MapReduce分区

[1. MapReduce分区](#1. MapReduce分区)
- [1.1 哈希分区](#1.1 哈希分区)
- [1.2 自定义分区](#1.2 自定义分区)
[2. 成绩分组](#2. 成绩分组)
- [2.1 Map](#2.1 Map)
- [2.2 Partition](#2.2 Partition)
- [2.3 Reduce](#2.3 Reduce)
[3. 代码和结果](#3. 代码和结果)
- [3.1 pom.xml中依赖配置](#3.1 pom.xml中依赖配置)
- [3.2 工具类util](#3.2 工具类util)
- [3.3 GroupScores](#3.3 GroupScores)
- [3.4 结果](#3.4 结果)
参考

本文引用的Apache Hadoop源代码基于Apache许可证 2.0，详情请参阅 Apache许可证2.0。

1. MapReduce分区

在默认情况下，MapReduce认为Reduce函数处理的是数据汇总操作，因此其针对的必定是一个Map函数清洗处理后的相对规模较小的数据集，且需要对整个集群中所有Map的中间输出结果进行统一处理，因此只会启动一个Reduce计算节点来处理。

这与某些特殊的应用需求并不相匹配。在某些特定的时刻，开发人员希望启动更多的Reduce并发节点来优化最终结果统计的性能，减小数据处理的延迟，这通过简单的设置代码即可完成；而在更定制化的环境中，开发人员希望符合特定规则的Map中间输出结果交由特定的Reduce节点处理，这就需要使用MapReduce分区，开发人员还可以提供自定义的分区规则。

如果有很多个Reduce任务，每个Map任务就会针对输出进行分区，即为每个Reduce任务建立一个分区。每个分区中有很多键，但每个键对应的键值对记录都在同一分区中。如果不给定自定义的分区规则，则Hadoop使用默认的哈希函数来分区，效率较高。

1.1 哈希分区

下面是Apache Hadoop中默认哈希分区的源代码。在这个分区规则中，选择Reduce节点的计算方法是(key.hashCode() & Integer.MAX_VALUE) % numReduceTasks。

java 复制代码

/**
 * 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.classification.InterfaceAudience;
import org.apache.hadoop.classification.InterfaceStability;
import org.apache.hadoop.mapreduce.Partitioner;

/** Partition keys by their {@link Object#hashCode()}. */
@InterfaceAudience.Public
@InterfaceStability.Stable
public class HashPartitioner<K, V> extends Partitioner<K, V> {

  /** Use {@link Object#hashCode()} to partition. */
  public int getPartition(K key, V value,
                          int numReduceTasks) {
    return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;
  }

}

1.2 自定义分区

如果希望实现自定义分区，需要继承Hadoop提供的分区类org.apache.hadoop.mapreduce.Partitioner，下面是该类的声明。继承该分区类的自定义分区类需要实现public abstract int getPartition(KEY key, VALUE value, int numPartitions)，该函数的作用是设置Map中间处理结果的分区规则，其中numPartitions是总分区的个数。此外，在自定义分区类时，通过函数返回了多少个分区，那么在MapReduce任务调度代码中需要设置Job.setNumReduceTasks(自定义分区个数)。

java 复制代码

/**
 * 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;

import org.apache.hadoop.classification.InterfaceAudience;
import org.apache.hadoop.classification.InterfaceStability;
import org.apache.hadoop.conf.Configurable;

/** 
 * Partitions the key space.
 * 
 * <p><code>Partitioner</code> controls the partitioning of the keys of the 
 * intermediate map-outputs. The key (or a subset of the key) is used to derive
 * the partition, typically by a hash function. The total number of partitions
 * is the same as the number of reduce tasks for the job. Hence this controls
 * which of the <code>m</code> reduce tasks the intermediate key (and hence the 
 * record) is sent for reduction.</p>
 *
 * <p>Note: A <code>Partitioner</code> is created only when there are multiple
 * reducers.</p>
 *
 * <p>Note: If you require your Partitioner class to obtain the Job's
 * configuration object, implement the {@link Configurable} interface.</p>
 * 
 * @see Reducer
 */
@InterfaceAudience.Public
@InterfaceStability.Stable
public abstract class Partitioner<KEY, VALUE> {
  
  /** 
   * Get the partition number for a given key (hence record) given the total 
   * number of partitions i.e. number of reduce-tasks for the job.
   *   
   * <p>Typically a hash function on a all or a subset of the key.</p>
   *
   * @param key the key to be partioned.
   * @param value the entry value.
   * @param numPartitions the total number of partitions.
   * @return the partition number for the <code>key</code>.
   */
  public abstract int getPartition(KEY key, VALUE value, int numPartitions);
  
}

2. 成绩分组

成绩文本如下，第一列为人名，第二列为成绩。目标是将成绩分为5段，分别为 [ 0 , 20 ) [0, 20) [0,20)， [ 20 , 40 ) [20, 40) [20,40)， [ 40 , 60 ) [40, 60) [40,60)， [ 60 , 80 ) [60, 80) [60,80)， [ 80 , 100 ] [80, 100] [80,100]。

txt 复制代码

2.1 Map

假设这个MapReduce作业使用了1个Map，Map的作用是从文本获取<人名，成绩>键值对，同时保证成绩在有效范围内。

2.2 Partition

根据成绩进行分区，其中1的范围是 [ 20 , 40 ) [20, 40) [20,40)，3的范围是 [ 60 , 80 ) [60, 80) [60,80)，2的范围是 [ 40 , 60 ) [40, 60) [40,60)，4的范围是 [ 80 , 100 ] [80, 100] [80,100]，0的范围是 [ 0 , 20 ) [0, 20) [0,20)。

2.3 Reduce

reduce不进行任何操作，直接将分区结果排序后写入5个文件中。

3. 代码和结果

3.1 pom.xml中依赖配置

xml 复制代码

  <dependencies>
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.11</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-common</artifactId>
      <version>3.3.6</version>
      <exclusions>
        <exclusion>
          <groupId>org.slf4j</groupId>
          <artifactId>slf4j-log4j12</artifactId>
        </exclusion>
      </exclusions>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-core</artifactId>
      <version>3.3.6</version>
      <type>pom</type>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-jobclient</artifactId>
      <version>3.3.6</version>
    </dependency>
  </dependencies>

3.2 工具类util

java 复制代码

import java.net.URI;
import java.util.regex.Matcher;
import java.util.regex.Pattern;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;


public class util {
    public static FileSystem getFileSystem(String uri, Configuration conf) throws Exception {
        URI add = new URI(uri);
        return FileSystem.get(add, conf);
    }

    public static void removeALL(String uri, Configuration conf, String path) throws Exception {
        FileSystem fs = getFileSystem(uri, conf);
        if (fs.exists(new Path(path))) {
            boolean isDeleted = fs.delete(new Path(path), true);
            System.out.println("Delete Output Folder? " + isDeleted);
        }
    }

    public static void  showResult(String uri, Configuration conf, String path) throws Exception {
        FileSystem fs = getFileSystem(uri, conf);
        String regex = "part-r-";
        Pattern pattern = Pattern.compile(regex);

        if (fs.exists(new Path(path))) {
            FileStatus[] files = fs.listStatus(new Path(path));
            for (FileStatus file : files) {
                Matcher matcher = pattern.matcher(file.getPath().toString());
                if (matcher.find()) {
                	System.out.println(file.getPath() + ":");
                    FSDataInputStream openStream = fs.open(file.getPath());
                    IOUtils.copyBytes(openStream, System.out, 1024);
                    openStream.close();
                }
            }
        }
    }
}

3.3 GroupScores

java 复制代码

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
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.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class App {
    public static class MyMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
        @Override
        protected void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            String[] splitStr = value.toString().split(" ");
            Text keyOut = new Text(splitStr[0]);
            int grade = Integer.parseInt(splitStr[1]);
            if (grade >= 0 && grade <= 100) {
                IntWritable valueOut = new IntWritable(grade);
                context.write(keyOut, valueOut);
            }
        }
    }

    public static class MyPartitioner extends Partitioner<Text, IntWritable> {
        @Override
        public int getPartition(Text key, IntWritable value, int numPartitions) {
            if (value.get() >= 80) {
                return 0;
            } else if (value.get() >= 60) {
                return 1;
            } else if (value.get() >= 40) {
                return 2;
            } else if (value.get() >= 20) {
                return 3;
            } else {
                return 4;
            }
        }
    }

    public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
        @Override
        protected void reduce(Text key, Iterable<IntWritable> values, Context context)
                throws IOException, InterruptedException {
            for (IntWritable value : values) {
                context.write(key, value);
            }
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        String[] myArgs = {
            "file:///home/developer/CodeArtsProjects/mapreduce-partitioner/values.txt",
            "hdfs://localhost:9000/user/developer/GroupScores/output"
        };
        util.removeALL("hdfs://localhost:9000", conf, myArgs[myArgs.length - 1]);
        Job job = Job.getInstance(conf, "GroupScores");
        job.setMapperClass(MyMapper.class);
        job.setPartitionerClass(MyPartitioner.class);
        job.setReducerClass(MyReducer.class);
        job.setNumReduceTasks(5);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        for (int i = 0; i < myArgs.length - 1; i++) {
            FileInputFormat.setInputPaths(job, new Path(myArgs[i]));
        }
        FileOutputFormat.setOutputPath(job, new Path(myArgs[myArgs.length - 1]));
        int res = job.waitForCompletion(true) ? 0 : 1;
        if (res == 0) {
            System.out.println("GroupScores结果为:");
            util.showResult("hdfs://localhost:9000", conf, myArgs[myArgs.length - 1]);
        }
        System.exit(res);
    }
}

3.4 结果

参考

吴章勇杨强著大数据Hadoop3.X分布式处理实战

目录

1. MapReduce分区

1.1 哈希分区

1.2 自定义分区

2. 成绩分组

2.1 Map

2.2 Partition

2.3 Reduce

3. 代码和结果

3.1 pom.xml中依赖配置

3.2 工具类util

3.3 GroupScores

3.4 结果

参考