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https://issues.apache.org/jira/browse/SPARK-18884?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15755778#comment-15755778
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Dongjoon Hyun commented on SPARK-18884:
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+1 for the idea!
> Support Array[_] in ScalaUDF
> ----------------------------
>
> Key: SPARK-18884
> URL: https://issues.apache.org/jira/browse/SPARK-18884
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.2
> Reporter: Takeshi Yamamuro
> Priority: Minor
>
> Throw an exception if we use `Array[_]` in `ScalaUDF`;
> {code}
> scala> import org.apache.spark.sql.execution.debug._
> scala> Seq((0, 1)).toDF("a", "b").select(array($"a",
> $"b").as("ar")).write.mode("overwrite").parquet("/Users/maropu/Desktop/data/")
> scala> val df = spark.read.load("/Users/maropu/Desktop/data/")
> scala> val df = Seq((0, 1)).toDF("a", "b").select(array($"a", $"b").as("ar"))
> scala> val testArrayUdf = udf { (ar: Array[Int]) => ar.sum }
> scala> df.select(testArrayUdf($"ar")).show
> Caused by: java.lang.ClassCastException:
> scala.collection.mutable.WrappedArray$ofRef cannot be cast to [I
> at $anonfun$1.apply(<console>:23)
> at
> org.apache.spark.sql.catalyst.expressions.ScalaUDF$$anonfun$2.apply(ScalaUDF.scala:89)
> at
> org.apache.spark.sql.catalyst.expressions.ScalaUDF$$anonfun$2.apply(ScalaUDF.scala:88)
> at
> org.apache.spark.sql.catalyst.expressions.ScalaUDF.eval(ScalaUDF.scala:1069)
> ... 99 more
> {code}
> On the other hand, the query below is passed;
> {code}
> scala> val testSeqUdf = udf { (ar: Seq[Int]) => ar.sum }
> scala> df.select(testSeqUdf($"ar")).show
> +-------+
> |UDF(ar)|
> +-------+
> | 1|
> +-------+
> {code}
> I'm not sure this behivour is an expected one. The curernt implementation
> querys argument types (`DataType`) by reflection
> (`ScalaReflection.schemaFor`) in `sql.functions.udf`, and then creates type
> converters (`CatalystTypeConverters`) from the types. `Seq[_]` and `Array[_]`
> are represented as `ArrayType` in `DataType` and both types are handled by
> using `ArrayConverter. However, since we cannot tell a difference between
> both types in `DataType`, ISTM it's difficut to support the two array types
> based on this current design. One idea (of curse, it's not the best) I have
> is to create type converters directly from `TypeTag` in `sql.functions.udf`.
> This is a prototype
> (https://github.com/apache/spark/compare/master...maropu:ArrayTypeUdf#diff-89643554d9757dd3e91abff1cc6096c7R740)
> to support both array types in `ScalaUDF`. I'm not sure this is acceptable
> and welcome any suggestion.
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