Analysts must count how often each service category appears across millions of 311 text blurbs. Which MapReduce thinking applies?
Select an answer to reveal the explanation.
Short Explanation
Word-count is the textbook MapReduce demo: emit each token, then total. Here the ‘words’ are 311 service categories across millions of blurbs. Same thinking—map emissions, reduce sums—just dressed in city service language.
Full Explanation
The canonical MapReduce word-count pattern maps tokens to counts and reduces by summing per key. Applied civically, mappers emit service-category keys from 311 text and reducers compute frequencies at scale. Single-threaded spreadsheet updates, email tallies, or browser-cookie storage do not provide that parallel batch pattern.