what is the pickling problem that persistent ids are used to solve here?

Pickle streams have a notion of internal references - if the same object occurs multiple times in a stream, it is stored only once and then just referenced. However, this only refers to what is already stored in the stream - a reference cannot point to an object outside the stream, such as the original object. The content of a pickle data stream is conceptually a copy of its original data.

import pickle

bar = (1, 2)
foo = {
   1: 1,
   2: (1, 1),
   'bar': bar
}

with open('foo.pkl', 'wb') as out_stream: # open a data stream...
   pickle.dump((bar, foo), out_stream) #...
   for pickle data

with open('foo.pkl', 'rb') as in_stream:
   bar2, foo2 = pickle.load(in_stream)

assert bar2 is foo2['bar'] # internal identity is preserved
assert bar is not bar2 # external identity is broken

Defining and using persistent IDs is not difficult. However, it requires some orchestration and bookkeeping. A very simple example looks like this:

import pickle

# some object to persist
# usually, one would have some store or bookkeeping in place
bar = (1, 2)

# The create / load implementation of the persistent id
# extends pickling / unpickling
class PersistentPickler(pickle.Pickler):
   def persistent_id(self, obj):
   ""
"Return a persistent id for the `bar` object only"
""
return "it's a bar"
if obj is bar
else None

class PersistentUnpickler(pickle.Unpickler):
   def persistent_load(self, pers_id):
   ""
"Return the object identified by the persistent id"
""
if pers_id == "it's a bar":
   return bar
raise pickle.UnpicklingError("This is just an example for one persistent object!")

# we can now dump and load the persistent object
foo = {
   'bar': bar
}
with open("foo.pkl", "wb") as out_stream:
   PersistentPickler(out_stream).dump(foo)

with open("foo.pkl", "rb") as in_stream:
   foo2 = PersistentUnpickler(in_stream).load()

assert foo2 is not foo # regular objects are not persistent
assert foo2['bar'] is bar # persistent object identity is preserved

Suggestion : 2

I'd appreciate if someone could explain: anycodings_python what is the pickling problem that persistent anycodings_python IDs are used to solve here? In other words, anycodings_python what problem will pickling have if not using anycodings_python persistent IDs?,Extracting positions of elements from two Matlab vectors satisfying some criteria,How to listen to web requests without persistent background page?,Power BI changing the behavior of on click selection of a table

Pickle streams have a notion of internal anycodings_python references - if the same object occurs anycodings_python multiple times in a stream, it is stored anycodings_python only once and then just referenced. anycodings_python However, this only refers to what is anycodings_python already stored in the stream - a anycodings_python reference cannot point to an object anycodings_python outside the stream, such as the original anycodings_python object. The content of a pickle data anycodings_python stream is conceptually a copy of its anycodings_python original data.

import pickle

bar = (1, 2)
foo = {
   1: 1,
   2: (1, 1),
   'bar': bar
}

with open('foo.pkl', 'wb') as out_stream: # open a data stream...
   pickle.dump((bar, foo), out_stream) #...
   for pickle data

with open('foo.pkl', 'rb') as in_stream:
   bar2, foo2 = pickle.load(in_stream)

assert bar2 is foo2['bar'] # internal identity is preserved
assert bar is not bar2 # external identity is broken

Defining and using persistent IDs is not anycodings_python difficult. However, it requires some anycodings_python orchestration and bookkeeping. A very anycodings_python simple example looks like this:

import pickle

# some object to persist
# usually, one would have some store or bookkeeping in place
bar = (1, 2)

# The create / load implementation of the persistent id
# extends pickling / unpickling
class PersistentPickler(pickle.Pickler):
   def persistent_id(self, obj):
   ""
"Return a persistent id for the `bar` object only"
""
return "it's a bar"
if obj is bar
else None

class PersistentUnpickler(pickle.Unpickler):
   def persistent_load(self, pers_id):
   ""
"Return the object identified by the persistent id"
""
if pers_id == "it's a bar":
   return bar
raise pickle.UnpicklingError("This is just an example for one persistent object!")

# we can now dump and load the persistent object
foo = {
   'bar': bar
}
with open("foo.pkl", "wb") as out_stream:
   PersistentPickler(out_stream).dump(foo)

with open("foo.pkl", "rb") as in_stream:
   foo2 = PersistentUnpickler(in_stream).load()

assert foo2 is not foo # regular objects are not persistent
assert foo2['bar'] is bar # persistent object identity is preserved

Suggestion : 3

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

class Foo:
   attr = 'A class attribute'

picklestring = pickle.dumps(Foo)

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

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

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

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

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

Suggestion : 4

This issue tracker has been migrated to GitHub, and is currently read-only. For more information, see the GitHub FAQs in the Python's Developer Guide., Help Tracker Documentation Tracker Development Report Tracker Problem , This issue has been migrated to GitHub: https://github.com/python/cpython/issues/61911

Python 2 allows pickling and unpickling non - ascii persistent ids.In Python 3 C implementation of pickle saves persistent ids with protocol version 0 as utf8 - encoded strings and loads as bytes.

   >>>
   import pickle, io >>>
   class MyPickler(pickle.Pickler):
   ...def persistent_id(self, obj):
   ...
   if isinstance(obj, str):
   ...
   return obj
      ...
      return None
         ...
         >>>
         class MyUnpickler(pickle.Unpickler):
         ...def persistent_load(self, pid):
         ...
         return pid
            ...
            >>>
            f = io.BytesIO();
MyPickler(f).dump('\u20ac');
data = f.getvalue() >>>
   MyUnpickler(io.BytesIO(data)).load()
'€' >>>
f = io.BytesIO();
MyPickler(f, 0).dump('\u20ac');
data = f.getvalue() >>>
   MyUnpickler(io.BytesIO(data)).load()
b '\xe2\x82\xac' >>>
   f = io.BytesIO();
MyPickler(f, 0).dump('a');
data = f.getvalue() >>>
   MyUnpickler(io.BytesIO(data)).load()
b 'a'

Python implementation in Python 3 doesn 't works with non-ascii persistant ids at all.

In protocol 0, the persistent ID is restricted to alphanumeric strings because of the problems that arise when the persistent ID contains newline characters._pickle likely should be changed to use the ASCII decoded.And perhaps, we should check
for embedded newline characters too.

Even
for alphanumeric strings Python 3 have a bug.It saves strings and load bytes objects.

Here 's a patch that fix the bug.

I think a string with character codes < 256 will be better
for test_protocol0_is_ascii_only().It can be latin1 encoded(Python 2 allows any 8 - bit strings).

PyUnicode_AsASCIIString() can be slower than _PyUnicode_AsStringAndSize()(actually PyUnicode_AsUTF8AndSize()) because the latter can use cached value.You can check
if the persistent id only contains ASCII characters by checking PyUnicode_GET_LENGTH(pid_str) == size.

And what are you going to do with the fact that in Python 2 you can pickle non - ascii persistent ids, which will not be able to unpickle in Python 3 ?

The patch is updated to current sources.Also optimized writing ASCII strings and fixed tests.

Suggestion : 5

The cPickle module supports serialization and de-serialization of Python objects, providing an interface and functionality nearly identical to the pickle module. There are several differences, the most important being performance and subclassability.,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. Pickling (and unpickling) is alternatively known as “serialization”, “marshalling,” [1] or “flattening”, however, to avoid confusion, the terms used here are “pickling” and “unpickling”.,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.,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.

mypickler.memo.clear()

class Foo:
   attr = 'a class attr'

picklestring = pickle.dumps(Foo)

obj = C.__new__(C, * args)

import pickle
from cStringIO
import StringIO

src = StringIO()
p = pickle.Pickler(src)

def persistent_id(obj):
   if hasattr(obj, 'x'):
   return 'the value %d' % obj.x
else:
   return None

p.persistent_id = persistent_id

class Integer:
   def __init__(self, x):
   self.x = x
def __str__(self):
   return 'My name is integer %d' % self.x

i = Integer(7)
print i
p.dump(i)

datastream = src.getvalue()
print repr(datastream)
dst = StringIO(datastream)

up = pickle.Unpickler(dst)

class FancyInteger(Integer):
   def __str__(self):
   return 'I am the integer %d' % self.x

def persistent_load(persid):
   if persid.startswith('the value '):
   value = int(persid.split()[2])
return FancyInteger(value)
else:
   raise pickle.UnpicklingError, 'Invalid persistent id'

up.persistent_load = persistent_load

j = up.load()
print j

import pickle

data1 = {
   'a': [1, 2.0, 3, 4 + 6 j],
   'b': ('string', u 'Unicode string'),
   'c': None
}

selfref_list = [1, 2, 3]
selfref_list.append(selfref_list)

output = open('data.pkl', 'wb')

# Pickle dictionary using protocol 0.
pickle.dump(data1, output)

# Pickle the list using the highest protocol available.
pickle.dump(selfref_list, output, -1)

output.close()

import pprint, pickle

pkl_file = open('data.pkl', 'rb')

data1 = pickle.load(pkl_file)
pprint.pprint(data1)

data2 = pickle.load(pkl_file)
pprint.pprint(data2)

pkl_file.close()

Suggestion : 6

Last Updated : 01 Jun, 2021,GATE CS 2021 Syllabus

Output :

WRITING: pickle(elkcip)
WRITING: cPickle(elkciPc)
WRITING: last(tsal)

class pickle.Pickler(file, protocol = None, *, fix_imports = True)
This class takes a binary file for writing a pickle data stream.
1. dump(obj) – This function is used to write a pickled representation of obj to the open file object given in the constructor.
2. persistent_id(obj) – If persistent_id() returns None, obj is pickled as usual. This does nothing by default and exists so that any subclass can override it.
3. Dispatch_table – A pickler object’s dispatch table is a mapping whose keys are classes and whose values are reduction functions.
  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.
  Example : The below code creates an instance of pickle.Pickler with a private dispatch table that handles the SomeClass class especially.

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

Output :

0: hi geeks!, this is line 1.
0: This is line 2.
0: hi geeks!, this is line 1.