Hadoop - WordCount代码示例

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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类:实现了Mapper和Reducer接口的基类（其中的方法只是实现接口，而未作任何事情）
    * Mapper接口：
    * WritableComparable接口：实现WritableComparable的类可以相互比较。所有被用作key的类应该实现此接口。
    * Reporter 则可用于报告整个应用的运行进度，本例中未使用。
    *
    */
    public static class Map extends MapReduceBase implements
            Mapper<LongWritable, Text, Text, IntWritable>
    {
        /**
        * LongWritable, IntWritable, Text 均是 Hadoop 中实现的用于封装 Java 数据类型的类，这些类实现了WritableComparable接口，
        * 都能够被串行化从而便于在分布式环境中进行数据交换，你可以将它们分别视为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)
        * 映射一个单个的输入k/v对到一个中间的k/v对
        * 输出对不需要和输入对是相同的类型，输入对可以映射到0个或多个输出对。
        * OutputCollector接口：收集Mapper和Reducer输出的<k,v>对。
        * OutputCollector接口的collect(k, v)方法:增加一个(k,v)对到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配置类，向hadoop框架描述map-reduce执行的工作
        * 构造方法：JobConf()、JobConf(Class exampleClass)、JobConf(Configuration conf)等
        */
        JobConf conf = new JobConf(WordCount.class);
        conf.setJobName("wordcount");          //设置一个用户定义的job名称
        conf.setOutputKeyClass(Text.class);    //为job的输出数据设置Key类
        conf.setOutputValueClass(IntWritable.class);  //为job输出设置value类
        conf.setMapperClass(Map.class);        //为job设置Mapper类
        conf.setCombinerClass(Reduce.class);      //为job设置Combiner类
        conf.setReducerClass(Reduce.class);        //为job设置Reduce类
        conf.setInputFormat(TextInputFormat.class);    //为map-reduce任务设置InputFormat实现类
        conf.setOutputFormat(TextOutputFormat.class);  //为map-reduce任务设置OutputFormat实现类
        /**
        * InputFormat描述map-reduce中对job的输入定义
        * setInputPaths():为map-reduce job设置路径数组作为输入列表
        * setInputPath()：为map-reduce job设置路径数组作为输出列表
        */
        FileInputFormat.setInputPaths(conf, new Path(args[0]));
        FileOutputFormat.setOutputPath(conf, new Path(args[1]));
        JobClient.runJob(conf);        //运行一个job
    }
}