简单记录在CentOS下安装Eclipse测试Hadoop的过程。
(一)，安装eclipse
1，下载eclipse，点这里
2，将文件上传到Centos7,可以用WinSCP
3，解压并安装eclipse
[root@Master opt]# tar zxvf '/home/s/eclipse-jee-neon-1a-linux-gtk-x86_64.tar.gz' -C/opt ---------------> 建立文件：[root@Master opt]# mkdir /usr/bin/eclipse ------------------》添加链接，即快捷方式：[root@Master opt]# ln -s /opt/eclipse/eclipse /usr/bin/eclipse -----------》点击eclipse，即可启动了
（二），建立Hadoop项目
1，下载hadoop plugin 2.7.3 链接： 密码：ms91
2，解压上述jar包插件，放到eclipse中plugins中，并重启eclipse
2， 在eclipse中加载dfs库,点击Windows 工具栏-------->选择show view如图：
2，打开resource 点击Window ----->Perspective----------->open Perspective 选择resource：
3，配置连接端口，点击eclipse下放的MapResource Location，点击添加：其中port号按照hdfs-site.xml 和core-site.xml来填写。
4，上传输入文件：使用hdfs dfs -put /home/file1 /data 即可在eclipse中看到如下：（要确保各个机器的防火墙都关闭，出现异常可以暂时不用关，后面跑下例子就全没了，呵呵）
(三），测试WordCount程序
1，新建项目：点击new ------------》project ----------->Map Reduce,如图：
2，给项目配置本地的hadoop文件，圆圈处写本地hadoop的路径：
3，新建个mappert类，写如下代码：
package word;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
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;
import org.apache.hadoop.util.GenericOptionsParser;
public class mapper {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println(otherArgs.length);
System.err.println("Usage: wordcount <in> <out>");
System.exit(2);
}
Job job = new Job(conf, "word count");
job.setJarByClass(mapper.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.out.print("ok");
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
2,点击run as ------------>RunConfigurations ---------->设置input和output文件参数
3，点击run,查看结果
文件的内容：
