Hadoop里面的MapReduce编程模型
Hadoop里面的MapReduce编程模型，非常灵活，大部分环节我们都可以重写它的API，来灵活定制我们自己的一些特殊需求。
今天散仙要说的这个分区函数Partitioner，也是一样如此，下面我们先来看下Partitioner的作用：
对map端输出的数据key作一个散列，使数据能够均匀分布在各个reduce上进行后续操作，避免产生热点区。
Hadoop默认使用的分区函数是Hash Partitioner，源码如下：
/**
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.hadoop.mapreduce.lib.partition;
import org.apache.hadoop.mapreduce.Partitioner;
/** Partition keys by their {@link Object#hashCode()}. */
public class HashPartitioner<K, V> extends Partitioner<K, V> {
/** Use {@link Object#hashCode()} to partition. */
public int getPartition(K key, V value,
int numReduceTasks) {
//默认使用key的hash值与上int的最大值，避免出现数据溢出 的情况
return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;
}
} /**
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.hadoop.mapreduce.lib.partition;
import org.apache.hadoop.mapreduce.Partitioner;
/** Partition keys by their {@link Object#hashCode()}. */
public class HashPartitioner<K, V> extends Partitioner<K, V> {
/** Use {@link Object#hashCode()} to partition. */
public int getPartition(K key, V value,
int numReduceTasks) {
//默认使用key的hash值与上int的最大值，避免出现数据溢出 的情况
return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;
}
}
大部分情况下，我们都会使用默认的分区函数，但有时我们又有一些，特殊的需求，而需要定制Partition来完成我们的业务，案例如下：
对如下数据，按字符串的长度分区，长度为1的放在一个，2的一个，3的各一个。
河南省;1 河南;2 中国;3 中国人;4 大;1 小;3 中;11
河南省;1 河南;2 中国;3 中国人;4 大;1 小;3 中;11
这时候，我们使用默认的分区函数，就不行了，所以需要我们定制自己的Partition，首先分析下，我们需要3个分区输出，所以在设置reduce的个数时，一定要设置为3，其次在partition里，进行分区时，要根据长度具体分区，而不是根据字符串的hash码来分区。核心代码如下：
/**
* Partitioner
*
*
* */
public static class PPartition extends Partitioner<Text, Text>{
@Override
public int getPartition(Text arg0, Text arg1, int arg2) {
/**
* 自定义分区，实现长度不同的字符串，分到不同的reduce里面
*
* 现在只有3个长度的字符串，所以可以把reduce的个数设置为3
* 有几个分区，就设置为几
* */
String key=arg0.toString();
if(key.length()==1){
return 1%arg2;
}else if(key.length()==2){
return 2%arg2;
}else if(key.length()==3){
return 3%arg2;
}
return 0;
}
} /**
* Partitioner
*
*
* */
public static class PPartition extends Partitioner<Text, Text>{
@Override
public int getPartition(Text arg0, Text arg1, int arg2) {
/**
* 自定义分区，实现长度不同的字符串，分到不同的reduce里面
*
* 现在只有3个长度的字符串，所以可以把reduce的个数设置为3
* 有几个分区，就设置为几
* */
String key=arg0.toString();
if(key.length()==1){
return 1%arg2;
}else if(key.length()==2){
return 2%arg2;
}else if(key.length()==3){
return 3%arg2;
}
return 0;
}
}
全部代码如下：
package com.partition.test;
import java.io.IOException;
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.mapred.JobConf;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import com.qin.operadb.PersonRecoder;
import com.qin.operadb.ReadMapDB;
/**
* @author qindongliang
*
* 大数据交流群：376932160
*
*
* **/
public class MyTestPartition {
/**
* map任务
*
* */
public static class PMapper extends Mapper<LongWritable, Text, Text, Text>{
@Override
protected void map(LongWritable key, Text value,Context context)
throws IOException, InterruptedException {
// System.out.println("进map了");
//mos.write(namedOutput, key, value);
String ss[]=value.toString().split(";");
context.write(new Text(ss[0]), new Text(ss[1]));
}
}
/**
* Partitioner
*
*
* */
public static class PPartition extends Partitioner<Text, Text>{
@Override
public int getPartition(Text arg0, Text arg1, int arg2) {
/**
* 自定义分区，实现长度不同的字符串，分到不同的reduce里面
*
* 现在只有3个长度的字符串，所以可以把reduce的个数设置为3
* 有几个分区，就设置为几
* */
String key=arg0.toString();
if(key.length()==1){
return 1%arg2;
}else if(key.length()==2){
return 2%arg2;
}else if(key.length()==3){
return 3%arg2;
}
return 0;
}
}
/***
* Reduce任务
*
* **/
public static class PReduce extends Reducer<Text, Text, Text, Text>{
@Override
protected void reduce(Text arg0, Iterable<Text> arg1, Context arg2)
throws IOException, InterruptedException {
String key=arg0.toString().split(",")[0];
System.out.println("key==> "+key);
for(Text t:arg1){
//System.out.println("Reduce: "+arg0.toString()+" "+t.toString());
arg2.write(arg0, t);
}
}
}
public static void main(String[] args) throws Exception{
JobConf conf=new JobConf(ReadMapDB.class);
//Configuration conf=new Configuration();
conf.set("mapred.job.tracker","192.168.75.130:9001");
//读取person中的数据字段
conf.setJar("tt.jar");
//注意这行代码放在最前面，进行初始化，否则会报
/**Job任务**/
Job job=new Job(conf, "testpartion");
job.setJarByClass(MyTestPartition.class);
System.out.println("模式： "+conf.get("mapred.job.tracker"));;
// job.setCombinerClass(PCombine.class);
job.setPartitionerClass(PPartition.class);
job.setNumReduceTasks(3);//设置为3
job.setMapperClass(PMapper.class);
// MultipleOutputs.addNamedOutput(job, "hebei", TextOutputFormat.class, Text.class, Text.class);
// MultipleOutputs.addNamedOutput(job, "henan", TextOutputFormat.class, Text.class, Text.class);
job.setReducerClass(PReduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
String path="hdfs://192.168.75.130:9000/root/outputdb";
FileSystem fs=FileSystem.get(conf);
Path p=new Path(path);
if(fs.exists(p)){
fs.delete(p, true);
System.out.println("输出路径存在，已删除！");
}
FileInputFormat.setInputPaths(job, "hdfs://192.168.75.130:9000/root/input");
FileOutputFormat.setOutputPath(job,p );
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
} package com.partition.test;
import java.io.IOException;
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.mapred.JobConf;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import com.qin.operadb.PersonRecoder;
import com.qin.operadb.ReadMapDB;
/**
* @author qindongliang
*
* 大数据交流群：376932160
*
*
* **/
public class MyTestPartition {
/**
* map任务
*
* */
public static class PMapper extends Mapper<LongWritable, Text, Text, Text>{
@Override
protected void map(LongWritable key, Text value,Context context)
throws IOException, InterruptedException {
// System.out.println("进map了");
//mos.write(namedOutput, key, value);
String ss[]=value.toString().split(";");
context.write(new Text(ss[0]), new Text(ss[1]));
}
}
/**
* Partitioner
*
*
* */
public static class PPartition extends Partitioner<Text, Text>{
@Override
public int getPartition(Text arg0, Text arg1, int arg2) {
/**
* 自定义分区，实现长度不同的字符串，分到不同的reduce里面
*
* 现在只有3个长度的字符串，所以可以把reduce的个数设置为3
* 有几个分区，就设置为几
* */
String key=arg0.toString();
if(key.length()==1){
return 1%arg2;
}else if(key.length()==2){
return 2%arg2;
}else if(key.length()==3){
return 3%arg2;
}
return 0;
}
}
/***
* Reduce任务
*
* **/
public static class PReduce extends Reducer<Text, Text, Text, Text>{
@Override
protected void reduce(Text arg0, Iterable<Text> arg1, Context arg2)
throws IOException, InterruptedException {
String key=arg0.toString().split(",")[0];
System.out.println("key==> "+key);
for(Text t:arg1){
//System.out.println("Reduce: "+arg0.toString()+" "+t.toString());
arg2.write(arg0, t);
}
}
}
public static void main(String[] args) throws Exception{
JobConf conf=new JobConf(ReadMapDB.class);
//Configuration conf=new Configuration();
conf.set("mapred.job.tracker","192.168.75.130:9001");
//读取person中的数据字段
conf.setJar("tt.jar");
//注意这行代码放在最前面，进行初始化，否则会报
/**Job任务**/
Job job=new Job(conf, "testpartion");
job.setJarByClass(MyTestPartition.class);
System.out.println("模式： "+conf.get("mapred.job.tracker"));;
// job.setCombinerClass(PCombine.class);
job.setPartitionerClass(PPartition.class);
job.setNumReduceTasks(3);//设置为3
job.setMapperClass(PMapper.class);
// MultipleOutputs.addNamedOutput(job, "hebei", TextOutputFormat.class, Text.class, Text.class);
// MultipleOutputs.addNamedOutput(job, "henan", TextOutputFormat.class, Text.class, Text.class);
job.setReducerClass(PReduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
String path="hdfs://192.168.75.130:9000/root/outputdb";
FileSystem fs=FileSystem.get(conf);
Path p=new Path(path);
if(fs.exists(p)){
fs.delete(p, true);
System.out.println("输出路径存在，已删除！");
}
FileInputFormat.setInputPaths(job, "hdfs://192.168.75.130:9000/root/input");
FileOutputFormat.setOutputPath(job,p );
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
运行情况如下：
模式： 192.168.75.130:9001 输出路径存在，已删除！ WARN - JobClient.copyAndConfigureFiles(746) | Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same. INFO - FileInputFormat.listStatus(237) | Total input paths to process : 1 WARN - NativeCodeLoader.<clinit>(52) | Unable to load native-hadoop library for your platform... using builtin-java classes where applicable WARN - LoadSnappy.<clinit>(46) | Snappy native library not loaded INFO - JobClient.monitorAndPrintJob(1380) | Running job: job_201404101853_0005 INFO - JobClient.monitorAndPrintJob(1393) | map 0% reduce 0% INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 0% INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 11% INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 22% INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 55% INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 100% INFO - JobClient.monitorAndPrintJob(1448) | Job complete: job_201404101853_0005 INFO - Counters.log(585) | Counters: 29 INFO - Counters.log(587) | Job Counters INFO - Counters.log(589) | Launched reduce tasks=3 INFO - Counters.log(589) | SLOTS_MILLIS_MAPS=7422 INFO - Counters.log(589) | Total time spent by all reduces waiting after reserving slots (ms)=0 INFO - Counters.log(589) | Total time spent by all maps waiting after reserving slots (ms)=0 INFO - Counters.log(589) | Launched map tasks=1 INFO - Counters.log(589) | Data-local map tasks=1 INFO - Counters.log(589) | SLOTS_MILLIS_REDUCES=30036 INFO - Counters.log(587) | File Output Format Counters INFO - Counters.log(589) | Bytes Written=61 INFO - Counters.log(587) | FileSystemCounters INFO - Counters.log(589) | FILE_BYTES_READ=93 INFO - Counters.log(589) | HDFS_BYTES_READ=179 INFO - Counters.log(589) | FILE_BYTES_WRITTEN=218396 INFO - Counters.log(589) | HDFS_BYTES_WRITTEN=61 INFO - Counters.log(587) | File Input Format Counters INFO - Counters.log(589) | Bytes Read=68 INFO - Counters.log(587) | Map-Reduce Framework INFO - Counters.log(589) | Map output materialized bytes=93 INFO - Counters.log(589) | Map input records=7 INFO - Counters.log(589) | Reduce shuffle bytes=93 INFO - Counters.log(589) | Spilled Records=14 INFO - Counters.log(589) | Map output bytes=61 INFO - Counters.log(589) | Total committed heap usage (bytes)=207491072 INFO - Counters.log(589) | CPU time spent (ms)=2650 INFO - Counters.log(589) | Combine input records=0 INFO - Counters.log(589) | SPLIT_RAW_BYTES=111 INFO - Counters.log(589) | Reduce input records=7 INFO - Counters.log(589) | Reduce input groups=7 INFO - Counters.log(589) | Combine output records=0 INFO - Counters.log(589) | Physical memory (bytes) snapshot=422174720 INFO - Counters.log(589) | Reduce output records=7 INFO - Counters.log(589) | Virtual memory (bytes) snapshot=2935713792 INFO - Counters.log(589) | Map output records=7
模式： 192.168.75.130:9001 输出路径存在，已删除！ WARN - JobClient.copyAndConfigureFiles(746) | Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same. INFO - FileInputFormat.listStatus(237) | Total input paths to process : 1 WARN - NativeCodeLoader.<clinit>(52) | Unable to load native-hadoop library for your platform... using builtin-java classes where applicable WARN - LoadSnappy.<clinit>(46) | Snappy native library not loaded INFO - JobClient.monitorAndPrintJob(1380) | Running job: job_201404101853_0005 INFO - JobClient.monitorAndPrintJob(1393) | map 0% reduce 0% INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 0% INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 11% INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 22% INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 55% INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 100% INFO - JobClient.monitorAndPrintJob(1448) | Job complete: job_201404101853_0005 INFO - Counters.log(585) | Counters: 29 INFO - Counters.log(587) | Job Counters INFO - Counters.log(589) | Launched reduce tasks=3 INFO - Counters.log(589) | SLOTS_MILLIS_MAPS=7422 INFO - Counters.log(589) | Total time spent by all reduces waiting after reserving slots (ms)=0 INFO - Counters.log(589) | Total time spent by all maps waiting after reserving slots (ms)=0 INFO - Counters.log(589) | Launched map tasks=1 INFO - Counters.log(589) | Data-local map tasks=1 INFO - Counters.log(589) | SLOTS_MILLIS_REDUCES=30036 INFO - Counters.log(587) | File Output Format Counters INFO - Counters.log(589) | Bytes Written=61 INFO - Counters.log(587) | FileSystemCounters INFO - Counters.log(589) | FILE_BYTES_READ=93 INFO - Counters.log(589) | HDFS_BYTES_READ=179 INFO - Counters.log(589) | FILE_BYTES_WRITTEN=218396 INFO - Counters.log(589) | HDFS_BYTES_WRITTEN=61 INFO - Counters.log(587) | File Input Format Counters INFO - Counters.log(589) | Bytes Read=68 INFO - Counters.log(587) | Map-Reduce Framework INFO - Counters.log(589) | Map output materialized bytes=93 INFO - Counters.log(589) | Map input records=7 INFO - Counters.log(589) | Reduce shuffle bytes=93 INFO - Counters.log(589) | Spilled Records=14 INFO - Counters.log(589) | Map output bytes=61 INFO - Counters.log(589) | Total committed heap usage (bytes)=207491072 INFO - Counters.log(589) | CPU time spent (ms)=2650 INFO - Counters.log(589) | Combine input records=0 INFO - Counters.log(589) | SPLIT_RAW_BYTES=111 INFO - Counters.log(589) | Reduce input records=7 INFO - Counters.log(589) | Reduce input groups=7 INFO - Counters.log(589) | Combine output records=0 INFO - Counters.log(589) | Physical memory (bytes) snapshot=422174720 INFO - Counters.log(589) | Reduce output records=7 INFO - Counters.log(589) | Virtual memory (bytes) snapshot=2935713792 INFO - Counters.log(589) | Map output records=7
运行后的结果文件如下： 
其中，part-r-000000里面的数据
中国人 4 河南省 1
中国人 4 河南省 1
其中，part-r-000001里面的数据
中 11 大 1 小 3
中 11 大 1 小 3
其中，part-r-000002里面的数据
中国 3 河南 2
中国 3 河南 2
至此，我们使用自定义的分区策略完美的实现了，数据分区了。
总结：引用一段话
(Partition)分区出现的必要性，如何使用Hadoop产生一个全局排序的文件？最简单的方法就是使用一个分区，但是该方法在处理大型文件时效率极低，因为一台机器必须处理所有输出文件，从而完全丧失了MapReduce所提供的并行架构的优势。事实上我们可以这样做，首先创建一系列排好序的文件；其次，串联这些文件（类似于归并排序）；最后得到一个全局有序的文件。主要的思路是使用一个partitioner来描述全局排序的输出。比方说我们有1000个1-10000的数据，跑10个ruduce任务， 如果我们运行进行partition的时候，能够将在1-1000中数据的分配到第一个reduce中，1001-2000的数据分配到第二个reduce中，以此类推。即第n个reduce所分配到的数据全部大于第n-1个reduce中的数据。这样，每个reduce出来之后都是有序的了，我们只要cat所有的输出文件，变成一个大的文件，就都是有序的了
基本思路就是这样，但是现在有一个问题，就是数据的区间如何划分，在数据量大，还有我们并不清楚数据分布的情况下。一个比较简单的方法就是采样，假如有一亿的数据，我们可以对数据进行采样，如取10000个数据采样，然后对采样数据分区间。在Hadoop中，patition我们可以用TotalOrderPartitioner替换默认的分区。然后将采样的结果传给他，就可以实现我们想要的分区。在采样时，我们可以使用hadoop的几种采样工具，RandomSampler,InputSampler,IntervalSampler。
这样，我们就可以对利用分布式文件系统进行大数据量的排序了，我们也可以重写Partitioner类中的compare函数，来定义比较的规则，从而可以实现字符串或其他非数字类型的排序，也可以实现二次排序乃至多次排序。
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