原文地址:http://www.linuxidc.com/Linux/2014-11/109200.htm
图文详解Windows 8.0上Eclipse 4.4.0 配置CentOS 6.5 上的Hadoop2.2.0开发环境,给需要的朋友参考学习。
Eclipse的Hadoop插件下载地址:https://github.com/winghc/hadoop2x-eclipse-plugin
将下载的压缩包解压,将hadoop-eclipse-kepler-plugin-2.2.0这个jar包扔到eclipse下面的dropins目录下,重启eclipse即可
进入windows->Preference配置根目录
,这里面的hadoop installation directory并不是你windows上装的hadoop目录,而仅仅是你在centos上编译好的源码,在windows上的解压路径而已,该路径仅仅是用于在创建MapReduce Project能从这个地方自动引入MapReduce所需要的jar
进入Window-->Open Perspective-->other-->Map/Reduce打开Map/Reduce窗口
找到
,右击选择,New Hadoop location,这个时候会出现
Map/Reduce(V2)中的配置对应于mapred-site.xml中的端口配置,DFS Master中的配置对应于core-site.xml中的端口配置,配置完成之后finish即可,这个时候可以查看
测试,新建一个MapReduce项目,
,要解决这个问题,你必须要完成如下几个步骤,在windows上配置HADOOP_HOME,然后将%HADOOP_HOME%in加入到path之中,然后去https://github.com/srccodes/hadoop-common-2.2.0-bin下载一个,下载之后将这个bin目录里面的东西全部拷贝到你自己windows上的HADOOP的bin目录下,覆盖即可,同时把hadoop.dll加到C盘下的system32中,如果这些都完成之后还是碰到:Exception in thread "main" java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z,那么就检查一下你的JDK,有可能是32位的JDK导致的,需要下载64位JDK安装,并且在eclipse将jre环境配置为你新安装的64位JRE环境
。如我的jre1.8是64位,jre7是32位,如果这里面没有,你直接add即可,选中你的64位jre环境之后,就会出现了。
之后写个wordcount程序测试一下,贴出我的代码如下,前提是你已经在hdfs上建好了input文件,并且在里面放些内容
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 {
// System.setProperty("hadoop.home.dir", "E:\hadoop2.2\");
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("hdfs://master:9000/input"));
FileOutputFormat.setOutputPath(job, new Path("hdfs://master:9000/output"));
boolean flag = job.waitForCompletion(true);
System.out.print("SUCCEED!" + flag);
System.exit(flag ? 0 : 1);
System.out.println();
}
}
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