Meenakshi Goyal walks us through the transformation functions available to you when using a Spark RDD:
The role of transformation in Spark is to create a new dataset from an existing one. Lazy transformations are those that are computed only when an action requires a result to be returned to the driver programme.
When we call an action, transformations are executed since they are inherently lazy. Not right away are they carried out. There are two primary types of transformations: map() and filter ().
The outcome RDD is always distinct from the parent RDD after the transformation. It could be smaller (filter, count, distinct, sample, for example), bigger (flatMap(), union(), Cartesian()), or the same size (e.g. map).
Read on to learn more about transformations, including examples of how each works. Even if you’re using the DataFrames API for Spark, it’s still important to understand that transformations are lazy.