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<!DOCTYPE document PUBLIC "-//APACHE//DTD Documentation V2.0//EN" "http://forrest.apache.org/dtd/document-v20.dtd">

<document>
  
  <header>
    <title>Hadoop Map-Reduce Tutorial</title>
  </header>
  
  <body>
  
    <section>
      <title>Purpose</title>
      
      <p>This document comprehensively describes all user-facing facets of the 
      Hadoop Map-Reduce framework and serves as a tutorial.
      </p>
    </section>
    
    <section>
      <title>Pre-requisites</title>
      
      <p>Ensure that Hadoop is installed, configured and is running. More
      details:</p> 
      <ul>
        <li>
          Hadoop <a href="quickstart.html">Quickstart</a> for first-time users.
        </li>
        <li>
          Hadoop <a href="cluster_setup.html">Cluster Setup</a> for large, 
          distributed clusters.
        </li>
      </ul>
    </section>
    
    <section>
      <title>Overview</title>
      
      <p>Hadoop Map-Reduce is a software framework for easily writing 
      applications which process vast amounts of data (multi-terabyte data-sets) 
      in-parallel on large clusters (thousands of nodes) of commodity 
      hardware in a reliable, fault-tolerant manner.</p>
      
      <p>A Map-Reduce <em>job</em> usually splits the input data-set into 
      independent chunks which are processed by the <em>map tasks</em> in a
      completely parallel manner. The framework sorts the outputs of the maps, 
      which are then input to the <em>reduce tasks</em>. Typically both the 
      input and the output of the job are stored in a file-system. The framework 
      takes care of scheduling tasks, monitoring them and re-executes the failed
      tasks.</p>
      
      <p>Typically the compute nodes and the storage nodes are the same, that is, 
      the Map-Reduce framework and the <a href="hdfs_design.html">Distributed 
      FileSystem</a> are running on the same set of nodes. This configuration
      allows the framework to effectively schedule tasks on the nodes where data 
      is already present, resulting in very high aggregate bandwidth across the 
      cluster.</p>
      
      <p>The Map-Reduce framework consists of a single master 
      <code>JobTracker</code> and one slave <code>TaskTracker</code> per 
      cluster-node. The master is responsible for scheduling the jobs' component 
      tasks on the slaves, monitoring them and re-executing the failed tasks. The 
      slaves execute the tasks as directed by the master.</p>
      
      <p>Minimally, applications specify the input/output locations and supply
      <em>map</em> and <em>reduce</em> functions via implementations of
      appropriate interfaces and/or abstract-classes. These, and other job 
      parameters, comprise the <em>job configuration</em>. The Hadoop 
      <em>job client</em> then submits the job (jar/executable etc.) and 
      configuration to the <code>JobTracker</code> which then assumes the 
      responsibility of distributing the software/configuration to the slaves, 
      scheduling tasks and monitoring them, providing status and diagnostic 
      information to the job-client.</p>
      
      <p>Although the Hadoop framework is implemented in Java<sup>TM</sup>, 
      Map-Reduce applications need not be written in Java.</p>
      <ul>
        <li>
          <a href="ext:api/org/apache/hadoop/streaming/package-summary">
          Hadoop Streaming</a> is a utility which allows users to create and run 
          jobs with any executables (e.g. shell utilities) as the mapper and/or 
          the reducer.
        </li>
        <li>
          <a href="ext:api/org/apache/hadoop/mapred/pipes/package-summary">
          Hadoop Pipes</a> is a <a href="http://www.swig.org/">SWIG</a>-
          compatible <em>C++ API</em> to implement Map-Reduce applications (non 
          JNI<sup>TM</sup> based).
        </li>
      </ul>
    </section>
    
    <section>
      <title>Inputs and Outputs</title>

      <p>The Map-Reduce framework operates exclusively on 
      <code>&lt;key, value&gt;</code> pairs, that is, the framework views the 
      input to the job as a set of <code>&lt;key, value&gt;</code> pairs and 
      produces a set of <code>&lt;key, value&gt;</code> pairs as the output of 
      the job, conceivably of different types.</p> 
      
      <p>The <code>key</code> and <code>value</code> classes have to be 
      serializable by the framework and hence need to implement the 
      <a href="ext:api/org/apache/hadoop/io/writable">Writable</a> 
      interface. Additionally, the <code>key</code> classes have to implement the
      <a href="ext:api/org/apache/hadoop/io/writablecomparable">
      WritableComparable</a> interface to facilitate sorting by the framework.
      </p>

      <p>Input and Output types of a Map-Reduce job:</p>
      <p>
        (input) <code>&lt;k1, v1&gt;</code> 
        -&gt; 
        <strong>map</strong> 
        -&gt; 
        <code>&lt;k2, v2&gt;</code> 
        -&gt; 
        <strong>combine</strong> 
        -&gt; 
        <code>&lt;k2, v2&gt;</code> 
        -&gt; 
        <strong>reduce</strong> 
        -&gt; 
        <code>&lt;k3, v3&gt;</code> (output)
      </p>
    </section>

    <section>
      <title>Example: WordCount v1.0</title>
      
      <p>Before we jump into the details, lets walk through an example Map-Reduce 
      application to get a flavour for how they work.</p>
      
      <p><code>WordCount</code> is a simple application that counts the number of
      occurences of each word in a given input set.</p>
      
      <p>This works with a 
      <a href="quickstart.html#Standalone+Operation">local-standalone</a>,
      <a href="quickstart.html#SingleNodeSetup">pseudo-distributed</a> or
      <a href="quickstart.html#Fully-Distributed+Operation">fully-distributed</a> 
      Hadoop installation.</p>
      
      <section>
        <title>Source Code</title>
        
        <table>
          <tr>
            <th></th>
            <th>WordCount.java</th>
          </tr>
          <tr>
            <td>1.</td>
            <td>
              <code>package org.myorg;</code>
            </td>
          </tr>
          <tr>
            <td>2.</td>
            <td></td>
          </tr>
          <tr>
            <td>3.</td>
            <td>
              <code>import java.io.IOException;</code>
            </td>
          </tr>
          <tr>
            <td>4.</td>
            <td>
              <code>import java.util.*;</code>
            </td>
          </tr>
          <tr>
            <td>5.</td>
            <td></td>
          </tr>
          <tr>
            <td>6.</td>
            <td>
              <code>import org.apache.hadoop.fs.Path;</code>
            </td>
          </tr>
          <tr>
            <td>7.</td>
            <td>
              <code>import org.apache.hadoop.conf.*;</code>
            </td>
          </tr>
          <tr>
            <td>8.</td>
            <td>
              <code>import org.apache.hadoop.io.*;</code>
            </td>
          </tr>
          <tr>
            <td>9.</td>
            <td>
              <code>import org.apache.hadoop.mapred.*;</code>
            </td>
          </tr>
          <tr>
            <td>10.</td>
            <td>
              <code>import org.apache.hadoop.util.*;</code>
            </td>
          </tr>
          <tr>
            <td>11.</td>
            <td></td>
          </tr>
          <tr>
            <td>12.</td>
            <td>
              <code>public class WordCount {</code>
            </td>
          </tr>
          <tr>
            <td>13.</td>
            <td></td>
          </tr>
          <tr>
            <td>14.</td>
            <td>
              &nbsp;&nbsp;
              <code>
                public static class Map extends MapReduceBase 
                implements Mapper&lt;LongWritable, Text, Text, IntWritable&gt; {
              </code>
            </td>
          </tr>
          <tr>
            <td>15.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                private final static IntWritable one = new IntWritable(1);
              </code>
            </td>
          </tr>
          <tr>
            <td>16.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>private Text word = new Text();</code>
            </td>
          </tr>
          <tr>
            <td>17.</td>
            <td></td>
          </tr>
          <tr>
            <td>18.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                public void map(LongWritable key, Text value, 
                OutputCollector&lt;Text, IntWritable&gt; output, 
                Reporter reporter) throws IOException {
              </code>
            </td>
          </tr>
          <tr>
            <td>19.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>String line = value.toString();</code>
            </td>
          </tr>
          <tr>
            <td>20.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>StringTokenizer tokenizer = new StringTokenizer(line);</code>
            </td>
          </tr>
          <tr>
            <td>21.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>while (tokenizer.hasMoreTokens()) {</code>
            </td>
          </tr>
          <tr>
            <td>22.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>word.set(tokenizer.nextToken());</code>
            </td>
          </tr>
          <tr>
            <td>23.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>output.collect(word, one);</code>
            </td>
          </tr>
          <tr>
            <td>24.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>25.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>26.</td>
            <td>
              &nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>27.</td>
            <td></td>
          </tr>
          <tr>
            <td>28.</td>
            <td>
              &nbsp;&nbsp;
              <code>
                public static class Reduce extends MapReduceBase implements 
                Reducer&lt;Text, IntWritable, Text, IntWritable&gt; {
              </code>
            </td>
          </tr>
          <tr>
            <td>29.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                public void reduce(Text key, Iterator&lt;IntWritable&gt; values,
                OutputCollector&lt;Text, IntWritable&gt; output, 
                Reporter reporter) throws IOException {
              </code>
            </td>
          </tr>
          <tr>
            <td>30.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>int sum = 0;</code>
            </td>
          </tr>
          <tr>
            <td>31.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>while (values.hasNext()) {</code>
            </td>
          </tr>
          <tr>
            <td>32.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>sum += values.next().get();</code>
            </td>
          </tr>
          <tr>
            <td>33.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>34.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>output.collect(key, new IntWritable(sum));</code>
            </td>
          </tr>
          <tr>
            <td>35.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>36.</td>
            <td>
              &nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>37.</td>
            <td></td>
          </tr>
          <tr>
            <td>38.</td>
            <td>
              &nbsp;&nbsp;
              <code>
                public static void main(String[] args) throws Exception {
              </code>
            </td>
          </tr>
          <tr>
            <td>39.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                JobConf conf = new JobConf(WordCount.class);
              </code>
            </td>
          </tr>
          <tr>
            <td>40.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setJobName("wordcount");</code>
            </td>
          </tr>
          <tr>
            <td>41.</td>
            <td></td>
          </tr>
          <tr>
            <td>42.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setOutputKeyClass(Text.class);</code>
            </td>
          </tr>
          <tr>
            <td>43.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setOutputValueClass(IntWritable.class);</code>
            </td>
          </tr>
          <tr>
            <td>44.</td>
            <td></td>
          </tr>
          <tr>
            <td>45.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setMapperClass(Map.class);</code>
            </td>
          </tr>
          <tr>
            <td>46.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setCombinerClass(Reduce.class);</code>
            </td>
          </tr>
          <tr>
            <td>47.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setReducerClass(Reduce.class);</code>
            </td>
          </tr>
          <tr>
            <td>48.</td>
            <td></td>
          </tr>
          <tr>
            <td>49.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setInputFormat(TextInputFormat.class);</code>
            </td>
          </tr>
          <tr>
            <td>50.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setOutputFormat(TextOutputFormat.class);</code>
            </td>
          </tr>
          <tr>
            <td>51.</td>
            <td></td>
          </tr>
          <tr>
            <td>52.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>FileInputFormat.setInputPaths(conf, new Path(args[0]));</code>
            </td>
          </tr>
          <tr>
            <td>53.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>FileOutputFormat.setOutputPath(conf, new Path(args[1]));</code>
            </td>
          </tr>
          <tr>
            <td>54.</td>
            <td></td>
          </tr>
          <tr>
            <td>55.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>JobClient.runJob(conf);</code>
            </td>
          </tr>
          <tr>
            <td>57.</td>
            <td>
              &nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>58.</td>
            <td>
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>59.</td>
            <td></td>
          </tr>
        </table>
      </section>
        
      <section>
        <title>Usage</title>
        
        <p>Assuming <code>HADOOP_HOME</code> is the root of the installation and 
        <code>HADOOP_VERSION</code> is the Hadoop version installed, compile 
        <code>WordCount.java</code> and create a jar:</p>
        <p>
          <code>$ mkdir wordcount_classes</code><br/>
          <code>
            $ javac -classpath ${HADOOP_HOME}/hadoop-${HADOOP_VERSION}-core.jar 
              -d wordcount_classes WordCount.java
          </code><br/>
          <code>$ jar -cvf /usr/joe/wordcount.jar -C wordcount_classes/ .</code> 
        </p>
        
        <p>Assuming that:</p>
        <ul>
          <li>
            <code>/usr/joe/wordcount/input</code>  - input directory in HDFS
          </li>
          <li>
            <code>/usr/joe/wordcount/output</code> - output directory in HDFS
          </li>
        </ul>
        
        <p>Sample text-files as input:</p>
        <p>
          <code>$ bin/hadoop dfs -ls /usr/joe/wordcount/input/</code><br/>
          <code>/usr/joe/wordcount/input/file01</code><br/>
          <code>/usr/joe/wordcount/input/file02</code><br/>
          <br/>
          <code>$ bin/hadoop dfs -cat /usr/joe/wordcount/input/file01</code><br/>
          <code>Hello World Bye World</code><br/>
          <br/>
          <code>$ bin/hadoop dfs -cat /usr/joe/wordcount/input/file02</code><br/>
          <code>Hello Hadoop Goodbye Hadoop</code>
        </p>

        <p>Run the application:</p>
        <p>
          <code>
            $ bin/hadoop jar /usr/joe/wordcount.jar org.myorg.WordCount 
              /usr/joe/wordcount/input /usr/joe/wordcount/output 
          </code>
        </p>

        <p>Output:</p>
        <p>
          <code>
            $ bin/hadoop dfs -cat /usr/joe/wordcount/output/part-00000
          </code>
          <br/>
          <code>Bye    1</code><br/>
          <code>Goodbye    1</code><br/>
          <code>Hadoop    2</code><br/>
          <code>Hello    2</code><br/>
          <code>World    2</code><br/>
        </p>
      </section>
      
      <section>
        <title>Walk-through</title>
        
        <p>The <code>WordCount</code> application is quite straight-forward.</p>
        
        <p>The <code>Mapper</code> implementation (lines 14-26), via the 
        <code>map</code> method (lines 18-25), processes one line at a time,
        as provided by the specified <code>TextInputFormat</code> (line 49). 
        It then splits the line into tokens separated by whitespaces, via the 
        <code>StringTokenizer</code>, and emits a key-value pair of 
        <code>&lt; &lt;word&gt;, 1&gt;</code>.</p>
        
        <p>
          For the given sample input the first map emits:<br/>
          <code>&lt; Hello, 1&gt;</code><br/>
          <code>&lt; World, 1&gt;</code><br/>
          <code>&lt; Bye, 1&gt;</code><br/>
          <code>&lt; World, 1&gt;</code><br/>
        </p>
        
        <p>
          The second map emits:<br/>
          <code>&lt; Hello, 1&gt;</code><br/>
          <code>&lt; Hadoop, 1&gt;</code><br/>
          <code>&lt; Goodbye, 1&gt;</code><br/>
          <code>&lt; Hadoop, 1&gt;</code><br/>
        </p>
        
        <p>We'll learn more about the number of maps spawned for a given job, and
        how to control them in a fine-grained manner, a bit later in the 
        tutorial.</p>
        
        <p><code>WordCount</code> also specifies a <code>combiner</code> (line 
        46). Hence, the output of each map is passed through the local combiner 
        (which is same as the <code>Reducer</code> as per the job 
        configuration) for local aggregation, after being sorted on the 
        <em>key</em>s.</p>

        <p>
          The output of the first map:<br/>
          <code>&lt; Bye, 1&gt;</code><br/>
          <code>&lt; Hello, 1&gt;</code><br/>
          <code>&lt; World, 2&gt;</code><br/>
        </p>
        
        <p>
          The output of the second map:<br/>
          <code>&lt; Goodbye, 1&gt;</code><br/>
          <code>&lt; Hadoop, 2&gt;</code><br/>
          <code>&lt; Hello, 1&gt;</code><br/>
        </p>

        <p>The <code>Reducer</code> implementation (lines 28-36), via the
        <code>reduce</code> method (lines 29-35) just sums up the values,
        which are the occurence counts for each key (i.e. words in this example).
        </p>
        
        <p>
          Thus the output of the job is:<br/>
          <code>&lt; Bye, 1&gt;</code><br/>
          <code>&lt; Goodbye, 1&gt;</code><br/>
          <code>&lt; Hadoop, 2&gt;</code><br/>
          <code>&lt; Hello, 2&gt;</code><br/>
          <code>&lt; World, 2&gt;</code><br/>
        </p>
        
        <p>The <code>run</code> method specifies various facets of the job, such 
        as the input/output paths (passed via the command line), key/value 
        types, input/output formats etc., in the <code>JobConf</code>.
        It then calls the <code>JobClient.runJob</code> (line  55) to submit the
        and monitor its progress.</p>

        <p>We'll learn more about <code>JobConf</code>, <code>JobClient</code>,
        <code>Tool</code> and other interfaces and classes a bit later in the 
        tutorial.</p>
      </section>
    </section>
    
    <section>
      <title>Map-Reduce - User Interfaces</title>
      
      <p>This section provides a reasonable amount of detail on every user-facing 
      aspect of the Map-Reduce framwork. This should help users implement, 
      configure and tune their jobs in a fine-grained manner. However, please 
      note that the javadoc for each class/interface remains the most 
      comprehensive documentation available; this is only meant to be a tutorial.
      </p>
      
      <p>Let us first take the <code>Mapper</code> and <code>Reducer</code> 
      interfaces. Applications typically implement them to provide the 
      <code>map</code> and <code>reduce</code> methods.</p>
      
      <p>We will then discuss other core interfaces including 
      <code>JobConf</code>, <code>JobClient</code>, <code>Partitioner</code>, 
      <code>OutputCollector</code>, <code>Reporter</code>, 
      <code>InputFormat</code>, <code>OutputFormat</code> and others.</p>
      
      <p>Finally, we will wrap up by discussing some useful features of the
      framework such as the <code>DistributedCache</code>, 
      <code>IsolationRunner</code> etc.</p>

      <section>
        <title>Payload</title>
        
        <p>Applications typically implement the <code>Mapper</code> and 
        <code>Reducer</code> interfaces to provide the <code>map</code> and 
        <code>reduce</code> methods. These form the core of the job.</p>
        
        <section>
          <title>Mapper</title>

          <p><a href="ext:api/org/apache/hadoop/mapred/mapper">
          Mapper</a> maps input key/value pairs to a set of intermediate 
          key/value pairs.</p>
 
          <p>Maps are the individual tasks that transform input records into 
          intermediate records. The transformed intermediate records do not need
          to be of the same type as the input records. A given input pair may 
          map to zero or many output pairs.</p> 
 
          <p>The Hadoop Map-Reduce framework spawns one map task for each 
          <code>InputSplit</code> generated by the <code>InputFormat</code> for 
          the job.</p>
          
          <p>Overall, <code>Mapper</code> implementations are passed the 
          <code>JobConf</code> for the job via the 
          <a href="ext:api/org/apache/hadoop/mapred/jobconfigurable/configure">
          JobConfigurable.configure(JobConf)</a> method and override it to 
          initialize themselves. The framework then calls 
          <a href="ext:api/org/apache/hadoop/mapred/mapper/map">
          map(WritableComparable, Writable, OutputCollector, Reporter)</a> for 
          each key/value pair in the <code>InputSplit</code> for that task.        
          Applications can then override the
          <a href="ext:api/org/apache/hadoop/io/closeable/close">
          Closeable.close()</a> method to perform any required cleanup.</p>
 

          <p>Output pairs do not need to be of the same types as input pairs. A 
          given input pair may map to zero or many output pairs.  Output pairs 
          are collected with calls to 
          <a href="ext:api/org/apache/hadoop/mapred/outputcollector/collect">
          OutputCollector.collect(WritableComparable,Writable)</a>.</p>

          <p>Applications can use the <code>Reporter</code> to report 
          progress, set application-level status messages and update 
          <code>Counters</code>, or just indicate that they are alive.</p>
 
          <p>All intermediate values associated with a given output key are 
          subsequently grouped by the framework, and passed to the
          <code>Reducer</code>(s) to  determine the final output. Users can 
          control the grouping by specifying a <code>Comparator</code> via 
          <a href="ext:api/org/apache/hadoop/mapred/jobconf/setoutputkeycomparatorclass">
          JobConf.setOutputKeyComparatorClass(Class)</a>.</p>

          <p>The <code>Mapper</code> outputs are sorted and then 
          partitioned per <code>Reducer</code>. The total number of partitions is 
          the same as the number of reduce tasks for the job. Users can control 
          which keys (and hence records) go to which <code>Reducer</code> by 
          implementing a custom <code>Partitioner</code>.</p>
 
          <p>Users can optionally specify a <code>combiner</code>, via 
          <a href="ext:api/org/apache/hadoop/mapred/jobconf/setcombinerclass">
          JobConf.setCombinerClass(Class)</a>, to perform local aggregation of 
          the intermediate outputs, which helps to cut down the amount of data 
          transferred from the <code>Mapper</code> to the <code>Reducer</code>.
          </p>
 
          <p>The intermediate, sorted outputs are always stored in files of 
          <a href="ext:api/org/apache/hadoop/io/sequencefile">
          SequenceFile</a> format. Applications can control if, and how, the 
          intermediate outputs are to be compressed and the 
          <a href="ext:api/org/apache/hadoop/io/compress/compressioncodec">
          CompressionCodec</a> to be used via the <code>JobConf</code>.
          </p>
          
          <section>
            <title>How Many Maps?</title>
             
            <p>The number of maps is usually driven by the total size of the 
            inputs, that is, the total number of blocks of the input files.</p>
  
            <p>The right level of parallelism for maps seems to be around 10-100 
            maps per-node, although it has been set up to 300 maps for very 
            cpu-light map tasks. Task setup takes awhile, so it is best if the 
            maps take at least a minute to execute.</p>
 
            <p>Thus, if you expect 10TB of input data and have a blocksize of 
            <code>128MB</code>, you'll end up with 82,000 maps, unless 
            <a href="ext:api/org/apache/hadoop/mapred/jobconf/setnummaptasks">
            setNumMapTasks(int)</a> (which only provides a hint to the framework) 
            is used to set it even higher.</p>
          </section>
        </section>
        
        <section>
          <title>Reducer</title>
          
          <p><a href="ext:api/org/apache/hadoop/mapred/reducer">
          Reducer</a> reduces a set of intermediate values which share a key to
          a smaller set of values.</p>
          
          <p>The number of reduces for the job is set by the user 
          via <a href="ext:api/org/apache/hadoop/mapred/jobconf/setnumreducetasks">
          JobConf.setNumReduceTasks(int)</a>.</p>
          
          <p>Overall, <code>Reducer</code> implementations are passed the 
          <code>JobConf</code> for the job via the 
          <a href="ext:api/org/apache/hadoop/mapred/jobconfigurable/configure">
          JobConfigurable.configure(JobConf)</a> method and can override it to 
          initialize themselves. The framework then calls   
          <a href="ext:api/org/apache/hadoop/mapred/reducer/reduce">
          reduce(WritableComparable, Iterator, OutputCollector, Reporter)</a>
          method for each <code>&lt;key, (list of values)&gt;</code> 
          pair in the grouped inputs. Applications can then override the           
          <a href="ext:api/org/apache/hadoop/io/closeable/close">
          Closeable.close()</a> method to perform any required cleanup.</p>

          <p><code>Reducer</code> has 3 primary phases: shuffle, sort and reduce.
          </p>
          
          <section>
            <title>Shuffle</title>
   
            <p>Input to the <code>Reducer</code> is the sorted output of the
            mappers. In this phase the framework fetches the relevant partition 
            of the output of all the mappers, via HTTP.</p>
          </section>
   
          <section>
            <title>Sort</title>
   
            <p>The framework groups <code>Reducer</code> inputs by keys (since 
            different mappers may have output the same key) in this stage.</p>
   
            <p>The shuffle and sort phases occur simultaneously; while 
            map-outputs are being fetched they are merged.</p>
      
            <section>
              <title>Secondary Sort</title>
   
              <p>If equivalence rules for grouping the intermediate keys are 
              required to be different from those for grouping keys before 
              reduction, then one may specify a <code>Comparator</code> via 
              <a href="ext:api/org/apache/hadoop/mapred/jobconf/setoutputvaluegroupingcomparator">
              JobConf.setOutputValueGroupingComparator(Class)</a>. Since 
              <a href="ext:api/org/apache/hadoop/mapred/jobconf/setoutputkeycomparatorclass">
              JobConf.setOutputKeyComparatorClass(Class)</a> can be used to 
              control how intermediate keys are grouped, these can be used in 
              conjunction to simulate <em>secondary sort on values</em>.</p>
            </section>
          </section>
   
          <section>   
            <title>Reduce</title>
   
            <p>In this phase the 
            <a href="ext:api/org/apache/hadoop/mapred/reducer/reduce">
            reduce(WritableComparable, Iterator, OutputCollector, Reporter)</a>
            method is called for each <code>&lt;key, (list of values)&gt;</code> 
            pair in the grouped inputs.</p>
            
            <p>The output of the reduce task is typically written to the 
            <a href="ext:api/org/apache/hadoop/fs/filesystem">
            FileSystem</a> via 
            <a href="ext:api/org/apache/hadoop/mapred/outputcollector/collect">
            OutputCollector.collect(WritableComparable, Writable)</a>.</p>
   
            <p>Applications can use the <code>Reporter</code> to report 
            progress, set application-level status messages and update 
            <code>Counters</code>, or just indicate that they are alive.</p>
 
           <p>The output of the <code>Reducer</code> is <em>not sorted</em>.</p>
          </section>
          
          <section>
            <title>How Many Reduces?</title>
 
            <p>The right number of reduces seems to be <code>0.95</code> or 
            <code>1.75</code> multiplied by (&lt;<em>no. of nodes</em>&gt; * 
            <code>mapred.tasktracker.reduce.tasks.maximum</code>).</p>
 
            <p>With <code>0.95</code> all of the reduces can launch immediately 
            and start transfering map outputs as the maps finish. With 
            <code>1.75</code> the faster nodes will finish their first round of 
            reduces and launch a second wave of reduces doing a much better job 
            of load balancing.</p>
 
            <p>Increasing the number of reduces increases the framework overhead, 
            but increases load balancing and lowers the cost of failures.</p>
 
            <p>The scaling factors above are slightly less than whole numbers to 
            reserve a few reduce slots in the framework for speculative-tasks and
            failed tasks.</p>
          </section>
          
          <section>
            <title>Reducer NONE</title>
            
            <p>It is legal to set the number of reduce-tasks to <em>zero</em> if 
            no reduction is desired.</p>
 
            <p>In this case the outputs of the map-tasks go directly to the
            <code>FileSystem</code>, into the output path set by 
            <a href="ext:api/org/apache/hadoop/mapred/fileoutputformat/setoutputpath">
            setOutputPath(Path)</a>. The framework does not sort the 
            map-outputs before writing them out to the <code>FileSystem</code>.
            </p>
          </section>
        </section>
        
        <section>
          <title>Partitioner</title>
          
          <p><a href="ext:api/org/apache/hadoop/mapred/partitioner">
          Partitioner</a> partitions the key space.</p>

          <p>Partitioner controls the partitioning of the keys of the 
          intermediate map-outputs. The key (or a subset of the key) is used to 
          derive the partition, typically by a <em>hash function</em>. The total 
          number of partitions is the same as the number of reduce tasks for the 
          job. Hence this controls which of the <code>m</code> reduce tasks the 
          intermediate key (and hence the record) is sent to for reduction.</p>
          
          <p><a href="ext:api/org/apache/hadoop/mapred/lib/hashpartitioner">
          HashPartitioner</a> is the default <code>Partitioner</code>.</p>
        </section>
        
        <section>
          <title>Reporter</title>
        
          <p><a href="ext:api/org/apache/hadoop/mapred/reporter">
          Reporter</a> is a facility for Map-Reduce applications to report 
          progress, set application-level status messages and update 
          <code>Counters</code>.</p>
 
          <p><code>Mapper</code> and <code>Reducer</code> implementations can use 
          the <code>Reporter</code> to report progress or just indicate 
          that they are alive. In scenarios where the application takes a
          significant amount of time to process individual key/value pairs, 
          this is crucial since the framework might assume that the task has 
          timed-out and kill that task. Another way to avoid this is to 
          set the configuration parameter <code>mapred.task.timeout</code> to a
          high-enough value (or even set it to <em>zero</em> for no time-outs).
          </p>

          <p>Applications can also update <code>Counters</code> using the 
          <code>Reporter</code>.</p>
        </section>
      
        <section>
          <title>OutputCollector</title>
        
          <p><a href="ext:api/org/apache/hadoop/mapred/outputcollector">
          OutputCollector</a> is a generalization of the facility provided by
          the Map-Reduce framework to collect data output by the 
          <code>Mapper</code> or the <code>Reducer</code> (either the 
          intermediate outputs or the output of the job).</p>
        </section>
      
        <p>Hadoop Map-Reduce comes bundled with a 
        <a href="ext:api/org/apache/hadoop/mapred/lib/package-summary">
        library</a> of generally useful mappers, reducers, and partitioners.</p>
      </section>
      
      <section>
        <title>Job Configuration</title>
        
        <p><a href="ext:api/org/apache/hadoop/mapred/jobconf">
        JobConf</a> represents a Map-Reduce job configuration.</p>
 
        <p><code>JobConf</code> is the primary interface for a user to describe
        a map-reduce job to the Hadoop framework for execution. The framework 
        tries to faithfully execute the job as described by <code>JobConf</code>, 
        however:</p> 
        <ul>
          <li>f
            Some configuration parameters may have been marked as 
            <a href="ext:api/org/apache/hadoop/conf/configuration/final_parameters">
            final</a> by administrators and hence cannot be altered.
          </li>
          <li>
            While some job parameters are straight-forward to set (e.g. 
            <a href="ext:api/org/apache/hadoop/mapred/jobconf/setnumreducetasks">
            setNumReduceTasks(int)</a>), other parameters interact subtly with 
            the rest of the framework and/or job configuration and are 
            more complex to set (e.g. 
            <a href="ext:api/org/apache/hadoop/mapred/jobconf/setnummaptasks">
            setNumMapTasks(int)</a>).
          </li>
        </ul>
 
        <p><code>JobConf</code> is typically used to specify the 
        <code>Mapper</code>, combiner (if any), <code>Partitioner</code>, 
        <code>Reducer</code>, <code>InputFormat</code> and 
        <code>OutputFormat</code> implementations. <code>JobConf</code> also 
        indicates the set of input files 
        (<a href="ext:api/org/apache/hadoop/mapred/fileinputformat/setinputpaths">setInputPaths(JobConf, Path...)</a>
        /<a href="ext:api/org/apache/hadoop/mapred/fileinputformat/addinputpath">addInputPath(JobConf, Path)</a>)
        and (<a href="ext:api/org/apache/hadoop/mapred/fileinputformat/setinputpathstring">setInputPaths(JobConf, String)</a>
        /<a href="ext:api/org/apache/hadoop/mapred/fileinputformat/addinputpathstring">addInputPaths(JobConf, String)</a>)
        and where the output files should be written
        (<a href="ext:api/org/apache/hadoop/mapred/fileoutputformat/setoutputpath">setOutputPath(Path)</a>).</p>

        <p>Optionally, <code>JobConf</code> is used to specify other advanced 
        facets of the job such as the <code>Comparator</code> to be used, files 
        to be put in the <code>DistributedCache</code>, whether intermediate 
        and/or job outputs are to be compressed (and how), debugging via 
        user-provided scripts
        (<a href="ext:api/org/apache/hadoop/mapred/jobconf/setmapdebugscript">setMapDebugScript(String)</a>/<a href="ext:api/org/apache/hadoop/mapred/jobconf/setreducedebugscript">setReduceDebugScript(String)</a>) 
        , whether job tasks can be executed in a <em>speculative</em> manner 
        (<a href="ext:api/org/apache/hadoop/mapred/jobconf/setmapspeculativeexecution">setMapSpeculativeExecution(boolean)</a>)/(<a href="ext:api/org/apache/hadoop/mapred/jobconf/setreducespeculativeexecution">setReduceSpeculativeExecution(boolean)</a>)
        , maximum number of attempts per task
        (<a href="ext:api/org/apache/hadoop/mapred/jobconf/setmaxmapattempts">setMaxMapAttempts(int)</a>/<a href="ext:api/org/apache/hadoop/mapred/jobconf/setmaxreduceattempts">setMaxReduceAttempts(int)</a>) 
        , percentage of tasks failure which can be tolerated by the job
        (<a href="ext:api/org/apache/hadoop/mapred/jobconf/setmaxmaptaskfailurespercent">setMaxMapTaskFailuresPercent(int)</a>/<a href="ext:api/org/apache/hadoop/mapred/jobconf/setmaxreducetaskfailurespercent">setMaxReduceTaskFailuresPercent(int)</a>) 
        etc.</p>
        
        <p>Of course, users can use 
        <a href="ext:api/org/apache/hadoop/conf/configuration/set">set(String, String)</a>/<a href="ext:api/org/apache/hadoop/conf/configuration/get">get(String, String)</a>
        to set/get arbitrary parameters needed by applications. However, use the 
        <code>DistributedCache</code> for large amounts of (read-only) data.</p>
      </section>

      <section>
        <title>Task Execution &amp; Environment</title>

        <p>The <code>TaskTracker</code> executes the <code>Mapper</code>/ 
        <code>Reducer</code>  <em>task</em> as a child process in a separate jvm.
        </p>
        
        <p>The child-task inherits the environment of the parent 
        <code>TaskTracker</code>. The user can specify additional options to the
        child-jvm via the <code>mapred.child.java.opts</code> configuration
        parameter in the <code>JobConf</code> such as non-standard paths for the 
        run-time linker to search shared libraries via 
        <code>-Djava.library.path=&lt;&gt;</code> etc. If the 
        <code>mapred.child.java.opts</code> contains the symbol <em>@taskid@</em> 
        it is interpolated with value of <code>taskid</code> of the map/reduce
        task.</p>
        
        <p>Here is an example with multiple arguments and substitutions, 
        showing jvm GC logging, and start of a passwordless JVM JMX agent so that
        it can connect with jconsole and the likes to watch child memory, 
        threads and get thread dumps. It also sets the maximum heap-size of the 
        child jvm to 512MB and adds an additional path to the 
        <code>java.library.path</code> of the child-jvm.</p>

        <p>
          <code>&lt;property&gt;</code><br/>
          &nbsp;&nbsp;<code>&lt;name&gt;mapred.child.java.opts&lt;/name&gt;</code><br/>
          &nbsp;&nbsp;<code>&lt;value&gt;</code><br/>
          &nbsp;&nbsp;&nbsp;&nbsp;<code>
                    -Xmx512M -Djava.library.path=/home/mycompany/lib
                    -verbose:gc -Xloggc:/tmp/@taskid@.gc</code><br/>
          &nbsp;&nbsp;&nbsp;&nbsp;<code>
                    -Dcom.sun.management.jmxremote.authenticate=false 
                    -Dcom.sun.management.jmxremote.ssl=false</code><br/>
          &nbsp;&nbsp;<code>&lt;/value&gt;</code><br/>
          <code>&lt;/property&gt;</code>
        </p>
        
        <p>Users/admins can also specify the maximum virtual memory 
        of the launched child-task using <code>mapred.child.ulimit</code>.</p>
        
        <p>When the job starts, the localized job directory
        <code> ${mapred.local.dir}/taskTracker/jobcache/$jobid/</code>
        has the following directories: </p>
        <ul>
        <li> A job-specific shared directory, created at location
        <code>${mapred.local.dir}/taskTracker/jobcache/$jobid/work/ </code>.
        This directory is exposed to the users through 
        <code>job.local.dir </code>. The tasks can use this space as scratch
        space and share files among them. The directory can accessed through 
        api <a href="ext:api/org/apache/hadoop/mapred/jobconf/getjoblocaldir">
        JobConf.getJobLocalDir()</a>. It is available as System property also.
        So,users can call <code>System.getProperty("job.local.dir")</code>;
        </li>
        <li>A jars directory, which has the job jar file and expanded jar </li>
        <li>A job.xml file, the generic job configuration </li>
        <li>Each task has directory <code>task-id</code> which again has the 
        following structure
        <ul>
        <li>A job.xml file, task localized job configuration </li>
        <li>A directory for intermediate output files</li>
        <li>The working directory of the task. 
        And work directory has a temporary directory 
        to create temporary files</li>
        </ul>
        </li>
        </ul>
 
        <p>The <a href="#DistributedCache">DistributedCache</a> can also be used
        as a rudimentary software distribution mechanism for use in the map 
        and/or reduce tasks. It can be used to distribute both jars and 
        native libraries. The 
        <a href="ext:api/org/apache/hadoop/filecache/distributedcache/addarchivetoclasspath">
        DistributedCache.addArchiveToClassPath(Path, Configuration)</a> or 
        <a href="ext:api/org/apache/hadoop/filecache/distributedcache/addfiletoclasspath">
        DistributedCache.addFileToClassPath(Path, Configuration)</a> api can 
        be used to cache files/jars and also add them to the <em>classpath</em> 
        of child-jvm. Similarly the facility provided by the 
        <code>DistributedCache</code> where-in it symlinks the cached files into
        the working directory of the task can be used to distribute native 
        libraries and load them. The underlying detail is that child-jvm always 
        has its <em>current working directory</em> added to the
        <code>java.library.path</code> and hence the cached libraries can be 
        loaded via <a href="http://java.sun.com/j2se/1.5.0/docs/api/java/lang/System.html#loadLibrary(java.lang.String)">
        System.loadLibrary</a> or <a href="http://java.sun.com/j2se/1.5.0/docs/api/java/lang/System.html#load(java.lang.String)">
        System.load</a>.</p>
      </section>
      
      <section>
        <title>Job Submission and Monitoring</title>
        
        <p><a href="ext:api/org/apache/hadoop/mapred/jobclient">
        JobClient</a> is the primary interface by which user-job interacts
        with the <code>JobTracker</code>.</p>
 
        <p><code>JobClient</code> provides facilities to submit jobs, track their 
        progress, access component-tasks' reports/logs, get the Map-Reduce 
        cluster's status information and so on.</p>
 
        <p>The job submission process involves:</p>
        <ol>
          <li>Checking the input and output specifications of the job.</li>
          <li>Computing the <code>InputSplit</code> values for the job.</li>
          <li>
            Setting up the requisite accounting information for the 
            <code>DistributedCache</code> of the job, if necessary.
          </li>
          <li>
            Copying the job's jar and configuration to the map-reduce system 
            directory on the <code>FileSystem</code>.
          </li>
          <li>
            Submitting the job to the <code>JobTracker</code> and optionally 
            monitoring it's status.
          </li>
        </ol>
        <p> Job history files are also logged to user specified directory
        <code>hadoop.job.history.user.location</code> 
        which defaults to job output directory. The files are stored in
        "_logs/history/" in the specified directory. Hence, by default they
        will be in mapred.output.dir/_logs/history. User can stop
        logging by giving the value <code>none</code> for 
        <code>hadoop.job.history.user.location</code></p>

        <p> User can view the history logs summary in specified directory 
        using the following command <br/>
        <code>$ bin/hadoop job -history output-dir</code><br/> 
        This command will print job details, failed and killed tip
        details. <br/>
        More details about the job such as successful tasks and 
        task attempts made for each task can be viewed using the  
        following command <br/>
       <code>$ bin/hadoop job -history all output-dir</code><br/></p> 
            
        <p> User can use 
        <a href="ext:api/org/apache/hadoop/mapred/outputlogfilter">OutputLogFilter</a>
        to filter log files from the output directory listing. </p>
        
        <p>Normally the user creates the application, describes various facets 
        of the job via <code>JobConf</code>, and then uses the 
        <code>JobClient</code> to submit the job and monitor its progress.</p>

        <section>
          <title>Job Control</title>
 
          <p>Users may need to chain map-reduce jobs to accomplish complex
          tasks which cannot be done via a single map-reduce job. This is fairly
          easy since the output of the job typically goes to distributed 
          file-system, and the output, in turn, can be used as the input for the 
          next job.</p>
 
          <p>However, this also means that the onus on ensuring jobs are 
          complete (success/failure) lies squarely on the clients. In such 
          cases, the various job-control options are:</p>
          <ul>
            <li>
              <a href="ext:api/org/apache/hadoop/mapred/jobclient/runjob">
              runJob(JobConf)</a> : Submits the job and returns only after the 
              job has completed.
            </li>
            <li>
              <a href="ext:api/org/apache/hadoop/mapred/jobclient/submitjob">
              submitJob(JobConf)</a> : Only submits the job, then poll the 
              returned handle to the 
              <a href="ext:api/org/apache/hadoop/mapred/runningjob">
              RunningJob</a> to query status and make scheduling decisions.
            </li>
            <li>
              <a href="ext:api/org/apache/hadoop/mapred/jobconf/setjobendnotificationuri">
              JobConf.setJobEndNotificationURI(String)</a> : Sets up a 
              notification upon job-completion, thus avoiding polling.
            </li>
          </ul>
        </section>
      </section>

      <section>
        <title>Job Input</title>
        
        <p><a href="ext:api/org/apache/hadoop/mapred/inputformat">
        InputFormat</a> describes the input-specification for a Map-Reduce job.
        </p> 
 
        <p>The Map-Reduce framework relies on the <code>InputFormat</code> of 
        the job to:</p>
        <ol>
          <li>Validate the input-specification of the job.</li>
          <li>
            Split-up the input file(s) into logical <code>InputSplit</code> 
            instances, each of which is then assigned to an individual 
            <code>Mapper</code>.
          </li>
          <li>
            Provide the <code>RecordReader</code> implementation used to
            glean input records from the logical <code>InputSplit</code> for 
            processing by the <code>Mapper</code>.
          </li>
        </ol>
 
        <p>The default behavior of file-based <code>InputFormat</code>
        implementations, typically sub-classes of 
        <a href="ext:api/org/apache/hadoop/mapred/fileinputformat">
        FileInputFormat</a>, is to split the input into <em>logical</em> 
        <code>InputSplit</code> instances based on the total size, in bytes, of 
        the input files. However, the <code>FileSystem</code> blocksize of the 
        input files is treated as an upper bound for input splits. A lower bound
        on the split size can be set via <code>mapred.min.split.size</code>.</p>
 
        <p>Clearly, logical splits based on input-size is insufficient for many
        applications since record boundaries must be respected. In such cases, 
        the application should implement a <code>RecordReader</code>, who is 
        responsible for respecting record-boundaries and presents a 
        record-oriented view of the logical <code>InputSplit</code> to the 
        individual task.</p>

        <p><a href="ext:api/org/apache/hadoop/mapred/textinputformat">
        TextInputFormat</a> is the default <code>InputFormat</code>.</p>
        
        <p>If <code>TextInputFormat</code> is the <code>InputFormat</code> for a 
        given job, the framework detects input-files with the <em>.gz</em> and 
        <em>.lzo</em> extensions and automatically decompresses them using the 
        appropriate <code>CompressionCodec</code>. However, it must be noted that
        compressed files with the above extensions cannot be <em>split</em> and 
        each compressed file is processed in its entirety by a single mapper.</p>
        
        <section>
          <title>InputSplit</title>
          
          <p><a href="ext:api/org/apache/hadoop/mapred/inputsplit">
          InputSplit</a> represents the data to be processed by an individual 
          <code>Mapper</code>.</p>

          <p>Typically <code>InputSplit</code> presents a byte-oriented view of
          the input, and it is the responsibility of <code>RecordReader</code>
          to process and present a record-oriented view.</p>
          
          <p><a href="ext:api/org/apache/hadoop/mapred/filesplit">
          FileSplit</a> is the default <code>InputSplit</code>. It sets 
          <code>map.input.file</code> to the path of the input file for the
          logical split.</p>
        </section>
        
        <section>
          <title>RecordReader</title>
          
          <p><a href="ext:api/org/apache/hadoop/mapred/recordreader">
          RecordReader</a> reads <code>&lt;key, value&gt;</code> pairs from an 
          <code>InputSplit</code>.</p>

          <p>Typically the <code>RecordReader</code> converts the byte-oriented 
          view of the input, provided by the <code>InputSplit</code>, and 
          presents a record-oriented to the <code>Mapper</code> implementations 
          for processing. <code>RecordReader</code> thus assumes the 
          responsibility of processing record boundaries and presents the tasks 
          with keys and values.</p>
        </section>
      </section>

      <section>
        <title>Job Output</title>
        
        <p><a href="ext:api/org/apache/hadoop/mapred/outputformat">
        OutputFormat</a> describes the output-specification for a Map-Reduce 
        job.</p>

        <p>The Map-Reduce framework relies on the <code>OutputFormat</code> of 
        the job to:</p>
        <ol>
          <li>
            Validate the output-specification of the job; for example, check that 
            the output directory doesn't already exist.
          </li>
          <li>
            Provide the <code>RecordWriter</code> implementation used to 
            write the output files of the job. Output files are stored in a 
            <code>FileSystem</code>.
          </li>
        </ol>
 
        <p><code>TextOutputFormat</code> is the default 
        <code>OutputFormat</code>.</p>
 
        <section>
          <title>Task Side-Effect Files</title>
 
          <p>In some applications, component tasks need to create and/or write to
          side-files, which differ from the actual job-output files.</p>
 
          <p>In such cases there could be issues with two instances of the same 
          <code>Mapper</code> or <code>Reducer</code> running simultaneously (for
          example, speculative tasks) trying to open and/or write to the same 
          file (path) on the <code>FileSystem</code>. Hence the 
          application-writer will have to pick unique names per task-attempt 
          (using the attemptid, say <code>attempt_200709221812_0001_m_000000_0</code>), 
          not just per task.</p> 
 
          <p>To avoid these issues the Map-Reduce framework maintains a special 
          <code>${mapred.output.dir}/_temporary/_${taskid}</code> sub-directory
          accessible via <code>${mapred.work.output.dir}</code>
          for each task-attempt on the <code>FileSystem</code> where the output
          of the task-attempt is stored. On successful completion of the 
          task-attempt, the files in the 
          <code>${mapred.output.dir}/_temporary/_${taskid}</code> (only) 
          are <em>promoted</em> to <code>${mapred.output.dir}</code>. Of course, 
          the framework discards the sub-directory of unsuccessful task-attempts. 
          This process is completely transparent to the application.</p>
 
          <p>The application-writer can take advantage of this feature by 
          creating any side-files required in <code>${mapred.work.output.dir}</code>
          during execution of a task via 
          <a href="ext:api/org/apache/hadoop/mapred/fileoutputformat/getworkoutputpath">
          FileOutputFormat.getWorkOutputPath()</a>, and the framework will promote them 
          similarly for succesful task-attempts, thus eliminating the need to 
          pick unique paths per task-attempt.</p>
          
          <p>Note: The value of <code>${mapred.work.output.dir}</code> during 
          execution of a particular task-attempt is actually 
          <code>${mapred.output.dir}/_temporary/_{$taskid}</code>, and this value is 
          set by the map-reduce framework. So, just create any side-files in the 
          path  returned by
          <a href="ext:api/org/apache/hadoop/mapred/fileoutputformat/getworkoutputpath">
          FileOutputFormat.getWorkOutputPath() </a>from map/reduce 
          task to take advantage of this feature.</p>
          
          <p>The entire discussion holds true for maps of jobs with 
           reducer=NONE (i.e. 0 reduces) since output of the map, in that case, 
           goes directly to HDFS.</p> 
        </section>
        
        <section>
          <title>RecordWriter</title>
          
          <p><a href="ext:api/org/apache/hadoop/mapred/recordwriter">
          RecordWriter</a> writes the output <code>&lt;key, value&gt;</code> 
          pairs to an output file.</p>

          <p>RecordWriter implementations write the job outputs to the 
          <code>FileSystem</code>.</p>
        </section>
      </section>
      
      <section>
        <title>Other Useful Features</title>
 
        <section>
          <title>Counters</title>
          
          <p><code>Counters</code> represent global counters, defined either by 
          the Map-Reduce framework or applications. Each <code>Counter</code> can 
          be of any <code>Enum</code> type. Counters of a particular 
          <code>Enum</code> are bunched into groups of type 
          <code>Counters.Group</code>.</p>
          
          <p>Applications can define arbitrary <code>Counters</code> (of type 
          <code>Enum</code>) and update them via 
          <a href="ext:api/org/apache/hadoop/mapred/reporter/incrcounter">
          Reporter.incrCounter(Enum, long)</a> in the <code>map</code> and/or 
          <code>reduce</code> methods. These counters are then globally 
          aggregated by the framework.</p>
        </section>       
        
        <section>
          <title>DistributedCache</title>
          
          <p><a href="ext:api/org/apache/hadoop/filecache/distributedcache">
          DistributedCache</a> distributes application-specific, large, read-only 
          files efficiently.</p>
 
          <p><code>DistributedCache</code> is a facility provided by the 
          Map-Reduce framework to cache files (text, archives, jars and so on) 
          needed by applications.</p>
 
          <p>Applications specify the files to be cached via urls (hdfs:// or 
          http://) in the <code>JobConf</code>. The <code>DistributedCache</code> 
          assumes that the files specified via hdfs:// urls are already present 
          on the <code>FileSystem</code>.</p>

          <p>The framework will copy the necessary files to the slave node 
          before any tasks for the job are executed on that node. Its 
          efficiency stems from the fact that the files are only copied once 
          per job and the ability to cache archives which are un-archived on 
          the slaves.</p> 
          
          <p><code>DistributedCache</code> tracks the modification timestamps of 
          the cached files. Clearly the cache files should not be modified by 
          the application or externally while the job is executing.</p>

          <p><code>DistributedCache</code> can be used to distribute simple, 
          read-only data/text files and more complex types such as archives and
          jars. Archives (zip, tar, tgz and tar.gz files) are 
          <em>un-archived</em> at the slave nodes.
          Optionally users can also direct the <code>DistributedCache</code> to 
          <em>symlink</em> the cached file(s) into the <code>current working 
          directory</code> of the task via the 
          <a href="ext:api/org/apache/hadoop/filecache/distributedcache/createsymlink">
          DistributedCache.createSymlink(Configuration)</a> api. Files 
          have <em>execution permissions</em> set.</p>
        </section>
        
        <section>
          <title>Tool</title>
          
          <p>The <a href="ext:api/org/apache/hadoop/util/tool">Tool</a> 
          interface supports the handling of generic Hadoop command-line options.
          </p>
          
          <p><code>Tool</code> is the standard for any Map-Reduce tool or 
          application. The application should delegate the handling of 
          standard command-line options to 
          <a href="ext:api/org/apache/hadoop/util/genericoptionsparser">
          GenericOptionsParser</a> via          
          <a href="ext:api/org/apache/hadoop/util/toolrunner/run">
          ToolRunner.run(Tool, String[])</a> and only handle its custom 
          arguments.</p>
          
          <p>
            The generic Hadoop command-line options are:<br/>
            <code>
              -conf &lt;configuration file&gt;
            </code>
            <br/>
            <code>
              -D &lt;property=value&gt;
            </code>
            <br/>
            <code>
              -fs &lt;local|namenode:port&gt;
            </code>
            <br/>
            <code>
              -jt &lt;local|jobtracker:port&gt;
            </code>
          </p>
        </section>
        
        <section>
          <title>IsolationRunner</title>
          
          <p><a href="ext:api/org/apache/hadoop/mapred/isolationrunner">
          IsolationRunner</a> is a utility to help debug Map-Reduce programs.</p>
          
          <p>To use the <code>IsolationRunner</code>, first set 
          <code>keep.failed.tasks.files</code> to <code>true</code> 
          (also see <code>keep.tasks.files.pattern</code>).</p>
          
          <p>
            Next, go to the node on which the failed task ran and go to the 
            <code>TaskTracker</code>'s local directory and run the 
            <code>IsolationRunner</code>:<br/>
            <code>$ cd &lt;local path&gt;/taskTracker/${taskid}/work</code><br/>
            <code>
              $ bin/hadoop org.apache.hadoop.mapred.IsolationRunner ../job.xml
            </code>
          </p>
          
          <p><code>IsolationRunner</code> will run the failed task in a single 
          jvm, which can be in the debugger, over precisely the same input.</p>
        </section>
        
        <section>
          <title>Debugging</title>
          <p>Map/Reduce framework provides a facility to run user-provided 
          scripts for debugging. When map/reduce task fails, user can run 
          script for doing post-processing on task logs i.e task's stdout,
          stderr, syslog and jobconf. The stdout and stderr of the
          user-provided debug script are printed on the diagnostics. 
          These outputs are also displayed on job UI on demand. </p>

          <p> In the following sections we discuss how to submit debug script
          along with the job. For submitting debug script, first it has to
          distributed. Then the script has to supplied in Configuration. </p>
          <section>
          <title> How to distribute script file: </title>
          <p>
          To distribute  the debug script file, first copy the file to the dfs.
          The file can be distributed by setting the property 
          "mapred.cache.files" with value "path"#"script-name". 
          If more than one file has to be distributed, the files can be added
          as comma separated paths. This property can also be set by APIs
          <a href="ext:api/org/apache/hadoop/filecache/distributedcache/addcachefile">
          DistributedCache.addCacheFile(URI,conf) </a> and
          <a href="ext:api/org/apache/hadoop/filecache/distributedcache/setcachefiles">
          DistributedCache.setCacheFiles(URIs,conf) </a> where URI is of 
          the form "hdfs://host:port/'absolutepath'#'script-name'". 
          For Streaming, the file can be added through 
          command line option -cacheFile.
          </p>
          
          <p>
          The files has to be symlinked in the current working directory of 
          of the task. To create symlink for the file, the property 
          "mapred.create.symlink" is set to "yes". This can also be set by
          <a href="ext:api/org/apache/hadoop/filecache/distributedcache/createsymlink">
          DistributedCache.createSymLink(Configuration) </a> api.
          </p>
          </section>
          <section>
          <title> How to submit script: </title>
          <p> A quick way to submit debug script is to set values for the 
          properties "mapred.map.task.debug.script" and 
          "mapred.reduce.task.debug.script" for debugging map task and reduce
          task respectively. These properties can also be set by using APIs 
          <a href="ext:api/org/apache/hadoop/mapred/jobconf/setmapdebugscript">
          JobConf.setMapDebugScript(String) </a> and
          <a href="ext:api/org/apache/hadoop/mapred/jobconf/setreducedebugscript">
          JobConf.setReduceDebugScript(String) </a>. For streaming, debug 
          script can be submitted with command-line options -mapdebug,
          -reducedebug for debugging mapper and reducer respectively.</p>
            
          <p>The arguments of the script are task's stdout, stderr, 
          syslog and jobconf files. The debug command, run on the node where
          the map/reduce failed, is: <br/>
          <code> $script $stdout $stderr $syslog $jobconf </code> </p> 

          <p> Pipes programs have the c++ program name as a fifth argument
          for the command. Thus for the pipes programs the command is <br/> 
          <code>$script $stdout $stderr $syslog $jobconf $program </code>  
          </p>
          </section>
          
          <section>
          <title> Default Behavior: </title>
          <p> For pipes, a default script is run to process core dumps under
          gdb, prints stack trace and gives info about running threads. </p>
          </section>
        </section>
        
        <section>
          <title>JobControl</title>
          
          <p><a href="ext:api/org/apache/hadoop/mapred/jobcontrol/package-summary">
          JobControl</a> is a utility which encapsulates a set of Map-Reduce jobs
          and their dependencies.</p>
        </section>
        
        <section>
          <title>Data Compression</title>
          
          <p>Hadoop Map-Reduce provides facilities for the application-writer to
          specify compression for both intermediate map-outputs and the
          job-outputs i.e. output of the reduces. It also comes bundled with
          <a href="ext:api/org/apache/hadoop/io/compress/compressioncodec">
          CompressionCodec</a> implementations for the 
          <a href="ext:zlib">zlib</a> and <a href="ext:lzo">lzo</a> compression 
          algorithms. The <a href="ext:gzip">gzip</a> file format is also
          supported.</p>
          
          <p>Hadoop also provides native implementations of the above compression
          codecs for reasons of both performance (zlib) and non-availability of
          Java libraries (lzo). More details on their usage and availability are
          available <a href="native_libraries.html">here</a>.</p>
          
          <section>
            <title>Intermediate Outputs</title>
            
            <p>Applications can control compression of intermediate map-outputs
            via the 
            <a href="ext:api/org/apache/hadoop/mapred/jobconf/setcompressmapoutput">
            JobConf.setCompressMapOutput(boolean)</a> api and the 
            <code>CompressionCodec</code> to be used via the
            <a href="ext:api/org/apache/hadoop/mapred/jobconf/setmapoutputcompressorclass">
            JobConf.setMapOutputCompressorClass(Class)</a> api. Since 
            the intermediate map-outputs are always stored in the 
            <a href="ext:api/org/apache/hadoop/io/sequencefile">SequenceFile</a>
            format, the
            <a href="ext:api/org/apache/hadoop/io/sequencefilecompressiontype">
            SequenceFile.CompressionType</a> (i.e.
            <a href="ext:api/org/apache/hadoop/io/sequencefilecompressiontype/record">
            RECORD</a> / 
            <a href="ext:api/org/apache/hadoop/io/sequencefilecompressiontype/block">
            BLOCK</a> - defaults to <code>RECORD</code>) can be specified via the
            <a href="ext:api/org/apache/hadoop/mapred/jobconf/setmapoutputcompressiontype">
            JobConf.setMapOutputCompressionType(SequenceFile.CompressionType)</a> 
            api.</p>
          </section>
          
          <section>
            <title>Job Outputs</title>
            
            <p>Applications can control compression of job-outputs via the
            <a href="ext:api/org/apache/hadoop/mapred/outputformatbase/setcompressoutput">
            OutputFormatBase.setCompressOutput(JobConf, boolean)</a> api and the 
            <code>CompressionCodec</code> to be used can be specified via the
            <a href="ext:api/org/apache/hadoop/mapred/outputformatbase/setoutputcompressorclass">
            OutputFormatBase.setOutputCompressorClass(JobConf, Class)</a> api.</p>
            
            <p>If the job outputs are to be stored in the 
            <a href="ext:api/org/apache/hadoop/mapred/sequencefileoutputformat">
            SequenceFileOutputFormat</a>, the required
            <code>SequenceFile.CompressionType</code> (i.e. <code>RECORD</code> / 
            <code>BLOCK</code> - defaults to <code>RECORD</code>)can be specified 
            via the 
            <a href="ext:api/org/apache/hadoop/mapred/sequencefileoutputformat/setoutputcompressiontype">
            SequenceFileOutputFormat.setOutputCompressionType(JobConf, 
            SequenceFile.CompressionType)</a> api.</p>
          </section>
        </section>
        
      </section>
    </section>

    <section>
      <title>Example: WordCount v2.0</title>
      
      <p>Here is a more complete <code>WordCount</code> which uses many of the
      features provided by the Map-Reduce framework we discussed so far.</p>
      
      <p>This needs the HDFS to be up and running, especially for the 
      <code>DistributedCache</code>-related features. Hence it only works with a 
      <a href="quickstart.html#SingleNodeSetup">pseudo-distributed</a> or
      <a href="quickstart.html#Fully-Distributed+Operation">fully-distributed</a> 
      Hadoop installation.</p>      
      
      <section>
        <title>Source Code</title>
        
        <table>
          <tr>
            <th></th>
            <th>WordCount.java</th>
          </tr>
          <tr>
            <td>1.</td>
            <td>
              <code>package org.myorg;</code>
            </td>
          </tr>
          <tr>
            <td>2.</td>
            <td></td>
          </tr>
          <tr>
            <td>3.</td>
            <td>
              <code>import java.io.*;</code>
            </td>
          </tr>
          <tr>
            <td>4.</td>
            <td>
              <code>import java.util.*;</code>
            </td>
          </tr>
          <tr>
            <td>5.</td>
            <td></td>
          </tr>
          <tr>
            <td>6.</td>
            <td>
              <code>import org.apache.hadoop.fs.Path;</code>
            </td>
          </tr>
          <tr>
            <td>7.</td>
            <td>
              <code>import org.apache.hadoop.filecache.DistributedCache;</code>
            </td>
          </tr>
          <tr>
            <td>8.</td>
            <td>
              <code>import org.apache.hadoop.conf.*;</code>
            </td>
          </tr>
          <tr>
            <td>9.</td>
            <td>
              <code>import org.apache.hadoop.io.*;</code>
            </td>
          </tr>
          <tr>
            <td>10.</td>
            <td>
              <code>import org.apache.hadoop.mapred.*;</code>
            </td>
          </tr>
          <tr>
            <td>11.</td>
            <td>
              <code>import org.apache.hadoop.util.*;</code>
            </td>
          </tr>
          <tr>
            <td>12.</td>
            <td></td>
          </tr>
          <tr>
            <td>13.</td>
            <td>
              <code>public class WordCount extends Configured implements Tool {</code>
            </td>
          </tr>
          <tr>
            <td>14.</td>
            <td></td>
          </tr>
          <tr>
            <td>15.</td>
            <td>
              &nbsp;&nbsp;
              <code>
                public static class Map extends MapReduceBase 
                implements Mapper&lt;LongWritable, Text, Text, IntWritable&gt; {
              </code>
            </td>
          </tr>
          <tr>
            <td>16.</td>
            <td></td>
          </tr>
          <tr>
            <td>17.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                static enum Counters { INPUT_WORDS }
              </code>
            </td>
          </tr>
          <tr>
            <td>18.</td>
            <td></td>
          </tr>
          <tr>
            <td>19.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                private final static IntWritable one = new IntWritable(1);
              </code>
            </td>
          </tr>
          <tr>
            <td>20.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>private Text word = new Text();</code>
            </td>
          </tr>
          <tr>
            <td>21.</td>
            <td></td>
          </tr>
          <tr>
            <td>22.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>private boolean caseSensitive = true;</code>
            </td>
          </tr>
          <tr>
            <td>23.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>private Set&lt;String&gt; patternsToSkip = new HashSet&lt;String&gt;();</code>
            </td>
          </tr>
          <tr>
            <td>24.</td>
            <td></td>
          </tr>
          <tr>
            <td>25.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>private long numRecords = 0;</code>
            </td>
          </tr>
          <tr>
            <td>26.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>private String inputFile;</code>
            </td>
          </tr>
          <tr>
            <td>27.</td>
            <td></td>
          </tr>
          <tr>
            <td>28.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>public void configure(JobConf job) {</code>
            </td>
          </tr>
          <tr>
            <td>29.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                caseSensitive = job.getBoolean("wordcount.case.sensitive", true);
              </code>
            </td>
          </tr>
          <tr>
            <td>30.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>inputFile = job.get("map.input.file");</code>
            </td>
          </tr>
          <tr>
            <td>31.</td>
            <td></td>
          </tr>
          <tr>
            <td>32.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>if (job.getBoolean("wordcount.skip.patterns", false)) {</code>
            </td>
          </tr>
          <tr>
            <td>33.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>Path[] patternsFiles = new Path[0];</code>
            </td>
          </tr>
          <tr>
            <td>34.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>try {</code>
            </td>
          </tr>
          <tr>
            <td>35.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                patternsFiles = DistributedCache.getLocalCacheFiles(job);
              </code>
            </td>
          </tr>
          <tr>
            <td>36.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>} catch (IOException ioe) {</code>
            </td>
          </tr>
          <tr>
            <td>37.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                System.err.println("Caught exception while getting cached files: " 
                + StringUtils.stringifyException(ioe));
              </code>
            </td>
          </tr>
          <tr>
            <td>38.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>39.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>for (Path patternsFile : patternsFiles) {</code>
            </td>
          </tr>
          <tr>
            <td>40.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>parseSkipFile(patternsFile);</code>
            </td>
          </tr>
          <tr>
            <td>41.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>42.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>43.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>44.</td>
            <td></td>
          </tr>
          <tr>
            <td>45.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>private void parseSkipFile(Path patternsFile) {</code>
            </td>
          </tr>
          <tr>
            <td>46.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>try {</code>
            </td>
          </tr>
          <tr>
            <td>47.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                BufferedReader fis = 
                  new BufferedReader(new FileReader(patternsFile.toString()));
              </code>
            </td>
          </tr>
          <tr>
            <td>48.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>String pattern = null;</code>
            </td>
          </tr>
          <tr>
            <td>49.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>while ((pattern = fis.readLine()) != null) {</code>
            </td>
          </tr>
          <tr>
            <td>50.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>patternsToSkip.add(pattern);</code>
            </td>
          </tr>
          <tr>
            <td>51.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>52.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>} catch (IOException ioe) {</code>
            </td>
          </tr>
          <tr>
            <td>53.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                System.err.println("Caught exception while parsing the cached file '" +
                                   patternsFile + "' : " + 
                                   StringUtils.stringifyException(ioe));
                
              </code>
            </td>
          </tr>
          <tr>
            <td>54.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>55.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>56.</td>
            <td></td>
          </tr>
          <tr>
            <td>57.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                public void map(LongWritable key, Text value, 
                OutputCollector&lt;Text, IntWritable&gt; output, 
                Reporter reporter) throws IOException {
              </code>
            </td>
          </tr>
          <tr>
            <td>58.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                String line = 
                  (caseSensitive) ? value.toString() : 
                                    value.toString().toLowerCase();
              </code>
            </td>
          </tr>
          <tr>
            <td>59.</td>
            <td></td>
          </tr>
          <tr>
            <td>60.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>for (String pattern : patternsToSkip) {</code>
            </td>
          </tr>
          <tr>
            <td>61.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>line = line.replaceAll(pattern, "");</code>
            </td>
          </tr>
          <tr>
            <td>62.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>63.</td>
            <td></td>
          </tr>
          <tr>
            <td>64.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>StringTokenizer tokenizer = new StringTokenizer(line);</code>
            </td>
          </tr>
          <tr>
            <td>65.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>while (tokenizer.hasMoreTokens()) {</code>
            </td>
          </tr>
          <tr>
            <td>66.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>word.set(tokenizer.nextToken());</code>
            </td>
          </tr>
          <tr>
            <td>67.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>output.collect(word, one);</code>
            </td>
          </tr>
          <tr>
            <td>68.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>reporter.incrCounter(Counters.INPUT_WORDS, 1);</code>
            </td>
          </tr>
          <tr>
            <td>69.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>70.</td>
            <td></td>
          </tr>
          <tr>
            <td>71.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>if ((++numRecords % 100) == 0) {</code>
            </td>
          </tr>
          <tr>
            <td>72.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                reporter.setStatus("Finished processing " + numRecords + 
                                   " records " + "from the input file: " + 
                                   inputFile);
              </code>
            </td>
          </tr>
          <tr>
            <td>73.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>74.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>75.</td>
            <td>
              &nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>76.</td>
            <td></td>
          </tr>
          <tr>
            <td>77.</td>
            <td>
              &nbsp;&nbsp;
              <code>
                public static class Reduce extends MapReduceBase implements 
                Reducer&lt;Text, IntWritable, Text, IntWritable&gt; {
              </code>
            </td>
          </tr>
          <tr>
            <td>78.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                public void reduce(Text key, Iterator&lt;IntWritable&gt; values,
                OutputCollector&lt;Text, IntWritable&gt; output, 
                Reporter reporter) throws IOException {
              </code>
            </td>
          </tr>
          <tr>
            <td>79.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>int sum = 0;</code>
            </td>
          </tr>
          <tr>
            <td>80.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>while (values.hasNext()) {</code>
            </td>
          </tr>
          <tr>
            <td>81.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>sum += values.next().get();</code>
            </td>
          </tr>
          <tr>
            <td>82.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>83.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>output.collect(key, new IntWritable(sum));</code>
            </td>
          </tr>
          <tr>
            <td>84.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>85.</td>
            <td>
              &nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>86.</td>
            <td></td>
          </tr>
          <tr>
            <td>87.</td>
            <td>
              &nbsp;&nbsp;
              <code>public int run(String[] args) throws Exception {</code>
            </td>
          </tr>
          <tr>
            <td>88.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                JobConf conf = new JobConf(getConf(), WordCount.class);
              </code>
            </td>
          </tr>
          <tr>
            <td>89.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setJobName("wordcount");</code>
            </td>
          </tr>
          <tr>
            <td>90.</td>
            <td></td>
          </tr>
          <tr>
            <td>91.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setOutputKeyClass(Text.class);</code>
            </td>
          </tr>
          <tr>
            <td>92.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setOutputValueClass(IntWritable.class);</code>
            </td>
          </tr>
          <tr>
            <td>93.</td>
            <td></td>
          </tr>
          <tr>
            <td>94.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setMapperClass(Map.class);</code>
            </td>
          </tr>
          <tr>
            <td>95.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setCombinerClass(Reduce.class);</code>
            </td>
          </tr>
          <tr>
            <td>96.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setReducerClass(Reduce.class);</code>
            </td>
          </tr>
          <tr>
            <td>97.</td>
            <td></td>
          </tr>
          <tr>
            <td>98.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setInputFormat(TextInputFormat.class);</code>
            </td>
          </tr>
          <tr>
            <td>99.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>conf.setOutputFormat(TextOutputFormat.class);</code>
            </td>
          </tr>
          <tr>
            <td>100.</td>
            <td></td>
          </tr>
          <tr>
            <td>101.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                List&lt;String&gt; other_args = new ArrayList&lt;String&gt;();
              </code>
            </td>
          </tr>
          <tr>
            <td>102.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>for (int i=0; i &lt; args.length; ++i) {</code>
            </td>
          </tr>
          <tr>
            <td>103.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>if ("-skip".equals(args[i])) {</code>
            </td>
          </tr>
          <tr>
            <td>104.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                DistributedCache.addCacheFile(new Path(args[++i]).toUri(), conf);
              </code>
            </td>
          </tr>
          <tr>
            <td>105.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                conf.setBoolean("wordcount.skip.patterns", true);
              </code>
            </td>
          </tr>
          <tr>
            <td>106.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>} else {</code>
            </td>
          </tr>
          <tr>
            <td>107.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>other_args.add(args[i]);</code>
            </td>
          </tr>
          <tr>
            <td>108.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>109.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>110.</td>
            <td></td>
          </tr>
          <tr>
            <td>111.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>FileInputFormat.setInputPaths(conf, new Path(other_args.get(0)));</code>
            </td>
          </tr>
          <tr>
            <td>112.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>FileOutputFormat.setOutputPath(conf, new Path(other_args.get(1)));</code>
            </td>
          </tr>
          <tr>
            <td>113.</td>
            <td></td>
          </tr>
          <tr>
            <td>114.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>JobClient.runJob(conf);</code>
            </td>
          </tr>
          <tr>
            <td>115.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>return 0;</code>
            </td>
          </tr>
          <tr>
            <td>116.</td>
            <td>
              &nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>117.</td>
            <td></td>
          </tr>
          <tr>
            <td>118.</td>
            <td>
              &nbsp;&nbsp;
              <code>
                public static void main(String[] args) throws Exception {
              </code>
            </td>
          </tr>
          <tr>
            <td>119.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>
                int res = ToolRunner.run(new Configuration(), new WordCount(), 
                                         args);
              </code>
            </td>
          </tr>
          <tr>
            <td>120.</td>
            <td>
              &nbsp;&nbsp;&nbsp;&nbsp;
              <code>System.exit(res);</code>
            </td>
          </tr>
          <tr>
            <td>121.</td>
            <td>
              &nbsp;&nbsp;
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>122.</td>
            <td>
              <code>}</code>
            </td>
          </tr>
          <tr>
            <td>123.</td>
            <td></td>
          </tr>
        </table>
      </section>
        
      <section>
        <title>Sample Runs</title>
        
        <p>Sample text-files as input:</p>
        <p>
          <code>$ bin/hadoop dfs -ls /usr/joe/wordcount/input/</code><br/>
          <code>/usr/joe/wordcount/input/file01</code><br/>
          <code>/usr/joe/wordcount/input/file02</code><br/>
          <br/>
          <code>$ bin/hadoop dfs -cat /usr/joe/wordcount/input/file01</code><br/>
          <code>Hello World, Bye World!</code><br/>
          <br/>
          <code>$ bin/hadoop dfs -cat /usr/joe/wordcount/input/file02</code><br/>
          <code>Hello Hadoop, Goodbye to hadoop.</code>
        </p>
        
        <p>Run the application:</p>
        <p>
          <code>
            $ bin/hadoop jar /usr/joe/wordcount.jar org.myorg.WordCount 
              /usr/joe/wordcount/input /usr/joe/wordcount/output 
          </code>
        </p>

        <p>Output:</p>
        <p>
          <code>
            $ bin/hadoop dfs -cat /usr/joe/wordcount/output/part-00000
          </code>
          <br/>
          <code>Bye    1</code><br/>
          <code>Goodbye    1</code><br/>
          <code>Hadoop,    1</code><br/>
          <code>Hello    2</code><br/>
          <code>World!    1</code><br/>
          <code>World,    1</code><br/>
          <code>hadoop.    1</code><br/>
          <code>to    1</code><br/>
        </p>
        
        <p>Notice that the inputs differ from the first version we looked at, 
        and how they affect the outputs.</p>

        <p>Now, lets plug-in a pattern-file which lists the word-patterns to be 
        ignored, via the <code>DistributedCache</code>.</p>
        
        <p>
          <code>$ hadoop dfs -cat /user/joe/wordcount/patterns.txt</code><br/>
          <code>\.</code><br/>
          <code>\,</code><br/>
          <code>\!</code><br/>
          <code>to</code><br/>
        </p>
        
        <p>Run it again, this time with more options:</p>
        <p>
          <code>
            $ bin/hadoop jar /usr/joe/wordcount.jar org.myorg.WordCount 
              -Dwordcount.case.sensitive=true /usr/joe/wordcount/input 
              /usr/joe/wordcount/output -skip /user/joe/wordcount/patterns.txt
          </code>
        </p>
        
        <p>As expected, the output:</p>
        <p>
          <code>
            $ bin/hadoop dfs -cat /usr/joe/wordcount/output/part-00000
          </code>
          <br/>
          <code>Bye    1</code><br/>
          <code>Goodbye    1</code><br/>
          <code>Hadoop    1</code><br/>
          <code>Hello    2</code><br/>
          <code>World    2</code><br/>
          <code>hadoop    1</code><br/>
        </p>
        
        <p>Run it once more, this time switch-off case-sensitivity:</p>
        <p>
          <code>
            $ bin/hadoop jar /usr/joe/wordcount.jar org.myorg.WordCount 
              -Dwordcount.case.sensitive=false /usr/joe/wordcount/input 
              /usr/joe/wordcount/output -skip /user/joe/wordcount/patterns.txt
          </code>
        </p>
        
        <p>Sure enough, the output:</p>
        <p>
          <code>
            $ bin/hadoop dfs -cat /usr/joe/wordcount/output/part-00000
          </code>
          <br/>
          <code>bye    1</code><br/>
          <code>goodbye    1</code><br/>
          <code>hadoop    2</code><br/>
          <code>hello    2</code><br/>
          <code>world    2</code><br/>
        </p>
      </section>
      
      <section>
        <title>Highlights</title>
        
        <p>The second version of <code>WordCount</code> improves upon the 
        previous one by using some features offered by the Map-Reduce framework:
        </p>
        <ul>
          <li>
            Demonstrates how applications can access configuration parameters
            in the <code>configure</code> method of the <code>Mapper</code> (and
            <code>Reducer</code>) implementations (lines 28-43).
          </li>
          <li>
            Demonstrates how the <code>DistributedCache</code> can be used to 
            distribute read-only data needed by the jobs. Here it allows the user 
            to specify word-patterns to skip while counting (line 104).
          </li>
          <li>
            Demonstrates the utility of the <code>Tool</code> interface and the
            <code>GenericOptionsParser</code> to handle generic Hadoop 
            command-line options (lines 87-116, 119).
          </li>
          <li>
            Demonstrates how applications can use <code>Counters</code> (line 68)
            and how they can set application-specific status information via 
            the <code>Reporter</code> instance passed to the <code>map</code> (and
            <code>reduce</code>) method (line 72).
          </li>
        </ul>
        
      </section>
    </section>

    <p>
      <em>Java and JNI are trademarks or registered trademarks of 
      Sun Microsystems, Inc. in the United States and other countries.</em>
    </p>
    
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