MapReduce高级案例①①

WordCount案例

数据

hello world
dog fish
hadoop 
spark
hello world
dog fish
hadoop 
spark
hello world
dog fish
hadoop 
spark

统计一堆文件中单词出现的个数（WordCount案例）

在一堆给定的文本文件中统计输出每一个单词出现的总次数
[collapse title="ruaDriver"]

package com.kami.demo04;

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

import java.io.IOException;

/**
 * @version v 1.0
 * @Author kamisamak
 * @Date 2020/6/17
 */
public class ruaDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration configuration = new Configuration();
        //配置提交到yarn上运行,windows和Linux变量不一致
        //configuration.set("mapreduce.framework.name", "yarn");
        //configuration.set("yarn.resourcemanager.hostname", "node22");
        Job job = Job.getInstance(configuration);

        //指定本程序的jar包所在的本地路径
        //job.setJar("/home/admin/wc.jar");
        job.setJarByClass(ruaDriver.class);

        job.setMapperClass(ruaMapper.class);
        job.setCombinerClass(ruaCombiner.class);
        job.setReducerClass(ruaReducer.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        //将job中配置的相关参数，以及job所用的java类所在的jar包， 提交给yarn去运行
        //job.submit();
        boolean result = job.waitForCompletion(true);
        System.exit(result?0:1);
    }
    static class ruaMapper extends Mapper<LongWritable, Text,Text, IntWritable>{
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String[] sps = value.toString().split(" ");
            for (String sp : sps) {
                context.write(new Text(sp), new IntWritable(1));
            }
        }
    }
    static class ruaCombiner extends Reducer<Text, IntWritable,Text, IntWritable> {
        @Override
        protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
            int count = 0;
            for (IntWritable value : values) {
                count++;
            }
            context.write(key,new IntWritable(count));
        }
    }
    static class ruaReducer extends Reducer<Text, IntWritable,Text, IntWritable> {
        @Override
        protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
            int count = 0;
            for (IntWritable value : values) {
                count+=value.get();
            }
            context.write(key,new IntWritable(count));
        }
    }
}

[/collapse]

把单词按照ASCII码奇偶分区（Partitioner）

[collapse title="ruaDriver"]

package com.kami.demo05;

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;

import java.io.IOException;

/**
 * @version v 1.0
 * @Author kamisamak
 * @Date 2020/6/17
 */
public class ruaDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration configuration = new Configuration();
        //配置提交到yarn上运行,windows和Linux变量不一致
        //configuration.set("mapreduce.framework.name", "yarn");
        //configuration.set("yarn.resourcemanager.hostname", "node22");
        Job job = Job.getInstance(configuration);

        //指定本程序的jar包所在的本地路径
        //job.setJar("/home/admin/wc.jar");
        job.setJarByClass(ruaDriver.class);

        job.setPartitionerClass(WordCountPartitioner.class);
        job.setNumReduceTasks(2);

        job.setMapperClass(ruaMapper.class);
        job.setCombinerClass(ruaCombiner.class);
        //job.setCombinerClass(ruaReducer.class);
        job.setReducerClass(ruaReducer.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        //将job中配置的相关参数，以及job所用的java类所在的jar包， 提交给yarn去运行
        //job.submit();
        boolean result = job.waitForCompletion(true);
        System.exit(result?0:1);
    }
    static class ruaMapper extends Mapper<LongWritable, Text,Text, IntWritable>{
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String[] sps = value.toString().split(" ");
            for (String sp : sps) {
                context.write(new Text(sp), new IntWritable(1));
            }
        }
    }
    static class ruaCombiner extends Reducer<Text, IntWritable,Text, IntWritable> {
        @Override
        protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
            int count = 0;
            for (IntWritable value : values) {
                count++;
            }
            context.write(key,new IntWritable(count));
        }
    }
    static class ruaReducer extends Reducer<Text, IntWritable,Text, IntWritable> {
        @Override
        protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
            int count = 0;
            for (IntWritable value : values) {
                count++;
            }
            context.write(key,new IntWritable(count));
        }
    }

    static class WordCountPartitioner extends Partitioner<Text, IntWritable> {
        @Override
        public int getPartition(Text key, IntWritable value, int numPartitions) {
            String firWord = key.toString().substring(0, 1);
            char[] charArray = firWord.toCharArray();
            int result = charArray[0];
            // int result  = key.toString().charAt(0);
            if (result % 2 == 0) {
                return 0;
            }else {
                return 1;
            }
        }
    }
}

[/collapse]

大量小文件的切片优化（CombineTextInputFormat）

将输入的大量小文件合并成一个切片统一处理。

// 如果不设置InputFormat，它默认用的是TextInputFormat.class
job.setInputFormatClass(CombineTextInputFormat.class);
CombineTextInputFormat.setMaxInputSplitSize(job, 4194304);// 4m
CombineTextInputFormat.setMinInputSplitSize(job, 2097152);// 2m

案例来源:https://www.cnblogs.com/frankdeng/p/9311481.html

MapReduce高级案例①①

WordCount案例

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