hadoop - 使用TotalOrderPartitioner MapReduce时遇到错误

我写了下面的程序。
我没有使用TotalOrderPartitioner来运行它，并且运行良好。因此，我认为Mapper或Reducer类没有任何问题。

但是，当我包括TotalOrderPartitioner的代码时，即编写分区文件然后将其放入DistributedCache中时，出现以下错误:真的不知道如何处理。

[train @ sandbox TOTALORDERPARTITIONER]平均$ hadoop jar totalorderpart.jar。平均工作县数

// counties是输入目录，totpart是输出目录



我的代码

package average;

import java.io.IOException;
import java.net.URI;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.util.StringUtils;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.partition.InputSampler;
import org.apache.hadoop.mapreduce.lib.partition.TotalOrderPartitioner;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;


public class AverageJob extends Configured implements Tool {

public enum Counters {MAP, COMINE, REDUCE};

public static class AverageMapper extends Mapper<LongWritable, Text, Text, Text> {

    private Text mapOutputKey = new Text();
    private Text mapOutputValue = new Text();
    @Override
    protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {

        String[] words = StringUtils.split(value.toString(), '\\', ',');
        mapOutputKey.set(words[1].trim());

        StringBuilder moValue = new StringBuilder();
        moValue.append(words[9].trim()).append(",1");
        mapOutputValue.set(moValue.toString());
        context.write(mapOutputKey, mapOutputValue);

        context.getCounter(Counters.MAP).increment(1);
    }
}

public static class AverageCombiner extends Reducer<Text, Text, Text, Text> {

    private Text combinerOutputValue = new Text();

    @Override
    protected void reduce(Text key, Iterable<Text> values, Context context)
            throws IOException, InterruptedException {

        int count=0;
        long sum=0;
        for(Text value: values)
        {
            String[] strValues = StringUtils.split(value.toString(), ',');
            sum+= Long.parseLong(strValues[0]);
            count+= Integer.parseInt(strValues[1]);
        }
        combinerOutputValue.set(sum + "," + count);
        context.write(key, combinerOutputValue);

        context.getCounter(Counters.COMINE).increment(1);
    }
}


public static class AverageReducer extends Reducer<Text, Text, Text, DoubleWritable> {


    private DoubleWritable reduceOutputKey = new DoubleWritable();

    @Override
    protected void reduce(Text key, Iterable<Text> values, Context context)
            throws IOException, InterruptedException {

        int count=0;
        double sum=0;
        for(Text value: values)
        {
            String[] strValues = StringUtils.split(value.toString(), ',');
            sum+= Double.parseDouble(strValues[0]);
            count+= Integer.parseInt(strValues[1]);
        }

        reduceOutputKey.set(sum/count);
        context.write(key, reduceOutputKey);

        context.getCounter(Counters.REDUCE).increment(1);
    }

}


@Override
public int run(String[] args) throws Exception {

    Configuration conf = getConf();
    Job job = Job.getInstance(conf);
    job.setJarByClass(getClass());

    Path in = new Path(args[0]);
    Path out = new Path(args[1]);
    FileInputFormat.setInputPaths(job, in);
    FileOutputFormat.setOutputPath(job, out);

    job.setInputFormatClass(TextInputFormat.class);
    job.setOutputFormatClass(TextOutputFormat.class);

    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(Text.class);

    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(DoubleWritable.class);

    job.setMapperClass(AverageMapper.class);
    job.setCombinerClass(AverageCombiner.class);

    job.setPartitionerClass(TotalOrderPartitioner.class);

    job.setReducerClass(AverageReducer.class);

    job.setNumReduceTasks(6);

    InputSampler.Sampler<Text, Text> sampler = new InputSampler.RandomSampler<Text, Text>(0.2, 6, 5);
    InputSampler.writePartitionFile(job, sampler);

    String partitionFile = TotalOrderPartitioner.getPartitionFile(conf);
    URI partitionUri = new URI(partitionFile + "#" + TotalOrderPartitioner.DEFAULT_PATH);
    job.addCacheFile(partitionUri);

    return job.waitForCompletion(true)?0:1;
}

public static void main(String[] args) {

    int result=0;
    try
    {
        result = ToolRunner.run(new Configuration(), new AverageJob(), args);
        System.exit(result);
    }
    catch (Exception e)
    {
        e.printStackTrace();
    }
}
}

最佳答案

TotalOrderPartitioner不会在Mapper的输出上运行其采样，而是在输入数据集上运行。您的输入格式将LongWritable作为键，将Text作为值。而是，您尝试调用RandomSampler，声称您的格式以Text为键，Text为值。这是InputSampler运行时发现的不匹配项，因此消息



这意味着它试图找到Text作为键(基于您的参数化)，但是却找到了LongWritable。