MapReduce编程实战（2）-词频统计结果存入mysql数据库

摘要

通过实现MapReduce计算结果保存到MySql数据库过程，掌握多种方式保存计算结果的技术，加深了对MapReduce的理解；

Api 文档地址：http://hadoop.apache.org/docs/current/api/index.html

maven资源库：https://mvnrepository.com/repos/central     ##用于配置pom.xml的时候查询资源

1.master主机安装mysql

参见文章：https://www.cnblogs.com/hemomo/p/11942661.html

创建maven项目，项目名称hdfs，这里不再说明。

2.修改pom.xml文件

红色部分为增加内容：

<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">
  <modelVersion>4.0.0</modelVersion>

  <groupId>com.scitc</groupId>
  <artifactId>hdfs</artifactId>
  <version>0.0.1-SNAPSHOT</version>
  <packaging>jar</packaging>

  <name>hdfs</name>
  <url>http://maven.apache.org</url>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <hadoop.version>2.7.5</hadoop.version>
  </properties>

  <dependencies>
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>3.8.1</version>
      <scope>test</scope>
    </dependency>
    
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-common</artifactId>
      <version>${hadoop.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-jobclient</artifactId>
      <version>${hadoop.version}</version>
      <scope>provided</scope>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>${hadoop.version}</version>
    </dependency>
    
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-yarn-common</artifactId>
      <version>${hadoop.version}</version>
    </dependency>
     
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-core</artifactId>
      <version>${hadoop.version}</version>
    </dependency> 
    
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-hdfs</artifactId>
      <version>${hadoop.version}</version>
    </dependency>
    
    <dependency>
      <groupId>mysql</groupId>
      <artifactId>mysql-connector-java</artifactId>
      <version>5.1.27</version>
      <scope>compile</scope>
      <optional>true</optional>
    </dependency>
    
    <dependency>  
      <groupId>jdk.tools</groupId>  
      <artifactId>jdk.tools</artifactId>  
      <version>1.8</version>  
      <scope>system</scope>  
      <systemPath>${JAVA_HOME}/lib/tools.jar</systemPath>  
   </dependency>
   
  </dependencies>
  
  <build>
    <plugins>
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-compiler-plugin</artifactId>
            <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>
                 <archive>
                     <manifest>
                         <mainClass></mainClass>
                     </manifest>
                 </archive>
             </configuration>
             <executions>
                 <execution>
                     <id>make-assembly</id>
                     <phase>package</phase>
                     <goals>
                         <goal>single</goal>
                     </goals>
                 </execution>
             </executions>
         </plugin>
    
    </plugins>
</build>
  
</project>

2. 自定义数据类型(WordCountTb)

Hadoop给封装了许多输入输出的类型，如LongWritable、Text、 IntWritable、NullWritable等基础类型，这些类型和Java的基本数据类型一样，不能满足实际的业务需求；因此，我们可以通关过自定义输入输出类型来实现。

在com.scitc.hdfs下新建WordCountTb.java类：

 代码如下：

package com.scitc.hdfs;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;

import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapreduce.lib.db.DBWritable;

public class WordCountTb implements Writable, DBWritable {  
    
    //定义字段和构造函数
    String name;
    int value;
    public WordCountTb(String name, int value) {
        this.name = name;
        this.value = value;
    }

    //获取数据库表的字段值
    @Override
    public void readFields(ResultSet resultSet) throws SQLException {
        // TODO Auto-generated method stub
        this.name = resultSet.getString(1);
        this.value = resultSet.getInt(2);
    }
     
    @Override
    public void write(PreparedStatement statement) throws SQLException {
        // TODO Auto-generated method stub
        statement.setString(1, this.name);
        statement.setInt(2, this.value);
    }

    @Override
    public void write(DataOutput out) throws IOException {
        // TODO Auto-generated method stub
        out.writeUTF(name);
        out.writeInt(value);
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        // TODO Auto-generated method stub
        name = in.readUTF();
        value = in.readInt();
    }

}

3.数据库属性类StaticConstant

普通类中定义常量：//参考https://blog.csdn.net/rlnlo2pnefx9c/article/details/81277528 

在com.scitc.hdfs下新建StaticConstant.java类

代码如下：

package com.scitc.hdfs;

public class StaticConstant {
    public static final String jdbcDriver = "com.mysql.jdbc.Driver";
    public static final String jdbcUrl = "jdbc:mysql://192.168.56.110:3306/test?useUnicode=true&characterEncoding=utf8";
    public static final String jdbcUser = "root";
    public static final String jdbcPassword = "bigData@123";
}

3.编写MapReduce类WordCountToDb

在com.scitc.hdfs下新建WordCountToDb.java类

 代码如下：

package com.scitc.hdfs;

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.Mapper.Context;
import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBOutputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

public class WordCountToDb {

static class Maps extends Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
// 将读入的每行数据按空格切分
String[] dataArr = value.toString().split(" ");
if(dataArr.length>0){
// 将每个单词作为map的key，value设置为1
for (String word : dataArr) {
context.write(new Text(word), one);}
}
}
}

static class Reduces extends Reducer<Text, IntWritable, WordCountTb, WordCountTb> {
@Override
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable value : values) {
sum += value.get();
}
context.write(new WordCountTb(key.toString(), sum), null);
}
}

public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
// 1：实例化Configuration类、配置数据库类DBConfiguration、新建一个job任务
Configuration conf = new Configuration();
DBConfiguration.configureDB(conf, StaticConstant.jdbcDriver,
StaticConstant.jdbcUrl, StaticConstant.jdbcUser, StaticConstant.jdbcPassword);
Job job = Job.getInstance(conf, "word-count");

//2：设置jar加载的路径
job.setJarByClass(WordCountToDb.class);

//3：设置Map类和reduce类
job.setMapperClass(Maps.class);
job.setReducerClass(Reduces.class);

//4：设置Map输出
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);

//5：设置reduce最终输出kv类型
job.setOutputKeyClass(WordCountTb.class);
job.setOutputValueClass(WordCountTb.class);

//6：设置输入路径
String inputPath = "hdfs://master:9000/he/input/wordcount.txt";
// 如果有传入文件地址，接收参数为输入文件地址
if(args != null && args.length > 0){
inputPath = args[0];
}
FileInputFormat.addInputPath(job, new Path(inputPath));

//7：设置数据库输出格式、输出到哪些表、字段
job.setOutputFormatClass(DBOutputFormat.class);
DBOutputFormat.setOutput(job, "wordcount", "name", "value");

//本地提交没问题，在集群提交会出现，Error: java.io.IOException: com.mysql.jdbc.Driver
job.addArchiveToClassPath(new Path("hdfs://master:9000/lib/mysql/mysql-connector-java-5.1.27.jar"));

//8：提交任务
boolean result = job.waitForCompletion(true);
System.exit(result?0:1);
}
}

4：本地运行程序

本地测试非常方便调试。省去排除错误的时候，来回打包在集群运行。

在WordCountToDb类的编辑界面上右击鼠标，在弹出的菜单中选中Run As -> Java Application开始运行该类。

eclipse的console输出如下：

打开数据库wordcount表查看运行结果：

5：打包、上传、在集群中运行

运行之前记得删除掉mysql中表wordcount里之前本地运行生成的数据

1.打包

项目名hdfs上右键>>Run As>>Maven clean

项目名hdfs上右键>>Run As>>Maven install

2.上传

项目根目录下的target文件夹中找到hdfs-0.0.1-SNAPSHOT.jar，改文件名为hdfs1.jar,上传到master的/opt/data/目录中

3.用hadoop jar 命令运行hdfs1.jar包

cd /opt/data

hadoop jar hdfs1.jar com.scitc.hdfs. WordCountToDb 

##命令语法：hadoop jar  jar包 类的全名称

查看结果：

在集群中运行，出现问题：Error: java.io.IOException: com.mysql.jdbc.Driver

解决方法1:

pom配置的插件maven-assembly-plugin

在mavne install之后有两个jar包

一个hdfs-0.0.1-SNAPSHOT-jar-with-dependencies.jar 包含所有依赖

因此在集群运行这个jar包，也会正常执行。  ##测试通过

但是这样jar包40多M，太大了。

解决方法2：（推荐）

把jar包传到集群上，命令如下

hadoop fs –mkdir –p /lib/mysql     ##创建目录

hadoop fs -put mysql-connector-java-5.1.27.jar /lib/mysql        ##上传驱动到hdfs的lib/mysql目录中

在WordCountToDb.java中提交任务代码前。添加如下代码：

job.addArchiveToClassPath(new Path("hdfs://master:9000/lib/mysql/mysql-connector-java-5.1.27.jar"));

//8：提交任务
boolean result = job.waitForCompletion(true);

查看结果：

查看集群执行结果：没问题，输出为0字节，因为我们是输出到mysql的。

 查看mysql数据库：

============================

问题集：

问题1：集群中运行jar包，报错：Error: java.io.IOException: com.mysql.jdbc.Driver

解决参考资料：https://www.cnblogs.com/codeOfLife/p/5464613.html

修改说明：

2020-02-22，增加WordCountToDb代码