Hadoop启蒙demo——单词统计WordCount(Intellij IDEA版)_hadoop实现单词计数idea-优快云博客

本文提供了一种使用Intellij IDEA运行Hadoop WordCount demo的方法，无需繁琐的打包和环境配置。通过在Mapper和Reducer类中实现逻辑，并在主函数中设置输出，可以在控制台直接观察到统计结果。

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写在代码之前

在网上搜了很多都说要打包jar，或者需要在环境变量中写入，比较繁琐。但是笔者写的这个代码也是能直接跑的，正常输出结果。主要是要有下面这行代码，会在控制台打印出运行结果并在完成后退出，这样看着比较直观一点。

System.exit(job.waitForCompletion(true) ? 0 : 1);

Mapper类

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

import java.io.IOException;
import java.util.Arrays;

public class MapperTest extends Mapper<LongWritable
        , Text, Text, IntWritable> {
    /**
     * @param key     文本的行号
     * @param value   待统计单词的文本
     * @param context
     * @throws IOException
     * @throws InterruptedException
     */
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //得到文件的文本,每一行为一个value
        String text = value.toString();
        String[] words = text.split(" ");
        //将每个单词进行统计 出现次数为1
        Arrays.stream(words).forEach(
                word -> {
                    try {
                        context.write(new Text(word), new IntWritable(1));
                    } catch (IOException e) {
                        e.printStackTrace();
                    } catch (InterruptedException e) {
                        e.printStackTrace();
                    }
                }
        );
    }
}

Reducer

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

import java.io.IOException;

public class ReducerTest extends Reducer<Text, IntWritable, Text, IntWritable> {
    /**
     * @param key     单词
     * @param values  对应频数
     * @param context
     */
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        //统计频数之和
        int sum = 0;
        for (IntWritable count : values) {
            sum += count.get();
            context.write(key, new IntWritable(sum));
        }
    }
}

启动类主函数

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 Runner {
    private static final String inpath = "D:\\codeWareCollections\\hadoop-2.9.1\\input\\README.txt";

    public static void main(String[] args) {
        Configuration conf = new Configuration();
        try {
            System.setProperty("hadoop.home.dir", "D:\\codeWareCollections\\hadoop-2.9.1");
            Job job = Job.getInstance(conf, "first Hadoop");
            job.setJarByClass(Runner.class);
            job.setMapperClass(MapperTest.class);
            job.setReducerClass(ReducerTest.class);

            // 输出key类型
            job.setOutputKeyClass(Text.class);
            // 输出value类型
            job.setOutputValueClass(IntWritable.class);
            //设置输入输出路径
            Path inputPath = new Path(inpath);
            FileInputFormat.addInputPath(job, inputPath);
            FileOutputFormat.setOutputPath(job, new Path("D:\\output"));
            System.exit(job.waitForCompletion(true) ? 0 : 1);

        } catch (IOException e) {
            e.printStackTrace();

        } catch (InterruptedException e) {
            e.printStackTrace();
        } catch (ClassNotFoundException e) {
            e.printStackTrace();
        }
    }
}