How to extend an one-to-one RDD of Spark that can be persisted?

[Peng Cheng <pc...@uow.edu.au>]

In my project I extend a new RDD type that wraps another RDD and some
metadata. The code I use is similar to FilteredRDD implementation:

case class PageRowRDD(
                   self: RDD[PageRow],
                   @transient keys: ListSet[KeyLike] = ListSet()
){
  override def getPartitions: Array[Partition] =
    firstParent[PageRow].partitions

  override val partitioner = self.partitioner

  override def compute(split: Partition, context: TaskContext) =
    firstParent[PageRow].iterator(split, context)
}
However when I try to persist and reuse it in 2 transformations. My logs and
debug shows that it is being computed twice, rather than being reused in
memory.

The problem is: there is no such problem for FilteredRDD. How do I avoid
this?



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