jacky created SPARK-40367:
-----------------------------
Summary: Total size of serialized results of 3730 tasks (64.0 GB)
is bigger than spark.driver.maxResultSize (64.0 GB)
Key: SPARK-40367
URL: https://issues.apache.org/jira/browse/SPARK-40367
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 2.3.2
Reporter: jacky
I use this code:
spark.sql("xx").selectExpr(spark.table(target).columns:_*).
write.mode("overwrite").
insertInto(target)
，I get an error
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure:
Total size of serialized results of 3730 tasks (64.0 GB) is bigger than
spark.driver.maxResultSize (64.0 GB)
at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1609)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1597)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1596)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1596)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at scala.Option.foreach(Option.scala:257)
at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1830)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1779)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1768)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2055)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2074)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2099)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:939)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.collect(RDD.scala:938)
at
org.apache.spark.sql.execution.SparkPlan.executeCollectIterator(SparkPlan.scala:304)
at
org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1$$anonfun$apply$1.apply(BroadcastExchangeExec.scala:76)
at
org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1$$anonfun$apply$1.apply(BroadcastExchangeExec.scala:73)
at
org.apache.spark.sql.execution.SQLExecution$.withExecutionId(SQLExecution.scala:97)
at
org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1.apply(BroadcastExchangeExec.scala:72)
at
org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1.apply(BroadcastExchangeExec.scala:72)
at
scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
at
scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
--conf spark.driver.maxResultSize=64g --conf spark.sql.broadcastTimeout=36000
--conf spark.sql.autoBroadcastJoinThreshold=204857600
--conf spark.memory.offHeap.enabled=true --conf spark.memory.offHeap.size=4g
--num-executors 500 --executor-memory 16g --executor-cores 2 --driver-memory
80G --conf spark.sql.shuffle.partitions=4000 --conf
spark.sql.adaptive.enabled=true
When I increase the spark.driver.maxResultSize，it also does not work
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