MapReduce 顺序组合， 迭代式，组合式，链式

1、顺序组合式

顺序组合式就是按照指定顺序执行任务如：mapreduce1 --> mapreduce2 --> mapreduce3

即：mapreduce1的输出是mapreduce2的输入，mapreduce2的输出式mapreduce3的输入

代码片段如下：

String inPath1 = "hdfs://hadoop0:9000/user/root/3D/";
String outPath1 = "hdfs://hadoop0:9000/user/root/3DZout/";
String outPath2 = "hdfs://hadoop0:9000/user/root/3DZout2/";
String outPath3 = "hdfs://hadoop0:9000/user/root/3DZout3/";

// job1配置
Job job1 = Job.getInstance(conf);
job1.setJarByClass(Mode.class);
job1.setMapperClass(Map1.class);
job1.setReducerClass(Reduce1.class);
job1.setMapOutputKeyClass(Text.class);
job1.setMapOutputValueClass(IntWritable.class);
job1.setOutputKeyClass(Text.class);
job1.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job1, new Path(inPath1));
FileOutputFormat.setOutputPath(job1, new Path(outPath1));
job1.waitForCompletion(true);

// job2配置
Job job2 = Job.getInstance(conf);
job2.setJarByClass(Mode.class);
job2.setMapperClass(Map2.class);
job2.setReducerClass(Reduce2.class);
job2.setMapOutputKeyClass(Text.class);
job2.setMapOutputValueClass(IntWritable.class);
job2.setOutputKeyClass(Text.class);
job2.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job2, new Path(inPath1));
FileOutputFormat.setOutputPath(job2, new Path(outPath2));
job2.waitForCompletion(true);

// job3配置
Job job3 = Job.getInstance(conf);
job3.setJarByClass(Mode.class);
job3.setMapperClass(Map3.class);
job3.setReducerClass(Reduce3.class);
job3.setMapOutputKeyClass(Text.class);
job3.setMapOutputValueClass(IntWritable.class);
job3.setOutputKeyClass(Text.class);
job3.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job3, new Path(outPath2));
FileOutputFormat.setOutputPath(job3, new Path(outPath3));
job3.waitForCompletion(true);

子任务作业配置代码运行后，将按照顺序逐个执行每个子任务作业。由于后一个子任务需要使用前一个子任务的输出数据，因此，每一个子任务

都需要等前一个子任务执行执行完毕后才允许执行，这是通过job.waitForCompletion(true)方法加以保证的。

2、迭代组合式

迭代也可以理解为for循环或while循环，当满足某些条件时，循环结束

mapreduce的迭代算法正在研究中，后续提供完整源码....

代码如下：

3、复杂的依赖组合式

处理复杂的要求的时候，有时候一个mapreduce程序完成不了，往往需要多个mapreduce程序 这个时候就牵扯到各个任务之间的依赖关系，

所谓依赖就是一个M/R job的处理结果是另外一个M/R的输入，以此类推，

这里的顺序是 job1 和 job2 单独执行， job3依赖job1和job2执行后的结果

代码如下：

package com.hadoop.mapreduce;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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.jobcontrol.ControlledJob;
import org.apache.hadoop.mapreduce.lib.jobcontrol.JobControl;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class Mode {

// 第一个Job
public static class Map1 extends Mapper<Object, Text, Text, IntWritable>{
Text word = new Text();
@Override
protected void map(Object key, Text value,Context context)
throws IOException, InterruptedException {
StringTokenizer st = new StringTokenizer(value.toString());
while(st.hasMoreTokens()){
word.set(st.nextToken());
context.write(word, new IntWritable(1));
}
}

}

public static class Reduce1 extends Reducer<Text, IntWritable, Text, IntWritable>{
IntWritable result = new IntWritable();
@Override
protected 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);
}

}

// 第二个Job
public static class Map2 extends Mapper<Object, Text, Text, IntWritable>{
Text word = new Text();
@Override
protected void map(Object key, Text value,Context context)
throws IOException, InterruptedException {
StringTokenizer st = new StringTokenizer(value.toString());
while(st.hasMoreTokens()){
word.set(st.nextToken());
context.write(word, new IntWritable(1));
}
}
}

public static class Reduce2 extends Reducer<Text, IntWritable, Text, IntWritable>{
IntWritable result = new IntWritable();
@Override
protected 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);
}
}

// 第三个Job
public static class Map3 extends Mapper<Object, Text, Text, IntWritable>{
Text word = new Text();
@Override
protected void map(Object key, Text value,Context context)
throws IOException, InterruptedException {
StringTokenizer st = new StringTokenizer(value.toString());
while(st.hasMoreTokens()){
word.set(st.nextToken());
context.write(word, new IntWritable(1));
}
}
}

public static class Reduce3 extends Reducer<Text, IntWritable, Text, IntWritable>{
IntWritable result = new IntWritable();
@Override
protected 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 IOException{
String inPath1 = "hdfs://hadoop0:9000/user/root/3D/";
String outPath1 = "hdfs://hadoop0:9000/user/root/3DZout/";
String outPath2 = "hdfs://hadoop0:9000/user/root/3DZout2/";
String outPath3 = "hdfs://hadoop0:9000/user/root/3DZout3/";
String[] inOut = {inPath1, outPath1};
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, inOut).getRemainingArgs();
if (otherArgs.length < 2) {
System.err.println("Usage: wordcount <in> [<in>...] <out>");
System.exit(2);
}
// 判断输出路径是否存在，如存在先删除
FileSystem hdfs = FileSystem.get(conf);
Path findFile = new Path(outPath1);
boolean isExists = hdfs.exists(findFile);
if(isExists){
hdfs.delete(findFile, true);
}
if(hdfs.exists(new Path(outPath2))){
hdfs.delete(new Path(outPath2), true);
}
if(hdfs.exists(new Path(outPath3))){
hdfs.delete(new Path(outPath3), true);
}

// job1配置
Job job1 = Job.getInstance(conf);
job1.setJarByClass(Mode.class);
job1.setMapperClass(Map1.class);
job1.setReducerClass(Reduce1.class);
job1.setMapOutputKeyClass(Text.class);
job1.setMapOutputValueClass(IntWritable.class);
job1.setOutputKeyClass(Text.class);
job1.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job1, new Path(inPath1));
FileOutputFormat.setOutputPath(job1, new Path(outPath1));
// 将job1加入控制容器
ControlledJob ctrljob1 = new ControlledJob(conf);
ctrljob1.setJob(job1);

// job2配置
Job job2 = Job.getInstance(conf);
job2.setJarByClass(Mode.class);
job2.setMapperClass(Map2.class);
job2.setReducerClass(Reduce2.class);
job2.setMapOutputKeyClass(Text.class);
job2.setMapOutputValueClass(IntWritable.class);
job2.setOutputKeyClass(Text.class);
job2.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job2, new Path(inPath1));
FileOutputFormat.setOutputPath(job2, new Path(outPath2));
// 将job2加入控制容器
ControlledJob ctrljob2 = new ControlledJob(conf);
ctrljob2.setJob(job2);

// job3配置
Job job3 = Job.getInstance(conf);
job3.setJarByClass(Mode.class);
job3.setMapperClass(Map3.class);
job3.setReducerClass(Reduce3.class);
job3.setMapOutputKeyClass(Text.class);
job3.setMapOutputValueClass(IntWritable.class);
job3.setOutputKeyClass(Text.class);
job3.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job3, new Path(outPath2));
FileOutputFormat.setOutputPath(job3, new Path(outPath3));
ControlledJob ctrljob3 = new ControlledJob(conf);
// 设置job3依赖job1和job2
ctrljob3.addDependingJob(ctrljob1);
ctrljob3.addDependingJob(ctrljob2);
ctrljob3.setJob(job3);

// 主控制器
JobControl jobCtrl = new JobControl("myctrl");
jobCtrl.addJob(ctrljob1);
jobCtrl.addJob(ctrljob2);
jobCtrl.addJob(ctrljob3);

// 在启动线程，记住一定要有这个
Thread t = new Thread(jobCtrl);
t.start();

while(true){
// 如果作业全部完成，就打印成功作业的信息
if(jobCtrl.allFinished()){
System.out.println(jobCtrl.getSuccessfulJobList());
jobCtrl.stop();
break;
}
}
}

}

3、链式组合式

所谓连式MapReduce就是用多个Mapper处理任务，最后用一个Reducer输出结果，注意和迭代式和组合式MapReduce的不同之处

一个MapReduce作业可能会有一些前处理和后处理步骤，将这些前后处理步骤以单独的MapReduce任务实现也可以达到目的，但由于

增加了多个MapReduce作业，将增加整个作业的处理周期，而且还会增加很多I/O操作，因此处理效率不高。

    Hadoop为此提供了专门的链式Mapper(ChainMapper)和链式Reducer(ChainReducer)来完成这种处理。

ChainMapper允许在一个单一Map任务中添加和使用多个Map子任务；而ChainReducer则允许在一个单一Reduce任务执行了Reduce处理

后，继续使用多个Map子任务完成一些后续处理。

package com.hadoop.mapreduce;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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.chain.ChainMapper;
import org.apache.hadoop.mapreduce.lib.chain.ChainReducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class Chain {

// 第一个Job
public static class Map1 extends Mapper<LongWritable, Text, Text, IntWritable>{
Text word = new Text();
@Override
protected void map(LongWritable key, Text value,Context context)
throws IOException, InterruptedException {
StringTokenizer st = new StringTokenizer(value.toString());
while(st.hasMoreTokens()){
word.set(st.nextToken());
context.write(word, new IntWritable(1));
}
}

}

public static class Reduce1 extends Reducer<Text, IntWritable, Text, IntWritable>{
IntWritable result = new IntWritable();
@Override
protected 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);
}

}

// 第二个Job
public static class Map2 extends Mapper<Text, IntWritable, Text, IntWritable>{
Text word = new Text();
@Override
protected void map(Text key, IntWritable value,Context context)
throws IOException, InterruptedException {
StringTokenizer st = new StringTokenizer(value.toString());
while(st.hasMoreTokens()){
word.set(st.nextToken());
context.write(word, new IntWritable(1));
}
}
}

public static class Reduce2 extends Reducer<Text, IntWritable, Text, IntWritable>{
IntWritable result = new IntWritable();
@Override
protected 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);
}
}

// 第三个Job
public static class Map3 extends Mapper<Text, IntWritable, Text, IntWritable>{
Text word = new Text();
@Override
protected void map(Text key, IntWritable value,Context context)
throws IOException, InterruptedException {
StringTokenizer st = new StringTokenizer(value.toString());
while(st.hasMo
ad35
reTokens()){
word.set(st.nextToken());
context.write(word, new IntWritable(1));
}
}
}

public static class Reduce3 extends Reducer<Text, IntWritable, Text, IntWritable>{
IntWritable result = new IntWritable();
@Override
protected 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 IOException, ClassNotFoundException, InterruptedException{
String inPath1 = "hdfs://hadoop0:9000/user/root/input/";
String outPath1 = "hdfs://hadoop0:9000/user/root/3DZout/";
String outPath2 = "hdfs://hadoop0:9000/user/root/3DZout2/";
String outPath3 = "hdfs://hadoop0:9000/user/root/3DZout3/";
String[] inOut = {inPath1, outPath1};
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, inOut).getRemainingArgs();
if (otherArgs.length < 2) {
System.err.println("Usage: wordcount <in> [<in>...] <out>");
System.exit(2);
}
// 判断输出路径是否存在，如存在先删除
FileSystem hdfs = FileSystem.get(conf);
Path findFile = new Path(outPath1);
boolean isExists = hdfs.exists(findFile);
if(isExists){
hdfs.delete(findFile, true);
}
if(hdfs.exists(new Path(outPath2))){
hdfs.delete(new Path(outPath2), true);
}
if(hdfs.exists(new Path(outPath3))){
hdfs.delete(new Path(outPath3), true);
}

// job1配置
Job job1 = Job.getInstance(conf);
job1.setJarByClass(Chain.class);
job1.setJobName("ChainJob");

FileInputFormat.addInputPath(job1, new Path(inPath1));
FileOutputFormat.setOutputPath(job1, new Path(outPath1));

// 连式编程要注意的是，可以有多个个Mapper，且后面Mapper的输入是是上一个Mapper的输出，最后一个Mapper的输出是Reducer的输入，
// 但全局只有一个Reducer
ChainMapper.addMapper(job1, Map1.class, LongWritable.class, Text.class, Text.class, IntWritable.class, conf);
ChainMapper.addMapper(job1, Map2.class, Text.class, IntWritable.class, Text.class, IntWritable.class, conf);
ChainMapper.addMapper(job1, Map3.class, Text.class, IntWritable.class, Text.class, IntWritable.class, conf);

// 执行顺序 map1 --> map2 --> map3 --> reduce1
ChainReducer.setReducer(job1, Reduce1.class, Text.class, IntWritable.class, Text.class, IntWritable.class, conf);

job1.waitForCompletion(true);

}

}