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[SPARK-29063][SQL] Modify fillValue approach to support joined dataframe #25768

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[SPARK-29063][SQL] Modify fillValue approach to support joined dataframe #25768

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3602807
Modify fillValue approach to support joined dataframe
xuanyuanking Sep 12, 2019
03305be
Add test for checking ambiguous field
xuanyuanking Sep 17, 2019
59106dc
simplify
gatorsmile Sep 20, 2019
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12 changes: 5 additions & 7 deletions sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -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 fillColumnsInfo = df.schema.fields.filter { f =>
val typeMatches = (targetType, f.dataType) match {
case (NumericType, dt) => dt.isInstanceOf[NumericType]
case (StringType, dt) => dt == StringType
Expand All @@ -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))
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@gatorsmile gatorsmile Sep 12, 2019

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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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@xuanyuanking xuanyuanking Sep 17, 2019

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When we fill the duplicate column, we'll still get AnalysisException: Reference xx is ambiguous. Add test cases in 03305be.

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@HyukjinKwon HyukjinKwon Sep 18, 2019

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@xuanyuanking, BTW, does this keep the order of columns? Seems previously the order of columns in its input DataFrame but seems now it can be changed.

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@xuanyuanking xuanyuanking Sep 18, 2019

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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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@gatorsmile gatorsmile Sep 20, 2019

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we can simplify the code.

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@xuanyuanking xuanyuanking Sep 20, 2019

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Ah, thanks for the help.

}
}
35 changes: 35 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
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Expand Up @@ -231,6 +231,41 @@ class DataFrameNaFunctionsSuite extends QueryTest with SharedSparkSession {
}
}

def createDFsWithSameFieldsName(): (DataFrame, DataFrame) = {
val df1 = Seq(
("f1-1", "f2", null),
("f1-2", null, null),
("f1-3", "f2", "f3-1"),
("f1-4", "f2", "f3-1")
).toDF("f1", "f2", "f3")
val df2 = Seq(
("f1-1", null, null),
("f1-2", "f2", null),
("f1-3", "f2", "f4-1")
).toDF("f1", "f2", "f4")
(df1, df2)
}

test("fill unambiguous field for join operation") {
val (df1, df2) = createDFsWithSameFieldsName()
val joined_df = df1.join(df2, Seq("f1"), joinType = "left_outer")
checkAnswer(joined_df.na.fill("", cols = Seq("f4")),
Row("f1-1", "f2", null, null, "") ::
Row("f1-2", null, null, "f2", "") ::
Row("f1-3", "f2", "f3-1", "f2", "f4-1") ::
Row("f1-4", "f2", "f3-1", null, "") :: Nil)
}

test("fill ambiguous field for join operation") {
val (df1, df2) = createDFsWithSameFieldsName()
val joined_df = df1.join(df2, Seq("f1"), joinType = "left_outer")

val message = intercept[AnalysisException] {
joined_df.na.fill("", cols = Seq("f2"))
}.getMessage
assert(message.contains("Reference 'f2' is ambiguous"))
}

test("replace") {
val input = createDF()

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