3 ms·

You use it in Python. OP writes the C code for you.

stefantalpalaru 10y ago
Yes, but all the performance related praises should go to C.
pg314 10y ago
As soon as you do anything else than return 'hello, world' in Python, your performance will drop dramatically. Just parsing a tiny JSON string a million times in python takes 7s (i5-2467M). $ time python -c "import json; for i in range(0, 1000000): json.loads('{\"data\": \"hello, world\"}')" real 0m7.055s user 0m6.951s sys 0m0.063s It is no use optimising one part of a system if other parts take 99% of the time. As it stands, the title of the post would be more accurately stated as 'million requests per second with C'.
- SEJeff 10y ago
  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.
- Twirrim 10y ago
  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
  - pg314 10y ago
    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.
- crdoconnor 10y ago
  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.
- jwilk 10y ago
  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
  - deleted 10y ago
    [deleted]

Yes, but all the performance related praises should go to C.

pg314 10y ago

As soon as you do anything else than return 'hello, world' in Python, your performance will drop dramatically. Just parsing a tiny JSON string a million times in python takes 7s (i5-2467M). $ time python -c "import json; for i in range(0, 1000000): json.loads('{\"data\": \"hello, world\"}')" real 0m7.055s user 0m6.951s sys 0m0.063s It is no use optimising one part of a system if other parts take 99% of the time. As it stands, the title of the post would be more accurately stated as 'million requests per second with C'.

SEJeff 10y ago
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.
Twirrim 10y ago
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
- pg314 10y ago
  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.
crdoconnor 10y ago
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.
jwilk 10y ago
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
- deleted 10y ago
  [deleted]