Python: performance comparison of using `pickle` or `marshal` and using `re`

问题

I am calculating some very large numbers using Python, and I'd like to store previously calculated results in Berkeley DB.

The problem is that Berkeley DB has to use strings, and I have to store an integer tuple for the calculation results.

For example, I get (m, n) as my result, one way is to store this as "%d,%d" % (m, n) and read it out using re. I can also store the tuple using pickle or marshal.

Which has the better performance?

回答1:

For pure speed, marshal will get you the fastest results.

Timings:

>>> timeit.timeit("pickle.dumps([1,2,3])","import pickle",number=10000)
0.2939901351928711
>>> timeit.timeit("json.dumps([1,2,3])","import json",number=10000)
0.09756112098693848
>>> timeit.timeit("pickle.dumps([1,2,3])","import cPickle as pickle",number=10000)
0.031056880950927734
>>> timeit.timeit("marshal.dumps([1,2,3])","import marshal", number=10000)
0.00703883171081543

回答2:

When somebody are thinking about performance he should to remember 3 things:

Don't trust anybody - any benchmark can lie (by a different reasons: unprofessional, marketing, etc.)
Always measure your case - for example, cache system and statistics have totally different requirements. In one case you need to read as fast as possible, in other case - write
Repeat tests - new version of any software could be faster/slower, so any update could introduce benefits/penalties

For example, here is results of my benchmark:

jimilian$ python3.5 serializators.py
iterations= 100000
data= 'avzvasdklfjhaskldjfhkweljrqlkjb*@&$Y)(!#&$G@#lkjabfsdflb(*!G@#$(GKLJBmnz,bv(PGDFLKJ'
==== DUMP ====
Pickle:
>> 0.09806302400829736
Json: 2.0.9
>> 0.12253901800431777
Marshal: 4
>> 0.09477431800041813
Msgpack: (0, 4, 7)
>> 0.16701826300413813

==== LOAD ====
Pickle:
>> 0.10376790800364688
Json: 2.0.9
>> 0.30041573599737603
Marshal: 4
>> 0.034003349996055476
Msgpack: (0, 4, 7)
>> 0.061493027009419166

jimilian$ python3.5 serializators.py
iterations= 100000
data= [1,2,3]*100
==== DUMP ====
Pickle:
>> 0.9678693519963417
Json: 2.0.9
>> 4.494351467001252
Marshal: 4
>> 0.8597690019960282
Msgpack: (0, 4, 7)
>> 1.2778299400088144

==== LOAD ====
Pickle:
>> 1.0350999219954247
Json: 2.0.9
>> 3.349724347004667
Marshal: 4
>> 0.468191737003508
Msgpack: (0, 4, 7)
>> 0.3629750510008307

jimilian$ python2.7 serializators.py
iterations= 100000
data= [1,2,3]*100
==== DUMP ====
Pickle:
>> 50.5894570351
Json: 2.0.9
>> 2.69190311432
cPickle: 1.71
>> 5.14689707756
Marshal: 2
>> 0.539206981659
Msgpack: (0, 4, 7)
>> 0.752672195435

==== LOAD ====
Pickle:
>> 58.8052768707
Json: 2.0.9
>> 3.50090789795
cPickle: 1.71
>> 8.46298909187
Marshal: 2
>> 0.469168901443
Msgpack: (0, 4, 7)
>> 0.315001010895

So, as you can see sometimes it's better to use Pickle (python3, long string, dump), sometimes - msgpack (python3, long array, load), in python2 - things works completely different. That's why nobody can give certain answer that will be valid for everybody (expect this one ;) ).