MapReduce从输入文件到Mapper处理之间的过程

FileInputFormat.setInputPaths(job, new Path(input)); //设置MapReduce输入格式
job.waitForCompletion(true);

public abstract class InputFormat<K, V> {
    //获取输入文件的分片，仅是逻辑分片，并没有物理分片
    public abstract  List<InputSplit> getSplits(JobContext context);
    
    //创建RecordReader，从InputSplit中读取数据
    public abstract  RecordReader<K,V> createRecordReader(InputSplit split,TaskAttemptContext context) ;
}

public abstract class InputSplit {
  public abstract long getLength();
  public abstract String[] getLocations();
}

public class FileSplit extends InputSplit implements Writable{
    private Path file; //文件路径
    private long start; //分片起始位置
    private long length;  //分片长度
    private String[] hosts; //存储分片的hosts
    
    public FileSplit(Path file, long start, long length, String[] hosts) {
        this.file = file;
        this.start = start;
        this.length = length;
        this.hosts = hosts;
    }
}

public List<InputSplit> getSplits(JobContext job) throws IOException {
    /**
     * getFormatMinSplitSize() = 1
     * job.getConfiguration().getLong(SPLIT_MINSIZE, 1L)
     * SPLIT_MINSIZE = "mapreduce.input.fileinputformat.split.minsize"
     * mapred-default.xml中参数为0
     */
    long minSize = Math.max(getFormatMinSplitSize(), getMinSplitSize(job)); //计算分片的最小值： max(1,0) = 1
    
    /**
     * SPLIT_MAXSIZE = "mapreduce.input.fileinputformat.split.maxsize"
     * mapred-default.xml中参数为空
     */
    long maxSize = getMaxSplitSize(job); //计算分片的最大值：Long.MAX_VALUE
   
    //存储输入文件的分片结果
    List<InputSplit> splits = new ArrayList<InputSplit>();
    List<FileStatus> files = listStatus(job);
    for (FileStatus file: files) {
        Path path = file.getPath();
        long length = file.getLen();
        if (length != 0) {
            ...
            if (isSplitable(job, path)) { //能分片
                long blockSize = file.getBlockSize();
                long splitSize = computeSplitSize(blockSize, minSize, maxSize);{
                    //max(1, min(Long.MAX_VALUE, 64M)) = 64M 默认情况下splitSize=blockSize
                    return Math.max(minSize, Math.min(maxSize, blockSize)); 
                }

                //循环分片，当剩余数据与分片大小比值大于Split_Slop时，继续分片，小于等于时，停止分片
                long bytesRemaining = length;
                while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) { //SPLIT_SLOP = 1.1
                    int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
                    splits.add(makeSplit(path, length-bytesRemaining, splitSize, blkLocations[blkIndex].getHosts()));
                    bytesRemaining -= splitSize;
                }

                //处理余下的数据
                if (bytesRemaining != 0) {
                    int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
                    splits.add(makeSplit(path, length-bytesRemaining, bytesRemaining, blkLocations[blkIndex].getHosts()));
                }
            } else { // 不可分片，整块返回（有些压缩后是不能分片处理的）
                splits.add(makeSplit(path, 0, length, blkLocations[0].getHosts()));
            }
        } else { 
            splits.add(makeSplit(path, 0, length, new String[0]));
        }
    }
    job.getConfiguration().setLong(NUM_INPUT_FILES, files.size()); // 设置输入文件数量
    LOG.debug("Total # of splits: " + splits.size());
    return splits;
}

protected List<FileStatus> listStatus(JobContext job) throws IOException {
    ......
    List<PathFilter> filters = new ArrayList<PathFilter>();
    filters.add(hiddenFileFilter);
    PathFilter jobFilter = getInputPathFilter(job);
    if (jobFilter != null) {
      filters.add(jobFilter);
    }
    PathFilter inputFilter = new MultiPathFilter(filters);
    ......
}

public interface PathFilter {
    boolean accept(Path path);
}

//过滤掉文件名以_或者.开头的文件
private static final PathFilter hiddenFileFilter = new PathFilter(){
    public boolean accept(Path p){
        String name = p.getName(); 
        return !name.startsWith("_") && !name.startsWith("."); 
    }
}; 

public abstract class RecordReader<KEYIN, VALUEIN> implements Closeable {
    //InputSplit初始化
    public abstract void initialize(InputSplit split,TaskAttemptContext context) ;
    
    //读取分片下一个<key, value>对
    public abstract boolean nextKeyValue() throws IOException, InterruptedException;
    
    //获得当前读取到的KEY
    public abstract KEYIN getCurrentKey() throws IOException, InterruptedException;
    
    //获得当前读取到的VALUE
     public abstract  VALUEIN getCurrentValue() throws IOException, InterruptedException;
    
    //跟踪读取分片的进度
    public abstract float getProgress() throws IOException, InterruptedException;
    
    //关闭RecordReader
    public abstract void close() throws IOException;
}

public class Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT> {
    public abstract class Context implements MapContext<KEYIN,VALUEIN,KEYOUT,VALUEOUT> {
    }
  
    //预处理，仅在map task启动时运行一次
    protected void setup(Context context) throws IOException, InterruptedException {
    }

    //对于InputSplit中的每一对<key, value>都会运行一次
    protected void map(KEYIN key, VALUEIN value, Context context) throws IOException, InterruptedException {
        context.write((KEYOUT) key, (VALUEOUT) value);
    }

    //扫尾工作，比如关闭流等
    protected void cleanup(Context context) throws IOException, InterruptedException {
    }
  
    public void run(Context context) throws IOException, InterruptedException {
        setup(context);
        try {
            while (context.nextKeyValue()) {
                map(context.getCurrentKey(), context.getCurrentValue(), context);
            }
        } finally {
            cleanup(context);
        }
    }
}