I have found that this even does not work with a struct as an input
parameter
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)
On 26 December 2015 at 02:42, Deenar Toraskar <deenar.toras...@gmail.com>
wrote:
> Hi
>
> I am trying to define an UDF that can take an array of tuples as input
>
> def effectiveExpectedExposure(expectedExposures: Seq[(Seq[Float],
> Seq[Float])])=
> expectedExposures.map(x=> x._1 * x._2).sum/expectedExposures.map(x=>
> x._1).sum
>
> sqlContext.udf.register("expectedPositiveExposure",
> expectedPositiveExposure _)
>
> I get the following message when I try calling this function, where
> noOfMonths and ee are both floats
>
> val df = sqlContext.sql(s"select (collect(struct(noOfMonths, ee))) as eee
> from netExposureCpty where counterparty = 'xyz'")
> df.registerTempTable("test")
> sqlContext.sql("select effectiveExpectedExposure(eee) from test")
>
> Error in SQL statement: AnalysisException: cannot resolve 'UDF(eee)' due
> to data type mismatch: argument 1 requires array<struct<_1:float,_2:float>>
> type, however, 'eee' is of array<struct<noofmonths:float,ee:float>> type.;
> line 1 pos 33
>
> Deenar
>
