eclipse hadoop远程调试

一、插件安装

我用的hadoop-2.6.0，插件下载地址

下载后扔到eclipse/dropins目录下即可，当然eclipse/plugins也是可以的，前者更为轻便，推荐；重启Eclipse，即可在透视图(Perspective)中看到Map/Reduce

二、配置

点击蓝色的小象图标，新建一个Hadoop连接：



注意，一定要填写正确，修改了某些端口，以及默认运行的用户名等

正常情况下，可以在项目区域可以看到



这样可以正常的进行HDFS分布式文件系统的管理：上传，删除等操作。

为下面测试做准备，需要先建了一个目录 user/root/input，然后上传两个txt文件到此目录：

三、Hadoop工程

新建一个Map/Reduce Project工程，设定好本地的hadoop目录



新建一个测试类HadoopTest：

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;
import org.apache.log4j.Logger;
public class HadoopTest{
private static final Logger log = Logger.getLogger(HadoopTest.class);

public static class Mapper1 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 {
log.info("Map key : " + key);
log.info("Map value : " + value);
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
String wordStr = itr.nextToken();
word.set(wordStr);
log.info("Map word : " + wordStr);
context.write(word, one);
}
}
}

public static class Reduce1 extends
Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException {
log.info("Reduce key : " + key);
log.info("Reduce value : " + values);
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
log.info("Reduce sum : " + 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("Usage: WordCountTest <in> <out>");
System.exit(2);
}

Job job = new Job(conf, "word count");
job.setJarByClass(HadoopTest.class);

job.setMapperClass(Mapper1.class);
job.setCombinerClass(Reducer1.class);
job.setReducerClass(Reducer1.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);

FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));

System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

右键，选择“Run Configurations”,弹出窗口，点击“Arguments”选项卡,在“Program argumetns”处预先输入参数:



备注：参数为了在本地调试使用，而非真实环境。

然后，点击“Apply”，然后“Close”。现在可以右键，选择“Run on Hadoop”，运行。

成功之后，在Eclipse下刷新HDFS目录，可以看到生成了ouput目录：