为什么在Python 3中未编译，重复使用的正则表达式如此之慢？

Question

When answering this question (and having read this answer to a similar question), I thought that I knew how Python caches regexes.

But then I thought I'd test it, comparing two scenarios:

1. a single compilation of a simple regex, then 10 applications of that compiled regex.
2. 10 applications of an uncompiled regex (where I would have expected slightly worse performance because the regex would have to be compiled once, then cached, and then looked up in the cache 9 times).

However, the results were staggering (in Python 3.3):

>>> import timeit
>>> timeit.timeit(setup="import re", 
... stmt='r=re.compile(r"\w+")\nfor i in range(10):\n r.search("  jkdhf  ")')
18.547793477671938
>>> timeit.timeit(setup="import re", 
... stmt='for i in range(10):\n re.search(r"\w+","  jkdhf  ")')
106.47892003890324

That's over 5.7 times slower! In Python 2.7, there is still an increase by a factor of 2.5, which is also more than I would have expected.

Has caching of regexes changed between Python 2 and 3? The docs don't seem to suggest that.

Answer 1

The code has changed.

In Python 2.7, the cache is a simple dictionary; if more than _MAXCACHE items are stored in it, the whole the cache is cleared before storing a new item. A cache lookup only takes building a simple key and testing the dictionary, see the 2.7 implementation of _compile()

In Python 3.x, the cache has been replaced by the @functools.lru_cache(maxsize=500, typed=True) decorator . This decorator does much more work and includes a thread-lock, adjusting the cache LRU queue and maintaining the cache statistics (accessible via re._compile.cache_info() ). See the 3.3.0 implementation of _compile() and of functools.lru_cache() .

Others have noticed the same slowdown, and filed issue 16389 in the Python bugtracker. I'd expect 3.4 to be a lot faster again; either the lru_cache implementation is improved or the re module will move to a custom cache again.

Update: With revision 4b4dddd670d0 (hg) / 0f606a6 (git) the cache change has been reverted back to the simple version found in 3.1. Python versions 3.2.4 and 3.3.1 include that revision.

Since then, in Python 3.7 the pattern cache was updated to a custom FIFO cache implementation based on a regular dict (relying on insertion order, and unlike a LRU, does not take into account how recently items already in the cache were used when evicting).