Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/916#issuecomment-45992111
@colorant Tried the following with the new implementation:
~~~
val rdd = sc.parallelize(0 until 1000000000, 10000).flatMap(i =>
Iterator.fill(10)(0)) // 10^10
rdd.takeSample(false, 1).size
rdd.takeSample(true, 1).size
~~~
Both worked well. We might need a better RNG for even smaller sampling
probabilities. Another solution is set a lower bound in
`comptueFractionForSampleSize`, e.g, `10^{-9}`. I prefer the latter to avoid
using expensive RNGs. Could you run some tests and derive a good lower bound?
Thanks!
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