Veit's Blog

Regular Expressions Made Simple


Because I enjoyed writing my last blog post so much, I decided to give this format another shot. One of the reviewers pointed me to a chapter of Beautiful Code that details the implementation of what is possibly the most simple regular expression engine ever, written by Rob Pike for educational purposes. The chapter is written by Brian Kernighan, and it's a promising preview for the book.

As in the previous post, the algorithm is suprisingly compact. It's originally written in C, and the code is very nice to look at. I've deviced to transcribe it into Scheme for the purposes of this blog post. If you are familiar with C and aren't interested in Lisp I urge you to read the original article, because it is very illuminating. If you're up for another round of parenthetical goodness, though, I'm happy to provide it!

The complete code used in this blog post can be downloaded here.

What is it & how can I use it?

This fairly restricted regular expression engine tries to emulate grep without the -E option provided, i.e. without enabling its extended regular expression engine. Figure 1 shows which special characters are provided. An important difference between most regular expression engines and this one is that the * wildcard matches lazily. This stays true to the algorithm shown in the book.

^: matches the start of the string
$: matches the end of the string
.: matches any one character
*: matches zero or more occurrences of the
   character that precedes it, lazily

Fig. 1: The meta-characters provided by the algorithm.

You might ask yourself how the algorithm handles escaping. The short answer: it doesn't. This means that literal matches of any of the characters are impossible. This is most likely due to the pedagogical intention of the implementation, as escaping would unnecessarily complexify the algorithm.

The interface provides a single function called match that takes a regular expression and a string to match. It will return a boolean that signals whether the match was successful. Figure 2 showcases the API by example.

(match ".*md" "")       ; => true
(match ".*md" "i_am_not_markdown.html") ; => false
(match "^...chron" "anachronism")       ; => true
(match "^...chron" "parachronism")      ; => false
(match "^...chron$" "anachronism")      ; => false

Fig. 2: Example usage of the algorithm.

While this is by far not a complete implementation of a regular expression engine, it is useful enough to be interesting. This makes for a compelling code golf exercise, and Rob Pike did a great job in keeping it terse yet readable. In this blog post we will try and do the same for Scheme. The source code is available here.

The implemenetation

Before we start I have to warn you that this implementation is not R5RS-compliant, meaning it will most likely not run on many implementations of Scheme–it does run on zepto, though. The reason why I didn't restrict myself to standard Scheme is pure convenience: car, cdr, and length in zepto are generic functions that accept any type that implements the traversable-collec protocol. Protocols in zepto are fairly interesting (and I might write a blog post about them in the future). However, for the purpose of this blog post it's sufficient to say that car will return the first character of the string while cdr will return the string from the second character onwards. length will return the length of the string.

The following definitions could serve as a drop-in replacement:

; substitute all occurrences of car with string-car
(define (string-car s) (string-ref s 0))

; substitute all occurrences of cdr with string-cdr
(define (string-cdr s) (substring s 1 (length s)))

; substitute all occurrences of length with string-length

Fig. 3: Drop-in replacements for R5RS Scheme.

With this out of the way, let's implement the top-level function match.

(define (match regexp text)
  (if (eq? (car regexp) #\^)
    (match-here (cdr regexp) text)
    (let loop ((text text))
        ((null? text) false)
        ((match-here regexp text) true)
        (else (loop (cdr text)))))))

Fig. 4: The top-level matching function.

First we test whether the string begins with a ^ character, which forces the match to begin at the start of the input. We defer the actual matching to a function called match-here, which, as we now postulate, will return a boolean signalling whether a match was found at exactly this location in the string. We strip off the first character before passing it over because we've already treated it, it's the ^.

If there is no such character we try to apply match-here in a loop that slices the first character of the text off progressively until we either run out of text to match, in which case we return false, or we find a match.

(define (match-here regexp text)
    ((null? regexp) #t)
    ((and (> (length regexp) 1)
          (eq? (cadr regexp) #\*))
      (match-star (car regexp) (cddr regexp) text))
    ((and (eq? (car regexp) #\$)
          (eq? (length regexp) 1))
      (null? text))
    ((and (not (null? text))
          (or (eq? (car regexp) #\.)
              (eq? (car regexp) (car text))))
      (match-here (cdr regexp) (cdr text)))
    (else #f)))
Fig. 5: The match-here function.

The match-here function is the bread-and-butter function of our algorithm. It deals with most of the special cases and the regular case, and does most of the heavy lifting.

We dispatch on a number of case, in descending order:

We get all of that logic in a handful of lines, which is pretty neat, if I do say so myself. We almost have a working regular expression engine now. All we have left to do is define a function match-star that takes care of the * wildcard. How hard can it be, right? With the newly-found confidence we've gained from defining match-here we try our hands on match-star and get:

(define (match-star chr regex text)
  (let loop ((text text))
      ((match-here r t) #t)
      ((or (null? text)
           (and (not (eq? (car text) chr))
                (not (eq? chr #\.))))
      (else (loop (cdr t))))))
Fig. 6: An implementation of match-star.

match-star is a looped function that, for every iteration, checks whether the string currently under scrutiny can be handled by the match-here function. This is where we rely on laziness for the * wildcard, because if match-here can take over it will, regardless of whether match-star could also match the input. If match-here cannot consume the input, we'll check whether the wildcard can consume the first character instead, and do the check over again.

This is all of the code needed for the matcher! It is, much like the pattern matching algorithm we looked at last time, reasonably simple in its idea. Playing around with it reveals that it works quite well to boot.

Exercises for the reader

Like last time I will propose a couple of exercises one could do to deepen the understanding of the algorithm:

In a language with tail-call optimization, the recursive version is probably more performant while retaining its elegance. In a language that lacks them, however, we might think of an iterative version as a performance optimization—I doubt the algorithm will often run into stack overflow problems.


Once again, I marvel at the beauty of this finely crafted algorithm. I don't think I could ever think of them myself, so I am grateful for the likes of Steele, Sussman, and Pike for coming up with algorithms that please me aesthetically.

See you soon!