跟我一起hadoop(1)-hadoop2.6安装与使用-阿里云开发者社区

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跟我一起hadoop(1)-hadoop2.6安装与使用

简介:

伪分布式

hadoop的三种安装方式:

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

image

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:

输入后得到:

image

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:

image

查看结果

image

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:

image

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image

  • 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

image

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:

image

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