一、概述

1.實驗使用的Hadoop集群為偽分布式模式，eclipse相關配置已完成；

2.軟件版本為hadoop-2.7.3.tar.gz、apache-maven-3.5.0.rar。

二、使用eclipse連接hadoop集群進行開發

1.在開發主機上配置hadoop

①將hadoop-2.7.3.tar.gz解壓到本地主機上

②使用windows版本的hadoop中的bin替換目標中的bin文件夾

③配置windows上的hadoop環境變量

2.在eclipse上配置hadoop集群信息

①在eclipse中添加hadoop路徑

②配置hadoop集群訪問信息

3.在hadoop集群中取消權限驗證

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

4.創建一個文件測試連接權限

5.安裝maven

①將maven解壓到開發主機上

②在eclipse上添加maven路徑

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.新建一個類用於測試（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將log4j.properties移動到WordCount類下

②設置WordCount的運行自變量

8.運行測試

三、jar包的導出與提交執行

1.導出WordCount

2.將導出的jar包上傳到hadoop集群

[[email protected] ~]$ ls
wc.jar

3.運行

[[email protected] ~]$ 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.查看運行結果

[[email protected] ~]$ 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
……

本文出自 “lullaby” 博客，請務必保留此出處http://lullaby.blog.51cto.com/10815696/1963352

eclipse上搭建hadoop開發環境