Scala for Data Science

DataFrame immutability and persistence

DataFrames, like RDDs, are immutable. When you define a transformation on a DataFrame, this always creates a new DataFrame. The original DataFrame cannot be modified in place (this is notably different to pandas DataFrames, for instance).

Operations on DataFrames can be grouped into two: transformations, which result in the creation of a new DataFrame, and actions, which usually return a Scala type or have a side-effect. Methods like filter or withColumn are transformations, while methods like show or head are actions.

Transformations are lazy, much like transformations on RDDs. When you generate a new DataFrame by transforming an existing DataFrame, this results in the elaboration of an execution plan for ...