What is the lowest CPU cost partitioning method for parallel stage to parallel stage?
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Range
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Modulus
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Entire
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Same
Same partitioning has the lowest CPU cost because it keeps data on its existing partition without movement. Range requires sorting, Modulus requires calculation, Entire sends all data to one partition (bottleneck). Same is essentially zero-cost when partitions are already compatible.
To answer this question, let's go through each option to understand why it is correct or incorrect:
Option A) Range - This option is incorrect because range partitioning involves dividing data based on a specified range of values. It is useful when the data can be evenly distributed across partitions based on the range, but it may not necessarily be the lowest CPU cost method for parallel stage to parallel stage.
Option B) Modulus - This option is incorrect because modulus partitioning involves dividing data based on the remainder of a division operation. It is useful when the data can be evenly distributed across partitions based on the modulus value, but it may not necessarily be the lowest CPU cost method for parallel stage to parallel stage.
Option C) Entire - This option is incorrect because entire partitioning involves processing the entire data set in each partition. This means that each partition will process all the data, which can lead to higher CPU costs compared to other partitioning methods.
Option D) Same - This option is correct because the "Same" partitioning method involves passing data from one parallel stage directly to the next parallel stage without any partitioning. This means that the data remains in the same partition, resulting in lower CPU costs as there is no need for data movement or redistribution.
The correct answer is D) Same. This option is correct because the "Same" partitioning method does not involve any data movement or redistribution, resulting in lower CPU costs.