[大数据]Hadoop（MapReduce）

1、MapReduce概述

1.1 定义

1.2 优缺点

优点：

缺点：

1.3 MapReduce 核心思想

1.4 MapReduce进程

1.5 官方 WordCount 源码

采用反编译工具反编译源码，发现 WordCount 案例有 Map 类、Reduce 类和驱动类。且数据的类型是 Hadoop 自身封装的序列化类型。

1.6 常用数据序列化类型

1.7 MapReduce编程规范

1.8 WordCount案例实操

1.8.1 本地测试

（3）环境准备
a.创建maven工程，MapReduceDemo
b.在pom.xml文件中添加如下依赖

    <dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>3.1.3</version>
        </dependency>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.12</version>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>1.7.30</version>
        </dependency>
    </dependencies>

c.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

d.创建包名com.xxxx.mapreduce.wordcount
e.编写Mapper类

package com.xxxx.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;

/**
 * KEYIN        map阶段输入的key: LongWritable
 * VALUEIN      map阶段输入的value: Text
 * KEYOUT       map阶段输出的key: Text
 * VALUEOUT     map阶段输出的value类型: IntWritable
 */
public class WordCountMapper extends Mapper<LongWritable, Text,Text, IntWritable> {

    private Text outK = new Text();
    private IntWritable outV = new IntWritable(1);

    @Override
    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException {

        //1.获取一行
        //atguigu atguigu
        String line = value.toString();

        //2.切割
        //atguigu
        //atguigu
        String[] words = line.split(" ");

        //3.循环写出
        for (String word : words){

            //封装outK
            outK.set(word);

            //写出
            context.write(outK,outV);
        }
    }
}

f.编写 Reducer 类

package com.xxxx.mapreduce.wordcount;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

/**
 * KEYIN        reduce阶段输入的key: Text
 * VALUEIN      reduce阶段输入的value: IntWritable
 * KEYOUT       reduce阶段输出的key: Text
 * VALUEOUT     reduce阶段输出的value类型: IntWritable
 */
public class WordCountReducer extends Reducer<Text, IntWritable,Text,IntWritable> {


    private IntWritable outV = new IntWritable();

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

        int sum=0;

        //atguigu (1,1)
        //1.累计求和
        for (IntWritable value : values){
            sum += value.get();
        }

        outV.set(sum);

        //写出
        context.write(key,outV);

    }
}

g.编写 Driver 驱动类

package com.xxxx.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;

/**
 * 注释内容
 *
 * @author : li.linnan
 * @create : 2022/9/28
 */
public class WordCountDriver {

    public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {

        // 1 获取配置信息以及获取 job 对象
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        // 2 关联本 Driver 程序的 jar
        job.setJarByClass(WordCountDriver.class);

        // 3 关联 Mapper 和 Reducer 的 jar
        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReducer.class);

        // 4 设置 Mapper 输出的 kv 类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        // 5 设置最终输出 kv 类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        // 6 设置输入和输出路径
        FileInputFormat.setInputPaths(job, new Path("D:\\input\\hello.txt"));
        FileOutputFormat.setOutputPath(job, new Path("D:\\input\\result"));

        // 7 提交 job
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);

    }

}

h.本地测试

文件内容：

atguigu	2
banzhang	1
cls	3
hadoop	1
jiao	1
ss	2
xue	1

1.8.2 提交到集群测试

集群上测试
（1）用 maven 打 jar 包，需要添加的打包插件依赖

    <build>
        <plugins>

            <plugin>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.6.1</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                </configuration>
            </plugin>

            <plugin>
                <artifactId>maven-assembly-plugin</artifactId>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>

（2）修改输入输出路径

        // 6 设置输入和输出路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

（3）将程序打成 jar 包

（4）修改不带依赖的 jar 包名称为 wc.jar，并拷贝该 jar 包到 Hadoop 集群的/opt/module/hadoop-3.1.3 路径。 （直接将jar包拖拽过来）

（5）执行命令

[lln@hadoop102 hadoop-3.1.3]$ hadoop jar wc.jar com.xxxx.mapreduce.wordcount.WordCountDriver /input /output

2、Hadoop序列化

2.1 概述

2.2 自定义bean对象实现序列化接口（Writable）

2.3 序列化案例实操

（1）需求

（2）需求分析

（3）编写MapReduce程序
a.编写流量统计的Bean对象

package com.xxxx.mapreduce.writable;

import org.apache.hadoop.io.Writable;

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

/**
 * 1、定义类，实现Writable接口
 * 2、重写序列化和反序列化方法
 * 3、重写空参构造
 * 4、toString方法
 *
 * @author : li.linnan
 * @create : 2022/9/29
 */
public class FlowBean implements Writable {

    private long upFlow;//上行流量
    private long downFlow;//下行流量
    private long sumFlow;//总流量

    //空参构造
    public FlowBean(){
    }

    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;
    }

    public void setSumFlow() {
        this.sumFlow = this.upFlow + this.downFlow;
    }

    @Override
    public void write(DataOutput dataOutput) throws IOException {

        dataOutput.writeLong(upFlow);
        dataOutput.writeLong(downFlow);
        dataOutput.writeLong(sumFlow);

    }

    @Override
    public void readFields(DataInput dataInput) throws IOException {

        this.upFlow = dataInput.readLong();
        this.downFlow = dataInput.readLong();
        this.sumFlow = dataInput.readLong();

    }

    @Override
    public String toString() {
        return upFlow + "\t" + downFlow + "\t" + sumFlow;
    }

}

b.编写Mapper类

package com.xxxx.mapreduce.writable;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

/**
 * 注释内容
 *
 * @author : li.linnan
 * @create : 2022/9/29
 */
public class FlowMapper extends Mapper<LongWritable, Text,Text,FlowBean> {

    private Text outK = new Text();
    private FlowBean outV = new FlowBean();

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

        //1、获取一行
        //1	13736230513	192.196.100.1	www.atguigu.com	2481	24681	200
        //2	13846544121	192.196.100.2			264	0	200
        String line = value.toString();

        //2 切割
        //1,13736230513,192.196.100.1,www.atguigu.com,2481,24681,200
        //2,13846544121,192.196.100.2,264,0,200
        String[] split = line.split("\t");

        //3、抓取想要的数据
        //手机号 13736230513
        //上行流量和下行流量 2481 24681
        String phone = split[1];
        String up = split[split.length-3];
        String down = split[split.length-2];

        //4、封装outK、outV
        outK.set(phone);
        outV.setUpFlow(Long.parseLong(up));
        outV.setDownFlow(Long.parseLong(down));
        outV.setSumFlow();

        //5、写出outK、outV
        context.write(outK,outV);


    }

}

c.编写Reducer类

package com.xxxx.mapreduce.writable;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

/**
 * 注释内容
 *
 * @author : li.linnan
 * @create : 2022/9/29
 */
public class FlowReducer extends Reducer<Text,FlowBean,Text,FlowBean> {

    private FlowBean outV = new FlowBean();

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

        long totalUp = 0;
        long totalDown = 0;

        //1、遍历values，将其中的上行流量，下行流量分别累加
        for(FlowBean flowBean : values){
            totalUp += flowBean.getUpFlow();
            totalDown += flowBean.getDownFlow();
        }

        //2、封装outV
        outV.setUpFlow(totalUp);
        outV.setDownFlow(totalDown);
        outV.setSumFlow();

        //3、写出outK、outV
        context.write(key,outV);
    }
}

d.编写Driver驱动类

package com.xxxx.mapreduce.writable;

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 java.io.IOException;

/**
 * 注释内容
 *
 * @author : li.linnan
 * @create : 2022/9/29
 */
public class FlowDriver {

    public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {

        //1 获取job
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        //2 设置jar
        job.setJarByClass(FlowDriver.class);

        //3 关联mapper 和 Reducer
        job.setMapperClass(FlowMapper.class);
        job.setReducerClass(FlowReducer.class);

        //4 设置mapper 输出的 key 和 value 类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);

        //5 设置最终数据输出的 key 和 value 类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);

        //6 设置数据的输入路径和输出路径
        FileInputFormat.setInputPaths(job, new Path("D:\\input\\phone.txt"));
        FileOutputFormat.setOutputPath(job, new Path("D:\\input\\result"));

        //7 提交job
        boolean b = job.waitForCompletion(true);
        System.exit(b?0:1);

    }

}

phone.txt 文件内容

1	13736230513	192.196.100.1	www.atguigu.com	2481	24681	200
2	13846544121	192.196.100.2			264	0	200
3 	13956435636	192.196.100.3			132	1512	200
4 	13966251146	192.168.100.1			240	0	404
5 	18271575951	192.168.100.2	www.atguigu.com	1527	2106	200
6 	84188413	192.168.100.3	www.atguigu.com	4116	1432	200
7 	13590439668	192.168.100.4			1116	954	200
8 	15910133277	192.168.100.5	www.hao123.com	3156	2936	200
9 	13729199489	192.168.100.6			240	0	200
10 	13630577991	192.168.100.7	www.shouhu.com	6960	690	200
11 	15043685818	192.168.100.8	www.baidu.com	3659	3538	200
12 	15959002129	192.168.100.9	www.atguigu.com	1938	180	500
13 	13560439638	192.168.100.10			918	4938	200
14 	13470253144	192.168.100.11			180	180	200
15 	13682846555	192.168.100.12	www.qq.com	1938	2910	200
16 	13992314666	192.168.100.13	www.gaga.com	3008	3720	200
17 	13509468723	192.168.100.14	www.qinghua.com	7335	110349	404
18 	18390173782	192.168.100.15	www.sogou.com	9531	2412	200
19 	13975057813	192.168.100.16	www.baidu.com	11058	48243	200
20 	13768778790	192.168.100.17			120	120	200
21 	13568436656	192.168.100.18	www.alibaba.com	2481	24681	200
22 	13568436656	192.168.100.19			1116	954	200

运行结果

13470253144	180	180	360
13509468723	7335	110349	117684
13560439638	918	4938	5856
13568436656	3597	25635	29232
13590439668	1116	954	2070
13630577991	6960	690	7650
13682846555	1938	2910	4848
13729199489	240	0	240
13736230513	2481	24681	27162
13768778790	120	120	240
13846544121	264	0	264
13956435636	132	1512	1644
13966251146	240	0	240
13975057813	11058	48243	59301
13992314666	3008	3720	6728
15043685818	3659	3538	7197
15910133277	3156	2936	6092
15959002129	1938	180	2118
18271575951	1527	2106	3633
18390173782	9531	2412	11943
84188413	4116	1432	5548

3、MapReduce框架原理

4、Hadoop数据压缩

5、常见错误和解决方案