MapReduce案例-流量统计
一、MapReduce案例-流量统计
1： 需求一: 统计求和
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 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
1.1: 自定义map的输出value对象FlowBean
package flowcount;
import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
/**
* @author MoooJL
* @data 2020/8/28-20:26
*/
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;
}
//序列化方法
@Override
public void write(DataOutput out) throws IOException {
out.writeInt(upFlow);
out.writeInt(downFlow);
out.writeInt(upCountFlow);
out.writeInt(downCountFlow);
}
@Override
public void readFields(DataInput in) throws IOException {
this.upFlow = in.readInt();
this.downFlow = in.readInt();
this.upCountFlow = in.readInt();
this.downCountFlow = in.readInt();
}
}
1.2：定义FlowMapper类
package flowcount;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.yarn.webapp.hamlet.Hamlet;
import java.io.IOException;
/**
* @author MoooJL
* @data 2020/8/28-20:31
*/
public class FlowCountMapper extends Mapper<LongWritable, Text,Text,FlowBean> {
/*
将K1和V1转为K2和V2:
K1 V1
0 1360021750219 128 1177 16852 200
------------------------------
K2 V2
13600217502 FlowBean(19 128 1177 16852)
*/
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//1:拆分行文本数据,得到手机号--->K2
String[] split = value.toString().split("\t");
String phoneNum = split[1];
//2:创建FlowBean对象,并从行文本数据拆分出流量的四个四段,并将四个流量字段的值赋给FlowBean对象
FlowBean flowBean = new FlowBean();
flowBean.setUpFlow(Integer.parseInt(split[6]));
flowBean.setDownFlow(Integer.parseInt(split[7]));
flowBean.setUpCountFlow(Integer.parseInt(split[8]));
flowBean.setDownCountFlow(Integer.parseInt(split[9]));
//3:将K2和V2写入上下文中
context.write(new Text(phoneNum), flowBean);
}
}
1.3：定义FlowReducer类
package flowcount;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
/**
* @author MoooJL
* @data 2020/8/28-21:54
*/
public class FlowCountReducer extends Reducer<Text,FlowBean,Text,FlowBean> {
@Override
protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
//1:遍历集合,并将集合中的对应的四个字段累计
Integer upFlow = 0; //上行数据包数
Integer downFlow = 0; //下行数据包数
Integer upCountFlow = 0; //上行流量总和
Integer downCountFlow = 0;//下行流量总和
for (FlowBean value : values) {
upFlow += value.getUpFlow();
downFlow += value.getDownFlow();
upCountFlow += value.getUpCountFlow();
downCountFlow += value.getDownCountFlow();
}
//2: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);
//3:将K3和V3下入上下文中
context.write(key, flowBean);
}
}
1.4：程序main函数入口FlowMain
package flowcount;
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;
/**
* @author MoooJL
* @data 2020/8/26-15:24
*/
public class jobMain extends Configured implements Tool {
//该方法用于指定一个job任务
@Override
public int run(String[] args) throws Exception {
//1、创建一个job对象
Job job = Job.getInstance(super.getConf(), "mapreduce_flowcount");
//2、配置job任务的8个对象
//第一步 指定文件的读取方式和路径
job.setInputFormatClass(TextInputFormat.class);
/*
如果打包运行出错 加配置
job.setJarByClass(jobMain.class);
*/
TextInputFormat.addInputPath(job,new Path("file:///D:\\input\\flowcount"));
//第二步 指定map阶段的处理方式和数据类型
job.setMapperClass(FlowCountMapper.class);
//设置map阶段k2的类型
job.setMapOutputKeyClass(Text.class);
//设置map阶段v2的类型
job.setMapOutputValueClass(FlowBean.class);
//第三(分区) 四(排序) 第五步(规约) 六(分组) 步 采用默认方式
//第七步 指定reduce阶段的处理方式和数据类型
job.setReducerClass(FlowCountReducer.class);
//设置k3 v3类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowBean.class);
//第八步 设置输出类型
job.setOutputFormatClass(TextOutputFormat.class);
//本地运行模式
TextOutputFormat.setOutputPath(job,new Path("file:///D:\\output\\flowcount"));
//等待任务结束
boolean b = job.waitForCompletion(true);
return b ? 0:1;
}
public static void main(String[] args) throws Exception {
Configuration configuration=new Configuration();
//启动job任务
int run = ToolRunner.run(configuration, new jobMain(), args);
System.exit(run);
}
}
1.5：运行截图
2：需求二: 上行流量倒序排序（递减排序）
分析，以需求一的输出数据作为排序的输入数据，自定义FlowBean,以FlowBean为map输出的key，以手机号作为Map输出的value，因为MapReduce程序会对Map阶段输出的key进行排序
2.1： 定义FlowBean实现WritableComparable实现比较排序
Java 的 compareTo 方法说明:
- compareTo 方法用于将当前对象与方法的参数进行比较。
- 如果指定的数与参数相等返回 0。
- 如果指定的数小于参数返回 -1。
- 如果指定的数大于参数返回 1。
例如：o1.compareTo(o2); 返回正数的话，当前对象（调用 compareTo 方法的对象 o1）要排在比较对象（compareTo 传参对象 o2）后面，返回负数的话，放在前面
package flowcount.sort;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
/**
* @author MoooJL
* @data 2020/8/28-20:26
*/
public class FlowBean implements WritableComparable<FlowBean> {
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;
}
//序列化方法
@Override
public void write(DataOutput out) throws IOException {
out.writeInt(upFlow);
out.writeInt(downFlow);
out.writeInt(upCountFlow);
out.writeInt(downCountFlow);
}
@Override
public void readFields(DataInput in) throws IOException {
this.upFlow = in.readInt();
this.downFlow = in.readInt();
this.upCountFlow = in.readInt();
this.downCountFlow = in.readInt();
}
//指定排序规则
@Override
public int compareTo(FlowBean flowBean) {
// return this.upFlow.compareTo(flowBean.getUpFlow()) * -1;
return flowBean.upFlow - this.upFlow ;
}
}
2.2：定义FlowMapper
package flowcount.sort;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
/**
* @author MoooJL
* @data 2020/8/29-0:04
*/
public class FlowSortMapper extends Mapper<LongWritable, Text,FlowBean,Text> {
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//1:拆分行文本数据(V1),得到四个流量字段,并封装FlowBean对象---->K2
String[] split = value.toString().split("\t");
FlowBean flowBean = new FlowBean();
flowBean.setUpFlow(Integer.parseInt(split[1]));
flowBean.setDownFlow(Integer.parseInt(split[2]));
flowBean.setUpCountFlow(Integer.parseInt(split[3]));
flowBean.setDownCountFlow(Integer.parseInt(split[4]));
//2:通过行文本数据,得到手机号--->V2
String phoneNum = split[0];
//3:将K2和V2下入上下文中
context.write(flowBean, new Text(phoneNum));
}
}
2.3:定义FlowReducer
package flowcount.sort;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
/**
* @author MoooJL
* @data 2020/8/29-0:10
*/
public class FlowSortReducer extends Reducer<FlowBean, Text,Text,FlowBean> {
@Override
protected void reduce(FlowBean key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
//1:遍历集合,取出 K3,并将K3和V3写入上下文中
for (Text value : values) {
context.write(value, key);
}
}
}
2.4:程序main函数入口
package flowcount.sort;
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;
import sort.SortBean;
/**
* @author MoooJL
* @data 2020/8/26-15:24
*/
public class jobMain extends Configured implements Tool {
//该方法用于指定一个job任务
@Override
public int run(String[] args) throws Exception {
//1、创建一个job对象
Job job = Job.getInstance(super.getConf(), "mapreduce_flowcount_cort");
//2、配置job任务的8个对象
//第一步 指定文件的读取方式和路径
job.setInputFormatClass(TextInputFormat.class);
/*
如果打包运行出错 加配置
job.setJarByClass(jobMain.class);
*/
TextInputFormat.addInputPath(job,new Path("file:///D:\\output\\flowcount"));
//第二步 指定map阶段的处理方式和数据类型
job.setMapperClass(FlowSortMapper.class);
//设置map阶段k2的类型
job.setMapOutputKeyClass(FlowBean.class);
//设置map阶段v2的类型
job.setMapOutputValueClass(Text.class);
//第三(分区) 四(排序) 第五步(规约) 六(分组) 步 采用默认方式
//第七步 指定reduce阶段的处理方式和数据类型
job.setReducerClass(FlowSortReducer.class);
//设置k3 v3类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(SortBean.class);
//第八步 设置输出类型
job.setOutputFormatClass(TextOutputFormat.class);
//本地运行模式
TextOutputFormat.setOutputPath(job,new Path("file:///D:\\output\\flowcount_sort"));
//等待任务结束
boolean b = job.waitForCompletion(true);
return b ? 0:1;
}
public static void main(String[] args) throws Exception {
Configuration configuration=new Configuration();
//启动job任务
int run = ToolRunner.run(configuration, new jobMain(), args);
System.exit(run);
}
}
2.5：运行截图
3：需求三: 手机号码分区
在需求一的基础上，继续完善，将不同的手机号分到不同的数据文件的当中去，需要自定义分区来实现，这里我们自定义来模拟分区，将以下数字开头的手机号进行分开
135 开头数据到一个分区文件
136 开头数据到一个分区文件
137 开头数据到一个分区文件
其他分区
3.1：自定义分区
package flowcount.partition;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;
/**
* @author MoooJL
* @data 2020/8/29-0:28
*/
public class FlowCountPartition extends Partitioner<Text,FlowBean> {
/*
该方法用来指定分区的规则:
135 开头数据到一个分区文件
136 开头数据到一个分区文件
137 开头数据到一个分区文件
其他分区
参数:
text : K2 手机号
flowBean: V2
i : ReduceTask的个数
*/
@Override
public int getPartition(Text text, FlowBean flowBean, int i) {
//1:获取手机号
String phoneNum = text.toString();
//2:判断手机号以什么开头,返回对应的分区编号(0-3)
if(phoneNum.startsWith("135")){
return 0;
}else if(phoneNum.startsWith("136")){
return 1;
}else if(phoneNum.startsWith("137")){
return 2;
}else{
return 3;
}
}
}
3.2：作业运行设置
job.setPartitionerClass(FlowPartition.class);
job.setNumReduceTasks(4);
3.3：运行结果
