MapReduce编程小案例.1st—求取手机号码上下限及总流量

MapReduce编程小案例.1st—求取手机号码上下限及总流量

利用MapReduce处理一个小案例，如下是一批手机号码上网所保存在日志的流量信息：

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.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		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
1363157985066 	13726238888	00-FD-07-A4-72-B8:CMCC	120.196.100.82	i02.c.aliimg.com		24	27	2481	24681	200
1363157993055 	13560436666	C4-17-FE-BA-DE-D9:CMCC	120.196.100.99			18	15	1116	954	200

设计Mapper类

package cn.edu360.mr.flow;

import java.io.IOException;

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

public class FlowCountMapper extends Mapper<LongWritable, Text, Text, FlowBean>{
	
	@Override
	protected void map(LongWritable key, Text value, Context context)
			throws IOException, InterruptedException {
		 String line = value.toString();
		 String[] fields = line.split("\t");
		 
		 String phone = fields[1];
		 
		 int upFlow = Integer.parseInt(fields[fields.length-3]);
		 int dFlow = Integer.parseInt(fields[fields.length-2]);
		 
		 context.write(new Text(phone), new FlowBean(phone, upFlow, dFlow));

	}
	

}

设计Reducer类

package cn.edu360.mr.flow;

import java.io.IOException;

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

public class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean>{
	
	@Override
	protected void reduce(Text key, Iterable<FlowBean> values, Reducer<Text, FlowBean, Text, FlowBean>.Context context)
			throws IOException, InterruptedException {
		
		int upSum = 0;
		int dSum = 0;
		
		for (FlowBean value : values) {
			upSum += value.getUpFlow();
			dSum += value.getdFlow();
		}
		
		context.write(key, new FlowBean(key.toString(), upSum, dSum));

	}

}

创建一个类封装【手机号码以及流量的信息】，要注意实现序列化

package cn.edu360.mr.flow;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.io.Writable;

public class FlowBean implements Writable{
	
	
	private int upFlow;
	private int dFlow;
	private String phone;
	private int amountFlow;
	
	public FlowBean() {
		
	}
	
	public FlowBean(String phone, int upFlow, int dFlow) {
		this.phone = phone;
		this.upFlow = upFlow;
		this.dFlow = dFlow;
		this.amountFlow = upFlow + dFlow;
		
	}

	public int getUpFlow() {
		return upFlow;
	}

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

	public int getdFlow() {
		return dFlow;
	}

	public void setdFlow(int dFlow) {
		this.dFlow = dFlow;
	}

	public String getPhone() {
		return phone;
	}

	public void setPhone(String phone) {
		this.phone = phone;
	}

	public int getAmountFlow() {
		return amountFlow;
	}

	public void setAmountFlow(int amountFlow) {
		this.amountFlow = amountFlow;
	}

	
	
	/*
	 * hadoop系统在序列化该类的对象时要调用的方法
	 */
	
	
	//反序列化
	public void readFields(DataInput in) throws IOException {
		this.upFlow = in.readInt();
		this.phone = in.readUTF();
		this.dFlow = in.readInt();
		this.amountFlow = in.readInt();
		
	}
    
	//序列化
	public void write(DataOutput out) throws IOException {
		out.writeInt(upFlow);
		out.writeUTF(phone);
		out.writeInt(dFlow);
		out.writeInt(amountFlow);
		
	}
	
	@Override
	public String toString() {
		
		return this.phone + "," + this.upFlow + "," + this.dFlow + "," + this.amountFlow;
		
	}

	

}

PS：添加一个新的功能，覆写一下MapTask分配kv给ReduceTask的规则

package cn.edu360.mr.flow;

import java.util.HashMap;

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

/*
 * 本类是提供给MapTask用的
 * MapTask通过这个类的getPartition方法，来计算它所产生的每一对kv数据该分发哪一个reduce task
 * @author Downey
 */

public class ProvincePartitioner extends Partitioner<Text, FlowBean>{
	
	static HashMap<String, Integer> codeMap = new HashMap<String, Integer>();
	
	static {
		
		codeMap.put("135", 0);
		codeMap.put("136", 1);
		codeMap.put("137", 2);
		codeMap.put("138", 3);
		codeMap.put("139", 4);

		
	}

	@Override
	public int getPartition(Text key, FlowBean value, int numPartitions) {
		
		Integer code = codeMap.get(key.toString().substring(0, 3));
		return code == null?5:code;

	}
	
	
	

}

最后实现JobSubmitter类的设计

package cn.edu360.mr.flow;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
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.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class JobSubmitter {
	
	public static void main(String[] args) throws Exception {
		
		Configuration conf = new Configuration();
		
		Job job = Job.getInstance(conf);
		
	    job.setJarByClass(JobSubmitter.class);
	    
	    job.setMapperClass(FlowCountMapper.class);
	    job.setReducerClass(FlowCountReducer.class);
	    
	    //设置参数：maptask在做数据分区时，用哪个分区逻辑类(如果不指定的话，它会默认的HashPartitioner)
	    job.setPartitionerClass(ProvincePartitioner.class);
	    //由于我们的ProvincePartitioner可能会产生6种分区号，
	    job.setNumReduceTasks(6);
	    
	    job.setMapOutputKeyClass(Text.class);
	    job.setMapOutputValueClass(FlowBean.class);
	    job.setOutputKeyClass(Text.class);
	    job.setOutputValueClass(FlowBean.class);
	    
	    FileInputFormat.setInputPaths(job, new Path("F:\\mrdata\\flow\\input"));
	    FileOutputFormat.setOutputPath(job, new Path("F:\\mrdata\\flow\\province-output"));
	    
		job.waitForCompletion(true);
		
	}
	

}