34 MAPREDUCE自定义inputFormat

需求

无论hdfs还是mapreduce，对于小文件都有损效率，实践中，又难免面临处理大量小文件的场景，此时，就需要有相应解决方案。

分析

小文件的优化无非以下几种方式：

1. 在数据采集的时候，就将小文件或小批数据合成大文件再上传HDFS。
2. 在业务处理之前，在HDFS上使用mapreduce程序对小文件进行合并。
3. 在mapreduce处理时，可采用combineInputFormat提高效率。

实现

本节实现的是上述第二种方式

程序的核心机制：

• 自定义一个InputFormat
• 改写RecordReader，实现一次读取一个完整文件封装为KV
• 在输出时使用SequenceFileOutPutFormat输出合并文件

代码如下：

自定义InputFromat

public class WholeFileInputFormat extends
		FileInputFormat {
	//设置每个小文件不可分片,保证一个小文件生成一个key-value键值对
	@Override
	protected boolean isSplitable(JobContext context, Path file) {
		return false;
	}

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

自定义RecordReader

class WholeFileRecordReader extends RecordReader {
	private FileSplit fileSplit;
	private Configuration conf;
	private BytesWritable value = new BytesWritable();
	private boolean processed = false;

	@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 NullWritable getCurrentKey() throws IOException,
			InterruptedException {
		return NullWritable.get();
	}

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

	@Override
	public float getProgress() throws IOException {
		return processed ? 1.0f : 0.0f;
	}

	@Override
	public void close() throws IOException {
		// do nothing
	}
}

定义mapreduce处理流程

public class SmallFilesToSequenceFileConverter extends Configured implements
		Tool {
	static class SequenceFileMapper extends
			Mapper {
		private Text 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, BytesWritable value,
				Context context) throws IOException, InterruptedException {
			context.write(filenameKey, value);
		}
	}

	@Override
	public int run(String[] args) throws Exception {
		Configuration conf = new Configuration();
		System.setProperty("HADOOP_USER_NAME", "hdfs");
		String[] otherArgs = new GenericOptionsParser(conf, args)
				.getRemainingArgs();
		if (otherArgs.length != 2) {
			System.err.println("Usage: combinefiles  ");
			System.exit(2);
		}
		
		Job job = Job.getInstance(conf,"combine small files to sequencefile");
//		job.setInputFormatClass(WholeFileInputFormat.class);
		job.setOutputFormatClass(SequenceFileOutputFormat.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(BytesWritable.class);
		job.setMapperClass(SequenceFileMapper.class);
		return job.waitForCompletion(true) ? 0 : 1;
	}

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