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

**Source code:** Lib/pickle.py

======================================================================

The "pickle" module implements binary protocols 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 (from a
*binary file* or *bytes-like object*) 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”.

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


12.1.1. Relationship to other Python modules
============================================


12.1.1.1. Comparison with "marshal"
-----------------------------------

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.


12.1.1.2. Comparison with "json"
--------------------------------

There are fundamental differences between the pickle protocols and
JSON (JavaScript Object Notation):

* JSON is a text serialization format (it outputs unicode text,
  although most of the time it is then encoded to "utf-8"), while
  pickle is a binary serialization format;

* JSON is human-readable, while pickle is not;

* JSON is interoperable and widely used outside of the Python
  ecosystem, while pickle is Python-specific;

* JSON, by default, can only represent a subset of the Python built-
  in types, and no custom classes; pickle can represent an extremely
  large number of Python types (many of them automatically, by clever
  usage of Python’s introspection facilities; complex cases can be
  tackled by implementing specific object APIs).

See also: The "json" module: a standard library module allowing JSON
  serialization and deserialization.


12.1.2. 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 JSON or 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 relatively compact binary
representation.  If you need optimal size characteristics, you can
efficiently compress pickled data.

The module "pickletools" contains tools for analyzing data streams
generated by "pickle".  "pickletools" source code has extensive
comments about opcodes used by pickle protocols.

There are currently 5 different protocols which can be used for
pickling. The higher the protocol used, the more recent the version of
Python needed to read the pickle produced.

* Protocol version 0 is the original “human-readable” protocol and
  is backwards compatible with earlier versions of Python.

* Protocol version 1 is an 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 information about improvements brought by protocol 2.

* Protocol version 3 was added in Python 3.0.  It has explicit
  support for "bytes" objects and cannot be unpickled by Python 2.x.
  This is the default protocol, and the recommended protocol when
  compatibility with other Python 3 versions is required.

* Protocol version 4 was added in Python 3.4.  It adds support for
  very large objects, pickling more kinds of objects, and some data
  format optimizations.  Refer to **PEP 3154** for information about
  improvements brought by protocol 4.

Note: 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 "shelve" module provides a simple interface to pickle
  and unpickle objects on DBM- style database files.


12.1.3. Module Interface
========================

To serialize an object hierarchy, you simply call the "dumps()"
function. Similarly, to de-serialize a data stream, you call the
"loads()" function. However, if you want more control over
serialization and de-serialization, you can create a "Pickler" or an
"Unpickler" object, respectively.

The "pickle" module provides the following constants:

pickle.HIGHEST_PROTOCOL

   An integer, the highest protocol version available.  This value can
   be passed as a *protocol* value to functions "dump()" and "dumps()"
   as well as the "Pickler" constructor.

pickle.DEFAULT_PROTOCOL

   An integer, the default protocol version used for pickling.  May be
   less than "HIGHEST_PROTOCOL".  Currently the default protocol is 3,
   a new protocol designed for Python 3.

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

pickle.dump(obj, file, protocol=None, *, fix_imports=True)

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

   The optional *protocol* argument, an integer, tells the pickler to
   use the given protocol; supported protocols are 0 to
   "HIGHEST_PROTOCOL". If not specified, the default is
   "DEFAULT_PROTOCOL".  If a negative number is specified,
   "HIGHEST_PROTOCOL" is selected.

   The *file* argument must have a write() method that accepts a
   single bytes argument.  It can thus be an on-disk file opened for
   binary writing, an "io.BytesIO" instance, or any other custom
   object that meets this interface.

   If *fix_imports* is true and *protocol* is less than 3, pickle will
   try to map the new Python 3 names to the old module names used in
   Python 2, so that the pickle data stream is readable with Python 2.

pickle.dumps(obj, protocol=None, *, fix_imports=True)

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

   Arguments *protocol* and *fix_imports* have the same meaning as in
   "dump()".

pickle.load(file, *, fix_imports=True, encoding="ASCII", errors="strict")

   Read a pickled object representation from the open *file object*
   *file* and return the reconstituted object hierarchy specified
   therein. This is equivalent to "Unpickler(file).load()".

   The protocol version of the pickle is detected automatically, so no
   protocol argument is needed.  Bytes past the pickled object’s
   representation are ignored.

   The argument *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 bytes.  Thus *file* can be
   an on-disk file opened for binary reading, an "io.BytesIO" object,
   or any other custom object that meets this interface.

   Optional keyword arguments are *fix_imports*, *encoding* and
   *errors*, which are used to control compatibility support for
   pickle stream generated by Python 2.  If *fix_imports* is true,
   pickle will try to map the old Python 2 names to the new names used
   in Python 3.  The *encoding* and *errors* tell pickle how to decode
   8-bit string instances pickled by Python 2; these default to
   ‘ASCII’ and ‘strict’, respectively.  The *encoding* can be ‘bytes’
   to read these 8-bit string instances as bytes objects. Using
   "encoding='latin1'" is required for unpickling NumPy arrays and
   instances of "datetime", "date" and "time" pickled by Python 2.

pickle.loads(bytes_object, *, fix_imports=True, encoding="ASCII", errors="strict")

   Read a pickled object hierarchy from a "bytes" object and return
   the reconstituted object hierarchy specified therein.

   The protocol version of the pickle is detected automatically, so no
   protocol argument is needed.  Bytes past the pickled object’s
   representation are ignored.

   Optional keyword arguments are *fix_imports*, *encoding* and
   *errors*, which are used to control compatibility support for
   pickle stream generated by Python 2.  If *fix_imports* is true,
   pickle will try to map the old Python 2 names to the new names used
   in Python 3.  The *encoding* and *errors* tell pickle how to decode
   8-bit string instances pickled by Python 2; these default to
   ‘ASCII’ and ‘strict’, respectively.  The *encoding* can be ‘bytes’
   to read these 8-bit string instances as bytes objects. Using
   "encoding='latin1'" is required for unpickling NumPy arrays and
   instances of "datetime", "date" and "time" pickled by Python 2.

The "pickle" module defines three exceptions:

exception pickle.PickleError

   Common base class for the other pickling exceptions.  It inherits
   "Exception".

exception pickle.PicklingError

   Error raised when an unpicklable object is encountered by
   "Pickler". It inherits "PickleError".

   Refer to What can be pickled and unpickled? to learn what kinds of
   objects can be pickled.

exception pickle.UnpicklingError

   Error raised when there is a problem unpickling an object, such as
   a data corruption or a security violation.  It inherits
   "PickleError".

   Note that other exceptions may also be raised during unpickling,
   including (but not necessarily limited to) AttributeError,
   EOFError, ImportError, and IndexError.

The "pickle" module exports two classes, "Pickler" and "Unpickler":

class pickle.Pickler(file, protocol=None, *, fix_imports=True)

   This takes a binary file for writing a pickle data stream.

   The optional *protocol* argument, an integer, tells the pickler to
   use the given protocol; supported protocols are 0 to
   "HIGHEST_PROTOCOL". If not specified, the default is
   "DEFAULT_PROTOCOL".  If a negative number is specified,
   "HIGHEST_PROTOCOL" is selected.

   The *file* argument must have a write() method that accepts a
   single bytes argument.  It can thus be an on-disk file opened for
   binary writing, an "io.BytesIO" instance, or any other custom
   object that meets this interface.

   If *fix_imports* is true and *protocol* is less than 3, pickle will
   try to map the new Python 3 names to the old module names used in
   Python 2, so that the pickle data stream is readable with Python 2.

   dump(obj)

      Write a pickled representation of *obj* to the open file object
      given in the constructor.

   persistent_id(obj)

      Do nothing by default.  This exists so a subclass can override
      it.

      If "persistent_id()" returns "None", *obj* is pickled as usual.
      Any other value causes "Pickler" to emit the returned value as a
      persistent ID for *obj*.  The meaning of this persistent ID
      should be defined by "Unpickler.persistent_load()".  Note that
      the value returned by "persistent_id()" cannot itself have a
      persistent ID.

      See Persistence of External Objects for details and examples of
      uses.

   dispatch_table

      A pickler object’s dispatch table is a registry of *reduction
      functions* of the kind which can be declared using
      "copyreg.pickle()".  It is a mapping whose keys are classes and
      whose values are reduction functions.  A reduction function
      takes a single argument of the associated class and should
      conform to the same interface as a "__reduce__()" method.

      By default, a pickler object will not have a "dispatch_table"
      attribute, and it will instead use the global dispatch table
      managed by the "copyreg" module. However, to customize the
      pickling for a specific pickler object one can set the
      "dispatch_table" attribute to a dict-like object.
      Alternatively, if a subclass of "Pickler" has a "dispatch_table"
      attribute then this will be used as the default dispatch table
      for instances of that class.

      See Dispatch Tables for usage examples.

      New in version 3.3.

   fast

      Deprecated. Enable fast mode if set to a true value.  The fast
      mode disables the usage of memo, therefore speeding the pickling
      process by not generating superfluous PUT opcodes.  It should
      not be used with self-referential objects, doing otherwise will
      cause "Pickler" to recurse infinitely.

      Use "pickletools.optimize()" if you need more compact pickles.

class pickle.Unpickler(file, *, fix_imports=True, encoding="ASCII", errors="strict")

   This takes a binary file for reading a pickle data stream.

   The protocol version of the pickle is detected automatically, so no
   protocol argument is needed.

   The argument *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 bytes.  Thus *file* can be
   an on-disk file object opened for binary reading, an "io.BytesIO"
   object, or any other custom object that meets this interface.

   Optional keyword arguments are *fix_imports*, *encoding* and
   *errors*, which are used to control compatibility support for
   pickle stream generated by Python 2.  If *fix_imports* is true,
   pickle will try to map the old Python 2 names to the new names used
   in Python 3.  The *encoding* and *errors* tell pickle how to decode
   8-bit string instances pickled by Python 2; these default to
   ‘ASCII’ and ‘strict’, respectively.  The *encoding* can be ‘bytes’
   to read these 8-bit string instances as bytes objects.

   load()

      Read a pickled object representation from the open file object
      given in the constructor, and return the reconstituted object
      hierarchy specified therein.  Bytes past the pickled object’s
      representation are ignored.

   persistent_load(pid)

      Raise an "UnpicklingError" by default.

      If defined, "persistent_load()" should return the object
      specified by the persistent ID *pid*.  If an invalid persistent
      ID is encountered, an "UnpicklingError" should be raised.

      See Persistence of External Objects for details and examples of
      uses.

   find_class(module, name)

      Import *module* if necessary and return the object called *name*
      from it, where the *module* and *name* arguments are "str"
      objects.  Note, unlike its name suggests, "find_class()" is also
      used for finding functions.

      Subclasses may override this to gain control over what type of
      objects and how they can be loaded, potentially reducing
      security risks. Refer to Restricting Globals for details.


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

The following types can be pickled:

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

* integers, floating point numbers, complex numbers

* strings, bytes, bytearrays

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

* functions defined at the top level of a module (using "def", not
  "lambda")

* 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 Pickling Class
  Instances 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 "RecursionError" 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. [2]  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. [3]

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 attribute'

   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.


12.1.5. Pickling Class Instances
================================

In this section, we describe the general mechanisms available to you
to define, customize, and control how class instances are pickled and
unpickled.

In most cases, no additional code is needed to make instances
picklable.  By default, pickle will retrieve the class and the
attributes of an instance via introspection. When a class instance is
unpickled, its "__init__()" method is usually *not* invoked.  The
default behaviour first creates an uninitialized instance and then
restores the saved attributes.  The following code shows an
implementation of this behaviour:

   def save(obj):
       return (obj.__class__, obj.__dict__)

   def load(cls, attributes):
       obj = cls.__new__(cls)
       obj.__dict__.update(attributes)
       return obj

Classes can alter the default behaviour by providing one or several
special methods:

object.__getnewargs_ex__()

   In protocols 2 and newer, classes that implements the
   "__getnewargs_ex__()" method can dictate the values passed to the
   "__new__()" method upon unpickling.  The method must return a pair
   "(args, kwargs)" where *args* is a tuple of positional arguments
   and *kwargs* a dictionary of named arguments for constructing the
   object.  Those will be passed to the "__new__()" method upon
   unpickling.

   You should implement this method if the "__new__()" method of your
   class requires keyword-only arguments.  Otherwise, it is
   recommended for compatibility to implement "__getnewargs__()".

   Changed in version 3.6: "__getnewargs_ex__()" is now used in
   protocols 2 and 3.

object.__getnewargs__()

   This method serves a similar purpose as "__getnewargs_ex__()", but
   supports only positional arguments.  It must return a tuple of
   arguments "args" which will be passed to the "__new__()" method
   upon unpickling.

   "__getnewargs__()" will not be called if "__getnewargs_ex__()" is
   defined.

   Changed in version 3.6: Before Python 3.6, "__getnewargs__()" was
   called instead of "__getnewargs_ex__()" in protocols 2 and 3.

object.__getstate__()

   Classes can further influence how their instances are pickled; if
   the class defines the method "__getstate__()", it is called and the
   returned object is pickled as the contents for the instance,
   instead of the contents of the instance’s dictionary.  If the
   "__getstate__()" method is absent, the instance’s "__dict__" is
   pickled as usual.

object.__setstate__(state)

   Upon unpickling, if the class defines "__setstate__()", it is
   called with the unpickled state.  In that case, there is no
   requirement for the state object to be a dictionary.  Otherwise,
   the pickled state must be a dictionary and its items are assigned
   to the new instance’s dictionary.

   Note: If "__getstate__()" returns a false value, the
     "__setstate__()" method will not be called upon unpickling.

Refer to the section Handling Stateful Objects for more information
about how to use the methods "__getstate__()" and "__setstate__()".

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 "__getnewargs__()" or
  "__getnewargs_ex__()" to establish such an invariant; otherwise,
  neither "__new__()" nor "__init__()" will be called.

As we shall see, pickle does not use directly the methods described
above.  In fact, these methods are part of the copy protocol which
implements the "__reduce__()" special method.  The copy protocol
provides a unified interface for retrieving the data necessary for
pickling and copying objects. [4]

Although powerful, implementing "__reduce__()" directly in your
classes is error prone.  For this reason, class designers should use
the high-level interface (i.e., "__getnewargs_ex__()",
"__getstate__()" and "__setstate__()") whenever possible.  We will
show, however, cases where using "__reduce__()" is the only option or
leads to more efficient pickling or both.

object.__reduce__()

   The interface is currently defined as follows.  The "__reduce__()"
   method takes no argument and shall return either a string or
   preferably a tuple (the returned object is often referred to as the
   “reduce value”).

   If a string is returned, the string should be interpreted as the
   name of a global variable.  It should be the object’s local name
   relative to its module; the pickle module searches the module
   namespace to determine the object’s module.  This behaviour is
   typically useful for singletons.

   When a tuple is returned, it must be between two and five items
   long. Optional items can either be omitted, or "None" can be
   provided as their value.  The semantics of each item are in order:

   * A callable object that will be called to create the initial
     version of the object.

   * A tuple of arguments for the callable object.  An empty tuple
     must be given if the callable does not accept any argument.

   * Optionally, the object’s state, which will be passed to the
     object’s "__setstate__()" method as previously described.  If the
     object has no such method then, the value must be a dictionary
     and it will be added to the object’s "__dict__" attribute.

   * Optionally, an iterator (and not a sequence) yielding
     successive items. These items will be appended to the object
     either using "obj.append(item)" or, in batch, using
     "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
     key- value pairs.  These items will be 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)

   Alternatively, a "__reduce_ex__()" method may be defined.  The only
   difference is this method should take a single integer argument,
   the protocol version.  When defined, pickle will prefer it over the
   "__reduce__()" method.  In addition, "__reduce__()" automatically
   becomes a synonym for the extended version.  The main use for this
   method is to provide backwards-compatible reduce values for older
   Python releases.


12.1.5.1. Persistence of 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 should
be either a string of alphanumeric characters (for protocol 0) [5] or
just an arbitrary object (for any newer protocol).

The resolution of such persistent IDs is not defined by the "pickle"
module; it will delegate this resolution to the user defined methods
on the pickler and unpickler, "persistent_id()" and
"persistent_load()" respectively.

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 object, along with a marker so that the unpickler will
recognize it as a persistent ID.

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

Here is a comprehensive example presenting how persistent ID can be
used to pickle external objects by reference.

   # Simple example presenting how persistent ID can be used to pickle
   # external objects by reference.

   import pickle
   import sqlite3
   from collections import namedtuple

   # Simple class representing a record in our database.
   MemoRecord = namedtuple("MemoRecord", "key, task")

   class DBPickler(pickle.Pickler):

       def persistent_id(self, obj):
           # Instead of pickling MemoRecord as a regular class instance, we emit a
           # persistent ID.
           if isinstance(obj, MemoRecord):
               # Here, our persistent ID is simply a tuple, containing a tag and a
               # key, which refers to a specific record in the database.
               return ("MemoRecord", obj.key)
           else:
               # If obj does not have a persistent ID, return None. This means obj
               # needs to be pickled as usual.
               return None


   class DBUnpickler(pickle.Unpickler):

       def __init__(self, file, connection):
           super().__init__(file)
           self.connection = connection

       def persistent_load(self, pid):
           # This method is invoked whenever a persistent ID is encountered.
           # Here, pid is the tuple returned by DBPickler.
           cursor = self.connection.cursor()
           type_tag, key_id = pid
           if type_tag == "MemoRecord":
               # Fetch the referenced record from the database and return it.
               cursor.execute("SELECT * FROM memos WHERE key=?", (str(key_id),))
               key, task = cursor.fetchone()
               return MemoRecord(key, task)
           else:
               # Always raises an error if you cannot return the correct object.
               # Otherwise, the unpickler will think None is the object referenced
               # by the persistent ID.
               raise pickle.UnpicklingError("unsupported persistent object")


   def main():
       import io
       import pprint

       # Initialize and populate our database.
       conn = sqlite3.connect(":memory:")
       cursor = conn.cursor()
       cursor.execute("CREATE TABLE memos(key INTEGER PRIMARY KEY, task TEXT)")
       tasks = (
           'give food to fish',
           'prepare group meeting',
           'fight with a zebra',
           )
       for task in tasks:
           cursor.execute("INSERT INTO memos VALUES(NULL, ?)", (task,))

       # Fetch the records to be pickled.
       cursor.execute("SELECT * FROM memos")
       memos = [MemoRecord(key, task) for key, task in cursor]
       # Save the records using our custom DBPickler.
       file = io.BytesIO()
       DBPickler(file).dump(memos)

       print("Pickled records:")
       pprint.pprint(memos)

       # Update a record, just for good measure.
       cursor.execute("UPDATE memos SET task='learn italian' WHERE key=1")

       # Load the records from the pickle data stream.
       file.seek(0)
       memos = DBUnpickler(file, conn).load()

       print("Unpickled records:")
       pprint.pprint(memos)


   if __name__ == '__main__':
       main()


12.1.5.2. Dispatch Tables
-------------------------

If one wants to customize pickling of some classes without disturbing
any other code which depends on pickling, then one can create a
pickler with a private dispatch table.

The global dispatch table managed by the "copyreg" module is available
as "copyreg.dispatch_table".  Therefore, one may choose to use a
modified copy of "copyreg.dispatch_table" as a private dispatch table.

For example

   f = io.BytesIO()
   p = pickle.Pickler(f)
   p.dispatch_table = copyreg.dispatch_table.copy()
   p.dispatch_table[SomeClass] = reduce_SomeClass

creates an instance of "pickle.Pickler" with a private dispatch table
which handles the "SomeClass" class specially.  Alternatively, the
code

   class MyPickler(pickle.Pickler):
       dispatch_table = copyreg.dispatch_table.copy()
       dispatch_table[SomeClass] = reduce_SomeClass
   f = io.BytesIO()
   p = MyPickler(f)

does the same, but all instances of "MyPickler" will by default share
the same dispatch table.  The equivalent code using the "copyreg"
module is

   copyreg.pickle(SomeClass, reduce_SomeClass)
   f = io.BytesIO()
   p = pickle.Pickler(f)


12.1.5.3. Handling Stateful Objects
-----------------------------------

Here’s an 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.

   class TextReader:
       """Print and number lines in a text file."""

       def __init__(self, filename):
           self.filename = filename
           self.file = open(filename)
           self.lineno = 0

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

       def __getstate__(self):
           # Copy the object's state from self.__dict__ which contains
           # all our instance attributes. Always use the dict.copy()
           # method to avoid modifying the original state.
           state = self.__dict__.copy()
           # Remove the unpicklable entries.
           del state['file']
           return state

       def __setstate__(self, state):
           # Restore instance attributes (i.e., filename and lineno).
           self.__dict__.update(state)
           # Restore the previously opened file's state. To do so, we need to
           # reopen it and read from it until the line count is restored.
           file = open(self.filename)
           for _ in range(self.lineno):
               file.readline()
           # Finally, save the file.
           self.file = file

A sample usage might be something like this:

   >>> reader = TextReader("hello.txt")
   >>> reader.readline()
   '1: Hello world!'
   >>> reader.readline()
   '2: I am line number two.'
   >>> new_reader = pickle.loads(pickle.dumps(reader))
   >>> new_reader.readline()
   '3: Goodbye!'


12.1.6. Restricting Globals
===========================

By default, unpickling will import any class or function that it finds
in the pickle data.  For many applications, this behaviour is
unacceptable as it permits the unpickler to import and invoke
arbitrary code.  Just consider what this hand-crafted pickle data
stream does when loaded:

   >>> import pickle
   >>> pickle.loads(b"cos\nsystem\n(S'echo hello world'\ntR.")
   hello world
   0

In this example, the unpickler imports the "os.system()" function and
then apply the string argument “echo hello world”.  Although this
example is inoffensive, it is not difficult to imagine one that could
damage your system.

For this reason, you may want to control what gets unpickled by
customizing "Unpickler.find_class()".  Unlike its name suggests,
"Unpickler.find_class()" is called whenever a global (i.e., a class or
a function) is requested.  Thus it is possible to either completely
forbid globals or restrict them to a safe subset.

Here is an example of an unpickler allowing only few safe classes from
the "builtins" module to be loaded:

   import builtins
   import io
   import pickle

   safe_builtins = {
       'range',
       'complex',
       'set',
       'frozenset',
       'slice',
   }

   class RestrictedUnpickler(pickle.Unpickler):

       def find_class(self, module, name):
           # Only allow safe classes from builtins.
           if module == "builtins" and name in safe_builtins:
               return getattr(builtins, name)
           # Forbid everything else.
           raise pickle.UnpicklingError("global '%s.%s' is forbidden" %
                                        (module, name))

   def restricted_loads(s):
       """Helper function analogous to pickle.loads()."""
       return RestrictedUnpickler(io.BytesIO(s)).load()

A sample usage of our unpickler working has intended:

   >>> restricted_loads(pickle.dumps([1, 2, range(15)]))
   [1, 2, range(0, 15)]
   >>> restricted_loads(b"cos\nsystem\n(S'echo hello world'\ntR.")
   Traceback (most recent call last):
     ...
   pickle.UnpicklingError: global 'os.system' is forbidden
   >>> restricted_loads(b'cbuiltins\neval\n'
   ...                  b'(S\'getattr(__import__("os"), "system")'
   ...                  b'("echo hello world")\'\ntR.')
   Traceback (most recent call last):
     ...
   pickle.UnpicklingError: global 'builtins.eval' is forbidden

As our examples shows, you have to be careful with what you allow to
be unpickled.  Therefore if security is a concern, you may want to
consider alternatives such as the marshalling API in "xmlrpc.client"
or third-party solutions.


12.1.7. Performance
===================

Recent versions of the pickle protocol (from protocol 2 and upwards)
feature efficient binary encodings for several common features and
built-in types. Also, the "pickle" module has a transparent optimizer
written in C.


12.1.8. Examples
================

For the simplest code, use the "dump()" and "load()" functions.

   import pickle

   # An arbitrary collection of objects supported by pickle.
   data = {
       'a': [1, 2.0, 3, 4+6j],
       'b': ("character string", b"byte string"),
       'c': {None, True, False}
   }

   with open('data.pickle', 'wb') as f:
       # Pickle the 'data' dictionary using the highest protocol available.
       pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)

The following example reads the resulting pickled data.

   import pickle

   with open('data.pickle', 'rb') as f:
       # The protocol version used is detected automatically, so we do not
       # have to specify it.
       data = pickle.load(f)

See also:

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

  Module "pickletools"
     Tools for working with and analyzing pickled data.

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

  Module "copy"
     Shallow and deep object copying.

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

-[ Footnotes ]-

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

[2] This is why "lambda" functions cannot be pickled:  all
    "lambda" functions share the same name:  "<lambda>".

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

[4] The "copy" module uses this protocol for shallow and deep
    copying operations.

[5] The limitation on alphanumeric characters is due to the fact
    the persistent IDs, in protocol 0, are delimited by the newline
    character.  Therefore if any kind of newline characters occurs in
    persistent IDs, the resulting pickle will become unreadable.
