A city 311 desk runs a MapReduce job over complaint tickets so analysts can tally each reason code. What should each mapper emit during the map phase?
Select an answer to reveal the explanation.
Short Explanation
Picture each mapper as a clerk stamping one sticky note per ticket: reason code on the left, a lonely 1 on the right. Those intermediate pairs are the map phase's job—not final citywide totals. Later stages do the grouping and summing.
Full Explanation
In classic MapReduce, the map phase transforms input records into intermediate key/value pairs. For a civic reason-code count, mappers typically emit (reason-code, 1) so the framework can group identical keys. Final aggregation and durable output belong to later reduce and commit steps, not to each mapper alone. Shared in-memory globals across mapper JVMs are not the MapReduce contract.