As we all know, a partition in Spark is actually an Iterator[T]. For some
purpose, I want to treat each partition not an Iterator but one whole
object. For example, treat Iterator[Int] to a
breeze.linalg.DenseVector[Int]. Thus I use 'mapPartitions' API to achieve
this, however, during the implementation, I observed some confused
observations.
I use Spark 1.3.0 on 10 executor nodes cluster, below is different attempts:
/import breeze.linalg.DenseVector/
val a = sc.parallelize( 1 to 100, 10)
val b = a.mapPartitions(iter =>{val v = Array.ofDim[Int](iter.size)
var ind = 0
while(iter.hasNext){
v(ind) = iter.next
ind += 1
}
println(v.mkString(","))
Iterator.single[DenseVector[Int]](DenseVector(v))}
)
b.count()
val c = a.mapPartitions(iter =>{val v = Array.ofDim[Int](iter.size)
iter.copyToArray(v, 0, 10)
println(v.mkString(","))
Iterator.single[DenseVector[Int]](DenseVector(v))}
)
c.count()
val d = a.mapPartitions(iter =>{val v = iter.toArray
println(v.mkString(","))
Iterator.single[DenseVector[Int]](DenseVector(v))}
)
d.count()
I can see the printed output in the executor's stdout, actually only attempt
'd' satisfy my needs, and other attempts only get a zero desevector, which
means the variable assignment from iterator to vector did not happen.
Hope for explanations for these observations.
Thanks.
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