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Which MapReduce Operation Calculates Data Ratios Like Male-to-Female Distribution?
Learn the exact MapReduce operation needed to calculate ratios like male-to-female distribution. Master the grouping keys and aggregating counts concept for your Big Data Hadoop, Pig, and Hive certification exams.
Question
Which MapReduce operation helps calculate ratios like male-to-female distribution?
A. Grouping keys and aggregating counts
B. Sampling
C. Sorting
D. Filtering
Answer
A. Grouping keys and aggregating counts
Explanation
To calculate a metric like a male-to-female ratio, MapReduce utilizes the fundamental pattern of grouping keys and aggregating counts. During the Map phase, the system reads each record, extracts the gender field, and outputs it as a key (e.g., “Male” or “Female”) alongside a value of 1. In the Reduce phase, MapReduce inherently groups all identical keys together, allowing the Reducer to sum (aggregate) the 1s for each group to find the total counts of males and females. Once these aggregates are calculated, the final ratio or percentage can be easily derived by dividing the grouped totals. Options B (Sampling), C (Sorting), and D (Filtering) are data manipulation techniques but do not perform the mathematical aggregation required to compute demographic totals and ratios.