akpatnam25 commented on a change in pull request #33644:
URL: https://github.com/apache/spark/pull/33644#discussion_r683853088
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File path: core/src/main/scala/org/apache/spark/rdd/RDD.scala
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@@ -1233,6 +1233,21 @@ abstract class RDD[T: ClassTag](
(i, iter) => iter.map((i % curNumPartitions, _))
}.foldByKey(zeroValue, new
HashPartitioner(curNumPartitions))(cleanCombOp).values
}
+ if (conf.get(ENABLE_EXECUTOR_TREE_AGGREGATE) &&
partiallyAggregated.partitions.length > 1) {
+ // define a new partitioner that results in only 1 partition
+ val constantPartitioner = new Partitioner {
+ override def numPartitions: Int = 1
+
+ override def getPartition(key: Any): Int = 0
+ }
+ // map the partially aggregated rdd into a key-value rdd
+ // do the computation in the single executor with one partition
+ // get the new RDD[U]
+ partiallyAggregated = partiallyAggregated
Review comment:
it would not be any less memory intensive. It is just that the
executors tend to be configured with higher memory, so there is a lesser chance
of this happening on executors. Additionally, executors can retry the task
several times, opposed to just 1 try and failing on the driver
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