MapReduce编程之Partitioner

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Partitioner：
在MR中,Partitioner决定了MapTask输出的数据交由哪个Reduce Task处理
默认实现：分发的key的hash值对Reduce Task个数取模
假设数据如下所示：

每行内容为水果和其数量，现要求将同种类的水果放在一起，得到该种水果的总数量

编译运行
hadoop jar /home/zq/lib/HDFS_Test-1.0-SNAPSHOT.jar MapReduce.ParititonerApp hdfs://zq:8020/partitioner hdfs://zq:8020/output/partitioner

可以看到有四个文件生成
每个就对应四种水果之一的总数量

详细代码如下：

package MapReduce;

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.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;

import java.io.IOException;

public class ParititonerApp {

    /**
     * Map：读取输入的文件
     */
    public static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable>{

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

            // 接收到的每一行数据
            String line = value.toString();

            //按照指定分隔符进行拆分
            String[] words = line.split(" ");

            context.write(new Text(words[0]), new LongWritable(Long.parseLong(words[1])));

        }
    }

    /**
     * Reduce：归并操作
     */
    public static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> {

        @Override
        protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {

            long sum = 0;
            for(LongWritable value : values) {
                // 求key出现的次数总和
                sum += value.get();
            }

            // 最终统计结果的输出
            context.write(key, new LongWritable(sum));
        }
    }

    public static class MyPartitioner extends Partitioner<Text, LongWritable> {

        @Override
        public int getPartition(Text key, LongWritable value, int numPartitions) {

            if(key.toString().equals("apple")) {
                return 0;
            }

            if(key.toString().equals("banana")) {
                return 1;
            }

            if(key.toString().equals("orange")) {
                return 2;
            }

            return 3;
        }
    }


    /**
     * 定义Driver：封装了MapReduce作业的所有信息
     */
    public static void main(String[] args) throws Exception{

        //创建Configuration
        Configuration configuration = new Configuration();

        // 准备清理已存在的输出目录
        Path outputPath = new Path(args[1]);
        FileSystem fileSystem = FileSystem.get(configuration);
        if(fileSystem.exists(outputPath)){
            fileSystem.delete(outputPath, true);
            System.out.println("output file exists, but it has deleted");
        }

        //创建Job
        Job job = Job.getInstance(configuration, "wordcount");

        //设置job的处理类
        job.setJarByClass(ParititonerApp.class);

        //设置作业处理的输入路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));

        //设置map相关参数
        job.setMapperClass(MyMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);

        //设置reduce相关参数
        job.setReducerClass(MyReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);

        //设置job的partition
        job.setPartitionerClass(MyPartitioner.class);
        //设置4个reducer，每个分区一个
        job.setNumReduceTasks(4);

        //设置作业处理的输出路径
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

更多代码以及详细信息见我的github相关项目
https://github.com/29DCH/Hadoop-MapReduce-Examples