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Answer: Apache Arrow is utilized by Pandas UDF to efficiently transfer data, enabling the use of Pandas API on Spark DataFrames.
Apache Arrow is pivotal in Pandas UDFs for efficient data transfer between Spark and pandas. It serves as a columnar in-memory data format that optimizes data exchange by eliminating unnecessary serialization and deserialization steps. This enhancement is crucial for performance when executing user-defined functions in Python with the Pandas API on Spark DataFrames. The incorrect options underestimate Arrow's role or its impact on Spark DataFrames, highlighting Arrow's essential function as a bridge for seamless and efficient data exchange.
Author: LeetQuiz Editorial Team
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In the context of Pandas UDF, how does Apache Arrow enhance the interaction between Spark and pandas?
A
Apache Arrow is solely beneficial for pandas and has no effect on Spark DataFrames.
B
Apache Arrow enables direct data exchange between Spark and pandas, bypassing any need for intermediaries.
C
Pandas UDF does not rely on Apache Arrow for data transfer between Spark and Pandas.
D
Apache Arrow is utilized by Pandas UDF to efficiently transfer data, enabling the use of Pandas API on Spark DataFrames.
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