Ubuntu 14.10 下Eclipse安装Hadoop插件
准备环境
1 安装好了Hadoop,之前安装了Hadoop 2.5.0,安装参考http://www.cnblogs.com/liuchangchun/p/4097286.html
2 安装Eclipse,这个直接在其官网下载即可
安装步骤
1 下载Eclipse插件,我找的是Hadoop 2.2 的插件,在Hadoop 2.5 下可以正常用,获取插件这里有两种方式
1.1 一是自己下载源码自己编译,过程如下
首先,下载eclipse-hadoop的插件,网址是https://github.com/winghc/hadoop2x-eclipse-plugin,你可以点击网页右下方的Download ZIP下载。下载之后,解压缩,。
然后,进入到 hadoop2x-eclipse-plugin-master/src/contrib/eclipse-plugin文件夹里面,执行命令
ant jar -Declipse.home=/usr/local/eclipse -Dhadoop.home=~/Downloads/hadoop-2.2.0 -Dversion=2.5.0
编译顺利通过,生成的插件在hadoop2x-eclipse-plugin-master/build/contrib/eclipse-plugin目录下。
1.2 或是直接下载编译好的插件,下载地址http://pan.baidu.com/s/1mgiHFok
2 将下载好的插件复制到eclipse/plugins目录下,需要重启Eclipse
3 配置Hadoop installation directory
3.1 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。
3.2 配置Map/Reduce Locations:打开Windows—Open Perspective—Other 选择Map/Reduce,点击OK
3.3 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。如果没有自己修改端口,那么一个是9001,一个是9000
3.4 点击左侧的DFSLocations—>Location Name(上一步配置的location name),如能看到Hadoop下的文件,那么表示安装成功。
4 测试MapReduce。Eclipse中,File—>Project,选择Map/Reduce Project,输入项目名称WordCount等。然后新建一个类,代码拷贝下
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); } }
5 运行项目,先需要做些准备工作
5.1、在HDFS上创建目录input
hadoop fs -mkdir input
5.2 、随便拷贝本地README.txt到HDFS的input里
hadoop fs -copyFromLocal /usr/local/hadoop/README.txt input
5.3、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/hadoop/input hdfs://localhost:9000/user/hadoop/output
5.4 注意,输入目录output不要在Hadoop中建立,否则会报错
6 查看结果,可以直接在DFS Locations刷新下就会看到多个目录,里面就有结果
----------------------------------------------------------------------------------------------------------------------------------------
WordCount程序上面是写在一个类里面,规范一点是Map类,Reduce类,MapRedcueDriver分开建立,低耦合
1 新建Map/Reduce工程wordcount。
2 新建Mapper.java,选择File——>New——>Mapper,输入包名及类名。
3 新建Reduccer.java,选择File——>New——>Reducer,输入包名及类名。
4 建立Map/Reduce Driver,选择File——>New——>MapReduce Driver,输入包名及类名。
5 运行,同上面
郑重声明:本站内容如果来自互联网及其他传播媒体,其版权均属原媒体及文章作者所有。转载目的在于传递更多信息及用于网络分享,并不代表本站赞同其观点和对其真实性负责,也不构成任何其他建议。