[ https://issues.apache.org/jira/browse/SPARK-10929?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Naden Franciscus updated SPARK-10929: ------------------------------------- Description: We are executing 20 Spark SQL jobs in parallel using Spark Job Server and hitting the following issue pretty routinely. 40GB heap x 6 nodes. Have tried adjusting shuffle.memoryFraction from 0.2 -> 0.1 with no difference. {code} .16): org.apache.spark.SparkException: Task failed while writing rows. at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:250) at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150) at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) at org.apache.spark.scheduler.Task.run(Task.scala:88) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) Caused by: java.io.IOException: Unable to acquire 16777216 bytes of memory at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:351) at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.<init>(UnsafeExternalSorter.java:138) at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.create(UnsafeExternalSorter.java:106) at org.apache.spark.sql.execution.UnsafeExternalRowSorter.<init>(UnsafeExternalRowSorter.java:68) at org.apache.spark.sql.execution.TungstenSort.org$apache$spark$sql$execution$TungstenSort$$preparePartition$1(sort.scala:146) at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169) at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169) at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.prepare(MapPartitionsWithPreparationRDD.scala:50) at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:83) at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:82) at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33) at scala.collection.TraversableLike$$anonfun$collect$1.apply(TraversableLike.scala:278) at scala.collection.immutable.List.foreach(List.scala:318) at scala.collection.TraversableLike$class.collect(TraversableLike.scala:278) at scala.collection.AbstractTraversable.collect(Traversable.scala:105) at org.apache.spark.rdd.ZippedPartitionsBaseRDD.tryPrepareParents(ZippedPartitionsRDD.scala:82) at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:97) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) {code} I have tried setting spark.buffer.pageSize to both 1MB and 64MB (in spark-defaults.conf) and it makes no difference. It also tries to acquire 33554432 bytes of memory in both cases. was: We are executing 20 Spark SQL jobs in parallel using Spark Job Server and hitting the following issue pretty routinely. 40GB heap x 6 nodes. Have tried adjusting shuffle.memoryFraction from 0.2 -> 0.1 with no difference. {code} .16): org.apache.spark.SparkException: Task failed while writing rows. at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:250) at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150) at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) at org.apache.spark.scheduler.Task.run(Task.scala:88) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) Caused by: java.io.IOException: Unable to acquire 16777216 bytes of memory at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:351) at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.<init>(UnsafeExternalSorter.java:138) at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.create(UnsafeExternalSorter.java:106) at org.apache.spark.sql.execution.UnsafeExternalRowSorter.<init>(UnsafeExternalRowSorter.java:68) at org.apache.spark.sql.execution.TungstenSort.org$apache$spark$sql$execution$TungstenSort$$preparePartition$1(sort.scala:146) at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169) at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169) at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.prepare(MapPartitionsWithPreparationRDD.scala:50) at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:83) at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:82) at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33) at scala.collection.TraversableLike$$anonfun$collect$1.apply(TraversableLike.scala:278) at scala.collection.immutable.List.foreach(List.scala:318) at scala.collection.TraversableLike$class.collect(TraversableLike.scala:278) at scala.collection.AbstractTraversable.collect(Traversable.scala:105) at org.apache.spark.rdd.ZippedPartitionsBaseRDD.tryPrepareParents(ZippedPartitionsRDD.scala:82) at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:97) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) {code} > Tungsten fails to acquire memory writing to HDFS > ------------------------------------------------ > > Key: SPARK-10929 > URL: https://issues.apache.org/jira/browse/SPARK-10929 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 1.5.0, 1.5.1 > Reporter: Naden Franciscus > Priority: Blocker > > We are executing 20 Spark SQL jobs in parallel using Spark Job Server and > hitting the following issue pretty routinely. > 40GB heap x 6 nodes. Have tried adjusting shuffle.memoryFraction from 0.2 -> > 0.1 with no difference. > {code} > .16): org.apache.spark.SparkException: Task failed while writing rows. > at > org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:250) > at > org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150) > at > org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150) > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) > at org.apache.spark.scheduler.Task.run(Task.scala:88) > at > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.io.IOException: Unable to acquire 16777216 bytes of memory > at > org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:351) > at > org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.<init>(UnsafeExternalSorter.java:138) > at > org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.create(UnsafeExternalSorter.java:106) > at > org.apache.spark.sql.execution.UnsafeExternalRowSorter.<init>(UnsafeExternalRowSorter.java:68) > at > org.apache.spark.sql.execution.TungstenSort.org$apache$spark$sql$execution$TungstenSort$$preparePartition$1(sort.scala:146) > at > org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169) > at > org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169) > at > org.apache.spark.rdd.MapPartitionsWithPreparationRDD.prepare(MapPartitionsWithPreparationRDD.scala:50) > at > org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:83) > at > org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:82) > at > scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33) > at > scala.collection.TraversableLike$$anonfun$collect$1.apply(TraversableLike.scala:278) > at scala.collection.immutable.List.foreach(List.scala:318) > at > scala.collection.TraversableLike$class.collect(TraversableLike.scala:278) > at scala.collection.AbstractTraversable.collect(Traversable.scala:105) > at > org.apache.spark.rdd.ZippedPartitionsBaseRDD.tryPrepareParents(ZippedPartitionsRDD.scala:82) > at > org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:97) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) > {code} > I have tried setting spark.buffer.pageSize to both 1MB and 64MB (in > spark-defaults.conf) and it makes no difference. > It also tries to acquire 33554432 bytes of memory in both cases. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org