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Posted to issues@spark.apache.org by "shashank (Jira)" <ji...@apache.org> on 2021/07/09 10:40:00 UTC
[jira] [Updated] (SPARK-36071) Spark driver requires large memory
space for serialized results even there are no data collected to the driver
[ https://issues.apache.org/jira/browse/SPARK-36071?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
shashank updated SPARK-36071:
-----------------------------
Priority: Critical (was: Major)
> 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: Critical
>
> 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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