[ https://issues.apache.org/jira/browse/SPARK-12714?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
James Eastwood resolved SPARK-12714. ------------------------------------ Resolution: Fixed [~marmbrus] Sorry for taking an age to get back to you -- I've tested this with 1.6.0-SNAPSHOT and it is indeed working. Thanks :). > Transforming Dataset with sequences of case classes to RDD causes Task Not > Serializable exception > ------------------------------------------------------------------------------------------------- > > Key: SPARK-12714 > URL: https://issues.apache.org/jira/browse/SPARK-12714 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 1.6.0 > Environment: linux 3.13.0-24-generic, scala 2.10.6 > Reporter: James Eastwood > > Attempting to transform a Dataset of a case class containing a nested > sequence of case classes causes an exception to be thrown: > `org.apache.spark.SparkException: Task not serializable`. > Here is a minimum repro: > {code} > import org.apache.spark.sql.SQLContext > import org.apache.spark.{SparkContext, SparkConf} > case class Top(a: String, nested: Array[Nested]) > case class Nested(b: String) > object scratch { > def main ( args: Array[String] ) { > lazy val sparkConf = new > SparkConf().setAppName("scratch").setMaster("local[1]") > lazy val sparkContext = new SparkContext(sparkConf) > lazy val sqlContext = new SQLContext(sparkContext) > val input = List( > """{ "a": "123", "nested": [{ "b": "123" }] }""" > ) > import sqlContext.implicits._ > val ds = sqlContext.read.json(sparkContext.parallelize(input)).as[Top] > ds.rdd.foreach(println) > sparkContext.stop() > } > } > {code} > {code} > scalaVersion := "2.10.6" > lazy val sparkVersion = "1.6.0" > libraryDependencies ++= List( > "org.apache.spark" %% "spark-core" % sparkVersion % "provided", > "org.apache.spark" %% "spark-sql" % sparkVersion % "provided", > "org.apache.spark" %% "spark-hive" % sparkVersion % "provided" > ) > {code} > Full stack trace: > {code} > [error] (run-main-0) org.apache.spark.SparkException: Task not serializable > org.apache.spark.SparkException: Task not serializable > at > org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:304) > at > org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:294) > at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:122) > at org.apache.spark.SparkContext.clean(SparkContext.scala:2055) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:707) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:706) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) > at org.apache.spark.rdd.RDD.withScope(RDD.scala:316) > at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:706) > at org.apache.spark.sql.Dataset.rdd(Dataset.scala:166) > at scratch$.main(scratch.scala:26) > at scratch.main(scratch.scala) > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:606) > Caused by: java.io.NotSerializableException: > scala.reflect.internal.Mirrors$Roots$EmptyPackageClass$ > Serialization stack: > - object not serializable (class: > scala.reflect.internal.Mirrors$Roots$EmptyPackageClass$, value: package > <empty>) > - field (class: scala.reflect.internal.Types$ThisType, name: sym, type: > class scala.reflect.internal.Symbols$Symbol) > - object (class scala.reflect.internal.Types$UniqueThisType, <empty>) > - field (class: scala.reflect.internal.Types$TypeRef, name: pre, type: > class scala.reflect.internal.Types$Type) > - object (class scala.reflect.internal.Types$TypeRef$$anon$6, Nested) > - field (class: > org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$constructorFor$2, > name: elementType$1, type: class scala.reflect.api.Types$TypeApi) > - object (class > org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$constructorFor$2, > <function0>) > - field (class: > org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$constructorFor$2$$anonfun$apply$1, > name: $outer, type: class > org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$constructorFor$2) > - object (class > org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$constructorFor$2$$anonfun$apply$1, > <function1>) > - field (class: org.apache.spark.sql.catalyst.expressions.MapObjects, > name: function, type: interface scala.Function1) > - object (class org.apache.spark.sql.catalyst.expressions.MapObjects, > mapobjects(<function1>,input[1, > ArrayType(StructType(StructField(b,StringType,true)),true)],StructField(b,StringType,true))) > - field (class: org.apache.spark.sql.catalyst.expressions.Invoke, name: > targetObject, type: class > org.apache.spark.sql.catalyst.expressions.Expression) > - object (class org.apache.spark.sql.catalyst.expressions.Invoke, > invoke(mapobjects(<function1>,input[1, > ArrayType(StructType(StructField(b,StringType,true)),true)],StructField(b,StringType,true)),array,ObjectType(class > [LNested;))) > - writeObject data (class: scala.collection.immutable.$colon$colon) > - object (class scala.collection.immutable.$colon$colon, > List(invoke(input[0, StringType],toString,ObjectType(class > java.lang.String)), invoke(mapobjects(<function1>,input[1, > ArrayType(StructType(StructField(b,StringType,true)),true)],StructField(b,StringType,true)),array,ObjectType(class > [LNested;)))) > - field (class: org.apache.spark.sql.catalyst.expressions.NewInstance, > name: arguments, type: interface scala.collection.Seq) > - object (class org.apache.spark.sql.catalyst.expressions.NewInstance, > newinstance(class Top,invoke(input[0, StringType],toString,ObjectType(class > java.lang.String)),invoke(mapobjects(<function1>,input[1, > ArrayType(StructType(StructField(b,StringType,true)),true)],StructField(b,StringType,true)),array,ObjectType(class > [LNested;)),false,ObjectType(class Top),None)) > - field (class: > org.apache.spark.sql.catalyst.encoders.ExpressionEncoder, name: > fromRowExpression, type: class > org.apache.spark.sql.catalyst.expressions.Expression) > - object (class > org.apache.spark.sql.catalyst.encoders.ExpressionEncoder, class[a[0]: string, > nested#ExprId(4,bc90ecfb-37ae-45bd-b0a1-7365a1a233d1): > array<struct<b:string>>]) > - field (class: org.apache.spark.sql.Dataset, name: boundTEncoder, > type: class org.apache.spark.sql.catalyst.encoders.ExpressionEncoder) > - object (class org.apache.spark.sql.Dataset, [a: string, nested: > array<struct<b:string>>]) > - field (class: org.apache.spark.sql.Dataset$$anonfun$rdd$1, name: > $outer, type: class org.apache.spark.sql.Dataset) > - object (class org.apache.spark.sql.Dataset$$anonfun$rdd$1, > <function1>) > at > org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:40) > at > org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:47) > at > org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:101) > at > org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:301) > at > org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:294) > at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:122) > at org.apache.spark.SparkContext.clean(SparkContext.scala:2055) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:707) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:706) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) > at org.apache.spark.rdd.RDD.withScope(RDD.scala:316) > at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:706) > at org.apache.spark.sql.Dataset.rdd(Dataset.scala:166) > at scratch$.main(scratch.scala:26) > at scratch.main(scratch.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) > {code} > Given the messages surrounding Datasets supporting only primitive types and > case classes I'm not 100% sure this is a bug or just as-yet unsupported. -- 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