Github user tgravescs commented on a diff in the pull request:
https://github.com/apache/spark/pull/14673#discussion_r75128458
--- Diff:
core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala ---
@@ -137,6 +139,17 @@ class JobProgressListener(conf: SparkConf) extends
SparkListener with Logging {
)
}
+ /** If Tasks is too large, remove and garbage collect old tasks */
+ private def trimTasksIfNecessary(taskData: HashMap[Long, TaskUIData]) =
synchronized {
+ if (taskData.size > retainedTasks) {
+ val toRemove = (taskData.size - retainedTasks)
+ val oldIds =
taskData.map(_._2.taskInfo.taskId).toList.sorted.take(toRemove)
--- End diff --
is there a reason we are using _._2.taskInfo.taskId instead of just the
Long ( _._1), which is the taskId also?
I'm a bit concerned with the sorting here. If someone is using really
large number of tasks , say 1 million I'm afraid this sorting might be
expensive, especially if we are doing it for every single task that comes in
after hitting the limit, we have to keep resorting every time.
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