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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2017/01/10 11:57:58 UTC
[jira] [Resolved] (SPARK-12809) Spark SQL UDF does not work with
struct input parameters
[ https://issues.apache.org/jira/browse/SPARK-12809?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-12809.
-------------------------------
Resolution: Duplicate
> 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)
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