编程语言
首页 > 编程语言> > MapReduce——客户端提交任务源码分析

MapReduce——客户端提交任务源码分析

作者:互联网

计算向数据移动

MR程序并不会在客户端执行任何的计算操作,它是为计算工作做好准备,例如计算出切片信息,直接影响到Map任务的并行度。

在Driver中提交任务时,会写到这样的语句:

  boolean result = job.waitForCompletion(true);

进入到waitForCompletion中:

public boolean waitForCompletion(boolean verbose) throws IOException, InterruptedException,
ClassNotFoundException {
    if (state == JobState.DEFINE) {
       // 提交任务语句
      submit();
    }
                                 ..............

继续跟进 submit():

 public void submit() throws IOException, InterruptedException, ClassNotFoundException {
     
    ensureState(JobState.DEFINE);
    setUseNewAPI();
    connect();
     
    final JobSubmitter submitter = 
        getJobSubmitter(cluster.getFileSystem(), cluster.getClient());
    status = ugi.doAs(new PrivilegedExceptionAction<JobStatus>() {
      public JobStatus run() throws IOException, InterruptedException, 
      ClassNotFoundException {
          // 执行提交任务
        return submitter.submitJobInternal(Job.this, cluster);
      }
    });
                    ..............
   }

上面代码可以看出,客户端经过连接集群,获得任务提交器submitter后执行了submitJobInternal(Job.this, cluster)方法,进入看(其实我只想看切片方法)

 /**
   * Internal method for submitting jobs to the system.
   * The job submission process involves:
   *   1、Checking the input and output specifications of the job.
   *   2、Computing the InputSplits for the job.
   *   3、Setup the requisite accounting information for the 
   *      DistributedCache of the job, if necessary.
   *   4、Copying the job's jar and configuration to the map-reduce system
   *      directory on the distributed file-system. 
   *   5、Submitting the job to the JobTracker and optionally
   *   monitoring it's status.
   */ 
..............
// Create the splits for the job
      LOG.debug("Creating splits at " + jtFs.makeQualified(submitJobDir));
      int maps = writeSplits(job, submitJobDir);
      conf.setInt(MRJobConfig.NUM_MAPS, maps);
      LOG.info("number of splits:" + maps);
..............

从这个方法头上的注释信息可以看到,在真正执行任务之前,客户端做了这么5件事,稍微翻译一下:

可以看到执行切片的方法时writeSplits(job, submitJobDir)

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);
    } else {
      maps = writeOldSplits(jConf, jobSubmitDir);
    }
    return maps;
  }

也有新旧API的区分,看新的writeNewSplits(job, jobSubmitDir)

private <T extends InputSplit>
  int writeNewSplits(JobContext job, Path jobSubmitDir) throws IOException,
      InterruptedException, ClassNotFoundException {
    ..................
        // 只看切片方法 
    List<InputSplit> splits = input.getSplits(job);
    T[] array = (T[]) splits.toArray(new InputSplit[splits.size()]); 
    ..............
        // 返回值是数组的长度,也就是切片的个数,也就是mapTask的并行度
    return array.length;
  }

进入切片方法,方法太长了,删除部分,留下核心业务逻辑。这个得好好说说

  public List<InputSplit> getSplits(JobContext job) throws IOException {
      
    // 如果没有指定的话,minSize = 1
    long minSize = Math.max(getFormatMinSplitSize(), getMinSplitSize(job));
    // 如果没有指定的话,maxSize = Long.Max
    long maxSize = getMaxSplitSize(job);

    // generate splits
    List<InputSplit> splits = new ArrayList<InputSplit>();
    // FileStatus这个概念来自于HDFS,存储客户端提交文件的元数据
    List<FileStatus> files = listStatus(job);
    for (FileStatus file: files) {
      // 获取到文件的路径
      Path path = file.getPath();
      // 获取到文件的长度
      long length = file.getLen();
      if (length != 0) {
        // 数据块位置数组,用于存储该文件对应的数据块的位置
        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)) {  // 没有指定,默认是可分片的
          long blockSize = file.getBlockSize();
            // 返回默认值:切片大小 = 块大小
          long splitSize = computeSplitSize(blockSize, minSize, maxSize);
           // 获取整个文件的长度,用于计算切片的偏移量
          long bytesRemaining = length;
           // SPLIT_SLOP 的大小是1.1
           // 这个判断表达式的含义是如果剩余的块体积大大于1.1倍的切片大小,继续切片
          while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) {
              // 在这计算了一步块索引
            int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
              
//-----------getBlockIndex() begin--------------------------------------------
protected int getBlockIndex(BlockLocation[] blkLocations, long offset) {
      for (int i = 0 ; i < blkLocations.length; i++) {
      // is the offset inside this block?
      if ((blkLocations[i].getOffset() <= offset) &&
          (offset < blkLocations[i].getOffset() + blkLocations[i].getLength())){
          // 代码逻辑非常简单,就是返回当前offset是在哪个block里面
        return i;
      }
    }
                    ....................
//-----------getBlockIndex() end----------------------------------------------
                        
            // 计算完成之后加入切片集合
            // 切片信息包括:路径,偏移量,切片大小,服务器节点【支撑计算向数据移动】
            splits.add(makeSplit(path, length-bytesRemaining, splitSize,
                        blkLocations[blkIndex].getHosts(),
                        blkLocations[blkIndex].getCachedHosts()));
            bytesRemaining -= splitSize;
          }

          // 计算剩余数据块的切片信息
          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 :不能切片,那就是一片
          splits.add(makeSplit(path, 0, length, blkLocations[0].getHosts(),
                      blkLocations[0].getCachedHosts()));
        }
      }
          ......
    // 返回切片文件的集合。根据集合中数据的个数,就可以计算出有多少个maptask
    return splits;
  }

标签:splits,int,MapReduce,切片,job,源码,file,blkLocations,客户端
来源: https://www.cnblogs.com/simon-1024/p/14867432.html