Hadoop MapReduce编程 API入门系列之统计学生成绩版本2（十八）
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>
/*
*
* @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>
{
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 tokens tokens tokens
String gender = tokens.toString();//性别
String nameAgeScore = tokens + "\t" + tokens + "\t"+ tokens;
//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>
{
/** 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>
{
@Override
public int getPartition(Text key, Text value, int numReduceTasks)
{
// TODO Auto-generated method stub
String[] nameAgeScore = value.toString().split("\t");
String age = nameAgeScore;//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>
{
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);
if (score > maxScore)
{
name = valTokens;
age = valTokens;
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>
{
@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);
if (score > maxScore)
{
name = valTokens;
age = valTokens;
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);
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));// 输入路径
FileOutputFormat.setOutputPath(job, new Path(args));// 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);
}
}
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