python - What is the difference between pickle and shelve?

Question

I am learning about object serialization for the first time. I tried reading and 'googling' for differences in the modules pickle and shelve but I am not sure I understand it. When to use which one? Pickle can turn every python object into stream of bytes which can be persisted into a file. Then why do we need the module shelve? Isn't pickle faster?

Answer 1

pickle is for serializing some object (or objects) as a single bytestream in a file.

shelve builds on top of pickle and implements a serialization dictionary where objects are pickled, but associated with a key (some string), so you can load your shelved data file and access your pickled objects via keys. This could be more convenient were you to be serializing many objects.

Here is an example of usage between the two. (should work in latest versions of Python 2.7 and Python 3.x).

pickle Example

import pickle

integers = [1, 2, 3, 4, 5]

with open('pickle-example.p', 'wb') as pfile:
    pickle.dump(integers, pfile)

This will dump the integers list to a binary file called pickle-example.p.

Now try reading the pickled file back.

import pickle

with open('pickle-example.p', 'rb') as pfile:
    integers = pickle.load(pfile)
    print integers

The above should output [1, 2, 3, 4, 5].

shelve Example

import shelve

integers = [1, 2, 3, 4, 5]

# If you're using Python 2.7, import contextlib and use
# the line:
# with contextlib.closing(shelve.open('shelf-example', 'c')) as shelf:
with shelve.open('shelf-example', 'c') as shelf:
    shelf['ints'] = integers

Notice how you add objects to the shelf via dictionary-like access.

Read the object back in with code like the following:

import shelve

# If you're using Python 2.7, import contextlib and use
# the line:
# with contextlib.closing(shelve.open('shelf-example', 'r')) as shelf:
with shelve.open('shelf-example', 'r') as shelf:
    for key in shelf.keys():
        print(repr(key), repr(shelf[key]))

The output will be 'ints', [1, 2, 3, 4, 5].