package sort;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.net.URI;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
public class SortApp {
static final String INPUT_PATH = "hdfs://hadoop:9000/newinput";
static final String OUT_PATH = "hdfs://hadoop:9000/newoutput";
public static void main(String[] args) throws Exception{
final Configuration configuration = new Configuration();
final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), configuration);
if(fileSystem.exists(new Path(OUT_PATH))){
fileSystem.delete(new Path(OUT_PATH), true);
}
final Job job = new Job(configuration, SortApp.class.getSimpleName());
//1.1 指定输入文件路径
FileInputFormat.setInputPaths(job, INPUT_PATH);
job.setInputFormatClass(TextInputFormat.class);//指定哪个类用来格式化输入文件
//1.2指定自定义的Mapper类
job.setMapperClass(MyMapper.class);
job.setMapOutputKeyClass(NewK2.class);//指定输出<k2,v2>的类型
job.setMapOutputValueClass(LongWritable.class);
//1.3 指定分区类
job.setPartitionerClass(HashPartitioner.class);
job.setNumReduceTasks(1);
//2.2 指定自定义的reduce类
job.setReducerClass(MyReducer.class);
job.setOutputKeyClass(LongWritable.class);//指定输出<k3,v3>的类型
job.setOutputValueClass(LongWritable.class);
//2.3 指定输出到哪里
FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));
job.setOutputFormatClass(TextOutputFormat.class);//设定输出文件的格式化类
job.waitForCompletion(true);//把代码提交给JobTracker执行
}
static class MyMapper extends Mapper<LongWritable, Text, NewK2, LongWritable>{
protected void map(LongWritable key, Text value,Context context) throws IOException ,InterruptedException {
final String[] splited = value.toString().split("\t");
final NewK2 k2 = new NewK2(Long.parseLong(splited[0]), Long.parseLong(splited[1]));
final LongWritable v2 = new LongWritable(Long.parseLong(splited[1]));
context.write(k2, v2);
};
}
static class MyReducer extends Reducer<NewK2, LongWritable, LongWritable, LongWritable>{
protected void reduce(NewK2 k2,Iterable<LongWritable> v2s,Context context) throws IOException ,InterruptedException {
context.write(new LongWritable(k2.first), new LongWritable(k2.second));
};
}
static class NewK2 implements WritableComparable<NewK2>{
Long first;
Long second;
public NewK2(){}
public NewK2(long first, long second){
this.first = first;
this.second = second;
}
@Override
public void readFields(DataInput in) throws IOException {
this.first = in.readLong();
this.second = in.readLong();
}
@Override
public void write(DataOutput out) throws IOException {
out.writeLong(first);
out.writeLong(second);
}
/**
* 当k2进行排序时,会调用该方法.
* 当第一列不同时,升序;当第一列相同时,第二列升序
*/
@Override
public int compareTo(NewK2 o) {
final long minus = this.first - o.first;
if(minus !=0){
return (int)minus;
}
return (int)(this.second - o.second);
}
@Override
public int hashCode() {
return this.first.hashCode()+this.second.hashCode();
}
@Override
public boolean equals(Object obj) {
if(!(obj instanceof NewK2)){
return false;
}
NewK2 oK2 = (NewK2)obj;
return (this.first==oK2.first)&&(this.second==oK2.second);
}
}
}
~~
~~
首先按照第一列升序排列,当第一列相同时,第二列升序排列
3 3
3 2
3 1
2 2
2 1
1 1
结果
1 1
2 1
2 2
3 1
3 2
3 3
~~
CompareTo 这个方法看不懂,为啥返回minus 就升序了 ?