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
        );

但其报告在第8行有错误：

此行有多个标记 - 'addMapper'的出现 - 方法addMapper（JobConf，Class＆gt;，Class，Class， ChainMapper类型中的Class，Class，boolean，JobConf）不适用于参数（Job，类，类，类，类，类，布尔值，配置） - 调试当前指令指针 - 方法addMapper（JobConf，Class＆gt;，Class，Class， ChainMapper类型中的Class，Class，boolean，JobConf）不适用于参数（JobConf，Class，Class，Class，Class，Class，boolean，JobConf）

Answer 1

在经历了很多“功夫”后，我能够使用ChainMapper/ChainReducer。感谢最后的评论 user864846。

/**
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

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);
    }
}

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

4 个答案: