In Spark, which statement accurately describes the difference between df.explain() and df.describe() or df.summary()?

In Spark, which statement accurately describes the difference between df.explain() and df.describe() or df.summary()?

• A. explain() prints plans; describe() or summary() compute statistics
• B. explain() prints data values; describe() extracts schema
• C. describe() prints execution plans; explain() prints statistics
• D. explain() and describe() both print plans

Answer: (A)

Spark treats planning and data inspection as two separate concerns. df.explain() reveals how Spark will run the computation: the planned logical and physical steps, operators, shuffles, and code generation details. It’s about execution strategy, not the actual data values. In contrast, df.describe() and df.summary() compute statistics from the data itself—counts, means, standard deviations, minimums and maximums, and other descriptive stats—resulting in a small summary table for the columns. So the best description is that explain prints plans, while describe/summary compute statistics.

Spark treats planning and data inspection as two separate concerns. df.explain() reveals how Spark will run the computation: the planned logical and physical steps, operators, shuffles, and code generation details. It’s about execution strategy, not the actual data values. In contrast, df.describe() and df.summary() compute statistics from the data itself—counts, means, standard deviations, minimums and maximums, and other descriptive stats—resulting in a small summary table for the columns. So the best description is that explain prints plans, while describe/summary compute statistics.