Mapreduce实例——倒排索引

实验步骤

1.开启Hadoop

2.新建mapreduce10目录

在Linux本地新建/data/mapreduce10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）

goods_0

袜子 189

毛衣 600

裤子 780

鞋子 30

呢子外套 90

牛仔外套 130

羽绒服 7

帽子 21

帽子 6

羽绒服 12

4.在HDFS中新建目录

首先在HDFS上新建/mymapreduce10/in目录，然后将Linux本地/data/mapreduce10目录下的goods_0文件导入到HDFS的/mymapreduce10/in目录中。

hadoop fs -mkdir -p /mymapreduce10/in

hadoop fs -put /root/data/mapreduce10/goods_0 /mymapreduce10/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mapreduce9。

在mapreduce9包下新建类，类名为ChainMapReduce。

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

ChainMapReduce.java

package mapreduce9;
import java.io.IOException;
import java.net.URI;
import org.apache.hadoop.conf.Configuration;
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.chain.ChainMapper;
import org.apache.hadoop.mapreduce.lib.chain.ChainReducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.io.DoubleWritable;
public class ChainMapReduce {
    private static final String INPUTPATH = "hdfs://192.168.109.10:9000/mymapreduce10/in/goods_0";
    private static final String OUTPUTPATH = "hdfs://192.168.109.10:9000/mymapreduce10/out";
    public static void main(String[] args) {
        try {
            Configuration conf = new Configuration();
            FileSystem fileSystem = FileSystem.get(new URI(OUTPUTPATH), conf);
            if (fileSystem.exists(new Path(OUTPUTPATH))) {
                fileSystem.delete(new Path(OUTPUTPATH), true);
            }
            Job job = new Job(conf, ChainMapReduce.class.getSimpleName());
            FileInputFormat.addInputPath(job, new Path(INPUTPATH));
            job.setInputFormatClass(TextInputFormat.class);
            ChainMapper.addMapper(job, FilterMapper1.class, LongWritable.class, Text.class, Text.class, DoubleWritable.class, conf);
            ChainMapper.addMapper(job, FilterMapper2.class, Text.class, DoubleWritable.class, Text.class, DoubleWritable.class, conf);
            ChainReducer.setReducer(job, SumReducer.class, Text.class, DoubleWritable.class, Text.class, DoubleWritable.class, conf);
            ChainReducer.addMapper(job, FilterMapper3.class, Text.class, DoubleWritable.class, Text.class, DoubleWritable.class, conf);
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(DoubleWritable.class);
            job.setPartitionerClass(HashPartitioner.class);
            job.setNumReduceTasks(1);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(DoubleWritable.class);
            FileOutputFormat.setOutputPath(job, new Path(OUTPUTPATH));
            job.setOutputFormatClass(TextOutputFormat.class);
            System.exit(job.waitForCompletion(true) ? 0 : 1);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
    public static class FilterMapper1 extends Mapper {
        private Text outKey = new Text();
        private DoubleWritable outValue = new DoubleWritable();
        @Override
        protected void map(LongWritable key, Text value, Mapper.Context context)
                throws IOException,InterruptedException {
            String line = value.toString();
            if (line.length() > 0) {
                String[] splits = line.split("\t");
                double visit = Double.parseDouble(splits[1].trim());
                if (visit <= 600) {
                    outKey.set(splits[0]);
                    outValue.set(visit);
                    context.write(outKey, outValue);
                }
            }
        }
    }
    public static class FilterMapper2 extends Mapper {
        @Override
        protected void map(Text key, DoubleWritable value, Mapper.Context context)
                throws IOException,InterruptedException {
            if (value.get() < 100) {
                context.write(key, value);
            }
        }
    }
    public  static class SumReducer extends Reducer {
        private DoubleWritable outValue = new DoubleWritable();
        @Override
        protected void reduce(Text key, Iterable values, Reducer.Context context)
                throws IOException, InterruptedException {
            double sum = 0;
            for (DoubleWritable val : values) {
                sum += val.get();
            }
            outValue.set(sum);
            context.write(key, outValue);
        }
    }
    public  static class FilterMapper3 extends Mapper {
        @Override
        protected void map(Text key, DoubleWritable value, Mapper.Context context)
                throws IOException, InterruptedException {
            if (key.toString().length() < 3) {
                System.out.println("写出去的内容为：" + key.toString() +"++++"+ value.toString());
                context.write(key, value);
            }
        }

    }

}

8.运行代码

在ChainMapReduce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/mymapreduce10/out查看实验结果。

hadoop fs -ls /mymapreduce10/out  

hadoop fs -cat /mymapreduce10/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