AngersZhuuuu commented on a change in pull request #28541:
URL: https://github.com/apache/spark/pull/28541#discussion_r426989811
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File path: core/src/main/scala/org/apache/spark/memory/ExecutionMemoryPool.scala
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@@ -138,6 +140,11 @@ private[memory] class ExecutionMemoryPool(
if (toGrant < numBytes && curMem + toGrant < minMemoryPerTask) {
logInfo(s"TID $taskAttemptId waiting for at least 1/2N of $poolName
pool to be free")
lock.wait()
+ } else if (toGrant == 0 && memoryFree > 0) {
Review comment:
> Or, if one specific pattern of tasks take significantly more memory
than other tasks, maybe you should tune the tasks themselves, instead of
modifying the Spark internal for a specific case.
yea, ad-hoc query sort with big key, spill 20G +. only run that query will
success, run with other sql with high concurrency , failed.
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