起风网一、测试数据：手机上网日志
1.1 关于这个日志
假设我们如下一个日志文件，这个文件的内容是来自某个电信运营商的手机上网日志，文件的内容已经经过了优化，格式比较规整，便于学习研究。
该文件的内容如下（这里我只截取了三行）：
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
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
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
每一行不同的字段有有不同的含义，具体的含义如下图所示：
1.2 要实现的目标
有了上面的测试数据—手机上网日志，那么问题来了，如何通过map-reduce实现统计不同手机号用户的上网流量信息？通过上表可知，第6~9个字段是关于流量的信息，也就是说我们需要为每个用户统计其upPackNum、downPackNum、upPayLoad以及downPayLoad这个四个字段的数量和，达到以下的显示结果：
13480253104 3 3 180 180
13502468823 57 102 7335 110349
二、解决思路：封装手机流量
2.1 Writable接口
经过上一篇的学习，我们知道了在Hadoop中操作所有的数据类型都需要实现一个叫Writable的接口，实现了该接口才能够支持序列化，才能方便地在Hadoop中进行读取和写入。
public interface Writable { /** * Serialize the fields of this object to <code>out</code>. */ void write(DataOutput out) throws IOException; /** * Deserialize the fields of this object from <code>in</code>. */ void readFields(DataInput in) throws IOException;}
从上面的代码中可以看到Writable 接口只有两个方法的定义，一个是write 方法，一个是readFields 方法。前者是把对象的属性序列化到DataOutput 中去，后者是从DataInput 把数据反序列化到对象的属性中。（简称“读进来”，“写出去”）
java 中的基本类型有char、byte、boolean、short、int、float、double 共7 中基本类型，除了char，都有对应的Writable 类型。但是，没有我们需要的对应类型。于是，我们需要仿照现有的对应Writable 类型封装一个自定义的数据类型，以供本次试验使用。
2.2 封装KpiWritable类型
我们需要为每个用户统计其upPackNum、downPackNum、upPayLoad以及downPayLoad这个四个字段的数量和，而这个四个字段又都是long 类型，于是我们可以封装以下代码：
/* * 自定义数据类型KpiWritable */ public class KpiWritable implements Writable { long upPackNum; // 上行数据包数，单位：个 long downPackNum; // 下行数据包数，单位：个 long upPayLoad; // 上行总流量，单位：byte long downPayLoad; // 下行总流量，单位：byte public KpiWritable() { } public KpiWritable(String upPack, String downPack, String upPay, String downPay) { upPackNum = Long.parseLong(upPack); downPackNum = Long.parseLong(downPack); upPayLoad = Long.parseLong(upPay); downPayLoad = Long.parseLong(downPay); } @Override public String toString() { String result = upPackNum + "\t" + downPackNum + "\t" + upPayLoad + "\t" + downPayLoad; return result; } @Override public void write(DataOutput out) throws IOException { out.writeLong(upPackNum); out.writeLong(downPackNum); out.writeLong(upPayLoad); out.writeLong(downPayLoad); } @Override public void readFields(DataInput in) throws IOException { upPackNum = in.readLong(); downPackNum = in.readLong(); upPayLoad = in.readLong(); downPayLoad = in.readLong(); } }
通过实现Writable接口的两个方法，就封装好了KpiWritable类型。
三、编程实现：依然MapReduce
3.1 自定义Mapper类
/* * 自定义Mapper类，重写了map方法 */ public static class MyMapper extends Mapper<LongWritable, Text, Text, KpiWritable> { protected void map( LongWritable k1, Text v1, org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, Text, KpiWritable>.Context context) throws IOException, InterruptedException { String[] spilted = v1.toString().split("\t"); String msisdn = spilted[1]; // 获取手机号码 Text k2 = new Text(msisdn); // 转换为Hadoop数据类型并作为k2 KpiWritable v2 = new KpiWritable(spilted[6], spilted[7], spilted[8], spilted[9]); context.write(k2, v2); }; }
这里将第6~9个字段的数据都封装到KpiWritable类型中，并将手机号和KpiWritable作为<k2,v2>传入下一阶段；
3.2 自定义Reducer类
/* * 自定义Reducer类，重写了reduce方法 */ public static class MyReducer extends Reducer<Text, KpiWritable, Text, KpiWritable> { protected void reduce( Text k2, java.lang.Iterable<KpiWritable> v2s, org.apache.hadoop.mapreduce.Reducer<Text, KpiWritable, Text, KpiWritable>.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; } KpiWritable v3 = new KpiWritable(upPackNum + "", downPackNum + "", upPayLoad + "", downPayLoad + ""); context.write(k2, v3); }; }
这里将Map阶段每个手机号所对应的流量记录都一一进行相加求和，最后生成一个新的KpiWritable类型对象与手机号作为新的<k3,v3>返回；
3.3 完整代码实现
完整的代码如下所示：
public class MyKpiJob extends Configured implements Tool { /* * 自定义数据类型KpiWritable */ public static class KpiWritable implements Writable { long upPackNum; // 上行数据包数，单位：个 long downPackNum; // 下行数据包数，单位：个 long upPayLoad; // 上行总流量，单位：byte long downPayLoad; // 下行总流量，单位：byte public KpiWritable() { } public KpiWritable(String upPack, String downPack, String upPay, String downPay) { upPackNum = Long.parseLong(upPack); downPackNum = Long.parseLong(downPack); upPayLoad = Long.parseLong(upPay); downPayLoad = Long.parseLong(downPay); } @Override public String toString() { String result = upPackNum + "\t" + downPackNum + "\t" + upPayLoad + "\t" + downPayLoad; return result; } @Override public void write(DataOutput out) throws IOException { out.writeLong(upPackNum); out.writeLong(downPackNum); out.writeLong(upPayLoad); out.writeLong(downPayLoad); } @Override public void readFields(DataInput in) throws IOException { upPackNum = in.readLong(); downPackNum = in.readLong(); upPayLoad = in.readLong(); downPayLoad = in.readLong(); } } /* * 自定义Mapper类，重写了map方法 */ public static class MyMapper extends Mapper<LongWritable, Text, Text, KpiWritable> { protected void map( LongWritable k1, Text v1, org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, Text, KpiWritable>.Context context) throws IOException, InterruptedException { String[] spilted = v1.toString().split("\t"); String msisdn = spilted[1]; // 获取手机号码 Text k2 = new Text(msisdn); // 转换为Hadoop数据类型并作为k2 KpiWritable v2 = new KpiWritable(spilted[6], spilted[7], spilted[8], spilted[9]); context.write(k2, v2); }; } /* * 自定义Reducer类，重写了reduce方法 */ public static class MyReducer extends Reducer<Text, KpiWritable, Text, KpiWritable> { protected void reduce( Text k2, java.lang.Iterable<KpiWritable> v2s, org.apache.hadoop.mapreduce.Reducer<Text, KpiWritable, Text, KpiWritable>.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; } KpiWritable v3 = new KpiWritable(upPackNum + "", downPackNum + "", upPayLoad + "", downPayLoad + ""); context.write(k2, v3); }; } // 输入文件目录 public static final String INPUT_PATH = "hdfs://hadoop-master:9000/testdir/input/HTTP_20130313143750.dat"; // 输出文件目录 public static final String OUTPUT_PATH = "hdfs://hadoop-master:9000/testdir/output/mobilelog"; @Override public int run(String[] args) throws Exception { // 首先删除输出目录已生成的文件 FileSystem fs = FileSystem.get(new URI(INPUT_PATH), getConf()); Path outPath = new Path(OUTPUT_PATH); if (fs.exists(outPath)) { fs.delete(outPath, true); } // 定义一个作业 Job job = new Job(getConf(), "MyKpiJob"); // 设置输入目录 FileInputFormat.setInputPaths(job, new Path(INPUT_PATH)); // 设置自定义Mapper类 job.setMapperClass(MyMapper.class); // 指定<k2,v2>的类型 job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(KpiWritable.class); // 设置自定义Reducer类 job.setReducerClass(MyReducer.class); // 指定<k3,v3>的类型 job.setOutputKeyClass(Text.class); job.setOutputKeyClass(KpiWritable.class); // 设置输出目录 FileOutputFormat.setOutputPath(job, new Path(OUTPUT_PATH)); // 提交作业 Boolean res = job.waitForCompletion(true); if(res){ System.out.println("Process success!"); System.exit(0); } else{ System.out.println("Process failed!"); System.exit(1); } return 0; } public static void main(String[] args) { Configuration conf = new Configuration(); try { int res = ToolRunner.run(conf, new MyKpiJob(), args); System.exit(res); } catch (Exception e) { e.printStackTrace(); } }}
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3.4 调试运行效果
附件下载
（1）本次用到的手机上网日志（部分版）：http://pan.baidu.com/s/1dDzqHWX
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