WritableComparable 案例 全排序
一、需求分析
1、需求
按照流量降序排序
2、分析
a、原文件的总流量是value，排序是按照key进行排序的，因此需要把 value -> key
b、自定义Hadoop序列化类，(需要有排序功能) 实现 WritableComparable
二、代码
1、自定义Hadoop序列化，实现WritableComparable
package com.sort;
import org.apache.hadoop.io.WritableComparable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
public class FlowBean implements WritableComparable {
private long upFlow;
private long downFlow;
private long sumFlow;
public FlowBean() {
}
// 排序
public int compareTo(FlowBean bean) {
int result;
if (this.sumFlow > bean.getSumFlow()){
result = -1;
}else if (this.sumFlow < bean.getSumFlow()){
result = 1;
}else {
result = 0;
}
return result;
}
// 序列化
public void write(DataOutput out) throws IOException {
out.writeLong(upFlow);
out.writeLong(downFlow);
out.writeLong(sumFlow);
}
// 反序列化
public void readFields(DataInput in) throws IOException {
this.upFlow = in.readLong();
this.downFlow = in.readLong();
this.sumFlow = in.readLong();
}
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;
}
public long getSumFlow() {
return sumFlow;
}
public void setSumFlow(long sumFlow) {
this.sumFlow = sumFlow;
}
@Override
public String toString() {
return upFlow + "\t" + downFlow + "\t" + sumFlow;
}
}
2、Mapper
package com.sort;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
public class SortMapper extends Mapper {
FlowBean k = new FlowBean();
Text v = new Text();
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
// 13509468723 7335 110349 117684
// 1. 读取一行数据
String line = value.toString();
String[] words = line.split("\t");
// 2.设置 key
k.setUpFlow(Long.parseLong(words[1]));
k.setDownFlow(Long.parseLong(words[2]));
k.setSumFlow(Long.parseLong(words[3]));
// 3.设置 value
v.set(words[0]);
// 4.写入
context.write(k, v);
}
}
注意：需要把FlowBean 作为输出的 Key，Text作为输出的 Value
3、Reducer
package com.sort;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class SortReducer extends Reducer {
@Override
protected void reduce(FlowBean key, Iterable values, Context context) throws IOException, InterruptedException {
// 1. 循环写入
for (Text value : values) {
context.write(value, key);
}
}
}
注意：Values含有一个数据，但为了以防万一，使用for循环遍历
4、Driver
package com.sort;
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;
import java.io.IOException;
public class SortDriver {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
args = new String[]{"E:\\a\\output", "E:\\a\\output1"};
// 1.获取job
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
// 2.设置jar
job.setJarByClass(SortDriver.class);
// 3.关联mapper和reducer
job.setMapperClass(SortMapper.class);
job.setReducerClass(SortReducer.class);
// 4.设置mapper输出的k v
job.setMapOutputKeyClass(FlowBean.class);
job.setMapOutputValueClass(Text.class);
// 5.设置整体输出的 k, v
job.setOutputKeyClass(Text.class);
job.setOutputKeyClass(FlowBean.class);
// 6.设置输入输出路径
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 7.提交job
boolean wait = job.waitForCompletion(true);
System.exit(wait? 0: 1);
}
}
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