Github user squito commented on a diff in the pull request: https://github.com/apache/spark/pull/7770#discussion_r36019144 --- Diff: core/src/main/scala/org/apache/spark/ui/ToolTips.scala --- @@ -62,6 +62,13 @@ private[spark] object ToolTips { """Time that the executor spent paused for Java garbage collection while the task was running.""" + val PEAK_EXECUTION_MEMORY = + """Execution memory refers to the memory used by internal data structures created during + shuffles, aggregations and joins when Tungsten is enabled. The value of this accumulator + should be approximately the sum of the peak sizes across all such data structures created + in this task. For SQL jobs, this only tracks all unsafe operators, broadcast joins, and + external sort.""" --- End diff -- I don't love the name "Execution Memory" -- I think a user will assume this is covering *all* memory that isn't for cached rdds. I don't even think that is 100% accurate for sql, but it'll be even less accurate for non-sql use. Not that I have much better suggestions ... "Peak Aggregator Memory" maybe?
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