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--- courses:mapreduce-tutorial:step-29 [2012/01/29 17:23]
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+++ courses:mapreduce-tutorial:step-29 [2012/02/05 18:49]
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@@ Line 1: / Line 1: @@
-====== MapReduce Tutorial : Custom input formats ======
+====== MapReduce Tutorial : Custom sorting and grouping comparators. ======
-Every custom format reading keys of type ''K'' and values of type ''V'' must subclass [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/InputFormat.html|InputFormat<K, V>]]. Usually it is easier to subclass [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/lib/input/FileInputFormat.html|FileInputFormat<K, V>]] -- the file listing and splitting is then solved by the ''FileInputFormat'' itself.
+====== Fast sorting comparator ======
-===== WholeFileInputFormat =====
+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:
-We start by creating ''WholeFileInputFormat'', which reads any file and return exactly one input pair (input_path, file_content) with types (''Text'', ''BytesWritable''). The format does not allow file splitting -- each file will be processed by exactly one mapper.
+<code java>
+public static class IntPair implements WritableComparable<IntPair> {
+  private int first = 0;
+  private int second = 0;
-The main functionality lays in ''WholeFileRecordReader'', a subclass of [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/RecordReader.html|RecordReader<Text, BytesWritable]].
+  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]]:
+====== 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.
+----
+<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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