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Safari | Python Developer's Handbook -> Object Serialization and Persistent Storage





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Object Serialization and Persistent Storage

These other modules provide persistent storage of arbitrary Python objects. Whenever you need to save objects whose value is not a simple string (such as None, integer, long integer, float, complex, tuple, list, dictionary, code object, and so on), you need to serialize the object before sending it to a file.

Both pickle and shelve modules save serializable objects to a file.

By using these persistent storage modules, Python objects can be stored in relational database systems. These modules abstract and hide the underlying database interfaces, such as the Sybase module and the Python Database API.

Included in the standard Python distribution, the pickle module can convert Python objects to and from a string representation.

The cPickle module is a faster implementation of the pickle module.

The copy_reg module extends the capabilities of the pickle and cpickle modules by registering support functions.

The marshal module is an alternate method to implement Python object serialization. It allows you to read/write information in a platform independent binary format and convert data to/from character strings (the module only supports the simple built-in types). Basically, it is just another way to do byte stream conversions by using serialized Python objects. This module is used to serialize the compiled bytecode for Python modules.

This module should be used for simple objects only. Use the pickle module to implement persistent objects in general.

Persistent Storage of Python Objects in Relational Databases is a paper by Joel Shprentz presented at the Sixth Python Conference. For more information, check out http://www.python.org/workshops/1997-10/proceedings/shprentz.html.

pickle Module

The pickle module serializes the contents of an object into a stream of bytes. Optionally, it can save the serialized object into a file object. It is slower than the marshal module.

						
>>> import pickle
>>> listobj = [1,2,3,4]
>>> filehandle = open(filename, 'w')
>>> pickle.dump(filehandle, listobj)
>>> filehandle = open(filename, 'r')
>>> listobj = pickle.load(filehandle)

					

The next functions are the ones implemented by the pickle module.

						
pickle.dump(object, filename [,bin])

					

This function serializes and saves an object into a file. The bin argument specifies that the information must be saved as binary data. This function is the same as the following:

						
p = pickle.Pickler(filename)
p.dump(object)

					

If an unsupported object type is serialized, a PicklingException is raised.

						
pickle.dumps(object [,bin])

					

This function has the same behavior of dump. The difference is that this one returns the serialized object.

						
pickle.load(file)

					

Restores a serialized object from a file. This function is the same as the following:

						
object = pickle.Unpickler(file).load()

					

The next example serializes the information and converts it back again.

						
>>> import pickle
>>> value = ("parrot", (1,2,3))
>>> data = pickle.dumps(value)
>>> print pickle.loads(data)
("parrot", (1,2,3)) 
				
					

cPickle Module

This module implements the same functions that the pickle module does. The difference is that cPickle is much faster because it doesn't support subclassing of the Pickler and Unpickler objects. See the next example code. It uses the fastest pickle module available on the system.

						
try:
     import cPickle
     pickle = cPickle
except ImportError:
     import pickle
				
					

copy_reg Module

This module registers new types to be used with the pickle module. It extends the capabilities of the pickle and cPickle modules by supporting the serialization of new object types defined in C extension modules.

The next example corrects the fact that the standard pickle implementation cannot handle Python code objects. It registers a code object handler by using two functions:

  • dumpdata Takes the code object and returns a tuple that can only contain simple data types.

  • loaddata Processes the tuple.

						
import copy_reg, pickle, marshal, types

def loaddata(data):
    return marshal.loads(data)

def dumpdata(code):
    return loaddata, (marshal.dumps(code),)

copy_reg.pickle(types.CodeType, dumpdata, loaddata)

script = """
x = 1
while x < 10:
   print x
   x = x - 1
"""

code = compile(script, "<string>", "exec")
codeobj = pickle.dumps(code)

exec pickle.loads(codeobj)
				
					

Note

Note that starting at Python 2.0, the copy-reg module can't be used to register pickle support for classes anymore. It can only be used to register pickle support for extension types. You will get a TypeError exception from the pick ... ... (Not finished, please download and read the complete file)

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