自定义排序及Hadoop序列化
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>
static final String INPUT_PATH = "hdfs://chaoren:9000/input";
static final String OUT_PATH = "hdfs://chaoren:9000/out";
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);
// 指定哪个类用来格式化输入文件
job.setInputFormatClass(TextInputFormat.class);
// 1.2指定自定义的Mapper类
job.setMapperClass(MyMapper.class);
// 指定输出<k2,v2>的类型
job.setMapOutputKeyClass(NewK2.class);
job.setMapOutputValueClass(LongWritable.class);
// 1.3 指定分区类
job.setPartitionerClass(HashPartitioner.class);
job.setNumReduceTasks(1);
// 1.4 TODO 排序、分区
// 1.5 TODO （可选）合并
// 2.2 指定自定义的reduce类
job.setReducerClass(MyReducer.class);
// 指定输出<k3,v3>的类型
job.setOutputKeyClass(LongWritable.class);
job.setOutputValueClass(LongWritable.class);
// 2.3 指定输出到哪里
FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));
// 设定输出文件的格式化类
job.setOutputFormatClass(TextOutputFormat.class);
// 把代码提交给JobTracker执行
job.waitForCompletion(true);
}
static>
Mapper<LongWritable, Text, NewK2, LongWritable> {
protected void map(
LongWritable key,
Text value,
org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, NewK2, LongWritable>.Context context)
throws java.io.IOException, InterruptedException {
final String[] splited = value.toString().split("\t");
final NewK2 k2 = new NewK2(Long.parseLong(splited),
Long.parseLong(splited));
final LongWritable v2 = new LongWritable(Long.parseLong(splited));
context.write(k2, v2);
};
}
static>
Reducer<NewK2, LongWritable, LongWritable, LongWritable> {
protected void reduce(
NewK2 k2,
java.lang.Iterable<LongWritable> v2s,
org.apache.hadoop.mapreduce.Reducer<NewK2, LongWritable, LongWritable, LongWritable>.Context context)
throws java.io.IOException, InterruptedException {
context.write(new LongWritable(k2.first), new LongWritable(
k2.second));
};
}
/**
* 问：为什么实现该类？ 答：因为原来的v2不能参与排序，把原来的k2和v2封装到一个类中，作为新的k2
*
*/
// WritableComparable：Hadoop的序列化
static>
Long first;
Long second;
public NewK2() {
}
public NewK2(long first, long second) {
this.first = first;
this.second = second;
}
public void readFields(DataInput in) throws IOException {
this.first = in.readLong();
this.second = in.readLong();
}
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
*/
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);
}
}
}
页:
[1]