Hadoop实验：关于MapReduce词频统计的实验步骤

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需要搭建帮助的可以去taobao搜索Easy Company技术服务，谢谢！！！

一、在本地创建两个文本文件

创建 wordfile1.txt 文件，内容为：

I love Spark

I love Hadoop

I love WangJianXiong

创建 wordfile2.txt 文件，内容为：

Hadoop is good 
Spark is fast
WangJianXiong is good

将本地文件复制到docker容器内

docker cp /usr/local/hadoop-docker/wordfile1.txt 88ec9e3975c7:/root/wordfile1.txt
docker cp /usr/local/hadoop-docker/wordfile2.txt 88ec9e3975c7:/root/wordfile2.txt

二、启动 HDFS 并创建 input 目录

hadoop fs -mkdir /input
hadoop fs -ls /

三、上传文件到 HDFS 的 input 目录

hadoop fs -put /root/wordfile1.txt /input
hadoop fs -put /root/wordfile2.txt /input
hadoop fs -ls /input

四、编写 MapReduce 程序

WordCount.java

import java.io.IOException;
import java.util.StringTokenizer;
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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WordCount {

    // Mapper 类
    public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                word.set(itr.nextToken());
                context.write(word, one);
            }
        }
    }

    // Reducer 类
    public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values, Context context)
                throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }

    // Main 方法
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(WordCount.class);
        job.setMapperClass(TokenizerMapper.class);
        job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

五、编译和运行程序

1.将代码保存到 WordCount.java 文件中

vi WordCount.java

按下 i 进入 插入模式，将上述的WordCount代码插入到此WordCount.java 文件中，:wq保存退出

2.保存完代码后，运行以下命令查看文件内容

cat WordCount.java

3.编译代码

javac -classpath $(hadoop classpath) -d . WordCount.java

$(hadoop classpath)：自动获取 Hadoop 所需的类路径。

-d .：编译输出目录设置为当前目录。

jar -cvf wordcount.jar *.class

-cvf：创建一个新的 JAR 文件，显示详细信息。

wordcount.jar：输出的 JAR 文件名。

检查 wordcount.jar 是否已生成

ls -l wordcount.jar

4.运行 MapReduce 作业

将打包的 wordcount.jar 运行在 Hadoop 集群上

hadoop jar wordcount.jar WordCount /input /output

六、查看输出结果

hadoop fs -cat /output/part-r-00000