Spark DataFrame groupby, sql, cube - alternatives and optimization

The below is a SQL query that will be executed using Spark

$Query =

select col1,col2, ... col7, SUM(col8,col9...)

from table1 where (condition1)

GROUPBY col1, col2 .... col7

Having SUM(col8, col9, col10...)

1. With Spark1.5.x and dataframes, can the above operation represented as df.groupBy($"col1",$"col2",.... $"col7").sum(col8, col9....col12) ? Is the dataframe.groupBy() optimized for the data locality (i.e. similar to reduce and aggregate operations) than doing a naive shuffle. With multiple groupBy columns, how effective the optimization would be for say a billion or two tuples. Does the resulting dataframe have the sum(col8,col9....) as a selected column.

2. If the above sql query is executed as Sql/HiveContext.sql(" $Query "). Is this any different than the dataframe.groupBy().sum()

-> Can we use dataframe.cube() for the above groupBy Query as

dataframe.cube($"col1",$"col2",.... $"col7") .sum(col8, col9....col12)

If yes, is one approach better than the other in terms of query optimization or execution (df.groupBy().agg() , SqlContext.sql("") , df.cube().sum() ). Thanks in advance @jason