Common use-cases for pickle in Python

Question

Some uses that I have come across:

1) saving a program's state data to disk so that it can carry on where it left off when restarted (persistence)

2) sending python data over a TCP connection in a multi-core or distributed system (marshalling)

3) storing python objects in a database

4) converting an arbitrary python object to a string so that it can be used as a dictionary key (e.g. for caching & memoization).

There are some issues with the last one - two identical objects can be pickled and result in different strings - or even the same object pickled twice can have different representations. This is because the pickle can include reference count information.

To emphasise @lunaryorn's comment - you should never unpickle a string from an untrusted source, since a carefully crafted pickle could execute arbitrary code on your system. For example see https://blog.nelhage.com/2011/03/exploiting-pickle/

Minimal roundtrip example..

>>> import pickle
>>> a = Anon()
>>> a.foo = 'bar'
>>> pickled = pickle.dumps(a)
>>> unpickled = pickle.loads(pickled)
>>> unpickled.foo
'bar'

Edit: but as for the question of real-world examples of pickling, perhaps the most advanced use of pickling (you'd have to dig quite deep into the source) is ZODB: http://svn.zope.org/

Otherwise, PyPI mentions several: http://pypi.python.org/pypi?:action=search&term=pickle&submit=search

I have personally seen several examples of pickled objects being sent over the network as an easy to use network transfer protocol.

I have used it in one of my projects. If the app was terminated during it's working (it did a lengthy task and processed lots of data), I needed to save the whole data structure and reload it after the app was run again. I used cPickle for this, as speed was a crucial thing and the size of data was really big.

Pickling is absolutely necessary for distributed and parallel computing.

Say you wanted to do a parallel map-reduce with multiprocessing (or across cluster nodes with pyina), then you need to make sure the function you want to have mapped across the parallel resources will pickle. If it doesn't pickle, you can't send it to the other resources on another process, computer, etc. Also see here for a good example.

To do this, I use dill, which can serialize almost anything in python. Dill also has some good tools for helping you understand what is causing your pickling to fail when your code fails.

And, yes, people use picking to save the state of a calculation, or your ipython session, or whatever.