picklepickle module implements a basic but powerful algorithm for
``pickling'' (a.k.a. serializing, marshalling or flattening) nearly
arbitrary Python objects. This is the act of converting objects to a
stream of bytes (and back: ``unpickling'').
This is a more primitive notion than
persistency --- although pickle reads and writes file objects,
it does not handle the issue of naming persistent objects, nor the
(even more complicated) area of concurrent access to persistent
objects. The pickle module can transform a complex object into
a byte stream and it can transform the byte stream into an object with
the same internal structure. The most obvious thing to do with these
byte streams is to write them onto a file, but it is also conceivable
to send them across a network or store them in a database. The module
shelve provides a simple interface to pickle and unpickle
objects on ``dbm''-style database files.
Unlike the built-in module marshal, pickle handles the
following correctly:
pickle is Python-specific. This has
the advantage that there are no restrictions imposed by external
standards such as CORBA (which probably can't represent pointer
sharing or recursive objects); however it means that non-Python
programs may not be able to reconstruct pickled Python objects.
The pickle data format uses a printable ASCII representation.
This is slightly more voluminous than a binary representation.
However, small integers actually take less space when
represented as minimal-size decimal strings than when represented as
32-bit binary numbers, and strings are only much longer if they
contain many control characters or 8-bit characters. The big
advantage of using printable ASCII (and of some other characteristics
of pickle's representation) is that for debugging or recovery
purposes it is possible for a human to read the pickled file with a
standard text editor. (I could have gone a step further and used a
notation like S-expressions, but the parser
(currently written in Python) would have been
considerably more complicated and slower, and the files would probably
have become much larger.)
The pickle module doesn't handle code objects, which the
marshal module does. I suppose pickle could, and maybe
it should, but there's probably no great need for it right now (as
long as marshal continues to be used for reading and writing
code objects), and at least this avoids the possibility of smuggling
Trojan horses into a program.
For the benefit of persistency modules written using pickle, it
supports the notion of a reference to an object outside the pickled
data stream. Such objects are referenced by a name, which is an
arbitrary string of printable ASCII characters. The resolution of
such names is not defined by the pickle module --- the
persistent object module will have to implement a method
persistent_load. To write references to persistent objects,
the persistent module must define a method persistent_id which
returns either None or the persistent ID of the object.
There are some restrictions on the pickling of class instances.
First of all, the class must be defined at the top level in a module.
Next, it must normally be possible to create class instances by
calling the class without arguments. If this is undesirable, the
class can define a method __getinitargs__(), which should
return a tuple containing the arguments to be passed to the
class constructor (__init__()).
Classes can further influence how their instances are pickled --- if the class
defines the method __getstate__(), it is called and the return
state is pickled as the contents for the instance, and if the class
defines the method __setstate__(), it is called with the
unpickled state. (Note that these methods can also be used to
implement copying class instances.) If there is no
__getstate__() method, the instance's __dict__ is
pickled. If there is no __setstate__() method, the pickled
object must be a dictionary and its items are assigned to the new
instance's dictionary. (If a class defines both __getstate__()
and __setstate__(), the state object needn't be a dictionary
--- these methods can do what they want.) This protocol is also used
by the shallow and deep copying operations defined in the copy
module.
Note that when class instances are pickled, their class's code and
data are not pickled along with them. Only the instance data are
pickled. This is done on purpose, so you can fix bugs in a class or
add methods and still load objects that were created with an earlier
version of the class. If you plan to have long-lived objects that
will see many versions of a class, it may be worthwhile to put a version
number in the objects so that suitable conversions can be made by the
class's __setstate__() method.
When a class itself is pickled, only its name is pickled --- the class definition is not pickled, but re-imported by the unpickling process. Therefore, the restriction that the class must be defined at the top level in a module applies to pickled classes as well.
The interface can be summarized as follows.
To pickle an object x onto a file f, open for writing:
p = pickle.Pickler(f)
p.dump(x)
pickle.dump(x, f)
x from a file f, open for reading:
u = pickle.Unpickler(f)
x = u.load()
x = pickle.load(f)
Pickler class only calls the method f.write with a
string argument. The Unpickler calls the methods f.read
(with an integer argument) and f.readline (without argument),
both returning a string. It is explicitly allowed to pass non-file
objects here, as long as they have the right methods.
The following types can be pickled:
None
__dict__ or
__setstate__() is picklable
PicklingError exception; when this happens, an unspecified
number of bytes may have been written to the file.
It is possible to make multiple calls to the dump() method of
the same Pickler instance. These must then be matched to the
same number of calls to the load() instance of the
corresponding Unpickler instance. If the same object is
pickled by multiple dump() calls, the load() will all
yield references to the same object. Warning: this is intended
for pickling multiple objects without intervening modifications to the
objects or their parts. If you modify an object and then pickle it
again using the same Pickler instance, the object is not
pickled again --- a reference to it is pickled and the
Unpickler will return the old value, not the modified one.
(There are two problems here: (a) detecting changes, and (b)
marshalling a minimal set of changes. I have no answers. Garbage
Collection may also become a problem here.)
Apart from the Pickler and Unpickler classes, the
module defines the following functions, and an exception:
Pickler(file).dump(object).
Unpickler(file).load().
Pickler.dump().