In Spark, SparkSQL is an independent module, which can independently complete SQL statement parsing, optimization, and execution without relying on SparkCore.

Question

Options

• ATrue
• BFalse

• A
  16% (8)
• B
  84% (43)

Explanation

B is correct because SparkSQL is not independent - it is built on top of SparkCore and relies on it for fundamental operations like task scheduling, memory management, fault tolerance, and distributed execution. SparkSQL adds a SQL layer (with Catalyst optimizer, Tungsten execution engine, and DataFrame/Dataset APIs), but the actual distributed computation is delegated to SparkCore's RDD engine underneath.

Why A is wrong: SparkSQL cannot parse, optimize, and execute SQL entirely on its own. While Catalyst handles parsing and optimization independently, the execution phase requires SparkCore to distribute and run the physical plan across the cluster.

Memory tip: Think of SparkSQL as a "smart frontend" - it handles the SQL brain work (parsing, planning, optimizing), but it still needs SparkCore as the "muscle" to actually run the job. No Core = no execution.

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