MethodPickle (methodpickle) is a quick library that allows simple pickling and unpickling of function and method invocation. Function & method module loading is handled automatically, and methods can be specified by name as well.
The ability to pickle a method invocation allows for queueing and delayed execution of arbitrary code. This is useful for parallelization, logging, queueing, etc.
Steve Lacy <firstname.lastname@example.org> Twitter: @sklacy http://slacy.com/blog
Please see the unit tests in test.py for some more verbose examples, but I’ll go through a quick example here.:
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)
For convenience, there’s also a decorator form of the defer function, called deferred. Again, see the implementation or test.py for more details.
includes ‘self’ for class method invocations
inner functions don’t have an easy-to-discover import path, so all the deferred functions should be at the top level of your module. I’d suggest putting them all in the same file (say, tasks.py)
if you pass a very large datastructure to the deferral methods, it may have a performance impact. In addition, if you pass a mutable datastructur (dict, list, etc.) then subsequent modifications will have no effect.
a feature and a caveat. Once you pickle a function call, that value could be unpickled and run more than once. Watch out for anything that has unexpected side effects!
TODO: Figure out how to actually get changelog content.
Changelog content for this version goes here.