MultipleOutputFormat in hadoop

Question

I'm a newbie in Hadoop. I'm trying out the Wordcount program.

Now to try out multiple output files, i use MultipleOutputFormat. this link helped me in doing it. http://hadoop.apache.org/common/docs/r0.19.0/api/org/apache/hadoop/mapred/lib/MultipleOutputs.html

in my driver class i had

    MultipleOutputs.addNamedOutput(conf, "even",
            org.apache.hadoop.mapred.TextOutputFormat.class, Text.class,
            IntWritable.class);

    MultipleOutputs.addNamedOutput(conf, "odd",
            org.apache.hadoop.mapred.TextOutputFormat.class, Text.class,
            IntWritable.class);`

and my reduce class became this

public static class Reduce extends MapReduceBase implements
        Reducer<Text, IntWritable, Text, IntWritable> {
    MultipleOutputs mos = null;

    public void configure(JobConf job) {
        mos = new MultipleOutputs(job);
    }

    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();
        }
        if (sum % 2 == 0) {
            mos.getCollector("even", reporter).collect(key, new IntWritable(sum));
        }else {
            mos.getCollector("odd", reporter).collect(key, new IntWritable(sum));
        }
        //output.collect(key, new IntWritable(sum));
    }
    @Override
    public void close() throws IOException {
        // TODO Auto-generated method stub
    mos.close();
    }
}

Things worked , but i get LOT of files, (one odd and one even for every map-reduce)

Question is : How can i have just 2 output files (odd & even) so that every odd output of every map-reduce gets written into that odd file, and same for even.

Answer 1

you may try to change the output file name (Reducer output), since HDFS supports append operations only, then it will collect all Temp-r-0000x files (partitions) from all reducers and put them together in one file.

here the class you need to create which overrides methods in TextOutputFormat:

import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import java.util.Map.Entry;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.RecordWriter;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;



public class CustomNameMultipleFileOutputFormat<K, V> extends TextOutputFormat<K, V> {

    private String folderName;

    private class MultipleFilesRecordWriter extends RecordWriter<K, V> {

        private Map<String, RecordWriter<K, V>> fileNameToWriter;
        private FolderNameExtractor<K, V> fileNameExtractor;
        private TaskAttemptContext job;
        
        

        public MultipleFilesRecordWriter(FolderNameExtractor<K, V> fileNameExtractor, TaskAttemptContext job) {
            fileNameToWriter = new HashMap<String, RecordWriter<K, V>>();
            this.fileNameExtractor = fileNameExtractor;
            this.job = job;
        }

        @Override
        public void write(K key, V value) throws IOException, InterruptedException {
            String fileName = "**[FOLDER_NAME_INCLUDING_SUB_DIRS]**";//fileNameExtractor.extractFolderName(key, value);
            
            RecordWriter<K, V> writer = fileNameToWriter.get(fileName);
            if (writer == null) {
                writer = createNewWriter(fileName, fileNameToWriter, job);
                if (writer == null) {
                    throw new IOException("Unable to create writer for path: " + fileName);
                }
            }
            writer.write(key, value);
        }

        @Override
        public void close(TaskAttemptContext context) throws IOException, InterruptedException {
            for (Entry<String, RecordWriter<K, V>> entry : fileNameToWriter.entrySet()) {
                entry.getValue().close(context);
            }
        }

    

    }

    private synchronized RecordWriter<K, V> createNewWriter(String folderName,
            Map<String, RecordWriter<K, V>> fileNameToWriter, TaskAttemptContext job) {
        try {
            this.folderName = folderName;
            RecordWriter<K, V> writer = super.getRecordWriter(job);
            this.folderName = null;
            fileNameToWriter.put(folderName, writer);
            
            return writer;
        } catch (Exception e) {
            e.printStackTrace();
            return null;
        }
    }

    @Override
    public Path getDefaultWorkFile(TaskAttemptContext context, String extension) throws IOException {
        Path path = super.getDefaultWorkFile(context, extension);
     
        if (folderName != null) {
            String newPath = path.getParent().toString() + "/" + folderName + "/**[ONE_FILE_NAME]**";
            
            path = new Path(newPath);
      
        }
        return path;
    }

    @Override
    public RecordWriter<K, V> getRecordWriter(TaskAttemptContext job) throws IOException, InterruptedException {
        return new MultipleFilesRecordWriter(getFolderNameExtractor(), job);
    }

    public FolderNameExtractor<K, V> getFolderNameExtractor() {
        return new KeyFolderNameExtractor<K, V>();
    }

    public interface FolderNameExtractor<K, V> {
        public String extractFolderName(K key, V value);
    }

    private static class KeyFolderNameExtractor<K, V> implements FolderNameExtractor<K, V> {
        public String extractFolderName(K key, V value) {
            return key.toString();
        }
    }

}

then Reducer/Mapper:

public static class ExtraLabReducer extends Reducer<CustomKeyComparable, Text, CustomKeyComparable, Text>
{
    MultipleOutputs multipleOutputs;

    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        multipleOutputs = new MultipleOutputs(context);
    }

    @Override
    public void reduce(CustomKeyComparable key, Iterable<Text> values, Context context) throws IOException, InterruptedException
    {
        for(Text d : values)
        {
             **multipleOutputs.write**("batta",key, d,**"[EXAMPLE_FILE_NAME]"**);
        }
        
    }
    
    @Override
    protected void cleanup(Context context) throws IOException, InterruptedException {
        multipleOutputs.close();
    }
    
}

then in job config:

        Job job = new Job(getConf(), "ExtraLab");
    job.setJarByClass(ExtraLab.class);

    job.setMapperClass(ExtraLabMapper.class);
    job.setReducerClass(ExtraLabReducer.class);

    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(DoubleWritable.class);
    
    job.setMapOutputKeyClass(CustomKeyComparable.class);
    job.setMapOutputValueClass(Text.class);

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

    
    FileInputFormat.addInputPath(job, new Path(args[0]));
    //adding one more reducer
    job.setNumReduceTasks(2);
    
    LazyOutputFormat.setOutputFormatClass(job, TextOutputFormat.class);

    MultipleOutputs.addNamedOutput(job,"batta", CustomNameMultipleFileOutputFormat.class,CustomKeyComparable.class,Text.class);

Answer 2

I wrote a class for doing this. Just use it your job:

job.setOutputFormatClass(m_customOutputFormatClass);

This is the my class:

import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import java.util.Map.Entry;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.RecordWriter;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

/**
 * TextOutputFormat extension which enables writing the mapper/reducer's output in multiple files.<br>
 * <p>
 * <b>WARNING</b>: The number of different folder shuoldn't be large for one mapper since we keep an
 * {@link RecordWriter} instance per folder name.
 * </p>
 * <p>
 * In this class the folder name is defined by the written entry's key.<br>
 * To change this behavior simply extend this class and override the
 * {@link HdMultipleFileOutputFormat#getFolderNameExtractor()} method and create your own
 * {@link FolderNameExtractor} implementation.
 * </p>
 * 
 * 
 * @author ykesten
 * 
 * @param <K> - Keys type
 * @param <V> - Values type
 */
public class HdMultipleFileOutputFormat<K, V> extends TextOutputFormat<K, V> {

    private String folderName;

    private class MultipleFilesRecordWriter extends RecordWriter<K, V> {

        private Map<String, RecordWriter<K, V>> fileNameToWriter;
        private FolderNameExtractor<K, V> fileNameExtractor;
        private TaskAttemptContext job;

        public MultipleFilesRecordWriter(FolderNameExtractor<K, V> fileNameExtractor, TaskAttemptContext job) {
            fileNameToWriter = new HashMap<String, RecordWriter<K, V>>();
            this.fileNameExtractor = fileNameExtractor;
            this.job = job;
        }

        @Override
        public void write(K key, V value) throws IOException, InterruptedException {
            String fileName = fileNameExtractor.extractFolderName(key, value);
            RecordWriter<K, V> writer = fileNameToWriter.get(fileName);
            if (writer == null) {
                writer = createNewWriter(fileName, fileNameToWriter, job);
                if (writer == null) {
                    throw new IOException("Unable to create writer for path: " + fileName);
                }
            }
            writer.write(key, value);
        }

        @Override
        public void close(TaskAttemptContext context) throws IOException, InterruptedException {
            for (Entry<String, RecordWriter<K, V>> entry : fileNameToWriter.entrySet()) {
                entry.getValue().close(context);
            }
        }

    }

    private synchronized RecordWriter<K, V> createNewWriter(String folderName,
            Map<String, RecordWriter<K, V>> fileNameToWriter, TaskAttemptContext job) {
        try {
            this.folderName = folderName;
            RecordWriter<K, V> writer = super.getRecordWriter(job);
            this.folderName = null;
            fileNameToWriter.put(folderName, writer);
            return writer;
        } catch (Exception e) {
            e.printStackTrace();
            return null;
        }
    }

    @Override
    public Path getDefaultWorkFile(TaskAttemptContext context, String extension) throws IOException {
        Path path = super.getDefaultWorkFile(context, extension);
        if (folderName != null) {
            String newPath = path.getParent().toString() + "/" + folderName + "/" + path.getName();
            path = new Path(newPath);
        }
        return path;
    }

    @Override
    public RecordWriter<K, V> getRecordWriter(TaskAttemptContext job) throws IOException, InterruptedException {
        return new MultipleFilesRecordWriter(getFolderNameExtractor(), job);
    }

    public FolderNameExtractor<K, V> getFolderNameExtractor() {
        return new KeyFolderNameExtractor<K, V>();
    }

    public interface FolderNameExtractor<K, V> {
        public String extractFolderName(K key, V value);
    }

    private static class KeyFolderNameExtractor<K, V> implements FolderNameExtractor<K, V> {
        public String extractFolderName(K key, V value) {
            return key.toString();
        }
    }

}

Answer 3

Multiple Output files will be generated based on number of reducers.

You can use hadoop dfs -getmerge to merged outputs

Answer 4

Each reducer uses an OutputFormat to write records to. So that's why you are getting a set of odd and even files per reducer. This is by design so that each reducer can perform writes in parallel.

If you want just a single odd and single even file, you'll need to set mapred.reduce.tasks to 1. But performance will suffer, because all the mappers will be feeding into a single reducer.

Another option is to change the process the reads these files to accept multiple input files, or write a separate process that merges these files together.