Details
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Bug
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Status: Resolved
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Major
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Resolution: Invalid
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2.0.0
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None
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None
Description
How to fix java.util.NoSuchElementException while saving data into HDFS using Spark ?
I'm trying to ingest a greenplum table into HDFS using spark-greenplum reader.
Below are the versions of Spark & Scala I am using:
spark-core: 2.0.0
spark-sql: 2.0.0
Scala version: 2.11.8
To do that, I wrote the following code:
val conf = new SparkConf().setAppName("TEST_YEAR").set("spark.executor.heartbeatInterval", "1200s") .set("spark.network.timeout", "12000s") .set("spark.sql.inMemoryColumnarStorage.compressed", "true") .set("spark.shuffle.compress", "true") .set("spark.shuffle.spill.compress", "true") .set("spark.sql.orc.filterPushdown", "true") .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") .set("spark.kryoserializer.buffer.max", "512m") .set("spark.serializer", classOf[org.apache.spark.serializer.KryoSerializer].getName) .set("spark.streaming.stopGracefullyOnShutdown", "true") .set("spark.yarn.driver.memoryOverhead", "8192") .set("spark.yarn.executor.memoryOverhead", "8192") .set("spark.sql.shuffle.partitions", "400") .set("spark.dynamicAllocation.enabled", "false") .set("spark.shuffle.service.enabled", "true") .set("spark.sql.tungsten.enabled", "true") .set("spark.executor.instances", "12") .set("spark.executor.memory", "13g") .set("spark.executor.cores", "4") .set("spark.files.maxPartitionBytes", "268435468") val flagCol = "del_flag" val spark = SparkSession.builder().config(conf).master("yarn").enableHiveSupport().config("hive.exec.dynamic.partition", "true").config("hive.exec.dynamic.partition.mode", "nonstrict").getOrCreate() import spark.implicits._ val dtypes = spark.read.format("jdbc").option("url", hiveMetaConURL).option("dbtable", "(select source_type, hive_type from hivemeta.types) as gpHiveDataTypes").option("user", metaUserName).option("password", metaPassword).load() val spColsDF = spark.read.format("jdbc").option("url", hiveMetaConURL) .option("dbtable", "(select source_columns, precision_columns, partition_columns from hivemeta.source_table where tablename='gpschema.empdocs') as colsPrecision") .option("user", metaUserName).option("password", metaPassword).load() val dataMapper = dtypes.as[(String, String)].collect().toMap val gpCols = spColsDF.select("source_columns").map(row => row.getString(0)).collect.mkString(",") val gpColumns = gpCols.split("\\|").map(e => e.split("\\:")).map(s => s(0)).mkString(",") val splitColumns = gpCols.split("\\|").toList val precisionCols = spColsDF.select("precision_columns").collect().map(_.getString(0)).mkString(",") val partition_columns = spColsDF.select("partition_columns").collect.flatMap(x => x.getAs[String](0).split(",")) val prtn_String_columns = spColsDF.select("partition_columns").collect().map(_.getString(0)).mkString(",") val partCList = prtn_String_columns.split(",").map(x => col(x)) var splitPrecisionCols = precisionCols.split(",") for (i <- splitPrecisionCols) { precisionColsText += i.concat(s"::${textType} as ").concat(s"${i}_text") textList += s"${i}_text:${textType}" } val pCols = precisionColsText.mkString(",") val allColumns = gpColumns.concat("," + pCols) val allColumnsSeq = allColumns.split(",").toSeq val allColumnsSeqC = allColumnsSeq.map(x => column(x)) val gpColSeq = gpColumns.split(",").toSeq def prepareFinalDF(splitColumns: List[String], textList: ListBuffer[String], allColumns: String, dataMapper: Map[String, String], partition_columns: Array[String], spark: SparkSession): DataFrame = { val yearDF = spark.read.format("io.pivotal.greenplum.spark.GreenplumRelationProvider").option("url", connectionUrl) .option("dbtable", "empdocs") .option("dbschema","gpschema") .option("user", devUserName).option("password", devPassword) .option("partitionColumn","header_id") .load() .where("year=2017 and month=12") .select(gpColSeq map col:_*) .withColumn(flagCol, lit(0)) val totalCols: List[String] = splitColumns ++ textList val allColsOrdered = yearDF.columns.diff(partition_columns) ++ partition_columns val allCols = allColsOrdered.map(colname => org.apache.spark.sql.functions.col(colname)) val resultDF = yearDF.select(allCols: _*) val stringColumns = resultDF.schema.fields.filter(x => x.dataType == StringType).map(s => s.name) val finalDF = stringColumns.foldLeft(resultDF) { (tempDF, colName) => tempDF.withColumn(colName, regexp_replace(regexp_replace(col(colName), "[\r\n]+", " "), "[\t]+", " ")) } finalDF } val dataDF = prepareFinalDF(splitColumns, textList, allColumns, dataMapper, partition_columns, spark) dataDF.write.format("csv").save("hdfs://usrdev/apps/hive/warehouse/empdocs/") } }
When I submit the job, I see the tasks at below lines complete:
val dataMapper = dtypes.as[(String, String)].collect().toMap val gpCols = spColsDF.select("source_columns").map(row => row.getString(0)).collect.mkString(",") val precisionCols = spColsDF.select("precision_columns").collect().map(_.getString(0)).mkString(",") val partition_columns = spColsDF.select("partition_columns").collect.flatMap(x => x.getAs[String](0).split(",")) val prtn_String_columns = spColsDF.select("partition_columns").collect().map(_.getString(0)).mkString(",")
Once the task of saving the prepared dataframe starts, which is:
dataDF.write.format("csv").save("hdfs://usrdev/apps/hive/warehouse/empdocs/")
job ends with the exception: {{}}
java.util.NoSuchElementException
I am submitting the job using below spark-submit command:
SPARK_MAJOR_VERSION=2 spark-submit --class com.partition.source.YearPartition --master=yarn --conf spark.ui.port=4090 --driver-class-path /home/hdpdevusr/jars/greenplum-spark_2.11-1.3.0.jar --conf spark.jars=/home/hdpdevusr/jars/greenplum-spark_2.11-1.3.0.jar --executor-cores 3 --executor-memory 13G --keytab /home/hdpdevusr/hdpdevusr.keytab --principal hdpdevusr@usrdev.COM --files /usr/hdp/current/spark2-client/conf/hive-site.xml,testconnection.properties --name Splinter --conf spark.executor.extraClassPath=/home/hdpdevusr/jars/greenplum-spark_2.11-1.3.0.jar splinter_2.11-0.1.jar
I see the command launches the executors as per the specified numbers in the code which is 12 executors with 4 cores each.
Only 5 out of 48 tasks will complete and the job ends with the exception:
[Stage 5:> (0 + 48) / 64]18/12/27 10:29:10 WARN TaskSetManager: Lost task 6.0 in stage 5.0 (TID 11, executor 11): java.util.NoSuchElementException: None.get at scala.None$.get(Option.scala:347) at scala.None$.get(Option.scala:345) at io.pivotal.greenplum.spark.jdbc.Jdbc$.copyTable(Jdbc.scala:43) at io.pivotal.greenplum.spark.externaltable.GreenplumRowIterator.liftedTree1$1(GreenplumRowIterator.scala:110) at io.pivotal.greenplum.spark.externaltable.GreenplumRowIterator.<init>(GreenplumRowIterator.scala:109) at io.pivotal.greenplum.spark.GreenplumRDD.compute(GreenplumRDD.scala:49) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at org.apache.spark.scheduler.Task.run(Task.scala:108) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) Caused by: org.apache.spark.SparkException: Job 5 cancelled because killed via the Web UI at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1517) at org.apache.spark.scheduler.DAGScheduler.handleJobCancellation(DAGScheduler.scala:1457) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleStageCancellation$1.apply$mcVI$sp(DAGScheduler.scala:1446) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleStageCancellation$1.apply(DAGScheduler.scala:1439) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleStageCancellation$1.apply(DAGScheduler.scala:1439) at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) at scala.collection.mutable.ArrayOps$ofInt.foreach(ArrayOps.scala:234) at org.apache.spark.scheduler.DAGScheduler.handleStageCancellation(DAGScheduler.scala:1439) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1701) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1687) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1676) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2029) at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:186) ... 44 more 18/12/27 10:30:53 WARN ShutdownHookManager: ShutdownHook '$anon$2' timeout, java.util.concurrent.TimeoutException java.util.concurrent.TimeoutException at java.util.concurrent.FutureTask.get(FutureTask.java:205) at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:67) 18/12/27 10:30:53 ERROR Utils: Uncaught exception in thread pool-6-thread-1 java.lang.InterruptedException at java.lang.Object.wait(Native Method) at java.lang.Thread.join(Thread.java:1249) at java.lang.Thread.join(Thread.java:1323) at org.apache.spark.scheduler.LiveListenerBus.stop(LiveListenerBus.scala:199) at org.apache.spark.SparkContext$$anonfun$stop$6.apply$mcV$sp(SparkContext.scala:1919) at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1317) at org.apache.spark.SparkContext.stop(SparkContext.scala:1918) at org.apache.spark.SparkContext$$anonfun$2.apply$mcV$sp(SparkContext.scala:581) at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:216) at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ShutdownHookManager.scala:188) at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188) at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188) at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1948) at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply$mcV$sp(ShutdownHookManager.scala:188) at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188) at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188) at scala.util.Try$.apply(Try.scala:192) at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188) at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178) at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) at java.util.concurrent.FutureTask.run(FutureTask.java:266) 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)
I don't understand where did it go wrong whether in code or in any configuration applied in the job.
I posted the same on Stackoverflow as well. For executor images, the below link can be referred:[
https://stackoverflow.com/questions/54002002/how-to-fix-java-util-nosuchelementexception-while-saving-data-into-hdfs-using-sp/54002423?noredirect=1#comment94843141_54002423|http://example.com]
Could anyone let me know how to fix this exception ?
