跟我一起hadoop（1）-hadoop2.6安装与使用

http://www.cnblogs.com/skyme/p/4606138.html

伪分布式

hadoop的三种安装方式：

Local (Standalone) Mode
Pseudo-Distributed Mode
Fully-Distributed Mode

安装之前需要

$ sudo apt-get install ssh

     $ sudo apt-get install rsync

详见：http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-common/SingleCluster.html

伪分布式配置

Configuration
修改下边:

etc/hadoop/core-site.xml:

<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://localhost:9000</value>
</property>
</configuration>

etc/hadoop/hdfs-site.xml:

<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
</configuration>

配置ssh

$ ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa
$ cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys

如果想运行在yarn上

需要执行下边的步骤：

Configure parameters as follows:
etc/hadoop/mapred-site.xml:

<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
</configuration>

etc/hadoop/yarn-site.xml:

<configuration>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
</configuration>

Start ResourceManager daemon and NodeManager daemon:

$ sbin/start-yarn.sh

Browse the web interface for the ResourceManager; by default it is available at:
ResourceManager - http://localhost:8088/
Run a MapReduce job.
When you're done, stop the daemons with:

$ sbin/stop-yarn.sh

输入：

http://localhost:8088/

可以看到





启动yarn后

Format the filesystem:

$ bin/hdfs namenode -format

Start NameNode daemon and DataNode daemon:

$ sbin/start-dfs.sh

The hadoop daemon log output is written to the $HADOOP_LOG_DIR directory (defaults to
$HADOOP_HOME/logs).

Browse the web interface for the NameNode; by default it is available at:
NameNode - http://localhost:50070/

输入后得到：





然后执行测试

Make the HDFS directories required to execute MapReduce jobs:

$ bin/hdfs dfs -mkdir /user
$ bin/hdfs dfs -mkdir /user/<username>

Copy the input files into the distributed filesystem:

$ bin/hdfs dfs -put etc/hadoop input

Run some of the examples provided:

$ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar grep input output 'dfs[a-z.]+'

Examine the output files:
Copy the output files from the distributed filesystem to the local filesystem and examine them:

$ bin/hdfs dfs -get output output
$ cat output/*

or

View the output files on the distributed filesystem:

$ bin/hdfs dfs -cat output/*

看运行的情况：





查看结果





测试执行成功，可以编写本地代码了。

eclipse hadoop2.6插件使用

下载源码：

git clone https://github.com/winghc/hadoop2x-eclipse-plugin.git

下载过程：





编译插件：

cd src/contrib/eclipse-plugin

ant jar -Dversion=2.6.0 -Declipse.home=/usr/local/eclipse -Dhadoop.home=/usr/local/hadoop-2.6.0  //路径根据自己的配置



复制编译好的jar到eclipse插件目录，重启eclipse
配置 hadoop 安装目录

window ->preference -> hadoop Map/Reduce -> Hadoop installation directory

配置Map/Reduce 视图

window ->Open Perspective -> other->Map/Reduce -> 点击“OK”

windows → show view → other->Map/Reduce Locations-> 点击“OK”

控制台会多出一个“Map/Reduce Locations”的Tab页

在“Map/Reduce Locations” Tab页 点击图标<大象+>或者在空白的地方右键，选择“New Hadoop location…”，弹出对话框“New hadoop location…”，配置如下内容：将ha1改为自己的hadoop用户



注意：MR Master和DFS Master配置必须和mapred-site.xml和core-site.xml等配置文件一致。

打开Project Explorer,查看HDFS文件系统。

新建Map/Reduce任务

File->New->project->Map/Reduce Project->Next

编写WordCount类：记得先把服务都起来

/**
*
*/
package com.zongtui;

/**
* ClassName: WordCount <br/>
* Function: TODO ADD FUNCTION. <br/>
* date: Jun 28, 2015 5:34:18 AM <br/>
*
* @author zhangfeng
* @version
* @since JDK 1.7
*/

import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;

public class WordCount {
public static class Map extends MapReduceBase implements
Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();

public void map(LongWritable key, Text value,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
word.set(tokenizer.nextToken());
output.collect(word, one);
}
}
}

public static class Reduce extends MapReduceBase implements
Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterator<IntWritable> values,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
int sum = 0;
while (values.hasNext()) {
sum += values.next().get();
}
output.collect(key, new IntWritable(sum));
}
}

public static void main(String[] args) throws Exception {
JobConf conf = new JobConf(WordCount.class);
conf.setJobName("wordcount");

conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);

conf.setMapperClass(Map.class);
conf.setReducerClass(Reduce.class);

conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);

FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));

JobClient.runJob(conf);
}
}

user/admin123/input/hadoop是你上传在hdfs的文件夹（自己创建），里面放要处理的文件。ouput1放输出结果





将程序放在hadoop集群上运行：右键-->Runas -->Run on Hadoop,最终的输出结果会在HDFS相应的文件夹下显示。至此，ubuntu下hadoop-2.6.0 eclipse插件配置完成。

遇到异常

Exception in thread "main" org.apache.hadoop.mapred.FileAlreadyExistsException: Output directory hdfs://localhost:9000/output already exists
at org.apache.hadoop.mapred.FileOutputFormat.checkOutputSpecs(FileOutputFormat.java:132)
at org.apache.hadoop.mapreduce.JobSubmitter.checkSpecs(JobSubmitter.java:564)
at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:432)
at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1296)
at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1293)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1628)
at org.apache.hadoop.mapreduce.Job.submit(Job.java:1293)
at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:562)
at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:557)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1628)
at org.apache.hadoop.mapred.JobClient.submitJobInternal(JobClient.java:557)
at org.apache.hadoop.mapred.JobClient.submitJob(JobClient.java:548)
at org.apache.hadoop.mapred.JobClient.runJob(JobClient.java:833)
at com.zongtui.WordCount.main(WordCount.java:83)

1、改变输出路径。

2、删除重新建。

运行完成后看结果：