ubuntu安装配置eclipse+hadoop开发环境（⼗分详
细）+WordCount实例
我的环境：
系统平台：Ubuntu14.04TLS（64位）
Hadoop环境：Hadoop2.8.3
Eclipse：Neon.2 Release(4.6.2)
Eclipse插件：hadoop-eclipse-plugin-2.8.3.jar
1.先确保已安装了jdk和hadoop，没有的可参考以下两篇⽂章，已经安装的跳过此步骤
ubuntu jdk安装教程
ubuntu搭建hadoop-2.8.3（伪分布式）
2.安装eclipse，下载对应的hadoop eclipse pulgin插件
注意：Hadoop插件要跟⾃⼰安装的Hadoop版本相对应
eclispe官⽹下载地址
eclipse官⽹下载
下载的是eclipse-installer⽂件，打开eclipse-inist，⼀般选择第⼀个（根据⾃⼰需求）
我的hadoop版本是hadoop2.8.3下载的是hadoop-eclipse-plugin-2.8.3.jar
3.在eclipse上安装Hadoop插件并重启eclipse
将插件hadoop-eclipse-plugin-2.8.3.jar放到Eclipse⽬录下的dropins⽂件夹中，并重启Eclipse。
4.在Eclipse中配置插件
在Eclipse软件中，单击【Windows】-【Preferences】，弹出Preferences对话框，若左侧出现【Hadoop Map/Reduce】选项，则表⽰插件放置成功。
单击【Hadoop Map/Reduce】选项，并在右侧设置Hadoop的安装⽬录。
【Windows】-【Open Perspective】-【Other】，弹出对话框，单击蓝⾊⼩象【Map/Reduce】，并确定
这时Eclipse下⽅输出窗⼝部分，出现【Map/Reduce Locations】选项卡，单击右上⾓的蓝⾊⼩象进⾏编辑
location name：名称随意填写
Host与l⽂件⾥的配置保持⼀致：
port第⼀个是9001，第⼆个是9000
点击Advanced parameters，到fs.defaultFS
添加fs.defaultFS路径，与l⽂件配置保持⼀致
到p.dir，修改路径，和l⽂件配置保持⼀致
配置完成之后，记得保存，点击finish
右键刷新hadoop，即可看到连接成功
5.创建hadoop项⽬，运⾏Wordcount实例
(1)创建hadoop项⽬，点击左上⾓file—>new—>other
(2)选择map/preduce下⾯的map/preduce project
(3)填写项⽬名称，点击finish
(4)导⼊jar包，新建⼀个⽂件夹hadoop_jar
将以下jar包直接复制粘贴到此⽂件夹内
hadoop-2.8.3/share/hadoop/mapreduce下的所有jar包（⼦⽂件夹下的jar包不⽤）hadoop-2.8.3/share/hadoop/common下的hadoop-common-2.8.3.jar
hadoop-2.8.3/share/hadoop/common/lib下的commons-cli-1.2.jar
注：若版本不同，注意jar包的不同
(5)在项⽬名称上右键点击Build Path，点击Configure Build Path
(6)点击Libraries，第⼆步点击Add JARS
(7)选中hadoop_jar下的所有jar包，点击ok
(8)点击Apply and Close，应⽤并退出
(9)Referenced Libraries库⾥就多了刚刚添加的⼏个jar包
(10)新建WordCount类，
WordCount实例api
package lib_test1;
import java.io.IOException;
import java.util.StringTokenizer;
import org.f.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 String());
while (itr.hasMoreTokens()) {
word.Token());
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 += ();
}
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) {
}
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
jdk下载具体步骤
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]));
}
}
(11)上传需要单词计数的⽂件⾄hdfs
file1⽂件内容：
file2的⽂件内容：
(12)右键运⾏WordCount实例，选择Run configurations
(13)双击java Application，选择WordCount，点击Arguments，第⼀个是输⼊路径，第⼆个是输出路径（输出⽂件qy_output是⾃动创建的，运⾏实例之前不能有，不然报错），输⼊输出路径中间以⼀个空格隔开，点击Apply，最后点击run
(14)运⾏过程
(15)运⾏结果