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--- courses:mapreduce-tutorial:step-29 [2012/02/05 19:03]
straka
+++ courses:mapreduce-tutorial:step-29 [2012/02/05 19:14] (current)
straka
@@ Line 58: / Line 58: @@
 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//.
+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.
@@ Line 65: / Line 65: @@
   - 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.
 ----

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