本文主要包含hadoop partitioner,partitioner,kafka partitioner,spark partitioner,hadoop高级编程等服务器相关知识,网友希望可以进行参考
Hadoop之——Partitioner编程，hadooppartitioner
转载请注明出处:http://blog.csdn.net/l1028386804/article/details/46136685
一、Mapper类的实现
static class MyMapper extends Mapper<LongWritable, Text, Text, KpiWritable>{
protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper<LongWritable,Text,Text,KpiWritable>.Context context) throws IOException ,InterruptedException {
final String[] splited = value.toString().split("\t");
final String msisdn = splited[1];
final Text k2 = new Text(msisdn);
final KpiWritable v2 = new KpiWritable(splited[6],splited[7],splited[8],splited[9]);
context.write(k2, v2);
};
}
二、Reducer类的实现
static class MyReducer extends Reducer<Text, KpiWritable, Text, KpiWritable>{
/**
* @param k2 表示整个文件中不同的手机号码
* @param v2s 表示该手机号在不同时段的流量的集合
*/
protected void reduce(Text k2, java.lang.Iterable<KpiWritable> v2s, org.apache.hadoop.mapreduce.Reducer<Text,KpiWritable,Text,KpiWritable>.Context context) throws IOException ,InterruptedException {
long upPackNum = 0L;
long downPackNum = 0L;
long upPayLoad = 0L;
long downPayLoad = 0L;
for (KpiWritable kpiWritable : v2s) {
upPackNum += kpiWritable.upPackNum;
downPackNum += kpiWritable.downPackNum;
upPayLoad += kpiWritable.upPayLoad;
downPayLoad += kpiWritable.downPayLoad;
}
final KpiWritable v3 = new KpiWritable(upPackNum+"", downPackNum+"", upPayLoad+"", downPayLoad+"");
context.write(k2, v3);
};
}
三、Partitioner类的实现
static class KpiPartitioner extends HashPartitioner<Text, KpiWritable>{
@Override
public int getPartition(Text key, KpiWritable value, int numReduceTasks) {
return (key.toString().length()==11)?0:1;
}
}
}
四、自定义Hadoop数据类型
class KpiWritable implements Writable{
long upPackNum;
long downPackNum;
long upPayLoad;
long downPayLoad;
public KpiWritable(){}
public KpiWritable(String upPackNum, String downPackNum, String upPayLoad, String downPayLoad){
this.upPackNum = Long.parseLong(upPackNum);
this.downPackNum = Long.parseLong(downPackNum);
this.upPayLoad = Long.parseLong(upPayLoad);
this.downPayLoad = Long.parseLong(downPayLoad);
}
@Override
public void readFields(DataInput in) throws IOException {
this.upPackNum = in.readLong();
this.downPackNum = in.readLong();
this.upPayLoad = in.readLong();
this.downPayLoad = in.readLong();
}
@Override
public void write(DataOutput out) throws IOException {
out.writeLong(upPackNum);
out.writeLong(downPackNum);
out.writeLong(upPayLoad);
out.writeLong(downPayLoad);
}
@Override
public String toString() {
return upPackNum + "\t" + downPackNum + "\t" + upPayLoad + "\t" + downPayLoad;
}
五、程序入口Main方法
public static void main(String[] args) throws Exception{
Configuration conf = new Configuration();
final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);
final Path outPath = new Path(OUT_PATH);
//如果已经存在输出文件，则先删除已存在的输出文件
if(fileSystem.exists(outPath)){
fileSystem.delete(outPath, true);
}
final Job job = new Job(new Configuration(), KpiApp.class.getSimpleName());
//打成jar包
job.setJarByClass(KpiApp.class);
//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(Text.class);
job.setMapOutputValueClass(KpiWritable.class);
//1.3 指定分区类
job.setPartitionerClass(KpiPartitioner.class);
job.setNumReduceTasks(2);
//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(Text.class);
job.setOutputValueClass(KpiWritable.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);
}
六、完整程序
package com.lyz.hadoop.p;
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.Writable;
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;
/**
* 分区的例子必须打成jar运行
* 用处 1.根据业务需要，产生多个输出文件
* 2.多个reduce任务在运行，提高整体job的运行效率
*/
public class KpiApp {
static final String INPUT_PATH = "hdfs://liuyazhuang:9000/wlan";
static final String OUT_PATH = "hdfs://liuyazhuang:9000/out";
public static void main(String[] args) throws Exception{
Configuration conf = new Configuration();
final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);
final Path outPath = new Path(OUT_PATH);
//如果已经存在输出文件，则先删除已存在的输出文件
if(fileSystem.exists(outPath)){
fileSystem.delete(outPath, true);
}
final Job job = new Job(new Configuration(), KpiApp.class.getSimpleName());
//打成jar包
job.setJarByClass(KpiApp.class);
//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(Text.class);
job.setMapOutputValueClass(KpiWritable.class);
//1.3 指定分区类
job.setPartitionerClass(KpiPartitioner.class);
job.setNumReduceTasks(2);
//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(Text.class);
job.setOutputValueClass(KpiWritable.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 class MyMapper extends Mapper<LongWritable, Text, Text, KpiWritable>{
protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper<LongWritable,Text,Text,KpiWritable>.Context context) throws IOException ,InterruptedException {
final String[] splited = value.toString().split("\t");
final String msisdn = splited[1];
final Text k2 = new Text(msisdn);
final KpiWritable v2 = new KpiWritable(splited[6],splited[7],splited[8],splited[9]);
context.write(k2, v2);
};
}
static class MyReducer extends Reducer<Text, KpiWritable, Text, KpiWritable>{
/**
* @param k2 表示整个文件中不同的手机号码
* @param v2s 表示该手机号在不同时段的流量的集合
*/
protected void reduce(Text k2, java.lang.Iterable<KpiWritable> v2s, org.apache.hadoop.mapreduce.Reducer<Text,KpiWritable,Text,K