[python] Saving an Object (Data persistence)

I've created an object like this:

company1.name = 'banana' 
company1.value = 40

I would like to save this object. How can I do that?

I think it's a pretty strong assumption to assume that the object is a class. What if it's not a class? There's also the assumption that the object was not defined in the interpreter. What if it was defined in the interpreter? Also, what if the attributes were added dynamically? When some python objects have attributes added to their __dict__ after creation, pickle doesn't respect the addition of those attributes (i.e. it 'forgets' they were added -- because pickle serializes by reference to the object definition).

In all these cases, pickle and cPickle can fail you horribly.

If you are looking to save an object (arbitrarily created), where you have attributes (either added in the object definition, or afterward)… your best bet is to use dill, which can serialize almost anything in python.

We start with a class…

Python 2.7.8 (default, Jul 13 2014, 02:29:54) 
[GCC 4.2.1 Compatible Apple Clang 4.1 ((tags/Apple/clang-421.11.66))] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pickle
>>> class Company:
...     pass
... 
>>> company1 = Company()
>>> company1.name = 'banana'
>>> company1.value = 40
>>> with open('company.pkl', 'wb') as f:
...     pickle.dump(company1, f, pickle.HIGHEST_PROTOCOL)
... 
>>> 

Now shut down, and restart...

Python 2.7.8 (default, Jul 13 2014, 02:29:54) 
[GCC 4.2.1 Compatible Apple Clang 4.1 ((tags/Apple/clang-421.11.66))] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pickle
>>> with open('company.pkl', 'rb') as f:
...     company1 = pickle.load(f)
... 
Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 1378, in load
    return Unpickler(file).load()
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 858, in load
dispatch[key](self)
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 1090, in load_global
    klass = self.find_class(module, name)
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 1126, in find_class
    klass = getattr(mod, name)
AttributeError: 'module' object has no attribute 'Company'
>>> 

Oops… pickle can't handle it. Let's try dill. We'll throw in another object type (a lambda) for good measure.

Python 2.7.8 (default, Jul 13 2014, 02:29:54) 
[GCC 4.2.1 Compatible Apple Clang 4.1 ((tags/Apple/clang-421.11.66))] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import dill       
>>> class Company:
...     pass
... 
>>> company1 = Company()
>>> company1.name = 'banana'
>>> company1.value = 40
>>> 
>>> company2 = lambda x:x
>>> company2.name = 'rhubarb'
>>> company2.value = 42
>>> 
>>> with open('company_dill.pkl', 'wb') as f:
...     dill.dump(company1, f)
...     dill.dump(company2, f)
... 
>>> 

And now read the file.

Python 2.7.8 (default, Jul 13 2014, 02:29:54) 
[GCC 4.2.1 Compatible Apple Clang 4.1 ((tags/Apple/clang-421.11.66))] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import dill
>>> with open('company_dill.pkl', 'rb') as f:
...     company1 = dill.load(f)
...     company2 = dill.load(f)
... 
>>> company1 
<__main__.Company instance at 0x107909128>
>>> company1.name
'banana'
>>> company1.value
40
>>> company2.name
'rhubarb'
>>> company2.value
42
>>>    

It works. The reason pickle fails, and dill doesn't, is that dill treats __main__ like a module (for the most part), and also can pickle class definitions instead of pickling by reference (like pickle does). The reason dill can pickle a lambda is that it gives it a name… then pickling magic can happen.

Actually, there's an easier way to save all these objects, especially if you have a lot of objects you've created. Just dump the whole python session, and come back to it later.

Python 2.7.8 (default, Jul 13 2014, 02:29:54) 
[GCC 4.2.1 Compatible Apple Clang 4.1 ((tags/Apple/clang-421.11.66))] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import dill
>>> class Company:
...     pass
... 
>>> company1 = Company()
>>> company1.name = 'banana'
>>> company1.value = 40
>>> 
>>> company2 = lambda x:x
>>> company2.name = 'rhubarb'
>>> company2.value = 42
>>> 
>>> dill.dump_session('dill.pkl')
>>> 

Now shut down your computer, go enjoy an espresso or whatever, and come back later...

Python 2.7.8 (default, Jul 13 2014, 02:29:54) 
[GCC 4.2.1 Compatible Apple Clang 4.1 ((tags/Apple/clang-421.11.66))] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import dill
>>> dill.load_session('dill.pkl')
>>> company1.name
'banana'
>>> company1.value
40
>>> company2.name
'rhubarb'
>>> company2.value
42
>>> company2
<function <lambda> at 0x1065f2938>

The only major drawback is that dill is not part of the python standard library. So if you can't install a python package on your server, then you can't use it.

However, if you are able to install python packages on your system, you can get the latest dill with git+https://github.com/uqfoundation/dill.git@master#egg=dill. And you can get the latest released version with pip install dill.

Quick example using company1 from your question, with python3.

import pickle

# Save the file
pickle.dump(company1, file = open("company1.pickle", "wb"))

# Reload the file
company1_reloaded = pickle.load(open("company1.pickle", "rb"))

However, as this answer noted, pickle often fails. So you should really use dill.

import dill

# Save the file
dill.dump(company1, file = open("company1.pickle", "wb"))

# Reload the file
company1_reloaded = dill.load(open("company1.pickle", "rb"))

You can use anycache to do the job for you. It considers all the details:

• It uses dill as backend, which extends the python pickle module to handle lambda and all the nice python features.
• It stores different objects to different files and reloads them properly.
• Limits cache size
• Allows cache clearing
• Allows sharing of objects between multiple runs
• Allows respect of input files which influence the result

Assuming you have a function myfunc which creates the instance:

from anycache import anycache

class Company(object):
    def __init__(self, name, value):
        self.name = name
        self.value = value

@anycache(cachedir='/path/to/your/cache')    
def myfunc(name, value)
    return Company(name, value)

Anycache calls myfunc at the first time and pickles the result to a file in cachedir using an unique identifier (depending on the function name and its arguments) as filename. On any consecutive run, the pickled object is loaded. If the cachedir is preserved between python runs, the pickled object is taken from the previous python run.

For any further details see the documentation