The calculation logic of the Spark application will be parsed into DNG. Which of the following functional modules will complete this parsing operation?

Question

Options

• AClient
• BApplication Master
• CExecutor
• DDriver

• A
  15% (5)
• B
  9% (3)
• C
  6% (2)
• D
  70% (23)

Explanation

Driver is correct because it is the central coordinator that runs the application's main() function and translates the user's RDD/DataFrame transformations into a DAG (Directed Acyclic Graph) of stages and tasks - this parsing happens before any execution begins.

Client (A) is wrong because its sole job is to submit the application to the cluster; it hands off responsibility immediately and plays no role in logic parsing. Application Master (B) is wrong because it handles resource negotiation with the cluster manager (e.g., YARN) and monitors task progress - it never touches the application's logical plan. Executor (C) is wrong because Executors only run the tasks they are assigned; they receive work from the Driver, they don't generate it.

Memory tip: Think "Driver drives the plan" - just as a driver plans a route before the car moves, the Spark Driver parses and plans the entire DAG before a single Executor does any work.

No community discussion yet for this question.