The city wants a parallel count of citation reason codes across a huge distributed ticket archive. How does MapReduce organize that work?
Select an answer to reveal the explanation.
Short Explanation
Many clerks stamp ‘code → 1’ on their own stacks, then a few desks total each code. That is MapReduce: parallel map, then aggregate reduce. It is not an OLTP row-rewrite or a phone-survey tally.
Full Explanation
MapReduce processes distributed data by running map tasks that emit intermediate key-value pairs, then reduce tasks that aggregate by key. A civic word-count style job on citation reason codes follows that pattern. It is parallel batch processing over cluster data, not single-row OLTP maintenance or manual citizen surveys.