【MapReduce】2.MapReduce示例——WordCount（统计单词）

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过程分析

统计单词，把数据中的单词分别统计出出现的次数

过程图（图片源自网络）：

实现Mapper、Reducer、Driver

WordCountMapper ：

public class WordCountMapper extends Mapper<LongWritable,Text,Text,IntWritable> {

    private Text k = new Text();
    private IntWritable v = new IntWritable(1);

    /**
     *  重写map方法
     * @param key 行号
     * @param value 行数据
     * @param context
     * @throws IOException
     * @throws InterruptedException
     */
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // 获取一行的数据
        String valueString = value.toString();
        // 分割一行的数据
        String[] strings = valueString.split(" ");
        // 输出K-V对
        for (String string : strings) {
            k.set(string);
            context.write(k,v);
        }

    }
}

WordCountReduce ：

public class WordCountReduce extends Reducer<Text,IntWritable,Text,IntWritable> {

    private IntWritable v = new IntWritable(0);
    /**
     *  reduce合并过程
     * @param key key值
     * @param values 同一个key的value值得列表
     * @param context
     * @throws IOException
     * @throws InterruptedException
     */
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        // 统计数字
        int count = 0;
        // 汇总数字
        for (IntWritable value : values) {
            count += value.get();
        }
        // 赋值
        v.set(count);
        // 输出
        context.write(key,v);
    }
}

WordCountDriver

public class WordCountDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        // 获取job对象
        System.setProperty("hadoop.home.dir", "E:\\hadoop-2.7.1");
        Configuration configuration = new Configuration();
        FileSystem fs = FileSystem.get(configuration);
        //configuration.set("mapreduce.framework.name","local");
        //configuration.set("fs.defaultFS","file:///");
        Job job = Job.getInstance(configuration);

        // 设置加载类
        job.setJarByClass(WordCountDriver.class);

        // 设置map和reduce类
        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReduce.class);
        // 设置mapper输出类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        // 设置最终输出类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        // 设置输入文件和输出文件
        FileInputFormat.setInputPaths(job,new Path("E:\\hdfs\\input\\word.txt"));
        Path outPath = new Path("E:\\hdfs\\output");
        if (fs.exists(outPath)) {
            fs.delete(outPath, true);
        }
        FileOutputFormat.setOutputPath(job, new Path("E:\\hdfs\\output"));
        boolean waitForCompletion = job.waitForCompletion(true);
        System.out.println(waitForCompletion);
        System.exit(waitForCompletion?0:1);

    }
}

pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.xing</groupId>
    <artifactId>MapReduce</artifactId>
    <version>1.0-SNAPSHOT</version>
    <dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.1</version>
        </dependency>
    </dependencies>


</project>

我用的本地windows开发环境，如果不知道怎么搭建本地开发环境可以看我这篇
【Spark】Windows运行本地spark程序——JAVA版本
搭建过程都是一样的。

结果：