Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/3291#issuecomment-63270709
One way to do this lazily is via shuffle:
~~~scala
val identityPartitioner = new Partitioner {
override def numPartitions: Int = p
override def getPartition(key: Any): Int = key.asInstanceOf[Int]
}
val startIndices = PartitionPruningRDD.create(this, _ < p - 1) //
skip the last partition
.mapPartitionsWithIndex { (split, iter) =>
val size = Utils.getIteratorSize(iter)
Iterator.range(split + 1, p).map { i =>
(i, size)
}
}.reduceByKey(identityPartitioner, _ + _)
.values
this.zipPartitions(startIndices) { (iter, startIndexIter) =>
val startIndex = if (startIndexIter.hasNext) startIndexIter.next()
else 0L
iter.zipWithIndex.map { case (item, localIndex) =>
(item, startIndex + localIndex)
}
}
~~~
But I think this is more expensive.
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