Github user erikerlandson commented on a diff in the pull request:
https://github.com/apache/spark/pull/2455#discussion_r19636555
--- Diff:
core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala ---
@@ -38,13 +41,45 @@ trait RandomSampler[T, U] extends Pseudorandom with
Cloneable with Serializable
/** take a random sample */
def sample(items: Iterator[T]): Iterator[U]
+ /** return a copy of the RandomSampler object */
override def clone: RandomSampler[T, U] =
throw new NotImplementedError("clone() is not implemented.")
}
+private [spark]
+object RandomSampler {
+ /** Default random number generator used by random samplers. */
+ def newDefaultRNG: Random = new XORShiftRandom
+
+ /**
+ * Default gap sampling maximum.
+ * For sampling fractions <= this value, the gap sampling optimization
will be applied.
+ * Above this value, it is assumed that "tradtional" Bernoulli sampling
is faster. The
+ * optimal value for this will depend on the RNG. More expensive RNGs
will tend to make
+ * the optimal value higher. The most reliable way to determine this
value for a given RNG
+ * is to experiment. I would expect a value of 0.5 to be close in most
cases.
--- End diff --
0.5 is what I recommend as an initial guess if one is using a new RNG.
(0.4 is what I got by experimenting with the current RNG)
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