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Cheng Lian commented on SPARK-13101: ------------------------------------ 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} -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org