Hadoop学习笔记—5.自定义类型处理手机上网日志

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();
        }
    }
}
View Code

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

 

posted @ 2015-02-13 01:03 EdisonZhou 阅读(...) 评论(...) 编辑 收藏