Problem 2 (5 points)We have four text files as follows, storing the student grades of four subjects.math.txt physics.txt chemistry.txt art.txtJames, 91 James, 57 James, 78 James, 67John, 89 John, 78 John, 92 John, 89Robert, 72 Robert, 68 Robert, 68 Robert, 88Michael, 81 Michael, 71 Michael, 91 Michael, 87David, 76 David, 79 David, 77 David, 68Mary, 79 Mary, 69 Mary, 74 Mary, 79Linda, 63 Linda, 79 Linda, 89 Linda, 94Susan, 67 Susan, 76 Susan, 87 Susan, 78Lisa, 76 Lisa, 74 Lisa, 92 Lisa, 91Our goal is to calculate the total scores of students in all four subjects. (In practice, we could have morestudents and more subjects.)(a) What are the relationships between MapReduce and Hadoop?(b) Write pseudo-code for map worker, including the (key, value) pairs of the input and output.(c) Write pseudo-code for reduce worker, including the (key, value) pairs of the input and output.(d) What are the concrete inputs and outputs of your implemented mapper and reducer when process-ing the above four text files?
Question
Problem 2 (5 points)We have four text files as follows, storing the student grades of four subjects.math.txt physics.txt chemistry.txt art.txtJames, 91 James, 57 James, 78 James, 67John, 89 John, 78 John, 92 John, 89Robert, 72 Robert, 68 Robert, 68 Robert, 88Michael, 81 Michael, 71 Michael, 91 Michael, 87David, 76 David, 79 David, 77 David, 68Mary, 79 Mary, 69 Mary, 74 Mary, 79Linda, 63 Linda, 79 Linda, 89 Linda, 94Susan, 67 Susan, 76 Susan, 87 Susan, 78Lisa, 76 Lisa, 74 Lisa, 92 Lisa, 91Our goal is to calculate the total scores of students in all four subjects. (In practice, we could have morestudents and more subjects.)(a) What are the relationships between MapReduce and Hadoop?(b) Write pseudo-code for map worker, including the (key, value) pairs of the input and output.(c) Write pseudo-code for reduce worker, including the (key, value) pairs of the input and output.(d) What are the concrete inputs and outputs of your implemented mapper and reducer when process-ing the above four text files?
Solution
(a) MapReduce and Hadoop are closely related as they are both used for processing large data sets. MapReduce is a programming model that allows for the processing and generating of big data sets with a parallel, distributed algorithm on a cluster. Hadoop, on the other hand, is an open-source software framework that supports data-intensive distributed applications, and it implements the MapReduce model.
(b) Pseudo-code for map worker:
function map(String name, String grades):
// split the grades by comma
gradeList = grades.split(",")
for grade in gradeList:
emit(name, Integer.parseInt(grade))
The input key-value pair is the student's name and their grades (e.g., "James", "91,57,78,67"). The output key-value pair is the student's name and each grade (e.g., "James", 91).
(c) Pseudo-code for reduce worker:
function reduce(String name, Iterator<Integer> grades):
int sum = 0
while(grades.hasNext()):
sum += grades.next()
emit(name, sum)
The input key-value pair is the student's name and a list of their grades (e.g., "James", [91,57,78,67]). The output key-value pair is the student's name and the sum of their grades (e.g., "James", 293).
(d) The concrete inputs for the mapper would be the lines from the text files, such as "James, 91" from math.txt, "James, 57" from physics.txt, etc. The output would be key-value pairs of student names and their grades, such as ("James", 91), ("James", 57), etc.
The concrete inputs for the reducer would be the output from the mapper, such as ("James", [91,57,78,67]). The output would be key-value pairs of student names and their total grades, such as ("James", 293).
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