MapReduce案例-关于流量统计的求和分区规约排序操作

需求: 统计求和的求和分区规约

统计每个手机号的上行数据包总和，下行数据包总和，上行总流量之和，下行总流量之和，并实现的分区及规约。
分析：以手机号码作为key值，上行流量，下行流量，上行总流量，下行总流量四个字段作为value值，然后以这个key，和value作为map阶段的输出，reduce阶段的输入。
原始数据：

1363157985066	13726230503	00-FD-07-A4-72-B8:CMCC	120.196.100.82	i02.c.aliimg.com	游戏娱乐	24	27	2481	24681	200
1363157995052 	13826544101	5C-0E-8B-C7-F1-E0:CMCC	120.197.40.4	jd.com	京东购物	4	0	264	0	200
1363157991076 	13926435656	20-10-7A-28-CC-0A:CMCC	120.196.100.99	taobao.com	淘宝购物	2	4	132	1512	200
1363154400022 	13926251106	5C-0E-8B-8B-B1-50:CMCC	120.197.40.4	cnblogs.com	技术门户	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 	19984138413	5C-0E-8B-8C-E8-20:7DaysInn	120.197.40.4	122.72.52.12	未知	20	16	4116	1432	200
1363157993055 	13560439658	C4-17-FE-BA-DE-D9:CMCC	120.196.100.99	sougou.com	综合门户	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	baidu.com	综合搜索	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	zhilian.com	招聘门户	15	9	918	4938	200
1363157986041 	13480253104	5C-0E-8B-C7-FC-80:CMCC-EASY	120.197.40.4	csdn.net	技术门户	3	3	180	180	200
1363157984040 	13602846565	5C-0E-8B-8B-B6-00:CMCC	120.197.40.4	2052.flash2-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	图片大全	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	http://youku.com/	视频网站	2	2	120	120	200
1363157985079 	13823070001	20-7C-8F-70-68-1F:CMCC	120.196.100.99	img.qfc.cn	图片浏览	6	3	360	180	200
1363157985069 	13600217502	00-1F-64-E2-E8-B1:CMCC	120.196.100.55	www.baidu.com	综合门户	18	138	1080	186852	200
1363157985059 	13600217502	00-1F-64-E2-E8-B1:CMCC	120.196.100.55	www.baidu.com	综合门户	19	128	1177	16852	200

Step 1: 自定义map的输出value对象FlowBean

package org.example.mapreduce.Flow;

import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

public class FlowBean implements Writable {
    

    private Integer upFlow; // 上行数据包数
    private Integer downFlow; //下行数据包数
    private Integer upCountFlow; //上行流量总和
    private Integer downCountFlow; //下行流量总和

    public Integer getUpFlow() {
    
        return upFlow;
    }

    public void setUpFlow(Integer upFlow) {
    
        this.upFlow = upFlow;
    }

    public Integer getDownFlow() {
    
        return downFlow;
    }

    public void setDownFlow(Integer downFlow) {
    
        this.downFlow = downFlow;
    }

    public Integer getUpCountFlow() {
    
        return upCountFlow;
    }

    public void setUpCountFlow(Integer upCountFlow) {
    
        this.upCountFlow = upCountFlow;
    }

    public Integer getDownCountFlow() {
    
        return downCountFlow;
    }

    public void setDownCountFlow(Integer downCountFlow) {
    
        this.downCountFlow = downCountFlow;
    }

    @Override
    public String toString() {
    
        return upFlow + "\t" + downFlow + "\t" + upCountFlow + "\t" +downCountFlow;
    }

    //序列化
    @Override
    public void write(DataOutput dataOutput) throws IOException {
    
        dataOutput.writeInt(upFlow);
        dataOutput.writeInt(downFlow);
        dataOutput.writeInt(upCountFlow);
        dataOutput.writeInt(downCountFlow);
    }

    //反序列化
    @Override
    public void readFields(DataInput dataInput) throws IOException {
    
        this.upFlow = dataInput.readInt();
        this.downFlow = dataInput.readInt();
        this.upCountFlow = dataInput.readInt();
        this.downCountFlow = dataInput.readInt();
    }
}

Step 2: 定义FlowMapper类

package org.example.mapreduce.Flow;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class FlowMapper extends Mapper<LongWritable, Text,Text,FlowBean> {
    
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
    
        //K1：行偏移量
        //V1: 一行字符串，首先对字符串进行切分
        String[] split = value.toString().split("\t");
        String phone = split[1];

        //创建FlowBean对象
        FlowBean flowBean = new FlowBean();

        //将字符串转换为数字
        flowBean.setUpFlow(Integer.parseInt(split[6]));
        flowBean.setDownFlow(Integer.parseInt(split[7]));
        flowBean.setUpCountFlow(Integer.parseInt(split[8]));
        flowBean.setDownCountFlow(Integer.parseInt(split[9]));

        //将k2 v2 写入上下文中
        //K2: 电话号
        //V2：flowBean
        context.write(new Text(phone),flowBean);
    }
}

Step 3:自定义分区Partition

package org.example.mapreduce.Flow;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;

public class FlowPartition extends Partitioner<Text,FlowBean> {
    
    /* text : K2 手机号 flowBean: V2 i : ReduceTask的个数 */
    @Override
    public int getPartition(Text text, FlowBean flowBean, int i) {
    
        //获得手机号
        String phone = text.toString();
        //判断手机号以什么开头，返回对应的分区编号
        if(phone.startsWith("135")) return 0;
        else if (phone.startsWith("136")) return 1;
        else if (phone.startsWith("137")) return 2;
        else if (phone.startsWith("138")) return 3;
        else if (phone.startsWith("139")) return 4;
        else return 5;

Step 4:自定义规约Combiner
概念： 每一个 map 都可能会产生大量的本地输出，Combiner 的作用就是对 map 端的输出先做一次合并，以减少在 map 和 reduce 节点之间的数据传输量，以提高网络IO 性能，是 MapReduce的一种优化手段之一。

• combiner 是 MR 程序中 Mapper 和 Reducer 之外的一种组件

• combiner 组件的父类就是 Reducer

• combiner 和 reducer 的区别在于运行的位置

  • Combiner 是在每一个 maptask 所在的节点运行

  • Reducer 是接收全局所有 Mapper 的输出结果

• combiner 的意义就是对每一个 maptask 的输出进行局部汇总，以减小网络传输量

• combiner 能够应用的前提是不能影响最终的业务逻辑，而且，combiner 的输出 kv 应该跟reducer 的输入key，value类型要对应起来。

package org.example.mapreduce.Flow;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

public class FLowCombiner extends Reducer<Text,FlowBean,Text,FlowBean> {
    
    @Override
    protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
    
        Integer upFlow = 0;
        Integer downFlow = 0;
        Integer upCountFlow = 0;
        Integer downCountFlow = 0;

        for (FlowBean value : values) {
    
            upFlow += value.getUpFlow();
            downFlow += value.getDownFlow();
            upCountFlow += value.getUpCountFlow();
            downCountFlow += value.getDownCountFlow();
        }
        //创建FlowBean对象，并给对象赋值 V3
        FlowBean flowBean = new FlowBean();
        flowBean.setUpFlow(upFlow);
        flowBean.setDownFlow(downFlow);
        flowBean.setUpCountFlow(upCountFlow);
        flowBean.setDownCountFlow(downCountFlow);

        //将新的K2和V2写入上下文中
        context.write(key,flowBean);
    }
}

Step 5:自定义定义FlowReducer

package org.example.mapreduce.Flow;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

public class FlowReducer extends Reducer<Text,FlowBean,Text,FlowBean> {
    
    @Override
    protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
    
        Integer upFlow = 0;
        Integer downFlow = 0;
        Integer upCountFlow = 0;
        Integer downCountFlow = 0;

        for (FlowBean value : values) {
    
            upFlow += value.getUpFlow();
            downFlow += value.getDownFlow();
            upCountFlow += value.getUpCountFlow();
            downCountFlow += value.getDownCountFlow();
        }
        //创建FlowBean对象，并给对象赋值 V3
        FlowBean flowBean = new FlowBean();
        flowBean.setUpFlow(upFlow);
        flowBean.setDownFlow(downFlow);
        flowBean.setUpCountFlow(upCountFlow);
        flowBean.setDownCountFlow(downCountFlow);

        //将K3和V3写入上下文中
        //k3: 电话号
        //V3: flpwBean
        context.write(key,flowBean);
    }
}

Step 6: 程序main函数入口

package org.example.mapreduce.Flow;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;


public class FlowJobMain extends Configured implements Tool {
    

    @Override
    public int run(String[] strings) throws Exception {
    
        //创建一个Job任务对象
        Job job = Job.getInstance(super.getConf(),"mapreduce_flowcount");

        //如果打包运行出错，则需要加该配置
        job.setJarByClass(FlowJobMain.class);
        //配置job任务
        //指定文件的读取方式和读取路径
        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.addInputPath(job,new Path("file:///C:\\Myprogram\\IN\\FlowCount"));

        //指定Map阶段的处理方式和数据类型
        job.setMapperClass(FlowMapper.class);
        //设置map阶段K2的数据类型
        job.setMapOutputKeyClass(Text.class);
        //V2的数据类型
        job.setMapOutputValueClass(FlowBean.class);

        // 第三（分区）
        job.setPartitionerClass(FlowPartition.class);
        job.setNumReduceTasks(6); //分区数量
        // 第四（排序）
        // 第五（规约）
        job.setCombinerClass(FLowCombiner.class);
        // 第六（分组）

        //第七 :指定reduce阶段的处理方式和数据类型
        job.setReducerClass(FlowReducer.class);
        // 设置K3的数据类型
        job.setOutputKeyClass(Text.class);
        // 设置V3的数据类型
        job.setOutputValueClass(FlowBean.class);

        //第八：设置输出类型
        job.setOutputFormatClass(TextOutputFormat.class);
        //设置输出路径
        TextOutputFormat.setOutputPath(job,new Path("file:///C:\\Myprogram\\OUT\\FlowCount_out0111"));

        boolean bl = job.waitForCompletion(true);
        return bl?0:1;
    }
    public static void main(String[] args) throws Exception {
    
        Configuration configuration = new Configuration();
        int run = ToolRunner.run(configuration,new FlowJobMain(),args);
        System.exit(run);
    }
}

完美撒花！