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Re: CIF-JSON new draft

>
> Since we are targeting this as a low overhead representation, which I associate with performance considerations, I am prepared to entertain arguments about performance impact.  I am not, however, prepared to accept unsupported assertions about performance.

Fair enough, here is a microbenchmark for you. First using Python2.7:

$ python -m timeit -s 'import json,sys' 'f=open("numbers.json"); numbers = json.load(f)["numbers"]'
100 loops, best of 3: 10.2 msec per loop
$ python -m timeit -s 'import json,sys' 'f=open("strings.json"); numbers = [float(x) for x in json.load(f)["strings"]]'
10 loops, best of 3: 58.1 msec per loop

In this case using strings is 5x slower.

Now with Python 3.5:

$ python3 -m timeit -s 'import json,sys' 'f=open("numbers.json"); numbers = json.load(f)["numbers"]'
100 loops, best of 3: 12.7 msec per loop
$ python3 -m timeit -s 'import json,sys' 'f=open("strings.json"); numbers = [float(x) for x in json.load(f)["strings"]]'
10 loops, best of 3: 27.4 msec per loop


The difference is smaller, but still >2x.

The input files were prepared using this script:

import json
import random
numbers = [round(random.uniform(-30, 30), 3) for _ in range(100000)]
with open('numbers.json', 'w') as f:
    json.dump({'numbers':numbers}, f)
with open('strings.json', 'w') as f:
    json.dump({'strings':[str(x) for x in numbers]}, f)


$ du -h strings.json numbers.json
984K    strings.json
788K    numbers.json


Marcin
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