实现mapreduce多文件自定义输出

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普通maprduce中通常是有map和reduce两个阶段，在不做设置的情况下，计算结果会以part-000*输出成多个文件，并且输出的文件数量和reduce数量一样，文件内容格式也不能随心所欲。这样不利于后续结果处理。

在hadoop中，reduce支持多个输出,输出的文件名也是可控的，就是继承MultipleTextOutputFormat类，重写generateFileNameForKey方法。如果只是想做到输出结果的文件名可控，实现自己的LogNameMultipleTextOutputFormat类，设置jobconf.setOutputFormat(LogNameMultipleTextOutputFormat.class);就可以了，但是这种方式只限于使用旧版本的hadoop api.如果想采用新版本的api接口或者自定义输出内容的格式等等更多的需求，那么就要自己动手重写一些hadoop api了。

首先需要构造一个自己的MultipleOutputFormat类实现FileOutputFormat类（注意是org.apache.hadoop.mapreduce.lib.output包的FileOutputFormat）

  

import java.io.DataOutputStream;
import java.io.IOException;
import java.util.HashMap;
import java.util.Iterator;


import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.GzipCodec;
import org.apache.hadoop.mapreduce.OutputCommitter;
import org.apache.hadoop.mapreduce.RecordWriter;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.ReflectionUtils;


/**
 * This abstract class extends the FileOutputFormat, allowing to write the
 * output data to different output files. There are three basic use cases for
 * this class. 
 * Created on 2012-07-08
 * @author zhoulongliu
 * @param <K>
 * @param <V>
 */
public abstract class MultipleOutputFormat<K extends WritableComparable<?>, V extends Writable> extends
        FileOutputFormat<K, V> {


   //接口类，需要在调用程序中实现generateFileNameForKeyValue来获取文件名
    private MultiRecordWriter writer = null;


    public RecordWriter<K, V> getRecordWriter(TaskAttemptContext job) throws IOException, InterruptedException {
        if (writer == null) {
            writer = new MultiRecordWriter(job, getTaskOutputPath(job));
        }
        return writer;
    }


    /**
     * get task output path
     * @param conf
     * @return
     * @throws IOException
     */
    private Path getTaskOutputPath(TaskAttemptContext conf) throws IOException {
        Path workPath = null;
        OutputCommitter committer = super.getOutputCommitter(conf);
        if (committer instanceof FileOutputCommitter) {
            workPath = ((FileOutputCommitter) committer).getWorkPath();
        } else {
            Path outputPath = super.getOutputPath(conf);
            if (outputPath == null) {
                throw new IOException("Undefined job output-path");
            }
            workPath = outputPath;
        }
        return workPath;
    }


    /**
     * 通过key, value, conf来确定输出文件名（含扩展名） Generate the file output file name based
     * on the given key and the leaf file name. The default behavior is that the
     * file name does not depend on the key.
     * 
     * @param key the key of the output data
     * @param name the leaf file name
     * @param conf the configure object
     * @return generated file name
     */
    protected abstract String generateFileNameForKeyValue(K key, V value, Configuration conf);


   /**
    * 实现记录写入器RecordWriter类
    * （内部类）
    * @author zhoulongliu
    *
    */
    public class MultiRecordWriter extends RecordWriter<K, V> {
        /** RecordWriter的缓存 */
        private HashMap<String, RecordWriter<K, V>> recordWriters = null;
        private TaskAttemptContext job = null;
        /** 输出目录 */
        private Path workPath = null;


        public MultiRecordWriter(TaskAttemptContext job, Path workPath) {
            super();
            this.job = job;
            this.workPath = workPath;
            recordWriters = new HashMap<String, RecordWriter<K, V>>();
        }


        @Override
        public void close(TaskAttemptContext context) throws IOException, InterruptedException {
            Iterator<RecordWriter<K, V>> values = this.recordWriters.values().iterator();
            while (values.hasNext()) {
                values.next().close(context);
            }
            this.recordWriters.clear();
        }


        @Override
        public void write(K key, V value) throws IOException, InterruptedException {
            // 得到输出文件名
            String baseName = generateFileNameForKeyValue(key, value, job.getConfiguration());
           //如果recordWriters里没有文件名，那么就建立。否则就直接写值。
            RecordWriter<K, V> rw = this.recordWriters.get(baseName);
            if (rw == null) {
                rw = getBaseRecordWriter(job, baseName);
                this.recordWriters.put(baseName, rw);
            }
            rw.write(key, value);
        }


        // ${mapred.out.dir}/_temporary/_${taskid}/${nameWithExtension}
        private RecordWriter<K, V> getBaseRecordWriter(TaskAttemptContext job, String baseName) throws IOException,
                InterruptedException {
            Configuration conf = job.getConfiguration();
           //查看是否使用解码器  
            boolean isCompressed = getCompressOutput(job);
            String keyValueSeparator = ",";
            RecordWriter<K, V> recordWriter = null;
            if (isCompressed) {
                Class<? extends CompressionCodec> codecClass = getOutputCompressorClass(job, GzipCodec.class);
                CompressionCodec codec = ReflectionUtils.newInstance(codecClass, conf);
                Path file = new Path(workPath, baseName + codec.getDefaultExtension());
                FSDataOutputStream fileOut = file.getFileSystem(conf).create(file, false);
                //这里我使用的自定义的OutputFormat 
                recordWriter = new LineRecordWriter<K, V>(new DataOutputStream(codec.createOutputStream(fileOut)),
                        keyValueSeparator);
            } else {
                Path file = new Path(workPath, baseName);
                FSDataOutputStream fileOut = file.getFileSystem(conf).create(file, false);
                //这里我使用的自定义的OutputFormat 
                recordWriter = new LineRecordWriter<K, V>(fileOut, keyValueSeparator);
            }
            return recordWriter;
        }
    }


}

接着你还需要自定义一个LineRecordWriter实现记录写入器RecordWriter类，自定义输出格式。

import java.io.DataOutputStream;
import java.io.IOException;
import java.io.UnsupportedEncodingException;

import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.RecordWriter;
import org.apache.hadoop.mapreduce.TaskAttemptContext;

/**
 * 
 * 重新构造实现记录写入器RecordWriter类
 * Created on 2012-07-08
 * @author zhoulongliu
 * @param <K>
 * @param <V>
 */
public class LineRecordWriter<K, V> extends RecordWriter<K, V> {

    private static final String utf8 = "UTF-8";//定义字符编码格式
    private static final byte[] newline;
    static {
        try {
            newline = "\n".getBytes(utf8);//定义换行符
        } catch (UnsupportedEncodingException uee) {
            throw new IllegalArgumentException("can't find " + utf8 + " encoding");
        }
    }
    protected DataOutputStream out;
    private final byte[] keyValueSeparator;

     //实现构造方法，出入输出流对象和分隔符
    public LineRecordWriter(DataOutputStream out, String keyValueSeparator) {
        this.out = out;
        try {
            this.keyValueSeparator = keyValueSeparator.getBytes(utf8);
        } catch (UnsupportedEncodingException uee) {
            throw new IllegalArgumentException("can't find " + utf8 + " encoding");
        }
    }

    public LineRecordWriter(DataOutputStream out) {
        this(out, "\t");
    }

    private void writeObject(Object o) throws IOException {
        if (o instanceof Text) {
            Text to = (Text) o;
            out.write(to.getBytes(), 0, to.getLength());
        } else {
            out.write(o.toString().getBytes(utf8));
        }
    }
   
    /**
     * 将mapreduce的key,value以自定义格式写入到输出流中
     */
    public synchronized void write(K key, V value) throws IOException {
        boolean nullKey = key == null || key instanceof NullWritable;
        boolean nullValue = value == null || value instanceof NullWritable;
        if (nullKey && nullValue) {
            return;
        }
        if (!nullKey) {
            writeObject(key);
        }
        if (!(nullKey || nullValue)) {
            out.write(keyValueSeparator);
        }
        if (!nullValue) {
            writeObject(value);
        }
        out.write(newline);
    }

    public synchronized void close(TaskAttemptContext context) throws IOException {
        out.close();
    }

}

接着，你实现刚刚重写MultipleOutputFormat类中的generateFileNameForKeyValue方法自定义返回需要输出文件的名称，我这里是以key值中以逗号分割取第一个字段的值作为输出文件名，这样第一个字段值相同的会输出到一个文件中并以其值作为文件名。

 public static class VVLogNameMultipleTextOutputFormat extends MultipleOutputFormat<Text, NullWritable> {
        
        @Override
        protected String generateFileNameForKeyValue(Text key, NullWritable value, Configuration conf) { 
            String sp[] = key.toString().split(",");
            String filename = sp[1];
            try {
                Long.parseLong(sp[1]);
            } catch (NumberFormatException e) {
                filename = "000000000000";
            }
            return filename;
        }


    }

最后就是在job调用时设置了

Configuration conf = getConf();
Job job = new Job(conf);
job.setNumReduceTasks(12);
......
job.setMapperClass(VVEtlMapper.class);
job.setReducerClass(EtlReducer.class);
job.setOutputFormatClass(VVLogNameMultipleTextOutputFormat.class);//设置自定义的多文件输出类
FileInputFormat.setInputPaths(job,new Path(args[0]));
FileOutputFormat.setOutputPath(job,new Path(args[1]));
FileOutputFormat.setCompressOutput(job, true);//设置输出结果采用压缩
FileOutputFormat.setOutputCompressorClass(job, LzopCodec.class); //设置输出结果采用lzo压缩

ok，这样你就完成了支持新的hadoop api自定义的多文件输出mapreduce编写。

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