Pro:
Easy to use. Just like dictionary accesses plus an open and close, and remembering not to change mutable values in place.
Pickle and Shelve have been standard Python modules for years.
Users needn't do anything to set them up: they're included with Python.
Con:
Shelve requires a dbm library (gdbm, ndbm, Berkely db, etc, or the slow-but-universal dumbdbm).
Each dbm library has its own file format. If you open a dbm file with the wrong library, it destroys the file.
No support for concurrency unless you write a daemon or module to broker the connections.
With large pickled lists or dictionaries, you can't just unpickle one element and modify it, you have to unpickle/repickle the entire list/dictionary.
-- MikeOrr - 31 Dec 2001
I've been fiddling with this a little, and have come up with a simple mixin and object that seem to deal nicely with the concurency issues, and make Pickle a nice alternative to something as heavy as ZODB.
The first part is a persistency mixin. It pickles or marshals your object, with locking, to a file. You are expected to use multiple files for multiple objects -- like shelve in the filesystem (and perhaps shelve would be faster...?)
The second is a simple factory-generator. The object constructor should take generally an ID, or list of IDs (no default arguments, no keyword arguments), and the factory will return an already-existing object or create a new one. This way their won't be multiple objects that are pickling to or from the same file.
Together, this makes it very easy to make a simple, persistent object. For instance:
class _UserPrefs(PersistMixin):
def __init__(self, userID):
self._userID = userID
self._prefs = self.load({}) # PersistMixin defines .load()
def setPref(self, pref, value):
self._prefs[pref] = value
self.changed() # This tells the Mixin that we need a commit
def pref(self, pref, default=None):
return self._prefs.get(pref, default)
## these next two methods are required by PersistMixin
def data(self):
## this is what will get pickled -- it goes with self.load() in __init__
return self._prefs
def filename(self):
## this is where the data will be pickled to
return '/var/userdata/user%i.pickle' % self._userID
UserPrefs = ParamFactory(_UserPrefs)
## UserPrefs is then the way to get a _UserPref object
It ain't fancy, but it's very transparent. You can look at Persist.py and ParamFactory.py
-- IanBicking - 03 Jan 2002