Deenar Toraskar created SPARK-13101: ---------------------------------------
Summary: 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 Fix For: 1.6.1 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 -- 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