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package spark.rdd
import java.util.Random
import cern.jet.random.Poisson
import cern.jet.random.engine.DRand
import spark.{OneToOneDependency, RDD, Split, TaskContext}
private[spark]
class SampledRDDSplit(val prev: Split, val seed: Int) extends Split with Serializable {
override val index: Int = prev.index
}
class SampledRDD[T: ClassManifest](
prev: RDD[T],
withReplacement: Boolean,
frac: Double,
seed: Int)
extends RDD[T](prev.context) {
@transient
val splits_ = {
val rg = new Random(seed)
prev.splits.map(x => new SampledRDDSplit(x, rg.nextInt))
}
override def splits = splits_.asInstanceOf[Array[Split]]
override val dependencies = List(new OneToOneDependency(prev))
override def preferredLocations(split: Split) =
prev.preferredLocations(split.asInstanceOf[SampledRDDSplit].prev)
override def compute(splitIn: Split, context: TaskContext) = {
val split = splitIn.asInstanceOf[SampledRDDSplit]
if (withReplacement) {
// For large datasets, the expected number of occurrences of each element in a sample with
// replacement is Poisson(frac). We use that to get a count for each element.
val poisson = new Poisson(frac, new DRand(split.seed))
prev.iterator(split.prev, context).flatMap { element =>
val count = poisson.nextInt()
if (count == 0) {
Iterator.empty // Avoid object allocation when we return 0 items, which is quite often
} else {
Iterator.fill(count)(element)
}
}
} else { // Sampling without replacement
val rand = new Random(split.seed)
prev.iterator(split.prev, context).filter(x => (rand.nextDouble <= frac))
}
}
}
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