Hadoop自定义类型处理手机上网日志

job提交源码分析

在eclipse中的写的代码如何提交作业到JobTracker中的哪？
(1)在eclipse中调用的job.waitForCompletion(true)实际上执行如下方法
　　connect();
　　info = jobClient.submitJobInternal(conf);
(2)在connect()方法中，实际上创建了一个JobClient对象。
　　在调用该对象的构造方法时，获得了JobTracker的客户端代理对象JobSubmissionProtocol。
　　JobSubmissionProtocol的实现类是JobTracker。
(3)在jobClient.submitJobInternal(conf)方法中，调用了
　　JobSubmissionProtocol.submitJob(...),
　　即执行的是JobTracker.submitJob(...)。

 

Hadoop数据类型

1.Hadoop的数据类型要求必须实现Writable接口。
2.java基本类型与Hadoop常见基本类型的对照
　　　　Long 　　　　LongWritable
　　　　Integer 　　  IntWritable
　　　　Boolean 　　 BooleanWritable
　　　　String 　　　 Text

java类型如何转化为hadoop基本类型？
　　　　调用hadoop类型的构造方法，或者调用set()方法。
　　　　　　new LongWritable(123L);

hadoop基本类型如何转化为java类型？
　　　　对于Text，需要调用toString()方法，其他类型调用get()方法。

 

使用Hadoop自定义类型处理手机上网日志

1、首先，将手机上网日志文件HTTP_20130313143750.dat通过WinSCP工具复制到/usr/local目录下

2、将日志文件上传到hdfs://chaoren:9000/wlan文件夹下

 

日志文件：

 日志文件中各字段含义：

 

 3、编写Java代码将日志文件中想要的数据统计出来。

 

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://chaoren:9000/wlan";//wlan是个文件夹，日志文件放在/wlan目录下
    static final String OUT_PATH = "hdfs://chaoren:9000/out";

    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<k2,v2>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）归约

        // 2.2 指定自定义的reduce类
        job.setReducerClass(MyReducer.class);
        // 指定输出<k3,v3>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<LongWritable, Text, Text, KpiWritable> {
        protected void map(
                LongWritable key,
                Text value,
                org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, Text, KpiWritable>.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<Text, KpiWritable, Text, KpiWritable> {
        /**
         * @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<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;
            }

            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);
    }

    public void readFields(DataInput in) throws IOException {
        this.upPackNum = in.readLong();
        this.downPackNum = in.readLong();
        this.upPayLoad = in.readLong();
        this.downPayLoad = in.readLong();
    }

    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;
    }
}

 

4、运行结果