11.1. "pickle" — Python object serialization
********************************************

The "pickle" module implements a fundamental, but powerful algorithm
for serializing and de-serializing a Python object structure.  «
Pickling » is the process whereby a Python object hierarchy is
converted into a byte stream, and « unpickling » is the inverse
operation, whereby a byte stream is converted back into an object
hierarchy.  Pickling (and unpickling) is alternatively known as «
serialization », « marshalling, » [1] or « flattening », however, to
avoid confusion, the terms used here are « pickling » and « unpickling
».

This documentation describes both the "pickle" module and the
"cPickle" module.

Avertissement: The "pickle" module is not secure against erroneous
  or maliciously constructed data.  Never unpickle data received from
  an untrusted or unauthenticated source.


11.1.1. Relationship to other Python modules
============================================

The "pickle" module has an optimized cousin called the "cPickle"
module.  As its name implies, "cPickle" is written in C, so it can be
up to 1000 times faster than "pickle".  However it does not support
subclassing of the "Pickler()" and "Unpickler()" classes, because in
"cPickle" these are functions, not classes.  Most applications have no
need for this functionality, and can benefit from the improved
performance of "cPickle". Other than that, the interfaces of the two
modules are nearly identical; the common interface is described in
this manual and differences are pointed out where necessary.  In the
following discussions, we use the term « pickle » to collectively
describe the "pickle" and "cPickle" modules.

The data streams the two modules produce are guaranteed to be
interchangeable.

Python has a more primitive serialization module called "marshal", but
in general "pickle" should always be the preferred way to serialize
Python objects.  "marshal" exists primarily to support Python’s ".pyc"
files.

The "pickle" module differs from "marshal" in several significant
ways:

* The "pickle" module keeps track of the objects it has already
  serialized, so that later references to the same object won’t be
  serialized again. "marshal" doesn’t do this.

  This has implications both for recursive objects and object sharing.
  Recursive objects are objects that contain references to themselves.
  These are not handled by marshal, and in fact, attempting to marshal
  recursive objects will crash your Python interpreter.  Object
  sharing happens when there are multiple references to the same
  object in different places in the object hierarchy being serialized.
  "pickle" stores such objects only once, and ensures that all other
  references point to the master copy.  Shared objects remain shared,
  which can be very important for mutable objects.

* "marshal" cannot be used to serialize user-defined classes and
  their instances.  "pickle" can save and restore class instances
  transparently, however the class definition must be importable and
  live in the same module as when the object was stored.

* The "marshal" serialization format is not guaranteed to be
  portable across Python versions.  Because its primary job in life is
  to support ".pyc" files, the Python implementers reserve the right
  to change the serialization format in non-backwards compatible ways
  should the need arise. The "pickle" serialization format is
  guaranteed to be backwards compatible across Python releases.

Note that serialization is a more primitive notion than persistence;
although "pickle" reads and writes file objects, it does not handle
the issue of naming persistent objects, nor the (even more
complicated) issue 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.  Perhaps 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.


11.1.2. Data stream format
==========================

The data format used by "pickle" is Python-specific.  This has the
advantage that there are no restrictions imposed by external standards
such as XDR (which can’t represent pointer sharing); however it means
that non-Python programs may not be able to reconstruct pickled Python
objects.

By default, the "pickle" data format uses a printable ASCII
representation. This is slightly more voluminous than a binary
representation.  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.

There are currently 3 different protocols which can be used for
pickling.

* Protocol version 0 is the original ASCII protocol and is backwards
  compatible with earlier versions of Python.

* Protocol version 1 is the old binary format which is also
  compatible with earlier versions of Python.

* Protocol version 2 was introduced in Python 2.3.  It provides much
  more efficient pickling of *new-style class*es.

Refer to **PEP 307** for more information.

If a *protocol* is not specified, protocol 0 is used. If *protocol* is
specified as a negative value or "HIGHEST_PROTOCOL", the highest
protocol version available will be used.

Modifié dans la version 2.3: Introduced the *protocol* parameter.

A binary format, which is slightly more efficient, can be chosen by
specifying a *protocol* version >= 1.


11.1.3. Usage
=============

To serialize an object hierarchy, you first create a pickler, then you
call the pickler’s "dump()" method.  To de-serialize a data stream,
you first create an unpickler, then you call the unpickler’s "load()"
method.  The "pickle" module provides the following constant:

pickle.HIGHEST_PROTOCOL

   The highest protocol version available.  This value can be passed
   as a *protocol* value.

   Nouveau dans la version 2.3.

Note: Be sure to always open pickle files created with protocols >=
  1 in binary mode. For the old ASCII-based pickle protocol 0 you can
  use either text mode or binary mode as long as you stay consistent.A
  pickle file written with protocol 0 in binary mode will contain lone
  linefeeds as line terminators and therefore will look « funny » when
  viewed in Notepad or other editors which do not support this format.

The "pickle" module provides the following functions to make the
pickling process more convenient:

pickle.dump(obj, file[, protocol])

   Write a pickled representation of *obj* to the open file object
   *file*.  This is equivalent to "Pickler(file, protocol).dump(obj)".

   If the *protocol* parameter is omitted, protocol 0 is used. If
   *protocol* is specified as a negative value or "HIGHEST_PROTOCOL",
   the highest protocol version will be used.

   Modifié dans la version 2.3: Introduced the *protocol* parameter.

   *file* must have a "write()" method that accepts a single string
   argument. It can thus be a file object opened for writing, a
   "StringIO" object, or any other custom object that meets this
   interface.

pickle.load(file)

   Read a string from the open file object *file* and interpret it as
   a pickle data stream, reconstructing and returning the original
   object hierarchy.  This is equivalent to "Unpickler(file).load()".

   *file* must have two methods, a "read()" method that takes an
   integer argument, and a "readline()" method that requires no
   arguments.  Both methods should return a string.  Thus *file* can
   be a file object opened for reading, a "StringIO" object, or any
   other custom object that meets this interface.

   This function automatically determines whether the data stream was
   written in binary mode or not.

pickle.dumps(obj[, protocol])

   Return the pickled representation of the object as a string,
   instead of writing it to a file.

   If the *protocol* parameter is omitted, protocol 0 is used. If
   *protocol* is specified as a negative value or "HIGHEST_PROTOCOL",
   the highest protocol version will be used.

   Modifié dans la version 2.3: The *protocol* parameter was added.

pickle.loads(string)

   Read a pickled object hierarchy from a string.  Characters in the
   string past the pickled object’s representation are ignored.

The "pickle" module also defines three exceptions:

exception pickle.PickleError

   A common base class for the other exceptions defined below.  This
   inherits from "Exception".

exception pickle.PicklingError

   This exception is raised when an unpicklable object is passed to
   the "dump()" method.

exception pickle.UnpicklingError

   This exception is raised when there is a problem unpickling an
   object. Note that other exceptions may also be raised during
   unpickling, including (but not necessarily limited to)
   "AttributeError", "EOFError", "ImportError", and "IndexError".

The "pickle" module also exports two callables [2], "Pickler" and
"Unpickler":

class pickle.Pickler(file[, protocol])

   This takes a file-like object to which it will write a pickle data
   stream.

   If the *protocol* parameter is omitted, protocol 0 is used. If
   *protocol* is specified as a negative value or "HIGHEST_PROTOCOL",
   the highest protocol version will be used.

   Modifié dans la version 2.3: Introduced the *protocol* parameter.

   *file* must have a "write()" method that accepts a single string
   argument. It can thus be an open file object, a "StringIO" object,
   or any other custom object that meets this interface.

   "Pickler" objects define one (or two) public methods:

   dump(obj)

      Write a pickled representation of *obj* to the open file object
      given in the constructor.  Either the binary or ASCII format
      will be used, depending on the value of the *protocol* argument
      passed to the constructor.

   clear_memo()

      Clears the pickler’s « memo ».  The memo is the data structure
      that remembers which objects the pickler has already seen, so
      that shared or recursive objects pickled by reference and not by
      value.  This method is useful when re-using picklers.

      Note: Prior to Python 2.3, "clear_memo()" was only available
        on the picklers created by "cPickle".  In the "pickle" module,
        picklers have an instance variable called "memo" which is a
        Python dictionary.  So to clear the memo for a "pickle" module
        pickler, you could do the following:

           mypickler.memo.clear()

        Code that does not need to support older versions of Python
        should simply use "clear_memo()".

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()" method 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. [3]

"Unpickler" objects are defined as:

class pickle.Unpickler(file)

   This takes a file-like object from which it will read a pickle data
   stream. This class automatically determines whether the data stream
   was written in binary mode or not, so it does not need a flag as in
   the "Pickler" factory.

   *file* must have two methods, a "read()" method that takes an
   integer argument, and a "readline()" method that requires no
   arguments.  Both methods should return a string.  Thus *file* can
   be a file object opened for reading, a "StringIO" object, or any
   other custom object that meets this interface.

   "Unpickler" objects have one (or two) public methods:

   load()

      Read a pickled object representation from the open file object
      given in the constructor, and return the reconstituted object
      hierarchy specified therein.

      This method automatically determines whether the data stream was
      written in binary mode or not.

   noload()

      This is just like "load()" except that it doesn’t actually
      create any objects.  This is useful primarily for finding what’s
      called « persistent ids » that may be referenced in a pickle
      data stream.  See section The pickle protocol below for more
      details.

      **Note:** the "noload()" method is currently only available on
      "Unpickler" objects created with the "cPickle" module. "pickle"
      module "Unpickler"s do not have the "noload()" method.


11.1.4. What can be pickled and unpickled?
==========================================

The following types can be pickled:

* "None", "True", and "False"

* integers, long integers, floating point numbers, complex numbers

* normal and Unicode strings

* tuples, lists, sets, and dictionaries containing only picklable
  objects

* functions defined at the top level of a module

* built-in functions defined at the top level of a module

* classes that are defined at the top level of a module

* instances of such classes whose "__dict__" or the result of
  calling "__getstate__()" is picklable  (see section The pickle
  protocol for details).

Attempts to pickle unpicklable objects will raise the "PicklingError"
exception; when this happens, an unspecified number of bytes may have
already been written to the underlying file. Trying to pickle a highly
recursive data structure may exceed the maximum recursion depth, a
"RuntimeError" will be raised in this case. You can carefully raise
this limit with "sys.setrecursionlimit()".

Note that functions (built-in and user-defined) are pickled by « fully
qualified » name reference, not by value.  This means that only the
function name is pickled, along with the name of the module the
function is defined in.  Neither the function’s code, nor any of its
function attributes are pickled.  Thus the defining module must be
importable in the unpickling environment, and the module must contain
the named object, otherwise an exception will be raised. [4]

Similarly, classes are pickled by named reference, so the same
restrictions in the unpickling environment apply.  Note that none of
the class’s code or data is pickled, so in the following example the
class attribute "attr" is not restored in the unpickling environment:

   class Foo:
       attr = 'a class attr'

   picklestring = pickle.dumps(Foo)

These restrictions are why picklable functions and classes must be
defined in the top level of a module.

Similarly, 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 to the class 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.


11.1.5. The pickle protocol
===========================

This section describes the « pickling protocol » that defines the
interface between the pickler/unpickler and the objects that are being
serialized.  This protocol provides a standard way for you to define,
customize, and control how your objects are serialized and de-
serialized.  The description in this section doesn’t cover specific
customizations that you can employ to make the unpickling environment
slightly safer from untrusted pickle data streams; see section
Subclassing Unpicklers for more details.


11.1.5.1. Pickling and unpickling normal class instances
--------------------------------------------------------

object.__getinitargs__()

   When a pickled class instance is unpickled, its "__init__()" method
   is normally *not* invoked.  If it is desirable that the
   "__init__()" method be called on unpickling, an old-style class can
   define a method "__getinitargs__()", which should return a *tuple*
   of positional arguments to be passed to the class constructor
   ("__init__()" for example).  Keyword arguments are not supported.
   The "__getinitargs__()" method is called at pickle time; the tuple
   it returns is incorporated in the pickle for the instance.

object.__getnewargs__()

   New-style types can provide a "__getnewargs__()" method that is
   used for protocol 2.  Implementing this method is needed if the
   type establishes some internal invariants when the instance is
   created, or if the memory allocation is affected by the values
   passed to the "__new__()" method for the type (as it is for tuples
   and strings).  Instances of a *new-style class* "C" are created
   using

      obj = C.__new__(C, *args)

   where *args* is the result of calling "__getnewargs__()" on the
   original object; if there is no "__getnewargs__()", an empty tuple
   is assumed.

object.__getstate__()

   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, instead
   of the contents of the instance’s dictionary.  If there is no
   "__getstate__()" method, the instance’s "__dict__" is pickled.

object.__setstate__(state)

   Upon unpickling, if the class also defines the method
   "__setstate__()", it is called with the unpickled state. [5] If
   there is no "__setstate__()" method, the pickled state 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 and
   these methods can do what they want. [6]

   Note: For *new-style class*es, if "__getstate__()" returns a
     false value, the "__setstate__()" method will not be called.

Note: At unpickling time, some methods like "__getattr__()",
  "__getattribute__()", or "__setattr__()" may be called upon the
  instance.  In case those methods rely on some internal invariant
  being true, the type should implement either "__getinitargs__()" or
  "__getnewargs__()" to establish such an invariant; otherwise,
  neither "__new__()" nor "__init__()" will be called.


11.1.5.2. Pickling and unpickling extension types
-------------------------------------------------

object.__reduce__()

   When the "Pickler" encounters an object of a type it knows nothing
   about — such as an extension type — it looks in two places for a
   hint of how to pickle it.  One alternative is for the object to
   implement a "__reduce__()" method.  If provided, at pickling time
   "__reduce__()" will be called with no arguments, and it must return
   either a string or a tuple.

   If a string is returned, it names a global variable whose contents
   are pickled as normal.  The string returned by "__reduce__()"
   should be the object’s local name relative to its module; the
   pickle module searches the module namespace to determine the
   object’s module.

   When a tuple is returned, it must be between two and five elements
   long. Optional elements can either be omitted, or "None" can be
   provided as their value.  The contents of this tuple are pickled as
   normal and used to reconstruct the object at unpickling time.  The
   semantics of each element are:

   * A callable object that will be called to create the initial
     version of the object.  The next element of the tuple will
     provide arguments for this callable, and later elements provide
     additional state information that will subsequently be used to
     fully reconstruct the pickled data.

     In the unpickling environment this object must be either a class,
     a callable registered as a « safe constructor » (see below), or
     it must have an attribute "__safe_for_unpickling__" with a true
     value. Otherwise, an "UnpicklingError" will be raised in the
     unpickling environment.  Note that as usual, the callable itself
     is pickled by name.

   * A tuple of arguments for the callable object.

     Modifié dans la version 2.5: Formerly, this argument could also
     be "None".

   * Optionally, the object’s state, which will be passed to the
     object’s "__setstate__()" method as described in section Pickling
     and unpickling normal class instances.  If the object has no
     "__setstate__()" method, then, as above, the value must be a
     dictionary and it will be added to the object’s "__dict__".

   * Optionally, an iterator (and not a sequence) yielding
     successive list items.  These list items will be pickled, and
     appended to the object using either "obj.append(item)" or
     "obj.extend(list_of_items)".  This is primarily used for list
     subclasses, but may be used by other classes as long as they have
     "append()" and "extend()" methods with the appropriate signature.
     (Whether "append()" or "extend()" is used depends on which pickle
     protocol version is used as well as the number of items to
     append, so both must be supported.)

   * Optionally, an iterator (not a sequence) yielding successive
     dictionary items, which should be tuples of the form "(key,
     value)".  These items will be pickled and stored to the object
     using "obj[key] = value". This is primarily used for dictionary
     subclasses, but may be used by other classes as long as they
     implement "__setitem__()".

object.__reduce_ex__(protocol)

   It is sometimes useful to know the protocol version when
   implementing "__reduce__()".  This can be done by implementing a
   method named "__reduce_ex__()" instead of "__reduce__()".
   "__reduce_ex__()", when it exists, is called in preference over
   "__reduce__()" (you may still provide "__reduce__()" for backwards
   compatibility).  The "__reduce_ex__()" method will be called with a
   single integer argument, the protocol version.

   The "object" class implements both "__reduce__()" and
   "__reduce_ex__()"; however, if a subclass overrides "__reduce__()"
   but not "__reduce_ex__()", the "__reduce_ex__()" implementation
   detects this and calls "__reduce__()".

An alternative to implementing a "__reduce__()" method on the object
to be pickled, is to register the callable with the "copy_reg" module.
This module provides a way for programs to register « reduction
functions » and constructors for user-defined types.   Reduction
functions have the same semantics and interface as the "__reduce__()"
method described above, except that they are called with a single
argument, the object to be pickled.

The registered constructor is deemed a « safe constructor » for
purposes of unpickling as described above.


11.1.5.3. Pickling and unpickling external objects
--------------------------------------------------

For the benefit of object persistence, the "pickle" module supports
the notion of a reference to an object outside the pickled data
stream.  Such objects are referenced by a « persistent id », which is
just an arbitrary string of printable ASCII characters. The resolution
of such names is not defined by the "pickle" module; it will delegate
this resolution to user defined functions on the pickler and
unpickler. [7]

To define external persistent id resolution, you need to set the
"persistent_id" attribute of the pickler object and the
"persistent_load" attribute of the unpickler object.

To pickle objects that have an external persistent id, the pickler
must have a custom "persistent_id()" method that takes an object as an
argument and returns either "None" or the persistent id for that
object. When "None" is returned, the pickler simply pickles the object
as normal. When a persistent id string is returned, the pickler will
pickle that string, along with a marker so that the unpickler will
recognize the string as a persistent id.

To unpickle external objects, the unpickler must have a custom
"persistent_load()" function that takes a persistent id string and
returns the referenced object.

Here’s a silly example that *might* shed more light:

   import pickle
   from cStringIO import StringIO

   src = StringIO()
   p = pickle.Pickler(src)

   def persistent_id(obj):
       if hasattr(obj, 'x'):
           return 'the value %d' % obj.x
       else:
           return None

   p.persistent_id = persistent_id

   class Integer:
       def __init__(self, x):
           self.x = x
       def __str__(self):
           return 'My name is integer %d' % self.x

   i = Integer(7)
   print i
   p.dump(i)

   datastream = src.getvalue()
   print repr(datastream)
   dst = StringIO(datastream)

   up = pickle.Unpickler(dst)

   class FancyInteger(Integer):
       def __str__(self):
           return 'I am the integer %d' % self.x

   def persistent_load(persid):
       if persid.startswith('the value '):
           value = int(persid.split()[2])
           return FancyInteger(value)
       else:
           raise pickle.UnpicklingError, 'Invalid persistent id'

   up.persistent_load = persistent_load

   j = up.load()
   print j

In the "cPickle" module, the unpickler’s "persistent_load" attribute
can also be set to a Python list, in which case, when the unpickler
reaches a persistent id, the persistent id string will simply be
appended to this list.  This functionality exists so that a pickle
data stream can be « sniffed » for object references without actually
instantiating all the objects in a pickle. [8]  Setting
"persistent_load" to a list is usually used in conjunction with the
"noload()" method on the Unpickler.


11.1.6. Subclassing Unpicklers
==============================

By default, unpickling will import any class that it finds in the
pickle data. You can control exactly what gets unpickled and what gets
called by customizing your unpickler.  Unfortunately, exactly how you
do this is different depending on whether you’re using "pickle" or
"cPickle". [9]

In the "pickle" module, you need to derive a subclass from
"Unpickler", overriding the "load_global()" method. "load_global()"
should read two lines from the pickle data stream where the first line
will the name of the module containing the class and the second line
will be the name of the instance’s class.  It then looks up the class,
possibly importing the module and digging out the attribute, then it
appends what it finds to the unpickler’s stack.  Later on, this class
will be assigned to the "__class__" attribute of an empty class, as a
way of magically creating an instance without calling its class’s
"__init__()". Your job (should you choose to accept it), would be to
have "load_global()" push onto the unpickler’s stack, a known safe
version of any class you deem safe to unpickle. It is up to you to
produce such a class.  Or you could raise an error if you want to
disallow all unpickling of instances.  If this sounds like a hack,
you’re right.  Refer to the source code to make this work.

Things are a little cleaner with "cPickle", but not by much. To
control what gets unpickled, you can set the unpickler’s "find_global"
attribute to a function or "None".  If it is "None" then any attempts
to unpickle instances will raise an "UnpicklingError".  If it is a
function, then it should accept a module name and a class name, and
return the corresponding class object.  It is responsible for looking
up the class and performing any necessary imports, and it may raise an
error to prevent instances of the class from being unpickled.

The moral of the story is that you should be really careful about the
source of the strings your application unpickles.


11.1.7. Exemple
===============

For the simplest code, use the "dump()" and "load()" functions.  Note
that a self-referencing list is pickled and restored correctly.

   import pickle

   data1 = {'a': [1, 2.0, 3, 4+6j],
            'b': ('string', u'Unicode string'),
            'c': None}

   selfref_list = [1, 2, 3]
   selfref_list.append(selfref_list)

   output = open('data.pkl', 'wb')

   # Pickle dictionary using protocol 0.
   pickle.dump(data1, output)

   # Pickle the list using the highest protocol available.
   pickle.dump(selfref_list, output, -1)

   output.close()

The following example reads the resulting pickled data.  When reading
a pickle-containing file, you should open the file in binary mode
because you can’t be sure if the ASCII or binary format was used.

   import pprint, pickle

   pkl_file = open('data.pkl', 'rb')

   data1 = pickle.load(pkl_file)
   pprint.pprint(data1)

   data2 = pickle.load(pkl_file)
   pprint.pprint(data2)

   pkl_file.close()

Here’s a larger example that shows how to modify pickling behavior for
a class. The "TextReader" class opens a text file, and returns the
line number and line contents each time its "readline()" method is
called. If a "TextReader" instance is pickled, all attributes *except*
the file object member are saved. When the instance is unpickled, the
file is reopened, and reading resumes from the last location. The
"__setstate__()" and "__getstate__()" methods are used to implement
this behavior.

   #!/usr/local/bin/python

   class TextReader:
       """Print and number lines in a text file."""
       def __init__(self, file):
           self.file = file
           self.fh = open(file)
           self.lineno = 0

       def readline(self):
           self.lineno = self.lineno + 1
           line = self.fh.readline()
           if not line:
               return None
           if line.endswith("\n"):
               line = line[:-1]
           return "%d: %s" % (self.lineno, line)

       def __getstate__(self):
           odict = self.__dict__.copy() # copy the dict since we change it
           del odict['fh']              # remove filehandle entry
           return odict

       def __setstate__(self, dict):
           fh = open(dict['file'])      # reopen file
           count = dict['lineno']       # read from file...
           while count:                 # until line count is restored
               fh.readline()
               count = count - 1
           self.__dict__.update(dict)   # update attributes
           self.fh = fh                 # save the file object

A sample usage might be something like this:

   >>> import TextReader
   >>> obj = TextReader.TextReader("TextReader.py")
   >>> obj.readline()
   '1: #!/usr/local/bin/python'
   >>> obj.readline()
   '2: '
   >>> obj.readline()
   '3: class TextReader:'
   >>> import pickle
   >>> pickle.dump(obj, open('save.p', 'wb'))

If you want to see that "pickle" works across Python processes, start
another Python session, before continuing.  What follows can happen
from either the same process or a new process.

   >>> import pickle
   >>> reader = pickle.load(open('save.p', 'rb'))
   >>> reader.readline()
   '4:     """Print and number lines in a text file."""'

Voir aussi:

  Module "copy_reg"
     Pickle interface constructor registration for extension types.

  Module "shelve"
     Indexed databases of objects; uses "pickle".

  Module "copy"
     Shallow and deep object copying.

  Module "marshal"
     High-performance serialization of built-in types.


11.2. "cPickle" — A faster "pickle"
***********************************

The "cPickle" module supports serialization and de-serialization of
Python objects, providing an interface and functionality nearly
identical to the "pickle" module.  There are several differences, the
most important being performance and subclassability.

First, "cPickle" can be up to 1000 times faster than "pickle" because
the former is implemented in C.  Second, in the "cPickle" module the
callables "Pickler()" and "Unpickler()" are functions, not classes.
This means that you cannot use them to derive custom pickling and
unpickling subclasses.  Most applications have no need for this
functionality and should benefit from the greatly improved performance
of the "cPickle" module.

The pickle data stream produced by "pickle" and "cPickle" are
identical, so it is possible to use "pickle" and "cPickle"
interchangeably with existing pickles. [10]

There are additional minor differences in API between "cPickle" and
"pickle", however for most applications, they are interchangeable.
More documentation is provided in the "pickle" module documentation,
which includes a list of the documented differences.

-[ Notes ]-

[1] Don’t confuse this with the "marshal" module

[2] In the "pickle" module these callables are classes, which you
    could subclass to customize the behavior.  However, in the
    "cPickle" module these callables are factory functions and so
    cannot be subclassed.  One common reason to subclass is to control
    what objects can actually be unpickled.  See section Subclassing
    Unpicklers for more details.

[3] *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: (1) detecting
    changes, and (2) marshalling a minimal set of changes.  Garbage
    Collection may also become a problem here.

[4] The exception raised will likely be an "ImportError" or an
    "AttributeError" but it could be something else.

[5] These methods can also be used to implement copying class
    instances.

[6] This protocol is also used by the shallow and deep copying
    operations defined in the "copy" module.

[7] The actual mechanism for associating these user defined
    functions is slightly different for "pickle" and "cPickle".  The
    description given here works the same for both implementations.
    Users of the "pickle" module could also use subclassing to effect
    the same results, overriding the "persistent_id()" and
    "persistent_load()" methods in the derived classes.

[8] We’ll leave you with the image of Guido and Jim sitting around
    sniffing pickles in their living rooms.

[9] A word of caution: the mechanisms described here use internal
    attributes and methods, which are subject to change in future
    versions of Python.  We intend to someday provide a common
    interface for controlling this behavior, which will work in either
    "pickle" or "cPickle".

[10] Since the pickle data format is actually a tiny stack-
     oriented programming language, and some freedom is taken in the
     encodings of certain objects, it is possible that the two modules
     produce different data streams for the same input objects.
     However it is guaranteed that they will always be able to read
     each other’s data streams.
