly eliminate all backtracking, making the regular expression more efficient.

    Solution

    \b\d++\b
    Regex options: None
    Regex flavors: Java, PCRE, Perl 5.10, Ruby 1.9

    The easiest solution is to use a possessive quantifier. But it is supported only in a few recent regex flavors.

    \b(?>\d+)\b
    Regex options: None
    Regex flavors: .NET, Java, PCRE, Perl, Ruby

    An atomic group provides exactly the same functionality, using a slightly less readable syntax. Support for atomic grouping is more widespread than support for possessive quantifiers.

    JavaScript and Python do not support possessive quantifiers or atomic grouping. There is no way to eliminate needless backtracking with these two regex flavors.

Discussion

A possessive quantifier is similar to a greedy quantifier: it tries to repeat as many times as possible. The difference is that a possessive quantifier will never give back, not even when giving back is the only way that the remainder of the regular expression could match. Possessive quantifiers do not keep backtracking positions.

You can make any quantifier possessive by placing a plus sign after it. For example, *+, ++, ?+, and {7,42}+ are all possessive quantifiers.

Possessive quantifiers are supported by Java 4 and later, the first Java release to include the java.util.regex package. All versions of PCRE discussed in this book (version 4 to 7) support possessive quantifiers. Perl supports them starting with Perl 5.10. Classic Ruby regular expressions do not support possessive quantifiers, but the Oniguruma engine, which is the default in Ruby 1.9, does support them.

Wrapping a greedy quantifier inside an atomic group has the exact same effect as using a possessive quantifier. When the regex engine exits the atomic group, all backtracking positions remembered by quantifiers and alternation inside the group are thrown away. The syntax is (?>), where is any regular expression. An atomic group is essentially a noncapturing group, with the extra job of refusing to backtrack. The question mark is not a quantifier; the opening bracket simply consists of the three characters (?>.

When you apply the regex \b\d++\b (possessive) to 123abc 456, \b matches at the start of the subject, and \d++ matches 123. So far, this is no different from what \b\d+\b (greedy) would do. But then the second \b fails to match between 3 and a.

The possessive quantifier did not store any backtracking positions. Since there are no other quantifiers or alternation in this regular expression, there are no further options to try when the second word boundary fails. The regex engine immediately declares failure for the match attempt starting at 1.

The regex engine does attempt the regex starting at the next character positions in the string, and using a possessive quantifier does not change that. If the regex must match the whole subject, use anchors, as discussed in Recipe 2.5. Eventually, the regex engine will attempt the regex starting at the 4 and find the match 456.

The difference with the greedy quantifier is that when the second \b fails during the first match attempt, the greedy quantifier will backtrack. The regex engine will then (needlessly) test \b between 2 and 3, and between 1 and 2.

The matching process using atomic grouping is essentially the same. When you apply the regex \b(?>\d+)\b (possessive) to 123abc 456, the word boundary matches at the start of the subject. The regex engine enters the atomic group, and \d+ matches 123. Now the engine exits the atomic group. At this point, the backtracking positions remembered by \d+ are thrown away. When the second \b fails, the regex engine is left without any further options, causing the match attempt to fail immediately. As with the possessive quantifier, eventually 456 will be found.

We describe the possessive quantifier as failing to remember backtracking positions, and the atomic group as throwing them away. This makes it easier to understand the matching process, but don’t get hung up on the difference, as it may not even exist in the regex flavor you’re working with. In many flavors, x++ is merely syntactic sugar for (?>x+), and both are implemented in exactly the same way. Whether the engine never remembers backtracking positions or throws them away later is irrelevant for the final outcome of the match attempt.

Where possessive quantifiers and atomic grouping differ is that a possessive quantifier applies only to a single regular expression token, whereas an atomic group can wrap a whole regular expression.

\w++\d++ and (?>\w+\d+) are not the same at all. \w++\d++, which is the same as (?>\w+)(?>\d+), will not match abc123. \w++ matches abc123 entirely. Then, the regex engine attempts \d++ at the end of the subject text. Since there are no further characters that can be matched, \d++ fails. Without any remembered backtracking positions, the match attempt fails.

(?>\w+\d+) has two greedy quantifiers inside the same atomic group. Within the atomic group, backtracking occurs normally. Backtracking positions are thrown away only when the engine exits the whole group. When the subject is abc123, \w+ matches abc123. The greedy quantifier does remember backtracking positions. When \d+ fails to match, \w+ gives up one character. \d+ then matches 3. Now, the engine exits the atomic group, throwing away all backtracking positions remembered for \w+ and \d+. Since the end of the regex has been reached, this doesn’t really make any difference. An overall match is found.

If the end had not been reached, as in (?>\w+\d+)\d+, we would be in the same situation as with \w++\d++. The second \d+ has nothing left to match at the end of the subject. Since the backtracking positions were thrown away, the regex engine can only declare failure.

Possessive quantifiers and atomic grouping don’t just optimize regular expressions. They can alter the matches found by a regular expression by eliminating those that would be reached through backtracking.

This recipe shows how to use possessive quantifiers and atomic grouping to make minor optimizations, which may not even show any difference in benchmarks. The next recipe will showcase how atomic grouping can make a dramatic difference.

See Also

Recipe 2.12 shows the different alternation operators supported by regular expressions.

Recipe 2.15 explains how to make sure the regex engine doesn’t needlessly try different ways of matching a group.