First, configure the environment variables as follows:
My Computer, right - "Properties -----" Advanced System Settings ------ "Environment Variables
-" add new variables in the system:
This get away part of
the following open eclipse
open position where the eclipse file before opening ---- "
copy the hadoop-eclipse-plugin-2.6.0.jar to the eclipse installation directory plugins
start eclipse
core-site.xml configuration file:
<name>fs.defaultFS</name>
<value>hdfs://master:9000</value>
<description>The name of the default file system.</description>
<name>hadoop.tmp.dir</name>
<!-- 注意创建相关的目录结构 -->
<value>/usr/setup/hadoop/temp</value>
<description>A base for other temporary directories.</description>
1 download plug-ins
hadoop-eclipse-plugin-2.5.1.jar
After downloading the source code required to compile on github. As used herein have been compiled plug-ins
2 Configure Plug
The plug-in into the ... \ under the eclipse \ plugins directory and restart eclipse, configure 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。（windows下只需把hadoop-2.5.1.tar.gz解压到指定目录）
3 配置Map/Reduce Locations
打开Windows—Open Perspective—Other，选择Map/Reduce，点击OK，控制台会出现：
Right-new Hadoop location configuration hadoop: Input
Location Name, any name.
Configuration Map / Reduce Master and DFS Mastrer, Host Port, and can be configured with the same core-site.xml settings.
Click "Finish" button to close the window.
Click on the left of DFSLocations-> master (Previous configured location name), as can be seen user, represents a successful installation
4 wordcount examples
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();
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://192.168.11.134:9000/in/test*.txt"));//路径1
FileOutputFormat.setOutputPath(job, new Path("hdfs://192.168.11.134:9000/output"));//输出路径
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
The above since paths 1 and 2 have been defined in the code, which need not be defined in a configuration file, when the above paths 1 and 2 codes:
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
This needs to be configured to run Path: Right-class Run As-> Run Configurations
Red part of the file path on hdfs configuration,
Click or or run: Run on Hadoop, operating results will appear in the DFS Locations. If an update is running, right-DFS Locations, point disconnect update
operation result:
package com.hpe.test;
import java.io.BufferedInputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;
import org.junit.After;
import org.junit.Before;
import org.junit.Test;
import org.omg.Messaging.SyncScopeHelper;
public class TestHdfs {
//引入配置文件
Configuration conf=null;
//创建文件流----引用的是hadoop内部封装的方法
FileSystem fs=null;
@Before
public void conn() throws Exception{
conf=new Configuration(true);//设置是否读取配置信息
fs=FileSystem.get(conf);
}
@After
public void close() throws Exception{
fs.close();
}
//创建、删除、重命名、判断是否存在
//创建文件
@Test
public void mkdir() throws Exception{
Path f=new Path("/aaa");
//判断是否存在
if(fs.exists(f)){
//删除
fs.delete(f);
}
//创建
fs.mkdirs(f);
}
//自己补充完成
public void exist(){
}
//重命名
@Test
public void rn() throws Exception{
Path p1 = new Path("/user/root/passwd");
Path p2 = new Path("/user/root/haha.txt");
boolean rename = fs.rename(p1, p2);
System.out.println(rename);
}
//上传文件
@Test
public void uploadFile() throws Exception{
//输出位置
Path inputFile=new Path("/tmpDir/haha.txt");
//相当于文件内容的输出
FSDataOutputStream output = fs.create(inputFile);
//输入位置，相当于文件内容的输入
InputStream input=new BufferedInputStream(new FileInputStream(new File("d:\\124.txt")));
IOUtils.copyBytes(input, output, conf, true);
}
//下载文件
@Test
public void downloadFile() throws Exception{
//上传文件到HDFS
Path src = new Path("/tmpDir/haha.txt");
//输入源：将我集群中的文件作为输入
FSDataInputStream input=fs.open(src);
//输出位置
FileOutputStream output=new FileOutputStream("F://aa.txt");
IOUtils.copyBytes(input, output, conf, true);
}
//上传下载
}