initial commit

sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala

@@ -41,7 +41,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    *
    * @since 1.3.1
    */
-  def drop(): DataFrame = drop("any", df.columns)
+  def drop(): DataFrame = drop0("any", outputAttributes)
 
   /**
    * Returns a new `DataFrame` that drops rows containing null or NaN values.
@@ -51,7 +51,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    *
    * @since 1.3.1
    */
-  def drop(how: String): DataFrame = drop(how, df.columns)
+  def drop(how: String): DataFrame = drop0(how, outputAttributes)
 
   /**
    * Returns a new `DataFrame` that drops rows containing any null or NaN values
@@ -90,11 +90,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    * @since 1.3.1
    */
   def drop(how: String, cols: Seq[String]): DataFrame = {
-    how.toLowerCase(Locale.ROOT) match {
-      case "any" => drop(cols.size, cols)
-      case "all" => drop(1, cols)
-      case _ => throw new IllegalArgumentException(s"how ($how) must be 'any' or 'all'")
-    }
+    drop0(how, toAttributes(cols))
   }
 
   /**
@@ -120,10 +116,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    * @since 1.3.1
    */
   def drop(minNonNulls: Int, cols: Seq[String]): DataFrame = {
-    // Filtering condition:
-    // only keep the row if it has at least `minNonNulls` non-null and non-NaN values.
-    val predicate = AtLeastNNonNulls(minNonNulls, cols.map(name => df.resolve(name)))
-    df.filter(Column(predicate))
+    drop0(minNonNulls, toAttributes(cols))
   }
 
   /**
@@ -488,6 +481,23 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
     df.queryExecution.analyzed.output
   }
 
+  private def drop0(how: String, cols: Seq[Attribute]): DataFrame = {
+    how.toLowerCase(Locale.ROOT) match {
+      case "any" => drop0(cols.size, cols)
+      case "all" => drop0(1, cols)
+      case _ => throw new IllegalArgumentException(s"how ($how) must be 'any' or 'all'")
+    }
+  }
+
+  private def drop0(minNonNulls: Int, cols: Seq[Attribute]): DataFrame = {
+    // Filtering condition:
+    // only keep the row if it has at least `minNonNulls` non-null and non-NaN values.
+    val predicate = AtLeastNNonNulls(
+      minNonNulls,
+      outputAttributes.filter{ col => cols.exists(_.semanticEquals(col)) })
+    df.filter(Column(predicate))
+  }
+
   /**
    * Returns a new `DataFrame` that replaces null or NaN values in the specified
    * columns. If a specified column is not a numeric, string or boolean column,

sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala

@@ -240,13 +240,14 @@ class DataFrameNaFunctionsSuite extends QueryTest with SharedSQLContext {
     }
   }
 
-  test("fill with col(*)") {
+  test("fill/drop with col(*)") {
     val df = createDF()
     // If columns are specified with "*", they are ignored.
     checkAnswer(df.na.fill("new name", Seq("*")), df.collect())
+    checkAnswer(df.na.drop("any", Seq("*")), df.collect())
   }
 
-  test("fill with nested columns") {
+  test("fill/drop with nested columns") {
     val schema = new StructType()
       .add("c1", new StructType()
         .add("c1-1", StringType)
@@ -263,8 +264,9 @@ class DataFrameNaFunctionsSuite extends QueryTest with SharedSQLContext {
     checkAnswer(df.select("c1.c1-1"),
       Row(null) :: Row("b1") :: Row(null) :: Nil)
 
-    // Nested columns are ignored for fill().
+    // Nested columns are ignored for fill() and drop().
     checkAnswer(df.na.fill("a1", Seq("c1.c1-1")), data)
+    checkAnswer(df.na.drop("any", Seq("c1.c1-1")), data)
   }
 
   test("replace") {
@@ -394,4 +396,21 @@ class DataFrameNaFunctionsSuite extends QueryTest with SharedSQLContext {
       df.na.fill("hello"),
       Row("1", "hello", "2") :: Row("3", "4", "hello") :: Nil)
   }
+
+  test("SPARK-30065: duplicate names are allowed for drop() if column names are not specified.") {
+    val left = Seq(("1", null), ("3", "4"), ("5", "6")).toDF("col1", "col2")
+    val right = Seq(("1", "2"), ("3", null), ("5", "6")).toDF("col1", "col2")
+    val df = left.join(right, Seq("col1"))
+
+    // If column names are specified, the following fails due to ambiguity.
+    val exception = intercept[AnalysisException] {
+      df.na.drop("any", Seq("col2"))
+    }
+    assert(exception.getMessage.contains("Reference 'col2' is ambiguous"))
+
+    // If column names are not specified, drop() is applied to all the eligible rows.
+    checkAnswer(
+      df.na.drop("any"),
+      Row("5", "6", "6") :: Nil)
+  }
 }