pickle — Python object serialization — Python 3.13.0 documentation

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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:

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

exceptionpickle.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.

exceptionpickle.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:

classpickle.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.

classpickle.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.

classpickle.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.

  • •

    • 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.

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.

  • •

    • 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 copyregPickle interface constructor registration for extension types. Module pickletoolsTools for working with and analyzing pickled data. Module shelveIndexed databases of objects; uses pickle. Module copyShallow and deep object copying. Module marshalHigh-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.

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Source code: Lib/copyreg.py

The copyreg module offers a way to define functions used while pickling specific objects. The pickle and copy modules use those functions when pickling/copying those objects. The module provides configuration information about object constructors which are not classes. Such constructors may be factory functions or class instances.

copyreg.constructor(object) Declares object to be a valid constructor. If object is not callable (and hence not valid as a constructor), raises TypeError.

copyreg.pickle(type, function, constructor_ob=None) Declares that function should be used as a “reduction” function for objects of type type. function must return either a string or a tuple containing between two and six elements. See the dispatch_table for more details on the interface of function. The constructor_ob parameter is a legacy feature and is now ignored, but if passed it must be a callable. Note that the dispatch_table attribute of a pickler object or subclass of pickle.Pickler can also be used for declaring reduction functions.

Example

The example below would like to show how to register a pickle function and how it will be used:

>>>

>>> import copyreg, copy, pickle
>>> class C:
...     def __init__(self, a):
...         self.a = a
...
>>> def pickle_c(c):
...     print("pickling a C instance...")
...     return C, (c.a,)
...
>>> copyreg.pickle(C, pickle_c)
>>> c = C(1)
>>> d = copy.copy(c)
pickling a C instance...
>>> p = pickle.dumps(c)
pickling a C instance...

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Source code: Lib/shelve.py

A “shelf” is a persistent, dictionary-like object. The difference with “dbm” databases is that the values (not the keys!) in a shelf can be essentially arbitrary Python objects — anything that the pickle module can handle. This includes most class instances, recursive data types, and objects containing lots of shared sub-objects. The keys are ordinary strings.

shelve.open(filename, flag='c', protocol=None, writeback=False) Open a persistent dictionary. The filename specified is the base filename for the underlying database. As a side-effect, an extension may be added to the filename and more than one file may be created. By default, the underlying database file is opened for reading and writing. The optional flag parameter has the same interpretation as the flag parameter of dbm.open(). By default, pickles created with pickle.DEFAULT_PROTOCOL are used to serialize values. The version of the pickle protocol can be specified with the protocol parameter. Because of Python semantics, a shelf cannot know when a mutable persistent-dictionary entry is modified. By default modified objects are written only when assigned to the shelf (see Example). If the optional writeback parameter is set to True, all entries accessed are also cached in memory, and written back on sync() and close(); this can make it handier to mutate mutable entries in the persistent dictionary, but, if many entries are accessed, it can consume vast amounts of memory for the cache, and it can make the close operation very slow since all accessed entries are written back (there is no way to determine which accessed entries are mutable, nor which ones were actually mutated). Changed in version 3.10: pickle.DEFAULT_PROTOCOL is now used as the default pickle protocol. Changed in version 3.11: Accepts path-like object for filename. Note Do not rely on the shelf being closed automatically; always call close() explicitly when you don’t need it any more, or use shelve.open() as a context manager: 

with shelve.open('spam') as db:
    db['eggs'] = 'eggs'

Warning

Because the shelve module is backed by pickle, it is insecure to load a shelf from an untrusted source. Like with pickle, loading a shelf can execute arbitrary code.

Shelf objects support most of methods and operations supported by dictionaries (except copying, constructors and operators | and |=). This eases the transition from dictionary based scripts to those requiring persistent storage.

Two additional methods are supported:

Shelf.sync() Write back all entries in the cache if the shelf was opened with writeback set to True. Also empty the cache and synchronize the persistent dictionary on disk, if feasible. This is called automatically when the shelf is closed with close().

Shelf.close() Synchronize and close the persistent dict object. Operations on a closed shelf will fail with a ValueError.

See also

Persistent dictionary recipe with widely supported storage formats and having the speed of native dictionaries.

Restrictions

  • The choice of which database package will be used (such as dbm.ndbm or dbm.gnu) depends on which interface is available. Therefore it is not safe to open the database directly using dbm. The database is also (unfortunately) subject to the limitations of dbm, if it is used — this means that (the pickled representation of) the objects stored in the database should be fairly small, and in rare cases key collisions may cause the database to refuse updates.
  • The shelve module does not support concurrent read/write access to shelved objects. (Multiple simultaneous read accesses are safe.) When a program has a shelf open for writing, no other program should have it open for reading or writing. Unix file locking can be used to solve this, but this differs across Unix versions and requires knowledge about the database implementation used.
  • On macOS dbm.ndbm can silently corrupt the database file on updates, which can cause hard crashes when trying to read from the database.

classshelve.Shelf(dict, protocol=None, writeback=False, keyencoding='utf-8') A subclass of collections.abc.MutableMapping which stores pickled values in the dict object. By default, pickles created with pickle.DEFAULT_PROTOCOL are used to serialize values. The version of the pickle protocol can be specified with the protocol parameter. See the pickle documentation for a discussion of the pickle protocols. If the writeback parameter is True, the object will hold a cache of all entries accessed and write them back to the dict at sync and close times. This allows natural operations on mutable entries, but can consume much more memory and make sync and close take a long time. The keyencoding parameter is the encoding used to encode keys before they are used with the underlying dict. A Shelf object can also be used as a context manager, in which case it will be automatically closed when the with block ends. Changed in version 3.2: Added the keyencoding parameter; previously, keys were always encoded in UTF-8. Changed in version 3.4: Added context manager support. Changed in version 3.10: pickle.DEFAULT_PROTOCOL is now used as the default pickle protocol.

classshelve.BsdDbShelf(dict, protocol=None, writeback=False, keyencoding='utf-8') A subclass of Shelf which exposes first(), next(), previous(), last() and set_location() methods. These are available in the third-party bsddb module from pybsddb but not in other database modules. The dict object passed to the constructor must support those methods. This is generally accomplished by calling one of bsddb.hashopen(), bsddb.btopen() or bsddb.rnopen(). The optional protocol, writeback, and keyencoding parameters have the same interpretation as for the Shelf class.

classshelve.DbfilenameShelf(filename, flag='c', protocol=None, writeback=False) A subclass of Shelf which accepts a filename instead of a dict-like object. The underlying file will be opened using dbm.open(). By default, the file will be created and opened for both read and write. The optional flag parameter has the same interpretation as for the open() function. The optional protocol and writeback parameters have the same interpretation as for the Shelf class.

Example

To summarize the interface (key is a string, data is an arbitrary object):

import shelve

d = shelve.open(filename)  # open -- file may get suffix added by low-level
                           # library

d[key] = data              # store data at key (overwrites old data if
                           # using an existing key)
data = d[key]              # retrieve a COPY of data at key (raise KeyError
                           # if no such key)
del d[key]                 # delete data stored at key (raises KeyError
                           # if no such key)

flag = key in d            # true if the key exists
klist = list(d.keys())     # a list of all existing keys (slow!)

# as d was opened WITHOUT writeback=True, beware:
d['xx'] = [0, 1, 2]        # this works as expected, but...
d['xx'].append(3)          # *this doesn't!* -- d['xx'] is STILL [0, 1, 2]!

# having opened d without writeback=True, you need to code carefully:
temp = d['xx']             # extracts the copy
temp.append(5)             # mutates the copy
d['xx'] = temp             # stores the copy right back, to persist it

# or, d=shelve.open(filename,writeback=True) would let you just code
# d['xx'].append(5) and have it work as expected, BUT it would also
# consume more memory and make the d.close() operation slower.

d.close()                  # close it