This method is useful for resizing partitions of an input data set that are not equal in size.
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Range
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Round Robin
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Hash
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Same
Round Robin partitioning redistributes data evenly across all partitions by sending rows to partitions in a rotating fashion (1,2,3,1,2,3...). This is specifically useful when input partitions are unequal in size - Round Robin will rebalance them so each partition has approximately equal data volume. Range partitioning divides based on value ranges, Hash partitions based on a hash function, and Same keeps the same partitioning. Only Round Robin explicitly handles resizing/rebalancing unequal partitions.
To answer this question, you need to understand different methods for resizing partitions of an input data set.
Option A) Range - This option is incorrect because the range method is used to divide a data set into partitions based on a specified range of values. It is not specifically designed for resizing partitions that are not equal in size.
Option B) Round Robin - This option is correct. The round robin method is a useful technique for resizing partitions of an input data set that are not equal in size. In this method, each partition is allocated an equal amount of data in a cyclic manner. This ensures that each partition receives a fair share of the data, even if the partitions have different sizes.
Option C) Hash - This option is incorrect. The hash method is used to allocate data to partitions based on a hash function. It is not specifically designed for resizing partitions that are not equal in size.
Option D) Same - This option is incorrect because the "Same" method implies that all partitions will have the same size. It is not suitable for resizing partitions that are not equal in size.
The correct answer is B) Round Robin. This option is correct because the round robin method is specifically designed for resizing partitions of an input data set that are not equal in size.