Download ##VERIFIED## Pickle Python

It is possible to construct malicious pickle data which will executearbitrary code during unpickling. Never unpickle data that could have comefrom an untrusted source, or that could have been tampered with.

This has implications both for recursive objects and object sharing. Recursiveobjects are objects that contain references to themselves. These are nothandled by marshal, and in fact, attempting to marshal recursive objects willcrash your Python interpreter. Object sharing happens when there are multiplereferences to the same object in different places in the object hierarchy beingserialized. pickle stores such objects only once, and ensures that allother references point to the master copy. Shared objects remain shared, whichcan be very important for mutable objects.

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

The marshal serialization format is not guaranteed to be portableacross Python versions. Because its primary job in life is to support.pyc files, the Python implementers reserve the right to change theserialization format in non-backwards compatible ways should the need arise.The pickle serialization format is guaranteed to be backwards compatibleacross Python releases provided a compatible pickle protocol is chosen andpickling and unpickling code deals with Python 2 to Python 3 type differencesif your data is crossing that unique breaking change language boundary.

Serialization is a more primitive notion than persistence; althoughpickle reads and writes file objects, it does not handle the issue ofnaming persistent objects, nor the (even more complicated) issue of concurrentaccess to persistent objects. The pickle module can transform a complexobject into a byte stream and it can transform the byte stream into an objectwith the same internal structure. Perhaps the most obvious thing to do withthese byte streams is to write them onto a file, but it is also conceivable tosend them across a network or store them in a database. The shelvemodule provides a simple interface to pickle and unpickle objects onDBM-style database files.

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

If buffer_callback is not None, then it can be called any numberof 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.

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

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

Special reducer that can be defined in Pickler subclasses. Thismethod has priority over any reducer in the dispatch_table. Itshould conform to the same interface as a __reduce__() method, andcan optionally return NotImplemented to fallback ondispatch_table-registered reducers to pickle obj.

If buffers is None (the default), then all data necessary fordeserialization must be contained in the pickle stream. This meansthat the buffer_callback argument was None when a Picklerwas instantiated (or when dump() or dumps() was called).

If buffers is not None, it should be an iterable of buffer-enabledobjects that is consumed each time the pickle stream referencesan out-of-band buffer view. Such buffers have beengiven in order to the buffer_callback of a Pickler object.

Read the pickled representation of an object from the open file objectgiven in the constructor, and return the reconstituted object hierarchyspecified therein. Bytes past the pickled representation of the objectare ignored.

Attempts to pickle unpicklable objects will raise the PicklingErrorexception; when this happens, an unspecified number of bytes may have alreadybeen written to the underlying file. Trying to pickle a highly recursive datastructure may exceed the maximum recursion depth, a RecursionError will beraised in this case. You can carefully raise this limit withsys.setrecursionlimit().

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

Classes can further influence how their instances are pickled by overridingthe method __getstate__(). It is called and the returned objectis pickled as the contents for the instance, instead of a default state.There are several cases:

Optionally, an iterator (and not a sequence) yielding successive items.These items will be appended to the object either usingobj.append(item) or, in batch, using obj.extend(list_of_items).This is primarily used for list subclasses, but may be used by otherclasses as long as they have append() and extend() methods withthe appropriate signature. (Whether append() or extend() isused depends on which pickle protocol version is used as well as the numberof items to append, so both must be supported.)

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

The resolution of such persistent IDs is not defined by the picklemodule; it will delegate this resolution to the user-defined methods on thepickler and unpickler, persistent_id() andpersistent_load() respectively.

To pickle objects that have an external persistent ID, the pickler must have acustom persistent_id() method that takes an object as anargument 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.

In some contexts, the pickle module is used to transfer massive amountsof data. Therefore, it can be important to minimize the number of memorycopies, to preserve performance and resource consumption. However, normaloperation of the pickle module, as it transforms a graph-like structureof objects into a sequential stream of bytes, intrinsically involves copyingdata to and from the pickle stream.

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

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

A PickleBuffer object signals that the underlying buffer iseligible for out-of-band data transfer. Those objects remain compatiblewith normal usage of the pickle module. However, consumers can alsoopt-in to tell pickle that they will handle those buffers bythemselves.

On the sending side, it needs to pass a buffer_callback argument toPickler (or to the dump() or dumps() function), whichwill be called with each PickleBuffer generated while picklingthe object graph. Buffers accumulated by the buffer_callback will notsee their data copied into the pickle stream, only a cheap marker will beinserted.

On the receiving side, it needs to pass a buffers argument toUnpickler (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 passedto buffer_callback. Those buffers will provide the data expected by thereconstructors of the objects whose pickling produced the originalPickleBuffer objects.

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

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

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

The limitation on alphanumeric characters is due to the factthat persistent IDs in protocol 0 are delimited by the newlinecharacter. Therefore if any kind of newline characters occurs inpersistent IDs, the resulting pickled data will become unreadable. ff782bc1db