Resilient Distributed Dataset (RDD) is a rudimentary data structure of Spark. RDDs are the immutable Distributed collections of objects of any type. It records the data from various nodes and prevents it from significant faults.
The Resilient Distributed Dataset (RDD) in Spark supports two types of operations. These are:
Transformations
Actions
RDD Transformation:
The transformation function generates new RDD from the pre-existing RDDs in Spark. Whenever the transformation occurs, it generates a new RDD by taking an existing RDD as input and producing one or more RDD as output. Due to its Immutable nature, the input RDDs don't change and remain constant.
Along with this, if we apply Spark transformation, it builds RDD lineage, including all parent RDDs of the final RDDs. We can also call this RDD lineage as RDD operator graph or RDD dependency graph. RDD Transformation is the logically executed plan, which means it is a Directed Acyclic Graph (DAG) of the continuous parent RDDs of RDD.
RDD Action:
The RDD Action works on an actual dataset by performing some specific actions. Whenever the action is triggered, the new RDD does not generate as happens in transformation. It depicts that Actions are Spark RDD operations that provide non-RDD values. The drivers and external storage systems store these non-RDD values of action. This brings all the RDDs into motion.
If appropriately defined, the action is how the data is sent from the Executor to the driver. Executors play the role of agents and the responsibility of executing a task. In comparison, the driver works as a JVM process facilitating the coordination of workers and task execution.
