Pandas msgpack vs pickle

Question

msgpack in Pandas is supposed to be a replacement for pickle.

Per the Pandas docs on msgpack:

This is a lightweight portable binary format, similar to binary JSON, that is highly space efficient, and provides good performance both on the writing (serialization), and reading (deserialization).

I find, however, that its performance does not appear to stack up against pickle.

df = pd.DataFrame(np.random.randn(10000, 100))  >>> %timeit df.to_pickle('test.p') 10 loops, best of 3: 22.4 ms per loop  >>> %timeit df.to_msgpack('test.msg') 10 loops, best of 3: 36.4 ms per loop  >>> %timeit pd.read_pickle('test.p') 100 loops, best of 3: 10.5 ms per loop  >>> %timeit pd.read_msgpack('test.msg') 10 loops, best of 3: 24.6 ms per loop 

Question: Asides from potential security issues with pickle, what are the benefits of msgpack over pickle? Is pickle still the preferred method of serializing data, or do better alternatives currently exist?

Answer 1

Pickle is better for the following:

1. Numerical data or anything that uses the buffer protocol (numpy arrays) (though only if you use a somewhat recent protocol=)
2. Python specific objects like classes, functions, etc.. (although here you should look at cloudpickle)

MsgPack is better for the following:

1. Cross language interoperation. It's an alternative to JSON with some improvements
2. Performance on text data and Python objects. It's a decent factor faster than Pickle at this under any setting.

As @Jeff noted above this blogpost may be of interest