Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/3193#issuecomment-63129260
For the quality of RNG, both python and numpy use Mersenne Twister
(http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.RandomState.html):
> The Python stdlib module ârandomâ also contains a Mersenne Twister
pseudo-random number generator with a number of methods that are similar to the
ones available in RandomState. RandomState, besides being NumPy-aware, has the
advantage that it provides a much larger number of probability distributions to
choose from.
I can tell numpy.random uses MT19937 from its source code, and perhaps
Python implements the same RNG. So quality-wise, there should be no issues with
always using Python's random.
But for the performance/code complexity trade-offs, maybe @JoshRosen should
decide.
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