In a MapReduce tally of transit delay reason codes, what is the reducer's primary responsibility?
Select an answer to reveal the explanation.
Short Explanation
Once every sticky note for "signal failure" lands in one pile, the reducer adds them up. That aggregation—summing the ones into a final tally—is the reduce phase's job. Splitting input and babysitting HDFS or Kafka live elsewhere.
Full Explanation
After shuffle groups values by key, reducers consume each key and its value iterable to produce aggregated output. For reason-code counting, that usually means summing ones into a durable per-key total. Input splitting is a map-side concern; NameNode health and Kafka broker ops are outside the MapReduce reduce contract.