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：

1. 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>

2. Start ResourceManager daemon and NodeManager daemon:

    $ sbin/start-yarn.sh

3. Browse the web interface for the ResourceManager; by default it is available at:
   • ResourceManager - http://localhost:8088/
4. Run a MapReduce job.
5. 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

1. Format the filesystem:

    $ bin/hdfs namenode -format

2. 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).

3. 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

1. Make the HDFS directories required to execute MapReduce jobs:

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

2. Copy the input files into the distributed filesystem:

    $ bin/hdfs dfs -put etc/hadoop input

3. 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.]+'

4. 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：