mapreduce将若干小文件合成大文件

1、思路：

http://blog.yfteach.com/?p=815，注意原文中有一个错误，就是FileInputformat中并没有找到createRecordReader这个方法，应该在TextInputFormat中有，而不是textFileInputFormat

2、编码：

      第一步:编写将整个文件作为一条记录处理的类，即实现FileInputFormat.

注意：FileInputFormat本身有很多子类，也实现了很多不同的输入格式，如下

       特别注意：KeyValueTextInputFormat这个是以一行的形式作为输入的，默认分隔符是Tab键，比如可以使用KeyValueTextInputFormat对WordCount程序进行修改

package com.SmallFilesToSequenceFileConverter.hadoop;

import java.io.IOException;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.JobContext;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

//将整个文件作为一条记录处理
public class WholeFileInputFormat extends FileInputFormat{

	//表示文件不可分
	@Override
	protected boolean isSplitable(JobContext context, Path filename) {
		return false;
	}

	@Override
	public RecordReader createRecordReader(
			InputSplit split, TaskAttemptContext context) throws IOException,
			InterruptedException {
		WholeRecordReader reader=new WholeRecordReader();
		reader.initialize(split, context);
		return reader;
	}
  
}

第二步：实现RecordReader,为自定义的InputFormat服务

package com.SmallFilesToSequenceFileConverter.hadoop;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;

//实现RecordReader,为自定义的InputFormat服务
public class WholeRecordReader extends RecordReader{

	private FileSplit fileSplit;
	private Configuration conf;
	private Text value=new Text();
	private boolean processed=false;//表示记录是否被处理过
	@Override
	public NullWritable getCurrentKey() throws IOException,
			InterruptedException {
		return NullWritable.get();
	}

	@Override
	public Text getCurrentValue() throws IOException,
			InterruptedException {
		return value;
	}

	@Override
	public float getProgress() throws IOException, InterruptedException {
		 return processed? 1.0f : 0.0f;
	}
	@Override
	public void initialize(InputSplit split, TaskAttemptContext context)
			throws IOException, InterruptedException {
		this.fileSplit=(FileSplit)split;
	    this.conf=context.getConfiguration();
	}

	@Override
	public boolean nextKeyValue() throws IOException, InterruptedException {
		if(!processed)
		{
			byte[]contents=new byte[(int)fileSplit.getLength()];
			Path file=fileSplit.getPath();
			FileSystem fs=file.getFileSystem(conf);
			FSDataInputStream in=null;
			try{
				in=fs.open(file);
				IOUtils.readFully(in, contents, 0, contents.length);
			    value.set(contents,0,contents.length);
			}
			finally{
				IOUtils.closeStream(in);
			}
			processed=true;
			return true;
		}
		return false;
	}

	

	@Override
	public void close() throws IOException {
		// TODO Auto-generated method stub
		
	}

}

第三步：编写主类

package com.SmallFilesToSequenceFileConverter.hadoop;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class SmallFilesToSequenceFileConverter{
	private static class SequenceFileMapper extends Mapper
	{
		private Text filenameKey;
		//setup在task之前调用，用来初始化filenamekey
		
		@Override
		protected void setup(Context context)
				throws IOException, InterruptedException {
			InputSplit split=context.getInputSplit();
		    Path path=((FileSplit)split).getPath();
		    filenameKey=new Text(path.toString());
		}
		
		@Override
		protected void map(NullWritable key,Text value,Context context)
				throws IOException, InterruptedException {
			context.write(filenameKey, value);
		}
	}
	
	public static void main(String[] args) throws Exception {
		Configuration conf=new Configuration();
		Job job=Job.getInstance(conf,"SmallFilesToSequenceFileConverter");
		
		job.setJarByClass(SmallFilesToSequenceFileConverter.class);
		
		job.setInputFormatClass(WholeFileInputFormat.class);
		//job.setOutputFormatClass(TextOutputFormat.class);
		
		
		job.setMapperClass(SequenceFileMapper.class);
		
		//设置最终的输出
	    job.setOutputKeyClass(Text.class);
	    job.setOutputValueClass(Text.class);
	    
		FileInputFormat.addInputPath(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job,new Path(args[1]));
	
	    System.exit(job.waitForCompletion(true) ? 0:1);
	}
}

3、测试

原始文件：在input目录下有a、b、c、d、e五个文件，

a文件的数据是:a a a a a 

                           a a a a a

其他同理，

得到最终的结果如下：

可以看到文件名和数据为一行存在同一个文件当中！