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https://issues.apache.org/jira/browse/SPARK-36071?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17378935#comment-17378935
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Hyukjin Kwon commented on SPARK-36071:
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[~vcshashank] can you show your codes?
> Spark driver requires large memory space for serialized results even there
> are no data collected to the driver
> --------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-36071
> URL: https://issues.apache.org/jira/browse/SPARK-36071
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.4.3
> Reporter: shashank
> Priority: Major
>
> Executing with large partition is causing the data transferred to driver
> exceed spark.driver.maxResultSize.
> Even when no data from the logic is being collected at by the driver. Looks
> like spark is sending metadata back which is causing it to exceed.
> {code:java}
> spark.driver.maxResultSize=8g{code}
>
> {code:java}
> Caused by: org.apache.spark.SparkException: Job aborted due to stage failure:
> Total size of serialized results of 104904 tasks (8.0 GB) is bigger than
> spark.driver.maxResultSize (8.0 GB)Caused by:
> org.apache.spark.SparkException: Job aborted due to stage failure: Total size
> of serialized results of 104904 tasks (8.0 GB) is bigger than
> spark.driver.maxResultSize (8.0 GB) at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2041)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2029)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2028)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2028)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
> at scala.Option.foreach(Option.scala:257) at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:966)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2262)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2211)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2200)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:777) at
> org.apache.spark.SparkContext.runJob(SparkContext.scala:2061) at
> org.apache.spark.SparkContext.runJob(SparkContext.scala:2082) at
> org.apache.spark.SparkContext.runJob(SparkContext.scala:2114) at
> org.apache.spark.internal.io.SparkHadoopWriter$.write(SparkHadoopWriter.scala:78)
> ... 54 more{code}
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