1,代码

package mr;

import java.io.IOException;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.ArrayWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
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; /**
* 使用ArrayWritable
*/
public class TrafficApp4 { public static void main(String[] args) throws Exception{
Configuration conf = new Configuration();
Job job = Job.getInstance(conf , TrafficApp4.class.getSimpleName());
job.setJarByClass(TrafficApp4.class); FileInputFormat.setInputPaths(job, args[]);
job.setMapperClass(TrafficMapper.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(LongArrayWritable.class); job.setReducerClass(TrafficReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(LongArrayWritable.class);
FileOutputFormat.setOutputPath(job, new Path(args[])); job.waitForCompletion(true);
} public static class TrafficMapper extends Mapper<LongWritable, Text, Text, LongArrayWritable>{
@Override
protected void map(LongWritable key, Text value,
Mapper<LongWritable, Text, Text, LongArrayWritable>.Context context)
throws IOException, InterruptedException {
String line = value.toString();
String[] splited = line.split("\t");
String phonenumber = splited[]; String upPackNum = splited[];
String downPackNum = splited[];
String upPayLoad = splited[];
String downPayLoad = splited[]; Text k2 = new Text(phonenumber);
LongArrayWritable v2 = new LongArrayWritable(upPackNum, downPackNum, upPayLoad, downPayLoad);
context.write(k2, v2);
}
} public static class TrafficReducer extends Reducer<Text, LongArrayWritable, Text, LongArrayWritable>{
@Override
protected void reduce(Text k2, Iterable<LongArrayWritable> v2s,
Reducer<Text, LongArrayWritable, Text, LongArrayWritable>.Context context)
throws IOException, InterruptedException { long upPackNum = 0L;
long downPackNum = 0L;
long upPayLoad = 0L;
long downPayLoad = 0L;
for (LongArrayWritable v2 : v2s) {
Writable[] values = v2.get();
upPackNum += ((LongWritable)values[]).get();
downPackNum += ((LongWritable)values[]).get();
upPayLoad += ((LongWritable)values[]).get();
downPayLoad += ((LongWritable)values[]).get();
} LongArrayWritable v3 = new LongArrayWritable(upPackNum, downPackNum, upPayLoad, downPayLoad);
context.write(k2, v3);
}
} public static class LongArrayWritable extends ArrayWritable{
/**
* 在调用的时候,首先调用该方法,然后调用set(Writable[])
*/
public LongArrayWritable() {
super(LongWritable.class);
}
/**
* 直接调用该方法即可
* @param values
*/
public LongArrayWritable(LongWritable[] values) {
super(LongWritable.class, values);
}
/**
* 直接调用该方法即可
* @param upPackNum
* @param downPackNum
* @param upPayLoad
* @param downPayLoad
*/
public LongArrayWritable(Long upPackNum, Long downPackNum, Long upPayLoad, Long downPayLoad) {
super(LongWritable.class);
LongWritable[] values = new LongWritable[];
values[] = new LongWritable(upPackNum);
values[] = new LongWritable(downPackNum);
values[] = new LongWritable(upPayLoad);
values[] = new LongWritable(downPayLoad);
super.set(values);
}
/**
* 直接调用该方法即可
* @param upPackNum
* @param downPackNum
* @param upPayLoad
* @param downPayLoad
*/
public LongArrayWritable(String upPackNum, String downPackNum, String upPayLoad, String downPayLoad) {
super(LongWritable.class);
LongWritable[] values = new LongWritable[];
values[] = new LongWritable(Long.parseLong(upPackNum));
values[] = new LongWritable(Long.parseLong(downPackNum));
values[] = new LongWritable(Long.parseLong(upPayLoad));
values[] = new LongWritable(Long.parseLong(downPayLoad));
super.set(values);
} @Override
public String toString() {
String[] array = super.toStrings();
return StringUtils.join(array, "\t");
}
} }

2,ArrayWritable的API

org.apache.hadoop.io
Class ArrayWritable

java.lang.Object
  

org.apache.hadoop.io.ArrayWritable
已实现的接口:
Writable

public class ArrayWritableextends Objectimplements Writable

A Writable for arrays containing instances of a class. The elements of this writable must all be instances of the same class. If this writable will be the input for a Reducer, you will need to create a subclass that sets the value to be of the proper type. For example: public class IntArrayWritable extends ArrayWritable { public IntArrayWritable() { super(IntWritable.class); } }


构造方法摘要
ArrayWritable(Class<? extends Writable> valueClass) 
           
ArrayWritable(Class<? extends Writable> valueClass, Writable[] values) 
           
ArrayWritable(String[] strings) 
           
方法摘要
 Writable[] get() 
           
 Class getValueClass() 
           
 void readFields(DataInput in) 
          Deserialize the fields of this object from in.
 void set(Writable[] values) 
           
 Object toArray() 
           
 String[] toStrings() 
           
 void write(DataOutput out) 
          Serialize the fields of this object to out.
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
构造方法详细信息

ArrayWritable

public ArrayWritable(Class<? extends Writable> valueClass)

ArrayWritable

public ArrayWritable(Class<? extends Writable> valueClass,
Writable[] values)

ArrayWritable

public ArrayWritable(String[] strings)
方法详细信息

getValueClass

public Class getValueClass()

toStrings

public String[] toStrings()

toArray

public Object toArray()

set

public void set(Writable[] values)

get

public Writable[] get()

readFields

public void readFields(DataInput in)
throws IOException
Description copied from interface: Writable
Deserialize the fields of this object from in.

For efficiency, implementations should attempt to re-use storage in the existing object where possible.

Specified by:
readFields in interface Writable
Parameters:
in - DataInput to deseriablize this object from.
Throws:
IOException

write

public void write(DataOutput out)
throws IOException
Description copied from interface: Writable
Serialize the fields of this object to out.

Specified by:
write in interface Writable
Parameters:
out - DataOuput to serialize this object into.
Throws:
IOException

 

 

基于MapReduce的手机流量统计分析的更多相关文章

  1. MapReduce的手机流量统计的案例

    程序:(另外一个关于单词计数的总结:http://www.cnblogs.com/DreamDrive/p/5492572.html) import java.io.IOException; impo ...

  2. 基于winpcap的以太网流量分析器(java)

    开发工具 IDE:eclipse -neon JDK:1.8 OS:Win10-64bit 主要功能 1.要求完成一个基于Winpcap的网络流量统计分析系统,具有易用.美观的界面. 2.完成局域网( ...

  3. 023_数量类型练习——Hadoop MapReduce手机流量统计

    1) 分析业务需求:用户使用手机上网,存在流量的消耗.流量包括两部分:其一是上行流量(发送消息流量),其二是下行流量(接收消息的流量).每种流量在网络传输过程中,有两种形式说明:包的大小,流量的大小. ...

  4. 第2节 mapreduce深入学习:8、手机流量汇总求和

    第2节 mapreduce深入学习:8.手机流量汇总求和 例子:MapReduce综合练习之上网流量统计. 数据格式参见资料夹 需求一:统计求和 统计每个手机号的上行流量总和,下行流量总和,上行总流量 ...

  5. MapReduce 经典案例手机流量排序的分析

    在进行流量排序之前,先要明白排序是发生在map阶段,排序之后(排序结束后map阶段才会显示100%完成)才会到reduce阶段(事实上reduce也会排序),.此外排序之前要已经完成了手机流量的统计工 ...

  6. 基于mapreduce的大规模连通图寻找算法

    基于mapreduce的大规模连通图寻找算法 当我们想要知道哪些账号是一个人的时候往往可以通过业务得到两个账号之间有联系,但是这种联系如何传播呢? 问题 已知每个账号之间的联系 如: A B B C ...

  7. 字节数转换为b,kb,mb,gb的方法 通用的手机流量计算方法

    //通用的手机流量计算方法 private String byteToMB(long size){ long kb = 1024; long mb = kb*1024; long gb = mb*10 ...

  8. MapReduce教程(一)基于MapReduce框架开发<转>

    1 MapReduce编程 1.1 MapReduce简介 MapReduce是一种编程模型,用于大规模数据集(大于1TB)的并行运算,用于解决海量数据的计算问题. MapReduce分成了两个部分: ...

  9. 基于MapReduce的贝叶斯网络算法研究参考文献

    原文链接(系列):http://blog.csdn.net/XuanZuoNuo/article/details/10472219 论文: 加速贝叶斯网络:Accelerating Bayesian ...

随机推荐

  1. Linux 下 PHP 扩展 PDO 编译安装

    1.进入 PHP 的软件包 pdo 扩展目录中(注:不是 PHP 安装目录) [root@tester /]# /home/tdweb/php-5.4.34/ext/pdo_mysql 执行 phpi ...

  2. Android adb shell启动应用程序的方法

    在Android中,除了从界面上启动程序之外,还可以从命令行启动程序,使用的是命令行工具am. usage: am [subcommand] [options] start an Activity: ...

  3. 修改有数据oracle字段类型 从number转为varchar

    --修改有数据oracle字段类型 从number转为varchar--例:修改ta_sp_org_invoice表中RESCUE_PHONE字段类型,从number转为varchar --step1 ...

  4. C变量之间的转换

    int main(){ //定义了三个变量分别是abc ab的值分别是5跟8 c没有赋值  把b的值给c 把a的值给b 把c的值给a 形成了一个ab值得转换: int a=5; int b=8; in ...

  5. LeetCode 410——分割数组的最大值

    1. 题目 2. 解答 此题目为 今日头条 2018 AI Camp 5 月 26 日在线笔试编程题第二道--最小分割分数. class Solution { public: // 若分割数组的最大值 ...

  6. Tensorflow多线程输入数据处理框架

    Tensorflow提供了一系列的对图像进行预处理的方法,但是复杂的预处理过程会减慢整个训练过程,所以,为了避免图像的预处理成为训练神经网络效率的瓶颈,Tensorflow提供了多线程处理输入数据的框 ...

  7. PHP 用Symfony VarDumper Component 调试

    Symfony VarDumper 类似 php var_dump() 官方文档写的安装方法 : 按照步骤 就可以在 running any PHP code  时候使用了 In order to h ...

  8. LCA(最近公共祖先)——dfs+ST 在线算法

    一.前人种树 博客:浅谈LCA的在线算法ST表 二.沙场练兵 题目:POJ 1330 Nearest Common Ancestors 题解博客:http://www.cnblogs.com/Miss ...

  9. 并查集——poj1182(带权并查集高阶)

    题目链接:食物链 题解:点击 说一声:这题关系推导值得学习.

  10. ByteArrayInputStream/ByteArrayOutputStream 学习

    ByteArrayInputStream: byte[] buff = new byte[1024]; ByteArrayInputStream bAIM = new ByteArrayInputSt ...