RDD.toLocalIterator return the partition one by one but with all elements in
the partition, which is not lazy calculated. Given the design of spark, it is
very hard to maintain the state of iterator across runJob.
def toLocalIterator: Iterator[T] = {
def collectPartition(p: Int): Array[T] = {
sc.runJob(this, (iter: Iterator[T]) => iter.toArray, Seq(p), allowLocal =
false).head
}
(0 until partitions.length).iterator.flatMap(i => collectPartition(i))
}
Thanks.
Zhan Zhang
On Oct 29, 2014, at 3:43 AM, Yanbo Liang <[email protected]> wrote:
> RDD.toLocalIterator() is the suitable solution.
> But I doubt whether it conform with the design principle of spark and RDD.
> All RDD transform is lazily computed until it end with some actions.
>
> 2014-10-29 15:28 GMT+08:00 Sean Owen <[email protected]>:
> Call RDD.toLocalIterator()?
>
> https://spark.apache.org/docs/latest/api/java/org/apache/spark/rdd/RDD.html
>
> On Wed, Oct 29, 2014 at 4:15 AM, Dai, Kevin <[email protected]> wrote:
> > Hi, ALL
> >
> >
> >
> > I have a RDD[T], can I use it like a iterator.
> >
> > That means I can compute every element of this RDD lazily.
> >
> >
> >
> > Best Regards,
> >
> > Kevin.
>
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