Hadoop MapReduce编程 API入门系列之统计学生成绩版本2（十八）

更新时间：2022-08-28 23:34:35

　统计出每个年龄段的男、女学生的最高分

　　这里，为了空格符的差错，直接，我们有时候，像如下这样的来排数据。

Hadoop MapReduce编程 API入门系列之统计学生成绩版本2（十八）

代码

package zhouls.bigdata.myMapReduce.Gender;

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/**
* 
* @function 统计不同年龄段内 男、女最高分数
*
*
*/

/*
Alice<tab>23<tab>female<tab>45
Bob<tab>34<tab>male<tab>89
Chris<tab>67<tab>male<tab>97
Kristine<tab>38<tab>female<tab>53
Connor<tab>25<tab>male<tab>27
Daniel<tab>78<tab>male<tab>95
James<tab>34<tab>male<tab>79
Alex<tab>52<tab>male<tab>69
Nancy<tab>7<tab>female<tab>98
Adam<tab>9<tab>male<tab>37
Jacob<tab>7<tab>male<tab>23
Mary<tab>6<tab>female<tab>93
Clara<tab>87<tab>female<tab>72
Monica<tab>56<tab>female<tab>92
*/
public class Gender extends Configured implements Tool {
/*
* 
* @function Mapper 解析输入数据，然后按需求输出
* @input key=行偏移量 value=学生数据
* @output key=gender value=name+age+score
* 
*/
public static class PCMapper extends Mapper<Object, Text, Text, Text>
{
public void map(Object key, Text value, Context context) throws IOException, InterruptedException 
{//拿Alice<tab>23<tab>female<tab>45
String[] tokens = value.toString().split("<tab>");//使用分隔符<tab>，将数据解析为数组 tokens
//得到Alice    23    female    45
//即tokens[0] tokens[1] tokens[2] tokens[3] 
String gender = tokens[2].toString();//性别
String nameAgeScore = tokens[0] + "\t" + tokens[1] + "\t"+ tokens[3];
//输出 key=gender value=name+age+score
//输出 key=female value=Alice    +23+45
context.write(new Text(gender), new Text(nameAgeScore));//将 （female ， Alice+ 23+ 45) 写入到context中
}
}
public static class MyHashPartitioner extends Partitioner<Text, Text> 
{
/** Use {@link Object#hashCode()} to partition. */
@Override
public int getPartition(Text key, Text value,int numReduceTasks) 
{
return (key.hashCode()) % numReduceTasks;
}

}
/**
* 
* @function Partitioner 根据 age 选择 reduce 分区
*
*/
public static class PCPartitioner extends Partitioner<Text, Text> 
{

@Override
public int getPartition(Text key, Text value, int numReduceTasks) 
{
// TODO Auto-generated method stub
String[] nameAgeScore = value.toString().split("\t");
String age = nameAgeScore[1];//学生年龄
int ageInt = Integer.parseInt(age);//按年龄段分区

// 默认指定分区 0
if (numReduceTasks == 0)
return 0;

//年龄小于等于20，指定分区0
if (ageInt <= 20) {
return 0;
}
// 年龄大于20，小于等于50，指定分区1
if (ageInt > 20 && ageInt <= 50) {

return 1 % numReduceTasks;
}
// 剩余年龄，指定分区2
else
return 2 % numReduceTasks;
}
}

/**
* 
* @function 定义Combiner 合并 Mapper 输出结果
*
*/
public static class PCCombiner extends Reducer<Text, Text, Text, Text> 
{
private Text text = new Text();

public void reduce(Text key, Iterable<Text> values, Context context)throws IOException, InterruptedException 
{
int maxScore = Integer.MIN_VALUE;
String name = " ";
String age = " ";
int score = 0;
for (Text val : values) 
{
String[] valTokens = val.toString().split("\\t");
score = Integer.parseInt(valTokens[2]);
if (score > maxScore) 
{
name = valTokens[0];
age = valTokens[1];
maxScore = score;
}
}
text.set(name + "\t" + age + "\t" + maxScore);
context.write(key, text);
}
}

/*
* 
* @function Reducer 统计出 不同年龄段、不同性别 的最高分
* input key=gender value=name+age+score
* output key=name value=age+gender+score
* 
*/
static class PCReducer extends Reducer<Text, Text, Text, Text> 
{
@Override
public void reduce(Text key, Iterable<Text> values, Context context)throws IOException, InterruptedException 
{
int maxScore = Integer.MIN_VALUE;
String name = " ";
String age = " ";
String gender = " ";
int score = 0;
// 根据key，迭代 values 集合，求出最高分
for (Text val : values)
{
String[] valTokens = val.toString().split("\\t");
score = Integer.parseInt(valTokens[2]);
if (score > maxScore) 
{
name = valTokens[0];
age = valTokens[1];
gender = key.toString();
maxScore = score;
}
}
context.write(new Text(name), new Text("age- " + age + "\t" + gender + "\tscore-" + maxScore));
}
}

/**
* @function 任务驱动方法
* @param args
* @return
* @throws Exception
*/
@Override
public int run(String[] args) throws Exception 
{
// TODO Auto-generated method stub
Configuration conf = new Configuration();//读取配置文件

Path mypath = new Path(args[1]);
FileSystem hdfs = mypath.getFileSystem(conf);
if (hdfs.isDirectory(mypath)) 
{
hdfs.delete(mypath, true);
}

@SuppressWarnings("deprecation")
Job job = new Job(conf, "gender");//新建一个任务
job.setJarByClass(Gender.class);//主类
job.setMapperClass(PCMapper.class);//Mapper
job.setReducerClass(PCReducer.class);//Reducer

job.setPartitionerClass(MyHashPartitioner.class);
//job.setPartitionerClass(PCPartitioner.class);//设置Partitioner类
job.setNumReduceTasks(3);// reduce个数设置为3

job.setMapOutputKeyClass(Text.class);//map 输出key类型
job.setMapOutputValueClass(Text.class);//map 输出value类型

job.setCombinerClass(PCCombiner.class);//设置Combiner类

job.setOutputKeyClass(Text.class);//输出结果 key类型
job.setOutputValueClass(Text.class);//输出结果 value 类型

FileInputFormat.addInputPath(job, new Path(args[0]));// 输入路径
FileOutputFormat.setOutputPath(job, new Path(args[1]));// 输出路径
job.waitForCompletion(true);//提交任务
return 0;
}
/**
* @function main 方法
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception
{
//    String[] args0 = {
//    "hdfs://HadoopMaster:9000/gender/gender.txt",
//    "hdfs://HadoopMaster:9000/out/partition/" };

String[] args0 = {
"./data/gender/gender.txt",
"./out/gender" };


int ec = ToolRunner.run(new Configuration(),new Gender(), args0);
System.exit(ec);
}
}

　　　　或者

　　　　代码

package com.dajiangtai.hadoop.junior;

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/**
 * 
 * @function 统计不同年龄段内    男、女最高分数
 * @author zhouls
 *
 */
 
 /*
Alice<tab>23<tab>female<tab>45
Bob<tab>34<tab>male<tab>89
Chris<tab>67<tab>male<tab>97
Kristine<tab>38<tab>female<tab>53
Connor<tab>25<tab>male<tab>27
Daniel<tab>78<tab>male<tab>95
James<tab>34<tab>male<tab>79
Alex<tab>52<tab>male<tab>69
Nancy<tab>7<tab>female<tab>98
Adam<tab>9<tab>male<tab>37
Jacob<tab>7<tab>male<tab>23
Mary<tab>6<tab>female<tab>93
Clara<tab>87<tab>female<tab>72
Monica<tab>56<tab>female<tab>92
*/
public class Gender extends Configured implements Tool {
    /*
     * 
     * @function Mapper 解析输入数据，然后按需求输出
     * @input  key=行偏移量   value=学生数据
     * @output key=gender  value=name+age+score
     * 
     */
    public static class PCMapper extends Mapper<Object, Text, Text, Text>
    {
        public void map(Object key, Text value, Context context) throws IOException, InterruptedException 
        {//拿Alice<tab>23<tab>female<tab>45
            String[] tokens = value.toString().split("<tab>");//使用分隔符<tab>，将数据解析为数组 tokens
                            //得到Alice        23         female            45
                            //即tokens[0]   tokens[1]  tokens[2]  tokens[3] 
            String gender = tokens[2].toString();//性别
            String nameAgeScore = tokens[0] + "\t" + tokens[1] + "\t"+ tokens[3];
            //输出  key=gender  value=name+age+score
            //输出     key=female  value=Alice    +23+45
            context.write(new Text(gender), new Text(nameAgeScore));//将 （female ， Alice+  23+ 45) 写入到context中
        }
    }
    public static class MyHashPartitioner extends Partitioner<Text, Text> 
    {
          /** Use {@link Object#hashCode()} to partition. */
          @Override
          public int getPartition(Text key, Text value,int numReduceTasks) 
          {
            return (key.hashCode()) % numReduceTasks;
          }

        }
    /**
     * 
     * @function Partitioner 根据 age 选择 reduce 分区
     *
     */
    public static class PCPartitioner extends Partitioner<Text, Text> 
    {

        @Override
        public int getPartition(Text key, Text value, int numReduceTasks) 
        {
            // TODO Auto-generated method stub
            String[] nameAgeScore = value.toString().split("\t");
            String age = nameAgeScore[1];//学生年龄
            int ageInt = Integer.parseInt(age);//按年龄段分区

            // 默认指定分区 0
            if (numReduceTasks == 0)
                return 0;

            //年龄小于等于20，指定分区0
            if (ageInt <= 20) {
                return 0;
            }
            // 年龄大于20，小于等于50，指定分区1
            if (ageInt > 20 && ageInt <= 50) {

                return 1 % numReduceTasks;
            }
            // 剩余年龄，指定分区2
            else
                return 2 % numReduceTasks;
        }
    }

    /**
     * 
     * @function 定义Combiner 合并 Mapper 输出结果
     *
     */
    public static class PCCombiner extends Reducer<Text, Text, Text, Text> 
    {
        private Text text = new Text();

        public void reduce(Text key, Iterable<Text> values, Context context)throws IOException, InterruptedException 
        {
            int maxScore = Integer.MIN_VALUE;
            String name = " ";
            String age = " ";
            int score = 0;
            for (Text val : values) 
            {
                String[] valTokens = val.toString().split("\\t");
                score = Integer.parseInt(valTokens[2]);
                if (score > maxScore) 
                {
                    name = valTokens[0];
                    age = valTokens[1];
                    maxScore = score;
                }
            }
            text.set(name + "\t" + age + "\t" + maxScore);
            context.write(key, text);
        }
    }

    /*
     * 
     * @function Reducer 统计出 不同年龄段、不同性别 的最高分
     * input key=gender value=name+age+score
     * output key=name value=age+gender+score
     * 
     */
    static class PCReducer extends Reducer<Text, Text, Text, Text>  
    {
        @Override
        public void reduce(Text key, Iterable<Text> values, Context context)throws IOException, InterruptedException 
        {
            int maxScore = Integer.MIN_VALUE;
            String name = " ";
            String age = " ";
            String gender = " ";
            int score = 0;
            // 根据key，迭代 values 集合，求出最高分
            for (Text val : values)
                {
                String[] valTokens = val.toString().split("\\t");
                score = Integer.parseInt(valTokens[2]);
                if (score > maxScore) 
                {
                    name = valTokens[0];
                    age = valTokens[1];
                    gender = key.toString();
                    maxScore = score;
                }
            }
            context.write(new Text(name), new Text("age- " + age + "\t" + gender + "\tscore-" + maxScore));
        }
    }

    /**
     * @function 任务驱动方法
     * @param args
     * @return
     * @throws Exception
     */
    @Override
    public int run(String[] args) throws Exception 
    {
        // TODO Auto-generated method stub
        Configuration conf = new Configuration();//读取配置文件

        Path mypath = new Path(args[1]);
        FileSystem hdfs = mypath.getFileSystem(conf);
        if (hdfs.isDirectory(mypath)) 
        {
            hdfs.delete(mypath, true);
        }

        @SuppressWarnings("deprecation")
        Job job = new Job(conf, "gender");//新建一个任务
        job.setJarByClass(Gender.class);//主类
        job.setMapperClass(PCMapper.class);//Mapper
        job.setReducerClass(PCReducer.class);//Reducer

        job.setPartitionerClass(MyHashPartitioner.class);
        //job.setPartitionerClass(PCPartitioner.class);//设置Partitioner类
        job.setNumReduceTasks(3);// reduce个数设置为3

        job.setMapOutputKeyClass(Text.class);//map 输出key类型
        job.setMapOutputValueClass(Text.class);//map 输出value类型

        job.setCombinerClass(PCCombiner.class);//设置Combiner类

        job.setOutputKeyClass(Text.class);//输出结果 key类型
        job.setOutputValueClass(Text.class);//输出结果 value 类型

        FileInputFormat.addInputPath(job, new Path(args[0]));// 输入路径
        FileOutputFormat.setOutputPath(job, new Path(args[1]));// 输出路径
        job.waitForCompletion(true);//提交任务
        return 0;
    }
    /**
     * @function main 方法
     * @param args
     * @throws Exception
     */
    public static void main(String[] args) throws Exception
    {
        String[] args0 = {
                "hdfs://master:9000/middle/partition/gender.txt",
                "hdfs://master:9000/middle/partition/out/" };
        int ec = ToolRunner.run(new Configuration(),new Gender(), args0);
        System.exit(ec);
    }
}

本文转自大数据躺过的坑博客园博客，原文链接：http://www.cnblogs.com/zlslch/p/6165704.html，如需转载请自行联系原作者

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Hadoop MapReduce编程 API入门系列之统计学生成绩版本2（十八）

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