工学1号馆

原创文章，转载请注明： 转载自工学1号馆

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。

13583746322 10086
13202930239 120
13434300000 13800138000
13583746322 13800138000
13687653322 110
13777777322 10086
13583234324 10000
13583746322 13800138000
13234328847 10086
13583000000 10000
13000039984 10000
13580006322 10086
13583216322 110

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 13583234324|13000039984|13583000000|
10086 13583746322|13777777322|13234328847|13580006322|
110 13583216322|13687653322|
120 13202930239|
13800138000 13583746322|13434300000|13583746322|
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

源代码如下：

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.NullWritable;
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.Mapper.Context;
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 invertedindex extends Configured implements Tool{
	enum Counter{
		LINESKIP,//记录出错的行
	}
/**
*Mapper<LongWritable,Text,NullWritable,Text>
*LongWritable,Text 是输入数据的key和value 如：路由日志的每一行的首字符的偏移量作为key,整一行的内容作为value
*NullWritable,Text 是输出数据的key和value
*
*/
public static class Map extends Mapper<LongWritable, Text, Text, Text>{

	//map(LongWritable key,Text value,Context context)
	//LongWritable key,Text value,和RouterMapper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{
		String line = value.toString();
		try{
			String[] lineSplit = line.split(" ");//分割原始数据  \\135, 10086
			String anum = lineSplit[0];
			String bnum = lineSplit[1];
			
			//输出
			context.write(new Text(bnum), new Text(anum));
		
		}catch(ArrayIndexOutOfBoundsException e) {
			//对异常数据进行处理，出现异常，令计数器+1
			context.getCounter(Counter.LINESKIP).increment(1);
			return;
		}
	}
}

public static class Reduce extends Reducer<Text, Text, Text, Text> {
	public void reduce(Text key, Iterable<Text>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));
	}
}

@Override
public int run(String[] args) throws Exception {
	Configuration conf = getConf();
	Job job = new Job(conf,"invertedindex");//指定任务名称
	job.setJarByClass(invertedindex.class);//指定Class
	FileInputFormat.addInputPath(job, new Path(args[0]));//输入路径
	FileOutputFormat.setOutputPath(job, new Path(args[1]));//输出路径
	
	job.setMapperClass(Map.class);//调用Mapper类作为Mapper的任务代码
	job.setReducerClass(Reduce.class);
	job.setOutputFormatClass(TextOutputFormat.class);
	
	job.setOutputKeyClass(Text.class);//指定输出的key格式，要和RouterMapper的输出数据格式一致
	job.setOutputValueClass(Text.class);//指定输出的value格式，要和RouterMapper的输出数据格式一致
	job.waitForCompletion(true);
	return job.isSuccessful()?0:1;
}

	//测试用的main方法
	//main方法运行的时候需要指定输入路径和输出路径
	public static void main(String[] args) throws Exception{
		int res = ToolRunner.run(new Configuration(), new invertedindex(), args);
		System.exit(res);
	}
}