The speed actually depends on the data, it’s content and size.
But, anyway, let’s take an example json data and see what is faster (Ubuntu 12.04, python 2.7.3) :
Giving this json structure dumped into test.json and test.pickle files:
{
"glossary": {
"title": "example glossary",
"GlossDiv": {
"title": "S",
"GlossList": {
"GlossEntry": {
"ID": "SGML",
"SortAs": "SGML",
"GlossTerm": "Standard Generalized Markup Language",
"Acronym": "SGML",
"Abbrev": "ISO 8879:1986",
"GlossDef": {
"para": "A meta-markup language, used to create markup languages such as DocBook.",
"GlossSeeAlso": ["GML", "XML"]
},
"GlossSee": "markup"
}
}
}
}
}
Testing script:
import timeit
import pickle
import cPickle
import json
import simplejson
import ujson
import yajl
def load_pickle(f):
return pickle.load(f)
def load_cpickle(f):
return cPickle.load(f)
def load_json(f):
return json.load(f)
def load_simplejson(f):
return simplejson.load(f)
def load_ujson(f):
return ujson.load(f)
def load_yajl(f):
return yajl.load(f)
print "pickle:"
print timeit.Timer('load_pickle(open("test.pickle"))', 'from __main__ import load_pickle').timeit()
print "cpickle:"
print timeit.Timer('load_cpickle(open("test.pickle"))', 'from __main__ import load_cpickle').timeit()
print "json:"
print timeit.Timer('load_json(open("test.json"))', 'from __main__ import load_json').timeit()
print "simplejson:"
print timeit.Timer('load_simplejson(open("test.json"))', 'from __main__ import load_simplejson').timeit()
print "ujson:"
print timeit.Timer('load_ujson(open("test.json"))', 'from __main__ import load_ujson').timeit()
print "yajl:"
print timeit.Timer('load_yajl(open("test.json"))', 'from __main__ import load_yajl').timeit()
Output:
pickle:
107.936687946
cpickle:
28.4231381416
json:
31.6450419426
simplejson:
20.5853149891
ujson:
16.9352178574
yajl:
18.9763481617
As you can see, unpickling via pickle is not that fast at all – cPickle is definetely the way to go if you choose pickling/unpickling option. ujson looks promising among these json parsers on this particular data.
Also, json and simplejson libraries load much faster on pypy (see Python JSON Performance).
See also:
It’s important to note that the results may differ on your particular system, on other type and size of data.