先简单介绍一下partitioner 和 combiner 

Partitioner类

• 用于在Map端对key进行分区
  • 默认使用的是HashPartitioner
    • 获取key的哈希值
    • 使用key的哈希值对Reduce任务数求模
  • 决定每条记录应该送到哪个Reducer处理
• 自定义Partitioner
  • 继承抽象类Partitioner，重写getPartition方法
  • job.setPartitionerClass(MyPartitioner.class)

Combiner类

• Combiner相当于本地化的Reduce操作
  • 在shuffle之前进行本地聚合
  • 用于性能优化，可选项
  • 输入和输出类型一致
• Reducer可以被用作Combiner的条件
  • 符合交换律和结合律
• 实现Combiner
  • job.setCombinerClass(WCReducer.class)

我们进入案例来看这两个知识点

一 案例需求

一个存放电话号码的文本，我们需要136 137，138 139和其它开头的号码分开存放统计其每个数字开头的号码个数

 二 PhoneMapper 类

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;import java.io.IOException;public class PhoneMapper extends Mapper<LongWritable, Text,Text, IntWritable> {@Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {String phone = value.toString();Text text = new Text(phone);IntWritable intWritable = new IntWritable(1);context.write(text,intWritable);}
}

三 PhoneReducer 类

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;public class PhoneReducer extends Reducer<Text, IntWritable,Text,IntWritable> {@Overrideprotected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {int count = 0;for (IntWritable intWritable : values){count += intWritable.get();}context.write(key, new IntWritable(count));}
}

四 PhonePartitioner 类

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;public class PhonePartitioner extends Partitioner<Text, IntWritable> {@Overridepublic int getPartition(Text text, IntWritable intWritable, int i) {//136,137   138,139     其它号码放一起if("136".equals(text.toString().substring(0,3)) || "137".equals(text.toString().substring(0,3))){return 0;}else if ("138".equals(text.toString().substring(0,3)) || "139".equals(text.toString().substring(0,3))){return 1;}else {return 2;}}
}

五 PhoneCombiner 类

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;public class PhoneCombiner extends Reducer<Text, IntWritable,Text,IntWritable> {@Overrideprotected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {int count = 0;for(IntWritable intWritable : values){count += intWritable.get();}context.write(new Text(key.toString().substring(0,3)), new IntWritable(count));}
}

六 PhoneDriver 类

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 PhoneDriver {public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {Configuration conf = new Configuration();Job job = Job.getInstance(conf);job.setJarByClass(PhoneDriver.class);job.setMapperClass(PhoneMapper.class);job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(IntWritable.class);job.setCombinerClass(PhoneCombiner.class);job.setPartitionerClass(PhonePartitioner.class);job.setNumReduceTasks(3);job.setReducerClass(PhoneReducer.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(IntWritable.class);Path inPath = new Path("in/demo4/phone.csv");FileInputFormat.setInputPaths(job, inPath);Path outPath = new Path("out/out6");FileSystem fs = FileSystem.get(outPath.toUri(),conf);if (fs.exists(outPath)){fs.delete(outPath, true);}FileOutputFormat.setOutputPath(job, outPath);job.waitForCompletion(true);}
}

七 小结

该案例新知识点在于分区（partition）和结合（combine）

这次代码的流程是 

driver——》mapper——》partitioner——》combiner——》reducer

map 每处理一条数据都经过一次 partitioner 分区然后存到环形缓存区中去，然后map再去处理下一条数据以此反复直至所有数据处理完成

combine 则是将环形缓存区溢出的缓存文件合并，并提前进行一次排序和计算（对每个溢出文件计算后再合并）最后将一个大的文件给到 reducer，这样大大减少了 reducer 的计算负担