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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-13101) Dataset complex types mapping to DataFrame (element nullability) mismatch
Date Tue, 02 Feb 2016 23:37:40 GMT

    [ https://issues.apache.org/jira/browse/SPARK-13101?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15129347#comment-15129347
] 

Apache Spark commented on SPARK-13101:
--------------------------------------

User 'cloud-fan' has created a pull request for this issue:
https://github.com/apache/spark/pull/11035

> 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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