Hadoop - WordCount代碼示例

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import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
/**
 * 
 * 描述：WordCount explains by Felix
 * @author Hadoop Dev Group
 */
public class WordCount
{
    /**
    * MapReduceBase類:實現了Mapper和Reducer接口的基類（其中的方法只是實現接口，而未做任何事情）
    * Mapper接口：
    * WritableComparable接口：實現WritableComparable的類能夠相互比較。全部被用做key的類應該實現此接口。
    * Reporter 則可用於報告整個應用的運行進度，本例中未使用。 
    * 
    */
    public static class Map extends MapReduceBase implements
            Mapper<LongWritable, Text, Text, IntWritable>
    {
        /**
        * LongWritable, IntWritable, Text 均是 Hadoop 中實現的用於封裝 Java 數據類型的類，這些類實現了WritableComparable接口，
        * 都可以被串行化從而便於在分佈式環境中進行數據交換，你能夠將它們分別視爲long,int,String 的替代品。
        */
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();
        
        /**
        * Mapper接口中的map方法：
        * void map(K1 key, V1 value, OutputCollector<K2,V2> output, Reporter reporter)
        * 映射一個單個的輸入k/v對到一箇中間的k/v對
        * 輸出對不須要和輸入對是相同的類型，輸入對能夠映射到0個或多個輸出對。
        * OutputCollector接口：收集Mapper和Reducer輸出的<k,v>對。
        * OutputCollector接口的collect(k, v)方法:增長一個(k,v)對到output
        */
        public void map(LongWritable key, Text value,
                OutputCollector<Text, IntWritable> output, Reporter reporter)
                throws IOException
        {
            String line = value.toString();
            StringTokenizer tokenizer = new StringTokenizer(line);
            while (tokenizer.hasMoreTokens())
            {
                word.set(tokenizer.nextToken());
                output.collect(word, one);
            }
        }
    }
    public static class Reduce extends MapReduceBase implements
            Reducer<Text, IntWritable, Text, IntWritable>
    {
        public void reduce(Text key, Iterator<IntWritable> values,
                OutputCollector<Text, IntWritable> output, Reporter reporter)
                throws IOException
        {
            int sum = 0;
            while (values.hasNext())
            {
                sum += values.next().get();
            }
            output.collect(key, new IntWritable(sum));
        }
    }
    public static void main(String[] args) throws Exception
    {
        /**
        * JobConf：map/reduce的job配置類，向hadoop框架描述map-reduce執行的工做
        * 構造方法：JobConf()、JobConf(Class exampleClass)、JobConf(Configuration conf)等
        */
        JobConf conf = new JobConf(WordCount.class);
        conf.setJobName("wordcount");          //設置一個用戶定義的job名稱
        conf.setOutputKeyClass(Text.class);    //爲job的輸出數據設置Key類
        conf.setOutputValueClass(IntWritable.class);  //爲job輸出設置value類
        conf.setMapperClass(Map.class);        //爲job設置Mapper類
        conf.setCombinerClass(Reduce.class);      //爲job設置Combiner類
        conf.setReducerClass(Reduce.class);        //爲job設置Reduce類
        conf.setInputFormat(TextInputFormat.class);    //爲map-reduce任務設置InputFormat實現類
        conf.setOutputFormat(TextOutputFormat.class);  //爲map-reduce任務設置OutputFormat實現類
        /**
        * InputFormat描述map-reduce中對job的輸入定義
        * setInputPaths():爲map-reduce job設置路徑數組做爲輸入列表
        * setInputPath()：爲map-reduce job設置路徑數組做爲輸出列表
        */
        FileInputFormat.setInputPaths(conf, new Path(args[0]));
        FileOutputFormat.setOutputPath(conf, new Path(args[1]));
        JobClient.runJob(conf);        //運行一個job
    }
}