hadoop处理手机流量小例子_JAVA_编程开发_程序员俱乐部

最近一直在学习hadoop  这是一个简单的关于MapReduce的示例
通过实现map和reduce2个函数完成操作

首先定义一个自定义对象

class="java">class MyWriterble implements Writable{
	long UpPackNum;
	long DownPackNum;
	long UpPayLoad;
	long DownPayLoad;
	
	public MyWriterble(){}
	public MyWriterble(String UpPackNum,String DownPackNum,String UpPayLoad,String DownPayLoad){
		this.UpPackNum=Long.parseLong(UpPackNum);
		this.DownPackNum=Long.parseLong(DownPackNum);
		this.UpPayLoad=Long.parseLong(UpPayLoad);
		this.DownPayLoad=Long.parseLong(DownPayLoad);
	}
	
	@Override
	public void readFields(DataInput in) throws IOException {
		// TODO Auto-generated method stub
		this.UpPackNum=in.readLong();
		this.DownPackNum=in.readLong();
		this.UpPayLoad=in.readLong();
		this.DownPayLoad=in.readLong();
		
	}

	@Override
	public void write(DataOutput out) throws IOException {
		out.writeLong(UpPackNum);
		out.writeLong(DownPackNum);
		out.writeLong(UpPayLoad);
		out.writeLong(DownPayLoad);
	}
	
	@Override
	public String toString() {
		// TODO Auto-generated method stub
		return UpPackNum + "\t" + DownPackNum + "\t" + UpPayLoad + "\t" + DownPayLoad;
	}
	
}

其中类必须实现Writable接口 和Java中的可序列化接口一样

然后分别定义2个类 用于重写map和reduce方法

class MyMappera extends Mapper<LongWritable,Text, Text,MyWriterble>{
	protected void map(LongWritable key, Text value,Context context) throws IOException ,InterruptedException {
		String vals[]=value.toString().split("\t");
		MyWriterble my=new MyWriterble(vals[21], vals[22], vals[23], vals[24]);
		context.write(new Text(vals[2]),my);
	};
}

Mapper中的泛型 分别代表k1,v1,k2,v2类型
k1,v1即使分割每一行数据得到的键值对，key为位置，value为行内容
k2,v2为输出值得类型，k2表示每个手机号，所以为Text

class myreducers extends Reducer<Text,MyWriterble,Text,MyWriterble>{
	protected void reduce(Text text, java.lang.Iterable<MyWriterble> wt,Context context) throws IOException ,InterruptedException {
		long UpPackNum=0l;
		long DownPackNum=0l;
		long UpPayLoad=0l;
		long DownPayLoad=0l;
		for(MyWriterble mt:wt){
			UpPackNum=UpPackNum+mt.UpPackNum;
			DownPackNum=DownPackNum+mt.DownPackNum;
			UpPayLoad=UpPayLoad+mt.UpPayLoad;
			DownPayLoad=DownPayLoad+DownPayLoad;
		}
		
		context.write(text, new MyWriterble(UpPackNum+"", DownPackNum+"", UpPayLoad+"", DownPayLoad+""));
		
	};

public class MyRIZHI {
	public static void main(String[] args) throws Exception{
		final String INPUT_PATHs = "hdfs://chaoren:9000/ncmdp_08500001_Net_20130515164000.dat";
		final String OUT_PATHs = "hdfs://chaoren:9000/out";
		Job job=new Job(new Configuration(),MyRIZHI.class.getSimpleName());
		//1.1 指定输入文件路径
		FileInputFormat.setInputPaths(job, INPUT_PATHs);
		//指定哪个类用来格式化输入文件
		job.setInputFormatClass(TextInputFormat.class);
		
		//1.2指定自定义的Mapper类
		job.setMapperClass(MyMappera.class);
		//指定输出<k2,v2>的类型
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(MyWriterble.class);
		//1.3 指定分区类
		job.setPartitionerClass(HashPartitioner.class);
		job.setNumReduceTasks(1);
		//1.4 TODO 排序、分区
		
		//1.5  TODO （可选）合并
				
		//2.2 指定自定义的reduce类
		job.setReducerClass(myreducers.class);
		//指定输出<k3,v3>的类型
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(MyWriterble.class);
		
		//2.3 指定输出到哪里
		FileOutputFormat.setOutputPath(job, new Path(OUT_PATHs));
		//设定输出文件的格式化类
		job.setOutputFormatClass(TextOutputFormat.class);
		//把代码提交给JobTracker执行
		job.waitForCompletion(true);
		
		
		
	}
}