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--- courses:mapreduce-tutorial:step-29 [2012/01/29 16:27]
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+++ courses:mapreduce-tutorial:step-29 [2012/02/05 19:14] (current)
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@@ Line 1: / Line 1: @@
-====== MapReduce Tutorial : Custom input formats ======
+====== MapReduce Tutorial : Custom sorting and grouping comparators. ======
-WholeFile
+====== Custom sorting comparator ======
-FileAsPath
-ParagraphFile
+The keys are sorted before processed by a reducer, using a
+[[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/io/RawComparator.html|Raw comparator]]. The default comparator uses the ''compareTo'' method provided by the key type, which is a subclass of [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/io/WritableComparable.html|WritableComparable]]. Consider for example the following ''IntPair'' type:
+<code java>
+public static class IntPair implements WritableComparable<IntPair> {
+  private int first = 0;
+  private int second = 0;
+  public void set(int left, int right) { first = left; second = right; }
+  public int getFirst() { return first; }
+  public int getSecond() { return second; }
+  public void readFields(DataInput in) throws IOException {
+    first = in.readInt();
+    second = in.readInt();
+  }
+  public void write(DataOutput out) throws IOException {
+    out.writeInt(first);
+    out.writeInt(second);
+  }
+  public int compareTo(IntPair o) {
+    if (first != o.first) return first < o.first ? -1 : 1;
+    else return second < o.second ? -1 : second == o.second ? 0 : 1;
+  }
+}
+</code>
+If we would like in a Hadoop job to sort the ''IntPair'' using the first element only, we can provide a ''RawComparator'' and set it using [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Job.html#setSortComparatorClass(java.lang.Class)|job.setSortComparatorClass]]:
+<code java>
+public static class IntPair implements WritableComparable<IntPair> {
+  ...
+  public static class FirstOnlyComparator implements RawComparator<IntPair> {
+    public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {
+      int first1 = WritableComparator.readInt(b1, s1);
+      int first2 = WritableComparator.readInt(b2, s2);
+      return first1 < first2 ? -1 : first1 == first2 ? 0 : 1;
+    }
+    public int compare(IntPair x, IntPair y) {
+      return x.getFirst() < y.getFirst() ? -1 : x.getFirst() == y.getFirst() ? 0 : 1;
+    }
+  }
+}
+...
+job.setSortComparatorClass(IntPair.FirstOnlyComparator.class);
+</code>
+Notice we used helper function ''readInt'' from [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/io/WritableComparator.html|WritableComparator]] class, which provides means of parsing primitive data types from byte streams.
+====== Grouping comparator ======
+In a reduce, it is guaranteed that keys are processed in ascending order. Sometimes it would be useful if the //values associated with one key// could also be processed in ascending order.
+That is possible only to some degree. The (key, value) pairs are compared //using the key only//. After the (key, value) pairs are sorted, the (key, value) pairs with the same key are grouped together. This grouping can be performed using a custom ''RawComparator'' -- it is therefore possible to group the input pairs using //only a part of the keys//. The custom grouping comparator can be specified using [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Job.html#setGroupingComparatorClass(java.lang.Class)|job.setGroupingComparatorClass]].
+As an example, consider that the input consists of (''IntWritable'', ''IntWritable'') pairs. We would like to perform a Hadoop job with these pairs, such that the values belonging to one key are sorted before processed by a reducer.
+  - The mapper produces (''IntPair'', ''IntWritable'') pairs. Notice that the key now consists of both numbers.
+  - These pairs are sorted by the ''IntPair'' keys -- i.e., by both numbers.
+  - The custom grouping comparator is used, which groups the ''IntPair'' keys using the first element only (using the ''RawComparator'' from the previous section):
+<code java>
+public static class IntPair implements WritableComparable<IntPair> {
+  ...
+  public static class FirstOnlyComparator implements RawComparator<IntPair> {
+    public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {
+      int first1 = WritableComparator.readInt(b1, s1);
+      int first2 = WritableComparator.readInt(b2, s2);
+      return first1 < first2 ? -1 : first1 == first2 ? 0 : 1;
+    }
+    public int compare(IntPair x, IntPair y) {
+      return x.getFirst() < y.getFirst() ? -1 : x.getFirst() == y.getFirst() ? 0 : 1;
+    }
+  }
+}
+...
+job.setGroupingComparatorClass(IntPair.FirstOnlyComparator.class);
+</code>
+====== Exercise ======
+Improve the [[.:step-28#exercise-1|inverted index exercise]] from the previous step to create for each word a //sorted// list of ''DocWithOccurrences<Text>''.
+Use the same approach as with the ''IntPair'' -- create a type ''TextPair'', which stores two values of type ''Text'' and let the mapper create ''(TextPair, DocWithOccurrences<Text>'' pairs, where the ''TextPair'' contains the word and then the document. Provide a ''FirstOnlyComparator'' which compares two ''TextPair''s using only the word (hint: use [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/io/Text.Comparator.html#compare(byte[],%20int,%20int,%20byte[],%20int,%20int)|Text.Comparator.compare]] when defining the byte version ''FirstOnlyComparator.compare'') and use it as a grouping comparator.
+----
+<html>
+<table style="width:100%">
+<tr>
+<td style="text-align:left; width: 33%; "></html>[[step-28|Step 28]]: Custom data types.<html></td>
+<td style="text-align:center; width: 33%; "></html>[[.|Overview]]<html></td>
+<td style="text-align:right; width: 33%; "></html>[[step-30|Step 30]]: Custom input formats.<html></td>
+</tr>
+</table>
+</html>

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