MapReduce之基本数据类的排序

一、需求说明

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• 要求：将各个部门降序排列，利用MapReduce实现将emp.csv中的部门倒序排列

二、测试数据

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1. 员工信息表：下载地址
2. 表字段说明：
   

三、编程思路

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• 因在MapReduce中基本数据类型（如int）默认是升序排序的，因此我们只需要写一个类继承IntWritable.Comparator，重写compare方法即可

四、实现步骤

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1. 在Idea或eclipse中创建maven项目

2. 在pom.xml中添加hadoop依赖

   <dependency>
   	<groupId>org.apache.hadoopgroupId>
   	<artifactId>hadoop-commonartifactId>
   	<version>2.7.3version>
   dependency>
   <dependency>
   	<groupId>org.apache.hadoopgroupId>
   	<artifactId>hadoop-hdfsartifactId>
   	<version>2.7.3version>
   dependency>
   <dependency>
   	<groupId>org.apache.hadoopgroupId>
   	<artifactId>hadoop-mapreduce-client-commonartifactId>
   	<version>2.7.3version>
   dependency>
   <dependency>
   	<groupId>org.apache.hadoopgroupId>
   	<artifactId>hadoop-mapreduce-client-coreartifactId>
   	<version>2.7.3version>
   dependency>
   

3. 添加log4j.properties文件在资源目录下即resources，文件内容如下：

   ### 配置根 ###
   log4j.rootLogger = debug,console,fileAppender
   ## 配置输出到控制台 ###
   log4j.appender.console = org.apache.log4j.ConsoleAppender
   log4j.appender.console.Target = System.out
   log4j.appender.console.layout = org.apache.log4j.PatternLayout
   log4j.appender.console.layout.ConversionPattern = %d{ABSOLUTE} %5p %c:%L - %m%n
   ### 配置输出到文件 ###
   log4j.appender.fileAppender = org.apache.log4j.FileAppender
   log4j.appender.fileAppender.File = logs/logs.log
   log4j.appender.fileAppender.Append = false
   log4j.appender.fileAppender.Threshold = DEBUG,INFO,WARN,ERROR
   log4j.appender.fileAppender.layout = org.apache.log4j.PatternLayout
   log4j.appender.fileAppender.layout.ConversionPattern = %-d{yyyy-MM-dd HH:mm:ss} [ %t:%r ] - [ %p ] %m%n
   

4. 编写序列化类Employee.java，用于映射到emp.csv文件内容

   import org.apache.hadoop.io.IntWritable;
   import org.apache.hadoop.io.Text;
   import org.apache.hadoop.io.Writable;
   
   import java.io.DataInput;
   import java.io.DataOutput;
   import java.io.IOException;
   
   public class Employee implements Writable {
          
   	//7369,SMITH,CLERK,7902,1980/12/17,800,,20
   	private IntWritable empNo;
   	private Text empName;
   	private Text empJob;
   	private IntWritable leaderNo;
   	private Text hireDate;
   	private IntWritable empSalary;
   	private Text empBonus;
   	private IntWritable deptNo;
   	
   	public Employee() {
          
   		this.empNo = new IntWritable();
   		this.empName = new Text("");
   		this.empJob = new Text("");
   		this.leaderNo = new IntWritable();
   		this.hireDate = new Text("");
   		this.empSalary =new IntWritable();
   		this.empBonus = new Text("");
   		this.deptNo = new IntWritable();
   	}
   	
   	public Employee(int empNo, String empName, String empJob, int leaderNo,
   	String hireDate, int empSalary, String empBonus, int deptNo) {
          
   		this.empNo = new IntWritable(empNo);
   		this.empName = new Text(empName);
   		this.empJob = new Text(empJob);
   		this.leaderNo = new IntWritable(leaderNo);
   		this.hireDate = new Text(hireDate);
   		this.empSalary =new IntWritable(empSalary);
   		this.empBonus = new Text(empBonus);
   		this.deptNo = new IntWritable(deptNo);
   	}
   	
   	@Override
   	public void write(DataOutput out) throws IOException {
          
   		//序列化
   		this.empNo.write(out);
   		this.empName.write(out);
   		this.empJob.write(out);
   		this.leaderNo.write(out);
   		this.hireDate.write(out);
   		this.empSalary.write(out);
   		this.empBonus.write(out);
   		this.deptNo.write(out);
   	}
   	
   	@Override
   	public void readFields(DataInput in) throws IOException {
          
   		this.empNo.readFields(in);
   		this.empName.readFields(in);
   		this.empJob.readFields(in);
   		this.leaderNo.readFields(in);
   		this.hireDate.readFields(in);
   		this.empSalary.readFields(in);
   		this.empBonus.readFields(in);
   		this.deptNo.readFields(in);
   	}
   	
   	@Override
   	public String toString() {
          
   		return "Employee{" +
   		"empNo=" + empNo +
   		", empName=" + empName +
   		", empJob=" + empJob +
   		", leaderNo=" + leaderNo +
   		", hireDate=" + hireDate +
   		", empSalary=" + empSalary +
   		", empBonus=" + empBonus +
   		", deptNo=" + deptNo +
   		'}';
   	}
   	
   	public IntWritable getEmpNo() {
          
   		return empNo;
   	}
   	
   	public void setEmpNo(IntWritable empNo) {
          
   		this.empNo = empNo;
   	}
   	
   	public Text getEmpName() {
          
   		return empName;
   	}
   	
   	public void setEmpName(Text empName) {
          
   		this.empName = empName;
   	}
   	
   	public Text getEmpJob() {
          
   		return empJob;
   	}
   	
   	public void setEmpJob(Text empJob) {
          
   		this.empJob = empJob;
   	}
   	
   	public IntWritable getLeaderNo() {
          
   		return leaderNo;
   	}
   	
   	public void setLeaderNo(IntWritable leaderNo) {
          
   		this.leaderNo = leaderNo;
   	}
   	
   	public Text getHireDate() {
          
   		return hireDate;
   	}
   	
   	public void setHireDate(Text hireDate) {
          
   		this.hireDate = hireDate;
   	}
   	
   	public IntWritable getEmpSalary() {
          
   		return empSalary;
   	}
   	
   	public void setEmpSalary(IntWritable empSalary) {
          
   		this.empSalary = empSalary;
   	}
   	
   	public Text getEmpBonus() {
          
   		return empBonus;
   	}
   	
   	public void setEmpBonus(Text empBonus) {
          
   		this.empBonus = empBonus;
   	}
   	
   	public IntWritable getDeptNo() {
          
   		return deptNo;
   	}
   	
   	public void setDeptNo(IntWritable deptNo) {
          
   		this.deptNo = deptNo;
   	}
   }
   

5. 编写自定义Mapper类

   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 EmployeeMapper extends Mapper<LongWritable, Text, IntWritable,Employee> {
          
   
   	@Override
   	protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
          
   		//数据格式：<0,7369,SMITH,CLERK,7902,1980/12/17,800,,20>
   		//1、分词
   		String[] splits = value.toString().split(",");
   		//2、创建Employee对象，并且赋值
   		Employee employee = null;
   		
   		//判断员工是否有上级领导，如果没有，则给该字段设置一个0
   		if (null == splits[3] || "".equals(splits[3])){
          
   			splits[3] = "0";
   		}
   		//判断员工是否有奖金
   		if(null != splits[6] && !"".equals(splits[6])){
          
   			employee = getEmpInstance(splits);
   		}else{
          
   			splits[6] = "0";
   			employee = getEmpInstance(splits);
   		}
   		//3、通过context写出去
   		context.write(employee.getDeptNo(),employee);
   	}
   	
   	private Employee getEmpInstance(String[] splits){
          
   		Employee employee = new Employee(
   		Integer.parseInt(splits[0]),splits[1],splits[2],
   		Integer.parseInt(splits[3]),splits[4],Integer.parseInt(splits[5]),
   		splits[6],Integer.parseInt(splits[7]));
   		return employee;
   	}
   }
   

6. 编写自定义Reducer类

   import org.apache.hadoop.io.IntWritable;
   import org.apache.hadoop.io.Text;
   import org.apache.hadoop.mapreduce.Reducer;
   
   import java.io.IOException;
   
   public class EmployeeReducer extends Reducer<IntWritable,Employee,IntWritable,IntWritable> {
          
   	@Override
   	protected void reduce(IntWritable key, Iterable<Employee> values, Context context) throws IOException, InterruptedException {
          
   		//1、对数据进行处理：取出工资和奖金，求和操作
   		int sum = 0;
   		for (Employee e: values) {
          
   			IntWritable salary = e.getEmpSalary();
   			Text bonus = e.getEmpBonus();
   			if (bonus.getLength() > 0){
          
   				sum += salary.get() + Integer.valueOf(bonus.toString());
   			}else{
          
   				sum += salary.get();
   			}
   		}
   		//2、将结果通过context写出去
   		context.write(key,new IntWritable(sum));
   	}
   }
   

7. 编写自定义比较器类NumComparator.java

   import org.apache.hadoop.io.IntWritable;
   
   public class NumComparator extends IntWritable.Comparator {
          
   @Override
   public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {
          
   	return -super.compare(b1, s1, l1, b2, s2, l2);//-代表降序
   }
   

8. 编写自定义Driver类

   import org.apache.hadoop.conf.Configuration;
   import org.apache.hadoop.fs.Path;
   import org.apache.hadoop.io.IntWritable;
   import org.apache.hadoop.mapreduce.Job;
   import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
   import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
   
   import java.util.Random;
   
   public class EmployeeJob {
          
   	public static void main(String[] args) throws Exception {
          
   		Job job = Job.getInstance(new Configuration());
   		job.setJarByClass(EmployeeJob.class);
   		
   		job.setMapperClass(EmployeeMapper.class);
   		job.setMapOutputKeyClass(IntWritable.class);
   		job.setMapOutputValueClass(Employee.class);//Employee
   		
   		job.setReducerClass(EmployeeReducer.class);
   		job.setOutputKeyClass(IntWritable.class);
   		job.setOutputValueClass(IntWritable.class);
   		
   		//设置比较器
   		job.setSortComparatorClass(NumComparator.class);
   		
   		//先使用本地文件做测试
   		FileInputFormat.setInputPaths(job,new Path("F:\\NIIT\\hadoopOnWindow\\input\\emp.csv"));
   		FileOutputFormat.setOutputPath(job,new Path(getOutputDir()));
   		
   		boolean result = job.waitForCompletion(true);
   		
   		System.out.println("result:" + result);
   	}
   	
   	//用于产生随机输出目录
   	public static String getOutputDir(){
          
   		String prefix = "F:\\NIIT\\hadoopOnWindow\\output\\";
   		long time = System.currentTimeMillis();
   		int random = new Random().nextInt();
   		return prefix + "result_" + time + "_" + random;
   	}
   
   }
   

9. 本地运行代码，测试下结果正确与否

四、打包上传到集群中运行

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1. 上传emp.csv到hdfs中的datas目录下

2. 本地运行测试结果正确后，需要对Driver类输入输出部分代码进行修改，具体修改如下：
   FileInputFormat.setInputPaths(job,new Path(args[0]));
   FileOutputFormat.setOutputPath(job,new Path(args[1]));

3. 将程序打成jar包，需要在pom.xml中配置打包插件

   <build>
           <plugins>
               <plugin>
                   <groupId>org.apache.maven.pluginsgroupId>
                   <artifactId> maven-assembly-plugin artifactId>
                   <configuration>
                       
                       <descriptorRefs>
                           <descriptorRef>jar-with-dependenciesdescriptorRef>
                       descriptorRefs>
                   configuration>
                   <executions>
                       <execution>
                           <id>make-assemblyid>
                           
                           <phase>packagephase>
                           <goals>
                               
                               <goal>singlegoal>
                           goals>
                       execution>
                   executions>
               plugin>
           plugins>
       build>
   

   按照如下图所示进行操作
   
   

4. 提交集群运行，执行如下命令：

   hadoop jar packagedemo-1.0-SNAPSHOT.jar  com.niit.mr.EmployeeJob /datas/emp.csv /output/emp/ 
   

   至此，所有的步骤已经完成，大家可以试试，祝大家好运~~~~