[GitHub] [spark] gatorsmile commented on a change in pull request #25768: [SPARK-29063][SQL] Modify fillValue approach to support joined dataframe
gatorsmile 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_r326727451
##
File path:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
##
@@ -488,7 +488,7 @@ final class DataFrameNaFunctions private[sql](df:
DataFrame) {
}
val columnEquals = df.sparkSession.sessionState.analyzer.resolver
-val projections = df.schema.fields.map { f =>
+val filledColumns = df.schema.fields.filter { f =>
Review comment:
We can also traverse df.logicalPlan.output to avoid calling withColumns, but
it might not be a big deal here.
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[GitHub] [spark] gatorsmile commented on a change in pull request #25768: [SPARK-29063][SQL] Modify fillValue approach to support joined dataframe
gatorsmile 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_r326722825
##
File path:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
##
@@ -497,12 +497,10 @@ final class DataFrameNaFunctions private[sql](df:
DataFrame) {
throw new IllegalArgumentException(s"$targetType is not matched at
fillValue")
}
// Only fill if the column is part of the cols list.
- if (typeMatches && cols.exists(col => columnEquals(f.name, col))) {
-fillCol[T](f, value)
- } else {
-df.col(f.name)
- }
+ typeMatches && cols.exists(col => columnEquals(f.name, col))
+}.map { col =>
+ (col.name, fillCol[T](col, value))
}
-df.select(projections : _*)
+df.withColumns(fillColumnsInfo.map(_._1), fillColumnsInfo.map(_._2))
Review comment:
we can simplify the code.
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[GitHub] [spark] gatorsmile commented on a change in pull request #25768: [SPARK-29063][SQL] Modify fillValue approach to support joined dataframe
gatorsmile commented on a change in pull request #25768: [SPARK-29063][SQL]
Modify fillValue approach to support joined dataframe
URL: https://github.com/apache/spark/pull/25768#discussion_r323549161
##
File path:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
##
@@ -497,12 +497,10 @@ final class DataFrameNaFunctions private[sql](df:
DataFrame) {
throw new IllegalArgumentException(s"$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:
When `df` has a duplicate column name, what is the behavior? Also, we need
to add test cases to ensure the behaviors are consistent.
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