Mapreduce实例——MapReduce自定义输入格式

实验步骤

1.开启Hadoop

2.新建mapreduce11目录

在Linux本地新建/data/mapreduce11目录

3. 上传文件到linux中

（自行生成文本文件，放到个人指定文件夹下）

cat1文件

52001 有机蔬果 601

52002 有机肉类水产 602

52003 有机粮油干货 603

52004 有机冲饮 604

52005 其它 605

4.在HDFS中新建目录

首先在HDFS上新建/mymapreduce11/in目录，然后将Linux本地/data/mapreduce11目录下的cat1文件导入到HDFS的/mymapreduce11/in目录中。

hadoop fs -mkdir -p /mymapreduce11/in

hadoop fs -put /root/data/mapreduce11/cat1 /mymapreduce11/in

5.新建Java Project项目

新建Java Project项目，项目名为mapreduce。

在mapreduce项目下新建包，包名为mapreduce10。

在mapreduce10包下新建类，类名为FileKeyInputFormat、FileKeyRecordReader、FileKeyMR。

6.添加项目所需依赖的jar包

右键项目，新建一个文件夹，命名为：hadoop2lib,用于存放项目所需的jar包。

将/data/mapreduce2目录下，hadoop2lib目录中的jar包，拷贝到eclipse中mapreduce2项目的hadoop2lib目录下。

hadoop2lib为自己从网上下载的，并不是通过实验教程里的命令下载的

选中所有项目hadoop2lib目录下所有jar包，并添加到Build Path中。

7.编写程序代码

（1）FileKeyInputFormat.java

package mapreduce10;
import java.io.IOException;
import java.util.List;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.JobContext;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
public class FileKeyInputFormat extends FileInputFormat{
    public FileKeyInputFormat(){}
    public RecordReader createRecordReader(InputSplit split,TaskAttemptContext tac)
            throws IOException,InterruptedException{
        FileKeyRecordReader fkrr=new FileKeyRecordReader();
        try {
            fkrr.initialize(split,tac);
        } catch (Exception e) {
            e.printStackTrace();
        }
        return fkrr;
    }
    protected long computeSplitSize(long blockSize,long minSize,long maxSize){
        return super.computeSplitSize(blockSize,minSize,maxSize);
    }
    public List getSplits(JobContext arg0)throws IOException{
        return super.getSplits(arg0);
    }
    protected boolean isSplitable(JobContext context,Path filename){
        return true;
    }
    protected List listStatus(JobContext arg0)throws IOException{
        return super.listStatus(arg0);
    }
}

（2）FileKeyMR.java

package mapreduce10;
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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.input.TextInputFormat;
public class FileKeyMR{
    public static class Map extends Mapper{
        public void map(Text key,Text value,Context context) throws IOException, InterruptedException{
            String line=value.toString();
            System.out.println(line);
            String str[]=line.split("\t");
            for(String st:str){
                context.write(key,new Text(st));
            }
            System.out.println(line);
        }
    }
    public static class Reduce extends Reducer{
        public void reduce(Text key,Iterable values,Context context) throws IOException, InterruptedException{
            String s=":";
            for(Text val:values){
                s+=val;
            }
            context.write(key,new Text(s));
        }
    }
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException{
        Configuration conf=new Configuration();
        Job job=new Job(conf,"FileKeyMR");
        job.setJarByClass(FileKeyMR.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setInputFormatClass(FileKeyInputFormat.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        FileInputFormat.addInputPath(job, new Path("hdfs://192.168.109.10:9000/mymapreduce11/in/cat1"));
        FileOutputFormat.setOutputPath(job, new Path("hdfs://192.168.109.10:9000/mymapreduce11/out"));
        System.exit(job.waitForCompletion(true)?0:1);
    }
}

（3）FileKeyRecordReader.java

package mapreduce10;
import java.io.IOException;
import java.io.InterruptedIOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.input.LineRecordReader;
public class FileKeyRecordReader extends RecordReader {
    public FileKeyRecordReader(){}
    String fn;
    LineRecordReader lrr=new LineRecordReader();
    public void initialize(InputSplit arg0,TaskAttemptContext arg1)
            throws IOException,InterruptedException{
        lrr.initialize(arg0, arg1);
        this.fn=((FileSplit)arg0).getPath().getName();
    }
    public void close()throws IOException{
        lrr.close();
    }
    public Text getCurrentKey()throws IOException,InterruptedException{
        System.out.println("CurrentKey");
        LongWritable lw=lrr.getCurrentKey();
        Text key =new Text("("+fn+"@"+lw+")");
        System.out.println("key--"+key);
        return key;
    }
    public Text getCurrentValue()throws IOException,InterruptedException{
        return lrr.getCurrentValue();
    }
    public float getProgress()throws IOException,InterruptedException{
        return 0;
    }
    public boolean nextKeyValue() throws IOException,InterruptedIOException{
        return lrr.nextKeyValue();
    }
}

8.运行代码

在FileKeyMR类文件中，右键并点击=>Run As=>Run on Hadoop选项，将MapReduce任务提交到Hadoop中。

9.查看实验结果

待执行完毕后，进入命令模式下，在HDFS中/mymapreduce11/out查看实验结果。

hadoop fs -ls /mymapreduce11/out  

hadoop fs -cat /mymapreduce11/out/part-r-00000  

图一为我的运行结果，图二为实验结果

经过对比，发现结果一样

此处为浏览器截图