将统计结果按照总流量倒序排序（全排序）
1、数据
https://www.jianshu.com/p/bbebc7b959a8
2、分析
（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
@Override
public int compareTo(FlowBean o) {
// 倒序排列，从大到小
return this.sumFlow > o.getSumFlow() ? -1 : 1;
}
3、FlowBean对象在在需求1基础上增加了比较功能
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) {
// 倒序排列，从大到小
return this.sumFlow > o.getSumFlow() ? -1 : 1;
}
}
4、Map方法优化为一个对象，reduce方法则直接输出结果即可，驱动函数根据输入输出重写配置即可。
import java.io.IOException;
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;
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<Text> 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);
}
}
5、将程序打成jar包，然后拷贝到hadoop集群中。
6、启动hadoop集群
7、执行flowcountsort程序
[victor@hadoop102 software]$ hadoop jar flowcountsort.jar com.victor.mr.sort.FlowCountSort /user/victor/flowcount/output /user/victor/flowcount/output_sort
8、查看结果
[victor@hadoop102 software]$ hadoop fs -cat /user/victor/flowcount/output_sort/part-r-00000
13502468823 7335 110349 117684
13925057413 11058 48243 59301
13726238888 2481 24681 27162
13726230503 2481 24681 27162
18320173382 9531 2412 11943
