java – Hadoop mapreduce：用于在MapReduce作业中链接映射器的驱动程序

我有mapreduce工作：
我的代码Map类：

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

    @Override
    public void map(Text key,Text value,Context context)
        throws IOException,InterruptedException {
    }
}

我想使用ChainMapper：

1. Job job = new Job(conf,"Job with chained tasks");
2. job.setJarByClass(MapReduce.class);
3. job.setInputFormatClass(TextInputFormat.class);
4. job.setOutputFormatClass(TextOutputFormat.class);

5. FileInputFormat.setInputPaths(job,new Path(InputFile));
6. FileOutputFormat.setOutputPath(job,new Path(OutputFile));

7. JobConf map1 = new JobConf(false);

8. ChainMapper.addMapper(
        job,MapClass.class,Text.class,true,map1
        );

但它的报告在第8行有一个错误：

Multiple markers at this line
– Occurrence of ‘addMapper’
– The method addMapper(JobConf,Class>,Class,
Class,boolean,JobConf) in the type ChainMapper is not applicable for the arguments (Job,Configuration)
– Debug Current Instruction Pointer
– The method addMapper(JobConf,JobConf) in the type ChainMapper is not applicable for the arguments
(JobConf,JobConf)

解决方法

经过大量的“功夫”,我能够使用ChainMapper / ChainReducer.感谢上次评论user864846.

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 */

package myPKG;

/* 
 * Ajitsen: Sample program for ChainMapper/ChainReducer. This program is modified version of WordCount example available in Hadoop-0.18.0. Added ChainMapper/ChainReducer and made to works in Hadoop 1.0.2. 
 */

import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.mapred.lib.ChainMapper;
import org.apache.hadoop.mapred.lib.ChainReducer;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class ChainWordCount extends Configured implements Tool {

    public static class Tokenizer extends MapReduceBase
    implements Mapper<LongWritable,IntWritable> {

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

        public void map(LongWritable key,OutputCollector<Text,IntWritable> output,Reporter reporter) throws IOException {
            String line = value.toString();
            System.out.println("Line:"+line);
            StringTokenizer itr = new StringTokenizer(line);
            while (itr.hasMoreTokens()) {
                word.set(itr.nextToken());
                output.collect(word,one);
            }
        }
    }

    public static class UpperCaser extends MapReduceBase
    implements Mapper<Text,IntWritable,IntWritable> {

        public void map(Text key,IntWritable value,Reporter reporter) throws IOException {
            String word = key.toString().toUpperCase();
            System.out.println("Upper Case:"+word);
            output.collect(new Text(word),value);    
        }
    }

    public static class Reduce extends MapReduceBase
    implements Reducer<Text,IntWritable> {

        public void reduce(Text key,Iterator<IntWritable> values,Reporter reporter) throws IOException {
            int sum = 0;
            while (values.hasNext()) {
                sum += values.next().get();
            }
            System.out.println("Word:"+key.toString()+"\tCount:"+sum);
            output.collect(key,new IntWritable(sum));
        }
    }

    static int printUsage() {
        System.out.println("wordcount <input> <output>");
        ToolRunner.printGenericCommandUsage(System.out);
        return -1;
    }

    public int run(String[] args) throws Exception {
        JobConf conf = new JobConf(getConf(),ChainWordCount.class);
        conf.setJobName("wordcount");

        if (args.length != 2) {
            System.out.println("ERROR: Wrong number of parameters: " +
                    args.length + " instead of 2.");
            return printUsage();
        }
        FileInputFormat.setInputPaths(conf,args[0]);
        FileOutputFormat.setOutputPath(conf,new Path(args[1]));

        conf.setInputFormat(TextInputFormat.class);
        conf.setOutputFormat(TextOutputFormat.class);

        JobConf mapAConf = new JobConf(false);
        ChainMapper.addMapper(conf,Tokenizer.class,LongWritable.class,IntWritable.class,mapAConf);

        JobConf mapBConf = new JobConf(false);
        ChainMapper.addMapper(conf,UpperCaser.class,mapBConf);

        JobConf reduceConf = new JobConf(false);
        ChainReducer.setReducer(conf,Reduce.class,reduceConf);

        JobClient.runJob(conf);
        return 0;
    }

    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(),new ChainWordCount(),args);
        System.exit(res);
    }
}

编辑最新版本(至少从hadoop 2.6),不需要addMapper中的真正标志. (实际上签名有变化抑制它).

所以它会是公正的

JobConf mapAConf = new JobConf(false);
ChainMapper.addMapper(conf,mapAConf);

java – Hadoop mapreduce：用于在MapReduce作业中链接映射器的驱动程序

解决方法

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