hadoop自定义inputformat源码

hadoop的inputformat包括他的子类reader是maptask读取数据的重要步骤

一、获得splits-mapper数

1. jobclinet的submitJobInternal，生成split，获取mapper数量

 

public 
  RunningJob submitJobInternal {
    return ugi.doAs(new PrivilegedExceptionAction<RunningJob>() {
....
int maps = writeSplits(context, submitJobDir);//生成split，获取mapper数量
....
}}

 jobclinet的writesplit方法

 

 

private int writeSplits(org.apache.hadoop.mapreduce.JobContext job,
      Path jobSubmitDir) throws IOException,
      InterruptedException, ClassNotFoundException {
    JobConf jConf = (JobConf)job.getConfiguration();
    int maps;
    if (jConf.getUseNewMapper()) {
      maps = writeNewSplits(job, jobSubmitDir);//新api调用此方法
    } else {
      maps = writeOldSplits(jConf, jobSubmitDir);
    }
    return maps;
  }

  2.writeNewSplits新api方法，反射inputformat类，调用getsplit方法，获取split数据，并排序，并返回mapper数量

private <T extends InputSplit>
  int writeNewSplits(JobContext job, Path jobSubmitDir) throws IOException,
      InterruptedException, ClassNotFoundException {
    Configuration conf = job.getConfiguration();
    InputFormat<?, ?> input =
      ReflectionUtils.newInstance(job.getInputFormatClass(), conf);//反射到inputsplit

    List<InputSplit> splits = input.getSplits(job);//调用inputformat子类实现的getsplits方法
    T[] array = (T[]) splits.toArray(new InputSplit[splits.size()]);//生成数组，这么简单的方法写的这么复杂，真够扯的，不懂这样为了什么

    // sort the splits into order based on size, so that the biggest
    // go first
    Arrays.sort(array, new SplitComparator());//splits排序
    JobSplitWriter.createSplitFiles(jobSubmitDir, conf,
        jobSubmitDir.getFileSystem(conf), array);
    return array.length;//mapper数量
  }

 

3.贴上最常用的FileInputSplit的getSplits方法

 

public List<InputSplit> getSplits(JobContext job
                                    ) throws IOException {
    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();
      FileSystem fs = path.getFileSystem(job.getConfiguration());
      long length = file.getLen();
      BlockLocation[] blkLocations = fs.getFileBlockLocations(file, 0, length);
      if ((length != 0) && isSplitable(job, path)) { 
        long blockSize = file.getBlockSize();
        long splitSize = computeSplitSize(blockSize, minSize, maxSize);//获得split文件的最大文件大小

        long bytesRemaining = length;
        while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) {//分解大文件
          int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
          splits.add(new FileSplit(path, length-bytesRemaining, splitSize, 
                                   blkLocations[blkIndex].getHosts()));
          bytesRemaining -= splitSize;
        }
        
        if (bytesRemaining != 0) {
          splits.add(new FileSplit(path, length-bytesRemaining, bytesRemaining, 
                     blkLocations[blkLocations.length-1].getHosts()));
        }
      } else if (length != 0) {
        splits.add(new FileSplit(path, 0, length, blkLocations[0].getHosts()));
      } else { 
        //Create empty hosts array for zero length files
        splits.add(new FileSplit(path, 0, length, new String[0]));
      }
    }
    
    // Save the number of input files in the job-conf
    job.getConfiguration().setLong(NUM_INPUT_FILES, files.size());

    LOG.debug("Total # of splits: " + splits.size());
    return splits;
  }

 

 

 

二、读取keyvalue的过程

1.实例化inputformat，初始化reader

在MapTask类的runNewMapper方法中，生成inputformat和recordreader，并进行初始化，运行mapper

MapTask$NewTrackingRecordReader 由 RecordReader组成，是它的一个代理类

 private <INKEY,INVALUE,OUTKEY,OUTVALUE>
  void runNewMapper {
 // 生成自定义inputformat
    org.apache.hadoop.mapreduce.InputFormat<INKEY,INVALUE> inputFormat =
      (org.apache.hadoop.mapreduce.InputFormat<INKEY,INVALUE>)
        ReflectionUtils.newInstance(taskContext.getInputFormatClass(), job);
.....
//生成自定义recordreader
org.apache.hadoop.mapreduce.RecordReader<INKEY,INVALUE> input =
      new NewTrackingRecordReader<INKEY,INVALUE>
          (split, inputFormat, reporter, job, taskContext);
.....
//初始化recordreader
input.initialize(split, mapperContext);
.....
//运行mapper
mapper.run(mapperContext);
   }

 

2.在运行mapper中，调用context让reader读取key和value，其中使用代理类MapTask$NewTrackingRecordReader，添加并推送读取记录

    mapper代码：

 public void run(Context context) throws IOException, InterruptedException {
    setup(context);
     
    while (context.nextKeyValue()) {
      map(context.getCurrentKey(), context.getCurrentValue(), context);
    }
    cleanup(context);
  }

 MapContext代码：

@Override
  public boolean nextKeyValue() throws IOException, InterruptedException {
    return reader.nextKeyValue();
  }
@Override
  public KEYIN getCurrentKey() throws IOException, InterruptedException {
    return reader.getCurrentKey();
  }

  @Override
  public VALUEIN getCurrentValue() throws IOException, InterruptedException {
    return reader.getCurrentValue();
  }

 MapTask$NewTrackingRecordReader的代码：

 @Override
    public boolean nextKeyValue() throws IOException, InterruptedException {
      boolean result = false;
      try {
        long bytesInPrev = getInputBytes(fsStats);
        result = real.nextKeyValue();//recordreader实际读取数据
        long bytesInCurr = getInputBytes(fsStats);

        if (result) {
          inputRecordCounter.increment(1);//添加读取记录
          fileInputByteCounter.increment(bytesInCurr - bytesInPrev);//记录读取数据
        }
        reporter.setProgress(getProgress());//将reporter的flag置为true，推送记录信息
      } catch (IOException ioe) {
        if (inputSplit instanceof FileSplit) {
          FileSplit fileSplit = (FileSplit) inputSplit;
          LOG.error("IO error in map input file "
              + fileSplit.getPath().toString());
          throw new IOException("IO error in map input file "
              + fileSplit.getPath().toString(), ioe);
        }
        throw ioe;
      }
      return result;
    }

 3.执行完mapper方法，返回到maptask，关闭reader

  

      mapper.run(mapperContext);
      input.close();//关闭inputformat
      output.close(mapperContext);

 

 两个步骤不在同一个线程中完成，生成splits后进入monitor阶段

以上也调用了所有的inputformat虚类的所有方法