Hadoop小程序倒排索引

　　有一批电话通信清单，保存了主叫和被叫的记录，记录格式下，主叫和被叫之间是以空格隔开的。

13400001111 10086
13500002222 10000
13600003333 114
13700004444 12580
13711111111 10086
13822222222 12580
13922225555 12580
18622220000 114
18800000000 114

　　现在需要做一个倒排索引，记录拨打给被叫的所有主叫号码，记录的格式如下，主叫号码之间以|分隔。

10000    13500002222|
10086    13400001111|13711111111|
114    13600003333|18622220000|18800000000|
12580    13700004444|13822222222|13922225555|

1、算法思路

　　源文件——》Mapper(分隔原始数据，以被叫作为key，以主叫作为value)——》Reducer(把拥有相同被叫的主叫号码用|分隔汇总)——》输出到HDFS

2、Hadoop程序

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
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;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;





public class App_2 extends Configured implements Tool{
    
    @Override
    public int run(String[] arg0) throws Exception {
        Configuration conf = getConf();
        Job job = new Job(conf,"App_2");
        job.setJarByClass(App_2.class);
        FileInputFormat.addInputPath(job, new Path(arg0[0]));
        FileOutputFormat.setOutputPath(job, new Path(arg0[1]));
        
        job.setMapperClass(CallMapper.class);
        job.setReducerClass(CallReducer.class);
        
        job.setOutputFormatClass(TextOutputFormat.class);
        
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        job.waitForCompletion(true);
        return job.isSuccessful()?0:1;
    }
    
    
    enum Counter{
        SKIPLINE,//记录出错的行数
    }
    
    /**
     *Mapper<LongWritable,Text,Text,Text>
     *LongWritable,Text 是输入数据的key和value  如：清单的每一行的首字符的偏移量作为key,整一行的内容作为value
     *Text,Text    是输出数据的key和value
     *
     */
    public static class CallMapper extends Mapper<LongWritable,Text,Text,Text> {
        
        //map(LongWritable key,Text value,Context context)
        //LongWritable key,Text value,和CallMapper类的输入数据的key、value对应
        //Context 上下文环境
        public void map(LongWritable key,Text value,Context context) throws IOException, InterruptedException {
            try {
                String line = value.toString();
                String[] call = line.split(" ");
                String caller = call[0];//主叫
                String callee = call[1];//被叫
                
                Text outKey = new Text(callee);
                Text outValue = new Text(caller);
                
                context.write(outKey, outValue);//被叫作为key,主叫作为value输出
            } catch(ArrayIndexOutOfBoundsException e) {
                context.getCounter(Counter.SKIPLINE).increment(1);//出错，行数+1
                return;
            }
            
        }
    }
    
    /**
     *Reducer<Text,Text,Text,Text>
     *Text,Text,是输入数据的key和value,对应Mapper中的输出数据
     *Text,Text    是最终输出数据的key和value
     *
     */
    public static class CallReducer extends Reducer<Text,Text,Text,Text>{
        
        //reduce(Text key,Text value,Context context)
        //Text key,Iterable<Text> values,和CallMapper类的输出数据的key、value对应，其中values是对应key的所有主叫的集合
        //Context 上下文环境
        public void reduce(Text key,Iterable<Text> values,Context context) throws IOException, InterruptedException {
            String result = "";
            String temp = "";
            //对主叫用|分隔
            for(Text value : values) {
                temp = value.toString();
                result += (temp + "|");
            }
            
            Text outValue = new Text(result);
            //最终输出：被叫    用|分隔的主叫
            context.write(key, outValue);
        }
    }
    
    public static void main(String[] args) throws Exception{
        int res = ToolRunner.run(new Configuration(), new App_2(), args);
        System.exit(res);
        
    }
    
}

3、可以在eclipse中运行程序，输入两个参数，一个是通话清单文件所在路径，一个是结果输出目录

4、也可以将程序打成jar包，用命令执行。

[coder@h1 hadoop-0.20.2]$ bin/hadoop jar /home/coder/call.jar /user/coder/in/call.txt /user/coder/output

　　注意：/user/coder/in/call.txt 和/user/coder/output都是HDFS中的路径