What should you expect when you call an action on an RDD?

When you call an action on an RDD (Resilient Distributed Dataset), you should expect that the data processing is executed. Actions are operations that trigger the actual execution of the transformations that have been defined on the RDD. Unlike transformations, which are lazily evaluated and build up a lineage of operations to perform, actions force a computation to be performed and return a value to the driver program or write data to an external storage system.

This execution occurs because until an action is called, Spark does not execute the transformations. The lazy evaluation allows Spark to optimize the workflow, but once an action is triggered, those optimizations are applied, and the data is processed according to the defined transformations. This is critical for performance and helps manage the resources effectively, enabling Spark to carry out operations on large datasets across a distributed environment.

While some other options mention aspects of RDD behavior, such as evaluation and caching, they do not capture the primary outcome of calling an action, which is the execution of data processing.