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本篇内容主要讲解“MapReduce怎么处理手机通信流量统计”，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习“MapReduce怎么处理手机通信流量统计”吧!
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模拟元数据如下 HTTP_20130313143750.dat
1363157985066 13726230503 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200
1363157995052 13826544101 5C-0E-8B-C7-F1-E0:CMCC 120.197.40.4 4 0 264 0 200
1363157991076 13926435656 20-10-7A-28-CC-0A:CMCC 120.196.100.99 2 4 132 1512 200
1363154400022 13926251106 5C-0E-8B-8B-B1-50:CMCC 120.197.40.4 4 0 240 0 200
1363157993044 18211575961 94-71-AC-CD-E6-18:CMCC-EASY 120.196.100.99 iface.qiyi.com 视频网站 15 12 1527 2106 200
1363157995074 84138413 5C-0E-8B-8C-E8-20:7DaysInn 120.197.40.4 122.72.52.12 20 16 4116 1432 200
1363157993055 13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200
1363157995033 15920133257 5C-0E-8B-C7-BA-20:CMCC 120.197.40.4 sug.so.360.cn 信息安全 20 20 3156 2936 200
1363157983019 13719199419 68-A1-B7-03-07-B1:CMCC-EASY 120.196.100.82 4 0 240 0 200
1363157984041 13660577991 5C-0E-8B-92-5C-20:CMCC-EASY 120.197.40.4 s19.cnzz.com 站点统计 24 9 6960 690 200
1363157973098 15013685858 5C-0E-8B-C7-F7-90:CMCC 120.197.40.4 rank.ie.sogou.com 搜索引擎 28 27 3659 3538 200
1363157986029 15989002119 E8-99-C4-4E-93-E0:CMCC-EASY 120.196.100.99 www.umeng.com 站点统计 3 3 1938 180 200
1363157992093 13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 15 9 918 4938 200
1363157986041 13480253104 5C-0E-8B-C7-FC-80:CMCC-EASY 120.197.40.4 3 3 180 180 200
1363157984040 13602846565 5C-0E-8B-8B-B6-00:CMCC 120.197.40.4 2052.flash3-http.qq.com 综合门户 15 12 1938 2910 200
1363157995093 13922314466 00-FD-07-A2-EC-BA:CMCC 120.196.100.82 img.qfc.cn 12 12 3008 3720 200
1363157982040 13502468823 5C-0A-5B-6A-0B-D4:CMCC-EASY 120.196.100.99 y0.ifengimg.com 综合门户 57 102 7335 110349 200
1363157986072 18320173382 84-25-DB-4F-10-1A:CMCC-EASY 120.196.100.99 input.shouji.sogou.com 搜索引擎 21 18 9531 2412 200
1363157990043 13925057413 00-1F-64-E1-E6-9A:CMCC 120.196.100.55 t3.baidu.com 搜索引擎 69 63 11058 48243 200
1363157988072 13760778710 00-FD-07-A4-7B-08:CMCC 120.196.100.82 2 2 120 120 200
1363157985079 13823070001 20-7C-8F-70-68-1F:CMCC 120.196.100.99 6 3 360 180 200
1363157985069 13600217502 00-1F-64-E2-E8-B1:CMCC 120.196.100.55 18 138 1080 186852 200
上面日志的格式如下
MapReduce代码如下
package MapReduce;
import java.io.DataInput;
import java.io.DataOutput;
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.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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
public class KpiApp {
static final String INPUT_PATH = "hdfs://hadoop:9000/wlan";
static final String OUT_PATH = "hdfs://hadoop:9000/outwlan";
public static void main(String[] args) throws Exception{
final Job job = new Job(new Configuration(), KpiApp.class.getSimpleName());
//1.1 指定输入文件路径
FileInputFormat.setInputPaths(job, INPUT_PATH);
//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(MyMapper.class);
//指定输出的类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(KpiWritable.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(MyReducer.class);
//指定输出的类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(KpiWritable.class);
//2.3 指定输出到哪里
FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));
//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);
}
static class MyMapper extends Mapper{
protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper.Context context) throws IOException ,InterruptedException {
final String[] splited = value.toString().split("\t");
final String msisdn = splited[1];
final Text k2 = new Text(msisdn);
final KpiWritable v2 = new KpiWritable(splited[6],splited[7],splited[8],splited[9]);
context.write(k2, v2);
};
}
static class MyReducer extends Reducer{
/**
* @param k2 表示整个文件中不同的手机号码
* @param v2s 表示该手机号在不同时段的流量的集合
*/
protected void reduce(Text k2, java.lang.Iterable v2s, org.apache.hadoop.mapreduce.Reducer.Context context) throws IOException ,InterruptedException {
long upPackNum = 0L;
long downPackNum = 0L;
long upPayLoad = 0L;
long downPayLoad = 0L;
for (KpiWritable kpiWritable : v2s) {
upPackNum += kpiWritable.upPackNum;
downPackNum += kpiWritable.downPackNum;
upPayLoad += kpiWritable.upPayLoad;
downPayLoad += kpiWritable.downPayLoad;
}
final KpiWritable v3 = new KpiWritable(upPackNum+"", downPackNum+"", upPayLoad+"", downPayLoad+"");
context.write(k2, v3);
};
}
}
class KpiWritable implements Writable{
long upPackNum;
long downPackNum;
long upPayLoad;
long downPayLoad;
public KpiWritable(){}
public KpiWritable(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 {
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() {
return upPackNum + "\t" + downPackNum + "\t" + upPayLoad + "\t" + downPayLoad;
}
} 将HTTP_20130313143750.dat上传至hadoop HDFS文件系统中
运行MapReduce代码，查看输出的/outwlan/part-*文件下的内容
到此，相信大家对“MapReduce怎么处理手机通信流量统计”有了更深的了解，不妨来实际操作一番吧！这里是创新互联网站，更多相关内容可以进入相关频道进行查询，关注我们，继续学习！