When is data pulled from the RDD if 5 transformations are applied followed by a single action?

In Apache Spark, transformations are operations that define a new dataset from an existing one, while actions trigger the execution of these transformations and return a result. When you apply multiple transformations to a resilient distributed dataset (RDD), Spark does not immediately compute the results after each transformation. Instead, it builds up a logical execution plan known as a lineage graph.

Data is pulled from the RDD and computed only when an action is invoked. Actions trigger the evaluation of the entire lineage of transformations defined up to that point, which means that all transformations are applied in one go when the action is called. This design allows Spark to optimize the execution plan and potentially minimize data shuffling and recomputation.

Therefore, the correct response is that data is only pulled and transformations are executed when the first action is called. This efficient execution model is a core feature of Spark’s laziness and allows users to chain transformations without incurring unnecessary resource consumption until necessary.