12.1. pickle — Python object serialization

Source code: Lib/pickle.py

The pickle module implements binary protocols for serializing and de-serializing a Python object structure. “Pickling” is the process whereby a Python object hierarchy is converted into a byte stream, and “unpickling” is the inverse operation, whereby a byte stream (from a binary file or bytes-like object) is converted back into an object hierarchy. Pickling (and unpickling) is alternatively known as “serialization”, “marshalling,” [1] or “flattening”; however, to avoid confusion, the terms used here are “pickling” and “unpickling”.

Warning

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

12.1.1. Relationship to other Python modules

12.1.1.1. Comparison with marshal

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

The pickle module differs from marshal in several significant ways:

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

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

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

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

12.1.1.2. Comparison with json

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

  • JSON is a text serialization format (it outputs unicode text, although most of the time it is then encoded to utf-8), while pickle is a binary serialization format;
  • JSON is human-readable, while pickle is not;
  • JSON is interoperable and widely used outside of the Python ecosystem, while pickle is Python-specific;
  • JSON, by default, can only represent a subset of the Python built-in types, and no custom classes; pickle can represent an extremely large number of Python types (many of them automatically, by clever usage of Python’s introspection facilities; complex cases can be tackled by implementing specific object APIs).

See also

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

12.1.2. Data stream format

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

By default, the pickle data format uses a relatively compact binary representation. If you need optimal size characteristics, you can efficiently compress pickled data.

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

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

  • Protocol version 0 is the original “human-readable” protocol and is backwards compatible with earlier versions of Python.
  • Protocol version 1 is an old binary format which is also compatible with earlier versions of Python.
  • Protocol version 2 was introduced in Python 2.3. It provides much more efficient pickling of new-style 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 is the default protocol, and the recommended protocol when compatibility with other Python 3 versions is required.
  • Protocol version 4 was added in Python 3.4. It adds support for very large objects, pickling more kinds of objects, and some data format optimizations. Refer to PEP 3154 for information about improvements brought by protocol 4.

Note

Serialization is a more primitive notion than persistence; although pickle reads and writes file objects, it does not handle the issue of naming persistent objects, nor the (even more complicated) issue of concurrent access to persistent objects. The pickle module can transform a complex object into a byte stream and it can transform the byte stream into an object with the same internal structure. Perhaps the most obvious thing to do with these byte streams is to write them onto a file, but it is also conceivable to send them across a network or store them in a database. The shelve module provides a simple interface to pickle and unpickle objects on DBM-style database files.

12.1.3. Module Interface

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

The pickle module provides the following constants:

pickle.HIGHEST_PROTOCOL

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

pickle.DEFAULT_PROTOCOL

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

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

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

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

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

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

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

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

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

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

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

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

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

The argument file must have two methods, a read() method that takes an integer argument, and a readline() method that requires no arguments. Both methods should return bytes. Thus file can be an on-disk file opened for binary reading, an io.BytesIO object, or any other custom object that meets this interface.

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

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

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

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

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

The pickle module defines three exceptions:

exception pickle.PickleError

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

exception pickle.PicklingError

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

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

exception pickle.UnpicklingError

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

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

The pickle module exports two classes, Pickler and Unpickler:

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

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

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

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

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

dump(obj)

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

persistent_id(obj)

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

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

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

dispatch_table

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

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

See Dispatch Tables for usage examples.

New in version 3.3.

fast

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

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

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

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

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

The argument file must have two methods, a read() method that takes an integer argument, and a readline() method that requires no arguments. Both methods should return bytes. Thus file can be an on-disk file object opened for binary reading, an io.BytesIO object, or any other custom object that meets this interface.

Optional keyword arguments are fix_imports, encoding and errors, which are used to control compatibility support for pickle stream generated by Python 2. If fix_imports is true, pickle will try to map the old Python 2 names to the new names used in Python 3. The encoding and errors tell pickle how to decode 8-bit string instances pickled by Python 2; these default to ‘ASCII’ and ‘strict’, respectively. The encoding can be ‘bytes’ to read these ß8-bit string instances as bytes objects.

load()

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

persistent_load(pid)

Raise an UnpicklingError by default.

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

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

find_class(module, name)

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

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

12.1.4. What can be pickled and unpickled?

The following types can be pickled:

  • None, True, and False
  • integers, floating point numbers, complex numbers
  • strings, bytes, bytearrays
  • tuples, lists, sets, and dictionaries containing only picklable objects
  • functions defined at the top level of a module (using def, not lambda)
  • built-in functions defined at the top level of a module
  • classes that are defined at the top level of a module
  • instances of such classes whose __dict__ or the result of calling __getstate__() is picklable (see section Pickling Class Instances for details).

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

Note that functions (built-in and user-defined) are pickled by “fully qualified” name reference, not by value. [2] This means that only the function name is pickled, along with the name of the module the function is defined in. Neither the function’s code, nor any of its function attributes are pickled. Thus the defining module must be importable in the unpickling environment, and the module must contain the named object, otherwise an exception will be raised. [3]

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

class Foo:
    attr = 'A class attribute'

picklestring = pickle.dumps(Foo)

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

Similarly, when class instances are pickled, their class’s code and data are not pickled along with them. Only the instance data are pickled. This is done on purpose, so you can fix bugs in a class or add methods to the class and still load objects that were created with an earlier version of the class. If you plan to have long-lived objects that will see many versions of a class, it may be worthwhile to put a version number in the objects so that suitable conversions can be made by the class’s __setstate__() method.

12.1.5. Pickling Class Instances

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

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

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

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

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

object.__getnewargs_ex__()

In protocols 4 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__().

object.__getnewargs__()

This method serve a similar purpose as __getnewargs_ex__() but for protocols 2 and newer. It must return a tuple of arguments args which will be passed to the __new__() method upon unpickling.

In protocols 4 and newer, __getnewargs__() will not be called if __getnewargs_ex__() is defined.

object.__getstate__()

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

object.__setstate__(state)

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

Note

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

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

Note

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

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

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

object.__reduce__()

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

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

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

  • A callable object that will be called to create the initial version of the object.
  • A tuple of arguments for the callable object. An empty tuple must be given if the callable does not accept any argument.
  • Optionally, the object’s state, which will be passed to the object’s __setstate__() method as previously described. If the object has no such method then, the value must be a dictionary and it will be added to the object’s __dict__ attribute.
  • Optionally, an iterator (and not a sequence) yielding successive items. These items will be appended to the object either using obj.append(item) or, in batch, using obj.extend(list_of_items). This is primarily used for list subclasses, but may be used by other classes as long as they have append() and extend() methods with the appropriate signature. (Whether append() or extend() is used depends on which pickle protocol version is used as well as the number of items to append, so both must be supported.)
  • Optionally, an iterator (not a sequence) yielding successive key-value pairs. These items will be stored to the object using obj[key] = value. This is primarily used for dictionary subclasses, but may be used by other classes as long as they implement __setitem__().
object.__reduce_ex__(protocol)

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

12.1.5.1. Persistence of External Objects

For the benefit of object persistence, the pickle module supports the notion of a reference to an object outside the pickled data stream. Such objects are referenced by a persistent ID, which should be either a string of alphanumeric characters (for protocol 0) [5] or just an arbitrary object (for any newer protocol).

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

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

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

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

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

import pickle
import sqlite3
from collections import namedtuple

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

class DBPickler(pickle.Pickler):

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


class DBUnpickler(pickle.Unpickler):

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

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


def main():
    import io
    import pprint

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

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

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

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

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

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


if __name__ == '__main__':
    main()

12.1.5.2. Dispatch Tables

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

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

For example

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

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

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

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

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

12.1.5.3. Handling Stateful Objects

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

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

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

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

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

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

A sample usage might be something like this:

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

12.1.6. Restricting Globals

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

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

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

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

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

import builtins
import io
import pickle

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

class RestrictedUnpickler(pickle.Unpickler):

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

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

A sample usage of our unpickler working has intended:

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

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

12.1.7. Performance

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

12.1.8. Examples

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

import pickle

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

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

The following example reads the resulting pickled data.

import pickle

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

See also

Module copyreg
Pickle interface constructor registration for extension types.
Module pickletools
Tools for working with and analyzing pickled data.
Module shelve
Indexed databases of objects; uses pickle.
Module copy
Shallow and deep object copying.
Module marshal
High-performance serialization of built-in types.

Footnotes

[1]Don’t confuse this with the marshal module
[2]This is why lambda functions cannot be pickled: all lambda functions share the same name: <lambda>.
[3]The exception raised will likely be an ImportError or an AttributeError but it could be something else.
[4]The copy module uses this protocol for shallow and deep copying operations.
[5]The limitation on alphanumeric characters is due to the fact the persistent IDs, in protocol 0, are delimited by the newline character. Therefore if any kind of newline characters occurs in persistent IDs, the resulting pickle will become unreadable.