程式扎記: [Python Std Library] Data Persistence : pickle — Python object serialization

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2012年5月23日 星期三

[Python Std Library] Data Persistence : pickle — Python object serialization


來源自 這裡
Preface :
The pickle module implements a fundamental, but powerful algorithm 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 is converted back into an object hierarchy.

Relationship to other Python modules :
The pickle module has an optimized cousin called the cPickle module. As its name implies, cPickle is written in C, so it can be up to 1000 times faster than pickle. However it does not support subclassing of the Pickler() and Unpickler() classes, because in cPickle these are functions, not classes. Most applications have no need for this functionality, and can benefit from the improved performance of cPickle. Other than that, the interfaces of the two modules are nearly identical; the common interface is described in this manual and differences are pointed out where necessary. In the following discussions, we use the term "pickle" to collectively describe the pickle and cPickle modules.

Note that 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 module shelve provides a simple interface to pickle and unpickle objects onDBM-style database files.

Data stream format :
The data format used by pickle is Python-specific. It means that non-Python programs may not be able to reconstruct pickled Python objects.

By default, the pickle data format uses a printable ASCII representation. This is slightly more voluminous than a binary representation. The big advantage of using printable ASCII (and of some other characteristics of pickle‘s representation) is that for debugging or recovery purposes it is possible for a human to read the pickled file with a standard text editor.

There are currently 3 different protocols which can be used for pickling :
* Protocol version 0 is the original ASCII protocol and is backwards compatible with earlier versions of Python.
* Protocol version 1 is the 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.

If a protocol is not specified, protocol 0 is used. If protocol is specified as a negative value or HIGHEST_PROTOCOL, the highest protocol version available will be used. (Changed in version 2.3: Introduced the protocol parameter.)

A binary format, which is slightly more efficient, can be chosen by specifying a protocol version >= 1.

Usage :
To serialize an object hierarchy, you first create a pickler, then you call the pickler’s dump() method. To de-serialize a data stream, you first create an unpickler, then you call the unpickler’s load() method. The pickle module provides the following constant :
pickle.HIGHEST_PROTOCOL
New in version 2.3.
The highest protocol version available. This value can be passed as a protocol value.

The pickle module provides the following functions to make the pickling process more convenient :
pickle.dump(obj, file[, protocol])
Changed in version 2.3: Introduced the protocol parameter.
Write a pickled representation of obj to the open file object file. This is equivalent to Pickler(file, protocol).dump(obj).

If the protocol parameter is omitted, protocol 0 is used. If protocol is specified as a negative value or HIGHEST_PROTOCOL, the highest protocol version will be used.

file must have a write() method that accepts a single string argument. It can thus be a file object opened for writing, a StringIO object, or any other custom object that meets this interface.

pickle.load(file)
Read a string from the open file object file and interpret it as a pickle data stream, reconstructing and returning the original object hierarchy. This is equivalent toUnpickler(file).load().

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 a string. Thus file can be a file object opened for reading, a StringIO object, or any other custom object that meets this interface.

This function automatically determines whether the data stream was written in binary mode or not.

pickle.dumps(obj[, protocol])
Changed in version 2.3: The protocol parameter was added.
Return the pickled representation of the object as a string, instead of writing it to a file.

If the protocol parameter is omitted, protocol 0 is used. If protocol is specified as a negative value or HIGHEST_PROTOCOL, the highest protocol version will be used.
>>> list = ['john', 'ken']
>>> pickle.dumps(list)
b'\x80\x03]q\x00(X\x04\x00\x00\x00johnq\x01X\x03\x00\x00\x00kenq\x02e.'

The pickle module exports two callables Pickler and Unpickler :
class pickle.Pickler(file[, protocol])
Changed in version 2.3: Introduced the protocol parameter.
This takes a file-like object to which it will write a pickle data stream.

file must have a write() method that accepts a single string argument. It can thus be an open file object, a StringIO object, or any other custom object that meets this interface. Pickler objects define one (or two) public methods :
dump(obj)
Write a pickled representation of obj to the open file object given in the constructor. Either the binary or ASCII format will be used, depending on the value of theprotocol argument passed to the constructor.

clear_memo()
Clears the pickler’s “memo”. The memo is the data structure that remembers which objects the pickler has already seen, so that shared or recursive objects pickled by reference and not by value. This method is useful when re-using picklers.

It is possible to make multiple calls to the dump() method of the same Pickler instance. These must then be matched to the same number of calls to the load() method of the corresponding Unpickler instance. If the same object is pickled by multiple dump() calls, the load() will all yield references to the same object.

- class pickle.Unpickler(file)
This takes a file-like object from which it will read a pickle data stream. This class automatically determines whether the data stream was written in binary mode or not, so it does not need a flag as in the Pickler factory.

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 a string. Thus file can be a file object opened for reading, a StringIO object, or any other custom object that meets this interface.

Unpickler objects have one (or two) public methods :
load()
Read a pickled object representation from the open file object given in the constructor, and return the reconstituted object hierarchy specified therein.

This method automatically determines whether the data stream was written in binary mode or not.

noload()
This is just like load() except that it doesn’t actually create any objects. This is useful primarily for finding what’s called “persistent ids” that may be referenced in a pickle data stream. See section The pickle protocol below for more details.

Note: the noload() method is currently only available on Unpickler objects created with the cPickle module.

What can be pickled and unpickled?
The following types can be pickled :
* NoneTrue, and False
* integers, long integers, floating point numbers, complex numbers
* normal and Unicode strings
* tuples, lists, sets, and dictionaries containing only picklable objects
* functions defined at the top level of a module
* 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 __setstate__() is picklable (see section The pickle protocol 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 RuntimeError will be raised in this case. You can carefully raise this limit with sys.setrecursionlimit().

Note that functions (built-in and user-definedare pickled by "fully qualified" name reference, not by value. 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.

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 :
  1. class Foo:  
  2.     attr = 'a class attr'  
  3.   
  4. 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.

Example :
For the simplest code, use the dump() and load() functions. Note that a self-referencing list is pickled and restored correctly :


The following example reads the resulting pickled data. When reading a pickle-containing file, you should open the file in binary mode because you can’t be sure if the ASCII or binary format was used : (pickle_ex2.py)


Below is the executing result :


Supplement :
The pickle protocol
This section describes the “pickling protocol” that defines the interface between the pickler/unpickler and the objects that are being serialized...

Subclassing Unpicklers
By default, unpickling will import any class that it finds in the pickle data. You can control exactly what gets unpickled and what gets called by customizing your unpickler...

cPickle — A faster pickle
This module supports serialization and de-serialization of Python objects, providing an interface and functionality nearly identical to the pickle module.
The difference between this module and pickle :
First, this module can be up to 1000 times faster than pickle because the former is implemented in C. Second, in the cPickle module the callables Pickler() and Unpickler() are functions, not classes...

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