myeclipse配备hadoop开发环境_Eclipse

myeclipse配置hadoop开发环境

3. 安装Hadoop开发插件：

下载hadoop-1.0.3-eclipse-plugin.jar(http://ishare.iask.sina.com.cn/f/24669534.html)拷贝到myeclipseeclipse根目录下/dropins目录下：

4. 启动Eclipse，打开Perspective：

【Window】->【Open Perspective】->【Other...】->【Map/Reduce】->【OK】

5. 打开一个View：

【Window】->【Show View】->【Other...】->【MapReduce Tools】->【Map/Reduce Locations】->【OK】

6. 添加Hadoop location：

在Advanced parameters中修改：

hadoop.tmp.dir=/home/xsj/hadoop/hadoop-xsj
mapred.child.java.opts=-Xmx512m

注意：hadoop.tmp.dir的修改将影响到mapred.local.dir、mapred.system.dir、mapred.temp.dir、fs.s3.buffer.dir、fs.checkpoint.dir、fs.checkpoint.edits.dir、dfs.name.dir、dfs.name.edits.dir、dfs.data.dir等项，因此修改后需要重新启动Eclipse。

mapred.child.java.opts的修改可能最开始没有这一项，可以在后面再行修改，这里的512是我的虚拟机Ubuntu的内存大小，具体情形具体分析。

修改之后可以看到以下HDFS的视图：

7. 添加文本文件：

$ ./hadoop fs -mkdir /user/xsj/input

$ ./hadoop fs -put ./*.txt /user/xsj/input

8. 新建Map/Reduce Project：

【File】->【New】->【Project...】->【Map/Reduce】->【Map/Reduce Project】->【Project name: WordCount】->【Configure Hadoop install directory...】->【Hadoop installation directory: /home/xsj/hadoop/hadoop-0.20.2】->【Apply】->【OK】->【Next】->【Allow output folders for source folders】->【Finish】

9. 新建WordCount类：

【WordCount】->【src】->【New】->【Class】->【Package: org.apache.hadoop.examples】->【Name: WordCount】->【Finish】
Ubuntu下开发Eclipse下的Hadoop应用

添加/编写源代码：

/home/xsj/hadoop/hadoop-0.20.2/src/examples/org/apache/hadoop/examples/WordCount.java

package org.apache.hadoop.examples;

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 WordCount {

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("Usage: wordcount <in> <out>");

System.exit(2);

}

Job job = new Job(conf, "word count");

job.setJarByClass(WordCount.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.exit(job.waitForCompletion(true) ? 0 : 1);

}

10. 配置运行参数：

【Run】->【Run Configurations】->【Java Application】->【Word Count】->【Arguments】->【Program arguments: hdfs://localhost:9000/user/xsj/input/* hdfs://localhost:9000/user/xsj/output】->【VM arguments: -Xms512m -Xmx512m】->【Apply】->【Close】->【Run】->【Run As】->【Run On Hadoop】

11. 运行：

控制台正常输出：

12/06/01 10:23:31 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=

12/06/01 10:23:33 WARN mapred.JobClient: No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String).

12/06/01 10:23:36 INFO input.FileInputFormat: Total input paths to process : 2

12/06/01 10:23:37 INFO mapred.JobClient: Running job: job_local_0001

12/06/01 10:23:37 INFO input.FileInputFormat: Total input paths to process : 2

12/06/01 10:23:37 INFO mapred.MapTask: io.sort.mb = 100

12/06/01 10:23:40 INFO mapred.MapTask: data buffer = 79691776/99614720

12/06/01 10:23:40 INFO mapred.MapTask: record buffer = 262144/327680

12/06/01 10:23:44 INFO mapred.JobClient: map 0% reduce 0%

12/06/01 10:23:52 INFO mapred.MapTask: Starting flush of map output

12/06/01 10:23:59 INFO mapred.LocalJobRunner:

12/06/01 10:23:59 INFO mapred.MapTask: Finished spill 0

12/06/01 10:24:00 INFO mapred.JobClient: map 100% reduce 0%

12/06/01 10:24:00 INFO mapred.TaskRunner: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting

12/06/01 10:24:03 INFO mapred.LocalJobRunner:

12/06/01 10:24:03 INFO mapred.TaskRunner: Task 'attempt_local_0001_m_000000_0' done.

12/06/01 10:24:04 INFO mapred.MapTask: io.sort.mb = 100

12/06/01 10:24:08 INFO mapred.MapTask: data buffer = 79691776/99614720

12/06/01 10:24:08 INFO mapred.MapTask: record buffer = 262144/327680

12/06/01 10:24:10 INFO mapred.MapTask: Starting flush of map output

12/06/01 10:24:10 INFO mapred.MapTask: Finished spill 0

12/06/01 10:24:11 INFO mapred.TaskRunner: Task:attempt_local_0001_m_000001_0 is done. And is in the process of commiting

12/06/01 10:24:12 INFO mapred.LocalJobRunner:

12/06/01 10:24:12 INFO mapred.TaskRunner: Task 'attempt_local_0001_m_000001_0' done.

12/06/01 10:24:14 INFO mapred.LocalJobRunner:

12/06/01 10:24:16 INFO mapred.Merger: Merging 2 sorted segments

12/06/01 10:24:17 INFO mapred.Merger: Down to the last merge-pass, with 2 segments left of total size: 77 bytes

12/06/01 10:24:17 INFO mapred.LocalJobRunner:

12/06/01 10:24:20 INFO mapred.TaskRunner: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting

12/06/01 10:24:20 INFO mapred.LocalJobRunner:

12/06/01 10:24:20 INFO mapred.TaskRunner: Task attempt_local_0001_r_000000_0 is allowed to commit now

12/06/01 10:24:21 INFO output.FileOutputCommitter: Saved output of task 'attempt_local_0001_r_000000_0' to hdfs://localhost:9000/user/xsj/output

12/06/01 10:24:21 INFO mapred.LocalJobRunner: reduce > reduce

12/06/01 10:24:21 INFO mapred.TaskRunner: Task 'attempt_local_0001_r_000000_0' done.

12/06/01 10:24:22 INFO mapred.JobClient: map 100% reduce 100%

12/06/01 10:24:22 INFO mapred.JobClient: Job complete: job_local_0001

12/06/01 10:24:22 INFO mapred.JobClient: Counters: 14

12/06/01 10:24:22 INFO mapred.JobClient: FileSystemCounters

12/06/01 10:24:22 INFO mapred.JobClient: FILE_BYTES_READ=50488

12/06/01 10:24:22 INFO mapred.JobClient: HDFS_BYTES_READ=120

12/06/01 10:24:22 INFO mapred.JobClient: FILE_BYTES_WRITTEN=102748

12/06/01 10:24:22 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=41

12/06/01 10:24:22 INFO mapred.JobClient: Map-Reduce Framework

12/06/01 10:24:22 INFO mapred.JobClient: Reduce input groups=5

12/06/01 10:24:22 INFO mapred.JobClient: Combine output records=6

12/06/01 10:24:22 INFO mapred.JobClient: Map input records=4

12/06/01 10:24:22 INFO mapred.JobClient: Reduce shuffle bytes=0

12/06/01 10:24:22 INFO mapred.JobClient: Reduce output records=5

12/06/01 10:24:22 INFO mapred.JobClient: Spilled Records=12

12/06/01 10:24:22 INFO mapred.JobClient: Map output bytes=81

12/06/01 10:24:22 INFO mapred.JobClient: Combine input records=8

12/06/01 10:24:22 INFO mapred.JobClient: Map output records=8

12/06/01 10:24:22 INFO mapred.JobClient: Reduce input records=6

12. 查看运行结果：

/user/xsj/output/part-r-00000文件为输出文件：