1.前提准备

1.1 启动hadoop（集群）

1. 启动HDFS

start-dfs.sh

2. 启动YARN

start-yarn.sh

3. 历史服务器

mapred --daemon start historyserver

2.创建Maven工程

2.1 使用idea创建Maven工程

请勿使用idea社区版

2.2 导入Hadoop的maven依赖

自行官网搜索依赖：mvnrepository
或直接将下文的xml的“dependency”中的“version”改为自己的hadoop版本

此处以hadoop3.3.4为例

1. 在pom.xml中新增hadoop的依赖

<dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>3.3.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>3.3.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-core</artifactId>
            <version>3.3.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>3.3.4</version>
            <scope>test</scope>
        </dependency>
    </dependencies>

2. 加载maven变更，此操作会更新本地maven仓库，需自动下载，稍等片刻

3.MapReduce程序

3.1 此处直接将课堂给的参考链接代码复制

参考链接

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

import java.io.IOException;

public class MyMap extends Mapper<LongWritable, Text,Text,LongWritable> {
    @Override
    protected void map(LongWritable key,Text value,Context context) throws IOException, InterruptedException {
        //1 get values string
        String valueString = value.toString();
        //2 split string
        String wArr[] = valueString.split(" ");
        //3 for iterator
        for(int i = 0;i < wArr.length;i++){
            //map out key/value
            context.write(new Text(wArr[i]), new LongWritable(1));
        }
    }
}

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

import java.io.IOException;
import java.util.Iterator;

public class MyReduce extends Reducer<Text, LongWritable,Text,LongWritable> {
    @Override
    protected void reduce(Text key,Iterable<LongWritable> valueIn,Context context) throws IOException, InterruptedException {
        Iterator<LongWritable> it = valueIn.iterator();
        long sum = 0;
        //iterator count arr
        while(it.hasNext()){
            sum += it.next().get();
        }
        context.write(key,new LongWritable(sum));
    }
}

import org.apache.hadoop.conf.Configuration;
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.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

public class TestJob {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        //1 get a job
        Job job = Job.getInstance(conf);
        //2 set jar main class
        job.setJarByClass(TestJob.class);
        //3 set map class and reducer class
        job.setMapperClass(MyMap.class);
        job.setReducerClass(MyReduce.class);
        //4 set map reduce output type
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        //5 set key/value output file format and input/output path
        FileInputFormat.setInputPaths(job,new Path("/input/word.txt"));
        FileOutputFormat.setOutputPath(job,new Path("/output"));
        //6 commit job
        job.waitForCompletion(true);
    }
}

4.打包

4.1打包为jar包

1. 依次点击clean和package
2. target文件夹下会生成jar文件
   

4.2（可选）jar包下载

1. 提供本人生成的jar包，可下载

wordcount的jar包下载链接

5.运行jar

5.1上传jar文件和input/word.txt至liunx

1. 上传jar文件和input文件夹至liunx的/data/temp
   

2. 上传input至hdfs

hdfs dfs -put /data/temp/input /input

5.2运行jar

1. 验证hdfs文件系统中没有output文件夹

hdfs dfs -ls /

2. 如有/output文件夹，删除

hdfs dfs -rm -r  /output

3. 再次验证

4. 运行jar

hadoop jar /data/temp/hadoop03-1.0.jar TestJob

5. 查看结果

hdfs dfs -cat /output/part-r-00000