% Weighted RegExp Matching
Efficient regular expression matching can be beautifully
simple. Revisiting ideas from theoretical computer science, it can be
implemented with linear worstcase time and space bounds in the purely
functional programming language Haskell.
Background
Since Plato wrote about philosophy in the form of dialogues, authors
have used this literary form to convey their ideas. The 15th
International Conference on Functional Programming features an
article on Regular Expressions written as a play, A Play on Regular
Expressions, which is meant to be elegant, instructive, and
fun. The play discusses an efficient, purely functional
algorithm for matching regular expressions. By generalizing from
Booleans to arbitrary semirings, this algorithm implements various
matching policies for weighted regular expressions.
Installation
An implementation of the ideas discussed in the Play on Regular
Expressions is available as a Haskell library. It is implemented in
pure Haskell rather than as a binding to an external library so you do
not need to install an external regular expression library to use it.

However, you need Haskell in order to use this library. By installing
the Haskell Platform you get a Haskell compiler with an
interactive environment as well as the package manager cabalinstall
and various preinstalled packages.


You can install the weightedregexp library by typing the following
into a terminal:
bash# cabal update
bash# cabal install weightedregexp

This will install the current version. Differences between versions
are listed in the changelog.
Correctness
The matching algorithm computes the same result as a simple inductive
specification (given in the Play on Regular Expressions) but
is more efficient than a direct translation of this
specification into Haskell. Although the ideas behind the algorithm
are not new but based on proven results from theoretical computer
science, there is no correctness proof for the equivalence of the
Haskell implementation of the algorithm with its specification. The
equivalence is therefore confirmed by testing.
It is difficult (and tedious) to write tests manually that cover all
interesting apsects of regular expression matching. Therefore,
QuickCheck is used to generate tests automatically and Haskell
Program Coverage (HPC) is used to monitor test coverage.
You can install the weightedregexp
library along with a test
program as follows:
bash# cabal install weightedregexp fQuickCheck
Using the QuickCheck
flag results in an additional program that you
can use to test the implementation. The program tests

the algebraic laws of semirings for all defined semirings, and

the equivalence of the matching algorithm with the specification
both for full and partial matchings.
For testing the equivalence, QuickCheck generates random regular
expressions and compares the result of the matching algorithm with the
result of its specification on random words. Moreover, the program
tests

the parser that provides common syntactic sugar like bounded
repetitions and character classes,

the use of the library to recognize nonregular languages using
infinite regular expressions, and

a combinator for parsing permutation sequences, that is, sequences
of regular expressions in arbitrary order.
For a more detailed description of the tested properties consider the
source code of the test program. In order to
generate an HPC report you need to download the sources of the
weightedregexp
package. But you may as well consult the
pregenerated coverage report instead of generating one
yourself.
The matching algorithm provided by this library is usually slower than
other libraries like pcre but has a better asymptotic
complexity. There are no corner cases for which matching takes forever
or eats all available memory. More specifically, the worstcase run
time for matching a word against a regular expression is linearly
bounded by the length of the word and the size of the regular
expression. It is in O(nm) if n is the length of the word and m
the size of the expression. The memory requirements are independent of
the length of the word and linear in the size of the regular
expression, that is, in O(m). Therefore, this library provides
similar asymptotic complexity guarantees as Google's re2.
Here are timings that have been obtained (on a MacBook) with the
current version of the library.
input regexp run time memory
100 MB of a's .*
8s (12 MB/s) 1 MB
5000 a's (a?){5000}a{5000}
13s 5 MB
~2M a's and b's .*a.{20}a.*
3.6s 1 MB
The first example measures the search speed for a simple regular
expression with a long string. There is room for improvement. No time
has been invested yet to improve the performance of the library with
regard to constant factors.
The second example demonstrates the good asymptotic complexity of the
algorithm. Unlike a backtracking implementation like pcre the
library finishes in reasonable time. However, the memory requirements
are higher than usual and on closer inspection one can see that almost
10 of 13 seconds are spent during garbage collection. This example
uses a large regular expression which leads to a lot of garbage in the
matching algorithm.
The third example pushes automata based approaches to the limit
because the deterministic finite automaton that corresponds to the
regular expression is exponentially large. The input has been chosen
to not match the expression but is otherwise random and probably
explores many different states of the automaton. The matching
algorithm produces states on the fly and discards them, hence, it is
fast in this example, in fact, faster than re2.
The benchmarks above all use large input and two of them are
specifically designed as corner cases of typical matching
algorithms. The run time of matching more common regular expressions
against short input has been measured using Criterion in order to
get statistically robust results.
You can install the weightedregexp
package with the Criterion
flag to generate a program that executes the benchmarks described below:
bash# cabal install weightedregexp fCriterion
You can call criterionre help
to see how to use the generated
program. It tests three different examples:

a unique full match with a regular expression for phone numbers,

an ambiguous full match with a regular expression for sequences of
HTML elements, and

a partial match with a regular expression for protein sequences in
RNA.
For a more detailed explanation consider the source
code of the benchmark program.
matching acceptance #matchings leftmost longest leftmost longest
unique full 3.8 us 4.8 us
ambiguous full 11.7 us 13.4 us
partial 20.4 us 27.2 us 26.2 us 27.5 us
Click on the numbers for a more detailed distribution of run times.
Collaboration

The source code of this library is on github. You can collaborate by
using it in your projects, report bugs and ask for new features in the
issue tracker, or provide patches that implement pending issues.

The algorithm discussed in the Play on Regular Expressions
has been implemented in different languages. In a series of two
blog posts, Carl Friedrich Bolz describes a Python
implementation that uses a Just In Time (JIT) compiler to achieve
impressive performance. He compares his version with corresponding C++
and Java programs.
For questions and feedback email Sebastian
Fischer.