[jira] [Created] (SPARK-26403) DataFrame pivot using array column fails with "Unsupported literal type class"

["Huon Wilson (JIRA)" <ji...@apache.org>]

Huon Wilson created SPARK-26403:
-----------------------------------

             Summary: DataFrame pivot using array column fails with "Unsupported literal type class"
                 Key: SPARK-26403
                 URL: https://issues.apache.org/jira/browse/SPARK-26403
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 2.4.0
            Reporter: Huon Wilson


Doing a pivot (using the {{pivot(pivotColumn: Column)}} overload) on a column containing arrays results in a runtime error:

{code:none}
scala> val df = Seq((1, Seq("a", "x"), 2), (1, Seq("b"), 3), (2, Seq("a", "x"), 10), (3, Seq(), 100)).toDF("x", "s", "y")
df: org.apache.spark.sql.DataFrame = [x: int, s: array<string> ... 1 more field]

scala> df.show
+---+------+---+
|  x|     s|  y|
+---+------+---+
|  1|[a, x]|  2|
|  1|   [b]|  3|
|  2|[a, x]| 10|
|  3|    []|100|
+---+------+---+


scala> df.groupBy("x").pivot("s").agg(collect_list($"y")).show
java.lang.RuntimeException: Unsupported literal type class scala.collection.mutable.WrappedArray$ofRef WrappedArray()
  at org.apache.spark.sql.catalyst.expressions.Literal$.apply(literals.scala:78)
  at org.apache.spark.sql.RelationalGroupedDataset$$anonfun$pivot$1.apply(RelationalGroupedDataset.scala:419)
  at org.apache.spark.sql.RelationalGroupedDataset$$anonfun$pivot$1.apply(RelationalGroupedDataset.scala:419)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
  at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
  at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
  at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
  at scala.collection.AbstractTraversable.map(Traversable.scala:104)
  at org.apache.spark.sql.RelationalGroupedDataset.pivot(RelationalGroupedDataset.scala:419)
  at org.apache.spark.sql.RelationalGroupedDataset.pivot(RelationalGroupedDataset.scala:397)
  at org.apache.spark.sql.RelationalGroupedDataset.pivot(RelationalGroupedDataset.scala:317)
  ... 49 elided
{code}

However, this doesn't seem to be a fundamental limitation with {{pivot}}, as it works fine using the {{pivot(pivotColumn: Column, values: Seq[Any])}} overload, as long as the arrays are mapped to the {{Array}} type:

{code:none}
scala> val rawValues = df.select("s").distinct.sort("s").collect
rawValues: Array[org.apache.spark.sql.Row] = Array([WrappedArray()], [WrappedArray(a, x)], [WrappedArray(b)])

scala> val values = rawValues.map(_.getSeq[String](0).to[Array])
values: Array[Array[String]] = Array(Array(), Array(a, x), Array(b))

scala> df.groupBy("x").pivot("s", values).agg(collect_list($"y")).show
+---+-----+------+---+
|  x|   []|[a, x]|[b]|
+---+-----+------+---+
|  1|   []|   [2]|[3]|
|  3|[100]|    []| []|
|  2|   []|  [10]| []|
+---+-----+------+---+
{code}

It would be nice if {{pivot}} was more resilient to Spark's own representation of array columns, and so the first version worked.



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