ByteEasy

I'm using their PI example. The Performance gains are great and when you see the code below you will realize that PICloud is really easy and intuitive to use. I'll be moving some of my python jobs to them. 0.0 0.0160000324249 0.047000169754 0.483999967575 4.64100003242 46.5150001049 Process Location Number of Tests Number in Parallel Wall Clock Time (sec) Pi calcPiLocal local 1 10 2 0.00 3.16000000 calcPiCloud cloud 8 10 2 30.37 3.04000000 calcPiLocal local 1 10 3 0.00 3.13200000 calcPiCloud cloud 8 10 3 5.31 3.08000000 calcPiLocal local 1 10 4 0.02 3.13640000 calcPiCloud cloud 8 10 4 4.31 3.13200000 calcPiLocal local 1 10 5 0.05 3.13664000 calcPiCloud cloud 8 10 5 1.22 3.13840000 calcPiLocal local 1 10 6 0.48 3.14185200 calcPiCloud cloud 8 10 6 2.30 3.14116000 calcPiLocal local 1 10 7 4.64 3.14092240 calcPiCloud cloud 8 10 7 2.31 3.14099920 calcPiLocal local 1 10 8 46.52 3.14138168 calcPiCloud cloud 8 10 8 8.50 3.14139276 calcPiLocal local 1 10...

Last week  I stuck my head out  in a meeting and declared that XML is verbose and slow to parse and that we should move to something like Google's protocols buffers,  or something readable such as json or YAML, which are  easier to parse etc etc etc! Well is this really true ? The statement seems logical considering how verbose XML can be. Still, after the meeting, some questions stayed in my mind. So I thought I would do some tests. I used  a FIX Globex (CME) swap trade confirmation message to test my theory. Size from Python to Python json cjson 2332 0.222238063812 0.0943419933319 pickle cPickle 1778 0.233518123627 0.128826141357 XML cElementTree 2083 0.407706975937 2.77832698822 json simplejson 2332 3.37723612785 5.11316084862 So this simple test shows that using XML with cElementTree parser  is not so slow, cjson wins in speed and the conclusion must be: Your performance will ultimately depend on your data and the quality of the l...