MapReduce是一种编程模型，用于处理和生成大数据集，它由两个阶段组成：Map阶段和Reduce阶段，在读取MySQL数据时，我们可以使用Hadoop的MapReduce框架来处理这些数据，以下是一个简单的示例，展示了如何使用MapReduce从MySQL数据库中读取数据。

1、确保已经安装了Hadoop和MySQL JDBC驱动程序。

2、创建一个Java项目，并添加以下依赖项到项目的pom.xml文件中（如果使用Maven）：

<dependencies>
    <dependency>
        <groupId>mysql</groupId>
        <artifactId>mysqlconnectorjava</artifactId>
        <version>8.0.26</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoopcore</artifactId>
        <version>3.3.1</version>
    </dependency>
</dependencies>

3、编写一个Mapper类，用于从MySQL数据库中读取数据：

import java.io.IOException;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.ResultSet;
import java.sql.Statement;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class MysqlMapper extends Mapper<LongWritable, Text, Text, Text> {
    private Connection connection;
    private Statement statement;
    private ResultSet resultSet;
    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        try {
            Class.forName("com.mysql.cj.jdbc.Driver");
            connection = DriverManager.getConnection("jdbc:mysql://localhost:3306/mydatabase", "username", "password");
            statement = connection.createStatement();
            resultSet = statement.executeQuery("SELECT * FROM mytable");
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        try {
            while (resultSet.next()) {
                String id = resultSet.getString("id");
                String name = resultSet.getString("name");
                context.write(new Text(id), new Text(name));
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
    @Override
    protected void cleanup(Context context) throws IOException, InterruptedException {
        try {
            if (resultSet != null) {
                resultSet.close();
            }
            if (statement != null) {
                statement.close();
            }
            if (connection != null) {
                connection.close();
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

4、编写一个Reducer类，用于处理从Mapper传递过来的数据：

import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class MysqlReducer extends Reducer<Text, Text, Text, Text> {
    @Override
    protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
        for (Text value : values) {
            context.write(key, value);
        }
    }
}

5、编写一个驱动程序，用于运行MapReduce作业：

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 MysqlMapReduce {
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "Mysql MapReduce");
        job.setJarByClass(MysqlMapReduce.class);
        job.setMapperClass(MysqlMapper.class);
        job.setReducerClass(MysqlReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

6、编译并运行程序，将MySQL数据作为输入传递给MapReduce作业，注意，这里的示例代码仅用于演示目的，实际应用中可能需要根据具体需求进行调整。