Which command is primarily used for filtering elements from an RDD?

The command that is primarily used for filtering elements from an RDD (Resilient Distributed Dataset) is indeed the filter function. In Apache Spark, an RDD is a fundamental data structure that represents a distributed collection of objects. The filter operation allows users to create a new RDD by applying a specified predicate (a function that returns a Boolean value) to each element of the original RDD.

When filter is called, it evaluates the predicate for each element and returns only those elements that satisfy the condition (i.e., return true). This operation is essential in data processing as it enables users to reduce the size of the dataset by focusing only on the relevant data needed for analysis.

The other choices do not relate directly to this functionality within Spark's RDD context. "View" suggests a method for observing data rather than filtering it. "Select" is more commonly associated with DataFrame operations in Spark, not RDDs. "Extract" typically implies retrieving data but lacks the specific context of applying a filtering condition directly on RDD elements. Thus, filter is the most accurate choice for the operation described.