Hadoop MapReduce统计手机流量案例学习(结合Partitioner)

统计手机上网的上行流量和下行流量
数据格式：



统计手机的 上网流量只需要“手机号”、“上行流量”、“下行流量”三个字段，根据这三个字段创建bean对象， 该对象要实现Writable接口，以便实现序列化， 并且要有无参构造方法，hadoop会使用反射创建对象

public class PhoneBean implements Writable {
	private String phone;
	private Long upPayLoad;
	private Long downPayLoad;
	private Long totalPayLoad;

	public PhoneBean() {
	}

	public PhoneBean(String phone, Long upPayLoad, Long downPayLoad) {
		super();
		this.phone = phone;
		this.upPayLoad = upPayLoad;
		this.downPayLoad = downPayLoad;
		this.totalPayLoad = upPayLoad + downPayLoad;
	}

	@Override
	public String toString() {
		return this.upPayLoad + "\t" + this.downPayLoad + "\t" + this.totalPayLoad;
	}

	@Override
	public void write(DataOutput out) throws IOException {
		out.writeUTF(phone);
		out.writeLong(upPayLoad);
		out.writeLong(downPayLoad);
	}

	@Override
	public void readFields(DataInput in) throws IOException {
		this.phone = in.readUTF();
		this.upPayLoad = in.readLong();
		this.downPayLoad = in.readLong();
	}
        setter/getter略
}

MapReduce程序

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class PhoneCount {

	public static class PCMapper extends Mapper<LongWritable, Text, Text, PhoneBean> {
		@Override
		protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, PhoneBean>.Context context) throws IOException, InterruptedException {
			String val = value.toString();
			String[] vals = val.split("\t");
			String phone = vals[1];
			Long upPayLoad = Long.parseLong(vals[8]);
			Long downPayLoad = Long.parseLong(vals[9]);
			PhoneBean bean = new PhoneBean(phone, upPayLoad, downPayLoad);           // 输出map结果 
			context.write(new Text(phone), bean);
		}
	}

	public static class PCReducer extends Reducer<Text, PhoneBean, Text, PhoneBean> {
		@Override
		protected void reduce(Text key, Iterable<PhoneBean> iterable, Reducer<Text, PhoneBean, Text, PhoneBean>.Context context) throws IOException, InterruptedException {
			Long upTotal = 0L;
			Long downTotal = 0L;
			for (PhoneBean pb : iterable) {
				upTotal += pb.getUpPayLoad();
				downTotal += pb.getDownPayLoad();
			}
                        // reduce输出结果
			context.write(key, new PhoneBean("", upTotal, downTotal));
		}
	}

	public static void main(String[] args) throws Exception {
		Configuration configuration = new Configuration();
		Job job = Job.getInstance(configuration);
// 设置jar对应的class文件
		job.setJarByClass(PhoneCount.class);
// 设置map class文件
		job.setMapperClass(PCMapper.class);
// 设置reduce class文件
                job.setReducerClass(PCReducer.class);
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(PhoneBean.class);
                job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(PhoneBean.class);
// 设置输入文件位置
		FileInputFormat.setInputPaths(job, new Path(args[0]));
// 设置输出文件位置
                FileOutputFormat.setOutputPath(job, new Path(args[1]));

		job.waitForCompletion(true);
	}
}

把需要的数据上传到hdfs，程序打包后运行

hadoop jar phone2.jar /phone/phone.dat /phone/output

通过partition对手机号进行划分，使用Map来模拟从数据库中查询出来的partition的规则

public static class PCPartitioner extends Partitioner<Text, PhoneBean> {

		private static Map<String, Integer> dataMap = new HashMap<String, Integer>();
		static {
// 第一分区
			dataMap.put("135", 1);
			dataMap.put("136", 1);
			dataMap.put("137", 1);
// 第二分区
			dataMap.put("138", 2);
			dataMap.put("139", 2);
// 第三分区
			dataMap.put("150", 3);
		}

		@Override
		public int getPartition(Text key, PhoneBean value, int numPartitions) {
			String phone = key.toString();
			String code = phone.substring(0, 3);
			Integer partition = dataMap.get(code);
			return partition == null ? 0 : partition;
		}
	}

总结：分区Partitioner主要作用在于以下两点

（1）根据业务需要，产生多个输出文件；

（2）多个reduce任务并发运行，提高整体job的运行效率

设置reduce的任务数，通过参数传入程序

		// 指定Partitioner文件
		job.setPartitionerClass(PCPartitioner.class);
// 设置Reduce任务数量
		job.setNumReduceTasks(Integer.parseInt(args[2]));

partition分了0、1、2、3个区总共四个分区， 但如果reduce的数量小于partition的会报一个IO的异常，因为每个reduce对应一个输出文件

#设置reduce的数量为3  
hadoop jar phone3.jar /phone/phone.dat /phone/output1 3  
#程序执行时的异常  
15/09/21 16:51:34 INFO mapreduce.Job: Task Id : attempt_1442818713228_0003_m_000000_0, Status : FAILED  
Error: java.io.IOException: Illegal partition for 15013685858 (3)  

如果设置的reduce的数量大于partition数量，写出的reduce文件将为空文件

#设置reduce数量为5
hadoop jar phone3.jar /phone/phone.dat /phone/output2 5

[root@centos1 sbin]# hadoop fs -ls /phone/output2
Found 6 items
-rw-r--r--   1 root supergroup          0 2015-09-21 16:53 /phone/output2/_SUCCESS
-rw-r--r--   1 root supergroup        156 2015-09-21 16:53 /phone/output2/part-r-00000
-rw-r--r--   1 root supergroup        241 2015-09-21 16:53 /phone/output2/part-r-00001
-rw-r--r--   1 root supergroup        127 2015-09-21 16:53 /phone/output2/part-r-00002
-rw-r--r--   1 root supergroup         27 2015-09-21 16:53 /phone/output2/part-r-00003
-rw-r--r--   1 root supergroup          0 2015-09-21 16:53 /phone/output2/part-r-00004

partiton的注意事项：
1、partition规则要清晰
2、reduce的数量要等于或大于partition数量


转自： http://mvplee.iteye.com/blog/2245011