1. Container是YARN中资源的抽象，它封装了某个节点上一定量的资源（CPU和内存两类资源）。
2. Container由ApplicationMaster向ResourceManager申请的，由ResouceManager中的资源调度器异步分配给ApplicationMaster；
/**
*
* @author 汤高
* Mapper<LongWritable, Text, Text, IntWritable>中 LongWritable,IntWritable是Hadoop数据类型表示长整型和整形
*
* LongWritable, Text表示输入类型 (比如本应用单词计数输入是 偏移量(字符串中的第一个单词的其实位置),对应的单词(值))
* Text, IntWritable表示输出类型 输出是单词 和他的个数
* 注意：map函数中前两个参数LongWritable key, Text value和输出类型不一致
* 所以后面要设置输出类型 要使他们一致
*/
//Map过程
public static class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
/***
*
*/
@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
throws IOException, InterruptedException {
//默认的map的value是每一行,我这里自定义的是以空格分割
String[] vs = value.toString().split("\\s");
for (String v : vs) {
//写出去
context.write(new Text(v), ONE);
}
}
}
//Reduce过程
/***
* @author 汤高
* Text, IntWritable输入类型,从map过程获得 既map的输出作为Reduce的输入
* Text, IntWritable输出类型
*/
public static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
@Override
protected void reduce(Text key, Iterable<IntWritable> values,
Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
int count=0;
for(IntWritable v:values){
count+=v.get();//单词个数加一
}
context.write(key, new IntWritable(count));
}
}public static void main(String[] args) {
Configuration conf=new Configuration();
try {
//args从控制台获取路径 解析得到域名
String[] paths=new GenericOptionsParser(conf,args).getRemainingArgs();
if(paths.length<2){
throw new RuntimeException("必須輸出 輸入 和输出路径");
}
//得到一个Job 并设置名字
Job job=Job.getInstance(conf,"wordcount");
//设置Jar 使本程序在Hadoop中运行
job.setJarByClass(WordCount.class);
//设置Map处理类
job.setMapperClass(WordCountMapper.class);
//设置map的输出类型,因为不一致,所以要设置
job.setMapOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
//设置Reduce处理类
job.setReducerClass(WordCountReducer.class);
//设置输入和输出目录
FileInputFormat.addInputPath(job, new Path(paths[0]));
FileOutputFormat.setOutputPath(job, new Path(paths[1]));
//启动运行
System.exit(job.waitForCompletion(true) ? 0:1);
} catch (IOException e) {
e.printStackTrace();
} catch (ClassNotFoundException e) {
e.printStackTrace();
} catch (InterruptedException e) {
e.printStackTrace();
}
}package hadoopday02;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
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 {
//计数变量
private static final IntWritable ONE = new IntWritable(1);
/**
*
* @author 汤高
* Mapper<LongWritable, Text, Text, IntWritable>中 LongWritable,IntWritable是Hadoop数据类型表示长整型和整形
*
* LongWritable, Text表示输入类型 (比如本应用单词计数输入是 偏移量(字符串中的第一个单词的其实位置),对应的单词(值))
* Text, IntWritable表示输出类型 输出是单词 和他的个数
* 注意：map函数中前两个参数LongWritable key, Text value和输出类型不一致
* 所以后面要设置输出类型 要使他们一致
*/
//Map过程
public static class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
/***
*
*/
@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
throws IOException, InterruptedException {
//默认的map的value是每一行,我这里自定义的是以空格分割
String[] vs = value.toString().split("\\s");
for (String v : vs) {
//写出去
context.write(new Text(v), ONE);
}
}
}
//Reduce过程
/***
* @author 汤高
* Text, IntWritable输入类型,从map过程获得 既map的输出作为Reduce的输入
* Text, IntWritable输出类型
*/
public static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
@Override
protected void reduce(Text key, Iterable<IntWritable> values,
Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
int count=0;
for(IntWritable v:values){
count+=v.get();//单词个数加一
}
context.write(key, new IntWritable(count));
}
}
public static void main(String[] args) {
Configuration conf=new Configuration();
try {
//args从控制台获取路径 解析得到域名
String[] paths=new GenericOptionsParser(conf,args).getRemainingArgs();
if(paths.length<2){
throw new RuntimeException("必須輸出 輸入 和输出路径");
}
//得到一个Job 并设置名字
Job job=Job.getInstance(conf,"wordcount");
//设置Jar 使本程序在Hadoop中运行
job.setJarByClass(WordCount.class);
//设置Map处理类
job.setMapperClass(WordCountMapper.class);
//设置map的输出类型,因为不一致,所以要设置
job.setMapOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
//设置Reduce处理类
job.setReducerClass(WordCountReducer.class);
//设置输入和输出目录
FileInputFormat.addInputPath(job, new Path(paths[0]));
FileOutputFormat.setOutputPath(job, new Path(paths[1]));
//启动运行
System.exit(job.waitForCompletion(true) ? 0:1);
} catch (IOException e) {
e.printStackTrace();
} catch (ClassNotFoundException e) {
e.printStackTrace();
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}Hadoop2.6（新版本）----MapReduce工作原理
原文：http://blog.csdn.net/tanggao1314/article/details/51275812