Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1562#discussion_r15439509
--- Diff: core/src/test/scala/org/apache/spark/PartitioningSuite.scala ---
@@ -102,6 +100,34 @@ class PartitioningSuite extends FunSuite with
SharedSparkContext with PrivateMet
partitioner.getPartition(Row(100))
}
+ test("RangePartitioner should run only one job if data is roughly
balanced") {
+ val rdd = sc.makeRDD(0 until 20, 20).flatMap { i =>
+ val random = new java.util.Random(i)
+ Iterator.fill(5000 * i)((random.nextDouble() + i, i))
+ }.cache()
+ for (numPartitions <- Seq(10, 20, 40)) {
+ val partitioner = new RangePartitioner(numPartitions, rdd)
+ assert(partitioner.numPartitions === numPartitions)
+ assert(partitioner.singlePass === true)
+ val counts = rdd.keys.map(key =>
partitioner.getPartition(key)).countByValue().values
+ assert(counts.max < 2.0 * counts.min)
+ }
+ }
+
+ test("RangePartitioner should work well on unbalanced data") {
+ val rdd = sc.makeRDD(0 until 20, 20).flatMap { i =>
+ val random = new java.util.Random(i)
+ Iterator.fill(20 * i * i * i)((random.nextDouble() + i, i))
+ }.cache()
+ for (numPartitions <- Seq(2, 4, 8)) {
+ val partitioner = new RangePartitioner(numPartitions, rdd)
+ assert(partitioner.numPartitions === numPartitions)
+ assert(partitioner.singlePass === false)
+ val counts = rdd.keys.map(key =>
partitioner.getPartition(key)).countByValue().values
+ assert(counts.max < 2.0 * counts.min)
+ }
+ }
+
--- End diff --
The first partition in this test contains 0 elements. I will add a test
where the whole RDD has 0 elements.
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