hadoop,WordCount,大厂面试题,手撸mapreduce 单词统计

有的大厂面试需要你手撸一个wordcount的例子,今天我们就从头到尾现一遍

1.在开始编程工作之前,先要规划好wordcount的步骤,规划的流程图和mapreduce原理如下:

 

2.前期准备

(1) 启动hadoop和日志

(2).准备要处理的文件,并上传到hdfs分布式文件系统中

(3) 将文件上传到hdfs文件系统中

hdfs dfs -mkdir /wordcount/
hdfs dfs -put wordcount.txt /wordcount/

3.自定义mapper

package com.alibaba.mapreduce;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

/**
 * @author Jiapeng Pang
 * @Position 大数据工程师
 * @date 2019/8/20 19:12.
 */
public class WordCountMapper extends Mapper{

    //map方法就是将转为

    /**
     * @param key      k1 行偏移量
     * @param value    v1 每一行的文本数据
     * @param context   上下文对象,传递
     * @throws IOException
     * @throws InterruptedException
     */
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        Text text = new Text();
        LongWritable longWritable = new LongWritable();
        //1.将一行的文本数据进行拆分
        String[] split = value.toString().split(",");
        //2.遍历数组,组装v2和k2
        for (String word : split) {
            text.set(word);
            longWritable.set(1);
            //3.将k2和v2 传入上下文中
            context.write(text,longWritable);

        }

    }
}

4.自定义reduce

package com.alibaba.mapreduce;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

/**
 * @author Jiapeng Pang
 * @Position 大数据工程师
 * @date 2019/8/20 19:32.
 *
 * KEYIN  k2
 * VALUEIN v2
 * KEYOUT k3
 * VALUEOUT v3
 *
 */

public class wordCountReducer extends Reducer {

    //将新的k2,v2转为k3和v3,将k3和v3写入上下文中

    /**
     *
     * @param key  新k2
     * @param values  新v2
     * @param context  上下文对象
     * @throws IOException
     * @throws InterruptedException
     */
    @Override
    protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
        long count=0;
        //遍历集合,将集合中的数字相加,得到v3
        for (LongWritable value : values) {
            count+=value.get();
        }
        context.write(key,new LongWritable(count));
        //将k3和v3写入上下文中

    }
}

5.编写主代码

package com.alibaba.mapreduce;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

import java.net.URI;

/**
 * @author Jiapeng Pang
 * @Position 大数据工程师
 * @date 2019/8/20 19:44.
 */
public class JobMain extends Configured implements Tool {

    //指定一个job任务
    @Override
    public int run(String[] strings) throws Exception {
        //1.创建一个job任务对象
        Job job = Job.getInstance(super.getConf(), "wordcount");
        //如果打包运行出错,需要加入该配置
        //job.setJarByClass(JobMain.class);
        //2.配置job任务对象(8个步骤)
        //第一步 指定文件的读取方式和读取路径
        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.addInputPath(job,new Path("hdfs://node01:8020/wordcount"));
        //TextInputFormat.addInputPath(job,new Path("file:///E:\\mapreduce\\input"));

        //第二步 指定map阶段处理方式和数据类型
        job.setMapperClass(WordCountMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);

        //3,4,5,6  采用默认方式
        //第七步 指定 redce 的处理方式和数据类型
        job.setReducerClass(wordCountReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);

        //第八步,设置输出类型
        job.setOutputFormatClass(TextOutputFormat.class);

        Path path =new Path("hdfs://node01:8020/wordcount_out");
        //设置输出路径
        TextOutputFormat.setOutputPath(job,path);
        //TextOutputFormat.setOutputPath(job,new Path("file:///E:\\mapreduce\\output"));
        FileSystem fileSystem = FileSystem.get(new URI("hdfs://node01:8020"), new Configuration());

        boolean a = fileSystem.exists(path);
        if(a){

            fileSystem.delete(path,true);
        }


        //等待任务结束
        boolean b = job.waitForCompletion(true);


        return b?0:1;
    }

    public static void main(String[] args) throws Exception {
        Configuration configuration = new Configuration();

        //启动job任务
        int run = ToolRunner.run(configuration, new JobMain(), args);
        System.exit(run);

    }
}

6.集群运行和本地运行

//集群运行
hadoop jar hadoop_hdfs_operate-1.0-SNAPSHOT.jar cn.itcast.mapreduce.JobMain
//本地运行,修改目录
TextInputFormat.addInputPath(job,new Path("file:///E:\\mapreduce\\input"));
TextOutputFormat.setOutputPath(job,new Path("file:///E:\\mapreduce\\output"));

7.查看结果