Databricks Machine Learning Associate Question 334
Single answerWhen using Pandas API on Spark, why might it not be as fast as native Spark APIs due to the role of an InternalFrame?
- A
InternalFrame requires additional data structure conversions between Pandas and Spark.
- B
InternalFrame caches all intermediate computations in memory, slowing down processing.
- C
InternalFrame introduces additional abstraction layers to manage Spark DataFrames with Pandas-like operations.
- D
InternalFrame does not support distributed processing across multiple nodes.
Show answer and explanation
Correct answer: A
Explanation
The InternalFrame in Pandas API on Spark acts as a bridge between Pandas operations and Spark DataFrames. This involves converting data between Pandas and Spark formats, which introduces additional overhead. This makes the Pandas API on Spark slower compared to native Spark APIs, which are optimized for distributed computation without such conversions.
- A. Correct.
InternalFrame requires additional data structure conversions between Pandas and Spark, which adds overhead and can make Pandas API on Spark slower than native Spark APIs.
- B. Incorrect.
InternalFrame does not inherently cache intermediate computations; caching is typically controlled by the user or the Spark environment.
- C. Incorrect.
While InternalFrame does add abstraction layers, this is not the primary reason for the performance difference. The main overhead comes from the conversions between Pandas and Spark data structures.
- D. Incorrect.
InternalFrame is designed to support distributed processing across multiple nodes in Spark. It is not a limitation of InternalFrame itself.