methodpickle 0.1.0

功能和用法

请参阅test.py中的单元测试以获取更多详细的示例，但是 我将在这里简单介绍一个示例。

from methodpickle.defer import defer

# These are the functions that we're going to defer
def some_function(x, y):
    return x*x + y*y

# methodpickle supports deferring execution of classmethods as well, so
# here's a simple class with a method:
def some_class(object):
    def __init__(self, x):
        self._x = x

    def calc(self, y):
        return (self._x * self._x + y * y)

if __name__ == '__main__':

    # the defer function takes a method and it's arguments, and turns it
    # into a pickleable object.
    storable_func = defer(some_function, 5, 4)

    # So, we pickle that guy into a string.
    method_str = pickle.dumps(storable_func)

    # You can now take method_str and do whatever you like with it.  Write
    # it to a database, send it to another process, put it in your logs,
    # whatever.

    # Then, you can unpickle the stored method invocation, and run it,
    # like this:
    recovered_func = pickle.loads()
    assert(recovered_func.run() == 5*5 + 4*4)

    # methodpickle also supports pickling of classmethods.  Note that your
    # class must support pickling and the methods should have no side
    # effects.

    i = some_class(2)
    storable_classmethod = defer(i, 3)

    classmethod_str = storable_method.dumps()
    recovered_classmethod = pickle.loads(classmethod_str)
    assert(recovered_classmethod.run() == 2*2 + 3*3)

为了方便起见，defer函数还有一种decorator形式，称为 推迟。同样，有关更多详细信息，请参见实现或test.py

注意事项

• 函数的所有参数本身都必须是可pickle的。this

  包括类方法调用的“self”

• 函数和类必须在模块级。内部类和

  内部函数没有容易发现的导入路径，因此 延迟函数应该位于模块的顶层我会的 建议将它们放在同一个文件中（比如tasks.py）

• 所有方法参数在延迟时都被深度复制。因此，

  如果将非常大的数据结构传递给延迟方法，则可能 对性能有影响。另外，如果你通过一个可变的 datastructur（dict、list等），随后的修改将具有 无效。

• 注意函数和方法的双重调用两者都是

  功能和警告。一旦对函数调用进行pickle操作，该值 可能会被解开并且跑不止一次。当心那些 有意想不到的副作用！