MapReduce编程实战2——倒排索引（jar包）

任务要求：

//输入文件格式

18661629496 110

13107702446 110

1234567 120

2345678 120

987654 110

2897839274 18661629496

//输出文件格式格式

11018661629496|13107702446|987654|18661629496|13107702446|987654|

1201234567|2345678|1234567|2345678|

186616294962897839274|2897839274|

mapreduce程序编写：

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 Test2 {        enum Counter        {                LINESKIP,//记录出错的行        }    public static class Map extends Mapper{        public void map(LongWritable key, Text value, Context context)                throws IOException, InterruptedException {                 String line = value.toString();//读取源数据             try             {                     //数据处理                     String [] lineSplit = line.split(" ");//18661629496,110                     String anum = lineSplit[0];                     String bnum = lineSplit[1];                       //输出格式：110,18661629496                                        context.write(new Text(bnum), new Text(anum));                                  }             catch(ArrayIndexOutOfBoundsException e)             {                     context.getCounter(Counter.LINESKIP).increment(1);//出错时计数器+1                     return;             }        }    }    public static class Reduce extends Reducer {        public void reduce(Text key, Iterable values, Context context)                throws IOException, InterruptedException {            String valueString;            String out="";            for(Text value:values)            {                    valueString=value.toString();                    out+=valueString+"|";            }            context.write(key, new Text(out));        }    }    public static void main(String[] args) throws Exception {        Configuration conf = new Configuration();        if (args.length != 2) {            System.err.println("请配置输入输出路径 ");            System.exit(2);        }        //各种配置        Job job = new Job(conf, "telephone ");//作业名称配置        //类配置        job.setJarByClass(Test2.class);        job.setMapperClass(Map.class);        job.setReducerClass(Reduce.class);        //map输出格式配置        job.setMapOutputKeyClass(Text.class);        job.setMapOutputValueClass(Text.class);        //作业输出格式配置        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(Text.class);        //增加输入输出路径        FileInputFormat.addInputPath(job, new Path(args[0]));        FileOutputFormat.setOutputPath(job, new Path(args[1]));        //任务完成时退出        System.exit(job.waitForCompletion(true) ? 0 : 1);    }}

将mapreduce程序打包为jar文件：

1.右键项目名称->Export->java->jar file

2.配置jar文件存储位置

3.选择main calss

4.运行jar文件

[liuqingjie@master hadoop-0.20.2]$ bin/hadoop jar /home/liuqingjie/test2.jar /user/liuqingjie/in /user/liuqingjie/out

15/05/14 01:46:47 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.

15/05/14 01:46:47 INFO input.FileInputFormat: Total input paths to process : 2

15/05/14 01:46:48 INFO mapred.JobClient: Running job: job_201505132004_0005

15/05/14 01:46:49 INFO mapred.JobClient: map 0% reduce 0%

15/05/14 01:46:57 INFO mapred.JobClient: map 100% reduce 0%

15/05/14 01:47:09 INFO mapred.JobClient: map 100% reduce 100%

……………………………………………………………………………………

查看结果

[liuqingjie@master hadoop-0.20.2]$ bin/hadoop dfs -cat ./out/*

cat: Source must be a file.

110 18661629496|13107702446|987654|18661629496|13107702446|987654|

120 1234567|2345678|1234567|2345678|

18661629496 2897839274|2897839274|