mapreduce【流量統計】求和——自定義資料型別
阿新 • • 發佈：2019-05-29
需求：統計一下檔案中，每一個使用者所耗費的總上行流量，總下行流量，總流量
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 視訊網站 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 資訊保安 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 站點統計 24 9 6960 690 200 1363157973098 15013685858 5C-0E-8B-C7-F7-90:CMCC 120.197.40.4 rank.ie.sogou.com 搜尋引擎 28 27 3659 3538 200 1363157986029 15989002119 E8-99-C4-4E-93-E0:CMCC-EASY 120.196.100.99 www.umeng.com 站點統計 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 綜合門戶 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 綜合門戶 57 102 7335 110349 200 1363157986072 18320173382 84-25-DB-4F-10-1A:CMCC-EASY 120.196.100.99 input.shouji.sogou.com 搜尋引擎 21 18 9531 2412 200 1363157990043 13925057413 00-1F-64-E1-E6-9A:CMCC 120.196.100.55 t3.baidu.com 搜尋引擎 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
思路：map階段：將每一行按tab切分成各欄位，提取其中的手機號作為輸出key，流量資訊封裝到FlowBean物件中，作為輸出的value
要點：自定義型別如何實現Hadoop的序列化介面
FlowBean:這種自定義資料型別必須實現Hadoop的序列化介面：Writable
實現其中的兩個方法：
1.readFields（in）——反序列化方法
2.write（out）——序列化方法
reduce階段：遍歷一組資料的所有value（flowbean），進行累加，然後以手機號作為key輸出，以總流量資訊bean作為value輸出。
程式碼實現
1.FlowBean
import org.apache.hadoop.io.Writable; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; /** * 本案例功能：演示自定義資料型別如何實現Hadoop的序列化介面 * 1，該類一定要保留空參構造器 * 2.write方法中輸出欄位二進位制資料的順序要與readFiles方法讀取資料的順序一致 */ public class FlowBean implements Writable { private int upFlow; private int dFlow; private String phone; private int amountFlow; 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 int getAmountFlow() { return amountFlow; } public void setAmountFlow(int amountFlow) { this.amountFlow = amountFlow; } public FlowBean() { } public FlowBean(int upFlow, int dFlow,String phone) { this.upFlow = upFlow; this.dFlow = dFlow; this.phone=phone; this.amountFlow=upFlow+dFlow; } /** * hadoop 系統在序列化該類的物件時要呼叫得方法 * @param dataOutput * @throws IOException */ public void write(DataOutput dataOutput) throws IOException { dataOutput.writeInt(upFlow); dataOutput.writeUTF(phone); dataOutput.writeInt(dFlow); dataOutput.writeInt(amountFlow); } /** * hadoop系統在反序列化時要呼叫的方法 * @param dataInput * @throws IOException */ public void readFields(DataInput dataInput) throws IOException { this.upFlow=dataInput.readInt(); this.phone=dataInput.readUTF(); this.dFlow=dataInput.readInt(); this.amountFlow=dataInput.readInt(); } @Override public String toString() { return this.upFlow+","+this.dFlow+","+this.amountFlow; } }
2.FlowCountMapper
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
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(upFlow,dFlow,phone));
}
}3.FlowCountReduce
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class FlowCountReduce extends Reducer<Text,FlowBean,Text,FlowBean> {
/**
*
* @param key:手機號
* @param values：某個手機號所產生的所有訪問記錄中的流量資料
* @param context
* @throws IOException
* @throws InterruptedException
*/
@Override
protected void reduce(Text key, Iterable<FlowBean> values, 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(upSum,dSum,key.toString()));
}
}
4.JobSubmitter
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(FlowCountReduce.class);
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\\output"));
boolean res = job.waitForCompletion(true);
System.exit(res ? 0:-1);
}
}
5.JobSubmitter程式執行統計結果【手機號 上行流量 下行流量 總流量】
13480253104 180,180,360
13502468823 7335,110349,117684
13560436666 1116,954,2070
13560439658 2034,5892,7926
13602846565 1938,2910,4848
13660577991 6960,690,7650
13719199419 240,0,240
13726230503 2481,24681,27162
13726238888 2481,24681,27162
13760778710 120,120,240
13826544101 264,0,264
13922314466 3008,3720,6728
13925057413 11058,48243,59301
13926251106 240,0,240
13926435656 132,1512,1644
15013685858 3659,3538,7197
15920133257 3156,2936,6092
15989002119 1938,180,2118
18211575961 1527,2106,3633
18320173382 9531,2412,11943
84138413 4116,1432,5548