多个单词的 Hadoop WordCount 未获得 public 变量

Question

我有一个简单的 Hadoop 程序，我需要为 mu 大学的一篇论文实现。这是一个替代的 WordCount 问题，它应该组合具有 n 个单词的 Text() 字符串，并且只用 reducer 总结大于等于 k 次出现的字符串。我已将 n 和 k 整数放在输入和输出文件夹（args[3] 和 args[4]）之后从命令行获取。问题是当 n 和 k 在 mapper 和 reducer 内部使用时它们是空的，尽管事实上从命令中正确地获取了它们的值。代码如下，哪里出错了？

public class MultiWordCount {

    public static int n;
    public static int k;

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

    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
    private StringBuilder phrase = new StringBuilder();

    public void map(Object key, Text value, Context context
    ) throws IOException, InterruptedException {
        StringTokenizer itr = new StringTokenizer(value.toString());
        while (itr.hasMoreTokens()) {
            for (int i=0; i<n; i++) {
                if (itr.hasMoreTokens()) {
                    phrase.append(itr.nextToken());
                    phrase.append(" ");

                }
            }
            word.set(phrase.toString());
            context.write(word, one);
            phrase.setLength(0);
        }
    }
}

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();
        }
        if(sum >= k) {
            result.set(sum);
            context.write(key, result);
        }
    }
}

public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
 n = Integer.parseInt(args[2]);
 k = Integer.parseInt(args[3]);
    Job job = Job.getInstance(conf, "multi 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);
}
}

Answer 1

尽管您这里基于 Java 的逻辑看起来很合理，但 Hadoop 中的 Map 和 Reduce 函数实现功能比人们想象的更短视或更独立。更准确地说，您在父 class 中声明 public 静态变量并在 driver/main 函数中初始化它们，但是 mapper/reducer 实例没有任何访问驱动程序的权限，但仅限于 TokenizerMapper 和 IntSumReducer class 范围内的严格范围。这就是为什么 n 和 k 在您查看映射器和缩减器内部时看起来是空的。

由于您的程序只有一个作业并且在单个 Hadoop Configuration 中执行，因此此处不需要 Hadoop Counters。您可以通过 TokenizerMapper 和 [=12] 中的 setup 函数，在执行 Map 和 Reduce 函数之前声明每个映射器和化简器将访问的基于 Configuration 的值=] classes.

要声明这些类型的值以便将它们传递给 MapReduce 函数，您可以在 driver/main 方法中执行如下操作：

conf.set("n", args[2]);

然后在 TokenizerMapper 和 IntSumReducer 的 setup 方法中访问此值（同时将其从 String 转换为 int）：

n = Integer.parseInt(context.getConfiguration().get("n"));

所以程序可以是这样的：

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;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.Counters;

import java.io.*;
import java.io.IOException;
import java.util.*;
import java.nio.charset.StandardCharsets;

public class MultiWordCount 
{
    public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>
    {
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();
        private StringBuilder phrase = new StringBuilder();
        private int n;

        protected void setup(Context context) throws IOException, InterruptedException 
        {
            n = Integer.parseInt(context.getConfiguration().get("n"));
        }

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException 
        {
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) 
            {
                for (int i = 0; i < n; i++) 
                {
                    if (itr.hasMoreTokens()) 
                    {
                        phrase.append(itr.nextToken());
                        phrase.append(" ");

                    }
                }

                word.set(phrase.toString());
                context.write(word, one);
                phrase.setLength(0);
            }
        }
    }

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

        protected void setup(Context context) throws IOException, InterruptedException 
        {
            k = Integer.parseInt(context.getConfiguration().get("k"));
        }

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

            if(sum >= k) 
            {
                result.set(sum);
                context.write(key, result);
            }
        }
    }

    public static void main(String[] args) throws Exception 
    {
        Configuration conf = new Configuration();

        conf.set("n", args[2]);
        conf.set("k", args[3]);

        FileSystem fs = FileSystem.get(conf);
        if(fs.exists(new Path(args[1])))
            fs.delete(new Path(args[1]), true);

        Job job = Job.getInstance(conf, "Multi Word Count");
        job.setJarByClass(MultiWordCount.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);
    }
}

而对于 n=3 和 k=1，输出看起来像这样（使用许多带有 Franz Kafka 句子的文本文件 here）：

多个单词的 Hadoop WordCount 未获得 public 变量

Hadoop WordCount for multiple words not getting the public variables

java

hadoop