xuanyuanking commented on a change in pull request #25768: [SPARK-29063][SQL]
Modify fillValue approach to support joined dataframe
URL: https://github.com/apache/spark/pull/25768#discussion_r325689461
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
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@@ -497,12 +497,10 @@ final class DataFrameNaFunctions private[sql](df:
DataFrame) {
throw new IllegalArgumentException(s"$targetType is not matched at
fillValue")
}
// Only fill if the column is part of the cols list.
- if (typeMatches && cols.exists(col => columnEquals(f.name, col))) {
- fillCol[T](f, value)
- } else {
- df.col(f.name)
- }
+ typeMatches && cols.exists(col => columnEquals(f.name, col))
+ }.map { col =>
+ (col.name, fillCol[T](col, value))
}
- df.select(projections : _*)
+ df.withColumns(fillColumnsInfo.map(_._1), fillColumnsInfo.map(_._2))
Review comment:
Yes, in the new approach, we only pass in the columns found in the existing
fields, and `withColumns` will replace the existing columns with the original
order.
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