MapReduce第一个项目

参考自林子雨大数据教学：http://dblab.xmu.edu.cn/blog/hadoop-build-project-using-eclipse/

整个过程按照实验要求

第一步创建文件夹；放入文本文件，填入一下数据。

1000481 2010-04-04 16:54:31
1001597 2010-04-07 15:07:52
1001560 2010-04-07 15:08:27
1001368 2010-04-08 08:20:30
1002061 2010-04-08 16:45:33
1003289 2010-04-12 10:50:55
1003290 2010-04-12 11:57:35
1003292 2010-04-12 12:05:29
1002420 2010-04-14 15:24:12
1001679 2010-04-14 19:46:04
1010675 2010-04-14 15:23:53
1002429 2010-04-14 17:52:45
1002427 2010-04-14 19:35:39
1003326 2010-04-20 12:54:44
1002420 2010-04-15 11:24:49
1002422 2010-04-15 11:35:54
1003066 2010-04-15 11:43:01
1003055 2010-04-15 11:43:06
1010183 2010-04-15 11:45:24
1002422 2010-04-15 11:45:49
1003100 2010-04-15 11:45:54
1003094 2010-04-15 11:45:57
1003064 2010-04-15 11:46:04
1010178 2010-04-15 16:15:20
1003101 2010-04-15 16:37:27
1003103 2010-04-15 16:37:05
1003100 2010-04-15 16:37:18
1003066 2010-04-15 16:37:31
1003103 2010-04-15 16:40:14
1003100 2010-04-15 16:40:16

将Linux的文件上传到HDFS/mapreduce1/in的目录下

下载： hadoop2x-eclipse-plugin

将 release 中的 hadoop-eclipse-kepler-plugin-2.6.0.jar 复制到 Eclipse 安装目录的 plugins 文件夹中运行 eclipse -clean

启动 Eclipse 后就可以在左侧的Project Explorer中看到 DFS Locations

第一步：选择 Window 菜单下的 Preference。

窗体的左侧会多出 Hadoop Map/Reduce 选项，点击此选项，选择 Hadoop 的安装目录

第二步：切换 Map/Reduce 开发视图，选择 Window 菜单下选择 Open Perspective -> Other（CentOS 是 Window -> Perspective -> Open Perspective -> Other），弹出一个窗体，从中选择 Map/Reduce 选项即可进行切换。

第三步：建立与 Hadoop 集群的连接，点击 Eclipse软件右下角的 Map/Reduce Locations 面板，在面板中单击右键，选择 New Hadoop Location。

Location name 随便起一个名字

运行测试代码WordCount

新建项目

在src文件夹下将hadoop安装目录中的配置文件复制过来

core-site.xml hdfs-site.xml log4j.properties

右击项目刷新（refresh）出现以下文件

创建Demo类

package org.apache.hadoop.examples;

import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 Demo {
public static void main(String[] args) throws IOException,ClassNotFoundException,InterruptedException {
Job job = Job.getInstance();
job.setJobName("WordCount");
job.setJarByClass(WordCount.class);
job.setMapperClass(doMapper.class);
job.setReducerClass(doReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
Path in = new Path("hdfs://localhost:9000/mymapreduce1/in/buyer_favorite1");
Path out = new Path("hdfs://localhost:9000/mymapreduce1/out");
FileInputFormat.addInputPath(job,in);
FileOutputFormat.setOutputPath(job,out);
System.exit(job.waitForCompletion(true)?0:1);
}
public static class doMapper extends Mapper<Object,Text,Text,IntWritable>{
public static final IntWritable one = new IntWritable(1);
public static Text word = new Text();
@Override
protected void map(Object key, Text value, Context context)
throws IOException,InterruptedException {
StringTokenizer tokenizer = new StringTokenizer(value.toString(),"  ");
word.set(tokenizer.nextToken());
context.write(word,one);
            }
}
public static class doReducer extends Reducer<Text,IntWritable,Text,IntWritable>
    {
private IntWritable result = new IntWritable();
@Override
protected void reduce(Text key,Iterable<IntWritable> values,Context context)
throws IOException,InterruptedException
    {
int sum = 0;
for (IntWritable value : values)
            {
sum += value.get();
            }
result.set(sum);
context.write(key,result);
        }
    }
}

运行截图：

MapReduce第一个项目

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