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Date: Wed, 23 Mar 2016 21:07:25 +0000 (UTC)
From: "Jacek Laskowski (JIRA)" <jira@apache.org>
To: issues@spark.apache.org
Message-ID: <JIRA.12941414.1456239336000.34396.1458767245517@Atlassian.JIRA>
In-Reply-To: <JIRA.12941414.1456239336000@Atlassian.JIRA>
References: <JIRA.12941414.1456239336000@Atlassian.JIRA>
 <JIRA.12941414.1456239336114@arcas>
Subject: [jira] [Reopened] (SPARK-13456) Cannot create encoders for case
 classes defined in Spark shell after upgrading to Scala 2.11
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     [ https://issues.apache.org/jira/browse/SPARK-13456?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Jacek Laskowski reopened SPARK-13456:
-------------------------------------

In today's Spark 2.0.0-SNAPSHOT:

{code}
scala> :pa
// Entering paste mode (ctrl-D to finish)

import sqlContext.implicits._

case class Token(name: String, productId: Int, score: Double)
val data = Token("aaa", 100, 0.12) ::
  Token("aaa", 200, 0.29) ::
  Token("bbb", 200, 0.53) ::
  Token("bbb", 300, 0.42) :: Nil

val ds = data.toDS


// Exiting paste mode, now interpreting.

java.lang.NullPointerException
  at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
  at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
  at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
  at java.lang.reflect.Method.invoke(Method.java:498)
  at org.apache.spark.sql.catalyst.encoders.OuterScopes$.getOuterScope(OuterScopes.scala:64)
  at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$resolveDeserializer$1.applyOrElse(Analyzer.scala:588)
  at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$resolveDeserializer$1.applyOrElse(Analyzer.scala:580)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:259)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:259)
  at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:67)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:258)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:248)
  at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.resolveDeserializer(Analyzer.scala:580)
  at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.resolve(ExpressionEncoder.scala:321)
  at org.apache.spark.sql.Dataset.<init>(Dataset.scala:197)
  at org.apache.spark.sql.Dataset.<init>(Dataset.scala:164)
  at org.apache.spark.sql.Dataset$.apply(Dataset.scala:53)
  at org.apache.spark.sql.SQLContext.createDataset(SQLContext.scala:448)
  at org.apache.spark.sql.SQLImplicits.localSeqToDatasetHolder(SQLImplicits.scala:152)
  ... 47 elided
{code}

> Cannot create encoders for case classes defined in Spark shell after upgrading to Scala 2.11
> --------------------------------------------------------------------------------------------
>
>                 Key: SPARK-13456
>                 URL: https://issues.apache.org/jira/browse/SPARK-13456
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.0.0
>            Reporter: Cheng Lian
>            Assignee: Wenchen Fan
>            Priority: Blocker
>             Fix For: 2.0.0
>
>
> Spark 2.0 started to use Scala 2.11 by default since [PR #10608|https://github.com/apache/spark/pull/10608].  Unfortunately, after this upgrade, Spark fails to create encoders for case classes defined in REPL:
> {code}
> import sqlContext.implicits._
> case class T(a: Int, b: Double)
> val ds = Seq(1 -> T(1, 1D), 2 -> T(2, 2D)).toDS()
> {code}
> Exception thrown:
> {noformat}
> org.apache.spark.sql.AnalysisException: Unable to generate an encoder for inner class `T` without access to the scope that this class was defined in.
> Try moving this class out of its parent class.;
>   at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$resolveDeserializer$1.applyOrElse(Analyzer.scala:565)
>   at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$resolveDeserializer$1.applyOrElse(Analyzer.scala:561)
>   at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:262)
>   at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:262)
>   at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
>   at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:261)
>   at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:267)
>   at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:267)
>   at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:304)
>   at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
>   at scala.collection.Iterator$class.foreach(Iterator.scala:742)
>   at scala.collection.AbstractIterator.foreach(Iterator.scala:1194)
>   at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
>   at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
>   at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
>   at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:308)
>   at scala.collection.AbstractIterator.to(Iterator.scala:1194)
>   at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:300)
>   at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1194)
>   at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:287)
>   at scala.collection.AbstractIterator.toArray(Iterator.scala:1194)
>   at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:353)
>   at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:267)
>   at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:267)
>   at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:267)
>   at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5$$anonfun$apply$11.apply(TreeNode.scala:333)
>   at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
>   at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
>   at scala.collection.immutable.List.foreach(List.scala:381)
>   at scala.collection.TraversableLike$class.map(TraversableLike.scala:245)
>   at scala.collection.immutable.List.map(List.scala:285)
>   at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331)
>   at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
>   at scala.collection.Iterator$class.foreach(Iterator.scala:742)
>   at scala.collection.AbstractIterator.foreach(Iterator.scala:1194)
>   at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
>   at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
>   at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
>   at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:308)
>   at scala.collection.AbstractIterator.to(Iterator.scala:1194)
>   at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:300)
>   at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1194)
>   at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:287)
>   at scala.collection.AbstractIterator.toArray(Iterator.scala:1194)
>   at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:353)
>   at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:267)
>   at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:251)
>   at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.resolveDeserializer(Analyzer.scala:561)
>   at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.resolve(ExpressionEncoder.scala:315)
>   at org.apache.spark.sql.Dataset.<init>(Dataset.scala:81)
>   at org.apache.spark.sql.Dataset.<init>(Dataset.scala:92)
>   at org.apache.spark.sql.SQLContext.createDataset(SQLContext.scala:482)
>   at org.apache.spark.sql.SQLImplicits.localSeqToDatasetHolder(SQLImplicits.scala:140)
>   ... 51 elided
> {noformat}
> However, existing Dataset REPL test case does pass:
> {code}
>   test("SPARK-2576 importing SQLContext.implicits._") {
>     // We need to use local-cluster to test this case.
>     val output = runInterpreter("local-cluster[1,1,1024]",
>       """
>         |val sqlContext = new org.apache.spark.sql.SQLContext(sc)
>         |import sqlContext.implicits._
>         |case class TestCaseClass(value: Int)
>         |sc.parallelize(1 to 10).map(x => TestCaseClass(x)).toDF().collect()
>         |
>         |// Test Dataset Serialization in the REPL
>         |Seq(TestCaseClass(1)).toDS().collect()
>       """.stripMargin)
>     assertDoesNotContain("error:", output)
>     assertDoesNotContain("Exception", output)
>   }
> {code}
> One possible clue is that, {{ReplSuite}} calls {{SparkILoop}} directly, while Spark shell is started by {{o.a.s.repl.Main}}, which also sets option {{-Yrepl-class-based}}.


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