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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-10637) DataFrames: saving with nested User Data Types
Date Fri, 02 Sep 2016 15:41:20 GMT

     [ https://issues.apache.org/jira/browse/SPARK-10637?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Sean Owen resolved SPARK-10637.
-------------------------------
    Resolution: Duplicate

> DataFrames: saving with nested User Data Types
> ----------------------------------------------
>
>                 Key: SPARK-10637
>                 URL: https://issues.apache.org/jira/browse/SPARK-10637
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.5.0
>            Reporter: Joao
>
> Cannot save data frames using nested UserDefinedType
> I wrote a simple example to show the error.
> It causes the following error java.lang.IllegalArgumentException: Nested type should
be repeated: required group array {
>   required int32 num;
> }
> {code}
> import org.apache.spark.sql.SaveMode
> import org.apache.spark.{SparkConf, SparkContext}
> import org.apache.spark.sql.catalyst.InternalRow
> import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
> import org.apache.spark.sql.types._
> @SQLUserDefinedType(udt = classOf[AUDT])
> case class A(list:Seq[B])
> class AUDT extends UserDefinedType[A] {
>   override def sqlType: DataType = StructType(Seq(StructField("list", ArrayType(BUDT,
containsNull = false), nullable = true)))
>   override def userClass: Class[A] = classOf[A]
>   override def serialize(obj: Any): Any = obj match {
>     case A(list) =>
>       val row = new GenericMutableRow(1)
>       row.update(0, new GenericArrayData(list.map(_.asInstanceOf[Any]).toArray))
>       row
>   }
>   override def deserialize(datum: Any): A = {
>     datum match {
>       case row: InternalRow => new A(row.getArray(0).toArray(BUDT).toSeq)
>     }
>   }
> }
> object AUDT extends AUDT
> @SQLUserDefinedType(udt = classOf[BUDT])
> case class B(num:Int)
> class BUDT extends UserDefinedType[B] {
>   override def sqlType: DataType = StructType(Seq(StructField("num", IntegerType, nullable
= false)))
>   override def userClass: Class[B] = classOf[B]
>   override def serialize(obj: Any): Any = obj match {
>     case B(num) =>
>       val row = new GenericMutableRow(1)
>       row.setInt(0, num)
>       row
>   }
>   override def deserialize(datum: Any): B = {
>     datum match {
>       case row: InternalRow => new B(row.getInt(0))
>     }
>   }
> }
> object BUDT extends BUDT
> object TestNested {
>   def main(args:Array[String]) = {
>     val col = Seq(new A(Seq(new B(1), new B(2))),
>                   new A(Seq(new B(3), new B(4))))
>     val sc = new SparkContext(new SparkConf().setMaster("local[1]").setAppName("TestSpark"))
>     val sqlContext = new org.apache.spark.sql.SQLContext(sc)
>     import sqlContext.implicits._
>     val df = sc.parallelize(1 to 2 zip col).toDF()
>     df.show()
>     df.write.mode(SaveMode.Overwrite).save(...)
>   }
> }
> {code}
> The error log is shown below:
> {code}
> 15/09/16 16:44:36 WARN : Your hostname, X resolves to a loopback/non-reachable address:
fe80:0:0:0:c4c7:8c4b:4a24:f8a1%14, but we couldn't find any external IP address!
> 15/09/16 16:44:38 INFO deprecation: mapred.job.id is deprecated. Instead, use mapreduce.job.id
> 15/09/16 16:44:38 INFO deprecation: mapred.tip.id is deprecated. Instead, use mapreduce.task.id
> 15/09/16 16:44:38 INFO deprecation: mapred.task.id is deprecated. Instead, use mapreduce.task.attempt.id
> 15/09/16 16:44:38 INFO deprecation: mapred.task.is.map is deprecated. Instead, use mapreduce.task.ismap
> 15/09/16 16:44:38 INFO deprecation: mapred.task.partition is deprecated. Instead, use
mapreduce.task.partition
> 15/09/16 16:44:38 INFO ParquetRelation: Using default output committer for Parquet: org.apache.parquet.hadoop.ParquetOutputCommitter
> 15/09/16 16:44:38 INFO DefaultWriterContainer: Using user defined output committer class
org.apache.parquet.hadoop.ParquetOutputCommitter
> 15/09/16 16:44:38 INFO BlockManagerInfo: Removed broadcast_0_piece0 on localhost:50986
in memory (size: 1402.0 B, free: 973.6 MB)
> 15/09/16 16:44:38 INFO ContextCleaner: Cleaned accumulator 1
> 15/09/16 16:44:39 INFO SparkContext: Starting job: save at TestNested.scala:73
> 15/09/16 16:44:39 INFO DAGScheduler: Got job 1 (save at TestNested.scala:73) with 1 output
partitions
> 15/09/16 16:44:39 INFO DAGScheduler: Final stage: ResultStage 1(save at TestNested.scala:73)
> 15/09/16 16:44:39 INFO DAGScheduler: Parents of final stage: List()
> 15/09/16 16:44:39 INFO DAGScheduler: Missing parents: List()
> 15/09/16 16:44:39 INFO DAGScheduler: Submitting ResultStage 1 (MapPartitionsRDD[1] at
rddToDataFrameHolder at TestNested.scala:69), which has no missing parents
> 15/09/16 16:44:39 INFO MemoryStore: ensureFreeSpace(59832) called with curMem=0, maxMem=1020914565
> 15/09/16 16:44:39 INFO MemoryStore: Block broadcast_1 stored as values in memory (estimated
size 58.4 KB, free 973.6 MB)
> 15/09/16 16:44:39 INFO MemoryStore: ensureFreeSpace(20794) called with curMem=59832,
maxMem=1020914565
> 15/09/16 16:44:39 INFO MemoryStore: Block broadcast_1_piece0 stored as bytes in memory
(estimated size 20.3 KB, free 973.5 MB)
> 15/09/16 16:44:39 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on localhost:50986
(size: 20.3 KB, free: 973.6 MB)
> 15/09/16 16:44:39 INFO SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:861
> 15/09/16 16:44:39 INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 1 (MapPartitionsRDD[1]
at rddToDataFrameHolder at TestNested.scala:69)
> 15/09/16 16:44:39 INFO TaskSchedulerImpl: Adding task set 1.0 with 1 tasks
> 15/09/16 16:44:39 INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 1, localhost,
PROCESS_LOCAL, 2354 bytes)
> 15/09/16 16:44:39 INFO Executor: Running task 0.0 in stage 1.0 (TID 1)
> 15/09/16 16:44:39 INFO deprecation: mapred.output.key.class is deprecated. Instead, use
mapreduce.job.output.key.class
> 15/09/16 16:44:39 INFO deprecation: mapred.output.value.class is deprecated. Instead,
use mapreduce.job.output.value.class
> 15/09/16 16:44:39 INFO deprecation: mapreduce.outputformat.class is deprecated. Instead,
use mapreduce.job.outputformat.class
> 15/09/16 16:44:39 INFO deprecation: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir
> 15/09/16 16:44:39 INFO DefaultWriterContainer: Using user defined output committer class
org.apache.parquet.hadoop.ParquetOutputCommitter
> 15/09/16 16:44:39 INFO CodecConfig: Compression: GZIP
> 15/09/16 16:44:39 INFO ParquetOutputFormat: Parquet block size to 134217728
> 15/09/16 16:44:39 INFO ParquetOutputFormat: Parquet page size to 1048576
> 15/09/16 16:44:39 INFO ParquetOutputFormat: Parquet dictionary page size to 1048576
> 15/09/16 16:44:39 INFO ParquetOutputFormat: Dictionary is on
> 15/09/16 16:44:39 INFO ParquetOutputFormat: Validation is off
> 15/09/16 16:44:39 INFO ParquetOutputFormat: Writer version is: PARQUET_1_0
> 15/09/16 16:44:39 ERROR Executor: Exception in task 0.0 in stage 1.0 (TID 1)
> java.lang.IllegalArgumentException: Nested type should be repeated: required group array
{
>   required int32 num;
> }
> 	at org.apache.parquet.schema.ConversionPatterns.listWrapper(ConversionPatterns.java:42)
> 	at org.apache.parquet.schema.ConversionPatterns.listType(ConversionPatterns.java:97)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:460)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convertField$1.apply(CatalystSchemaConverter.scala:522)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convertField$1.apply(CatalystSchemaConverter.scala:521)
> 	at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51)
> 	at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60)
> 	at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:108)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:521)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:526)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convert$1.apply(CatalystSchemaConverter.scala:311)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convert$1.apply(CatalystSchemaConverter.scala:311)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> 	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> 	at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
> 	at org.apache.spark.sql.types.StructType.foreach(StructType.scala:92)
> 	at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> 	at org.apache.spark.sql.types.StructType.map(StructType.scala:92)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convert(CatalystSchemaConverter.scala:311)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetTypesConverter$.convertFromAttributes(ParquetTypesConverter.scala:58)
> 	at org.apache.spark.sql.execution.datasources.parquet.RowWriteSupport.init(ParquetTableSupport.scala:55)
> 	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:288)
> 	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:262)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetRelation.scala:94)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetRelation$$anon$3.newInstance(ParquetRelation.scala:272)
> 	at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:234)
> 	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> 	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> 	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> 	at org.apache.spark.scheduler.Task.run(Task.scala:88)
> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> 	at java.lang.Thread.run(Thread.java:745)
> 15/09/16 16:44:39 WARN TaskSetManager: Lost task 0.0 in stage 1.0 (TID 1, localhost):
java.lang.IllegalArgumentException: Nested type should be repeated: required group array {
>   required int32 num;
> }
> 	at org.apache.parquet.schema.ConversionPatterns.listWrapper(ConversionPatterns.java:42)
> 	at org.apache.parquet.schema.ConversionPatterns.listType(ConversionPatterns.java:97)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:460)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convertField$1.apply(CatalystSchemaConverter.scala:522)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convertField$1.apply(CatalystSchemaConverter.scala:521)
> 	at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51)
> 	at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60)
> 	at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:108)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:521)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:526)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convert$1.apply(CatalystSchemaConverter.scala:311)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convert$1.apply(CatalystSchemaConverter.scala:311)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> 	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> 	at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
> 	at org.apache.spark.sql.types.StructType.foreach(StructType.scala:92)
> 	at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> 	at org.apache.spark.sql.types.StructType.map(StructType.scala:92)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convert(CatalystSchemaConverter.scala:311)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetTypesConverter$.convertFromAttributes(ParquetTypesConverter.scala:58)
> 	at org.apache.spark.sql.execution.datasources.parquet.RowWriteSupport.init(ParquetTableSupport.scala:55)
> 	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:288)
> 	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:262)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetRelation.scala:94)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetRelation$$anon$3.newInstance(ParquetRelation.scala:272)
> 	at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:234)
> 	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)
> 15/09/16 16:44:39 ERROR TaskSetManager: Task 0 in stage 1.0 failed 1 times; aborting
job
> 15/09/16 16:44:39 INFO TaskSchedulerImpl: Removed TaskSet 1.0, whose tasks have all completed,
from pool 
> 15/09/16 16:44:39 INFO TaskSchedulerImpl: Cancelling stage 1
> 15/09/16 16:44:39 INFO DAGScheduler: ResultStage 1 (save at TestNested.scala:73) failed
in 0.069 s
> 15/09/16 16:44:39 INFO DAGScheduler: Job 1 failed: save at TestNested.scala:73, took
0.097322 s
> 15/09/16 16:44:39 ERROR InsertIntoHadoopFsRelation: Aborting job.
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0
failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1, localhost): java.lang.IllegalArgumentException:
Nested type should be repeated: required group array {
>   required int32 num;
> }
> 	at org.apache.parquet.schema.ConversionPatterns.listWrapper(ConversionPatterns.java:42)
> 	at org.apache.parquet.schema.ConversionPatterns.listType(ConversionPatterns.java:97)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:460)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convertField$1.apply(CatalystSchemaConverter.scala:522)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convertField$1.apply(CatalystSchemaConverter.scala:521)
> 	at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51)
> 	at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60)
> 	at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:108)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:521)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:526)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convert$1.apply(CatalystSchemaConverter.scala:311)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convert$1.apply(CatalystSchemaConverter.scala:311)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> 	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> 	at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
> 	at org.apache.spark.sql.types.StructType.foreach(StructType.scala:92)
> 	at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> 	at org.apache.spark.sql.types.StructType.map(StructType.scala:92)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convert(CatalystSchemaConverter.scala:311)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetTypesConverter$.convertFromAttributes(ParquetTypesConverter.scala:58)
> 	at org.apache.spark.sql.execution.datasources.parquet.RowWriteSupport.init(ParquetTableSupport.scala:55)
> 	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:288)
> 	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:262)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetRelation.scala:94)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetRelation$$anon$3.newInstance(ParquetRelation.scala:272)
> 	at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:234)
> 	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> 	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> 	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> 	at org.apache.spark.scheduler.Task.run(Task.scala:88)
> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> 	at java.lang.Thread.run(Thread.java:745)
> Driver stacktrace:
> 	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$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$$anon$1.run(EventLoop.scala:48)
> 	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:567)
> 	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1813)
> 	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1826)
> 	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1903)
> 	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$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 app.TestNested$.main(TestNested.scala:73)
> 	at app.TestNested.main(TestNested.scala)
> 	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> 	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
> 	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> 	at java.lang.reflect.Method.invoke(Method.java:606)
> 	at com.intellij.rt.execution.application.AppMain.main(AppMain.java:140)
> Caused by: java.lang.IllegalArgumentException: Nested type should be repeated: required
group array {
>   required int32 num;
> }
> 	at org.apache.parquet.schema.ConversionPatterns.listWrapper(ConversionPatterns.java:42)
> 	at org.apache.parquet.schema.ConversionPatterns.listType(ConversionPatterns.java:97)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:460)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convertField$1.apply(CatalystSchemaConverter.scala:522)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convertField$1.apply(CatalystSchemaConverter.scala:521)
> 	at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51)
> 	at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60)
> 	at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:108)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:521)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:526)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convert$1.apply(CatalystSchemaConverter.scala:311)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convert$1.apply(CatalystSchemaConverter.scala:311)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> 	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> 	at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
> 	at org.apache.spark.sql.types.StructType.foreach(StructType.scala:92)
> 	at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> 	at org.apache.spark.sql.types.StructType.map(StructType.scala:92)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convert(CatalystSchemaConverter.scala:311)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetTypesConverter$.convertFromAttributes(ParquetTypesConverter.scala:58)
> 	at org.apache.spark.sql.execution.datasources.parquet.RowWriteSupport.init(ParquetTableSupport.scala:55)
> 	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:288)
> 	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:262)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetRelation.scala:94)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetRelation$$anon$3.newInstance(ParquetRelation.scala:272)
> 	at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:234)
> 	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> 	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> 	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> 	at org.apache.spark.scheduler.Task.run(Task.scala:88)
> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> 	at java.lang.Thread.run(Thread.java:745)
> 15/09/16 16:44:40 ERROR DefaultWriterContainer: Job job_201509161644_0000 aborted.
> Exception in thread "main" org.apache.spark.SparkException: Job aborted.
> 	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply$mcV$sp(InsertIntoHadoopFsRelation.scala:156)
> 	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 app.TestNested$.main(TestNested.scala:73)
> 	at app.TestNested.main(TestNested.scala)
> 	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> 	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
> 	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> 	at java.lang.reflect.Method.invoke(Method.java:606)
> 	at com.intellij.rt.execution.application.AppMain.main(AppMain.java:140)
> Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1, localhost):
java.lang.IllegalArgumentException: Nested type should be repeated: required group array {
>   required int32 num;
> }
> 	at org.apache.parquet.schema.ConversionPatterns.listWrapper(ConversionPatterns.java:42)
> 	at org.apache.parquet.schema.ConversionPatterns.listType(ConversionPatterns.java:97)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:460)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convertField$1.apply(CatalystSchemaConverter.scala:522)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convertField$1.apply(CatalystSchemaConverter.scala:521)
> 	at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51)
> 	at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60)
> 	at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:108)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:521)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:526)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convert$1.apply(CatalystSchemaConverter.scala:311)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convert$1.apply(CatalystSchemaConverter.scala:311)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> 	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> 	at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
> 	at org.apache.spark.sql.types.StructType.foreach(StructType.scala:92)
> 	at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> 	at org.apache.spark.sql.types.StructType.map(StructType.scala:92)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convert(CatalystSchemaConverter.scala:311)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetTypesConverter$.convertFromAttributes(ParquetTypesConverter.scala:58)
> 	at org.apache.spark.sql.execution.datasources.parquet.RowWriteSupport.init(ParquetTableSupport.scala:55)
> 	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:288)
> 	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:262)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetRelation.scala:94)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetRelation$$anon$3.newInstance(ParquetRelation.scala:272)
> 	at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:234)
> 	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> 	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> 	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> 	at org.apache.spark.scheduler.Task.run(Task.scala:88)
> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> 	at java.lang.Thread.run(Thread.java:745)
> Driver stacktrace:
> 	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$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$$anon$1.run(EventLoop.scala:48)
> 	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:567)
> 	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1813)
> 	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1826)
> 	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1903)
> 	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply$mcV$sp(InsertIntoHadoopFsRelation.scala:150)
> 	... 23 more
> Caused by: java.lang.IllegalArgumentException: Nested type should be repeated: required
group array {
>   required int32 num;
> }
> 	at org.apache.parquet.schema.ConversionPatterns.listWrapper(ConversionPatterns.java:42)
> 	at org.apache.parquet.schema.ConversionPatterns.listType(ConversionPatterns.java:97)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:460)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convertField$1.apply(CatalystSchemaConverter.scala:522)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convertField$1.apply(CatalystSchemaConverter.scala:521)
> 	at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51)
> 	at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60)
> 	at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:108)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:521)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:526)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convertField(CatalystSchemaConverter.scala:318)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convert$1.apply(CatalystSchemaConverter.scala:311)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter$$anonfun$convert$1.apply(CatalystSchemaConverter.scala:311)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> 	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> 	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> 	at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
> 	at org.apache.spark.sql.types.StructType.foreach(StructType.scala:92)
> 	at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> 	at org.apache.spark.sql.types.StructType.map(StructType.scala:92)
> 	at org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.convert(CatalystSchemaConverter.scala:311)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetTypesConverter$.convertFromAttributes(ParquetTypesConverter.scala:58)
> 	at org.apache.spark.sql.execution.datasources.parquet.RowWriteSupport.init(ParquetTableSupport.scala:55)
> 	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:288)
> 	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:262)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetRelation.scala:94)
> 	at org.apache.spark.sql.execution.datasources.parquet.ParquetRelation$$anon$3.newInstance(ParquetRelation.scala:272)
> 	at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:234)
> 	at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$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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