Hadoop InputFormat详解

InputFormat是MapReduce编程模型包括5个可编程组件之一，其余4个是Mapper、Partitioner、Reducer和OutputFormat。

新版Hadoop InputFormat是一个抽象类，之前的InputFormat是一个接口。

InputFormat类有两个抽象方法。

方法getSplits将输入数据切分成InputSlits，InputSplits的个数即为map tasks的个数，InputSplits的大小默认为块大小，即64M
public abstract List<InputSplit> getSplits(JobContext context) throws IOException, InterruptedException;
方法createRecordReader将每个InputSplit解析成RecordReader, 再依次将RecordReader解析成<K,V>对 
public abstract RecordReader<K,V> createRecordReader(InputSplit split,TaskAttemptContext context) throws IOException,InterruptedException;

也就是说InputFormat完成以下工作：

 InputFile --> InputSplits --> RecordReader --> <K,V>

FileInputFormat类的getSplits方法实现了文件切分。

 

InputFormat的子类，其中TextInputFormat便是最常用的，它的<K,V>就代表<行偏移,该行内容>

自己实现的一个RecordReader

package tokenize.inputformat;

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.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.CombineFileSplit;

public class MyRecordReader extends RecordReader<Text, Text> {

    private CombineFileSplit combineFileSplit; // 当前处理的分片
    private int totalLength;                   // 分片包含的文件数量
    private int currentIndex;                  // 当前处理的文件索引
    private float currentProgress = 0;         // 当前的进度
    private Text currentKey = new Text();      // 当前的Key
    private Text currentValue = new Text();    // 当前的Value
    private Configuration conf;                // 任务信息
    private boolean processed;                 // 记录当前文件是否已经读取

    public MyRecordReader(CombineFileSplit combineFileSplit,
            TaskAttemptContext context, Integer index) throws IOException {
        super();
        this.currentIndex = index;
        this.combineFileSplit = combineFileSplit;
        conf = context.getConfiguration();
        totalLength = combineFileSplit.getPaths().length;
        processed = false;
    }

    @Override
    public void initialize(InputSplit split, TaskAttemptContext context)
            throws IOException, InterruptedException {
    }

    @Override
    public Text getCurrentKey() throws IOException, InterruptedException {
        return currentKey;
    }

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

    @Override
    public float getProgress() throws IOException {
        if (currentIndex >= 0 && currentIndex < totalLength) {
            currentProgress = (float) currentIndex / totalLength;
            return currentProgress;
        }
        return currentProgress;
    }

    @Override
    public void close() throws IOException {
    }

    @Override
    public boolean nextKeyValue() throws IOException {
        if (!processed) {    // 如果文件未处理则读取文件并设置key-value
            // set key
            Path file = combineFileSplit.getPath(currentIndex);
            currentKey.set(file.getParent().getName()); // category's name
            // set value
            FSDataInputStream in = null;
            byte[] contents = new byte[(int)combineFileSplit.getLength(currentIndex)];
            try {
                FileSystem fs = file.getFileSystem(conf);
                in = fs.open(file);
                in.readFully(contents);
                currentValue.set(contents);
            } catch (Exception e) {
            } finally {
                in.close();
            }
            processed = true;
            return true;
        }
        return false;        //如果文件已经处理，必须返回false
    }

}

package tokenize.inputformat;

import java.io.IOException;

import org.apache.hadoop.fs.Path;
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.CombineFileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.CombineFileRecordReader;
import org.apache.hadoop.mapreduce.lib.input.CombineFileSplit;

public class MyInputFormat extends CombineFileInputFormat<Text, Text> {
    /**
     *   make sure file will not be splitted
     */
    @Override
    protected boolean isSplitable(JobContext context, Path file) {
        return false;
    }

    /**
     *   specify record reader
     */
    @Override
    public RecordReader<Text, Text> createRecordReader(InputSplit split, TaskAttemptContext context) throws IOException {
        CombineFileRecordReader<Text, Text> recordReader =     new CombineFileRecordReader<Text, Text>(
                (CombineFileSplit)split, context, MyRecordReader.class);
        return recordReader;
    }

}