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https://issues.apache.org/jira/browse/SPARK-10294?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yin Huai updated SPARK-10294:
-----------------------------
Summary: When save data to a data source table, we should bound the size of
a saved file (was: When saving a file larger than S3 size limit to S3, Parquet
writer's close method is called twice and then NPE is thrown.)
> When save data to a data source table, we should bound the size of a saved
> file
> -------------------------------------------------------------------------------
>
> Key: SPARK-10294
> URL: https://issues.apache.org/jira/browse/SPARK-10294
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.5.0
> Reporter: Yin Huai
> Attachments: screenshot-1.png
>
>
> When a task saves a large parquet file (larger than the S3 file size limit)
> to S3, looks like we still call parquet writer's close twice and triggers NPE
> reported in SPARK-7837. Eventually, job failed and I got NPE as the
> exception. Actually, the real problem was that the file was too large for S3.
> {code}
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1280)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1268)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1267)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1267)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)
> at scala.Option.foreach(Option.scala:236)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:697)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1493)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1455)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1444)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:567)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1818)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1831)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1908)
> at
> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply$mcV$sp(InsertIntoHadoopFsRelation.scala:150)
> at
> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108)
> at
> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)
> at
> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:108)
> at
> org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57)
> at
> org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57)
> at
> org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:69)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:140)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:138)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:138)
> at
> org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute(SQLContext.scala:927)
> at
> org.apache.spark.sql.SQLContext$QueryExecution.toRdd(SQLContext.scala:927)
> at
> org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:197)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:146)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:137)
> at
> com.databricks.spark.sql.perf.tpcds.Tables$Table.genData(Tables.scala:147)
> at
> com.databricks.spark.sql.perf.tpcds.Tables$$anonfun$genData$2.apply(Tables.scala:192)
> at
> com.databricks.spark.sql.perf.tpcds.Tables$$anonfun$genData$2.apply(Tables.scala:190)
> at scala.collection.immutable.List.foreach(List.scala:318)
> at com.databricks.spark.sql.perf.tpcds.Tables.genData(Tables.scala:190)
> at Notebook$$anonfun$1$$anonfun$apply$1.apply(<console>:40)
> at Notebook$$anonfun$1$$anonfun$apply$1.apply(<console>:39)
> at scala.collection.immutable.List.foreach(List.scala:318)
> at Notebook$$anonfun$1.apply(<console>:39)
> at Notebook$$anonfun$1.apply(<console>:38)
> at scala.collection.immutable.List.foreach(List.scala:318)
> Caused by: org.apache.spark.SparkException: Task failed while writing rows.
> at
> org.apache.spark.sql.execution.datasources.DynamicPartitionWriterContainer.writeRows(WriterContainer.scala:391)
> at
> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> at
> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:88)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: java.lang.NullPointerException
> at
> org.apache.parquet.hadoop.InternalParquetRecordWriter.flushRowGroupToStore(InternalParquetRecordWriter.java:147)
> at
> org.apache.parquet.hadoop.InternalParquetRecordWriter.close(InternalParquetRecordWriter.java:113)
> at
> org.apache.parquet.hadoop.ParquetRecordWriter.close(ParquetRecordWriter.java:112)
> at
> org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.close(ParquetRelation.scala:98)
> at
> org.apache.spark.sql.execution.datasources.DynamicPartitionWriterContainer.writeRows(WriterContainer.scala:382)
> at
> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$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:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> {code}
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