public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{m

这是一个 Java 类的定义，名为 TokenizerMapper，它继承了 Mapper<Object, Text, Text, IntWritable> 这个泛型类。其中，Object 表示输入的键类型，Text 表示输入的值类型，Text 表示输出的键类型，IntWritable 表示输出的值类型。Mapper 是 Hadoop 中的一个抽象类，用于将输入数据按照一定的逻辑进行映射，输出为键值对形式的数据。在这个 TokenizerMapper 类中，将实现 Mapper 类中的 map 方法，完成具体的映射逻辑。

相关问题

public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{ Mapper和text标红是要导入什么包吗

是的，Mapper和Text标红说明它们没有被正确导入。在Java程序中，需要使用import语句导入所需的包或类，以便在程序中使用它们。对于这段代码中使用的Mapper和Text类，需要导入org.apache.hadoop.mapreduce包中的类。可以添加以下import语句：

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

这样就可以正确使用Mapper、Text和IntWritable类了。

帮我解释下面的代码：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; import org.apache.hadoop.util.GenericOptionsParser; public class WordCount { 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); } } } 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); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if (otherArgs.length != 2) { System.err.println("Usage: wordcount <in> <out>"); System.exit(2); } Job job = new Job(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(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } }

这段代码是一个 Hadoop MapReduce 的 WordCount（词频统计）程序，主要包括以下几个部分：

1. 引入需要用到的类库：

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;
import org.apache.hadoop.util.GenericOptionsParser;

2. 定义 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);
        }
    }
}

Mapper 类主要是将输入的文本分割成单词，然后对每个单词输出一个键值对，其中键为单词，值为 1。

3. 定义 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);
    }
}

Reducer 类主要是将 Mapper 输出的键值对按照键进行分组，然后对每组键值进行统计，输出每个单词出现的次数。

4. 定义主函数：

public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
    if (otherArgs.length != 2) {
        System.err.println("Usage: wordcount <in> <out>");
        System.exit(2);
    }
    Job job = new Job(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(otherArgs[0]));
    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
}

主函数主要是设置 MapReduce 作业的相关参数，包括输入路径、输出路径、Mapper 类、Reducer 类、输出键值对类型等。最后调用 `waitForCompletion` 函数提交作业并等待作业完成。