执行中的 Hadoop 错误：映射中的键类型不匹配：预期为 org.apache.hadoop.io.Text，已收到 org.apache.hadoop.io.LongWritable

我正在 Hadoop 上实现 PageRank 算法，正如标题所示，我在尝试执行代码时遇到了以下错误：

映射中的键类型不匹配：预期为 org.apache.hadoop.io.Text，已收到 org.apache.hadoop.io.LongWritable

在我的输入文件中，我将图形节点 ID 存储为键，并将有关它们的一些信息存储为值。我的输入文件具有以下格式：

1 \t 3.4,2,5,6,67

4 \t 4.2,77,2,7,83

... ...

试图理解错误的含义，我尝试使用 LongWritable 作为我的主要变量类型，如下面的代码所示。这意味着我有：

映射

减少

但是，我也尝试过：

地图

减少

并且：

地图

减少

我总是会犯同样的错误。我想我很难理解错误中预期和收到的含义。这是否意味着我的地图函数需要从我的输入文件中获取 LongWritable 并且它得到了 Text？我使用的输入文件的格式或变量类型是否有问题？

这是完整的代码，你能告诉我要更改什么以及在哪里吗？：

import java.io.IOException;
import java.util.*;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
import java.lang.Object.*;

import org.apache.commons.cli.ParseException;
import org.apache.commons.lang.StringUtils;
import org.apache.commons.configuration.Configuration;
import org.apache.hadoop.security.Credentials;
import org.apache.log4j.*;
import org.apache.commons.logging.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.JobContext;
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.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;



public class Pagerank
{   


public static class PRMap extends Mapper<LongWritable, LongWritable, LongWritable, LongWritable>
{

    public void map(LongWritable lineNum, LongWritable line, OutputCollector<LongWritable, LongWritable> outputCollector, Reporter reporter) throws IOException, InterruptedException
    {
        if (line.toString().length() == 0) {
            return;
        }

        Text key = new Text();
        Text value = new Text();
        LongWritable valuel = new LongWritable();
        StringTokenizer spline = new StringTokenizer(line.toString(),"\t");
        key.set(spline.nextToken());  
        value.set(spline.nextToken());  

        valuel.set(Long.parseLong(value.toString()));
        outputCollector.collect(lineNum,valuel);


        String info = value.toString();
        String splitter[] = info.split(",");

        if(splitter.length >= 3)
        {
            float f = Float.parseFloat(splitter[0]);
            float pagerank = f / (splitter.length - 2);

            for(int i=2;i<splitter.length;i++)
            {
                LongWritable key2 = new LongWritable();
                LongWritable value2 = new LongWritable();
                long l;

                l = Long.parseLong(splitter[i]);
                key2.set(l);
                //key2.set(splitter[i]);
                value2.set((long)f);

                outputCollector.collect(key2, value2);
            }
        }
    }
}

public static class PRReduce extends Reducer<LongWritable,LongWritable,LongWritable,LongWritable>
{
    private Text result = new Text();
    public void reduce(LongWritable key, Iterator<LongWritable> values,OutputCollector<LongWritable, LongWritable> results, Reporter reporter) throws IOException, InterruptedException
    {

        float pagerank = 0;
        String allinone = ",";
        while(values.hasNext())
        {
            LongWritable temp = values.next();
            String converted = temp.toString();
            String[] splitted = converted.split(",");

            if(splitted.length > 1)
            {                   
                for(int i=1;i<splitted.length;i++)
                {
                    allinone = allinone.concat(splitted[i]);
                    if(i != splitted.length - 1)
                        allinone = allinone.concat(",");
                }
            }
            else
            {
                float f = Float.parseFloat(splitted[0]);
                pagerank = pagerank + f;
            }
        }
        String last = Float.toString(pagerank);
        last = last.concat(allinone);

        LongWritable value = new LongWritable();
        value.set(Long.parseLong(last));

        results.collect(key, value);
    }      
}



public static void main(String[] args) throws Exception
{      


    org.apache.hadoop.conf.Configuration conf = new org.apache.hadoop.conf.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, "pagerank_itr0");

    job.setJarByClass(Pagerank.class);      
    job.setMapperClass(Pagerank.PRMap.class);       
    job.setReducerClass(Pagerank.PRReduce.class);    


    job.setOutputKeyClass(LongWritable.class);            
    job.setOutputValueClass(LongWritable.class);            
    FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
    job.waitForCompletion(true);

}
}

您没有在作业配置中设置映射器输出类。 尝试使用以下方法从作业设置键和值类：

setMapOutputKeyClass();

setMapOutputValueClass();