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https://issues.apache.org/jira/browse/SPARK-13101?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15126890#comment-15126890
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Cheng Lian commented on SPARK-13101:
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I think this is expected behavior. The problem in snippet mentioned in the JIRA
description is that it tries to convert a nullable DataFrame field into a
non-nullable Dataset field, which shouldn't be allowed. This can be further
illustrated by the following spark-shell snippet:
{code}
// Converting non-nullable DF field to nullable DS field, OK
case class Rec(a: Array[Integer])
val df = Seq(Array(0)).map(Tuple1(_)).toDF("a")
df.printSchema
df.as[Rec]
// Converting nullable DF field to non-nullable DS field, failure
case class Rec(a: Array[Integer])
val df = Seq(Array(0)).map(Tuple1(_)).toDF("a")
df.printSchema
df.as[Rec]
{code}
> Dataset complex types mapping to DataFrame (element nullability) mismatch
> --------------------------------------------------------------------------
>
> Key: SPARK-13101
> URL: https://issues.apache.org/jira/browse/SPARK-13101
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.1
> Reporter: Deenar Toraskar
> Priority: Blocker
>
> There seems to be a regression between 1.6.0 and 1.6.1 (snapshot build). By
> default a scala {{Seq\[Double\]}} is mapped by Spark as an ArrayType with
> nullable element
> {noformat}
> |-- valuations: array (nullable = true)
> | |-- element: double (containsNull = true)
> {noformat}
> This could be read back to as a Dataset in Spark 1.6.0
> {code}
> val df = sqlContext.table("valuations").as[Valuation]
> {code}
> But with Spark 1.6.1 the same fails with
> {code}
> val df = sqlContext.table("valuations").as[Valuation]
> org.apache.spark.sql.AnalysisException: cannot resolve 'cast(valuations as
> array<double>)' due to data type mismatch: cannot cast
> ArrayType(DoubleType,true) to ArrayType(DoubleType,false);
> {code}
> Here's the classes I am using
> {code}
> case class Valuation(tradeId : String,
> counterparty: String,
> nettingAgreement: String,
> wrongWay: Boolean,
> valuations : Seq[Double], /* one per scenario */
> timeInterval: Int,
> jobId: String) /* used for hdfs partitioning */
> val vals : Seq[Valuation] = Seq()
> val valsDF = sqlContext.sparkContext.parallelize(vals).toDF
> valsDF.write.partitionBy("jobId").mode(SaveMode.Overwrite).saveAsTable("valuations")
> {code}
> even the following gives the same result
> {code}
> val valsDF = vals.toDS.toDF
> {code}
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