Speed tests for *json and cPickle in Python

And because caching systems are typically a means for speeding up reads, where the article only focuses on writes.

Python 2.5 -- including pickle's (faster) bin mode

    Starting simplejson dumps...
    done: 31.7636611462
    Starting cjson encode...
    done: 3.80062890053
    Starting pickle dumps...
    done: 10.4873199463
    Starting pickle dumps (protocol=-1 -- force HIGHEST_PROTOCOL)...
    done: 5.98110699654
    Starting simplejson loads...
    done: 138.709590197
    Starting cjson decode...
    done: 1.85300803185
    Starting pickle loads...
    done: 5.24735999107
    Starting pickle loads (protocol=-1 -- force HIGHEST_PROTOCOL)...
    done: 3.66428518295

He doesn't use the cPickle binary format, which is significantly faster: http://docs.python.org/library/pickle.html#pickle.HIGHEST_PR...

Python 2.7 on Ubuntu:

  $ python -mtimeit -s "from json import dumps; 
  d = {
      'foo': 'bar',
      'food': 'barf',
      'good': 'bars',
      'dood': 'wheres your car?',
      'wheres your car': 'dude?',
  }
  " "dumps(d)"
  100000 loops, best of 3: 6.89 usec per loop

  $ python -mtimeit -s "from cPickle import dumps; 
  d = {
      'foo': 'bar',
      'food': 'barf',
      'good': 'bars',
      'dood': 'wheres your car?',
      'wheres your car': 'dude?',
  }
  " "dumps(d)"
  100000 loops, best of 3: 7.78 usec per loop

So `json` seems faster than `cPickle`. Right? Wrong!:

  $ python -mtimeit -s "from cPickle import dumps; 
  d = {
      'foo': 'bar',
      'food': 'barf',
      'good': 'bars',
      'dood': 'wheres your car?',
      'wheres your car': 'dude?',
  }
  " "dumps(d, -1)"
  100000 loops, best of 3: 3.59 usec per loop

Apple and oranges (even though they're mostly used for the same thing). pickle is a Turing complete language used to recreate python objects, while json is only used for serialization. pickle is slightly more powerful, but should also not be used to load untrusted data. See http://nadiana.com/python-pickle-insecure

We were just talking about this a few weeks back, with a few more libraries compared with interesting results: https://convore.com/python/faster-json-library/

I'm confused, is cjson still recommended? Is it still maintained? Its status seems ambiguous based on the PyPi discussion. Furthermore it seems there's a number of issues with cjson that need to be resolved...

insane