[SPARK-14063][SQL] SQLContext.range should return Dataset[java.lang.Long]

## What changes were proposed in this pull request?
This patch changed the return type for SQLContext.range from `Dataset[Long]` (Scala primitive) to `Dataset[java.lang.Long]` (Java boxed long).

Previously, SPARK-13894 changed the return type of range from `Dataset[Row]` to `Dataset[Long]`. The problem is that due to https://issues.scala-lang.org/browse/SI-4388, Scala compiles primitive types in generics into just Object, i.e. range at bytecode level now just returns `Dataset[Object]`. This is really bad for Java users because they are losing type safety and also need to add a type cast every time they use range.

Talked to Jason Zaugg from Lightbend (Typesafe) who suggested the best approach is to return `Dataset[java.lang.Long]`. The downside is that when Scala users want to explicitly type a closure used on the dataset returned by range, they would need to use `java.lang.Long` instead of the Scala `Long`.

## How was this patch tested?
The signature change should be covered by existing unit tests and API tests. I also added a new test case in DatasetSuite for range.

Author: Reynold Xin <rxin@databricks.com>

Closes #11880 from rxin/SPARK-14063.

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

@@ -725,7 +725,7 @@ class SQLContext private[sql](
    * @group dataset
    */
   @Experimental
-  def range(end: Long): Dataset[Long] = range(0, end)
+  def range(end: Long): Dataset[java.lang.Long] = range(0, end)
 
   /**
    * :: Experimental ::
@@ -736,7 +736,7 @@ class SQLContext private[sql](
    * @group dataset
    */
   @Experimental
-  def range(start: Long, end: Long): Dataset[Long] = {
+  def range(start: Long, end: Long): Dataset[java.lang.Long] = {
     range(start, end, step = 1, numPartitions = sparkContext.defaultParallelism)
   }
 
@@ -749,7 +749,7 @@ class SQLContext private[sql](
     * @group dataset
     */
   @Experimental
-  def range(start: Long, end: Long, step: Long): Dataset[Long] = {
+  def range(start: Long, end: Long, step: Long): Dataset[java.lang.Long] = {
     range(start, end, step, numPartitions = sparkContext.defaultParallelism)
   }
 
@@ -763,8 +763,8 @@ class SQLContext private[sql](
    * @group dataset
    */
   @Experimental
-  def range(start: Long, end: Long, step: Long, numPartitions: Int): Dataset[Long] = {
-    new Dataset(this, Range(start, end, step, numPartitions), implicits.newLongEncoder)
+  def range(start: Long, end: Long, step: Long, numPartitions: Int): Dataset[java.lang.Long] = {
+    new Dataset(this, Range(start, end, step, numPartitions), Encoders.LONG)
   }
 
   /**

sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java

@@ -329,7 +329,7 @@ public class JavaDataFrameSuite {
 
   @Test
   public void testCountMinSketch() {
-    Dataset df = context.range(1000);
+    Dataset<Long> df = context.range(1000);
 
     CountMinSketch sketch1 = df.stat().countMinSketch("id", 10, 20, 42);
     Assert.assertEquals(sketch1.totalCount(), 1000);
@@ -354,7 +354,7 @@ public class JavaDataFrameSuite {
 
   @Test
   public void testBloomFilter() {
-    Dataset df = context.range(1000);
+    Dataset<Long> df = context.range(1000);
 
     BloomFilter filter1 = df.stat().bloomFilter("id", 1000, 0.03);
     Assert.assertTrue(filter1.expectedFpp() - 0.03 < 1e-3);

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

@@ -44,6 +44,15 @@ class DatasetSuite extends QueryTest with SharedSQLContext {
       1, 1, 1)
   }
 
+  test("range") {
+    assert(sqlContext.range(10).map(_ + 1).reduce(_ + _) == 55)
+    assert(sqlContext.range(10).map{ case i: java.lang.Long => i + 1 }.reduce(_ + _) == 55)
+    assert(sqlContext.range(0, 10).map(_ + 1).reduce(_ + _) == 55)
+    assert(sqlContext.range(0, 10).map{ case i: java.lang.Long => i + 1 }.reduce(_ + _) == 55)
+    assert(sqlContext.range(0, 10, 1, 2).map(_ + 1).reduce(_ + _) == 55)
+    assert(sqlContext.range(0, 10, 1, 2).map{ case i: java.lang.Long => i + 1 }.reduce(_ + _) == 55)
+  }
+
   test("SPARK-12404: Datatype Helper Serializability") {
     val ds = sparkContext.parallelize((
       new Timestamp(0),