CSci415 Assignment 3 Due: Nov 7th, 11:59pm. You will also have to give a presentation/demo in class. In this programming assignment, you will practice parallel programming. You are recommended to form. a group of three, although individual work is allowed. Do not accept late submission. Objectives The goal of this programming assignment is to enable you to gain experience in: 1. Basic features of Hadoop distributed file system and MapReduce 2. Writing a program for finding common friends between all pairs of nodes in a large Network. Language: You are required to use Java as the implementation language. Final Submission and Demo: 3. Submit a report + code to blackboard You should hand in a (1) report which presents your implementation, (2) a README file explaining how to run your program, and of course (3) your source code and the executable files. 4. You need to demonstrate working of your implementation to the class on Demo Day. The grading will be based on all these parts. Total Marks: 100 --------------------------------------------------------------------------- Description In large-scale social networks, there are normally tens of millions of users. The task of this assignment is to implement a MapReduce program to identify common friends among all pairs of users. Let U be a set of all users: {U1, U2, ..., Un}. Then the goal is to find common friends for every pair of (Ui, Uj) where i ≠ j. The input files have the following format: A B C D E B A C D C A B D A B E E A D where the first token in the line is the user and the remaining tokens in the line are the friends. So, for line 1, A has four friends, B, C, D, and E. For example, A, and B have C, and D as their common friends. Also, A and E have only D as their common friend. Use the social network profiles provided that is attached with this assignment as input to your program. The network has been partitioned into three files: file01, file02, file03. The output will be stored in a file in the output directory. Below is a tutorial helping you getting started with the Hadoop installed in the department cluster. Getting Started with Hadoop Logging In First, make sure you can log in to the head node with SSH, currently at zoidberg.cs.ndsu.nodak.edu. You can log in to this server with your CS Domain password or your Blackboard password. Example: WordCount Before we jump into the details, lets walk through an example MapReduce application to get a flavor for how they work. Source Code import java.io.IOException; import java.util.StringTokenizer; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class WordCount { public static class TokenizerMapper extends Mapper{ private final static IntWritable ne = new IntWritable(1); private Text word = new Text(); public void map(Object key, Text value, Context context ) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); } } } public static class IntSumReducer extends Reducer { private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable values, Context context ) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); context.write(key, result); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, &"word count&"); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } } Usage Set environment variables: export JAVA_HOME=/path/to/your/jdk (path where java jdk is installed, e.g., export JAVA_HOME=&"/usr/lib/jvm/java-8-openjdk-amd64&") export HADOOP_CLASSPATH=${JAVA_HOME}/lib/tools.jar Compile WordCount.java and create a jar: $ bin/hadoop com.sun.tools.javac.Main WordCount.java (or hadoop com.sun.tools.javac.Main WordCount.java) $ jar cf wc.jar WordCount*.class Setting Up Input Files This program can use the Hadoop Distributed File System (HDFS) that is set up in the CS department. This file system spans all the Linux lab machines and provides distributed storage for use specifically with Hadoop. You can work with HDFS with UNIX-like file commands. The list of file commands can be found here. First, make a directory to store the input for the program (use your username). yourName@zoidberg:~$ hadoop fs -mkdir /user/yourName/wordcount yourName@zoidberg:~$ hadoop fs -mkdir /user/yourName/wordcount/input To set up input for the WordCount program, create two files as follows: file01: Hello World Bye World file02: Hello Hadoop Goodbye Hadoop Save these to your home folder on the head node. To move them into HDFS, use the following commands: yourName@zoidberg:~$ hadoop fs -copyFromLocal /home/yourName/file01 /user/yourName/wordcount/input/file01 yourName@zoidberg:~$ hadoop fs -copyFromLocal /home/yourName/file02 /user/yourName/wordcount/input/file02 Again, use your username where applicable. The syntax here is “hadoop fs -copyFromLocal ”, in this case were going to copy file01 from the local system into HDFS under our HDFS user directory into the wordcount/input/ directory. Running the WordCount Program You can now run the WordCount program using the following command: hadoop jar wc.jar WordCount /user/yourName/wordcount/input /user/yourName/wordcount/output The command syntax is: “hadoop jar ” In this case, we use the wc.jar JAR file, running the class WordCount with two parameters, an input directory and an output directory. The output directory must not already exist in HDFS, it will be created by the program. View Output You can check the output directory with: hadoop fs -ls /user/yourName/wordcount/output/ You should then see something similar to: yourName@zoidberg:~$ hadoop fs -ls /user/yourName/wordcount/output Found 2 items -rw-r--r-- 3 yourName nogroup 41 2011-11-08 11:23 /user/yourName/wordcount/output/part-r-00000 The part-r-00000 file contains the results of the word counting. You can look at the file using the cat command. yourName@zoidberg:~$ hadoop fs -cat /user/yourName/wordcount/output/part-r-00000 Bye 1 Goodbye 1 Hadoop 2 Hello 2 World 2 Another Example: Bigram The following example may help you write your assignment program. Bigrams are simply sequences of two consecutive words. For example, the previous sentence contains the following bigrams: &"Bigrams are&", &"are simply&", &"simply sequences&", &"sequences of&", etc. A Bigram Counting application that can be composed as a two-stage Map Reduce job. o The first stage counts bigrams. o The second stage MapReduce job takes the output of the first stage (bigram counts). You can read and run the attached Bigram source code to learn. (To run the program, you need to create input file(s). 转自：http://ass.3daixie.com/2018110618113583.html
