MR mapper数量怎么确定？

看过MR的处理流程的人应该都知道，在MR处理的时候有个split，这个split数量决定了mapper的数量，那split是怎么来的呢？我们在写MR代码的时候也没有接口可以定义split的数量，那split怎么来的？ 有人说是block数量，真是是这样吗？ 我们来看一下源码：

public List<InputSplit> getSplits(JobContext job) throws IOException {
    StopWatch sw = new StopWatch().start();
    long minSize = Math.max(getFormatMinSplitSize(), getMinSplitSize(job));
    long maxSize = getMaxSplitSize(job);

    // generate splits
    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) {
        BlockLocation[] blkLocations;
        if (file instanceof LocatedFileStatus) {
          blkLocations = ((LocatedFileStatus) file).getBlockLocations();
        } else {
          FileSystem fs = path.getFileSystem(job.getConfiguration());
          blkLocations = fs.getFileBlockLocations(file, 0, length);
        }
        if (isSplitable(job, path)) {
          long blockSize = file.getBlockSize();
          long splitSize = computeSplitSize(blockSize, minSize, maxSize);//这里计算splitSize

          long bytesRemaining = length;
          while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) {
            int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
            splits.add(makeSplit(path, length-bytesRemaining, splitSize,
                        blkLocations[blkIndex].getHosts(),
                        blkLocations[blkIndex].getCachedHosts()));
            bytesRemaining -= splitSize;
          }

          if (bytesRemaining != 0) {
            int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
            splits.add(makeSplit(path, length-bytesRemaining, bytesRemaining,
                       blkLocations[blkIndex].getHosts(),
                       blkLocations[blkIndex].getCachedHosts()));
          }
        } else { // not splitable
          splits.add(makeSplit(path, 0, length, blkLocations[0].getHosts(),
                      blkLocations[0].getCachedHosts()));
        }
      } else { 
        //Create empty hosts array for zero length files
        splits.add(makeSplit(path, 0, length, new String[0]));
      }
    }
    // Save the number of input files for metrics/loadgen
    job.getConfiguration().setLong(NUM_INPUT_FILES, files.size());
    sw.stop();
    if (LOG.isDebugEnabled()) {
      LOG.debug("Total # of splits generated by getSplits: " + splits.size()
          + ", TimeTaken: " + sw.now(TimeUnit.MILLISECONDS));
    }
    return splits;
  }

  protected long computeSplitSize(long blockSize, long minSize,
                                  long maxSize) {
    return Math.max(minSize, Math.min(maxSize, blockSize)); //获取splitSize
  }

其中的getMinSplitSize和getMaxSplitSize方法分别用于获取最小InputSplit和最大InputSplit的值，对应的配置参数分别为mapreduce.input.fileinputformat.split.minsize，默认值为1L和mapreduce.input.fileinputformat.split.maxsize，默认值为Long.MAX_VALUE

所以，hadoop中默认的block size为128M，所以split的size一般对应为block的大小，所以，Mapper的数量就是文件个数的数量；

这样可以做到数据本地性，提示效率；

1.如果根据特殊情况的需要非要自定义mapper的数量怎么办？

那就只有修改块的大小、split的最小值和最大值来影响mapper的数量；