Question

MapReduce computing in big data system takes a list of key-value pairs as input and computes the results in two phases: Map and Reduce. Map will process the input list and output

A.N key-value pairs, and then Reduce computes the N sub-lists into a single list
B. a shorter list, and then Reduce computes the shorter list to obtain single pair as output.
C. two half lists, and then Reduce merge the two half lists into a single list.
D.two half lists, and then Reduce process the two half lists to obtain a single pair as output.

Answer 1

MapReduce computing processes key-value pairs through a Map phase, outputting N key-value pairs, followed by a Reduce phase, which aggregates these into a shorter list, contrary to just a single pair. So the correct answer is option A.

Step-by-step explanation:

MapReduce computing in a big data system indeed processes data in two phases: Map and Reduce. During the Map phase, the big data system takes a list of key-value pairs as input, processes it, and outputs multiple key-value pairs.

Then, during the Reduce phase, these outputted key-value pairs are combined, usually by aggregating values that have the same key, to produce a shorter list of key-value pairs. To be more specific, the correct representation of these phases is Map will process the input list and output N key-value pairs.

Then Reduce computes these key-value pairs to produce a shorter list, which is not necessarily a single pair but typically a reduced set of key-value pairs that contain the results of the applied aggregation or computation.