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What Is the Main Role of the Reducer Phase in Hadoop Big Data Processing?
Learn the main role of the Reduce function in Hadoop MapReduce for exam success. Understand how the Reducer aggregates all values for each key into a single output, producing the final Big Data results.
Question
What is the main role of the Reduce function in Hadoop?
A. To aggregate values for the same key into a single output
B. To replicate blocks across racks
C. To configure hostnames of nodes
D. To break input into key-value pairs
Answer
A. To aggregate values for the same key into a single output
Explanation
The Reduce function takes all intermediate values associated with the same key (after the shuffle and sort phase) and combines them into a single, consolidated result for that key, such as summing counts or merging lists. It does not handle data replication, node configuration, or breaking raw input into key-value pairs; those responsibilities belong to HDFS and the Map function, respectively. By aggregating per-key values, Reduce produces the final output dataset that is typically written back to HDFS for further analysis or consumption.