I know that in RDD's we were discouraged from using groupByKey, and encouraged to use alternatives such as reduceByKey(), and aggregateByKey() since these other methods would reduce first on each partition, and then perform groupByKey() and thus reduces the amount of data being shuffled.
Now, my question is if this still applies to Dataset/Dataframe? I was thinking that since catalyst engine does a lot of optimization, that the catalyst will automatically know that it should reduce on each partition, and then perform the groupBy. Am I correct? Or we still need to take steps to ensure reduction on each partition is performed before groupBy.
The groupBy should be used at Dataframes and Datasets. You thinking is complete right, the Catalyst Optimizer will build the plan and optimize all the entrances in GroupBy and other aggregations that you want to do.
There is a good example, that is in spark 1.4 on this link that show the comparison of reduceByKey with RDD and GroupBy with DataFrame.
And you can see that is really much more faster than RDD, so groupBy optimize all the execution for more details you can see the oficial post of DataBricks with the introduction of DataFrames
来源：https://stackoverflow.com/questions/47933210/should-i-avoid-groupby-in-dataset-dataframe