"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**. Only unpickle data you
  trust.It is possible to construct malicious pickle data which will
  **execute arbitrary code during unpickling**. Never unpickle data
  that could have come from an untrusted source, or that could have
  been tampered with.Consider signing data with "hmac" if you need to
  ensure that it has not been tampered with.Safer serialization
  formats such as "json" may be more appropriate if you are processing
  untrusted data. See Comparison with json.


Relationship to other Python modules
====================================


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
  provided a compatible pickle protocol is chosen and pickling and
  unpickling code deals with Python 2 to Python 3 type differences if
  your data is crossing that unique breaking change language boundary.


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);

* Unlike pickle, deserializing untrusted JSON does not in itself
  create an arbitrary code execution vulnerability.

See also:

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


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 (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 6 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 classes*.  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 was
  the default protocol in Python 3.0--3.7.

* 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.  It is the default protocol starting with
  Python 3.8. Refer to **PEP 3154** for information about improvements
  brought by protocol 4.

* Protocol version 5 was added in Python 3.8.  It adds support for
  out-of-band data and speedup for in-band data.  Refer to **PEP 574**
  for information about improvements brought by protocol 5.

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.


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 4,
   first introduced in Python 3.4 and incompatible with previous
   versions.

   Changed in version 3.0: The default protocol is 3.

   Changed in version 3.8: The default protocol is 4.

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

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

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

   Arguments *file*, *protocol*, *fix_imports* and *buffer_callback*
   have the same meaning as in the "Pickler" constructor.

   Changed in version 3.8: The *buffer_callback* argument was added.

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

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

   Arguments *protocol*, *fix_imports* and *buffer_callback* have the
   same meaning as in the "Pickler" constructor.

   Changed in version 3.8: The *buffer_callback* argument was added.

pickle.load(file, *, fix_imports=True, encoding='ASCII', errors='strict', buffers=None)

   Read the pickled representation of an object 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 representation
   of the object are ignored.

   Arguments *file*, *fix_imports*, *encoding*, *errors*, *strict* and
   *buffers* have the same meaning as in the "Unpickler" constructor.

   Changed in version 3.8: The *buffers* argument was added.

pickle.loads(data, /, *, fix_imports=True, encoding='ASCII', errors='strict', buffers=None)

   Return the reconstituted object hierarchy of the pickled
   representation *data* of an object. *data* must be a *bytes-like
   object*.

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

   Arguments *fix_imports*, *encoding*, *errors*, *strict* and
   *buffers* have the same meaning as in the "Unpickler" constructor.

   Changed in version 3.8: The *buffers* argument was added.

The "pickle" module defines three exceptions:

exception pickle.PickleError

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

exception pickle.PicklingError

   Error raised when an unpicklable object is encountered by
   "Pickler". It inherits from "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 from
   "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 three classes, "Pickler", "Unpickler" and
"PickleBuffer":

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

   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.

   If *buffer_callback* is "None" (the default), buffer views are
   serialized into *file* as part of the pickle stream.

   If *buffer_callback* is not "None", then it can be called any
   number of times with a buffer view.  If the callback returns a
   false value (such as "None"), the given buffer is out-of-band;
   otherwise the buffer is serialized in-band, i.e. inside the pickle
   stream.

   It is an error if *buffer_callback* is not "None" and *protocol* is
   "None" or smaller than 5.

   Changed in version 3.8: The *buffer_callback* argument was added.

   dump(obj)

      Write the 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.

      Changed in version 3.13: Add the default implementation of this
      method in the C implementation of "Pickler".

   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.

      Added in version 3.3.

   reducer_override(obj)

      Special reducer that can be defined in "Pickler" subclasses.
      This method has priority over any reducer in the
      "dispatch_table".  It should conform to the same interface as a
      "__reduce__()" method, and can optionally return
      "NotImplemented" to fallback on "dispatch_table"-registered
      reducers to pickle "obj".

      For a detailed example, see Custom Reduction for Types,
      Functions, and Other Objects.

      Added in version 3.8.

   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', buffers=None)

   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 three methods, a read() method that
   takes an integer argument, a readinto() method that takes a buffer
   argument and a readline() method that requires no arguments, as in
   the "io.BufferedIOBase" interface.  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.

   The optional arguments *fix_imports*, *encoding* and *errors* 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.

   If *buffers* is "None" (the default), then all data necessary for
   deserialization must be contained in the pickle stream.  This means
   that the *buffer_callback* argument was "None" when a "Pickler" was
   instantiated (or when "dump()" or "dumps()" was called).

   If *buffers* is not "None", it should be an iterable of buffer-
   enabled objects that is consumed each time the pickle stream
   references an out-of-band buffer view.  Such buffers have been
   given in order to the *buffer_callback* of a Pickler object.

   Changed in version 3.8: The *buffers* argument was added.

   load()

      Read the pickled representation of an object from the open file
      object given in the constructor, and return the reconstituted
      object hierarchy specified therein.  Bytes past the pickled
      representation of the object 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.

      Changed in version 3.13: Add the default implementation of this
      method in the C implementation of "Unpickler".

   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.

      Raises an auditing event "pickle.find_class" with arguments
      "module", "name".

class pickle.PickleBuffer(buffer)

   A wrapper for a buffer representing picklable data.  *buffer* must
   be a buffer-providing object, such as a *bytes-like object* or a
   N-dimensional array.

   "PickleBuffer" is itself a buffer provider, therefore it is
   possible to pass it to other APIs expecting a buffer-providing
   object, such as "memoryview".

   "PickleBuffer" objects can only be serialized using pickle protocol
   5 or higher.  They are eligible for out-of-band serialization.

   Added in version 3.8.

   raw()

      Return a "memoryview" of the memory area underlying this buffer.
      The returned object is a one-dimensional, C-contiguous
      memoryview with format "B" (unsigned bytes).  "BufferError" is
      raised if the buffer is neither C- nor Fortran-contiguous.

   release()

      Release the underlying buffer exposed by the PickleBuffer
      object.


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

The following types can be pickled:

* built-in constants ("None", "True", "False", "Ellipsis", and
  "NotImplemented");

* integers, floating-point numbers, complex numbers;

* strings, bytes, bytearrays;

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

* functions (built-in and user-defined) accessible from the top level
  of a module (using "def", not "lambda");

* classes accessible from the top level of a module;

* instances of such classes whose 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*, not by value. [2]  This means that only the function
name is pickled, along with the name of the containing module and
classes.  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 fully qualified name, 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 at 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.


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 restore(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 by
   overriding the method "__getstate__()".  It is called and the
   returned object is pickled as the contents for the instance,
   instead of a default state. There are several cases:

   * For a class that has no instance "__dict__" and no "__slots__",
     the default state is "None".

   * For a class that has an instance "__dict__" and no "__slots__",
     the default state is "self.__dict__".

   * For a class that has an instance "__dict__" and "__slots__", the
     default state is a tuple consisting of two dictionaries:
     "self.__dict__", and a dictionary mapping slot names to slot
     values.  Only slots that have a value are included in the latter.

   * For a class that has "__slots__" and no instance "__dict__", the
     default state is a tuple whose first item is "None" and whose
     second item is a dictionary mapping slot names to slot values
     described in the previous bullet.

   Changed in version 3.11: Added the default implementation of the
   "__getstate__()" method in the "object" class.

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 "__reduce__()" returns a state with value "None" at pickling,
     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 "__new__()" to establish such
  an invariant, as "__init__()" is not called when unpickling an
  instance.

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 six 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__()".

   * Optionally, a callable with a "(obj, state)" signature. This
     callable allows the user to programmatically control the state-
     updating behavior of a specific object, instead of using "obj"'s
     static "__setstate__()" method. If not "None", this callable will
     have priority over "obj"'s "__setstate__()".

     Added in version 3.8: The optional sixth tuple item, "(obj,
     state)", was added.

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.


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()


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 private dispatch table.  On the other hand, the code

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

modifies the global dispatch table shared by all users of the
"copyreg" module.


Handling Stateful Objects
-------------------------

Here's an example that shows how to modify pickling behavior for a
class. The "TextReader" class below 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!'


Custom Reduction for Types, Functions, and Other Objects
========================================================

Added in version 3.8.

Sometimes, "dispatch_table" may not be flexible enough. In particular
we may want to customize pickling based on another criterion than the
object's type, or we may want to customize the pickling of functions
and classes.

For those cases, it is possible to subclass from the "Pickler" class
and implement a "reducer_override()" method. This method can return an
arbitrary reduction tuple (see "__reduce__()"). It can alternatively
return "NotImplemented" to fallback to the traditional behavior.

If both the "dispatch_table" and "reducer_override()" are defined,
then "reducer_override()" method takes priority.

Note:

  For performance reasons, "reducer_override()" may not be called for
  the following objects: "None", "True", "False", and exact instances
  of "int", "float", "bytes", "str", "dict", "set", "frozenset",
  "list" and "tuple".

Here is a simple example where we allow pickling and reconstructing a
given class:

   import io
   import pickle

   class MyClass:
       my_attribute = 1

   class MyPickler(pickle.Pickler):
       def reducer_override(self, obj):
           """Custom reducer for MyClass."""
           if getattr(obj, "__name__", None) == "MyClass":
               return type, (obj.__name__, obj.__bases__,
                             {'my_attribute': obj.my_attribute})
           else:
               # For any other object, fallback to usual reduction
               return NotImplemented

   f = io.BytesIO()
   p = MyPickler(f)
   p.dump(MyClass)

   del MyClass

   unpickled_class = pickle.loads(f.getvalue())

   assert isinstance(unpickled_class, type)
   assert unpickled_class.__name__ == "MyClass"
   assert unpickled_class.my_attribute == 1


Out-of-band Buffers
===================

Added in version 3.8.

In some contexts, the "pickle" module is used to transfer massive
amounts of data.  Therefore, it can be important to minimize the
number of memory copies, to preserve performance and resource
consumption.  However, normal operation of the "pickle" module, as it
transforms a graph-like structure of objects into a sequential stream
of bytes, intrinsically involves copying data to and from the pickle
stream.

This constraint can be eschewed if both the *provider* (the
implementation of the object types to be transferred) and the
*consumer* (the implementation of the communications system) support
the out-of-band transfer facilities provided by pickle protocol 5 and
higher.


Provider API
------------

The large data objects to be pickled must implement a
"__reduce_ex__()" method specialized for protocol 5 and higher, which
returns a "PickleBuffer" instance (instead of e.g. a "bytes" object)
for any large data.

A "PickleBuffer" object *signals* that the underlying buffer is
eligible for out-of-band data transfer.  Those objects remain
compatible with normal usage of the "pickle" module.  However,
consumers can also opt-in to tell "pickle" that they will handle those
buffers by themselves.


Consumer API
------------

A communications system can enable custom handling of the
"PickleBuffer" objects generated when serializing an object graph.

On the sending side, it needs to pass a *buffer_callback* argument to
"Pickler" (or to the "dump()" or "dumps()" function), which will be
called with each "PickleBuffer" generated while pickling the object
graph.  Buffers accumulated by the *buffer_callback* will not see
their data copied into the pickle stream, only a cheap marker will be
inserted.

On the receiving side, it needs to pass a *buffers* argument to
"Unpickler" (or to the "load()" or "loads()" function), which is an
iterable of the buffers which were passed to *buffer_callback*. That
iterable should produce buffers in the same order as they were passed
to *buffer_callback*.  Those buffers will provide the data expected by
the reconstructors of the objects whose pickling produced the original
"PickleBuffer" objects.

Between the sending side and the receiving side, the communications
system is free to implement its own transfer mechanism for out-of-band
buffers. Potential optimizations include the use of shared memory or
datatype-dependent compression.


Example
-------

Here is a trivial example where we implement a "bytearray" subclass
able to participate in out-of-band buffer pickling:

   class ZeroCopyByteArray(bytearray):

       def __reduce_ex__(self, protocol):
           if protocol >= 5:
               return type(self)._reconstruct, (PickleBuffer(self),), None
           else:
               # PickleBuffer is forbidden with pickle protocols <= 4.
               return type(self)._reconstruct, (bytearray(self),)

       @classmethod
       def _reconstruct(cls, obj):
           with memoryview(obj) as m:
               # Get a handle over the original buffer object
               obj = m.obj
               if type(obj) is cls:
                   # Original buffer object is a ZeroCopyByteArray, return it
                   # as-is.
                   return obj
               else:
                   return cls(obj)

The reconstructor (the "_reconstruct" class method) returns the
buffer's providing object if it has the right type.  This is an easy
way to simulate zero-copy behaviour on this toy example.

On the consumer side, we can pickle those objects the usual way, which
when unserialized will give us a copy of the original object:

   b = ZeroCopyByteArray(b"abc")
   data = pickle.dumps(b, protocol=5)
   new_b = pickle.loads(data)
   print(b == new_b)  # True
   print(b is new_b)  # False: a copy was made

But if we pass a *buffer_callback* and then give back the accumulated
buffers when unserializing, we are able to get back the original
object:

   b = ZeroCopyByteArray(b"abc")
   buffers = []
   data = pickle.dumps(b, protocol=5, buffer_callback=buffers.append)
   new_b = pickle.loads(data, buffers=buffers)
   print(b == new_b)  # True
   print(b is new_b)  # True: no copy was made

This example is limited by the fact that "bytearray" allocates its own
memory: you cannot create a "bytearray" instance that is backed by
another object's memory.  However, third-party datatypes such as NumPy
arrays do not have this limitation, and allow use of zero-copy
pickling (or making as few copies as possible) when transferring
between distinct processes or systems.

See also: **PEP 574** -- Pickle protocol 5 with out-of-band data


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 as 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.


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.


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+4j],
       '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 that
    persistent IDs in protocol 0 are delimited by the newline
    character.  Therefore if any kind of newline characters occurs in
    persistent IDs, the resulting pickled data will become unreadable.
