瞌睡中的葡萄虎

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公告

1、MapReduce代码入口

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

2、InputFormat分析

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) ;
}

不同的InputFormat会各自实现不同的文件读取方式以及分片方式,每个输入分片(InputSplit)会被单独的map task作为数据源

3、InputSplit

Mapper的输入是一个一个的输入分片(InputSplit)

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;
    }
}

一个FileSplit对应Mapper的一个输入文件,不管这个文件有多么的小,也是作为一个单独的InputSplit来处理;
在输入文件是由大量小文件组成的场景下,就会有大量的InputSplit,从而需要大量的Mapper的处理;
大量的Mapper Task创建和销毁开销将是巨大的;可以采用CombineFileSplit将多个小文件进行合并再交由Mapper Task处理;

4、FileInputFormat

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;
}

5、PathFilter

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);
    ......
}

PathFilter文件筛选器接口,使用它我们可以控制哪些文件要作为输入,哪些不作为输入;
PathFilter有一个accept(Path)方法,当接收的Path要被包含进来,就返回true,否则返回false;

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("."); 
    }
}; 

6、RecordReader

RecordReader将InputSplit拆分成KEY-VALUE对

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;
}

7、Mapper

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);
        }
    }
}

模板模式的应用:run方法:
1)setup
2)循环从InputSplit中获得到的KV对调用map函数进行处理
3)cleanup

 

至此完成了MapReduce的输入文件是如何被过滤分片读取读出“K-V对”,然后交给Mapper类来处理

 

 

 

posted on 2014-11-28 23:34 瞌睡中的葡萄虎 阅读(...) 评论(...) 编辑 收藏