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https://issues.apache.org/jira/browse/SPARK-13101?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15126932#comment-15126932
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Cheng Lian commented on SPARK-13101:
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The reason why 1.6.0 allows this illegal situation is that we didn't do
nullability check there.
Another tricky thing here is about Parquet. When writing Parquet files, all
non-nullable fields are converted to nullable fields intentionally. This
behavior is for better interoperability with Hive. So in your case, after
writing the {{Valuation}} records into a Parquet file and then reading them
back, the {{valuations}} field becomes a nullable array. One possible
workaround for your use case is to use {{Seq\[java.lang.Double\]}} for the
{{valuations}} field.
> 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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