//mapreduce程序import java.io.IOException;import java.util.StringTokenizer;import org.apache.hadoop.conf.Configuration;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.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;public class WordCount { /** * TokenizerMapper 继续自 Mapper<LongWritable, Text, Text, IntWritable> * * [一个文件就一个map,两个文件就会有两个map] * map[这里读入输入文件内容 以" \t\n\r\f" 进行分割，然后设置 word ==> one 的key/value对] * * @param Object Input key Type: * @param Text Input value Type: * @param Text Output key Type: * @param IntWritable Output value Type: * * Writable的主要特点是它使得Hadoop框架知道对一个Writable类型的对象怎样进行serialize以及deserialize. * WritableComparable在Writable的基础上增加了compareT接口，使得Hadoop框架知道怎样对WritableComparable类型的对象进行排序。 * * @ author liuqingjie * */ public static class TokenizerMapper extends Mapper<LongWritable, Text, Text, IntWritable>{ private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); } } } /** * IntSumReducer 继承自 Reducer<Text,IntWritable,Text,IntWritable> * * [不管几个Map,都只有一个Reduce,这是一个汇总] * reduce[循环所有的map值,把word ==> one 的key/value对进行汇总] * * 这里的key为Mapper设置的word[每一个key/value都会有一次reduce] * * 当循环结束后，最后的确context就是最后的结果. * * @author liuqingjie * */ public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> { private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable<IntWritable> values, Context context ) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); context.write(key, result); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); if (args.length != 2) { System.err.println("请配置路径 "); System.exit(2); } Job job = new Job(conf, "wordcount"); job.setJarByClass(WordCount.class);//主类 job.setMapperClass(TokenizerMapper.class);//mapper job.setReducerClass(IntSumReducer.class);//reducer job.setMapOutputKeyClass(Text.class);//设置map输出数据的关键类 job.setMapOutputValueClass(IntWritable.class);//设置map输出值类 job.setOutputKeyClass(Text.class);//设置作业输出数据的关键类 job.setOutputValueClass(IntWritable.class);//设置作业输出值类 FileInputFormat.addInputPath(job, new Path(otherArgs[0]));//文件输入 FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));//文件输出 System.exit(job.waitForCompletion(true) ? 0 : 1);//等待完成退出. }} |
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Job job = new Job(conf, "word count"); job.setJarByClass(WordCount.class);//主类 job.setMapperClass(TokenizerMapper.class);//mapper job.setReducerClass(IntSumReducer.class);//reducer job.setMapOutputKeyClass(Text.class);//设置map输出数据的关键类 job.setMapOutputValueClass(IntWritable.class);//设置map输出值类 job.setOutputKeyClass(Text.class);//设置作业输出数据的关键类 job.setOutputValueClass(IntWritable.class);//设置作业输出值类 FileInputFormat.addInputPath(job, new Path(otherArgs[0]));//文件输入 FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));//文件输出 System.exit(job.waitForCompletion(true) ? 0 : 1);//等待完成退出. |
原文：http://blog.csdn.net/sunlei1980/article/details/46473163