概述

分布式运算程序；

优点：易于编程；良好扩展性；高容错性；适合PB级以上海量数据的离线处理；

缺点：不擅长实时计算；不擅长流式计算；不擅长DAG有向图计算；

核心思想：

1）分布式的运算程序往往需要分成至少2个阶段。

2）第一个阶段的MapTask并发实例，完全并行运行，互不相干。

3）第二个阶段的ReduceTask并发实例互不相干，但是他们的数据依赖于上一个阶段的所有MapTask并发实例的输出。

4）MapReduce编程模型只能包含一个Map阶段和一个Reduce阶段，如果用户的业务逻辑非常复杂，那就只能多个MapReduce程序，串行运行。

一个完整的MapReduce在分布式运行时有3类实例进程：

MrAppMaster：负责整个程序的过程调度及状态协调；

MapTask：负责Map阶段的整个数据处理流程；

ReduceTask：负责ReduceTask阶段的整个数据处理流程；

数据序列化类型

常用的数据类型对应的Hadoop数据序列化类型 Java类型 　　　Hadoop Writable类型 Boolean 　　　BooleanWritable Byte ByteWritable Int IntWritable Float FloatWritable Long LongWritable Double 　　　 DoubleWritable String 　　　 Text Map MapWritable Array 　　　　 ArrayWritable Null 　 NullWritable

MapReduce编程规范：

用户编写的程序分成三个部分：Mapper、Reducer和Driver。

Mapper阶段：

自定义的Mapper继承父类；输入数据以K，V对的形式；业务逻辑写在map( )方法；

输出数据以K，V形式；map（）方法（MapTask进程）对每一个k，v调用一次

Reduce阶段：

自定义的Reducer继承父类；输入数据类型对应Mapper的输出类型以K，V对的形式；业务逻辑写在reduce( )方法；

输出数据以K，V形式；（ReduceTask进程）对每一组相同k的k，v调用一次reduce方法

Driver 阶段：

Driver 相当于yarn集群的客户端，提交（封装了MapReduce程序相关运行参数的job对象）整个程序到yarn集群

在pom.xml文件中添加如下依赖

junit junit RELEASE org.apache.logging.log4j log4j-core 2.8.2 org.apache.hadoop hadoop-common 2.7.2 org.apache.hadoop hadoop-client 2.7.2 org.apache.hadoop hadoop-hdfs 2.7.2

在项目的src/main/resources目录下，新建一个文件，命名为“log4j.properties”，在文件中填入。

log4j.rootLogger=INFO, stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d %p [%c] - %m%n
log4j.appender.logfile=org.apache.log4j.FileAppender
log4j.appender.logfile.File=target/spring.log
log4j.appender.logfile.layout=org.apache.log4j.PatternLayout
log4j.appender.logfile.layout.ConversionPattern=%d %p [%c] - %m%n

编写Mapper类

package com.xxx.mapreduce.wordcount;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;

public class WcMapper extends Mapper {
//定义泛型: 输入是以行号: 一行文本这种形式; 输出是以aaa: 1这种形式
private Text word = new Text(); //对象定义为类的私有,是为了防止垃圾,对象太多会占用很大的JVM堆空间;
private IntWritable one = new IntWritable(1);

@Override  
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {  
    //1.切分行数据  
    String\[\] split = value.toString().split(" ");  
    for (String str : split) {  
        this.word.set(str);  
        //context贯彻整个页面的,  
        context.write(this.word, one);  
    }

}  

}

WcReduce类

package com.xxx.mapreduce.wordcount;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
import java.util.Iterator;

public class WcReduce extends Reducer {
//泛型 输入aaa 1; 输出是对所有的进行统计汇总aaa 3;
private IntWritable sumAll = new IntWritable();

@Override  
protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {  
    int sum = 0;  
    Iterator<IntWritable> iterator = values.iterator();  
    while (iterator.hasNext()){  
        sum += iterator.next().get();  
    }  
    this.sumAll.set(sum);  
    context.write(key, this.sumAll);  
}  

}

WcDriver

package com.atguigu.mapreduce.wordcount;

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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

public class WcDriver {

public static void main(String\[\] args) throws IOException, ClassNotFoundException, InterruptedException {  
    //1.获取一个任务实例; 获取配置信息和封装任务  
    Job job = Job.getInstance(new Configuration());  
    //2.设置jar类加载路径  
    job.setJarByClass(WcDriver.class);  
    //3.设置Mapper和Reduce类  
    job.setMapperClass(WcMapper.class);  
    job.setReducerClass(WcReduce.class);  
    //4.设置Mapper和Reduce最终输出的k  v类型  
    job.setMapOutputKeyClass(Text.class);  
    job.setMapOutputValueClass(IntWritable.class);

    //5.设置输入和输出路径  
    FileInputFormat.setInputPaths(job,new Path(args\[0\]));  
    FileOutputFormat.setOutputPath(job, new Path(args\[1\]));

    //6.提交任务  
    boolean b = job.waitForCompletion(true);  
    System.exit(b ? 0 : 1);  
}  

}

打包jar，copy到Hadoop集群上传，然后在集群中运行

[kris@hadoop101 hadoop-2.7.2]$ rz -E //上传jar包WordCount-1.0-SNAPSHOT.jar

[kris@hadoop101 hadoop-2.7.2]$ hadoop jar WordCount-1.0-SNAPSHOT.jar com.atguigu.mapreduce.wordcount.WcDriver /2.txt /output //运行

Hadoop序列化

注意：

反序列化时，需要反射调用空参构造函数，所以必须有空参构造

注意反序列化的顺序和序列化的顺序完全一致

要想把结果显示在文件中，需要重写toString()，可用”\t”分开，方便后续用。

如果需要将自定义的bean放在key中传输，则还需要实现Comparable接口（WritableComparable< >），因为MapReduce框中的Shuffle过程要求对key必须能排序。

@Override

public int compareTo(FlowBean o) {

// 倒序排列，从大到小

return xxx ;

}

自定义bean对象实现序列化接口（Writable）

package flow;

import org.apache.hadoop.io.Writable;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

//1.实现Writable接口
public class FlowBean implements Writable {
private long upFlow;
private long downFlow;
private long sumFlow;

public FlowBean() {  
    super();  
}

public void set(long upFlow, long downFlow) {  
    this.upFlow = upFlow;  
    this.downFlow = downFlow;  
    this.sumFlow = this.upFlow + this.downFlow;  
}

public long getUpFlow() {  
    return upFlow;  
}  
public void setUpFlow(long upFlow) {  
    this.upFlow = upFlow;  
}

public long getDownFlow() {  
    return downFlow;  
}

public void setDownFlow(long downFlow) {  
    this.downFlow = downFlow;  
}

public long getSumFlow() {  
    return sumFlow;  
}

public void setSumFlow(long sumFlow) {  
    this.sumFlow = sumFlow;  
}

@Override  
public String toString() {  
    return "上行流量=" + upFlow +  
            ",下行流量=" + downFlow +  
            ",总流量=" + sumFlow;  
}  
//写序列化方法;  
public void write(DataOutput dataOutput) throws IOException {  
    dataOutput.writeLong(upFlow);  
    dataOutput.writeLong(downFlow);  
    dataOutput.writeLong(sumFlow);  
}  
//反序列化方法必须和序列化方法顺序一致;  
public void readFields(DataInput dataInput) throws IOException {  
    this.upFlow = dataInput.readLong();  
    this.downFlow = dataInput.readLong();  
    this.sumFlow = dataInput.readLong();

}  

}

//写序列化方法;  
public void write(DataOutput dataOutput) throws IOException {  
    dataOutput.writeLong(upFlow);  
    dataOutput.writeLong(downFlow);  
    dataOutput.writeLong(sumFlow);  
}  
//反序列化方法必须和序列化方法顺序一致;  
public void readFields(DataInput dataInput) throws IOException {  
    this.upFlow = dataInput.readLong();  
    this.downFlow = dataInput.readLong();  
    this.sumFlow = dataInput.readLong();

FlowMapper类
//1.泛型是输入:行号+一行的内容; 输出:key字符手机号+类对象
public class FlowMapper extends Mapper {
private Text phone = new Text();
FlowBean flowBean = new FlowBean();

@Override  
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {  
    String\[\] split = value.toString().split("\\t");  
    phone.set(split\[1\]); //获取手机号key  
    flowBean.set(Long.parseLong(split\[split.length-3\]), Long.parseLong(split\[split.length-2\]));//获取upFlow和downFlow作为v  
    context.write(phone, flowBean);  
}  

}

FlowReducer类

public class FlowReduce extends Reducer {
private FlowBean flowBean = new FlowBean();
@Override
protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
super.reduce(key, values, context);
int sumUpFlow = 0;
int sumDownFlow = 0;
for (FlowBean value : values) {
sumUpFlow += value.getUpFlow();
sumDownFlow += value.getDownFlow();
}
flowBean.set(sumUpFlow, sumDownFlow);
context.write(key, flowBean);
}
}

FlowDriver类

public class FlowDriver {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
//1.获取job实例;获取配置信息
Job job = Job.getInstance(new Configuration());
//2.设置类路径;指定被程序的jar包所在的路径
job.setJarByClass(FlowDriver.class);
//3.设置Mapper和Reducer 指定本业务job要使用的mapper/Reducer业务类
job.setMapperClass(FlowMapper.class);
job.setReducerClass(FlowReduce.class);
//4.设置输出类型 指定mapper输出数据的kv类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(FlowBean.class);
// 指定最终输出的数据的kv类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowBean.class);
//5.设置输入输出路径
FileInputFormat.setInputPaths(job, new Path("F:\\input"));
FileOutputFormat.setOutputPath(job, new Path("F:\\output"));
//6.提交
boolean b = job.waitForCompletion(true);
System.exit(b ? 0 : 1);
}
}