Python设计模式编程中的备忘录模式与对象池模式示例

import copy

def Memento(obj, deep=False):

  # 对你要做快照的对象做快照
  state = (copy.copy if deep else copy.deepcopy)(obj.__dict__)
  def Restore():
    obj.__dict__ = state
  return Restore

class Transaction:

  deep = False
  def __init__(self, *targets):
    self.targets = targets
    self.Commit()
  # 模拟事务提交，其实就是初始化给每个对象往self.targets赋值
  def Commit(self):
    self.states = [Memento(target, self.deep) for target in self.targets]
  # 回滚其实就是调用Memento函数，执行其中的闭包，将__dict__恢复
  def Rollback(self):
    for state in self.states:
      state()

# 装饰器的方式给方法添加这个事务的功能
def transactional(method):
  # 这里的self其实就是要保存的那个对象，和类的实例无关
  def wrappedMethod(self, *args, **kwargs):
    state = Memento(self)
    try:
      return method(self, *args, **kwargs)
    except:
      # 和上面的回滚一样，异常就恢复
      state()
      raise
  return wrappedMethod

class NumObj(object):
  def __init__(self, value):
    self.value = value
  def __repr__(self):
    return '<%s: %r>' % (self.__class__.__name__, self.value)
  def Increment(self):
    self.value += 1

  @transactional
  def DoStuff(self):
    # 赋值成字符串，再自增长肯定会报错的
    self.value = '1111'
    self.Increment()

if __name__ == '__main__':

  n = NumObj(-1)
  print n
  t = Transaction(n)
  try:
    for i in range(3):
      n.Increment()
      print n
    # 这里事务提交会保存状态从第一次的-1到2
    t.Commit()
    print '-- commited'
    for i in range(3):
      n.Increment()
      print n
    n.value += 'x' # will fail
    print n
  except:
    # 回滚只会回顾到上一次comit成功的2 而不是-1
    t.Rollback()
    print '-- rolled back'
  print n
  print '-- now doing stuff ...'
  try:
    n.DoStuff()
  except:
    print '-> doing stuff failed!'
    import traceback
    traceback.print_exc(0)
    pass
  # 第二次的异常回滚n还是2, 整个过程都是修改NumObj的实例对象
  print n

import Queue
import types
import threading
from contextlib import contextmanager

class ObjectPool(object):

  def __init__(self, fn_cls, *args, **kwargs):
    super(ObjectPool, self).__init__()
    self.fn_cls = fn_cls
    self._myinit(*args, **kwargs)

  def _myinit(self, *args, **kwargs):
    self.args = args
    self.maxSize = int(kwargs.get("maxSize",1))
    self.queue = Queue.Queue()
  def _get_obj(self):
    # 因为传进来的可能是函数，还可能是类
    if type(self.fn_cls) == types.FunctionType:
      return self.fn_cls(self.args)
    # 判断是经典或者新类
    elif type(self.fn_cls) == types.ClassType or type(self.fn_cls) == types.TypeType:
      return apply(self.fn_cls, self.args)
    else:
      raise "Wrong type"

  def borrow_obj(self):
    # 这个print 没用，只是在你执行的时候告诉你目前的队列数，让你发现对象池的作用
    print self.queue._qsize()
    # 要是对象池大小还没有超过设置的最大数，可以继续放进去新对象
    if self.queue.qsize()<self.maxSize and self.queue.empty():
      self.queue.put(self._get_obj())
    # 都会返回一个对象给相关去用
    return self.queue.get() 
  # 回收
  def recover_obj(self,obj):
    self.queue.put(obj)

# 测试用函数和类
def echo_func(num):
  return num

class echo_cls(object):
  pass

# 不用构造含有__enter__, __exit__的类就可以使用with，当然你可以直接把代码放到函数去用
@contextmanager
def poolobj(pool):
  obj = pool.borrow_obj()
  try:
    yield obj
  except Exception, e:
    yield None
  finally:
    pool.recover_obj(obj)

obj = ObjectPool(echo_func, 23, maxSize=4)
obj2 = ObjectPool(echo_cls, maxSize=4)

class MyThread(threading.Thread):

  def run(self):
    # 为了实现效果，我搞了个简单的多线程，2个with放在一个地方了，只为测试用
    with poolobj(obj) as t:
      print t
    with poolobj(obj2) as t:
      print t

if __name__ == '__main__':
  threads = []
  for i in range(200):
    t = MyThread()
    t.start()
    threads.append(t)
  for t in threads:
    t.join(True)