MapReduce case - summation partition protocol sorting operation on traffic statistics

demand : Summation partition protocol of statistical summation

Count the total uplink packets of each mobile phone number , Total downlink packets , Sum of total uplink traffic , Sum of total downlink traffic , And realize the partition and protocol .
analysis ： Take the mobile phone number as key value , Uplink traffic , Downstream traffic , Total uplink traffic , The four fields of total downlink traffic are used as value value , And then with this key, and value As map Output of stage ,reduce Stage input .
Raw data ：

1363157985066	13726230503	00-FD-07-A4-72-B8:CMCC	120.196.100.82	i02.c.aliimg.com	 Entertainment 	24	27	2481	24681	200
1363157995052 	13826544101	5C-0E-8B-C7-F1-E0:CMCC	120.197.40.4	jd.com	 Jingdong shopping 	4	0	264	0	200
1363157991076 	13926435656	20-10-7A-28-CC-0A:CMCC	120.196.100.99	taobao.com	 Taobao shopping 	2	4	132	1512	200
1363154400022 	13926251106	5C-0E-8B-8B-B1-50:CMCC	120.197.40.4	cnblogs.com	 Technology portal 	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 	19984138413	5C-0E-8B-8C-E8-20:7DaysInn	120.197.40.4	122.72.52.12	 Unknown 	20	16	4116	1432	200
1363157993055 	13560439658	C4-17-FE-BA-DE-D9:CMCC	120.196.100.99	sougou.com	 Integrated portal 	18	15	1116	954	200
1363157995033 	15920133257	5C-0E-8B-C7-BA-20:CMCC	120.197.40.4	sug.so.360.cn	 Information security 	20	20	3156	2936	200
1363157983019 	13719199419	68-A1-B7-03-07-B1:CMCC-EASY	120.196.100.82	baidu.com	 Comprehensive search 	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 engine 	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	zhilian.com	 Recruitment portal 	15	9	918	4938	200
1363157986041 	13480253104	5C-0E-8B-C7-FC-80:CMCC-EASY	120.197.40.4	csdn.net	 Technology portal 	3	3	180	180	200
1363157984040 	13602846565	5C-0E-8B-8B-B6-00:CMCC	120.197.40.4	2052.flash2-http.qq.com	 Integrated portal 	15	12	1938	2910	200
1363157995093 	13922314466	00-FD-07-A2-EC-BA:CMCC	120.196.100.82	img.qfc.cn	 Picture collection 	12	12	3008	3720	200
1363157982040 	13502468823	5C-0A-5B-6A-0B-D4:CMCC-EASY	120.196.100.99	y0.ifengimg.com	 Integrated 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 engine 	21	18	9531	2412	200
1363157990043 	13925057413	00-1F-64-E1-E6-9A:CMCC	120.196.100.55	t3.baidu.com	 Search engine 	69	63	11058	48243	200
1363157988072 	13760778710	00-FD-07-A4-7B-08:CMCC	120.196.100.82	http://youku.com/	 Video website 	2	2	120	120	200
1363157985079 	13823070001	20-7C-8F-70-68-1F:CMCC	120.196.100.99	img.qfc.cn	 Picture view 	6	3	360	180	200
1363157985069 	13600217502	00-1F-64-E2-E8-B1:CMCC	120.196.100.55	www.baidu.com	 Integrated portal 	18	138	1080	186852	200
1363157985059 	13600217502	00-1F-64-E2-E8-B1:CMCC	120.196.100.55	www.baidu.com	 Integrated portal 	19	128	1177	16852	200

Step 1: Customize map Output value object 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; //  Number of uplink packets 
    private Integer downFlow; // Number of downlink packets 
    private Integer upCountFlow; // Sum of uplink traffic 
    private Integer downCountFlow; // Total downlink traffic 

    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;
    }

    // serialize 
    @Override
    public void write(DataOutput dataOutput) throws IOException {
    
        dataOutput.writeInt(upFlow);
        dataOutput.writeInt(downFlow);
        dataOutput.writeInt(upCountFlow);
        dataOutput.writeInt(downCountFlow);
    }

    // Deserialization 
    @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: Definition FlowMapper class

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： Row offset 
        //V1:  One line string , First, the string is segmented 
        String[] split = value.toString().split("\t");
        String phone = split[1];

        // establish FlowBean object 
        FlowBean flowBean = new FlowBean();

        // Convert string to number 
        flowBean.setUpFlow(Integer.parseInt(split[6]));
        flowBean.setDownFlow(Integer.parseInt(split[7]));
        flowBean.setUpCountFlow(Integer.parseInt(split[8]));
        flowBean.setDownCountFlow(Integer.parseInt(split[9]));

        // take k2 v2  Write in context 
        //K2:  Phone number 
        //V2：flowBean
        context.write(new Text(phone),flowBean);
    }
}

Step 3: Custom partition 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  cell-phone number  flowBean: V2 i : ReduceTask The number of  */
    @Override
    public int getPartition(Text text, FlowBean flowBean, int i) {
    
        // Get phone number 
        String phone = text.toString();
        // Determine what the phone number starts with , Return the corresponding partition number 
        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: Custom protocol Combiner
Concept ： every last map Can produce a lot of local output ,Combiner The function of is to map The output of the terminal is merged first , In order to reduce the map and reduce Data transfer between nodes , To improve the network IO performance , yes MapReduce It's one of the ways to optimize .

• combiner yes MR In the program Mapper and Reducer A component other than

• combiner The parent class of a component is Reducer

• combiner and reducer The difference is the location of the operation

  • Combiner It's in every maptask The node is running

  • Reducer Receive global ownership Mapper Output result of

• combiner The meaning of this is for every maptask Local summary of the output of , To reduce network traffic

• combiner The premise of application is not to affect the final business logic , and ,combiner Output kv It should be with reducer The input of key,value Types should be matched .

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();
        }
        // establish FlowBean object , And assign a value to the object  V3
        FlowBean flowBean = new FlowBean();
        flowBean.setUpFlow(upFlow);
        flowBean.setDownFlow(downFlow);
        flowBean.setUpCountFlow(upCountFlow);
        flowBean.setDownCountFlow(downCountFlow);

        // New K2 and V2 Write in context 
        context.write(key,flowBean);
    }
}

Step 5: Custom definition 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();
        }
        // establish FlowBean object , And assign a value to the object  V3
        FlowBean flowBean = new FlowBean();
        flowBean.setUpFlow(upFlow);
        flowBean.setDownFlow(downFlow);
        flowBean.setUpCountFlow(upCountFlow);
        flowBean.setDownCountFlow(downCountFlow);

        // take K3 and V3 Write in context 
        //k3:  Phone number 
        //V3: flpwBean
        context.write(key,flowBean);
    }
}

Step 6: Program main Function entrance

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 {
    
        // Create a Job Task object 
        Job job = Job.getInstance(super.getConf(),"mapreduce_flowcount");

        // If the package runs incorrectly , You need to add this configuration 
        job.setJarByClass(FlowJobMain.class);
        // To configure job Mission 
        // Specify the reading method and path of the file 
        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.addInputPath(job,new Path("file:///C:\\Myprogram\\IN\\FlowCount"));

        // Appoint Map Stage processing and data type 
        job.setMapperClass(FlowMapper.class);
        // Set up map Stage K2 Data type of 
        job.setMapOutputKeyClass(Text.class);
        //V2 Data type of 
        job.setMapOutputValueClass(FlowBean.class);

        //  Third （ Partition ）
        job.setPartitionerClass(FlowPartition.class);
        job.setNumReduceTasks(6); // Zoning quantity 
        //  Fourth （ Sort ）
        //  The fifth （ Statute ）
        job.setCombinerClass(FLowCombiner.class);
        //  The sixth （ grouping ）

        // The seventh  : Appoint reduce Stage processing and data type 
        job.setReducerClass(FlowReducer.class);
        //  Set up K3 Data type of 
        job.setOutputKeyClass(Text.class);
        //  Set up V3 Data type of 
        job.setOutputValueClass(FlowBean.class);

        // The eighth ： Set output type 
        job.setOutputFormatClass(TextOutputFormat.class);
        // Set output path 
        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);
    }
}

Perfect flowers ！