如何在Hadoop中使用CombineFileInputFormat

Question

我想使用 Hadoop 0.20.0

/0.20.2 的 CombineFileInputFormat，这样它就可以处理每条记录 1 个文件，并且也不会在数据上妥协 - 局部性（它通常会处理）。

Tom White的Hadoop Definitive Guide中提到了这一点，但他没有展示如何做到这一点。相反，他转向序列文件。

我对记录阅读器中处理变量的含义感到非常困惑。任何代码示例都将提供巨大的帮助。

提前谢谢..

Answer 1

检查以下用于组合文件输入格式的输入格式。

import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
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.CombineFileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.CombineFileRecordReader;
import org.apache.hadoop.mapreduce.lib.input.CombineFileSplit;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.input.LineRecordReader;

/**
 * CustomInputformat which implements the createRecordReader of abstract class CombineFileInputFormat
 */
public class MyCombineFileInputFormat extends CombineFileInputFormat {
    public static class MyRecordReader extends RecordReader<LongWritable,Text>{
        private LineRecordReader delegate=null;
        private int idx;
        public MyRecordReader(CombineFileSplit split,TaskAttemptContext taskcontext ,Integer idx) throws IOException {
            this.idx=idx;
            delegate = new LineRecordReader();
        }
        @Override
        public void close() throws IOException {
            delegate.close();
        }
        @Override
        public float getProgress() {
            try {
                return delegate.getProgress();
            }
            catch(Exception e) {
                return 0;
            }
        }
        @Override
        public void initialize(InputSplit split, TaskAttemptContext taskcontext) throws IOException {
            CombineFileSplit csplit=(CombineFileSplit)split;
            FileSplit fileSplit = new FileSplit(csplit.getPath(idx), csplit.getOffset(idx), csplit.getLength(idx), csplit.getLocations());
            delegate.initialize(fileSplit, taskcontext);
        }
        @Override
        public LongWritable getCurrentKey() throws IOException,
                InterruptedException {
            return delegate.getCurrentKey();
        }

        @Override
        public Text getCurrentValue() throws IOException, InterruptedException {
            return delegate.getCurrentValue();
        }
        @Override
        public boolean nextKeyValue() throws IOException, InterruptedException {
            return delegate.nextKeyValue();
        }
    }
    @SuppressWarnings("unchecked")
    @Override
    public RecordReader createRecordReader(InputSplit split,TaskAttemptContext taskcontext) throws IOException {
        return new CombineFileRecordReader((CombineFileSplit) split, taskcontext, MyRecordReader.class);
    }
}

Answer 2

这是从所谓的"新API"使用CombineFileInputFormat的最简单方法。 假设您的实际输入格式是 MyFormat，它适用于 MyKey 的键和 MyValue 的值（例如，可能是 SequenceFileInputFormat< MyKey, MyValue > 的某个子类）。

public class CombinedMyFormat extends CombineFileInputFormat< MyKey, MyValue > {
    // exists merely to fix the key/value types and
    // inject the delegate format to the superclass
    // if MyFormat does not use state, consider a constant instead
    private static class CombineMyKeyMyValueReaderWrapper
    extends CombineFileRecordReaderWrapper< MyKey, MyValue > {
        protected CombineMyKeyMyValueReaderWrapper(
            CombineFileSplit split, TaskAttemptContext ctx, Integer idx
        ) throws IOException, InterruptedException {
            super( new MyFormat(), split, ctx, idx );
        }
    }
    @Override
    public RecordReader< MyKey, MyValue > createRecordReader(
        InputSplit split, TaskAttemptContext ctx
    ) throws IOException {
        return new CombineFileRecordReader< MyKey, MyValue >(
            ( CombineFileSplit )split, ctx, CombineMyKeyMyValueReaderWrapper.class
        );
    }
}

在您的工作驱动程序中，您现在应该能够直接CombinedMyFormat进行MyFormat。 您还应该设置最大拆分大小属性，以防止 Hadoop 将整个输入合并为单个拆分。