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https://issues.apache.org/jira/browse/SPARK-13101?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Josh Rosen updated SPARK-13101:
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Fix Version/s: (was: 1.6.1)
> 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
> |-- valuations: array (nullable = true)
> | |-- element: double (containsNull = true)
> This could be read back to as a Dataset in Spark 1.6.0
> val df = sqlContext.table("valuations").as[Valuation]
> But with Spark 1.6.1 the same fails with
> 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);
> Here's the classes I am using
> 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")
> even the following gives the same result
> val valsDF = vals.toDS.toDF
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