分析Python中设计模式之Decorator装饰器模式的要点
先给出一个四人团对Decorator mode的定义：动态地给一个对象添加一些额外的职责。
再来说说这个模式的好处：认证，权限检查，记日志，检查参数，加锁，等等等等，这些功能和系统业务无关，但又是系统所必须的，说的更明白一点，就是面向方面的编程（AOP）。
在Python中Decorator mode可以按照像其它编程语言如C++, Java等的样子来实现，但是Python在应用装饰概念方面的能力上远不止于此，Python提供了一个语法和一个编程特性来加强这方面的功能。Python提供的语法就是装饰器语法(decorator)，如下：
@aoo def foo(): pass def aoo(fn): return fn
装饰模式强调动态地给对象添加额外的功能。 Python内置了很多对装饰器的支持，因此在Python中使用装饰模式是非常容易的，下面是一个典型的例子，给函数增加日志功能：
import functools def log_wrapper(fun): @functools.wraps(fun) def wrapper(*args, **kwargs): print '在函数执行前加日志' ret = fun(*args, **kwargs) print '在函数执行后家日志' return ret return wrapper @log_wrapper def test(): print 'Hello, 世界'
functools.wraps是Python标准库提供的一个特殊的装饰器，用来解决装饰器带来的一些常规问题，如函数名称、doc等的不一致问题。@是Python针对装饰器提供的一个语法糖，上面的@log_wrapper相当于wrap_test = log_rapper(test)，用@后，这个步骤由解释器代劳了。
装饰器是Python编程必须掌握的一项技能，在编码过程中经常会用到。
这里只是一个普通的内嵌函数
def foo(x):
y = x
def foo1 ():
a = 1
return a
return foo1
而下面boo则是一个闭包
def aoo(a, b):
c = a
def boo (x):
x = b + 1
return x
return boo
boo的特殊性在于引用了外部变量b，当aoo返回后，只要返回值（boo）一直存在，则对b的引用就会一直存在。
上面的知识可能需要花些时间消化，如果你觉得已经掌握了这些知识，下面就回归正题，看看这些语言特性是怎样来实现Python中装饰的概念的。
还是让我们先看一个简单的例子，然后逐步深入。这个例子就是加锁，怎样实现加锁的功能？
具体需求是这样的：我有一个对象，实现了某些功能并提供了一些接口供其它模块调用，这个对象是运行在并发的环境中的，因此我需要对接口的调用进行同步，第一版的代码如下：
class Foo(object):
def __init__(self, …):
self.lock = threading.Lock()
def interface1(self, …):
self.lock.acquire()
try:
do something
finally:
self.lock.release()
def interface2(self, …):
same as interface1()
…
这版代码的问题很明显，那就是每个接口函数都有相同的加锁/解锁代码，重复的代码带来的是更多的键入，更多的阅读，更多的维护，以及更多的修改，最主要的是，程序员本应集中在业务上的的精力被分散了，而且请注意，真正的业务代码在距离函数定义2次缩进处开始，即使你的显示器是宽屏，这也会带来一些阅读上的困难。
你直觉的认为，可以把这些代码收进一个函数中，以达到复用的目的，但是请注意，这些代码不是一个完整同一的代码块，而是在中间嵌入了业务代码。
现在我们用装饰器语法来改进这部分代码，得到第2版代码：
def sync(func):
def wrapper(*args, **kv):
self = args[0]
self.lock.acquire()
try:
return func(*args, **kv)
finally:
self.lock.release()
return wrapper
class Foo(object):
def __init__(self, …):
self.lock = threading.Lock()
@sync
def interface1(self, …):
do something
@sync
def interface2(self, …):
do something
…
一个装饰器函数的第一个参数是所要装饰的那个函数对象，而且装饰器函数必须返回一个函数对象。如sync函数，当其装饰interface1时，参数func的值就是interface1，返回值是wrapper，但类Foo实例的interface1被调用时，实际调用的是wrapper函数，在wrapper函数体中间接调用实际的interface1；当interface2被调用时，也调用的是wrapper函数，不过由于在装饰时func已经变成interface2，所以会间接地调用到实际的interface2函数。
使用装饰器语法的好处：
代码量大大的减少了，更少的代码意味着更少的维护，更少的阅读，更少的键入，好处不一而足（可复用，可维护）
用户基本上将绝大部分精力放在了业务代码上，而且少了加减锁的代码，可读性也提高了
缺点：
业务对象Foo中有一个非业务数据成员lock，很碍眼；
相当程度的耦合，wrapper的第一个参数必须是对象本身，而且被装饰的对象中必须有一个lock对象存在，这给客户对象添加了限制，使用起来不是很舒服。
我们可以更进一步想一想：
lock对象必须要放在Foo中吗？
为每个接口函数都键入@sync还是很烦人的重复性人工工作，如果漏添加一个，还是会造成莫名其妙的运行时错误，为什么不集中处理呢？
为了解决上述的缺点，第3版代码如下：
class DecorateClass(object):
def decorate(self):
for name, fn in self.iter():
if not self.filter(name, fn):
continue
self.operate(name, fn)
class LockerDecorator(DecorateClass):
def __init__(self, obj, lock = threading.RLock()):
self.obj = obj
self.lock = lock
def iter(self):
return [(name, getattr(self.obj, name)) for name in dir(self.obj)]
def filter(self, name, fn):
if not name.startswith('_') and callable(fn):
return True
else:
return False
def operate(self, name, fn):
def locker(*args, **kv):
self.lock.acquire()
try:
return fn(*args, **kv)
finally:
self.lock.release()
setattr(self.obj, name, locker)
class Foo(object):
def __init__(self, …):
…
LockerDecorator(self).decorate()
def interface1(self, …):
do something
def interface2(self, …):
do something
…
对对象的功能装饰是一个更一般的功能，不仅限于为接口加锁，我用2个类来完成这一功能，DecorateClass是一个基类，只定义了遍历并应用装饰功能的算法代码(template method)，LockerDecorator实现了为对象加锁的功能，其中iter是迭代器，定义了怎样遍历对象中的成员（包括数据成员和成员函数），filter是过滤器，定义了符合什么规则的成员才能成为一个接口，operate是执行函数，具体实施了为对象接口加锁的功能。
而在业务类Foo的__init__函数中，只需要在最后添加一行代码：LockerDecorator(self).decorate()，就可以完成为对象加锁的功能。
如果你的对象提供的接口有特殊性，完全可以通过直接改写filter或者继承LockerDecorator并覆盖filter的方式来实现；此外，如果要使用其他的装饰功能，可以写一个继承自DecorateClass的类，并实现iter，filter和operate三个函数即可。
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