gagafunctor commented on a change in pull request #23983: [SPARK-26881][mllib]
Heuristic for tree aggregate depth
URL: https://github.com/apache/spark/pull/23983#discussion_r267751485
##########
File path:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
##########
@@ -775,6 +778,35 @@ class RowMatrix @Since("1.0.0") (
s"The number of rows $m is different from what specified or previously
computed: ${nRows}.")
}
}
+
+ /**
+ * Computing desired tree aggregate depth necessary to avoid exceeding
+ * driver.MaxResultSize during aggregation.
+ * 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) = {
+ 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)
+
+ require(maxDriverResultSizeInBytes > aggregatedObjectSizeInBytes,
Review comment:
I don't have strong opinion about that either. But I slightly prefer failing
here rather than let a long job (depth 10 involves a lot of shuffle) that is
doomed to fail run.
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