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Revert "[SPARK-1784] Add a new partitioner to allow specifying # of keys per partition"
This reverts commit 92cebad.
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core/src/main/scala/org/apache/spark/Partitioner.scala

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@@ -156,64 +156,3 @@ class RangePartitioner[K : Ordering : ClassTag, V](
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false
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}
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}
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/**
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* A [[org.apache.spark.Partitioner]] that partitions records into specified bounds
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* Default value is 1000. Once all partitions have bounds elements, the partitioner
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* allocates 1 element per partition so eventually the smaller partitions are at most
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* off by 1 key compared to the larger partitions.
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*/
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class BoundaryPartitioner[K : Ordering : ClassTag, V](
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partitions: Int,
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@transient rdd: RDD[_ <: Product2[K,V]],
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private val boundary: Int = 1000)
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extends Partitioner {
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// this array keeps track of keys assigned to a partition
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// counts[0] refers to # of keys in partition 0 and so on
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private val counts: Array[Int] = {
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new Array[Int](numPartitions)
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}
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def numPartitions = math.abs(partitions)
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/*
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* Ideally, this should've been calculated based on # partitions and total keys
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* But we are not calling count on RDD here to avoid calling an action.
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* User has the flexibility of calling count and passing in any appropriate boundary
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*/
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def keysPerPartition = boundary
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var currPartition = 0
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/*
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* Pick current partition for the key until we hit the bound for keys / partition,
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* start allocating to next partition at that time.
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*
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* NOTE: In case where we have lets say 2000 keys and user says 3 partitions with 500
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* passed in as boundary, the first 500 will goto P1, 501-1000 go to P2, 1001-1500 go to P3,
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* after that, next keys go to one partition at a time. So 1501 goes to P1, 1502 goes to P2,
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* 1503 goes to P3 and so on.
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*/
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def getPartition(key: Any): Int = {
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val partition = currPartition
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counts(partition) = counts(partition) + 1
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/*
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* Since we are filling up a partition before moving to next one (this helps in maintaining
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* order of keys, in certain cases, it is possible to end up with empty partitions, like
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* 3 partitions, 500 keys / partition and if rdd has 700 keys, 1 partition will be entirely
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* empty.
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*/
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if(counts(currPartition) >= keysPerPartition)
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currPartition = (currPartition + 1) % numPartitions
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partition
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}
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override def equals(other: Any): Boolean = other match {
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case r: BoundaryPartitioner[_,_] =>
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(r.counts.sameElements(counts) && r.boundary == boundary
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&& r.currPartition == currPartition)
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case _ =>
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false
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}
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}

core/src/test/scala/org/apache/spark/PartitioningSuite.scala

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@@ -66,40 +66,6 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet
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assert(descendingP4 != p4)
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}
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test("BoundaryPartitioner equality") {
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// Make an RDD where all the elements are the same so that the partition range bounds
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// are deterministically all the same.
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val rdd = sc.parallelize(1.to(4000)).map(x => (x, x))
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val p2 = new BoundaryPartitioner(2, rdd, 1000)
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val p4 = new BoundaryPartitioner(4, rdd, 1000)
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val anotherP4 = new BoundaryPartitioner(4, rdd)
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assert(p2 === p2)
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assert(p4 === p4)
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assert(p2 != p4)
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assert(p4 != p2)
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assert(p4 === anotherP4)
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assert(anotherP4 === p4)
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}
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test("BoundaryPartitioner getPartition") {
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val rdd = sc.parallelize(1.to(2000)).map(x => (x, x))
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val partitioner = new BoundaryPartitioner(4, rdd, 500)
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1.to(2000).map { element => {
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val partition = partitioner.getPartition(element)
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if (element <= 500) {
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assert(partition === 0)
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} else if (element > 501 && element <= 1000) {
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assert(partition === 1)
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} else if (element > 1001 && element <= 1500) {
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assert(partition === 2)
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} else if (element > 1501 && element <= 2000) {
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assert(partition === 3)
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}
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}}
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}
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test("RangePartitioner getPartition") {
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val rdd = sc.parallelize(1.to(2000)).map(x => (x, x))
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// We have different behaviour of getPartition for partitions with less than 1000 and more than

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