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Answer: Iterator UDFs are preferred for large datasets because they allow for lazy evaluation of data, reducing memory usage.
Iterator UDFs are preferred for handling large datasets in Spark because they allow for lazy evaluation of data. Instead of loading the entire dataset into memory, iterator UDFs process the data in chunks, reducing memory usage and improving performance. This is particularly useful when working with large datasets that do not fit into memory. By processing the data in smaller batches, iterator UDFs can efficiently handle large datasets without running into memory limitations.
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Why are iterator UDFs preferred for handling large datasets in Spark? Provide a detailed explanation and an example of how you would implement an iterator UDF in Spark.
A
Iterator UDFs are preferred for large datasets because they allow for lazy evaluation of data, reducing memory usage.
B
Iterator UDFs are preferred for large datasets because they enable parallel processing of data, improving performance.
C
Iterator UDFs are preferred for large datasets because they provide better error handling and debugging capabilities.
D
Iterator UDFs are not preferred for large datasets in Spark.