[GitHub] [spark] karenfeng commented on a change in pull request #29925: [SPARK-33043][ML] Handle spark.driver.maxResultSize=0 in RowMatrix heuristic computation

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

karenfeng commented on a change in pull request #29925:
URL: https://github.com/apache/spark/pull/29925#discussion_r498460591



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File path: mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
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@@ -786,11 +786,15 @@ class RowMatrix @Since("1.0.0") (
    * Based on the formulae: (numPartitions)^(1/depth) * objectSize <= DriverMaxResultSize
    * @param aggregatedObjectSizeInBytes the size, in megabytes, of the object being tree aggregated
    */
-  private[spark] def getTreeAggregateIdealDepth(aggregatedObjectSizeInBytes: Long) = {
+  private[spark] def getTreeAggregateIdealDepth(aggregatedObjectSizeInBytes: Long): Int = {
     require(aggregatedObjectSizeInBytes > 0,
       "Cannot compute aggregate depth heuristic based on a zero-size object to aggregate")
 
     val maxDriverResultSizeInBytes = rows.conf.get[Long](MAX_RESULT_SIZE)
+    if (maxDriverResultSizeInBytes == 0) {
+      // Unlimited result size, so 1 is OK
+      return 1

Review comment:
       Out of curiosity, why is this 1 given that the default argument for depth in `rdd.treeAggregate` is [2](https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/RDD.scala#L1131)?




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