[ https://issues.apache.org/jira/browse/SPARK-12809?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15814182#comment-15814182 ]
Hyukjin Kwon commented on SPARK-12809: -------------------------------------- Is this a duplicate of SPARK-12823? > Spark SQL UDF does not work with struct input parameters > -------------------------------------------------------- > > Key: SPARK-12809 > URL: https://issues.apache.org/jira/browse/SPARK-12809 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 1.6.0 > Reporter: Deenar Toraskar > > Spark SQL UDFs dont work with struct input parameters > def testUDF(expectedExposures: (Float, Float))= { > (expectedExposures._1 * expectedExposures._2 /expectedExposures._1) > } > sqlContext.udf.register("testUDF", testUDF _) > sqlContext.sql("select testUDF(struct(noofmonths,ee)) from netExposureCpty") > The full stacktrace is given below > com.databricks.backend.common.rpc.DatabricksExceptions$SQLExecutionException: > org.apache.spark.sql.AnalysisException: cannot resolve > 'UDF(struct(noofmonths,ee))' due to data type mismatch: argument 1 requires > struct<_1:float,_2:float> type, however, 'struct(noofmonths,ee)' is of > struct<noofmonths:float,ee:float> type.; line 1 pos 33 > at > org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42) > at > org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:65) > at > org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:57) > at > org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:319) > at > org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:319) > at > org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53) > at > org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:318) > at > org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:316) > at > org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:316) > at > org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265) > at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) > at scala.collection.Iterator$class.foreach(Iterator.scala:727) > at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) > at > scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) > at > scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) > at > scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47) > at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273) > at scala.collection.AbstractIterator.to(Iterator.scala:1157) > at > scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265) -- 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