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！