LantaoJin commented on a change in pull request #29869:
URL: https://github.com/apache/spark/pull/29869#discussion_r497203916
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File path: core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
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@@ -274,8 +274,36 @@ private[spark] class DAGScheduler(
accumUpdates: Seq[AccumulatorV2[_, _]],
metricPeaks: Array[Long],
taskInfo: TaskInfo): Unit = {
+ // It may cause heavy full GC problem if some heavy external accumulators
keep in memory.
+ // We update these heavy accumulators before they entering into Spark
listener event loop.
+ val (heavyAccumUpdates, otherAccumUpdates) = accumUpdates.partition { acc
=>
Review comment:
Yes. "Heavy" means the size is too big, such as the accumulator value
contains thousands of file paths. I know this looks not a good solution. Do you
mean to use a size threshold to filter out the big accumulator values is a
better solution? The problem that way is we split the same accumulators (same
name with different sizes). Actually, Spark uses METRICS_PREFIX to distinguish
the internal metrics from all accumulators `val METRICS_PREFIX =
"internal.metrics."`. We can split to `val (externalAccumUpdates,
internalAccumUpdates)` and update/merge the `externalAccumUpdates` before they
entering into event loop. But, you know, we still cannot forbid developers to
use the "internal.metrics." prefix. We only could add some comments in code and
document it.
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