json:

    $ time python -c "import json;
    for i in range(0, 1000000):
          json.loads('{\"data\": \"hello, world\"}')"

    real	0m3.142s
    user	0m3.098s
    sys	0m0.034s

ujson

    $ time python -c "import ujson as json;
    for i in range(0, 1000000):
          json.loads('{\"data\": \"hello, world\"}')"

    real	0m0.444s
    user	0m0.399s
    sys	0m0.028s

Like with many things, it's a question of being smart about your choice of libraries.

Also, just because I was curious:

    $ time pypy -c "import json;
    for i in range(0, 1000000):
          json.loads('{\"data\": \"hello, world\"}')"

    real	0m0.422s
    user	0m0.386s
    sys	0m0.032s

If you use a C library and have hardly any Python code running it obviously goes faster. That misses the point I was making. At some point you'll have to write some Python code, and that will lower the number of requests dramatically. If you're talking millions of requests per second, you will have maybe 10s of thousands of CPU cycles per request. That doesn't buy you much in Python. Case in point: parsing a trivial json string in Python eats up your budget.

Of course, most websites aren't dealing with anywhere near that volume of requests, and then Python is fine.

Pypy is interesting, but last time I looked it didn't support C extensions and hence can't be used with the poster's library.

FWIW, you can use ujson, which is much faster than the builtin stdlib json parser as well. I've seen some larger webapps in python do just this.

You might want to use the timeit module instead:

  $ python -m timeit --setup 'import json' "json.loads('{\"data\": \"hello, world\"}')"
  100000 loops, best of 3: 9.34 usec per loop

time python -c "import ujson; for i in xrange(0, 1000000): json.loads('{\"data\": \"hello, world\"}')"

0.291s

Well, that was an easy way to reduce 7 seconds to 0.3.