Tutorial: Creating a Hadoop Cluster with StarCluster on Amazon AWS

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--D. Thiebaut (talk) 09:40, 6 November 2013 (EST)
Updated --D. Thiebaut (talk) 13:47, 16 March 2017 (EDT)


Contents

AmazonAWS.jpgHadoopCartoon.png

This tutorial supersedes the previous two tutorials on this subject. It assumes that you have installed MIT's starcluster (see this tutorial for background material) on your local machine, and have obtained proper credentials to access the Amazon Web Services (AWS). This tutorial is also used to set up a Hadoop cluster in the CSC352 class on distributed processing taught Fall 2013 Spring 2017 at Smith College. Some information is pertinent to the students taking the class, and may not necessarily match other users setup.







Setup


  • If you have already installed starcluster on your local machine, and have already tested it on AWS, creating a hadoop cluster takes no time!
  • If you do not have starcluster on your laptop, please do this:
    • Follow the directions in this tutorial to setup Starcluster on your local machine.
    • Follow the directions presented in MIT's Starcluster documentation to add the hadoop plugin to the starcluster on your local machine.

This section is only visible to computers located at Smith College

  • Note that you should have created a new cluster definition in your starcluster config file for your hadoop cluster. You may want to set the default cluster definition to the new cluster. These are the lines in config that implement this:
DEFAULT_TEMPLATE=hadoopcluster

[cluster hadoopcluster]
plugins = hadoop
KEYNAME = mykeyABC       
CLUSTER_SIZE = 1
CLUSTER_USER = sgeadmin
CLUSTER_SHELL = bash
NODE_IMAGE_ID = ami-3393a45a
NODE_INSTANCE_TYPE = m3.medium
AVAILABILITY_ZONE = us-east-1c



Note 1: It is recommend to start with just 1 node in the cluster. Hadoop will work fine with one node, and it is less expensive to develop and test on 1 node first and deploy on a larger number once the application is fully debugged.



Starting the 1-Node Hadoop Cluster


Simply start the cluster using starcluster in a Terminal window of your laptop (replace ABC with your initials):

starcluster start myhadoopclusterABC
StarCluster - (http://star.mit.edu/cluster) (v. 0.95.6)
Software Tools for Academics and Researchers (STAR)
Please submit bug reports to starcluster@mit.edu

*** WARNING - Setting 'EC2_PRIVATE_KEY' from environment...
*** WARNING - Setting 'EC2_CERT' from environment...
>>> Using default cluster template: hadoopcluster
>>> Validating cluster template settings...
>>> Cluster template settings are valid
>>> Starting cluster...
>>> Launching a 1-node cluster...
>>> Creating security group @sc-hadoopcluster...
Reservation:r-0d83f6831c469e632
>>> Waiting for instances to propagate...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Waiting for cluster to come up... (updating every 30s)
>>> Waiting for all nodes to be in a 'running' state...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Waiting for SSH to come up on all nodes...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Waiting for cluster to come up took 1.246 mins
>>> The master node is ec2-54-204-245-225.compute-1.amazonaws.com
>>> Configuring cluster...
>>> Running plugin starcluster.clustersetup.DefaultClusterSetup
>>> Configuring hostnames...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Creating cluster user: sgeadmin (uid: 1001, gid: 1001)
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Configuring scratch space for user(s): sgeadmin
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Configuring /etc/hosts on each node
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Starting NFS server on master
>>> Setting up NFS took 0.028 mins
>>> Configuring passwordless ssh for root
>>> Configuring passwordless ssh for sgeadmin
>>> Running plugin starcluster.plugins.sge.SGEPlugin
>>> Configuring SGE...
>>> Setting up NFS took 0.000 mins
>>> Installing Sun Grid Engine...
>>> Creating SGE parallel environment 'orte'
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Adding parallel environment 'orte' to queue 'all.q'
>>> Running plugin hadoop
>>> Configuring Hadoop...
>>> Adding user sgeadmin to hadoop group
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Installing configuration templates...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Configuring environment...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Configuring MapReduce Site...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Configuring Core Site...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Configuring HDFS Site...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Configuring masters file...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Configuring slaves file...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Configuring HDFS...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Configuring dumbo...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Starting namenode...
>>> Starting secondary namenode...
>>> Starting datanode on all nodes...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Starting jobtracker...
>>> Starting tasktracker on all nodes...
1/1 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100%  
>>> Job tracker status: http://ec2-54-204-245-225.compute-1.amazonaws.com:50030
>>> Namenode status: http://ec2-54-204-245-225.compute-1.amazonaws.com:50070
>>> Configuring cluster took 3.356 mins
>>> Starting cluster took 4.653 mins

The cluster is now ready to use. To login to the master node
as root, run:

    $ starcluster sshmaster myhadoopclusterABC

If you're having issues with the cluster you can reboot the
instances and completely reconfigure the cluster from
scratch using:

    $ starcluster restart myhadoopclusterABC

When you're finished using the cluster and wish to terminate
it and stop paying for service:

    $ starcluster terminate myhadoopclusterABC

Alternatively, if the cluster uses EBS instances, you can
use the 'stop' command to shutdown all nodes and put them
into a 'stopped' state preserving the EBS volumes backing
the nodes:

    $ starcluster stop myhadoopclusterABC

WARNING: Any data stored in ephemeral storage (usually /mnt)
will be lost!

You can activate a 'stopped' cluster by passing the -x
option to the 'start' command:

    $ starcluster start -x myhadoopclusterABC

This will start all 'stopped' nodes and reconfigure the
cluster.


Note that the information displayed by starcluster shows status lines that show the hadoop environment being started in the form of

  • a MapReduce site,
  • an HDFS site,
  • namenodes,
  • secondary namenodes,
  • job trackers, and
  • tasks trackers.


Connecting to the Hadoop Cluster


  • This is done by ssh-ing from the Terminal window on your laptop to the master node of your AWS cluster. Starcluster understands the concept of a cluster in the same way MPI does, where there's always a master node and several worker nodes:
starcluster sshmaster myhadoopclusterABC
 StarCluster - (http://star.mit.edu/cluster) (v. 0.95.6)
 Software Tools for Academics and Researchers (STAR)
 Please submit bug reports to starcluster@mit.edu
 ...
 root@master:~#


  • We are now connected to the master of our 1-node cluster and ready to enter create and run our first Hadoop program.


Running our First Hadoop Program


The "hello world!" program of the MapReduce environment is a word-count program that takes one or several documents (typically books from the Gutenberg Web site) and computes the frequency of occurrence of each word.

We will run this program two different ways: the first is to use the wordcount program included in the examples folder of the hadoop distribution package. The second is to create the Wordcount.java program ourselves and go through the complete set of compilation and execution steps ourselves.

Running the Canned Wordcount Program


  • The steps required to run a MapReduce program on a set of files are as follows:
    1. create a folder in the Hadoop distributed file system (HDFS) for our data files
    2. upload the data files to the new folder
    3. run the MapReduce java program
    4. download the result file(s) from the HDFS and inspect them.


We now proceed to follow these steps.

Change to User sgeadmin


root@master# su - sgeadmin


Create data files


  • First we download a couple books from the Gutenberg.org project which contains many books on line, copyright free. Unfortunately the Gutenberg site prevents downloads coming from an AWS instance, as it assumes it is coming from a robot, so instead we'll download a couple of books from a local repository:
 sgeadmin@master:~$ wget http://cs.smith.edu/~dthiebaut/gutenberg/4300-8.txt
 sgeadmin@master:~$ wget http://cs.smith.edu/~dthiebaut/gutenberg/12241.txt

The first book is Ulysses., by James Joyce, and the second are Poems. by Emily Dickinson, 3rd series.

Put the data files in HDFS

  • run the following commands on the cluster:
sgeadmin@master:~$ hadoop dfs -mkdir books
sgeadmin@master:~$  hadoop dfs -copyFromLocal 4300-8.txt books
sgeadmin@master:~$  hadoop dfs -copyFromLocal 12241.txt books
sgeadmin@master:~$  hadoop dfs -ls books
Found 2 items
Found 2 items
-rw-r--r--   3 sgeadmin supergroup      78956 2017-03-18 13:47 /user/sgeadmin/books/12241.txt
-rw-r--r--   3 sgeadmin supergroup    1573082 2017-03-18 13:46 /user/sgeadmin/books/4300-8.txt


  • Good! Our data files are now in HDFS


Run the Program

  • Next we need to locate the hadoop distribution folder and in particular the example jar file:
sgeadmin@master:~$ find /usr -name "*hadoop-examples.jar" -print 2>&1 | grep -v Permission
/usr/lib/hadoop-0.20/hadoop-examples.jar
/usr/share/doc/hadoop-0.20/examples/hadoop-examples.jar
It's in /usr/lib/hadoop-0.20. We also just learnt that we are using hadoop Version 0.20.
  • Run the Wordcount program from the examples jar:
sgeadmin@master:~$ hadoop jar /usr/lib/hadoop-0.20/hadoop-examples.jar wordcount books output1
17/03/18 13:50:36 INFO input.FileInputFormat: Total input paths to process : 2
17/03/18 13:50:36 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java  classes where applicable
17/03/18 13:50:36 WARN snappy.LoadSnappy: Snappy native library not loaded
17/03/18 13:50:37 INFO mapred.JobClient: Running job: job_201703181316_0001
17/03/18 13:50:38 INFO mapred.JobClient:  map 0% reduce 0%
17/03/18 13:50:59 INFO mapred.JobClient:  map 100% reduce 0%
17/03/18 13:51:11 INFO mapred.JobClient:  map 100% reduce 33%
17/03/18 13:51:15 INFO mapred.JobClient:  map 100% reduce 100%
17/03/18 13:51:19 INFO mapred.JobClient: Job complete: job_201703181316_0001
17/03/18 13:51:19 INFO mapred.JobClient: Counters: 26
17/03/18 13:51:19 INFO mapred.JobClient:   Job Counters 
17/03/18 13:51:19 INFO mapred.JobClient:     Launched reduce tasks=1
...
17/03/18 13:51:19 INFO mapred.JobClient:     Reduce output records=51580
17/03/18 13:51:19 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=2232815616
17/03/18 13:51:19 INFO mapred.JobClient:     Map output records=280727

  • That's it! The two files have been processed. In the next section we take a look at the output file.


Check the Output


The output of the MapReduce application is always in a folder. The typical use for the folder is to download it or the result file inside it to your laptop, take a look at it or shape it for visualization, and delete the folder from the HDFS.

  • Below are steps necessary to look at the output
sgeadmin@master:~$ hadoop dfs -ls
Found 2 items
drwxr-xr-x   - sgeadmin supergroup          0 2017-03-18 13:47 /user/sgeadmin/books
drwxr-xr-x   - sgeadmin supergroup          0 2017-03-18 13:51 /user/sgeadmin/output1

sgeadmin@master:~$ hadoop dfs -ls output1
Found 3 items
-rw-r--r--   3 sgeadmin supergroup          0 2017-03-18 13:51 /user/sgeadmin/output1/_SUCCESS
drwxr-xr-x   - sgeadmin supergroup          0 2017-03-18 13:50 /user/sgeadmin/output1/_logs
-rw-r--r--   3 sgeadmin supergroup     543078 2017-03-18 13:51 /user/sgeadmin/output1/part-r-00000

  • We copy the output file from HDFS to our directory:
sgeadmin@master:~$ hadoop dfs -copyToLocal output1/part-r-00000 .
  • We clean up the HDFS of the output directory which we don't need any longer.
sgeadmin@master:~$ hadoop dfs -rmr output1
Deleted hdfs://master:54310/user/sgeadmin/output1
  • We look at some areas of the output file to verify that it lists words in alphabetical order, followed by a count of the number of times each word is found:
sgeadmin@master:~$ head part-r-00000 
"A      1
"Come   1
"Defects,"      2
"I      2
"Information    2
"J"     1
"Plain  4
"Project        10
"Right  2
"Viator"        1

sgeadmin@master:~$ tail part-r-00000 
zone    1
zones:  1
zoo.    1
zoological      1
zouave's        1
zrads,  2
zrads.  1 
�     7
�lus,_        1 
�tat_.        1

  • Looking at the book of poems by Emily Dickinson, we pick the editor's email address as a (somewhat) unique pattern to search for. Indeed, we see that it was picked up a couple times by the program:
sgeadmin@master:~$ grep -i "jtinsley@pobox.com" part-r-00000 
<jtinsley@pobox.com>    2


Copying Files to/from Your Laptop



Terminate the Cluster


  • Terminate the cluster, just to get familiar with this process. Remember to always turn off your cluster when you are done with a project, or else you keep on paying for hours of idle use!


  • First exit from your ssh connection:
sgeadmin@master:~$ exit
logout
root@master:~# exit
logout
  • Then, back at the prompt in your laptop terminal/console, terminate the cluster:
starcluster terminate hadoopclusterABC




Challenge # 1

QuestionMark3.jpg


Go through the same steps, download a couple more books (or other documents) from http://cs.smith.edu/~dthiebaut/gutenberg/ using the wget command, create a 2-node Starcluster MPI cluster with the hadoop plugin, upload your document to hdfs on your cluster, run the Wordcount application, and verify that you get a histogram of the occurrences of words in the documents.
For reference, here are some files mirrored from the gutenberg.org web site (which blocks accesses coming from AWS):

  • 12241.txt Poems: Third Series, by Emily Dickinson (78KB)
  • 1661.txt The Adventures of Sherlock Holmes (594KB)
  • 4300-8.txt Ulysses (1.5MB)
  • pg100.txt Complete Works of William Shakespeare (5.5GB)
  • pg10.txt The King James Bible (4.45GB)
  • pg135.txt Les Miserables, by Victor Hugo (3.3GB)




Running our Own Java WordCount Program


  • Running our own Java program as a MapReduce application requires a few more steps than the previous method. We have to
    1. upload the data to HDFS (we'll repeat the step we've followed it in the previous section)
    2. create the source file (in our case we'll copy it from the hadoop examples folder),
    3. figure out where the hadoop java library resides
    4. compile the java program and create a directory of java class files
    5. create the jar file
    6. run the program,
    7. download the result file(s)
    8. cleanup up the HDFS


Start the Cluster Again


  • Start the hadoop cluster again, as you did in the previous section
  • su to the sgeadmin user


Upload the Data to HDFS



Creating the Java Source


  • We will simply copy paste the code below (taken from Apache) into emacs and create our own copy of WordCount.java. A few lines have been edited (highlighted in yellow) from the original file.
sgeadmin@master:~$ emacs MyWordCount.java -nw


/**
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

//package org.apache.hadoop.examples;
package org.myorg;

import java.io.IOException;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

/**
 * This is an example Hadoop Map/Reduce application.
 * It reads the text input files, breaks each line into words
 * and counts them. The output is a locally sorted list of words and the
 * count of how often they occurred.
 *
 * To run: bin/hadoop jar build/hadoop-examples.jar wordcount
 *            [-m <i>maps</i>] [-r <i>reduces</i>] <i>in-dir</i> <i>out-dir</i>
 */
public class MyWordCount extends Configured implements Tool {
 
  /**
   * Counts the words in each line.
   * For each line of input, break the line into words and emit them as
   * (<b>word</b>, <b>1</b>).
   */
  public static class MapClass extends MapReduceBase
    implements Mapper<LongWritable, Text, Text, IntWritable> {
   
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
   
    public void map(LongWritable key, Text value,
                    OutputCollector<Text, IntWritable> output,
                    Reporter reporter) throws IOException {
      String line = value.toString();
      StringTokenizer itr = new StringTokenizer(line);
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        output.collect(word, one);
      }
    }
  }
 
  /**
   * A reducer class that just emits the sum of the input values.
   */
  public static class Reduce extends MapReduceBase
    implements Reducer<Text, IntWritable, Text, IntWritable> {
   
    public void reduce(Text key, Iterator<IntWritable> values,
                       OutputCollector<Text, IntWritable> output,
                       Reporter reporter) throws IOException {
      int sum = 0;
      while (values.hasNext()) {
        sum += values.next().get();
      }
      output.collect(key, new IntWritable(sum));
    }
  }
 
  static int printUsage() {
    System.out.println("wordcount [-m <maps>] [-r <reduces>] <input> <output>");
    ToolRunner.printGenericCommandUsage(System.out);
    return -1;
  }
 
  /**
   * The main driver for word count map/reduce program.
   * Invoke this method to submit the map/reduce job.
   * @throws IOException When there is communication problems with the
   *                     job tracker.
   */
  public int run(String[] args) throws Exception {
    JobConf conf = new JobConf(getConf(), MyWordCount.class);
    conf.setJobName("wordcount");
 
    // the keys are words (strings)
    conf.setOutputKeyClass(Text.class);
    // the values are counts (ints)
    conf.setOutputValueClass(IntWritable.class);
   
    conf.setMapperClass(MapClass.class);        
    conf.setCombinerClass(Reduce.class);
    conf.setReducerClass(Reduce.class);
   
    List<String> other_args = new ArrayList<String>();
    for(int i=0; i < args.length; ++i) {
      try {
        if ("-m".equals(args[i])) {
          conf.setNumMapTasks(Integer.parseInt(args[++i]));
        } else if ("-r".equals(args[i])) {
          conf.setNumReduceTasks(Integer.parseInt(args[++i]));
        } else {
          other_args.add(args[i]);
        }
      } catch (NumberFormatException except) {
        System.out.println("ERROR: Integer expected instead of " + args[i]);
        return printUsage();
      } catch (ArrayIndexOutOfBoundsException except) {
        System.out.println("ERROR: Required parameter missing from " +
                           args[i-1]);
        return printUsage();
      }
    }
    // Make sure there are exactly 2 parameters left.
    if (other_args.size() != 2) {
      System.out.println("ERROR: Wrong number of parameters: " +
                         other_args.size() + " instead of 2.");
      return printUsage();
    }
    FileInputFormat.setInputPaths(conf, other_args.get(0));
    FileOutputFormat.setOutputPath(conf, new Path(other_args.get(1)));
       
    JobClient.runJob(conf);
    return 0;
  }
 
 
  public static void main(String[] args) throws Exception {
    int res = ToolRunner.run(new Configuration(), new MyWordCount(), args);
    System.exit(res);
  }

}


Locating the Hadoop Core library

sgeadmin@master:~$ find /usr/lib -name "hadoop-core.jar" -print 2>&1 | grep -v denied
/usr/lib/hadoop-0.20/hadoop-core.jar


Compiling the MapReduce Application, Creating the JAR File


Now that we know where the hadoop library is, we can compile our program against this library:

sgeadmin@master:~$   mkdir MyWordCount_classes

sgeadmin@master:~$   javac -classpath /usr/lib/hadoop-0.20/hadoop-core.jar -d MyWordCount_classes \
 MyWordCount.java

sgeadmin@master:~$  jar -cvf MyWordCount.jar -C MyWordCount_classes/ .
added manifest
adding: org/(in = 0) (out= 0)(stored 0%)
adding: org/myorg/(in = 0) (out= 0)(stored 0%)
adding: org/myorg/MyWordCount$MapClass.class(in = 1954) (out= 805)(deflated 58%)
adding: org/myorg/MyWordCount.class(in = 3324) (out= 1679)(deflated 49%)
adding: org/myorg/MyWordCount$Reduce.class(in = 1617) (out= 651)(deflated 59%)

We now have the jar file in our directory:

sgeadmin@master:~$  ls -ltr | tail -1
-rw-rw-r-- 1 sgeadmin sgeadmin 4130 Mar 18 15:38 MyWordCount.jar


Running the MapReduce Application


sgeadmin@master:~$  hadoop jar MyWordCount.jar org.myorg.MyWordCount books output2
17/03/18 15:47:20 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/03/18 15:47:20 WARN snappy.LoadSnappy: Snappy native library not loaded
17/03/18 15:47:20 INFO mapred.FileInputFormat: Total input paths to process : 3
17/03/18 15:47:21 INFO mapred.JobClient: Running job: job_201703181531_0002
17/03/18 15:47:22 INFO mapred.JobClient:  map 0% reduce 0%
17/03/18 15:47:45 INFO mapred.JobClient:  map 50% reduce 0%
 ...
17/03/18 15:48:17 INFO mapred.JobClient:     Reduce output records=58671
17/03/18 15:48:17 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=3715158016
17/03/18 15:48:17 INFO mapred.JobClient:     Map output records=388260


Check the Output


We proceed as we did in the previous section:

sgeadmin@master:~$ hadoop dfs -ls
Found 2 items
drwxr-xr-x   - sgeadmin supergroup          0 2017-03-18 15:46 /user/sgeadmin/books
drwxr-xr-x   - sgeadmin supergroup          0 2017-03-18 15:48 /user/sgeadmin/output2

sgeadmin@master:~$ hadoop dfs -ls output2
Found 3 items
-rw-r--r--   3 sgeadmin supergroup          0 2017-03-18 15:48 /user/sgeadmin/output2/_SUCCESS
drwxr-xr-x   - sgeadmin supergroup          0 2017-03-18 15:47 /user/sgeadmin/output2/_logs
-rw-r--r--   3 sgeadmin supergroup     623321 2017-03-18 15:48 /user/sgeadmin/output2/part-00000

sgeadmin@master:/data/hadoop# hadoop dfs -copyToLocal output2/part-00000 .

sgeadmin@master:/data/hadoop# hadoop dfs -rmr output2
Deleted hdfs://master:54310/user/root/output2
 
sgeadmin@master:~$ head -n 10 part-00000; tail -n 10 part-00000
"'A	1
"'About	1
"'Absolute	1
"'Ah!'	2
"'Ah,	2
"'Ample.'	1
"'And	10
"'Arthur!'	1
"'As	1
"'At	1
zones:	1
zoo.	1
zoological	1
zouave's	1
zrads,	2
zrads.	1
Project	1 
�	 7
�lus,_	1
�tat_.	1

Terminate The Hadoop Cluster

Exit from the SSH session to the Master node, and terminate the cluster.

sgeadmin@master:~$ exit
logout
root@master:~# exit
logout
Connection to ec2-23-23-0-53.compute-1.amazonaws.com closed.

[11:54:09] ~$: starcluster terminate hadoopcluster
StarCluster - (http://star.mit.edu/cluster) (v. 0.95.6)
Software Tools for Academics and Researchers (STAR)
Please submit bug reports to starcluster@mit.edu
 




Challenge # 2

QuestionMark1.jpg


A MapReduce applications is essentially the combination of two functions, a map() function, and a reduce() function. Take a look at how these two functions together, with the MapReduce infrastructure of Hadoop, perform the computation of a histogram (which is really what the wordcount program generates). Map() gets single words from the document(s) and generates tuples of the form (word, 1) that it stores in a tuple world. These tuples are automatically sorted by their first field, which is a word from the document, and passed on as blocks of tuples with identical first field to reduce() which counts how many tuples have identical first field and outputs the result in the output file as a line containing the word first, and the count second.

Modify either map() or reduce() so that your word count program outputs only words that start with an uppercase letter. Verify that your program works.

Hints: Hadoop introduces a lot of new types. One is Text, that is Hadoop's version of the Java String. You can go from a Text variable to a String variable using the toString() method.

Text word;
String s;
s = word.toString();

To go from String to Text, you can do this:

Text word;
String s;
word = new Text( s );

Stack-Overflow has a good recipe for testing if the first character of a String is uppercase:

String s;

if ( Character.isUpperCase(s.charAt( 0 ) ) ) {
    ...
}




...