It is interesting how you can actually use these high-level APIs using SparkSQL to save on coding. For example, just look at this one line of code:
topMovieIDs = movieDataset.groupBy("movieID").count().orderBy("count", ascending=False).cache() Remember that to do the same thing earlier, we had to kind of jump through some hoops and create key/value RDDs, reduce the RDD, and do all sorts of things that weren't very intuitive. Using SparkSQL and DataSets, however, you can do these exercises in a much more intuitive manner. At the same time, you allow Spark the opportunity to represent its data more compactly and optimize those queries in a more efficient manner.
Again, DataFrames are the way of the future with Spark. If you do have the choice between using an RDD and a DataFrame to do the same problem, opt for a DataFrame. It is not only more efficient, but it will also give you more interoperability with more components within Spark going forward. So there you have it: Spark SQL DataFrames...
