Hive Input Split 相关的代码

HiveInputSplit.java

public InputSplit[] getSplits(JobConf job, int numSplits) throws IOException {
    //扫描每一个分区
    for (Path dir : dirs) {
      PartitionDesc part = getPartitionDescFromPath(pathToPartitionInfo, dir);
    //获取分区的输入格式
      Class inputFormatClass = part.getInputFileFormatClass();
      InputFormat inputFormat = getInputFormatFromCache(inputFormatClass, job);
    //按照相应格式的分片算法获取分片
    //注意：这里的Inputformat只是old version API：org.apache.hadoop.mapred而不是org.apache.hadoop.mapreduce，因此不能采用新的API，否则在查询时会报异常：Input format must implement InputFormat.区别就是新的API的计算inputsplit size（Math.max(minSize, Math.min(maxSize, blockSize)）和老的（Math.max(minSize, Math.min(goalSize, blockSize))）不一样；
      InputSplit[] iss = inputFormat.getSplits(newjob, numSplits / dirs.length);
      for (InputSplit is : iss) {
    //封装结果，返回
        result.add(new HiveInputSplit(is, inputFormatClass.getName()));
      }
    }
    return result.toArray(new HiveInputSplit[result.size()]);
}












public InputSplit[] getSplits(JobConf job, int numSplits) throws IOException {
    //加载CombineFileInputFormatShim，这个类继承了org.apache.hadoop.mapred.lib.CombineFileInputFormat
    CombineFileInputFormatShim combine = ShimLoader.getHadoopShims()
        .getCombineFileInputFormat();
if (combine == null) {
//若为空则采用HiveInputFormat的方式，下同
      return super.getSplits(job, numSplits);
    }
    Path[] paths = combine.getInputPathsShim(job);
for (Path path : paths) {
//若是外部表，则按照HiveInputFormat方式分片
      if ((tableDesc != null) && tableDesc.isNonNative()) {
        return super.getSplits(job, numSplits);
      }
      Class inputFormatClass = part.getInputFileFormatClass();
      String inputFormatClassName = inputFormatClass.getName();
      InputFormat inputFormat = getInputFormatFromCache(inputFormatClass, job);
      if (this.mrwork != null && !this.mrwork.getHadoopSupportsSplittable()) {
        if (inputFormat instanceof TextInputFormat) {
         if ((new CompressionCodecFactory(job)).getCodec(path) != null)
//在未开启hive.hadoop.supports.splittable.combineinputformat(MAPREDUCE-1597)参数情况下，对于TextInputFormat并且为压缩则采用HiveInputFormat分片算法
                    return super.getSplits(job, numSplits);
        }
      }
    //对于连接式同上
      if (inputFormat instanceof SymlinkTextInputFormat) {
        return super.getSplits(job, numSplits);
      }
      CombineFilter f = null;
      boolean done = false;
Path filterPath = path;
//由参数hive.mapper.cannot.span.multiple.partitions控制，默认false;如果没true，则对每一个partition创建一个pool，以下省略为true的处理；对于同一个表的同一个文件格式的split创建一个pool为combine做准备；
      if (!mrwork.isMapperCannotSpanPartns()) {
        opList = HiveFileFormatUtils.doGetWorksFromPath(
                   pathToAliases, aliasToWork, filterPath);
        f = poolMap.get(new CombinePathInputFormat(opList, inputFormatClassName));
      }
      if (!done) {
        if (f == null) {
          f = new CombineFilter(filterPath);
          combine.createPool(job, f);
        } else {
          f.addPath(filterPath);
        }
      }
    }
if (!mrwork.isMapperCannotSpanPartns()) {
//到这里才调用combine的分片算法，继承了org.apache.hadoop.mapred.lib.CombineFileInputFormat extends 新版本CombineFileInputformat
      iss = Arrays.asList(combine.getSplits(job, 1));
}
//对于sample查询特殊处理
    if (mrwork.getNameToSplitSample() != null && !mrwork.getNameToSplitSample().isEmpty()) {
      iss = sampleSplits(iss);
}
//封装结果返回
    for (InputSplitShim is : iss) {
      CombineHiveInputSplit csplit = new CombineHiveInputSplit(job, is);
      result.add(csplit);
    }
    return result.toArray(new CombineHiveInputSplit[result.size()]);
  }









public List<InputSplit> getSplits(JobContext job)
    throws IOException {
        //决定切分的几个参数
    if (minSplitSizeNode != 0) {
      minSizeNode = minSplitSizeNode;
    } else {
      minSizeNode = conf.getLong(SPLIT_MINSIZE_PERNODE, 0);
    }
    if (minSplitSizeRack != 0) {
      minSizeRack = minSplitSizeRack;
    } else {
      minSizeRack = conf.getLong(SPLIT_MINSIZE_PERRACK, 0);
    }
    if (maxSplitSize != 0) {
      maxSize = maxSplitSize;
    } else {
      maxSize= = conf.getLong("mapreduce.input.fileinputformat.split.maxsize", 0);
    }
        for (MultiPathFilter onepool : pools) {
      ArrayList<Path> myPaths = new ArrayList<Path>();
      // create splits for all files in this pool.
      getMoreSplits(job, myPaths.toArray(new Path[myPaths.size()]),
                    maxSize, minSizeNode, minSizeRack, splits);
    }
}