5

I'm relatively new to Hadoop and trying to figure out how to programmatically chain jobs (multiple mappers, reducers) with ChainMapper, ChainReducer. I've found a few partial examples, but not a single complete and working one.

My current test code is

public class ChainJobs extends Configured implements Tool {

public static class Map 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();
        StringTokenizer tokenizer = new StringTokenizer(line);
        while (tokenizer.hasMoreTokens()) {
            word.set(tokenizer.nextToken());
            output.collect(word, one);
        }
    }
}

public static class Map2 extends MapReduceBase implements Mapper<Text, IntWritable, Text, IntWritable> {

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

    @Override
    public void map(Text key, IntWritable value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
        String line = value.toString();
        StringTokenizer tokenizer = new StringTokenizer(line);
        while (tokenizer.hasMoreTokens()) {
            word.set(tokenizer.nextToken().concat("Justatest"));
            output.collect(word, one);
        }
    }
}

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

    @Override
    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();
        }
        output.collect(key, new IntWritable(sum));
    }
}

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

    Configuration conf = getConf();
    JobConf job = new JobConf(conf);

    job.setJobName("TestforChainJobs");
    FileInputFormat.setInputPaths(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));

    JobConf map1Conf = new JobConf(false);
    ChainMapper.addMapper(job, Map.class, LongWritable.class, Text.class, Text.class, IntWritable.class, true, map1Conf);

    JobConf map2Conf = new JobConf(false);
    ChainMapper.addMapper(job, Map2.class, Text.class, IntWritable.class, Text.class, IntWritable.class, true, map2Conf);

    JobConf reduceConf = new JobConf(false);
    ChainReducer.setReducer(job, Reduce.class, Text.class, IntWritable.class, Text.class, IntWritable.class, true, reduceConf);

    JobClient.runJob(job);
    return 0;

     }

}

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

But it fails with

MapAttempt TASK_TYPE="MAP" TASKID="task_201210162337_0009_m_000000" TASK_ATTEMPT_ID="attempt_201210162337_0009_m_000000_0" TASK_STATUS="FAILED" FINISH_TIME="1350397216365" HOSTNAME="localhost\.localdomain" ERROR="java\.lang\.RuntimeException: Error in configuring object
    at org\.apache\.hadoop\.util\.ReflectionUtils\.setJobConf(ReflectionUtils\.java:106)
    at org\.apache\.hadoop\.util\.ReflectionUtils\.setConf(ReflectionUtils\.java:72)
    at org\.apache\.hadoop\.util\.ReflectionUtils\.newInstance(ReflectionUtils\.java:130)
    at org\.apache\.hadoop\.mapred\.MapTask\.runOldMapper(MapTask\.java:389)
    at org\.apache\.hadoop\.mapred\.MapTask\.run(MapTask\.java:327)
    at org\.apache\.hadoop\.mapred\.Child$4\.run(Child\.java:268)
    at java\.security\.AccessController\.doPrivileged(Native Method)
    at javax\.security\.auth\.Subject\.doAs(Subject\.java:396)

Any hints or a very simple working example much appreciated.

4

1 回答 1

7


我已经基于链映射器编写了一个字数统计作业。该代码已在新 API 上编写,并且运行良好 :)

import java.io.IOException;


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.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.chain.ChainMapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

//implementing CHAIN MAPREDUCE without using custom format




//SPLIT MAPPER
class SplitMapper extends Mapper<Object,Text,Text,IntWritable>
{
    private IntWritable dummyValue=new IntWritable(1);
    //private String content;
    private String tokens[];
    @Override
    public void map(Object key,Text value,Context context)throws IOException,InterruptedException{
        tokens=value.toString().split(" ");
        for(String x:tokens)
        {
        context.write(new Text(x), dummyValue);
        }
    }   
}




//UPPER CASE MAPPER
class UpperCaseMapper extends Mapper<Text,IntWritable,Text,IntWritable>
{
    @Override
    public void map(Text key,IntWritable value,Context context)throws IOException,InterruptedException{
        String val=key.toString().toUpperCase();
        Text newKey=new Text(val);
        context.write(newKey, value);
    }
}



//ChainMapReducer
class ChainMapReducer extends Reducer<Text,IntWritable,Text,IntWritable>
{
    private int sum=0;
    @Override
    public void reduce(Text key,Iterable<IntWritable>values,Context context)throws IOException,InterruptedException{
        for(IntWritable value:values)
        {
            sum+=value.get();
        }
        context.write(key, new IntWritable(sum));
    }
}
public class FirstClass extends Configured implements Tool{
    static Configuration cf;
    public int run (String args[])throws IOException,InterruptedException,ClassNotFoundException{
        cf=new Configuration();

        //bypassing the GenericOptionsParser part and directly running into job declaration part
        Job j=Job.getInstance(cf);

        /**************CHAIN MAPPER AREA STARTS********************************/
        Configuration splitMapConfig=new Configuration(false);
        //below we add the 1st mapper class under ChainMapper Class
        ChainMapper.addMapper(j, SplitMapper.class, Object.class, Text.class, Text.class, IntWritable.class, splitMapConfig);

        //configuration for second mapper
        Configuration upperCaseConfig=new Configuration(false);
        //below we add the 2nd mapper that is the lower case mapper to the Chain Mapper class
        ChainMapper.addMapper(j, UpperCaseMapper.class, Text.class, IntWritable.class, Text.class, IntWritable.class, upperCaseConfig);
        /**************CHAIN MAPPER AREA FINISHES********************************/

        //now proceeding with the normal delivery
        j.setJarByClass(FirstClass.class);
        j.setCombinerClass(ChainMapReducer.class);
        j.setOutputKeyClass(Text.class);
        j.setOutputValueClass(IntWritable.class);
        Path p=new Path(args[1]);

        //set the input and output URI
        FileInputFormat.addInputPath(j, new Path(args[0]));
        FileOutputFormat.setOutputPath(j, p);
        p.getFileSystem(cf).delete(p, true);
        return j.waitForCompletion(true)?0:1;
    }
    public static void main(String args[])throws Exception{
        int res=ToolRunner.run(cf, new FirstClass(), args);
        System.exit(res);
    }
}

部分输出如下所示

A       619
ACCORDING       636
ACCOUNT 638
ACROSS? 655
ADDRESSES       657
AFTER   674
AGGREGATING,    687
AGO,    704
ALL     721
ALMOST  755
ALTERING        768
AMOUNT  785
AN      819
ANATOMY 820
AND     1198
ANXIETY 1215
ANY     1232
APACHE  1300
APPENDING       1313
APPLICATIONS    1330
APPLICATIONS.   1347
APPLICATIONS.�        1364
APPLIES 1381
ARCHITECTURE,   1387
ARCHIVES        1388
ARE     1405
AS      1422
BASED   1439

您可能会看到一些特殊或不需要的字符,因为我没有使用任何清理来删除标点符号。我只是专注于链映射器的工作。谢谢 :)

于 2015-11-19T13:12:53.443 回答