[GitHub] [spark] Ngone51 commented on a change in pull request #31480: [SPARK-32384][CORE] repartitionAndSortWithinPartitions avoid shuffle with same partitioner

[GitBox <gi...@apache.org>]

Ngone51 commented on a change in pull request #31480:
URL: https://github.com/apache/spark/pull/31480#discussion_r571082348



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File path: core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
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@@ -73,7 +75,21 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
    * because it can push the sorting down into the shuffle machinery.
    */
   def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)] = self.withScope {
-    new ShuffledRDD[K, V, V](self, partitioner).setKeyOrdering(ordering)
+    if (self.partitioner == Some(partitioner)) {
+      self.mapPartitions(iter => {
+        val context = TaskContext.get
+        val sorter = new ExternalSorter[K, V, V](context, None, None, Some(ordering))
+        sorter.insertAll(iter)
+        context.taskMetrics.incDiskBytesSpilled(sorter.diskBytesSpilled)
+        context.taskMetrics.incMemoryBytesSpilled(sorter.memoryBytesSpilled)
+        context.taskMetrics.incPeakExecutionMemory(sorter.peakMemoryUsedBytes)
+        val outputIter = new InterruptibleIterator(context,
+          sorter.iterator.asInstanceOf[Iterator[(K, V)]])
+        CompletionIterator[(K, V), Iterator[(K, V)]](outputIter, sorter.stop)

Review comment:
       I think it's better to add `sorter.stop` to task completion listener. So we can always release resources even if task fails.




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