Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?
A . The vectorized pandas UDFs allow for the use of type hints
B . The vectorized pandas UDFs process data in batches rather than one row at a time
C . The vectorized pandas UDFs allow for pandas API use inside of the function
D . The vectorized pandas UDFs work on distributed DataFrames
E . The vectorized pandas UDFs process data in memory rather than spilling to disk

View Answer

Answer: B

Explanation:

Vectorized pandas UDFs, also known as Pandas UDFs, are a powerful feature in PySpark that allows for more efficient operations than standard UDFs. They operate by processing data in batches, utilizing vectorized operations that leverage pandas to perform operations on whole batches of data at once. This approach is much more efficient than processing data row by row as is typical with standard PySpark UDFs, which can significantly speed up the computation.

Reference PySpark Documentation on UDFs:

https://spark.apache.org/docs/latest/api/python/user_guide/sql/arrow_pandas.html#pandas-udfs-a-k-a-vectorized-udfs