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--- courses:mapreduce-tutorial:step-25 [2012/01/27 23:42]
straka
+++ courses:mapreduce-tutorial:step-25 [2012/01/31 15:12] (current)
majlis Fixed code for sorting execution.
@@ Line 1: / Line 1: @@
 ====== MapReduce Tutorial : Reducers, combiners and partitioners. ======
-A //reducer// in a Hadoop job must be a subclass of [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Reducer.html|Reducer<Kin, Vin, Kout, Vout>].
+A //reducer// in a Hadoop job must be a subclass of [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Reducer.html|Reducer<Kin, Vin, Kout, Vout>]].
 As in the Perl API, any reducer can be used as a //combiner//.
@@ Line 75: / Line 75: @@
 }
 </file>
+  wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_export/code/courses:mapreduce-tutorial:step-25?codeblock=0' -O 'WordCount.java'
+  make -f /net/projects/hadoop/java/Makefile WordCount.jar
+  rm -rf step-25-out-ex1; /net/projects/hadoop/bin/hadoop WordCount.jar -r 1 /home/straka/wiki/cs-text-small step-25-out-ex1
+  less step-25-out-ex1/part-*
+==== Remarks ====
+  * The reducer is used also as combiner.
+  * The reducer could have defined ''setup'' and ''cleanup'' methods, just like in Perl API.
+  * Mind the way how the code uses ''Text'' and ''IntWritable''. By design, all the ''Writable'' subclasses should be //reused as often as possible to avoid allocation//. Therefore a ''Writable'' instance is usually allocated only once per mapper / reducer and methods ''get'' and ''set'' should be used to work with its content. That is why the last two lines in the reducer are <code>sumWritable.set(sum);
+context.write(key, sumWritable)</code> instead of <code>context.write(key, new IntWritable(sum));</code>
+  * **VIM users**: As in the mapper, code completion is puzzled by the 'Context' type. This time it can be replaced by ''ReduceContext<Text, IntWritable, Text, IntWritable>'' for the code completion to work.
+===== Partitioner =====
+A //partitioner// is a subclass of [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Partitioner.html|Partitioner<K, V>]], which has the only method ''getPartition(KEY key, VALUE value, int numPartitions)''.
+===== Example =====
+A slightly complicated example follows. We want a Hadoop job which will parse (wiki_article_name, wiki_article_content) pairs and should create two outputs -- a sorted list of article names and a sorted list of words present int he article content. We use:
+  * ''Mapper<Text, Text, Text, IntWritable>'' which outputs (wiki_article_name, 0) and (word, 1) pairs.
+  * ''Partitioner<Text, IntWritable>'' which for pair (text, number) outputs the value of ''number''.
+  * ''Combiner<Text, IntWritable, Text, IntWritable>'' which for a key and many values outputs one pair (key, value). Ideally we would like to output (key, null) pair, but that is not possible -- a Combiner must not change the type of keys and values.
+  * ''Reducer<Text, IntWritable, Text, NullWritable>'' which discards the value and outputs key only.
+The solution is a bit clumsy. If the mapper could output (key, value, partition) instead of just (key, value), we would not have to use the ''value'' as a partition number and the types would be simplified.
+**Remark:** If the type of keys or values which the mapper outputs //is different// than the type of keys and values the reducer outputs, then [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Job.html#setMapOutputKeyClass(java.lang.Class)|job.setMapOutputKeyClass]] or [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Job.html#setMapOutputValueClass(java.lang.Class)|job.setMapOutputValueClass]] must be used. If they are not used, the type of keys and values produced by the mapper is expected to be the same as from the reducer.
+<file java ArticlesAndWords.java>
+import java.io.IOException;
+import java.util.StringTokenizer;
+import org.apache.hadoop.conf.*;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.io.*;
+import org.apache.hadoop.mapreduce.*;
+import org.apache.hadoop.mapreduce.lib.input.*;
+import org.apache.hadoop.mapreduce.lib.output.*;
+import org.apache.hadoop.util.*;
+public class ArticlesAndWords extends Configured implements Tool {
+  public static class TheMapper extends Mapper<Text, Text, Text, IntWritable>{
+    private Text word = new Text();
+    private IntWritable zero = new IntWritable(0);
+    private IntWritable one = new IntWritable(1);
+    public void map(Text key, Text value, Context context) throws IOException, InterruptedException {
+      context.write(key, zero);
+      for (String token : value.toString().split("\\W+")) {
+        word.set(token);
+        context.write(word, one);
+      }
+    }
+  }
+  public static class ThePartitioner extends Partitioner<Text, IntWritable> {
+    public int getPartition(Text key, IntWritable value, int numPartitions) {
+      return value.get();
+    }
+  }
+  public static class TheCombiner extends Reducer<Text, IntWritable, Text, IntWritable> {
+    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
+      for (IntWritable value : values) {
+        context.write(key, value);
+        return;
+      }
+    }
+  }
+  public static class TheReducer extends Reducer<Text, IntWritable, Text, NullWritable> {
+    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
+      context.write(key, NullWritable.get());
+    }
+  }
+  public int run(String[] args) throws Exception {
+    if (args.length < 2) {
+      System.err.printf("Usage: %s.jar in-path out-path", this.getClass().getName());
+      return 1;
+    }
+    Job job = new Job(getConf(), this.getClass().getName());
+    job.setJarByClass(this.getClass());
+    job.setMapperClass(TheMapper.class);
+    job.setPartitionerClass(ThePartitioner.class);
+    job.setCombinerClass(TheCombiner.class);
+    job.setReducerClass(TheReducer.class);
+    job.setOutputKeyClass(Text.class);
+    job.setOutputValueClass(NullWritable.class);
+    job.setMapOutputValueClass(IntWritable.class);
+    job.setInputFormatClass(KeyValueTextInputFormat.class);
+    FileInputFormat.addInputPath(job, new Path(args[0]));
+    FileOutputFormat.setOutputPath(job, new Path(args[1]));
+    return job.waitForCompletion(true) ? 0 : 1;
+  }
+  public static void main(String[] args) throws Exception {
+    int res = ToolRunner.run(new ArticlesAndWords(), args);
+    System.exit(res);
+  }
+}
+</file>
+  wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_export/code/courses:mapreduce-tutorial:step-25?codeblock=3' -O 'ArticlesAndWords.java'
+  make -f /net/projects/hadoop/java/Makefile ArticlesAndWords.jar
+  rm -rf step-25-out-ex2; /net/projects/hadoop/bin/hadoop ArticlesAndWords.jar -c 2 -r 2 /home/straka/wiki/cs-text-small step-25-out-ex2
+  less step-25-out-ex2/part-*
+===== Exercise =====
+Implement the [[.:step-13|sorting exercise]] in Java -- only the part with uniform keys.
+**Remark:** Values of type ''Text'' are sorted lexicographically, but values of type ''IntWritable'' are sorted according to value. Your mapper should therefore produce pairs of types (''IntWritable'', ''Text'').
+You can download the {{:courses:mapreduce-tutorial:step-25.txt|Sorting.java}} template and execute it.
+  wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_media/courses:mapreduce-tutorial:step-25.txt' -O 'SortingUniform.java'
+  # NOW VIEW THE FILE
+  # $EDITOR SortingUniform.java
+  make -f /net/projects/hadoop/java/Makefile SortingUniform.jar
+  rm -rf step-25-out-uniform; /net/projects/hadoop/bin/hadoop SortingUniform.jar -c 2 -r 2 /net/projects/hadoop/examples/inputs/numbers-small step-25-out-uniform
+  less step-25-out-uniform/part-*
+  wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_media/courses:mapreduce-tutorial:step-25.txt' -O 'SortingNonuniform.java'
+  # NOW VIEW THE FILE
+  # $EDITOR SortingUniform.java
+  make -f /net/projects/hadoop/java/Makefile SortingNonuniform.jar
+  rm -rf step-25-out-nonuniform; /net/projects/hadoop/bin/hadoop SortingNonuniform.jar -c 2 -r 2 /net/projects/hadoop/examples/inputs/nonuniform-small  step-25-out-nonuniform
+  less step-25-out-nonuniform/part-*
+----
+<html>
+<table style="width:100%">
+<tr>
+<td style="text-align:left; width: 33%; "></html>[[step-24|Step 24]]: Mappers, running Java Hadoop jobs, counters.<html></td>
+<td style="text-align:center; width: 33%; "></html>[[.|Overview]]<html></td>
+<td style="text-align:right; width: 33%; "></html>[[step-26|Step 26]]: Compression and job configuration.<html></td>
+</tr>
+</table>
+</html>

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