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CSE 482: Big Data Analysis (Spring 2020) Homework 2
Due date: Monday, February 19, 2020
Please make sure you submit a PDF version of your homework via D2L.
1. Write the corresponding HDFS commands to perform the tasks described
for each question below. Type hadoop fs -help for the list of HDFS
commands available. You can also refer to the documentation available at
https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/
FileSystemShell.html. To double-check your answers, you should test
the commands to make sure they work correctly.
(a) Suppose you are connected to a master node on AWS running hadoop
with Linux operating system. Assume you have created a data directory
named logs (on the Linux filesystem of the master node),
which currently contains 1000 Web log files to be processed. Write
the hadoop DFS commands needed to upload all the Web log files
from the logs directory to the directory named /user/hadoop/data
on HDFS. Assume the /user/hadoop/data directory has not existed
yet on HDFS. Therefore, you need to create the directory first before
transferring the files.
(b) Write the HDFS command to move the Web log files from the /user/
hadoop/data directory on HDFS to a shared directory named /user/
share/ on HDFS. After the move, all the files should now be located
in the /user/share/data/ directory. Write the HDFS command to
list all the files and subdirectories located in /user/share directory.
To make sure the files have been moved, write the corresponding
HDFS command to list all the files and subdirectories located in the
directory named /user/hadoop to verify that the data subdirectory
no longer exists.
(c) Suppose one of the files located in the /user/share/data/ directory
named 2020-01-01.txt is corrupted. You need to replace the
corrupted file with a new file named 2020-01-01-new.txt, which
is currently located in the logs/new directory on the local (Linux)
filesystem of the AWS master node. Write the HDFS commands
to (1) delete the corrupted file from /user/share/data/ directory
on HDFS, (2) Upload the new file from logs/new directory to the
/user/share/data/ directory on HDFS, and (3) rename the new file
on HDFS from 2020-01-01-new.txt to 2020-01-01.txt.
(d) Write the HDFS command to display the content of the file 2020-01-01.
txt, which is currently stored in the /user/share/data/ directory
on HDFS. As the file is huge, write another HDFS command to display
the last kilobyte of the file to standard output.
1
2. Consider a Hadoop program written to solve each computational problem
and dataset described below. State how would you setup the (key,value)
pairs as inputs and outputs of its mapper and reducer classes. Assume
your Hadoop program uses TextInputFormat as its input format (where
each record corresponds to a line of the input file). Since the inputs for the
mappers are the same (byte offset, content of the line) for all the problems
below, you only have to specify the mappers’ outputs as well as reducers’
inputs and outputs. You must also explain the operations performed by
the map and reduce functions of the Hadoop program. If the problem
requires more than one mapreduce jobs, you should explain what each job
is trying to do along with its input and output key-value pairs. You should
solve the computation problem with minimum number of mapreduce jobs.
Example:
Data set: Collections of text documents.
Problem: Count the frequency of nouns that appear at least 100 times in
the documents.
Answer:
(i) Mapper function: Tokenize each line into a set of terms (words), and filter out
terms that are not nouns.
(ii) Mapper output: key is a noun, value is 1.
(iii) Reducer input: key is a word, value is list of 1’s.
(iv) Reduce function: sums up the 1’s for each key (noun).
(v) Reducer output: key is a noun, value is frequency of the word (filter the nouns
whose frequencies are below 100).
(a) Data set: Car for sale data. Each line in the data file has 5 columns
(seller id, car make, car model, car year, price). For example:
1234,honda,accord,2010,10500
2331,ford,taurus,2005,2400
Problem: Find the median price (over all years) for each make and
model of vehicle. For example, the median price for ford taurus could
be 8000.
(b) Data set: Netflix movie rental data. Each record in the data file
contains the following 4 columns: userID, rental date, movie title,
movie genre. For example, the record
user111 12-20-2019 star_wars scifi
user111 12-21-2019 aladdin animation
user111 12-25-2019 lion_king animation
Problem: Find the favorite movie genre of each user. In the above
example, the favorite genre for user111 is animation.
2
(c) Data set: Youtube subscriber data. Each line in the data file is
a 2-tuple (user, subscriber). For example, the following lines in the
data file:
john mary
john bob
mary john
show that mary and bob are subscribers of John’s Youtube videos.
Problem: Find all pairs of users who subscribe to each others’
videos. In the example above, john and mary are such pair of subscribers,
but john and bob are not (since john does not subscribe to
bob’s videos)
(d) Data set: Loan applicant data. Each line in the data file contains
the following attributes: marital status, age group, employment status,
home ownership, credit rating, and class (approve/reject).
single, 18-25, employed, none, poor, reject.
single, 25-45, employed, yes, good, approve.
Problem: Compute the entropy of each attribute (marital status,
age group, etc) with respect to the class variable.
(e) Data set: Document data. Each record in the dataset corresponds
to a document with its ID and set of words that appear in the document.
For example, the following records contain the set of words
that appear in documents 12345, 12346, and 12347, respectively.
12345 team won goal result
12346 political party won election result
12347 lunch party restaurant
Problem: Compute the cosine similarity between every pair of documents
in the dataset. Given a pair of documents, say, u and v, their
cosine similarity is computed as follows:
cosine(u, v) = nuv √
nu × nv
,
where nuv is the number of words that appear in both u and v, nu
is the number of words that appear in document u and nv is the
number of words that appear in document v. For the above example,
cosine(12345,12346) = 2/

20 whereas cosine(12346,12347) = 1/

15.
Hint: You will need two mapreduce (Hadoop) jobs for this problem.
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3. Download the data file Titanic.csv from the class Web site. Each line
in the data file has the following comma-separated attribute values:
PassengerGroup,Age,Gender,Outcome
For this question, you need to write a Hadoop program that computes the
mutual information between every pair of attributes. The reducer output
will contain the following key-value pair:
• key is name of attribute pair, e.g., (Age, Outcome).
• Value is the their mutual information.
Deliverable: Your hadoop source code (*.java), the archived (jar) files,
and the reducer output file, which must have 2 tab-separated columns:
attribute pair and its mutual information value.

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