MapReduce编程之wordcount

实践

MapReduce编程之wordcount

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.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

/**
 * 使用MapReduce开发WordCount的应用程序
 */
public class WordCountApp {

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

        LongWritable one = new LongWritable(1);
        @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(" ");
            for(String word : words){
                // 通过上下文把map的处理结果输出
                context.write(new Text(word),one);
            }
        }

    }

    /**
     * 归并操作
     */
    public static class MyReduce 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出现的次数和
                sum += value.get();
            }
            context.write(key, new LongWritable(sum));
        }
    }

    /**
     * 定义Driver：封装lMapReduce作业的所有信息
     * @param args
     */
    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("out file exists,but is has deleted!");
        }
        //创建job
        Job job = Job.getInstance(configuration,"WordCount");
        //设置job的处理类
        job.setJarByClass(WordCountApp.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(MyReduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        //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);
    }
}
运行
hadoop jar hadoop-train-1.0-SNAPSHOT.jar WordCountApp /hdfsapi/test/b.txt /hdfsapi/test/out

MapReduce编程之Combiner

  • 本地reduce（map端reduce）

  • 减少Map Tasks输出的数据量及数据网络传输量

  • combiner案例开发

  • 使用场景：求和、求次数

  • 代码

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.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

/**
 * 使用MapReduce开发WordCount的应用程序
 */
public class CombinerApp {

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

        LongWritable one = new LongWritable(1);
        @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(" ");
            for(String word : words){
                // 通过上下文把map的处理结果输出
                context.write(new Text(word),one);
            }
        }

    }

    /**
     * 归并操作
     */
    public static class MyReduce 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出现的次数和
                sum += value.get();
            }
            context.write(key, new LongWritable(sum));
        }
    }

    /**
     * 定义Driver：封装lMapReduce作业的所有信息
     * @param args
     */
    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("out file exists,but is has deleted!");
        }
        //创建job
        Job job = Job.getInstance(configuration,"WordCount");
        //设置job的处理类
        job.setJarByClass(CombinerApp.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(MyReduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        //通过job的设置combiner处理类，其实逻辑上和我们的reduce是一模一样的
        job.setCombinerClass(MyReduce.class);
        //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);
    }
}
  • 执行命令
hadoop jar hadoop-train-1.0-SNAPSHOT.jar WordCountApp /hdfsapi/test/b.txt /hdfsapi/test/out

MapReduce编程之Partitioner

  • partitioner决定MapTask输出的数据交由哪个ReduceTask处理

  • 默认实现：分发的key的hash值对ReduceTask个数取模

  • partitioner案例开发

  • 代码
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;

/**
 * 使用MapReduce开发WordCount的应用程序
 */
public class PartitionerApp {

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

        LongWritable one = new LongWritable(1);
        @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])));
        }
    }

    /**
     * 归并操作
     */
    public static class MyReduce 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出现的次数和
                sum += value.get();
            }
            context.write(key, new LongWritable(sum));
        }
    }

    public static class MyPartitioner extends Partitioner<Text,LongWritable>{
        @Override
        public int getPartition(Text key, LongWritable longWritable, int i) {
            if(key.toString().equals("xiaomi")){
                return 0;
            }
            if(key.toString().equals("huawei")){
                return 1;
            }
            if(key.toString().equals("iphone")){
                return 2;
            }
            return 3;
        }
    }
    /**
     * 定义Driver：封装lMapReduce作业的所有信息
     * @param args
     */
    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("out file exists,but is has deleted!");
        }
        //创建job
        Job job = Job.getInstance(configuration,"WordCount");
        //设置job的处理类
        job.setJarByClass(PartitionerApp.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(MyReduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        //通过job的设置partition
        job.setPartitionerClass(MyPartitioner.class);
        //设置4个reduce，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);
    }
}
  • 执行命令
hadoop jar hadoop-train-1.0-SNAPSHOT.jar PartitionerApp /hdfsapi/test/partitioner /hdfsapi/test/outpartitioner
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原文地址：https://www.cnblogs.com/zxbdboke/p/10468101.html