流量汇总程序案例
[infobox title=”标题内容”]
统计手机号耗费的总上行流量、下行流量、总流量（序列化）
Map阶段：
（1）读取一行数据，切分字段
（2）抽取手机号、上行流量、下行流量
（3）以手机号为key，bean对象为value输出，即context.write(手机号,bean);
Reduce阶段：
（1）累加上行流量和下行流量得到总流量。
（2）实现自定义的bean来封装流量信息，并将bean作为map输出的key来传输
（3）MR程序在处理数据的过程中会对数据排序(map输出的kv对传输到reduce之前，会排序)，排序的依据是map输出的key
所以，我们如果要实现自己需要的排序规则，则可以考虑将排序因素放到key中，让key实现接口：WritableComparable。
然后重写key的compareTo方法。
[collapse title=”phone_date.txt”]
13726230503 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200
13826544101 5C-0E-8B-C7-F1-E0:CMCC 120.197.40.4 4 0 264 0 200
13926435656 20-10-7A-28-CC-0A:CMCC 120.196.100.99 2 4 132 1512 200
13926251106 5C-0E-8B-8B-B1-50:CMCC 120.197.40.4 4 0 240 0 200
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
84138413 5C-0E-8B-8C-E8-20:7DaysInn 120.197.40.4 122.72.52.12 20 16 4116 1432 200
13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200
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
13719199419 68-A1-B7-03-07-B1:CMCC-EASY 120.196.100.82 4 0 240 0 200
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
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
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
13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 15 9 918 4938 200
13480253104 5C-0E-8B-C7-FC-80:CMCC-EASY 120.197.40.4 3 3 180 180 200
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
13922314466 00-FD-07-A2-EC-BA:CMCC 120.196.100.82 img.qfc.cn 12 12 3008 3720 200
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
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
13925057413 00-1F-64-E1-E6-9A:CMCC 120.196.100.55 t3.baidu.com 搜索引擎 69 63 11058 48243 200
13760778710 00-FD-07-A4-7B-08:CMCC 120.196.100.82 2 2 120 120 200
13560436666 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200
13560436666 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200[/collapse]
[/infobox]
[infobox title=”code”]
[collapse title=”FlowBean”]
package com.kami.demo02;
import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
/**
* @version v 1.0
* @Author kamisamak
* @Date 2020/6/17
*/
public class FlowBean implements Writable {
private long upFlow;
private long downFlow;
private long sumFlow;
// 反序列化时，需要反射调用空参构造函数，所以必须有
public FlowBean() {
super();
}
public FlowBean(long upFlow, long downFlow) {
super();
this.upFlow = upFlow;
this.downFlow = downFlow;
this.sumFlow = upFlow + downFlow;
}
public long getSumFlow() {
return sumFlow;
}
public void setSumFlow(long sumFlow) {
this.sumFlow = sumFlow;
}
public long getUpFlow() {
return upFlow;
}
public void setUpFlow(long upFlow) {
this.upFlow = upFlow;
}
public long getDownFlow() {
return downFlow;
}
public void setDownFlow(long downFlow) {
this.downFlow = downFlow;
}
/**
* 序列化方法
*
* @param out
* @throws IOException
*/
@Override
public void write(DataOutput out) throws IOException {
out.writeLong(upFlow);
out.writeLong(downFlow);
out.writeLong(sumFlow);
}
/**
* 反序列化方法
注意反序列化的顺序和序列化的顺序完全一致
*
* @param in
* @throws IOException
*/
@Override
public void readFields(DataInput in) throws IOException {
upFlow = in.readLong();
downFlow = in.readLong();
sumFlow = in.readLong();
}
@Override
public String toString() {
return upFlow + "\t" + downFlow + "\t" + sumFlow;
}
}[/collapse]
[collapse title=”FlowCount”]
package com.kami.demo02;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;
/**
* @version v 1.0
* @Author kamisamak
* @Date 2020/6/17
*/
public class FlowCount {
static class FlowCountMapper extends Mapper<LongWritable, Text, Text, FlowBean> {
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
// 1 将一行内容转成string
String ling = value.toString();
// 2 切分字段
String[] fields = ling.split("\t");
// 3 取出手机号码
String phoneNum = fields[1];
// 4 取出上行流量和下行流量
long upFlow = Long.parseLong(fields[fields.length - 3]);
long downFlow = Long.parseLong(fields[fields.length - 2]);
// 5 写出数据
context.write(new Text(phoneNum), new FlowBean(upFlow, downFlow));
}
}
static class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean> {
@Override
protected void reduce(Text key, Iterable values, Context context)
throws IOException, InterruptedException {
long sum_upFlow = 0;
long sum_downFlow = 0;
// 1 遍历所用bean，将其中的上行流量，下行流量分别累加
for (FlowBean bean : values) {
sum_upFlow += bean.getUpFlow();
sum_downFlow += bean.getDownFlow();
}
// 2 封装对象
FlowBean resultBean = new FlowBean(sum_upFlow, sum_downFlow);
context.write(key, resultBean);
}
}
public static void main(String[] args) throws Exception {
args = new String[]{"data\\d02","output\\d02"};
// 1 获取配置信息，或者job对象实例
Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration);
// 6 指定本程序的jar包所在的本地路径
job.setJarByClass(FlowCount.class);
// 2 指定本业务job要使用的mapper/Reducer业务类
job.setMapperClass(FlowCountMapper.class);
job.setReducerClass(FlowCountReducer.class);
// 3 指定mapper输出数据的kv类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(FlowBean.class);
// 4 指定最终输出的数据的kv类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowBean.class);
// 5 指定job的输入原始文件所在目录
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 7 将job中配置的相关参数，以及job所用的java类所在的jar包， 提交给yarn去运行
boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1);
}
}[/collapse]
[/infobox]
[infobox title=”自定义分区”]
将统计结果按照手机归属地不同省份输出到不同文件中（Partitioner）
[collapse title=”ProvincePartitioner”]
package com.kami.demo02;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;
/**
* @version v 1.0
* @Author kamisamak
* @Date 2020/6/17
*/
public class ProvincePartitioner extends Partitioner<Text, FlowBean> {
@Override
public int getPartition(Text key, FlowBean value, int numPartitions) {
// 1 获取电话号码的前三位
String preNum = key.toString().substring(0, 3);
int partition = 4;
// 2 判断是哪个省
if ("136".equals(preNum)) {
partition = 0;
}else if ("137".equals(preNum)) {
partition = 1;
}else if ("138".equals(preNum)) {
partition = 2;
}else if ("139".equals(preNum)) {
partition = 3;
}
return partition;
}
}
[/collapse]
在Driver中指定job.setPartitionerClass(ProvincePartitioner.class);
[/infobox]
[infobox title=”自定义全排序”]
将统计结果按照总流量倒序排序（全排序）
（1）把程序分两步走，第一步正常统计总流量，第二步再把结果进行排序
（2）context.write(总流量，手机号)
（3）FlowBean实现WritableComparable接口重写compareTo方法
[collapse title=”FlowCountSort”]
package com.kami.demo03;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;
/**
* @version v 1.0
* @Author kamisamak
* @Date 2020/6/17
*/
public class FlowCountSort {
static class FlowCountSortMapper extends Mapper<LongWritable, Text, FlowBean, Text> {
FlowBean bean = new FlowBean();
Text v = new Text();
@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
// 1 拿到的是上一个统计程序输出的结果，已经是各手机号的总流量信息
String line = value.toString();
// 2 截取字符串并获取电话号、上行流量、下行流量
String[] fields = line.split("\t");
String phoneNbr = fields[0];
long upFlow = Long.parseLong(fields[1]);
long downFlow = Long.parseLong(fields[2]);
// 3 封装对象
bean.set(upFlow, downFlow);
v.set(phoneNbr);
// 4 输出
context.write(bean, v);
}
}
static class FlowCountSortReducer extends Reducer<FlowBean, Text, Text, FlowBean> {
@Override
protected void reduce(FlowBean bean, Iterable values, Context context)
throws IOException, InterruptedException {
context.write(values.iterator().next(), bean);
}
}
public static void main(String[] args) throws Exception {
// 1 获取配置信息，或者job对象实例
Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration);
// 6 指定本程序的jar包所在的本地路径
job.setJarByClass(FlowCountSort.class);
// 2 指定本业务job要使用的mapper/Reducer业务类
job.setMapperClass(FlowCountSortMapper.class);
job.setReducerClass(FlowCountSortReducer.class);
// 3 指定mapper输出数据的kv类型
job.setMapOutputKeyClass(FlowBean.class);
job.setMapOutputValueClass(Text.class);
// 4 指定最终输出的数据的kv类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowBean.class);
// 5 指定job的输入原始文件所在目录
FileInputFormat.setInputPaths(job, new Path(args[0]));
Path outPath = new Path(args[1]);
// FileSystem fs = FileSystem.get(configuration);
// if (fs.exists(outPath)) {
// fs.delete(outPath, true);
// }
FileOutputFormat.setOutputPath(job, outPath);
// 7 将job中配置的相关参数，以及job所用的java类所在的jar包， 提交给yarn去运行
boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1);
}
}[/collapse]
[collapse title=”FlowBean”]
package com.kami.demo03;
import org.apache.hadoop.io.WritableComparable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
/**
* @version v 1.0
* @Author kamisamak
* @Date 2020/6/17
*/
public class FlowBean implements WritableComparable {
private long upFlow;
private long downFlow;
private long sumFlow;
// 反序列化时，需要反射调用空参构造函数，所以必须有
public FlowBean() {
super();
}
public FlowBean(long upFlow, long downFlow) {
super();
this.upFlow = upFlow;
this.downFlow = downFlow;
this.sumFlow = upFlow + downFlow;
}
public void set(long upFlow, long downFlow) {
this.upFlow = upFlow;
this.downFlow = downFlow;
this.sumFlow = upFlow + downFlow;
}
public long getSumFlow() {
return sumFlow;
}
public void setSumFlow(long sumFlow) {
this.sumFlow = sumFlow;
}
public long getUpFlow() {
return upFlow;
}
public void setUpFlow(long upFlow) {
this.upFlow = upFlow;
}
public long getDownFlow() {
return downFlow;
}
public void setDownFlow(long downFlow) {
this.downFlow = downFlow;
}
/**
* 序列化方法
* @param out
* @throws IOException
*/
@Override
public void write(DataOutput out) throws IOException {
out.writeLong(upFlow);
out.writeLong(downFlow);
out.writeLong(sumFlow);
}
/**
* 反序列化方法 注意反序列化的顺序和序列化的顺序完全一致
* @param in
* @throws IOException
*/
@Override
public void readFields(DataInput in) throws IOException {
upFlow = in.readLong();
downFlow = in.readLong();
sumFlow = in.readLong();
}
@Override
public String toString() {
return upFlow + "\t" + downFlow + "\t" + sumFlow;
}
@Override
public int compareTo(FlowBean o) {
// 倒序排列，从大到小
return this.sumFlow > o.getSumFlow() ? -1 : 1;
}
}[/collapse]
[/infobox]
[infobox title=”自定义局部排序”]
不同省份输出文件内部排序（部分排序）
要求每个省份手机号输出的文件中按照总流量内部排序。
基于需求3，增加自定义分区类即可。
[collapse title=”FlowSortPartitioner”]
package com.kami.demo03;
/**
* @version v 1.0
* @Author kamisamak
* @Date 2020/6/17
*/
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;
public class FlowSortPartitioner extends Partitioner<FlowBean, Text> {
@Override
public int getPartition(FlowBean key, Text value, int numPartitions) {
int partition = 0;
String preNum = value.toString().substring(0, 3);
if (" ".equals(preNum)) {
partition = 5;
} else {
if ("136".equals(preNum)) {
partition = 1;
} else if ("137".equals(preNum)) {
partition = 2;
} else if ("138".equals(preNum)) {
partition = 3;
} else if ("139".equals(preNum)) {
partition = 4;
}
}
return partition;
}
}[/collapse]
在Driver中添加分区类
[kbd]job.setPartitionerClass(FlowSortPartitioner.class);
job.setNumReduceTasks(5);[/kbd]
[/infobox]
from:https://www.cnblogs.com/frankdeng/p/9256252.html
发表回复