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    <p>One reason to define your own custom binary format could be optimization. pickle (and shelve, which uses pickle) is a generic serialization framework; it can store almost any Python data. It's easy to use pickle in a lot of situations, but it takes time to inspect all the objects and serialize their data and the data itself is stored in a generic, verbose format. If you are storing specific known data a custom-built serializer can be both faster and more concise. </p> <p>It takes 37 bytes to pickle an object with a single integer value:</p> <pre>>>> import pickle >>> class Foo: pass... >>> foo = Foo() >>> foo.x = 3 >>> print repr(pickle.dumps(foo)) "(i__main__\nFoo\np0\n(dp1\nS'x'\np2\nI3\nsb." </pre> <p>Embedded in that data is the name of the property and its type. A custom serializer for Foo (and Foo alone) could dispense with that and just store the number, saving both time and space.</p> <p>Another reason for a custom serialization framework is you can easily do custom validation and versioning of data. If you change your object types and need to load an old version of data it can be tricky via pickle. Your own code can be easily customized to handle older data formats. </p> <p>In practice, I'd build something using the generic cPickle module and only replace it if profiling indicated it was really important. Maintaining a separate serialization framework is a significant amount of work.</p> <p>One final resource you may find useful: <a href="http://kbyanc.blogspot.com/2007/07/python-serializer-benchmarks.html" rel="nofollow noreferrer">some synthetic serializer benchmarks</a>. cPickle is pretty fast.</p>
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    1. POWhat are the benefits of not using cPickle to create a persistent storage for data?
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