Question

I have a function that returns True if a string matches at least one
regular expression in a list and False otherwise. The function is called
often enough that performance is an issue.

When running it through cProfile, the function is spending about 65% of
its time doing matches and 35% of its time iterating over the list.

I would think there would be a way to use map() or something but I can’t
think of a way to have it stop iterating after it finds a match.

Is there a way to make the function faster while still having it return
upon finding the first match?

def matches_pattern(str, patterns):
    for pattern in patterns:
        if pattern.match(str):
            return True
    return False

Answer 1

The first thing that comes to mind is pushing the loop to the C side by using a generator expression:

def matches_pattern(s, patterns):
    return any(p.match(s) for p in patterns)

Probably you don’t even need a separate function for that.

Another thing you should try out is to build a single, composite regex using the | alternation operator, so that the engine has a chance to optimize it for you. You can also create the regex dynamically from a list of string patterns, if this is necessary:

def matches_pattern(s, patterns):
    return re.match('|'.join('(?:%s)' % p for p in patterns), s)

Of course you need to have your regexes in string form for that to work. Just profile both of these and check which one is faster 🙂

You might also want to have a look at a general tip for debugging regular expressions in Python. This can also help to find opportunities to optimize.

UPDATE: I was curious and wrote a little benchmark:

import timeit

setup = """
import re
patterns = [".*abc", "123.*", "ab.*", "foo.*bar", "11010.*", "1[^o]*"]*10
strings = ["asdabc", "123awd2", "abasdae23", "fooasdabar", "111", "11010100101", "xxxx", "eeeeee", "dddddddddddddd", "ffffff"]*10
compiled_patterns = list(map(re.compile, patterns))

def matches_pattern(str, patterns):
    for pattern in patterns:
        if pattern.match(str):
            return True
    return False

def test0():
    for s in strings:
        matches_pattern(s, compiled_patterns)

def test1():
    for s in strings:
        any(p.match(s) for p in compiled_patterns)

def test2():
    for s in strings:
        re.match('|'.join('(?:%s)' % p for p in patterns), s)

def test3():
    r = re.compile('|'.join('(?:%s)' % p for p in patterns))
    for s in strings:
        r.match(s)
"""

import sys
print(timeit.timeit("test0()", setup=setup, number=1000))
print(timeit.timeit("test1()", setup=setup, number=1000))
print(timeit.timeit("test2()", setup=setup, number=1000))
print(timeit.timeit("test3()", setup=setup, number=1000))

The output on my machine:

1.4120500087738037
1.662621021270752
4.729579925537109
0.1489570140838623

So any doesn’t seem to be faster than your original approach. Building up a regex dynamically also isn’t really fast. But if you can manage to build up a regex upfront and use it several times, this might result in better performance. You can also adapt this benchmark to test some other options 🙂