1.MapReduce实现WordCount

2.MapReduce原理概览：

3.MAP端join

4.reduce端jion

5.补充:

前景提要：打开idea，选择maven，搜索quick

maven内容：

<?xml version="1.0" encoding="UTF-8"?>

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"

xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">

<artifactId>pratice</artifactId>

<version>1.0-SNAPSHOT</version>

<name>pratice</name>

<url>http://www.example.com</url>

<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>

<maven.compiler.source>1.8</maven.compiler.source>

<maven.compiler.target>1.8</maven.compiler.target>

</properties>

<groupId>junit</groupId>

<artifactId>junit</artifactId>

</dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-common</artifactId>

</dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-hdfs</artifactId>

</dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-auth</artifactId>

</dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-client</artifactId>

</dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-mapreduce-client-core</artifactId>

</dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-mapreduce-client-jobclient</artifactId>

</dependency>

</dependencies>

</project>

1.MapReduce实现WordCount

流程图：

过程如下：

1.现在d盘创建一个test1文件夹，里面在加入一个a.txt

2.代码如下：

导入的包：

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<LongWritable, Text,Text, IntWritable> {

@Override

//重写map方法

protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

//将每行转成string类型

String line = value.toString();

//在将每行的内容按照空格进行分割，并将分割后的value放到数组中

String[] words = line.split(" ");

//遍历分割后的word，并进行写，每写一个计数1

for (String word : words) {

context.write(new Text(word),new IntWritable(1));

}

导入的包：

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

代码：

public class WCReducer extends Reducer<Text, IntWritable,Text,IntWritable> {

@Override

protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {

int total=0;

for (IntWritable value : values) {

total+=value.get();

}

context.write(key,new IntWritable(total));

}

导入的包：

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;

代码：

public class WCDriver {

public static void main(String[] args)throws Exception {

//1.建立连接

Configuration cfg=new Configuration();

Job job=Job.getInstance(cfg,"job_wc");

job.setJarByClass(WCDriver.class);

//2.指定mapper输出

job.setMapperClass(WCMapper.class);

//3.指定reducer输出

job.setReducerClass(WCReducer.class);

//4.指定mapper输出类型

job.setMapOutputKeyClass(Text.class);

job.setMapOutputValueClass(IntWritable.class);

//5.指定reducer输出类型

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

//6.指定输入输出路径(默认是hdfs路径，但想要其读取本地路径，则file:///)

FileInputFormat.setInputPaths(job,new Path("file:///D:/test1/a.txt"));

//输出路径必须不存在

FileOutputFormat.setOutputPath(job,new Path("file:///D:/test1/wcResult/"));

//7.判断是否结果是否成功输出

boolean result = job.waitForCompletion(true);

System.out.println(result?"成功":"失败");

//运行结束退出

System.exit(result?0:1);

}

最后结果可在本地文件中查看到。

2.分片

新建一个类

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Partitioner;

//接收map的输出

public class WCPartitioner extends Partitioner<Text, IntWritable> {

//第三个参数表示分片数，reducer有几个，i就为几

@Override

public int getPartition(Text text, IntWritable intWritable,int i) {

return text.hashCode()%i;

}

并在WCDriver类中增加

//补充：指定partitioner

job.setPartitionerClass(WCPartitioner.class);

job.setNumReduceTasks(4);

结果生成四个分片：

3.用hadoop运行jar包，并将文件上传到hdfs中

先打jar包：

点击ProjectStructure

选择Artifact,点击加号

记得main class选择自己的main方法

点击apply，ok

点击build，然后再点击build artifacts,点击build

最后在out文件中，会出现jar包，右键点击show in explorer,将jar包拖到linux中

然后在linux中

hdfs dfs -mkdir /test/

再创建一个文本文件

vi a.txt

将文件上传到hdfs中

hdfs dfs -put a.txt /test/a.txt

使用mapreduce方法

hadoop jar pratice.jar cn.kgc.kb09.mr.WCDriver /test/a.txt /test/result

查看hdfs中的文件

hdfs dfs -cat /test/result/part-r-00000

2.MapReduce原理概览：

map读取文件，reduce进行计算

注：行的起始偏移量指一行的字节数

3.MAP端join

创建目录data，并放入两个文本

package pratice.join;

import org.apache.hadoop.io.Writable;

import java.io.DataInput;

import java.io.DataOutput;

import java.io.IOException;

public class CustomOrder implements Writable {

private String customId;

private String customName;

private String orderId;

private String orderStatus;

private String tableFlag;//为0时是custom表，为1是order表

@Override

public String toString() {

return

"customId='" + customId + '\'' +

", customName='" + customName + '\'' +

", orderId='" + orderId + '\'' +

", orderStatus='" + orderStatus + '\'' +

", tableFlag='" + tableFlag ;

}

public String getCustomId() {

return customId;

}

public void setCustomId(String customId) {

this.customId = customId;

}

public String getCustomName() {

return customName;

}

public void setCustomName(String customName) {

this.customName = customName;

}

public String getOrderId() {

return orderId;

}

public void setOrderId(String orderId) {

this.orderId = orderId;

}

public String getOrderStatus() {

return orderStatus;

}

public void setOrderStatus(String orderStatus) {

this.orderStatus = orderStatus;

}

public String getTableFlag() {

return tableFlag;

}

public void setTableFlag(String tableFlag) {

this.tableFlag = tableFlag;

}

@Override

public void write(DataOutput out) throws IOException {

out.writeUTF(customId);

out.writeUTF(customName);

out.writeUTF(orderId);

out.writeUTF(orderStatus);

out.writeUTF(tableFlag);

}

@Override

public void readFields(DataInput in) throws IOException {

this.customId=in.readUTF();

this.customName=in.readUTF();

this.orderId=in.readUTF();

this.orderStatus=in.readUTF();

this.tableFlag=in.readUTF();

}

package pratice.join;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class WCMapperJoin extends Mapper <LongWritable, Text,Text, CustomOrder>{

@Override

protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

String line=value.toString();

String[] columns = line.split(",");

CustomOrder co=new CustomOrder();

if(columns.length==4){//order表

co.setCustomId(columns[2]);

co.setCustomName("");

co.setOrderId(columns[0]);

co.setOrderStatus(columns[3]);

co.setTableFlag("1");

}else if (columns.length==9){

co.setCustomId(columns[0]);

co.setCustomName(columns[1]+"·"+columns[2]);

co.setOrderId("");

co.setOrderStatus("");

co.setTableFlag("0");

}

context.write(new Text(co.getCustomId()),co);

}

package pratice.join;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

public class COReducejoin extends Reducer <Text,CustomOrder,CustomOrder, NullWritable>{

@Override

protected void reduce(Text key, Iterable<CustomOrder> values, Context context) throws IOException, InterruptedException {

StringBuffer orderId=new StringBuffer();

StringBuffer statues=new StringBuffer();

CustomOrder customOrder=null;

for (CustomOrder co : values) {

if(co.getCustomName().equals("")){

orderId.append(co.getOrderId()+"|");

statues.append(co.getOrderStatus()+"|");

}else{

customOrder=co;

}

customOrder.setOrderId(orderId.toString());

customOrder.setOrderStatus(statues.toString());

context.write(customOrder, NullWritable.get());

}

package pratice.join;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

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;

public class CODriver {

public static void main(String[] args)throws Exception {

Configuration cfg=new Configuration();

Job job=Job.getInstance(cfg,"co_job");

job.setJarByClass(CODriver.class);

job.setMapperClass(WCMapperJoin.class);

job.setReducerClass(COReducejoin.class);

job.setMapOutputKeyClass(Text.class);

job.setMapOutputValueClass(CustomOrder.class);

job.setOutputKeyClass(CustomOrder.class);

job.setOutputValueClass(CustomOrder.class);

FileInputFormat.setInputPaths(job,new Path("file:///D:/pratice914/data"));

FileOutputFormat.setOutputPath(job,new Path("file:///D:/test/a1"));

boolean result=job.waitForCompletion(true);

System.out.println(result?"执行成功":"执行失败");

System.exit(result?0:1);

}

4.reduce端jion

package pratice.join;

import org.apache.hadoop.io.Writable;

import java.io.DataInput;

import java.io.DataOutput;

import java.io.IOException;

public class StudentJoin implements Writable {

private String course;

private String stuId;

private String name;

private String score;

public String getCourse() {

return course;

}

public void setCourse(String course) {

this.course = course;

}

public String getStuId() {

return stuId;

}

public void setStuId(String stuId) {

this.stuId = stuId;

}

public String getName() {

return name;

}

public void setName(String name) {

this.name = name;

}

public String getScore() {

return score;

}

public void setScore(String score) {

this.score = score;

}

@Override

public String toString() {

return

"course='" + course + '\'' +

", stuId='" + stuId + '\'' +

", name='" + name + '\'' +

", score='" + score + '\''

;

}

@Override

public void write(DataOutput out) throws IOException {

out.writeUTF(stuId);

out.writeUTF(name);

out.writeUTF(course);

out.writeUTF(score);

}

@Override

public void readFields(DataInput in) throws IOException {

this.stuId=in.readUTF();

this.name=in.readUTF();

this.course=in.readUTF();

this.score=in.readUTF();

}

package pratice.reducejoin;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

import pratice.join.StudentJoin;

import java.io.BufferedReader;

import java.io.FileReader;

import java.io.IOException;

import java.net.URI;

import java.util.HashMap;

import java.util.Map;

public class COJoinMapper extends Mapper<LongWritable, Text,Text, StudentJoin> {

Map <String,String>map=new HashMap();

@Override

protected void setup(Context context) throws IOException, InterruptedException {

URI[] cacheFiles = context.getCacheFiles();

if(cacheFiles!=null&&cacheFiles.length>0){

String filePath = cacheFiles[0].getPath();

FileReader fr=new FileReader(filePath);

BufferedReader br=new BufferedReader(fr);

String line;

while((line=br.readLine())!=null&&!"".equals(line)){

String [] columns=line.split(",");

map.put(columns[0],columns[1]);

}

@Override

protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

String line=value.toString();

String[] columns = line.split(",");

StudentJoin stu=new StudentJoin();

String stuid=columns[0];

String name=map.get(stuid);

String course=columns[1];

String score=columns[2];

stu.setStuId(stuid);

stu.setName(name);

stu.setCourse(course);

stu.setScore(score);

context.write(new Text(stuid),stu);

}

package pratice.reducejoin;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

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 pratice.join.StudentJoin;

import java.net.URI;

public class COJoinDriver {

public static void main(String[] args)throws Exception {

Job job=Job.getInstance(new Configuration(),"job_a");

job.setJarByClass(COJoinDriver.class);

job.setMapperClass(COJoinMapper.class);

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(StudentJoin.class);

String inPath="file:///D:/pratice914/data/student_score.txt";

String outPath="file:///D:/test/a7";

String cachePath="file:///D:/pratice914/data/student.txt";

job.addCacheFile(new URI(cachePath));

FileInputFormat.setInputPaths(job,new Path(inPath));

FileOutputFormat.setOutputPath(job,new Path(outPath));

boolean result=job.waitForCompletion(true);

System.out.println(result?"执行成功":"执行失败");

System.exit(result?0:1);

}

5.补充:

1.mapreduce流程

job-->TaskTracker-->Map/Reduce Task

resourcemanager-->app master-->container-->

scheduler

split-->map-->combiner-->partitioner-->reducer-->resoucemanager

2.hadoop1.0和2.0的区别

1.0管理和计算都是由mapreduce来完成

2.0管理由yarn负责,计算框架扩展

3.driver类的核心逻辑

Job对象-->指定driver类为驱动类-->设置map和reduce/partitioner -->设置mapper和reducer的输出类型-->设置输入输出文件路径-->运行

4.文件关联的逻辑

mapper端负责把文件合并，并且根据业务逻辑，把相同的key归类

reducer端负责根据相同的key进行关联

补充2：

1.mapjoin和reducejoin的区别

mapjoin是会利用cachefile接入数据，与map端接入的数据进行逻辑关联

不需要写reducer

reducejoin是map端只完成文件合并，利用相同的关联条件（例如id）作为key，输出到reduce端，reduce端根据key聚合达到关联的效果

2.shuffle

2020.9.10MapReduce原理及编程

1.MapReduce实现WordCount

2.MapReduce原理概览：

3.MAP端join

4.reduce端jion

5.补充:

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