在MapReduce中运行WordCount以及字数统计中遇到的问题

Posted 2022-12-07 fadeless_3

运行环境：Ubantu的eclipse下
此操作需要在配置好了hadoop和hdfs的基础上运行MapReduce

常见问题：

1. 打开eclipse后查看不到MapReduceLocaltion，原因可能是没有开启hadoop，或者eclipse文件没有配置好
2. 运行WordCount.java，代码没有错误，运行报错问题，原因可能是output下已存在输出文件，解决方法，换一个输出文件夹，如output2，或者删除output文件夹下的文件
3. 代码中出现红叉，提示当前jdk不符合当前代码的运行，右键红叉点击兼容，编译器会自动向下兼容。（只能进行高版本jdk兼容低版本jdk）
4. 路径错误，导致文件无法执行
5. 打开eclipse后的MapReduceLocation后，打开后，文件目录下无文件，可通过命令行方式建立文件夹input(在/usr/local/hadoop下，开启hadoop后执行)

$./bin/hdfs dfs –mkdir –p /user/hadoop/input

运行过程

1. 开启hadoop（这里需要进到/usr/local/hadoop目录下开启）
2. 将本地/home/hadoop/sadworld.txt（所需要字符统计的）文件上传到hdfs上
   
3. 创建MapReduce 项目（WordCount.java文件）
   并编写WordCount.java程序（这里的输入文件是input1，输出文件是output2）

package org.apache.hadoop.examples;
import java.io.IOException;
import java.util.Iterator;
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 WordCount() 
    
    public static void main(String[] args)
throws Exception 
        Configuration conf = new Configuration();
//        String[] otherArgs = (new GenericOptionsParser(conf,args)).getRemainingArgs();
       String[] otherArgs=new String[]“input1”,”output2”;
        if(otherArgs.length < 2) 
            System.err.println("Usage:wordcount <in> [<in>...] <out>");
            System.exit(2);
        
        Job job = Job.getInstance(conf,"word count");
        job.setJarByClass(WordCount.class);
 	job.setMapperClass(WordCount.TokenizerMapper.class);      
	job.setCombinerClass(WordCount.IntSumReducer.class);
        job.setReducerClass(WordCount.IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
	job.setOutputValueClass(IntWritable.class);
        for(int i = 0; i < otherArgs.length - 1; ++i) 
            FileInputFormat.addInputPath(job,new Path(otherArgs[i]));
        
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[otherArgs.length - 1]));
	System.exit(job.waitForCompletion(true)?0:1);
    
     public static class IntSumReducer extends
Reducer<Text, IntWritable, Text, IntWritable> 
        private IntWritable result = new IntWritable();
        public IntSumReducer() 
     
        public void reduce(Text key,Iterable<IntWritable> values, Reducer<Text, IntWritable, Text,IntWritable>.Context context) throws IOException,InterruptedException 
            int sum = 0;
            IntWritable val;
            for(Iterator i$ =
values.iterator(); i$.hasNext(); sum += val.get()) 
                val = (IntWritable)i$.next();
            
            this.result.set(sum);
            context.write(key, this.result);
        
    
    public static class TokenizerMapper extends
Mapper<Object, Text, Text, IntWritable> 
        private static final IntWritable one = new IntWritable(1);
        private Text word = new Text();
        public TokenizerMapper() 
        
        public void map(Object key, Text value,
Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException 
            StringTokenizer itr = new
StringTokenizer(value.toString());
            while(itr.hasMoreTokens()) 
                this.word.set(itr.nextToken());
                context.write(this.word, one);
            
        
    

4. 运行WordCount程序,统计sadworld.txt中字符出现的次数
   

5. 执行完毕，查看字符统计情况