[SPARK-17124][SQL] RelationalGroupedDataset.agg should preserve order and...

[SPARK-17124][SQL] RelationalGroupedDataset.agg should preserve order and allow multiple aggregates per column

## What changes were proposed in this pull request?
This patch fixes a longstanding issue with one of the RelationalGroupedDataset.agg function. Even though the signature accepts vararg of pairs, the underlying implementation turns the seq into a map, and thus not order preserving nor allowing multiple aggregates per column.

This change also allows users to use this function to run multiple different aggregations for a single column, e.g.
```
agg("age" -> "max", "age" -> "count")
```

## How was this patch tested?
Added a test case in DataFrameAggregateSuite.

Author: petermaxlee <petermaxlee@gmail.com>

Closes #14697 from petermaxlee/SPARK-17124.

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

@@ -128,7 +128,7 @@ class RelationalGroupedDataset protected[sql](
   }
 
   /**
-   * (Scala-specific) Compute aggregates by specifying a map from column name to
+   * (Scala-specific) Compute aggregates by specifying the column names and
    * aggregate methods. The resulting [[DataFrame]] will also contain the grouping columns.
    *
    * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
@@ -143,7 +143,9 @@ class RelationalGroupedDataset protected[sql](
    * @since 1.3.0
    */
   def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
-    agg((aggExpr +: aggExprs).toMap)
+    toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+      strToExpr(expr)(df(colName).expr)
+    })
   }
 
   /**

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

@@ -87,6 +87,16 @@ class DataFrameAggregateSuite extends QueryTest with SharedSQLContext {
     )
   }
 
+  test("SPARK-17124 agg should be ordering preserving") {
+    val df = spark.range(2)
+    val ret = df.groupBy("id").agg("id" -> "sum", "id" -> "count", "id" -> "min")
+    assert(ret.schema.map(_.name) == Seq("id", "sum(id)", "count(id)", "min(id)"))
+    checkAnswer(
+      ret,
+      Row(0, 0, 1, 0) :: Row(1, 1, 1, 1) :: Nil
+    )
+  }
+
   test("rollup") {
     checkAnswer(
       courseSales.rollup("course", "year").sum("earnings"),