程序代写代做代考 Hive Java hadoop database CSE 482: Big Data Analysis (Spring 2017) Homework 5
CSE 482: Big Data Analysis (Spring 2017) Homework 5
Due date: Monday, April 3, 2017 (before 9:00am)
Please make sure you submit the homework via the handin system (go to https:
//secure.cse.msu.edu/handin.
1. Write the corresponding HDFS commands to perform the following tasks.
Each of these tasks must be accomplished with a single HDFS command.
Hint: type hadoop fs -help for the list of commands available. Note the
difference between the local (Linux) file directory and HDFS directory. To
double-check your answers, you’re encouraged to test the commands (on
your own HDFS directory) to make sure they work correctly.
(a) Make a directory on HDFS named /user/hadoop.
(b) Upload a file named data.txt from the local filesystem to HDFS.
(c) Rename the uploaded file on HDFS from data.txt to temp.txt
(d) Move the file from its current location at /user/hadoop/temp.txt
to /user/hduser/temp.txt.
(e) Copy the file from /user/hduser/temp.txt to /user/hadoop/data.
txt (note that the filename is also changed).
(f) Display the content of the file /user/hduser/temp.txt.
(g) Delete the file /user/hadoop/data.txt.
(h) Download the file named /user/hduser/temp.txt to the local filesys-
tem directory /user/cse482.
(i) Merge all the files under the HDFS directory /user/hadoop/results
into a single file output.txt to be stored in your current working
directory.
(j) Changing permission of the HDFS directory /user/hduser so that
only the user hduser can read and write files stored in the directory
(but not other users).
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2. For each problem and data set described below, state how would you setup
the (key,value) pairs as inputs and outputs for the mappers and reducers.
Also, explain the operation performed by the map and reduce functions
given their input. 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, con-
tent of the line), you only need to specify the mappers’ outputs. If the
problem requires more than one mapreduce jobs, specify the role of each
map/reduce function along with their input and output key-value pairs).
You should solve the 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:
Mapper function: Tokenize each line into a set of terms (words), and filter out
terms that are not nouns.
Mapper output: key is a noun, value is 1.
Reducer input: key is a word, value is list of 1’s.
Reduce function: sums up the 1’s for each key (noun).
Reducer output: key is a noun, value is frequency of the word (filter the nouns
whose frequencies are below 100).
(a) Data set: Student transcript database. Each record consists the
following information: student ID, course code, semester enrolled,
number of credits, and GPA for the course. For example:
A123456074 CSE260 FS2013 3 3.5
GPA is a numeric-valued attribute ranging from 0.0 to 4.0.
Problem: Compute the average GPA for each student.
(b) Data set: Facebook friendship graph. Each record corresponds to a
Facebook user, followed by the list of his/her friends. For example,
the graph data may contain the following records:
john123 mary456 tom312 lee222
mary456 john123
tom312 john123 lee222
lee222 john123 tom312
Problem: Find pairs of users who are not friends with each other but
share one or more common friends. This is known in network science
literature as the “friend-of-a-friend” (FOF) problem. For example,
mary456 and tom312 share a common friend, john123, but they are
not friends with each other. In this case, the hadoop program should
output the pair (mary456, tom312) only once, i.e., it should not
output the duplicate pair (tom312, mary456).
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(c) Data set: Online music streaming data. Each line in the data file
has 4 columns (user id, song title, artist, vote), where vote is an
integer value with the following values: +1 (the user liked the song),
0 (the user did not rate the song), -1 (the user disliked the song).
Problem: For each user, list his/her favorite artist, i.e., the artist
that received the highest net vote. Do not output anything if the net
vote is non-positive (0 or negative).
(d) Data set: Measurements of total daily precipitation for weather
stations from around the world. Each line in the data files has 3
columns (station id, date, total precipitation).
Problem: Find the station id and date of anomalous precipitation
values. A precipitation value is considered anomalous if it is negative-
valued or is 3 standard deviations or more away from its mean.
(e) Data set: Online 2-player gaming data. Each line in the data file
has the following information:
gameID player1 player2 player1Score player2Score
For example, the line
123311 Wchampion lansing233 50 24
suggests that Wchampion won the game by a score of 50 to 24 (the
winner is the player who achieves a score of 50 or higher). Note that a
player can play against the same opponent more than once. Assume
each player in the dataset has played more than 100 games.
Problem: For each player, find the toughest opponent to play against
(i.e., the opponent to whom they have lowest winning percentage).
For example, if Wchampion has a winning percentage of only 0.20
when playing against msu4life and has a winning percentage higher
than 0.2 when playing against other opponents, the program should
output the pair (playerID, toughestOpponent):
Wchampion, msu4life
For tie-breaking cases (i.e., if a player has more than one toughest op-
ponent with lowest winning percentage), the program may randomly
select one of the toughest opponents as its output. If a player has
won all of his/her games, then the program should output the pair
(playerID, NoToughestOpponent).
3. Write a Hadoop program that computes the correlation between pairs of
stocks from the stock market dataset stocks.csv provided on the class
webpage. Every line in the data file has the following information:
date, AAPL, BAC, C, F, GOOG, HMC, MSFT, TYM, WFC
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where the first column is the trading date, followed by the closing prices
for 9 different stocks (same data was used in homework 3). An example
of the output reducer file may look as follows:
(AAPL, BAC) 0.42344
Deliverable: Your hadoop source code (*.java), the archived (jar) file,
and the output result file (part-r-00000) that contains the pairwise corre-
lation values.
4. Use the Best Buy online search query data bbuy.csv from the class web-
page for this question. Each line in the query file has the following comma-
separated attribute values:
user,sku,category,query,click time,query time
where user is the id of user, sku is the id of the product page clicked on by
user, category is the product category, and query is the search string. For
this exercise, you need to write a Hadoop program that identifies the most
popular product page clicked on for each query. Specifically, the output
of the reducer must have 2 tab-separated columns: (query, sku), where
sku is the id of the product page most frequently clicked by users for a
given query.
Deliverable: Your hadoop source code (*.java), the archived (jar) files,
and the reducer output.
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