MapReduce怎么求每个部门最高工资的员工姓名

MapReduce如何求每个部门工资最高的员工姓名，求Reduce的代码，Reduce代码如何写

帮你找了个相似的问题, 你可以看下: https://ask.csdn.net/questions/7605785
我还给你找了一篇非常好的博客，你可以看看是否有帮助，链接：Mapreduce执行机制之Map和Reduce
除此之外, 这篇博客: MapReduce实战之 MapReduce中多表合并案例中的 需求1：Reduce端表合并（数据倾斜） 部分也许能够解决你的问题, 你可以仔细阅读以下内容或跳转源博客中阅读:

通过将关联条件作为map输出的key，将两表满足join条件的数据并携带数据所来源的文件信息，发往同一个reduce task，在reduce中进行数据的串联。

1）创建商品和订合并后的bean类

package com.atguigu.mapreduce.table;

import java.io.DataInput;

import java.io.DataOutput;

import java.io.IOException;

import org.apache.hadoop.io.Writable;

public class TableBean implements Writable {

private String order_id; // 订单id

private String p_id; // 产品id

private int amount; // 产品数量

private String pname; // 产品名称

private String flag;// 表的标记

public TableBean() {

super();

}

public TableBean(String order_id, String p_id, int amount, String pname, String flag) {

super();

this.order_id = order_id;

this.p_id = p_id;

this.amount = amount;

this.pname = pname;

this.flag = flag;

}

public String getFlag() {

return flag;

}

public void setFlag(String flag) {

this.flag = flag;

}

public String getOrder_id() {

return order_id;

}

public void setOrder_id(String order_id) {

this.order_id = order_id;

}

public String getP_id() {

return p_id;

}

public void setP_id(String p_id) {

this.p_id = p_id;

}

public int getAmount() {

return amount;

}

public void setAmount(int amount) {

this.amount = amount;

}

public String getPname() {

return pname;

}

public void setPname(String pname) {

this.pname = pname;

}

@Override

public void write(DataOutput out) throws IOException {

out.writeUTF(order_id);

out.writeUTF(p_id);

out.writeInt(amount);

out.writeUTF(pname);

out.writeUTF(flag);

}

@Override

public void readFields(DataInput in) throws IOException {

this.order_id = in.readUTF();

this.p_id = in.readUTF();

this.amount = in.readInt();

this.pname = in.readUTF();

this.flag = in.readUTF();

}

@Override

public String toString() {

return order_id + "\t" + pname + "\t" + amount + "\t" ;

}

2）编写TableMapper程序

package com.atguigu.mapreduce.table;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

import org.apache.hadoop.mapreduce.lib.input.FileSplit;

public class TableMapper extends Mapper<LongWritable, Text, Text, TableBean>{

TableBean bean = new TableBean();

Text k = new Text();

@Override

protected void map(LongWritable key, Text value, Context context)

throws IOException, InterruptedException {

// 1 获取输入文件类型

FileSplit split = (FileSplit) context.getInputSplit();

String name = split.getPath().getName();

// 2 获取输入数据

String line = value.toString();

// 3 不同文件分别处理

if (name.startsWith("order")) {// 订单表处理

// 3.1 切割

String[] fields = line.split("\t");

// 3.2 封装bean对象

bean.setOrder_id(fields[0]);

bean.setP_id(fields[1]);

bean.setAmount(Integer.parseInt(fields[2]));

bean.setPname("");

bean.setFlag("0");

k.set(fields[1]);

}else {// 产品表处理

// 3.3 切割

String[] fields = line.split("\t");

// 3.4 封装bean对象

bean.setP_id(fields[0]);

bean.setPname(fields[1]);

bean.setFlag("1");

bean.setAmount(0);

bean.setOrder_id("");

k.set(fields[0]);

}

// 4 写出

context.write(k, bean);

}

3）编写TableReducer程序

package com.atguigu.mapreduce.table;

import java.io.IOException;

import java.util.ArrayList;

import org.apache.commons.beanutils.BeanUtils;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

public class TableReducer extends Reducer<Text, TableBean, TableBean, NullWritable> {

@Override

protected void reduce(Text key, Iterable<TableBean> values, Context context)

throws IOException, InterruptedException {

// 1准备存储订单的集合

ArrayList<TableBean> orderBeans = new ArrayList<>();

// 2 准备bean对象

TableBean pdBean = new TableBean();

for (TableBean bean : values) {

if ("0".equals(bean.getFlag())) {// 订单表

// 拷贝传递过来的每条订单数据到集合中

TableBean orderBean = new TableBean();

try {

BeanUtils.copyProperties(orderBean, bean);

} catch (Exception e) {

e.printStackTrace();

}

orderBeans.add(orderBean);

} else {// 产品表

try {

// 拷贝传递过来的产品表到内存中

BeanUtils.copyProperties(pdBean, bean);

} catch (Exception e) {

e.printStackTrace();

}

// 3 表的拼接

for(TableBean bean:orderBeans){

bean.setPname (pdBean.getPname());

// 4 数据写出去

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

}

4）编写TableDriver程序

package com.atguigu.mapreduce.table;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.NullWritable;

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 TableDriver {

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

// 1 获取配置信息，或者job对象实例

Configuration configuration = new Configuration();

Job job = Job.getInstance(configuration);

// 2 指定本程序的jar包所在的本地路径

job.setJarByClass(TableDriver.class);

// 3 指定本业务job要使用的mapper/Reducer业务类

job.setMapperClass(TableMapper.class);

job.setReducerClass(TableReducer.class);

// 4 指定mapper输出数据的kv类型

job.setMapOutputKeyClass(Text.class);

job.setMapOutputValueClass(TableBean.class);

// 5 指定最终输出的数据的kv类型

job.setOutputKeyClass(TableBean.class);

job.setOutputValueClass(NullWritable.class);

// 6 指定job的输入原始文件所在目录

FileInputFormat.setInputPaths(job, new Path(args[0]));

FileOutputFormat.setOutputPath(job, new Path(args[1]));

// 7 将job中配置的相关参数，以及job所用的java类所在的jar包，提交给yarn去运行

boolean result = job.waitForCompletion(true);

System.exit(result ? 0 : 1);

}

3）运行程序查看结果

1001 小米 1

1002 华为 2

1003 格力 3

缺点：这种方式中，合并的操作是在reduce阶段完成，reduce端的处理压力太大，map节点的运算负载则很低，资源利用率不高，且在reduce阶段极易产生数据倾斜

解决方案： map端实现数据合并

您还可以看一下尚硅谷IT教育老师的大数据技术课程中的 MapReduce排序分类小节, 巩固相关知识点

以下回答由chatgpt基于相关博客总结生成:

我会尝试解决这个问题：

首先，我们需要定义一个数据结构来存储员工的信息，包括姓名和工资。可以定义一个类来实现这个功能，如下所示：

public class Employee {
    private String name;
    private double salary;

    // constructor, getters, and setters
}

接下来，我们需要编写 Map 和 Reduce 函数来实现对每个部门中工资最高的员工的姓名的求解。首先是 Map 函数，它可以将每个员工的信息映射到一个 key-value 键值对上，其中 key 是部门名称，value 是一个 Employee 类型的对象。代码如下所示：

public static class DeptMaxSalaryMapper extends Mapper<Object, Text, Text, Employee> {

    private Text department = new Text();
    private Employee employeeRecord = new Employee();

    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
        // Split the input CSV line into fields
        String[] fields = value.toString().split(",");
        // Parse the employee name, salary, and department from the input line
        String name = fields[0];
        double salary = Double.parseDouble(fields[1]);
        String dept = fields[2];
        // Create an Employee object with the parsed values
        employeeRecord.setName(name);
        employeeRecord.setSalary(salary);
        // Set the department as the key for this record
        department.set(dept);
        // Emit the department and its associated employee record
        context.write(department, employeeRecord);
    }
}

在 Map 函数中，我们首先将输入的 CSV 行拆分成各个字段，然后从中解析出员工的姓名、工资和部门信息。接下来，我们创建一个 Employee 对象，并将从 CSV 行中解析出的值赋给它。最后，我们将该员工所在的部门作为 key，将该员工记录作为 value，通过 context 对象输出。

接下来是 Reduce 函数，它接收一个 department 和其对应的 Employee 列表，然后输出该部门工资最高的员工的姓名。代码如下所示：

public static class DeptMaxSalaryReducer extends Reducer<Text, Employee, Text, Text> {

    private Employee highestSalaryEmployee = null;

    public void reduce(Text key, Iterable<Employee> values, Context context) throws IOException, InterruptedException {
        // Find the highest salary employee
        for (Employee emp : values) {
            if (highestSalaryEmployee == null || emp.getSalary() > highestSalaryEmployee.getSalary()) {
                highestSalaryEmployee = emp;
            }
        }
        // Emit the department and name of the employee with the highest salary
        if (highestSalaryEmployee != null) {
            context.write(key, new Text(highestSalaryEmployee.getName()));
        }
    }
}

在 Reduce 函数中，我们首先定义一个变量来存储部门中工资最高的员工的信息。然后，我们遍历输入的 Employee 列表，在其中查找工资最高的员工。一旦找到这个员工，我们就使用 context 对象输出部门名称和该员工的姓名。

最后，我们需要将 Map 和 Reduce 函数注册到我们的 MapReduce 作业中。完整的代码如下所示：

public class MaxSalaryByDept {

    public static class DeptMaxSalaryMapper extends Mapper<Object, Text, Text, Employee> {

        private Text department = new Text();
        private Employee employeeRecord = new Employee();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            // Split the input CSV line into fields
            String[] fields = value.toString().split(",");
            // Parse the employee name, salary, and department from the input line
            String name = fields[0];
            double salary = Double.parseDouble(fields[1]);
            String dept = fields[2];
            // Create an Employee object with the parsed values
            employeeRecord.setName(name);
            employeeRecord.setSalary(salary);
            // Set the department as the key for this record
            department.set(dept);
            // Emit the department and its associated employee record
            context.write(department, employeeRecord);
        }
    }

    public static class DeptMaxSalaryReducer extends Reducer<Text, Employee, Text, Text> {

        private Employee highestSalaryEmployee = null;

        public void reduce(Text key, Iterable<Employee> values, Context context) throws IOException, InterruptedException {
            // Find the highest salary employee
            for (Employee emp : values) {
                if (highestSalaryEmployee == null || emp.getSalary() > highestSalaryEmployee.getSalary()) {
                    highestSalaryEmployee = emp;
                }
            }
            // Emit the department and name of the employee with the highest salary
            if (highestSalaryEmployee != null) {
                context.write(key, new Text(highestSalaryEmployee.getName()));
            }
        }
    }

    public static void main(String[] args) throws Exception {
        // Create a new MapReduce job
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "Max salary by department");
        job.setJarByClass(MaxSalaryByDept.class);

        // Set the mapper and reducer classes
        job.setMapperClass(DeptMaxSalaryMapper.class);
        job.setReducerClass(DeptMaxSalaryReducer.class);

        // Set the output key and value classes
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        // Set the input and output paths
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        // Submit the job and wait for it to complete
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

在 main 方法中，我们首先创建一个新的 MapReduce 作业。然后，我们设置了 Map 和 Reduce 函数的类，同时指定了输出的 key 和 value 类型。接下来，我们设置了输入和输出路径，并提交了该作业。

以上就是解决该问题的代码实现，希望能对您有所帮助！