尚硅谷大数据技术之Hadoop（MapReduce）（新）第2章 Hadoop序列化
3.3.4 WritableComparable排序
1．排序的分类
2．自定义排序WritableComparable
（1）原理分析
bean对象做为key传输，需要实现WritableComparable接口重写compareTo方法，就可以实现排序。
@Override
public int compareTo(FlowBean o) {
int result;
// 按照总流量大小，倒序排列
if (sumFlow > bean.getSumFlow()) {
result = -1;
}else if (sumFlow < bean.getSumFlow()) {
result = 1;
}else {
result = 0;
}
return result;
}
3.3.5 WritableComparable排序案例实操（全排序）
1．需求
根据案例2.3产生的结果再次对总流量进行排序。
（1）输入数据
原始数据
第一次处理后的数据
（2）期望输出数据
13509468723 7335 110349 117684
13736230513 2481 24681 27162
13956435636 132 1512 1644
13846544121 264 0 264
。。。 。。。
2．需求分析
3．代码实现
（1）FlowBean对象在在需求1基础上增加了比较功能
package com.atguigu.mapreduce.sort;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.WritableComparable;
public class FlowBean implements WritableComparable<FlowBean> {
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) {
int result;
// 按照总流量大小，倒序排列
if (sumFlow > bean.getSumFlow()) {
result = -1;
}else if (sumFlow < bean.getSumFlow()) {
result = 1;
}else {
result = 0;
}
return result;
}
}
（2）编写Mapper类
package com.atguigu.mapreduce.sort;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public 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 获取一行
String line = value.toString();
// 2 截取
String[] fields = line.split("\t");
// 3 封装对象
String phoneNbr = fields[0];
long upFlow = Long.parseLong(fields[1]);
long downFlow = Long.parseLong(fields[2]);
bean.set(upFlow, downFlow);
v.set(phoneNbr);
// 4 输出
context.write(bean, v);
}
}
（3）编写Reducer类
package com.atguigu.mapreduce.sort;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class FlowCountSortReducer extends Reducer<FlowBean, Text, Text, FlowBean>{
@Override
protected void reduce(FlowBean key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
// 循环输出，避免总流量相同情况
for (Text text : values) {
context.write(text, key);
}
}
}
（4）编写Driver类
package com.atguigu.mapreduce.sort;
import java.io.IOException;
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 FlowCountSortDriver {
public static void main(String[] args) throws ClassNotFoundException, IOException, InterruptedException {
// 输入输出路径需要根据自己电脑上实际的输入输出路径设置
args = new String[]{"e:/output1","e:/output2"};
// 1 获取配置信息，或者job对象实例
Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration);
// 2 指定本程序的jar包所在的本地路径
job.setJarByClass(FlowCountSortDriver.class);
// 3 指定本业务job要使用的mapper/Reducer业务类
job.setMapperClass(FlowCountSortMapper.class);
job.setReducerClass(FlowCountSortReducer.class);
// 4 指定mapper输出数据的kv类型
job.setMapOutputKeyClass(FlowBean.class);
job.setMapOutputValueClass(Text.class);
// 5 指定最终输出的数据的kv类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowBean.class);
// 6 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);
}
}
