java - 错误值类 : class org. apache.hadoop.io.Text 不是类 org.apache.hadoop.io.IntWritable

Question

我使用了一个映射器、一个缩减器和一个组合器类，但出现如下错误:

java.io.IOException: wrong value class: class org.apache.hadoop.io.Text is not class org.apache.hadoop.io.IntWritable
at org.apache.hadoop.mapred.IFile$Writer.append(IFile.java:199)
at org.apache.hadoop.mapred.Task$CombineOutputCollector.collect(Task.java:1307)
at org.apache.hadoop.mapred.Task$NewCombinerRunner$OutputConverter.write(Task.java:1623)
at org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write(TaskInputOutputContextImpl.java:89)
at org.apache.hadoop.mapreduce.lib.reduce.WrappedReducer$Context.write(WrappedReducer.java:105)
at BookPublished1$Combine.reduce(BookPublished1.java:47)
at BookPublished1$Combine.reduce(BookPublished1.java:1)
at org.apache.hadoop.mapreduce.Reducer.run(Reducer.java:171)
at org.apache.hadoop.mapred.Task$NewCombinerRunner.combine(Task.java:1644)
at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.sortAndSpill(MapTask.java:1618)
at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.flush(MapTask.java:1467)
at org.apache.hadoop.mapred.MapTask$NewOutputCollector.close(MapTask.java:699)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:769)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:339)
at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:162)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1491)
at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:157)

我的整个程序如下所示:

import java.io.IOException;

import org.apache.hadoop.io.FloatWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.fs.Path;


public class BookPublished1 {

    public static class Map extends Mapper<LongWritable,Text,Text,IntWritable>{

        public void map(LongWritable key, Text value,Context context)
                throws IOException,InterruptedException {

            String line = value.toString();
            String [] strYear = line.split(";");
            context.write(new Text(strYear[3]), new IntWritable(1));
            }


        }


    public static class Combine extends Reducer<Text,IntWritable,Text,Text>{

        public void reduce(Text key, Iterable<IntWritable> values,Context context)
                throws IOException,InterruptedException {
            int sum=0;
            // TODO Auto-generated method stub
            for(IntWritable x: values)
            {
                sum+=x.get();
            }



            context.write(new Text("BookSummary"), new Text(key + "_"+ sum));

        }

    }
 public static class Reduce extends Reducer<Text,Text,Text,FloatWritable>{

        public void reduce(Text key, Iterable<Text> values,Context context)throws IOException,InterruptedException
             {
            Long publishYear =0L, max=Long.MAX_VALUE;
                            Text publishYear1 = null,maxYear=null;
                            Long publishValue= 0L;
            String compositeString;
            String compositeStringArray[];
            // TODO Auto-generated method stub
            for(Text  x: values)
            {
                                                compositeString = x.toString();
                compositeStringArray = compositeString.split("_");
                publishYear1=new Text(compositeStringArray[0]);
                publishValue=new Long(compositeStringArray[1]);
                if(publishValue > max){
                max=publishValue;
                maxYear=publishYear1;

            }
            }
        Text keyText= new Text("max" + " ( " + maxYear.toString() + ") : ");

            context.write(keyText, new FloatWritable(max));



    }
 }


    public static void main(String[] args) throws Exception {
        Configuration conf= new Configuration();
        Job job = new Job(conf,"BookPublished");

        job.setJarByClass(BookPublished1.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setCombinerClass(Combine.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FloatWritable.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);



        Path outputPath = new Path(args[1]);    
                FileInputFormat.addInputPath(job, new Path(args[0]));
                FileOutputFormat.setOutputPath(job, new Path(args[1]));


        outputPath.getFileSystem(conf).delete(outputPath);
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

}

请帮我解决问题。

Answer 1

组合器的输出类型必须匹配映射器的输出类型。 Hadoop 不保证组合器被应用了多少次，甚至根本不保证它被应用了。这就是你的情况。

来自 map ( <Text, IntWritable> ) 的值直接进入 reduce 类型 <Text, Text>预计。