[
https://issues.apache.org/jira/browse/SPARK-13326?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15195454#comment-15195454
]
Koert Kuipers commented on SPARK-13326:
---------------------------------------
I cannot reproduce it either anymore for Option. However i do still run
into issues with user defined types, but with a different (and more
helpful) error message now.
For example we have a user defined type that simply wraps any java
Serializable so it can be serialized in spark sql as a byte array. It looks
something like this:
@SQLUserDefinedType(udt = classOf[SerializableType])
case class SerializableHolder(value: JSerializable)
class SerializableType extends UserDefinedType[SerializableHolder] {
def deserialize(datum: Any): SerializableHolder = { ... } // go from
Array[Byte] to SerializableHolder
def serialize(obj: Any): Any = { ... } // go from SerializableHolder to
Array[Byte]
def sqlType: DataType = BinaryType
def userClass: Class[SerializableHolder] = classOf[SerializableHolder]
override def equals(other: Any): Boolean = other match { case _:
SerializableType => true; case _ => false }
}
With spark 2.0 if i do:
val schema = ScalaReflection.schemaFor[SerializableHolder]
val df1 = sc.makeRDD(1 to 3).toDF.as[Int]
.map{ x: Int => Row.fromSeq(Seq(SerializableHolder(x)))
}(RowEncoder(StructType(Seq(StructField("_1", schema.dataType, true)))))
i get:
[info] org.apache.spark.sql.AnalysisException: cannot resolve
'(IF(input[0, object].isNullAt, CAST(NULL AS BINARY), newInstance(class
com.tresata.spark.sql.fieldsapi.SerializableType).serialize))' due to data
type mismatch: differing types in '(IF(input[0, object].isNullAt, CAST(NULL
AS BINARY), newInstance(class
com.tresata.spark.sql.fieldsapi.SerializableType).serialize))'
(serializable and binary).;
it seems to confuse the sqlType (binary) with the type of the payload
(serializable)?
this works fine in spark 1.x.x
> Dataset in spark 2.0.0-SNAPSHOT missing columns
> -----------------------------------------------
>
> Key: SPARK-13326
> URL: https://issues.apache.org/jira/browse/SPARK-13326
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0
> Reporter: koert kuipers
> Priority: Minor
>
> i noticed some things stopped working on datasets in spark 2.0.0-SNAPSHOT,
> and with a confusing error message (cannot resolved some column with input
> columns []).
> for example in 1.6.0-SNAPSHOT:
> {noformat}
> scala> val ds = sc.parallelize(1 to 10).toDS
> ds: org.apache.spark.sql.Dataset[Int] = [value: int]
> scala> ds.map(x => Option(x))
> res0: org.apache.spark.sql.Dataset[Option[Int]] = [value: int]
> {noformat}
> and same commands in 2.0.0-SNAPSHOT:
> {noformat}
> scala> val ds = sc.parallelize(1 to 10).toDS
> ds: org.apache.spark.sql.Dataset[Int] = [value: int]
> scala> ds.map(x => Option(x))
> org.apache.spark.sql.AnalysisException: cannot resolve 'value' given input
> columns: [];
> at
> org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:60)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:57)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:284)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:284)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:283)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:162)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:172)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:176)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:245)
> at scala.collection.immutable.List.map(List.scala:285)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:176)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:181)
> at scala.collection.Iterator$$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.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:181)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:57)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:50)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:122)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:121)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:121)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:121)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:50)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:46)
> at
> org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.resolve(ExpressionEncoder.scala:322)
> 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.Dataset.mapPartitions(Dataset.scala:339)
> at org.apache.spark.sql.Dataset.map(Dataset.scala:323)
> ... 43 elided
> {noformat}
> i observed similar issues with user defined types
> (org.apache.spark.sql.types.UserDefinedType) in Dataset. trying to insert a
> UserDefinedType in Dataset[Row] fails with input columns [].
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
