:mod:`pickle` --- Python object serialization
=============================================
.. module:: pickle
:synopsis: Convert Python objects to streams of bytes and back.
.. sectionauthor:: Jim Kerr <jbkerr@sr.hp.com>.
.. sectionauthor:: Barry Warsaw <barry@python.org>
**Source code:** :source:`Lib/pickle.py`
.. index::
single: persistence
pair: persistent; objects
pair: serializing; objects
pair: marshalling; objects
pair: flattening; objects
pair: pickling; objects
--------------
The :mod:`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 :term:`binary file` or :term:`bytes-like object`) is converted
back into an object hierarchy. Pickling (and unpickling) is alternatively
known as "serialization", "marshalling," [#]_ 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 :mod:`hmac` if you need to ensure that it has not
been tampered with.
Safer serialization formats such as :mod:`json` may be more appropriate if
you are processing untrusted data. See :ref:`comparison-with-json`.
Relationship to other Python modules
------------------------------------
Comparison with ``marshal``
^^^^^^^^^^^^^^^^^^^^^^^^^^^
Python has a more primitive serialization module called :mod:`marshal`, but in
general :mod:`pickle` should always be the preferred way to serialize Python
objects. :mod:`marshal` exists primarily to support Python's :file:`.pyc`
files.
The :mod:`pickle` module differs from :mod:`marshal` in several significant ways:
* The :mod:`pickle` module keeps track of the objects it has already serialized,
so that later references to the same object won't be serialized again.
:mod:`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. :mod:`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.
* :mod:`marshal` cannot be used to serialize user-defined classes and their
instances. :mod:`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 :mod:`marshal` serialization format is not guaranteed to be portable
across Python versions. Because its primary job in life is to support
:file:`.pyc` files, the Python implementers reserve the right to change the
serialization format in non-backwards compatible ways should the need arise.
The :mod:`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:
Comparison with ``json``
^^^^^^^^^^^^^^^^^^^^^^^^
There are fundamental differences between the pickle protocols and
`JSON (JavaScript Object Notation) <https://json.org>`_:
* 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 :ref:`specific object APIs <pickle-inst>`);
* Unlike pickle, deserializing untrusted JSON does not in itself create an
arbitrary code execution vulnerability.
.. seealso::
The :mod:`json` module: a standard library module allowing JSON
serialization and deserialization.
.. _pickle-protocols:
Data stream format
------------------
.. index::
single: External Data Representation
The data format used by :mod:`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 :mod:`pickle` data format uses a relatively compact binary
representation. If you need optimal size characteristics, you can efficiently
:doc:`compress <archiving>` pickled data.
The module :mod:`pickletools` contains tools for analyzing data streams
generated by :mod:`pickle`. :mod:`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 :term:`new-style classes <new-style class>`. 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
:class:`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
:mod:`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 :mod:`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 :mod:`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 :func:`dumps` function.
Similarly, to de-serialize a data stream, you call the :func:`loads` function.
However, if you want more control over serialization and de-serialization,
you can create a :class:`Pickler` or an :class:`Unpickler` object, respectively.
The :mod:`pickle` module provides the following constants:
.. data:: HIGHEST_PROTOCOL
An integer, the highest :ref:`protocol version <pickle-protocols>`
available. This value can be passed as a *protocol* value to functions
:func:`dump` and :func:`dumps` as well as the :class:`Pickler`
constructor.
.. data:: DEFAULT_PROTOCOL
An integer, the default :ref:`protocol version <pickle-protocols>` used
for pickling. May be less than :data:`HIGHEST_PROTOCOL`. Currently the
default protocol is 4, first introduced in Python 3.4 and incompatible
with previous versions.
.. versionchanged:: 3.0
The default protocol is 3.
.. versionchanged:: 3.8
The default protocol is 4.
The :mod:`pickle` module provides the following functions to make the pickling
process more convenient:
.. function:: dump(obj, file, protocol=None, *, fix_imports=True, buffer_callback=None)
Write the pickled representation of the object *obj* to the open
:term:`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 :class:`Pickler` constructor.
.. versionchanged:: 3.8
The *buffer_callback* argument was added.
.. function:: dumps(obj, protocol=None, *, fix_imports=True, buffer_callback=None)
Return the pickled representation of the object *obj* as a :class:`bytes` object,
instead of writing it to a file.
Arguments *protocol*, *fix_imports* and *buffer_callback* have the same
meaning as in the :class:`Pickler` constructor.
.. versionchanged:: 3.8
The *buffer_callback* argument was added.
.. function:: load(file, *, fix_imports=True, encoding="ASCII", errors="strict", buffers=None)
Read the pickled representation of an object from the open :term:`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 :class:`Unpickler` constructor.
.. versionchanged:: 3.8
The *buffers* argument was added.
.. function:: 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 :term:`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 :class:`Unpickler` constructor.
.. versionchanged:: 3.8
The *buffers* argument was added.
The :mod:`pickle` module defines three exceptions:
.. exception:: PickleError
Common base class for the other pickling exceptions. It inherits
:exc:`Exception`.
.. exception:: PicklingError
Error raised when an unpicklable object is encountered by :class:`Pickler`.
It inherits :exc:`PickleError`.
Refer to :ref:`pickle-picklable` to learn what kinds of objects can be
pickled.
.. exception:: UnpicklingError
Error raised when there is a problem unpickling an object, such as a data
corruption or a security violation. It inherits :exc:`PickleError`.
Note that other exceptions may also be raised during unpickling, including
(but not necessarily limited to) AttributeError, EOFError, ImportError, and
IndexError.
The :mod:`pickle` module exports three classes, :class:`Pickler`,
:class:`Unpickler` and :class:`PickleBuffer`:
.. class:: 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 :data:`HIGHEST_PROTOCOL`.
If not specified, the default is :data:`DEFAULT_PROTOCOL`. If a negative
number is specified, :data:`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
:class:`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 :ref:`out-of-band <pickle-oob>`;
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.
.. versionchanged:: 3.8
The *buffer_callback* argument was added.
.. method:: dump(obj)
Write the pickled representation of *obj* to the open file object given in
the constructor.
.. method:: persistent_id(obj)
Do nothing by default. This exists so a subclass can override it.
If :meth:`persistent_id` returns ``None``, *obj* is pickled as usual. Any
other value causes :class:`Pickler` to emit the returned value as a
persistent ID for *obj*. The meaning of this persistent ID should be
defined by :meth:`Unpickler.persistent_load`. Note that the value
returned by :meth:`persistent_id` cannot itself have a persistent ID.
See :ref:`pickle-persistent` for details and examples of uses.
.. attribute:: dispatch_table
A pickler object's dispatch table is a registry of *reduction
functions* of the kind which can be declared using
:func:`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 :meth:`__reduce__`
method.
By default, a pickler object will not have a
:attr:`dispatch_table` attribute, and it will instead use the
global dispatch table managed by the :mod:`copyreg` module.
However, to customize the pickling for a specific pickler object
one can set the :attr:`dispatch_table` attribute to a dict-like
object. Alternatively, if a subclass of :class:`Pickler` has a
:attr:`dispatch_table` attribute then this will be used as the
default dispatch table for instances of that class.
See :ref:`pickle-dispatch` for usage examples.
.. versionadded:: 3.3
.. method:: reducer_override(obj)
Special reducer that can be defined in :class:`Pickler` subclasses. This
method has priority over any reducer in the :attr:`dispatch_table`. It
should conform to the same interface as a :meth:`__reduce__` method, and
can optionally return ``NotImplemented`` to fallback on
:attr:`dispatch_table`-registered reducers to pickle ``obj``.
For a detailed example, see :ref:`reducer_override`.
.. versionadded:: 3.8
.. attribute:: 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 :class:`Pickler` to
recurse infinitely.
Use :func:`pickletools.optimize` if you need more compact pickles.
.. class:: 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
:class:`io.BufferedIOBase` interface. Thus *file* can be an on-disk file
opened for binary reading, an :class:`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 :class:`~datetime.datetime`, :class:`~datetime.date` and
:class:`~datetime.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 :class:`Pickler`
was instantiated (or when :func:`dump` or :func:`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 :ref:`out-of-band <pickle-oob>` buffer view. Such buffers have been
given in order to the *buffer_callback* of a Pickler object.
.. versionchanged:: 3.8
The *buffers* argument was added.
.. method:: 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.
.. method:: persistent_load(pid)
Raise an :exc:`UnpicklingError` by default.
If defined, :meth:`persistent_load` should return the object specified by
the persistent ID *pid*. If an invalid persistent ID is encountered, an
:exc:`UnpicklingError` should be raised.
See :ref:`pickle-persistent` for details and examples of uses.
.. method:: find_class(module, name)
Import *module* if necessary and return the object called *name* from it,
where the *module* and *name* arguments are :class:`str` objects. Note,
unlike its name suggests, :meth:`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
:ref:`pickle-restrict` for details.
.. audit-event:: pickle.find_class module,name pickle.Unpickler.find_class
.. class:: PickleBuffer(buffer)
A wrapper for a buffer representing picklable data. *buffer* must be a
:ref:`buffer-providing <bufferobjects>` object, such as a
:term:`bytes-like object` or a N-dimensional array.
:class:`PickleBuffer` is itself a buffer provider, therefore it is
possible to pass it to other APIs expecting a buffer-providing object,
such as :class:`memoryview`.
:class:`PickleBuffer` objects can only be serialized using pickle
protocol 5 or higher. They are eligible for
:ref:`out-of-band serialization <pickle-oob>`.
.. versionadded:: 3.8
.. method:: raw()
Return a :class:`memoryview` of the memory area underlying this buffer.
The returned object is a one-dimensional, C-contiguous memoryview
with format ``B`` (unsigned bytes). :exc:`BufferError` is raised if
the buffer is neither C- nor Fortran-contiguous.
.. method:: release()
Release the underlying buffer exposed by the PickleBuffer object.
.. _pickle-picklable:
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 (built-in and user-defined) accessible from the top level of a
module (using :keyword:`def`, not :keyword:`lambda`);
* classes accessible from the top level of a module;
* instances of such classes whose the result of calling :meth:`__getstate__`
is picklable (see section :ref:`pickle-inst` for details).
Attempts to pickle unpicklable objects will raise the :exc:`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 :exc:`RecursionError` will be
raised in this case. You can carefully raise this limit with
:func:`sys.setrecursionlimit`.
Note that functions (built-in and user-defined) are pickled by fully
:term:`qualified name`, not by value. [#]_ 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. [#]_
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 :meth:`__setstate__` method.
.. _pickle-inst:
Pickling Class Instances
------------------------
.. currentmodule:: None
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 :meth:`__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:
.. method:: object.__getnewargs_ex__()
In protocols 2 and newer, classes that implements the
:meth:`__getnewargs_ex__` method can dictate the values passed to the
:meth:`__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 :meth:`__new__` method upon
unpickling.
You should implement this method if the :meth:`__new__` method of your
class requires keyword-only arguments. Otherwise, it is recommended for
compatibility to implement :meth:`__getnewargs__`.
.. versionchanged:: 3.6
:meth:`__getnewargs_ex__` is now used in protocols 2 and 3.
.. method:: object.__getnewargs__()
This method serves a similar purpose as :meth:`__getnewargs_ex__`, but
supports only positional arguments. It must return a tuple of arguments
``args`` which will be passed to the :meth:`__new__` method upon unpickling.
:meth:`__getnewargs__` will not be called if :meth:`__getnewargs_ex__` is
defined.
.. versionchanged:: 3.6
Before Python 3.6, :meth:`__getnewargs__` was called instead of
:meth:`__getnewargs_ex__` in protocols 2 and 3.
.. method:: object.__getstate__()
Classes can further influence how their instances are pickled by overriding
the method :meth:`__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 :attr:`~object.__dict__` and no
:attr:`~object.__slots__`, the default state is ``None``.
* For a class that has an instance :attr:`~object.__dict__` and no
:attr:`~object.__slots__`, the default state is ``self.__dict__``.
* For a class that has an instance :attr:`~object.__dict__` and
:attr:`~object.__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 :attr:`~object.__slots__` and no instance
:attr:`~object.__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.
.. versionchanged:: 3.11
Added the default implementation of the ``__getstate__()`` method in the
:class:`object` class.
.. method:: object.__setstate__(state)
Upon unpickling, if the class defines :meth:`__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 :meth:`__getstate__` returns a false value, the :meth:`__setstate__`
method will not be called upon unpickling.
Refer to the section :ref:`pickle-state` for more information about how to use
the methods :meth:`__getstate__` and :meth:`__setstate__`.
.. note::
At unpickling time, some methods like :meth:`__getattr__`,
:meth:`__getattribute__`, or :meth:`__setattr__` may be called upon the
instance. In case those methods rely on some internal invariant being
true, the type should implement :meth:`__new__` to establish such an
invariant, as :meth:`__init__` is not called when unpickling an
instance.
.. index:: pair: copy; protocol
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
:meth:`__reduce__` special method. The copy protocol provides a unified
interface for retrieving the data necessary for pickling and copying
objects. [#]_
Although powerful, implementing :meth:`__reduce__` directly in your classes is
error prone. For this reason, class designers should use the high-level
interface (i.e., :meth:`__getnewargs_ex__`, :meth:`__getstate__` and
:meth:`__setstate__`) whenever possible. We will show, however, cases where
using :meth:`__reduce__` is the only option or leads to more efficient pickling
or both.
.. method:: object.__reduce__()
The interface is currently defined as follows. The :meth:`__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:
.. XXX Mention __newobj__ special-case?
* 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
:meth:`__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 :attr:`~object.__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 :meth:`append` and :meth:`extend` methods with
the appropriate signature. (Whether :meth:`append` or :meth:`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 :meth:`__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
:meth:`__setstate__` method. If not ``None``, this callable will have
priority over ``obj``'s :meth:`__setstate__`.
.. versionadded:: 3.8
The optional sixth tuple item, ``(obj, state)``, was added.
.. method:: object.__reduce_ex__(protocol)
Alternatively, a :meth:`__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 :meth:`__reduce__`
method. In addition, :meth:`__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.
.. currentmodule:: pickle
.. _pickle-persistent:
Persistence of External Objects
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. index::
single: persistent_id (pickle protocol)
single: persistent_load (pickle protocol)
For the benefit of object persistence, the :mod:`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) [#]_ or just an arbitrary object (for
any newer protocol).
The resolution of such persistent IDs is not defined by the :mod:`pickle`
module; it will delegate this resolution to the user-defined methods on the
pickler and unpickler, :meth:`~Pickler.persistent_id` and
:meth:`~Unpickler.persistent_load` respectively.
To pickle objects that have an external persistent ID, the pickler must have a
custom :meth:`~Pickler.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
:meth:`~Unpickler.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.
.. literalinclude:: ../includes/dbpickle.py
.. _pickle-dispatch:
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 :mod:`copyreg` module is
available as :data:`copyreg.dispatch_table`. Therefore, one may
choose to use a modified copy of :data:`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 :class:`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 :mod:`copyreg` module.
.. _pickle-state:
Handling Stateful Objects
^^^^^^^^^^^^^^^^^^^^^^^^^
.. index::
single: __getstate__() (copy protocol)
single: __setstate__() (copy protocol)
Here's an example that shows how to modify pickling behavior for a class.
The :class:`TextReader` class opens a text file, and returns the line number and
line contents each time its :meth:`!readline` method is called. If a
:class:`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 :meth:`__setstate__` and
:meth:`__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!'
.. _reducer_override:
Custom Reduction for Types, Functions, and Other Objects
--------------------------------------------------------
.. versionadded:: 3.8
Sometimes, :attr:`~Pickler.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 :class:`Pickler` class and
implement a :meth:`~Pickler.reducer_override` method. This method can return an
arbitrary reduction tuple (see :meth:`__reduce__`). It can alternatively return
``NotImplemented`` to fallback to the traditional behavior.
If both the :attr:`~Pickler.dispatch_table` and
:meth:`~Pickler.reducer_override` are defined, then
:meth:`~Pickler.reducer_override` method takes priority.
.. Note::
For performance reasons, :meth:`~Pickler.reducer_override` may not be
called for the following objects: ``None``, ``True``, ``False``, and
exact instances of :class:`int`, :class:`float`, :class:`bytes`,
:class:`str`, :class:`dict`, :class:`set`, :class:`frozenset`, :class:`list`
and :class:`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
.. _pickle-oob:
Out-of-band Buffers
-------------------
.. versionadded:: 3.8
In some contexts, the :mod:`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 :mod:`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 :meth:`__reduce_ex__`
method specialized for protocol 5 and higher, which returns a
:class:`PickleBuffer` instance (instead of e.g. a :class:`bytes` object)
for any large data.
A :class:`PickleBuffer` object *signals* that the underlying buffer is
eligible for out-of-band data transfer. Those objects remain compatible
with normal usage of the :mod:`pickle` module. However, consumers can also
opt-in to tell :mod:`pickle` that they will handle those buffers by
themselves.
Consumer API
^^^^^^^^^^^^
A communications system can enable custom handling of the :class:`PickleBuffer`
objects generated when serializing an object graph.
On the sending side, it needs to pass a *buffer_callback* argument to
:class:`Pickler` (or to the :func:`dump` or :func:`dumps` function), which
will be called with each :class:`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
:class:`Unpickler` (or to the :func:`load` or :func:`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
:class:`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 :class:`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 :class:`bytearray` allocates its
own memory: you cannot create a :class:`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.
.. seealso:: :pep:`574` -- Pickle protocol 5 with out-of-band data
.. _pickle-restrict:
Restricting Globals
-------------------
.. index::
single: find_class() (pickle protocol)
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 :func:`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
:meth:`Unpickler.find_class`. Unlike its name suggests,
:meth:`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
:mod:`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
.. XXX Add note about how extension codes could evade our protection
mechanism (e.g. cached classes do not invokes find_class()).
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 :mod:`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 :mod:`pickle` module has a transparent optimizer written in C.
.. _pickle-example:
Examples
--------
For the simplest code, use the :func:`dump` and :func:`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)
.. XXX: Add examples showing how to optimize pickles for size (like using
.. pickletools.optimize() or the gzip module).
.. seealso::
Module :mod:`copyreg`
Pickle interface constructor registration for extension types.
Module :mod:`pickletools`
Tools for working with and analyzing pickled data.
Module :mod:`shelve`
Indexed databases of objects; uses :mod:`pickle`.
Module :mod:`copy`
Shallow and deep object copying.
Module :mod:`marshal`
High-performance serialization of built-in types.
.. rubric:: Footnotes
.. [#] Don't confuse this with the :mod:`marshal` module
.. [#] This is why :keyword:`lambda` functions cannot be pickled: all
:keyword:`!lambda` functions share the same name: ``<lambda>``.
.. [#] The exception raised will likely be an :exc:`ImportError` or an
:exc:`AttributeError` but it could be something else.
.. [#] The :mod:`copy` module uses this protocol for shallow and deep copying
operations.
.. [#] 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.
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