java - Hadoop mapreduce : Driver for chaining mappers within a MapReduce job

Question

我有 mapreduce 工作:我的代码 map 类:

public static class MapClass extends Mapper<Text, 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, 
        Text.class, 
        Text.class, 
        Text.class, 
        true, 
        map1
        ); 

但它的报告在第 8 行有错误:

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

Answer 1

经过大量的“功夫”，我能够使用ChainMapper/ChainReducer。感谢 user864846 的最后评论。

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 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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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, Text, Text, IntWritable> {

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

        public void map(LongWritable key, Text value, 
                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, Text, IntWritable> {

        public void map(Text key, IntWritable value, 
                OutputCollector<Text, IntWritable> output, 
                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, Text, IntWritable> {

        public void reduce(Text key, Iterator<IntWritable> values,
                OutputCollector<Text, IntWritable> output, 
                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, Text.class, Text.class, IntWritable.class, true, mapAConf);

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

        JobConf reduceConf = new JobConf(false);
        ChainReducer.setReducer(conf, Reduce.class, Text.class, IntWritable.class, Text.class, IntWritable.class, true, 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 在最新版本中(至少从 hadoop 2.6 开始)，不需要 addMapper 中的 true 标志。 (实际上签名有变化抑制它`)。

那就是

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