Hadoop示例程序WordCount详解

package com.felix; 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 } }