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7CCSMBDT – Big Data Technologies Coursework

创建日期：2024-04-03 14:55:12

所属分类：统计学statistics

标签： 7CCSMBDT

Big Data Technologies Coursework

7CCSMBDT – Big Data Technologies Coursework 1

Overview: The coursework aims to make you familiar with the following concepts:
(i) Big Data collection, and (iii) programming using the MapReduce framework.
This coursework is formally assessed and is worth 10% of your final mark.
You will receive some feedback as part of the marking of the coursework after 4 weeks
from the coursework submission deadline. You will also receive additional feedback in
the form of examples solutions soon after.

Submission: Include BOTH files below:
(i) A file, Coursework1.PDF, containing your answers. For tasks that require writing
code, write your code and comments as part of the answer. For tasks that require
showing output of a program, show the output or a small part of the output.
(ii) A file, Coursework1_code.ZIP, containing, for each program, the code of the
program (.py file) and a file containing the entire output of applying the program to the
required dataset. Name the code and output to indicate the task it corresponds to (e.g.,
task2.py for the code and task2.out for the output of Task 2).
Evaluation: The maximum number of marks (out of 100) for each task is given in
square brackets [] next to each question.

Plagiarism: “Plagiarism is passing off someone else’s work as your own, or submitting
a piece of your own work that you have already submitted as part of a different
programme, module or at a different institution. The penalties for plagiarising by the
College can be severe. Uploading work to KEATS is regarded by the Department as a
statement by the student concerned, confirming that the work has not been plagiarised.”

Late submission: "If you are submitting your coursework after the deadline, you must
submit a Mitigating Circumstances Form (MCF), with evidence to justify why you have
not submitted on time. If you do not do this or your reasons are not acceptable, your
coursework may be given a mark of zero." Please speak to your personal tutor about
the MCF. Lecturers cannot provide deadline extensions.
7CCSMBDT – Big Data Technologies Coursework 1

Task 1. Big data collection using Apache Sqoop.
Describe three features of Apache Sqoop that help import data into a distributed file
system efficiently. Your description should state the feature and a brief justification of
what the feature does and how it can help efficiency.
[30]
Task 2. MapReduce frequency-based computations.

Write a program task2.py using mrjob, which outputs the 10 most frequent values in the
Age attribute of the Adult dataset and their frequency. That is, the ten values in Age that
appear the largest number of times in the dataset and the number of times each of
these values appears.
The format of the output should be as follows:
98 "6"
88 "1"
This format says that the value 6 is the most frequent and appears 98 times followed by
value 1 that appears 88 times.
In your report, provide the output file (result) from applying the program to the first 10
lines of the dataset. Also, provide your code along with comments and description about
what each step does.
In your zip file, provide the code with comments (a task2.py file) and also the output file
(a task2.out file) from applying the program to the entire dataset. [30]
Note: You can use redirection (e.g., python3.6 myprogram.py > myoutput.txt) to get the
output. You can execute the program in local mode (i.e., without -r hadoop).
[35]
Task 3. MapReduce inverted index.
An inverted index is a data structure commonly used to map symbols into their location.
Many search engines utilize this data structure to efficiently process user queries. In this
task, you will implement a simple inverted index and apply it to a web dataset coming
from msn.com.
7CCSMBDT – Big Data Technologies Coursework 1

Download the dataset msnbc_lines.seq from KEATS.Each record (line) in the dataset
contains a first number which is the line-id, and one or more symbols (integers) that can
appear one or more times in a line.
Write a program task3.py using mrjob. Given the dataset, your program must output
each symbol of the dataset along with a set of line-ids indicating the lines in which the
symbol appears.
For example, there is a file named test on KEATs which contains the following lines:
1 1 1
2 2
3 3 2 2 4 2 2 2 3 3
4 5
5 1
6 6
7 1 1
8 6
9 6 7 7 7 6 6 8 8 8 8
10 6 9 4 4 4 10 3 10 5 10 4 4 4
The first line in test contains 1 as line-id and then the symbol 1 two times. The second
line contains 2 as line-id and then the symbol 2 once. Your program on test should
output the following lines (the order may differ):
"5" [4, 10]
"6" [6, 8, 9, 10]
"3" [3, 10]
"4" [3, 10]
"7" [9]
"8" [9]
"9" [10]
"1" [1, 5, 7]
"10" [10]
"2" [2, 3]
7CCSMBDT – Big Data Technologies Coursework 1

In this output, the symbol “5” is associated with [4, 10] which means that it appears in
lines with line-ids 4 and 10.
In your report, provide the output file (result) from applying the program to the first 10
lines of the msnbc_lines.seq dataset. Also, provide your code along with comments
and description about what each step does.
In your zip file, provide the code with comments (a task3.py file) and also the output file
(a task3.out file) from applying the program to the entire msnbc_lines.seq dataset.
You can execute the program in local mode (i.e., without -r hadoop).

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