Demand: in the document, the total upstream traffic, total downstream traffic and total traffic consumed by each user
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 Video website 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 information safety 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 Site statistics 24 9 6960 690 200 1363157973098 15013685858 5C-0E-8B-C7-F7-90:CMCC 120.197.40.4 rank.ie.sogou.com Search Engines 28 27 3659 3538 200 1363157986029 15989002119 E8-99-C4-4E-93-E0:CMCC-EASY 120.196.100.99 www.umeng.com Site statistics 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 Comprehensive portal 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 Comprehensive portal 57 102 7335 110349 200 1363157986072 18320173382 84-25-DB-4F-10-1A:CMCC-EASY 120.196.100.99 input.shouji.sogou.com Search Engines 21 18 9531 2412 200 1363157990043 13925057413 00-1F-64-E1-E6-9A:CMCC 120.196.100.55 t3.baidu.com Search Engines 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
Idea: map stage: divide each line into various fields according to the tab, extract the cell phone number as the output key, encapsulate the traffic information into the FlowBean object as the output value
Important: how to implement the serialization interface of Hadoop with custom type
FlowBean: this custom data type must implement the serialization interface of Hadoop: Writable
There are two ways to do this:
Readfields (in) -- deserialization method
Write (out) - serialization method
reduce stage: traverse all the values (flow beans) of a set of data, accumulate them, and then output them with the mobile phone number as the key and the total flow information bean as the value.
code implementation
1.FlowBean
import org.apache.hadoop.io.Writable; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; /** * Function of this case: demonstrate how to implement the serialization interface of Hadoop with custom data type * 1，This class must keep the null constructor * 2.write The order of the binary data of the output field in the method should be the same as that of the readFiles method */ 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 Method to be called by the system when serializing objects of this class * @param dataOutput * @throws IOException */ public void write(DataOutput dataOutput) throws IOException { dataOutput.writeInt(upFlow); dataOutput.writeUTF(phone); dataOutput.writeInt(dFlow); dataOutput.writeInt(amountFlow); } /** * hadoop Method the system calls when deserializing * @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:Cell-phone number * @param values: Traffic data in all access records generated by a mobile phone number * @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. Statistical results of jobsubmitter program operation [total uplink and downlink traffic of mobile number]
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