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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2018/12/19 09:45:00 UTC
[jira] [Assigned] (SPARK-26403) DataFrame pivot using array column
fails with "Unsupported literal type class"
[ https://issues.apache.org/jira/browse/SPARK-26403?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-26403:
------------------------------------
Assignee: (was: Apache Spark)
> 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
> Priority: Minor
>
> 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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