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17.3. Map Reduce Example
The following example uses a word count application to demonstrate MapReduce and its distributed task abilities.
This example assumes we have a mapping of the key
sentence stored on JBoss Data Grid nodes.
- Key is a String.
- Each sentence is a String.
All words that appear in all sentences must be counted.
The following defines the implementation of this distributed task:
public class WordCountExample {
/**
* In this example replace c1 and c2 with
* real Cache references
*
* @param args
*/
public static void main(String[] args) {
Cache c1 = null;
Cache c2 = null;
c1.put("1", "Hello world here I am");
c2.put("2", "Infinispan rules the world");
c1.put("3", "JUDCon is in Boston");
c2.put("4", "JBoss World is in Boston as well");
c1.put("12","WildFly");
c2.put("15", "Hello world");
c1.put("14", "Infinispan community");
c2.put("15", "Hello world");
c1.put("111", "Infinispan open source");
c2.put("112", "Boston is close to Toronto");
c1.put("113", "Toronto is a capital of Ontario");
c2.put("114", "JUDCon is cool");
c1.put("211", "JBoss World is awesome");
c2.put("212", "JBoss rules");
c1.put("213", "JBoss division of RedHat ");
c2.put("214", "RedHat community");
MapReduceTask<String, String, String, Integer> t =
new MapReduceTask<String, String, String, Integer>(c1);
t.mappedWith(new WordCountMapper())
.reducedWith(new WordCountReducer());
Map<String, Integer> wordCountMap = t.execute();
}
static class WordCountMapper implements Mapper<String,String,String,Integer> {
/** The serialVersionUID */
private static final long serialVersionUID = -5943370243108735560L;
@Override
public void map(String key, String value, Collector<String, Integer> collector) {
StringTokenizer tokens = new StringTokenizer(value);
for(String token : value.split("\\w")) {
collector.emit(token, 1);
}
}
}
static class WordCountReducer implements Reducer<String, Integer> {
/** The serialVersionUID */
private static final long serialVersionUID = 1901016598354633256L;
@Override
public Integer reduce(String key, Iterator<Integer> iter) {
int sum = 0;
while (iter.hasNext()) {
Integer i = (Integer) iter.next();
sum += i;
}
return sum;
}
}
}
In this second example, a
Collator is defined, which will transform the result of MapReduceTask Map<KOut,VOut> into a String that is returned to a task invoker. The Collator is a transformation function applied to a final result of MapReduceTask.
MapReduceTask<String, String, String, Integer> t = new MapReduceTask<String, String, String, Integer>(cache);
t.mappedWith(new WordCountMapper()).reducedWith(new WordCountReducer());
String mostFrequentWord = t.execute(
new Collator<String,Integer,String>() {
@Override
public String collate(Map<String, Integer> reducedResults) {
String mostFrequent = "";
int maxCount = 0;
for (Entry<String, Integer> e : reducedResults.entrySet()) {
Integer count = e.getValue();
if(count > maxCount) {
maxCount = count;
mostFrequent = e.getKey();
}
}
return mostFrequent;
}
});
System.out.println("The most frequent word is " + mostFrequentWord);
