Github user maropu commented on a diff in the pull request:
https://github.com/apache/spark/pull/22316#discussion_r214761811
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/DataFramePivotSuite.scala ---
@@ -308,4 +308,27 @@ class DataFramePivotSuite extends QueryTest with
SharedSQLContext {
assert(exception.getMessage.contains("aggregate functions are not
allowed"))
}
+
+ test("pivoting column list with values") {
+ val expected = Row(2012, 10000.0, null) :: Row(2013, 48000.0, 30000.0)
:: Nil
+ val df = trainingSales
+ .groupBy($"sales.year")
+ .pivot(struct(lower($"sales.course"), $"training"), Seq(
+ struct(lit("dotnet"), lit("Experts")),
+ struct(lit("java"), lit("Dummies")))
+ ).agg(sum($"sales.earnings"))
+
+ checkAnswer(df, expected)
+ }
+
+ test("pivoting column list") {
+ val exception = intercept[RuntimeException] {
+ trainingSales
+ .groupBy($"sales.year")
+ .pivot(struct(lower($"sales.course"), $"training"))
+ .agg(sum($"sales.earnings"))
+ .collect()
--- End diff --
I tried in your branch;
```
scala> df.show
+--------+--------------------+
|training| sales|
+--------+--------------------+
| Experts|[dotNET, 2012, 10...|
| Experts|[JAVA, 2012, 2000...|
| Dummies|[dotNet, 2012, 50...|
| Experts|[dotNET, 2013, 48...|
| Dummies|[Java, 2013, 3000...|
+--------+--------------------+
scala> df.groupBy($"sales.year").pivot(struct(lower($"sales.course"),
$"training")).agg(sum($"sales.earnings"))
java.lang.RuntimeException: Unsupported literal type class
org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema [dotnet,Dummies]
at
org.apache.spark.sql.catalyst.expressions.Literal$.apply(literals.scala:78)
at
org.apache.spark.sql.catalyst.expressions.Literal$$anonfun$create$2.apply(literals.scala:164)
at
org.apache.spark.sql.catalyst.expressions.Literal$$anonfun$create$2.apply(literals.scala:164)
at scala.util.Try.getOrElse(Try.scala:79)
at
org.apache.spark.sql.catalyst.expressions.Literal$.create(literals.scala:163)
at org.apache.spark.sql.functions$.typedLit(functions.scala:127)
```
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