MapReduce wordcount案例测试代码

pom.xml 导包：

        
            org.apache.hadoop
            hadoop-common
            2.7.5
        
        
            org.apache.hadoop
            hadoop-client
            2.7.5
        
        
            org.apache.hadoop
            hadoop-hdfs
            2.7.5
        
        
            org.apache.hadoop
            hadoop-mapreduce-client-core
            2.7.5
        
        
            junit
            junit
            RELEASE
        
        
            junit
            junit
            RELEASE
        
        
        
            org.projectlombok
            lombok
            1.18.0
            provided
        
    
    
        
            
                org.apache.maven.plugins
                maven-compiler-plugin
                3.1
                
                    1.8
                    1.8
                    UTF-8
                    
                
            
            
                org.apache.maven.plugins
                maven-shade-plugin
                2.4.3
                
                    
                        package
                        
                            shade
                        
                        
                            true
                        
                    
                
            

        
    
    

wordcountmapper 类

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

import java.io.IOException;

/**
 * 四个泛型
 * KEYIN:k1的类型
 * VALUEIN:V1的类型
 * KEYOUT: K2的类型
 * VALUEOUT:V2的类型
 *
 * 使用自定义的类  序列化 对基本类型的封装
 *
 */
public class WordCountMapper extends Mapper<LongWritable,Text,Text,LongWritable> {

    /**
     * map 方法就是将k1 v1转 k2 v2
     * @param key  k1   行偏移量
     * @param value  v1 每一行的文本数据
     * @param context 表示上下文对象 起到桥梁
     * @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K2 V2
        for(String word:split){
            //3.将k2 v2写入上下文中
            text.set(word);
            longWritable.set(1);
            context.write(text,longWritable);
        }

    }
}

reduce 类

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

import org.apache.hadoop.io.Text;
import java.io.IOException;

/**
 * 四个泛型解释
 * KEYIN  : 新k2 类型
 * VALUE  : 新V2 类型
 * KEYOUT :K3 类型
 * VALUEOUT :V3 类型
 */
public class WordCountReducer extends Reducer<Text,LongWritable,Text,LongWritable> {

    /**
     * reduce方法作用：将新的k2 v2 转为k3 v3,将k3 v3写入上下文中
     * @param key 新k2
     * @param values  集合 新的v2
     * @param context 上下文
     * @throws IOException
     * @throws InterruptedException
     */
    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
        //1.遍历集合，将集合中的数字相加，得到V3
        long count = 0;
        for(LongWritable value:values){
            count+=value.get();
        }
        //2.将结果写入上下文中
        context.write(key,new LongWritable(count));
    }
}

jobMain 运行类

```java

```java

```java

```java

```java

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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;



public class JobMain extends Configured implements Tool {

    /**
     * 该方法用于指定一个job任务
     * @param strings
     * @return
     * @throws Exception
     */
    @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");
        //打包到集群上面运行时候，必须要添加以下配置，指定程序的main函数
        job.setJarByClass(JobMain.class);
        //2.配置job任务对象(八个步骤)
        //第一步 指定文件的读取方式和读取路径
        job.setInputFormatClass(TextInputFormat.class);
        //本地文件系统
        TextInputFormat.addInputPath(job,new Path("file:///D:\\mapreduce\\input"));
//        TextInputFormat.addInputPath(job,new Path("hdfs://node01:8020/wordcount"));
        //第二步 指定map阶段的处理方式 和数据类型
        job.setMapperClass(WordCountMapper.class);
        job.setMapOutputKeyClass(Text.class);//设置map阶段k2的类型
        job.setMapOutputValueClass(LongWritable.class);//设置map节点的v2的类型
        //第三 四 五 六   shuffle阶段采用默认的方式 不做处理

        //第七步  指定reduce阶段处理方式和数据类型
        job.setReducerClass(WordCountReducer.class);
        job.setOutputKeyClass(Text.class);//设置k3的类型
        job.setOutputValueClass(LongWritable.class);//设置v3的类型
        //第八步 设置输出类型
        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job,new Path("file:///D:\\mapreduce\\output"));//目标文件夹不能存在 否则会报错
//        TextOutputFormat.setOutputPath(job,new Path("hdfs://node01:8020/wordcount_out"));//设置输出的路径
        //等待任务结束

        boolean b = job.waitForCompletion(true);
        return b?0:1;
    }

    public static void main(String[] args) throws Exception {
        Configuration entries = new Configuration();
        //启动job任务
        int run = ToolRunner.run(entries, new JobMain(), args);
        System.exit(run); //0表示执行成功

    }
}