ODIn/spark-instrumented-optimizer
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Remove commented code

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This commit is contained in:
Matei Zaharia 2011-03-06 23:38:41 -08:00
parent bce95b8458
commit 467f056e29
1 changed files with 0 additions and 26 deletions
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26
core/src/main/scala/spark/RDD.scala
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@ -283,32 +283,6 @@ extends RDD[Array[T]](prev.context) {
(k, (vs.asInstanceOf[Seq[V]], w1s.asInstanceOf[Seq[W1]], w2s.asInstanceOf[Seq[W2]]))
}
}
/*
def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Seq[V], Seq[W]))] = {
if (self.partitioner != None) {
val part = self.partitoner.get
if (other.partitioner != None && other.partitioner.get == part) {
// Can do a partition-wise cogroup
return new PartitionWiseGroupedRDD(self, other)
}
}
val vs: RDD[(K, Either[V, W])] = self.map { case (k, v) => (k, Left(v)) }
val ws: RDD[(K, Either[V, W])] = other.map { case (k, w) => (k, Right(w)) }
(vs ++ ws).groupByKey(numSplits).flatMap {
case (k, seq) => {
val vbuf = new ArrayBuffer[V]
val wbuf = new ArrayBuffer[W]
seq.foreach(_ match {
case Left(v) => vbuf += v
case Right(w) => wbuf += w
})
for (v <- vbuf; w <- wbuf) yield (k, (v, w))
}
}
}
*/
}
class MappedValuesRDD[K, V, U](