流量汇总案例
1.需求
现在我们采集到了一份用户访问流量的数据，我们需要从这份数据中统计出每个用户的流量数据。
部分测试数据如下：可以拷贝出去做测试
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
1363157993055 13560436646 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200
1363157984041 13660573991 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 15989102119 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 13480453104 5C-0E-8B-C7-FC-80:CMCC-EASY 120.197.40.4 3 3 180 180 200
1363157984040 13602246565 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 13922114466 00-FD-07-A2-EC-BA:CMCC 120.196.100.82 img.qfc.cn 12 12 3008 3720 200
1363157982040 13506468823 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 13925047413 00-1F-64-E1-E6-9A:CMCC 120.196.100.55 t3.baidu.com 搜索引擎 69 63 11058 48243 200
1363157988072 13760758710 00-FD-07-A4-7B-08:CMCC 120.196.100.82 2 2 120 120 200
1363157985066 13726228888 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200
1363157993055 13560416666 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200
1363157993055 13560436766 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200
2.需求分析
Map阶段读取一行数据需要记录’上行流量’,‘下行流量’以及’总流量’,单个基本数据类型不方便保存,引入自定义对象保存，但需要序列化.
3.具体实现
3.1创建自定义对象
注意需要实现序列化，此处我们实现Writable接口，重写相关的方法
package com.sxt.mr.flow;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.Writable;
/**
* 存储流量相关数据
* @author 波波烤鸭
*
*/
public class Flow implements Writable {
// 上下流量
private long upFlow;
// 下行流量
private long downFlow;
// 总流量
private long sumFlow;
public long getUpFlow() {
return upFlow;
}
public void setUpFlow(long upFlow) {
this.upFlow = upFlow;
}
public long getDownFlow() {
return downFlow;
}
public void setDownFlow(long downFlow) {
this.downFlow = downFlow;
}
public Flow(long upFlow, long downFlow) {
super();
this.upFlow = upFlow;
this.downFlow = downFlow;
this.sumFlow = upFlow + downFlow;
}
/**
* 无参构造方法必须要有 反射的时候需要用到
*/
public Flow() {
super();
}
/**
* 序列化方法
*/
public void write(DataOutput out) throws IOException {
// TODO Auto-generated method stub
out.writeLong(upFlow);
out.writeLong(downFlow);
out.writeLong(sumFlow);
}
public String toString() {
return upFlow + "\t" + downFlow + "\t" + sumFlow;
}
/**
* 反序列化 反序列化的顺序和序列化的顺序一致
*/
public void readFields(DataInput in) throws IOException {
// TODO Auto-generated method stub
upFlow = in.readLong();
downFlow = in.readLong();
sumFlow = in.readLong();
}
public long getSumFlow() {
return sumFlow;
}
public void setSumFlow(long sumFlow) {
this.sumFlow = sumFlow;
}
}
3.2 自定义MapTask
注意输出的value就是我们自定义的类型
public class MyMapTask extends Mapper<LongWritable, Text, Text, Flow>{
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
// 将一行数据转换为String
String line = value.toString();
// 切分字段
String[] fields = line.split("\t");
// 取出手机号
String phoneNum = fields[1];
// 取出上行流量下行流量
long upFlow = Long.parseLong(fields[fields.length-3]);
long downFlow = Long.parseLong(fields[fields.length-2]);
Flow flow = new Flow(upFlow,downFlow);
context.write(new Text(phoneNum), flow);
}
}
3.3 自定义ReduceTask
public class MyReduceTask extends Reducer<Text, Flow, Text, Flow>{
protected void reduce(Text key, Iterable<Flow> values, Context context)
throws IOException, InterruptedException {
long sum_upflow = 0;
long sum_downflow = 0;
for (Flow flow : values) {
sum_upflow += flow.getUpFlow();
sum_downflow += flow.getDownFlow();
}
Flow f = new Flow(sum_upflow,sum_downflow);
// 必须重写Flow的toString方法
context.write(new Text(key), f);
}
}
3.4 启动类测试
采用本地模式运行
package com.sxt.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 FlowTest {
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration(true);
conf.set("mapreduce.framework.name", "local");
// 输出到HDFS文件系统中
// conf.set("fs.defaultFS", "hdfs://hadoop-node01:9000");
// 输出到本地文件系统
conf.set("fs.defaultFS", "file:///");
Job job = Job.getInstance(conf);
job.setJarByClass(FlowTest.class);
// 指定本job要使用的map/reduce的工具类
job.setMapperClass(MyMapTask.class);
job.setReducerClass(MyReduceTask.class);
// 指定mapper输出kv的类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Flow.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Flow.class);
// 指定job的原始文件输入目录
// 6.设置输出输出类
FileInputFormat.setInputPaths(job, new Path("c:/tools/bigdata/mr/flow/input/"));
FileOutputFormat.setOutputPath(job, new Path("c:/tools/bigdata/mr/flow/output/"));
//将job中配置的相关参数，以及job所用的jar包提交给yarn运行
//job.submit(); waitForCompletion等待执行完成
boolean flag = job.waitForCompletion(true);
System.exit(flag?0:1);
}
}
3.5 输出结果
4. 总结
本案例主要是演示了自定义对象在MapReduce任务中的使用，注意序列化！
