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Eclipse上搭建Hadoop开发环境

[日期:2017-10-15] 来源:Linux社区  作者:lullaby [字体: ]

一、概述
1.实验使用的Hadoop集群为伪分布式模式,Eclipse相关配置已完成;
2.软件版本为hadoop-2.7.3.tar.gz、apache-maven-3.5.0.rar。
 
二、使用eclipse连接hadoop集群进行开发
1.在开发主机上配置hadoop
①将hadoop-2.7.3.tar.gz解压到本地主机上

②使用windows版本的hadoop中的bin替换目标中的bin文件夹

③配置windows上的hadoop环境变量

2.在eclipse上配置hadoop集群信息
①在eclipse中添加hadoop路径

②配置hadoop集群访问信息

3.在hadoop集群中取消权限验证

 hdfs-site.xml
<property>
    <name>dfs.permissions</name>
    <value>false</value>
</property>

4.创建一个文件测试连接权限

5.安装maven
①将maven解压到开发主机上

②在eclipse上添加maven路径

5.新建maven工程

6.修改maven配置文件(maven/pom.xml)
  <dependencies>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-client</artifactId>
        <version>2.7.3</version>
    </dependency>
    <dependency> 
          <groupId>junit</groupId>
          <artifactId>junit</artifactId>
          <version>3.8.1</version>
          <scope>test</scope>
    </dependency>
  </dependencies>

7.新建一个类用于测试(WordCount)

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> [<in>...] <out>");
      System.exit(2);
    }
    Job job = Job.getInstance(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);
    for (int i = 0; i < otherArgs.length - 1; ++i) {
      FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
    }
    FileOutputFormat.setOutputPath(job,
      new Path(otherArgs[otherArgs.length - 1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

8.配置WordCount
①将log4j.properties移动到WordCount类下
②设置WordCount的运行自变量


 
8.运行测试


 
三、jar包的导出与提交执行
1.导出WordCount

2.将导出的jar包上传到hadoop集群

[hadoop@hadoop ~]$ ls
wc.jar

3.运行

[hadoop@hadoop ~]$ hadoop jar wc.jar WordCount /user/hadoop/input/* /user/hadoop/output/out
17/09/06 22:36:56 INFO client.RMProxy: Connecting to ResourceManager at hadoop/192.168.100.141:8032
17/09/06 22:36:57 INFO input.FileInputFormat: Total input paths to process : 1
17/09/06 22:36:58 INFO mapreduce.JobSubmitter: number of splits:1
17/09/06 22:36:58 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1504744740212_0001
17/09/06 22:36:59 INFO impl.YarnClientImpl: Submitted application application_1504744740212_0001
17/09/06 22:36:59 INFO mapreduce.Job: The url to track the job: http://hadoop:8088/proxy/application_1504744740212_0001/
17/09/06 22:36:59 INFO mapreduce.Job: Running job: job_1504744740212_0001
17/09/06 22:37:36 INFO mapreduce.Job: Job job_1504744740212_0001 running in uber mode : false
17/09/06 22:37:36 INFO mapreduce.Job:  map 0% reduce 0%
17/09/06 22:38:26 INFO mapreduce.Job:  map 100% reduce 0%
17/09/06 22:38:42 INFO mapreduce.Job:  map 100% reduce 100%
17/09/06 22:38:46 INFO mapreduce.Job: Job job_1504744740212_0001 completed successfully

4.查看运行结果

[hadoop@hadoop ~]$ hdfs dfs -cat /user/hadoop/output/out/part-r-00000
"AS              1
"GCC        1
"License");    1
&            1
'Aalto      1
'Apache        4
'ArrayDeque',    1
'Bouncy        1
'Caliper',      1
'Compress-LZF',  1
……

Hadoop2.3-HA高可用集群环境搭建  http://www.chineselinuxuniversity.net/Linux/2017-03/142155.htm

Hadoop项目之基于CentOS7的Cloudera 5.10.1(CDH)的安装部署  http://www.chineselinuxuniversity.net/Linux/2017-04/143095.htm

Hadoop2.7.2集群搭建详解(高可用)  http://www.chineselinuxuniversity.net/Linux/2017-03/142052.htm

使用Ambari来部署Hadoop集群(搭建内网HDP源)  http://www.chineselinuxuniversity.net/Linux/2017-03/142136.htm

Ubuntu 14.04下Hadoop集群安装  http://www.chineselinuxuniversity.net/Linux/2017-02/140783.htm

CentOS 6.7安装Hadoop 2.7.2  http://www.chineselinuxuniversity.net/Linux/2017-08/146232.htm

Ubuntu 16.04上构建分布式Hadoop-2.7.3集群  http://www.chineselinuxuniversity.net/Linux/2017-07/145503.htm

CentOS 7 下 Hadoop 2.6.4 分布式集群环境搭建  http://www.chineselinuxuniversity.net/Linux/2017-06/144932.htm

Hadoop2.7.3+Spark2.1.0完全分布式集群搭建过程  http://www.chineselinuxuniversity.net/Linux/2017-06/144926.htm

更多Hadoop相关信息见Hadoop 专题页面 http://www.chineselinuxuniversity.net/topicnews.aspx?tid=13

本文永久更新链接地址http://www.chineselinuxuniversity.net/Linux/2017-10/147639.htm

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