Eclipse下搭建Hadoop2.7.0开发环境
2015-06-18 00:05
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一、安装Eclipse
二、在eclipse上安装hadoop插件
1、下载hadoop插件http://download.csdn.net/detail/tondayong1981/8680589
2、把插件放到eclipse/plugins目录下
3、重启eclipse,配置Hadoop installation directory
如果插件安装成功,打开Windows—Preferences后,在窗口左侧会有Hadoop Map/Reduce选项,点击此选项,在窗口右侧设置Hadoop安装路径。
![](https://oscdn.geek-share.com/Uploads/Images/Content/202005/09/3edd240df9ba80d4e1fcad64b76f4e7b.png)
4、配置Map/Reduce Locations
打开Windows—Open Perspective—Other
![](https://oscdn.geek-share.com/Uploads/Images/Content/202005/09/e8f461ffa591282c73fe710673da8248.png)
选择Map/Reduce,点击OK
在右下方看到如下图所示
![](https://oscdn.geek-share.com/Uploads/Images/Content/202005/09/1c3407424f955941a78114186cd12912.png)
点击Map/Reduce Location选项卡,点击右边小象图标,打开Hadoop Location配置窗口:
输入Location Name,任意名称即可.配置Map/Reduce Master和DFS Mastrer,Host和Port配置成与core-site.xml的设置一致即可。(貌似Map/Reduce Master 的端口设置任何数字都可以?)
![](https://oscdn.geek-share.com/Uploads/Images/Content/202005/09/933bd9bc2bde1c13f1c014c799940503.png)
![](https://oscdn.geek-share.com/Uploads/Images/Content/202005/09/f8a35ff21cdb87a0b03ceb5157fb46b7.png)
点击"Finish"按钮,关闭窗口。
点击左侧的DFSLocations—>myhadoop(上一步配置的location name),如能看到user,表示安装成功
![](https://oscdn.geek-share.com/Uploads/Images/Content/202005/09/c1eb25ffd2bb8de79e1f6821409dd46b.png)
如果如下图所示表示安装失败,请检查Hadoop是否启动,以及eclipse配置是否正确。
![](https://oscdn.geek-share.com/Uploads/Images/Content/202005/09/b800455ca3d13524aac69f4d1792d450.png)
三、新建WordCount项目
File—>Project,选择Map/Reduce Project,输入项目名称WordCount等。在WordCount项目里新建class,名称为WordCount,代码如下:
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> <out>"); System.exit(2); } Job job = new Job(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); FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } }
四、运行
1、在HDFS上创建目录inputhadoop fs -mkdir /user
hadoop fs -mkdir /user/inhput
2、拷贝本地README.txt到HDFS的input里
hadoop fs -copyFromLocal /opt/hadoop/README.txt /user/input
3、点击WordCount.java,右键,点击Run As—>Run Configurations,配置运行参数,即输入和输出文件夹
hdfs://localhost:9000/user/input hdfs://localhost:9000/user/output
![](https://oscdn.geek-share.com/Uploads/Images/Content/202005/09/ea5d13793d8d48cad6b0309473315c46.png)
点击Run按钮,运行程序。
4、运行完成后,查看运行结果
方法1:
hadoop fs -ls output
可以看到有两个输出结果,_SUCCESS和part-r-00000
执行hadoop fs -cat output/*
方法2:
展开DFS Locations,如下图所示,双击打开part-r00000查看结果
![](https://oscdn.geek-share.com/Uploads/Images/Content/202005/09/daa3671ae86051dbb54ed4428ecfa33b.png)
参考:
http://www.cnblogs.com/kinglau/p/3802705.html
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