[SPARK-18555][SQL] DataFrameNaFunctions.fill miss up original values in long integers

## What changes were proposed in this pull request?

   DataSet.na.fill(0) used on a DataSet which has a long value column, it will change the original long value.

   The reason is that the type of the function fill's param is Double, and the numeric columns are always cast to double(`fillCol[Double](f, value)`) .
```
  def fill(value: Double, cols: Seq[String]): DataFrame = {
    val columnEquals = df.sparkSession.sessionState.analyzer.resolver
    val projections = df.schema.fields.map { f =>
      // Only fill if the column is part of the cols list.
      if (f.dataType.isInstanceOf[NumericType] && cols.exists(col => columnEquals(f.name, col))) {
        fillCol[Double](f, value)
      } else {
        df.col(f.name)
      }
    }
    df.select(projections : _*)
  }
```

 For example:
```
scala> val df = Seq[(Long, Long)]((1, 2), (-1, -2), (9123146099426677101L, 9123146560113991650L)).toDF("a", "b")
df: org.apache.spark.sql.DataFrame = [a: bigint, b: bigint]

scala> df.show
+-------------------+-------------------+
|                  a|                  b|
+-------------------+-------------------+
|                  1|                  2|
|                 -1|                 -2|
|9123146099426677101|9123146560113991650|
+-------------------+-------------------+

scala> df.na.fill(0).show
+-------------------+-------------------+
|                  a|                  b|
+-------------------+-------------------+
|                  1|                  2|
|                 -1|                 -2|
|9123146099426676736|9123146560113991680|
+-------------------+-------------------+
 ```

the original values changed [which is not we expected result]:
```
 9123146099426677101 -> 9123146099426676736
 9123146560113991650 -> 9123146560113991680
```

## How was this patch tested?

unit test added.

Author: root <root@iZbp1gsnrlfzjxh82cz80vZ.(none)>

Closes #15994 from windpiger/nafillMissupOriginalValue.

2 files changed, 80 insertions, 27 deletions

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
index 184c5a1129..28820681cd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
@@ -128,6 +128,12 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
   /**
    * Returns a new `DataFrame` that replaces null or NaN values in numeric columns with `value`.
    *
+   * @since 2.2.0
+   */
+  def fill(value: Long): DataFrame = fill(value, df.columns)
+
+  /**
+   * Returns a new `DataFrame` that replaces null or NaN values in numeric columns with `value`.
    * @since 1.3.1
    */
   def fill(value: Double): DataFrame = fill(value, df.columns)
@@ -143,6 +149,14 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    * Returns a new `DataFrame` that replaces null or NaN values in specified numeric columns.
    * If a specified column is not a numeric column, it is ignored.
    *
+   * @since 2.2.0
+   */
+  def fill(value: Long, cols: Array[String]): DataFrame = fill(value, cols.toSeq)
+
+  /**
+   * Returns a new `DataFrame` that replaces null or NaN values in specified numeric columns.
+   * If a specified column is not a numeric column, it is ignored.
+   *
    * @since 1.3.1
    */
   def fill(value: Double, cols: Array[String]): DataFrame = fill(value, cols.toSeq)
@@ -151,20 +165,18 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    * (Scala-specific) Returns a new `DataFrame` that replaces null or NaN values in specified
    * numeric columns. If a specified column is not a numeric column, it is ignored.
    *
+   * @since 2.2.0
+   */
+  def fill(value: Long, cols: Seq[String]): DataFrame = fillValue(value, cols)
+
+  /**
+   * (Scala-specific) Returns a new `DataFrame` that replaces null or NaN values in specified
+   * numeric columns. If a specified column is not a numeric column, it is ignored.
+   *
    * @since 1.3.1
    */
-  def fill(value: Double, cols: Seq[String]): DataFrame = {
-    val columnEquals = df.sparkSession.sessionState.analyzer.resolver
-    val projections = df.schema.fields.map { f =>
-      // Only fill if the column is part of the cols list.
-      if (f.dataType.isInstanceOf[NumericType] && cols.exists(col => columnEquals(f.name, col))) {
-        fillCol[Double](f, value)
-      } else {
-        df.col(f.name)
-      }
-    }
-    df.select(projections : _*)
-  }
+  def fill(value: Double, cols: Seq[String]): DataFrame = fillValue(value, cols)
+
 
   /**
    * Returns a new `DataFrame` that replaces null values in specified string columns.
@@ -180,18 +192,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    *
    * @since 1.3.1
    */
-  def fill(value: String, cols: Seq[String]): DataFrame = {
-    val columnEquals = df.sparkSession.sessionState.analyzer.resolver
-    val projections = df.schema.fields.map { f =>
-      // Only fill if the column is part of the cols list.
-      if (f.dataType.isInstanceOf[StringType] && cols.exists(col => columnEquals(f.name, col))) {
-        fillCol[String](f, value)
-      } else {
-        df.col(f.name)
-      }
-    }
-    df.select(projections : _*)
-  }
+  def fill(value: String, cols: Seq[String]): DataFrame = fillValue(value, cols)
 
   /**
    * Returns a new `DataFrame` that replaces null values.
@@ -210,7 +211,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    *
    * @since 1.3.1
    */
-  def fill(valueMap: java.util.Map[String, Any]): DataFrame = fill0(valueMap.asScala.toSeq)
+  def fill(valueMap: java.util.Map[String, Any]): DataFrame = fillMap(valueMap.asScala.toSeq)
 
   /**
    * (Scala-specific) Returns a new `DataFrame` that replaces null values.
@@ -230,7 +231,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    *
    * @since 1.3.1
    */
-  def fill(valueMap: Map[String, Any]): DataFrame = fill0(valueMap.toSeq)
+  def fill(valueMap: Map[String, Any]): DataFrame = fillMap(valueMap.toSeq)
 
   /**
    * Replaces values matching keys in `replacement` map with the corresponding values.
@@ -368,7 +369,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
     df.select(projections : _*)
   }
 
-  private def fill0(values: Seq[(String, Any)]): DataFrame = {
+  private def fillMap(values: Seq[(String, Any)]): DataFrame = {
     // Error handling
     values.foreach { case (colName, replaceValue) =>
       // Check column name exists
@@ -435,4 +436,38 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
     case v => throw new IllegalArgumentException(
       s"Unsupported value type ${v.getClass.getName} ($v).")
   }
+
+  /**
+   * Returns a new `DataFrame` that replaces null or NaN values in specified
+   * numeric, string columns. If a specified column is not a numeric, string column,
+   * it is ignored.
+   */
+  private def fillValue[T](value: T, cols: Seq[String]): DataFrame = {
+    // the fill[T] which T is  Long/Double,
+    // should apply on all the NumericType Column, for example:
+    // val input = Seq[(java.lang.Integer, java.lang.Double)]((null, 164.3)).toDF("a","b")
+    // input.na.fill(3.1)
+    // the result is (3,164.3), not (null, 164.3)
+    val targetType = value match {
+      case _: Double | _: Long => NumericType
+      case _: String => StringType
+      case _ => throw new IllegalArgumentException(
+        s"Unsupported value type ${value.getClass.getName} ($value).")
+    }
+
+    val columnEquals = df.sparkSession.sessionState.analyzer.resolver
+    val projections = df.schema.fields.map { f =>
+      val typeMatches = (targetType, f.dataType) match {
+        case (NumericType, dt) => dt.isInstanceOf[NumericType]
+        case (StringType, dt) => dt == StringType
+      }
+      // 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)
+      }
+    }
+    df.select(projections : _*)
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
index 47b55e2547..fd829846ac 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
@@ -138,6 +138,24 @@ class DataFrameNaFunctionsSuite extends QueryTest with SharedSQLContext {
     checkAnswer(
       Seq[(String, String)]((null, null)).toDF("col1", "col2").na.fill("test", "col1" :: Nil),
       Row("test", null))
+
+    checkAnswer(
+      Seq[(Long, Long)]((1, 2), (-1, -2), (9123146099426677101L, 9123146560113991650L))
+        .toDF("a", "b").na.fill(0),
+      Row(1, 2) :: Row(-1, -2) :: Row(9123146099426677101L, 9123146560113991650L) :: Nil
+    )
+
+    checkAnswer(
+      Seq[(java.lang.Long, java.lang.Double)]((null, 1.23), (3L, null), (4L, 3.45))
+        .toDF("a", "b").na.fill(2.34),
+      Row(2, 1.23) :: Row(3, 2.34) :: Row(4, 3.45) :: Nil
+    )
+
+    checkAnswer(
+      Seq[(java.lang.Long, java.lang.Double)]((null, 1.23), (3L, null), (4L, 3.45))
+        .toDF("a", "b").na.fill(5),
+      Row(5, 1.23) :: Row(3, 5.0) :: Row(4, 3.45) :: Nil
+    )
   }
 
   test("fill with map") {