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hadoop中的MultipleOutputFormat
EN

Stack Overflow用户
提问于 2010-08-16 14:42:31
回答 3查看 8.5K关注 0票数 16

我是Hadoop的新手。我正在试用字数统计程序。

现在,为了尝试多个输出文件,我使用了MultipleOutputFormat。这个链接帮助我做到了这一点。http://hadoop.apache.org/common/docs/r0.19.0/api/org/apache/hadoop/mapred/lib/MultipleOutputs.html

在我的司机课上我有

代码语言:javascript
复制
    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);`

我的reduce类变成了这个

代码语言:javascript
复制
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();
    }
}

一切正常,但我得到了很多文件,(每个map-reduce有一个奇数和一个偶数)

问题是:我如何才能只有2个输出文件(奇数和偶数),以便每个map-reduce的每个奇数输出都写入到那个奇数文件中,并且对偶数也是如此。

EN

回答 3

Stack Overflow用户

回答已采纳

发布于 2010-08-16 21:01:38

每个reducer都使用一个OutputFormat来写入记录。这就是为什么你会在每个reducer中得到一组奇偶文件。这是为了让每个reducer可以并行执行写操作而设计的。

如果您只需要一个奇数和单个偶数文件,则需要将mapred.reduce.tasks设置为1。但是性能将受到影响,因为所有映射器都将提供给单个reducer。

另一种选择是更改读取这些文件的过程以接受多个输入文件,或者编写一个将这些文件合并在一起的单独过程。

票数 3
EN

Stack Overflow用户

发布于 2012-03-07 22:54:51

我为此编写了一个类。把它用在你的工作中:

代码语言:javascript
复制
job.setOutputFormatClass(m_customOutputFormatClass);

这是我的班级:

代码语言:javascript
复制
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();
        }
    }

}
票数 3
EN

Stack Overflow用户

发布于 2010-08-17 15:17:58

将根据reducers的数量生成多个输出文件。

您可以使用hadoop dfs -getmerge合并输出

票数 1
EN
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接:

https://stackoverflow.com/questions/3491105

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