在Python中对正则表达式使用compile有什么好处吗?
h = re.compile('hello')
h.match('hello world')
vs
re.match('hello', 'hello world')
在Python中对正则表达式使用compile有什么好处吗?
h = re.compile('hello')
h.match('hello world')
vs
re.match('hello', 'hello world')
当前回答
我想说的是,预编译在概念上和“字面上”(如在“文学编程”中)都是有利的。看看这段代码片段:
from re import compile as _Re
class TYPO:
def text_has_foobar( self, text ):
return self._text_has_foobar_re_search( text ) is not None
_text_has_foobar_re_search = _Re( r"""(?i)foobar""" ).search
TYPO = TYPO()
在你的应用程序中,你可以这样写:
from TYPO import TYPO
print( TYPO.text_has_foobar( 'FOObar ) )
this is about as simple in terms of functionality as it can get. because this is example is so short, i conflated the way to get _text_has_foobar_re_search all in one line. the disadvantage of this code is that it occupies a little memory for whatever the lifetime of the TYPO library object is; the advantage is that when doing a foobar search, you'll get away with two function calls and two class dictionary lookups. how many regexes are cached by re and the overhead of that cache are irrelevant here.
将其与更常见的风格进行比较,如下所示:
import re
class Typo:
def text_has_foobar( self, text ):
return re.compile( r"""(?i)foobar""" ).search( text ) is not None
在应用中:
typo = Typo()
print( typo.text_has_foobar( 'FOObar ) )
我很乐意承认我的风格在python中是非常不寻常的,甚至可能是有争议的。然而,在更接近python的使用方式的示例中,为了进行一次匹配,我们必须实例化一个对象,进行三次实例字典查找,并执行三次函数调用;此外,当使用超过100个正则表达式时,我们可能会遇到重新缓存的麻烦。此外,正则表达式被隐藏在方法体中,这在大多数情况下并不是一个好主意。
可以说,每一个措施的子集——有针对性的,别名的import语句;别名方法(如适用);减少函数调用和对象字典查找——可以帮助减少计算和概念的复杂性。
其他回答
对我来说,re.compile的最大好处是能够将正则表达式的定义与其使用分开。
即使是一个简单的表达式,如0|[1-9][0-9]*(以10为基数,不带前导零的整数),也可能非常复杂,以至于您宁愿不重新输入它,检查是否有任何拼写错误,然后在开始调试时重新检查是否有拼写错误。另外,使用像num或num_b10这样的变量名比0|[1-9][0-9]*更好。
当然可以存储字符串并将它们传递给re.match;然而,这就不那么容易读了:
num = "..."
# then, much later:
m = re.match(num, input)
与编译:
num = re.compile("...")
# then, much later:
m = num.match(input)
虽然它很接近,但当重复使用时,第二句的最后一行感觉更自然、更简单。
大多数情况下,是否使用re.compile没有什么区别。在内部,所有函数都是按照编译步骤实现的:
def match(pattern, string, flags=0):
return _compile(pattern, flags).match(string)
def fullmatch(pattern, string, flags=0):
return _compile(pattern, flags).fullmatch(string)
def search(pattern, string, flags=0):
return _compile(pattern, flags).search(string)
def sub(pattern, repl, string, count=0, flags=0):
return _compile(pattern, flags).sub(repl, string, count)
def subn(pattern, repl, string, count=0, flags=0):
return _compile(pattern, flags).subn(repl, string, count)
def split(pattern, string, maxsplit=0, flags=0):
return _compile(pattern, flags).split(string, maxsplit)
def findall(pattern, string, flags=0):
return _compile(pattern, flags).findall(string)
def finditer(pattern, string, flags=0):
return _compile(pattern, flags).finditer(string)
此外,re.compile()绕过了额外的间接和缓存逻辑:
_cache = {}
_pattern_type = type(sre_compile.compile("", 0))
_MAXCACHE = 512
def _compile(pattern, flags):
# internal: compile pattern
try:
p, loc = _cache[type(pattern), pattern, flags]
if loc is None or loc == _locale.setlocale(_locale.LC_CTYPE):
return p
except KeyError:
pass
if isinstance(pattern, _pattern_type):
if flags:
raise ValueError(
"cannot process flags argument with a compiled pattern")
return pattern
if not sre_compile.isstring(pattern):
raise TypeError("first argument must be string or compiled pattern")
p = sre_compile.compile(pattern, flags)
if not (flags & DEBUG):
if len(_cache) >= _MAXCACHE:
_cache.clear()
if p.flags & LOCALE:
if not _locale:
return p
loc = _locale.setlocale(_locale.LC_CTYPE)
else:
loc = None
_cache[type(pattern), pattern, flags] = p, loc
return p
除了使用re.compile带来的小速度好处外,人们还喜欢命名潜在复杂的模式规范并将其与应用的业务逻辑分离所带来的可读性:
#### Patterns ############################################################
number_pattern = re.compile(r'\d+(\.\d*)?') # Integer or decimal number
assign_pattern = re.compile(r':=') # Assignment operator
identifier_pattern = re.compile(r'[A-Za-z]+') # Identifiers
whitespace_pattern = re.compile(r'[\t ]+') # Spaces and tabs
#### Applications ########################################################
if whitespace_pattern.match(s): business_logic_rule_1()
if assign_pattern.match(s): business_logic_rule_2()
注意,另一位受访者错误地认为pyc文件直接存储已编译的模式;然而,在现实中,每次PYC加载时,它们都会被重新构建:
>>> from dis import dis
>>> with open('tmp.pyc', 'rb') as f:
f.read(8)
dis(marshal.load(f))
1 0 LOAD_CONST 0 (-1)
3 LOAD_CONST 1 (None)
6 IMPORT_NAME 0 (re)
9 STORE_NAME 0 (re)
3 12 LOAD_NAME 0 (re)
15 LOAD_ATTR 1 (compile)
18 LOAD_CONST 2 ('[aeiou]{2,5}')
21 CALL_FUNCTION 1
24 STORE_NAME 2 (lc_vowels)
27 LOAD_CONST 1 (None)
30 RETURN_VALUE
上面的分解来自于一个包含tmp.py的PYC文件:
import re
lc_vowels = re.compile(r'[aeiou]{2,5}')
用下面的例子:
h = re.compile('hello')
h.match('hello world')
上面例子中的匹配方法和下面的不一样:
re.match('hello', 'hello world')
Re.compile()返回一个正则表达式对象,这意味着h是一个正则表达式对象。
regex对象有自己的匹配方法,带有可选的pos和endpos参数:
的。匹配(字符串[线程][线程]])
pos
可选的第二个参数pos给出了字符串中的一个索引 搜寻就要开始了;缺省值为0。这并不完全是 相当于对字符串进行切片;'^'模式字符匹配于 字符串的真正开始和在a之后的位置 换行符,但不一定在搜索到的索引处 开始。
尾部
可选参数endpos限制了字符串的长度 搜索;这就好像字符串有endpos个字符那么长 只搜索从pos到endpos - 1的字符 匹配。如果endpos小于pos,则找不到匹配;否则, 如果rx是编译后的正则表达式对象,则rx。搜索(字符串,0, 50)等于rx。搜索(字符串(:50),0)。
regex对象的search、findall和finditer方法也支持这些参数。
Re.match (pattern, string, flags=0)不支持,如你所见, 它的search、findall和finditer也没有。
match对象具有补充这些参数的属性:
match.pos
的search()或match()方法传递的pos的值 一个正则表达式对象。这是正则表达式所在字符串的索引 引擎开始寻找匹配。
match.endpos
传递给search()或match()方法的endpos值 正则表达式对象的。对象超出的字符串的索引 RE引擎不会去。
一个regex对象有两个唯一的,可能有用的属性:
regex.groups
模式中捕获组的数量。
regex.groupindex
将(?P)定义的任何符号组名映射到的字典 组数字。如果没有使用符号组,则字典为空 在模式中。
最后,match对象有这个属性:
match.re
其match()或search()方法的正则表达式对象 生成此匹配实例。
下面是一个使用re.compile的示例,在请求时速度超过50倍。
这一点与我在上面的评论中所说的是一样的,即当您的使用从编译缓存中获益不多时,使用re.compile可能是一个显著的优势。这种情况至少发生在一个特定的情况下(我在实践中遇到过),即当以下所有情况都成立时:
您有很多regex模式(不仅仅是re._MAXCACHE,它目前的默认值是512),以及 你经常使用这些正则表达式,而且 相同模式的连续使用之间被多个re._MAXCACHE其他正则表达式分隔,因此每个正则表达式在连续使用之间从缓存中刷新。
import re
import time
def setup(N=1000):
# Patterns 'a.*a', 'a.*b', ..., 'z.*z'
patterns = [chr(i) + '.*' + chr(j)
for i in range(ord('a'), ord('z') + 1)
for j in range(ord('a'), ord('z') + 1)]
# If this assertion below fails, just add more (distinct) patterns.
# assert(re._MAXCACHE < len(patterns))
# N strings. Increase N for larger effect.
strings = ['abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz'] * N
return (patterns, strings)
def without_compile():
print('Without re.compile:')
patterns, strings = setup()
print('searching')
count = 0
for s in strings:
for pat in patterns:
count += bool(re.search(pat, s))
return count
def without_compile_cache_friendly():
print('Without re.compile, cache-friendly order:')
patterns, strings = setup()
print('searching')
count = 0
for pat in patterns:
for s in strings:
count += bool(re.search(pat, s))
return count
def with_compile():
print('With re.compile:')
patterns, strings = setup()
print('compiling')
compiled = [re.compile(pattern) for pattern in patterns]
print('searching')
count = 0
for s in strings:
for regex in compiled:
count += bool(regex.search(s))
return count
start = time.time()
print(with_compile())
d1 = time.time() - start
print(f'-- That took {d1:.2f} seconds.\n')
start = time.time()
print(without_compile_cache_friendly())
d2 = time.time() - start
print(f'-- That took {d2:.2f} seconds.\n')
start = time.time()
print(without_compile())
d3 = time.time() - start
print(f'-- That took {d3:.2f} seconds.\n')
print(f'Ratio: {d3/d1:.2f}')
我在笔记本电脑上获得的示例输出(Python 3.7.7):
With re.compile:
compiling
searching
676000
-- That took 0.33 seconds.
Without re.compile, cache-friendly order:
searching
676000
-- That took 0.67 seconds.
Without re.compile:
searching
676000
-- That took 23.54 seconds.
Ratio: 70.89
I didn't bother with timeit as the difference is so stark, but I get qualitatively similar numbers each time. Note that even without re.compile, using the same regex multiple times and moving on to the next one wasn't so bad (only about 2 times as slow as with re.compile), but in the other order (looping through many regexes), it is significantly worse, as expected. Also, increasing the cache size works too: simply setting re._MAXCACHE = len(patterns) in setup() above (of course I don't recommend doing such things in production as names with underscores are conventionally “private”) drops the ~23 seconds back down to ~0.7 seconds, which also matches our understanding.
我想说的是,预编译在概念上和“字面上”(如在“文学编程”中)都是有利的。看看这段代码片段:
from re import compile as _Re
class TYPO:
def text_has_foobar( self, text ):
return self._text_has_foobar_re_search( text ) is not None
_text_has_foobar_re_search = _Re( r"""(?i)foobar""" ).search
TYPO = TYPO()
在你的应用程序中,你可以这样写:
from TYPO import TYPO
print( TYPO.text_has_foobar( 'FOObar ) )
this is about as simple in terms of functionality as it can get. because this is example is so short, i conflated the way to get _text_has_foobar_re_search all in one line. the disadvantage of this code is that it occupies a little memory for whatever the lifetime of the TYPO library object is; the advantage is that when doing a foobar search, you'll get away with two function calls and two class dictionary lookups. how many regexes are cached by re and the overhead of that cache are irrelevant here.
将其与更常见的风格进行比较,如下所示:
import re
class Typo:
def text_has_foobar( self, text ):
return re.compile( r"""(?i)foobar""" ).search( text ) is not None
在应用中:
typo = Typo()
print( typo.text_has_foobar( 'FOObar ) )
我很乐意承认我的风格在python中是非常不寻常的,甚至可能是有争议的。然而,在更接近python的使用方式的示例中,为了进行一次匹配,我们必须实例化一个对象,进行三次实例字典查找,并执行三次函数调用;此外,当使用超过100个正则表达式时,我们可能会遇到重新缓存的麻烦。此外,正则表达式被隐藏在方法体中,这在大多数情况下并不是一个好主意。
可以说,每一个措施的子集——有针对性的,别名的import语句;别名方法(如适用);减少函数调用和对象字典查找——可以帮助减少计算和概念的复杂性。