Spark Parquet file split

---

title: Spark Parquet file split
date: 2018-10-22 20:14:43

tags: Spark

在实际使用 spark + parquet 的时候, 遇到一个问题:我们只有一个 parquet 文件, 但是有四个 tasks, 但是只有一个 task 处理了全部的数据.这就牵涉到对于 parquet, spark 是如何来进行切分 partitions, 以及每个 partition 要处理哪部分数据

先说结论, spark 中, parquet 是 splitable 的, 代码见ParquetFileFormat#isSplitable. 那会不会把数据切碎? 答案是不会, 因为是以 row group 为最小单位切分的, 这也导致一些 partitions 会没有数据.

处理流程

1.根据 parquet 按文件大小切块生成 partitions:

在 FileSourceScanExec#createNonBucketedReadRDD 中, 如果文件是 splitable 的 , 按照 maxSplitBytes 把文件切分, 最后生成的数量, 就是 RDD partition 的数量, 代码如下:

val maxSplitBytes = Math.min(defaultMaxSplitBytes, Math.max(openCostInBytes, bytesPerCore))
logInfo(s"Planning scan with bin packing, max size: $maxSplitBytes bytes, " +
      s"open cost is considered as scanning $openCostInBytes bytes.")

val splitFiles = selectedPartitions.flatMap { partition =>
  partition.files.flatMap { file =>
    val blockLocations = getBlockLocations(file)
    if (fsRelation.fileFormat.isSplitable(
        fsRelation.sparkSession, fsRelation.options, file.getPath)) {
      (0L until file.getLen by maxSplitBytes).map { offset =>
        val remaining = file.getLen - offset
        val size = if (remaining > maxSplitBytes) maxSplitBytes else remaining
        val hosts = getBlockHosts(blockLocations, offset, size)
        PartitionedFile(
          partition.values, file.getPath.toUri.toString, offset, size, hosts)
      }
    } else {
      val hosts = getBlockHosts(blockLocations, 0, file.getLen)
      Seq(PartitionedFile(
        partition.values, file.getPath.toUri.toString, 0, file.getLen, hosts))
    }
  }
}.toArray.sortBy(_.length)(implicitly[Ordering[Long]].reverse)

val partitions = new ArrayBuffer[FilePartition]
val currentFiles = new ArrayBuffer[PartitionedFile]
var currentSize = 0L

/** Close the current partition and move to the next. */
def closePartition(): Unit = {
  if (currentFiles.nonEmpty) {
    val newPartition =
      FilePartition(
        partitions.size,
        currentFiles.toArray.toSeq) // Copy to a new Array.
    partitions += newPartition
  }
  currentFiles.clear()
  currentSize = 0
}

// Assign files to partitions using "First Fit Decreasing" (FFD)
splitFiles.foreach { file =>
  if (currentSize + file.length > maxSplitBytes) {
    closePartition()
  }
  // Add the given file to the current partition.
  currentSize += file.length + openCostInBytes
  currentFiles += file
}
closePartition()

new FileScanRDD(fsRelation.sparkSession, readFile, partitions)

2.使用 ParquetInputSplit 构造 reader:

在 ParquetFileFormat#buildReaderWithPartitionValues 实现中, 会使用 split 来初始化 reader, 并且根据配置可以把 reader 分为否是 vectorized 的:

• vectorizedReader.initialize(split, hadoopAttemptContext)
• reader.initialize(split, hadoopAttemptContext)

关于 2 在画外中还有更详细的代码, 但与本文的主流程关系不大, 这里先不表.

3. 划分 parquet 的 row group s 到不同的 partitions 中去

在 1 中根据文件大小均分了一些 partitions, 但不是所有这些 partitions 最后都会有数据.

接回 2 中的 init, 在 SpecificParquetRecordReaderBase#initialize 中, 会在 readFooter 的时候传入一个 RangeMetadataFilter, 这个 filter 的range 是根据你的 split 的边界来的, 最后会用这个 range 来划定 row group 的归属:

public void initialize(InputSplit inputSplit, TaskAttemptContext taskAttemptContext)
    throws IOException, InterruptedException {
    ...
    footer = readFooter(configuration, file, range(inputSplit.getStart(), inputSplit.getEnd()));
    ...
}

parquet 的ParquetFileReader#readFooter方法会用到ParquetMetadataConverter#converter.readParquetMetadata(f, filter);, 这个readParquetMetadata对于RangeMetadataFilter的处理是:

@Override
public FileMetaData visit(RangeMetadataFilter filter) throws IOException {
  return filterFileMetaDataByMidpoint(readFileMetaData(from), filter);
}

终于到了最关键的切分的地方, 最关键的就是这一段, 谁拥有这个 row group的中点, 谁就可以处理这个 row group.

现在假设我们有一个40m 的文件, 只有一个 row group, 10m 一分, 那么将会有4个 partitions, 但是只有一个 partition 会占有这个 row group 的中点, 所以也只有这一个 partition 会有数据.

long midPoint = startIndex + totalSize / 2;
if (filter.contains(midPoint)) {
  newRowGroups.add(rowGroup);
}

完整代码如下:

static FileMetaData filterFileMetaDataByMidpoint(FileMetaData metaData, RangeMetadataFilter filter) {
  List<RowGroup> rowGroups = metaData.getRow_groups();
  List<RowGroup> newRowGroups = new ArrayList<RowGroup>();
  for (RowGroup rowGroup : rowGroups) {
    long totalSize = 0;
    long startIndex = getOffset(rowGroup.getColumns().get(0));
    for (ColumnChunk col : rowGroup.getColumns()) {
      totalSize += col.getMeta_data().getTotal_compressed_size();
    }
    long midPoint = startIndex + totalSize / 2;
    if (filter.contains(midPoint)) {
      newRowGroups.add(rowGroup);
    }
  }
  metaData.setRow_groups(newRowGroups);
  return metaData;
}

画外:

2 中的代码其实是 spark 正儿八经如何读文件的代码, 最后返回一个FileScanRDD, 完整代码如下

    (file: PartitionedFile) => {
      assert(file.partitionValues.numFields == partitionSchema.size)

      val fileSplit =
        new FileSplit(new Path(new URI(file.filePath)), file.start, file.length, Array.empty)

      val split =
        new org.apache.parquet.hadoop.ParquetInputSplit(
          fileSplit.getPath,
          fileSplit.getStart,
          fileSplit.getStart + fileSplit.getLength,
          fileSplit.getLength,
          fileSplit.getLocations,
          null)

      val attemptId = new TaskAttemptID(new TaskID(new JobID(), TaskType.MAP, 0), 0)
      val hadoopAttemptContext =
        new TaskAttemptContextImpl(broadcastedHadoopConf.value.value, attemptId)

      // Try to push down filters when filter push-down is enabled.
      // Notice: This push-down is RowGroups level, not individual records.
      if (pushed.isDefined) {
        ParquetInputFormat.setFilterPredicate(hadoopAttemptContext.getConfiguration, pushed.get)
      }
      val parquetReader = if (enableVectorizedReader) {
        val vectorizedReader = new VectorizedParquetRecordReader()
        vectorizedReader.initialize(split, hadoopAttemptContext)
        logDebug(s"Appending $partitionSchema ${file.partitionValues}")
        vectorizedReader.initBatch(partitionSchema, file.partitionValues)
        if (returningBatch) {
          vectorizedReader.enableReturningBatches()
        }
        vectorizedReader
      } else {
        logDebug(s"Falling back to parquet-mr")
        // ParquetRecordReader returns UnsafeRow
        val reader = pushed match {
          case Some(filter) =>
            new ParquetRecordReader[UnsafeRow](
              new ParquetReadSupport,
              FilterCompat.get(filter, null))
          case _ =>
            new ParquetRecordReader[UnsafeRow](new ParquetReadSupport)
        }
        reader.initialize(split, hadoopAttemptContext)
        reader
      }

      val iter = new RecordReaderIterator(parquetReader)
      Option(TaskContext.get()).foreach(_.addTaskCompletionListener(_ => iter.close()))

      // UnsafeRowParquetRecordReader appends the columns internally to avoid another copy.
      if (parquetReader.isInstanceOf[VectorizedParquetRecordReader] &&
          enableVectorizedReader) {
        iter.asInstanceOf[Iterator[InternalRow]]
      } else {
        val fullSchema = requiredSchema.toAttributes ++ partitionSchema.toAttributes
        val joinedRow = new JoinedRow()
        val appendPartitionColumns = GenerateUnsafeProjection.generate(fullSchema, fullSchema)

        // This is a horrible erasure hack...  if we type the iterator above, then it actually check
        // the type in next() and we get a class cast exception.  If we make that function return
        // Object, then we can defer the cast until later!
        if (partitionSchema.length == 0) {
          // There is no partition columns
          iter.asInstanceOf[Iterator[InternalRow]]
        } else {
          iter.asInstanceOf[Iterator[InternalRow]]
            .map(d => appendPartitionColumns(joinedRow(d, file.partitionValues)))
        }
      }
    }

这个返回的(PartitionedFile) => Iterator[InternalRow] , 是在FileSourceScanExec#inputRDD用的

private lazy val inputRDD: RDD[InternalRow] = {
  val readFile: (PartitionedFile) => Iterator[InternalRow] =
    relation.fileFormat.buildReaderWithPartitionValues(
      sparkSession = relation.sparkSession,
      dataSchema = relation.dataSchema,
      partitionSchema = relation.partitionSchema,
      requiredSchema = requiredSchema,
      filters = pushedDownFilters,
      options = relation.options,
      hadoopConf = relation.sparkSession.sessionState.newHadoopConfWithOptions(relation.options))

  relation.bucketSpec match {
    case Some(bucketing) if relation.sparkSession.sessionState.conf.bucketingEnabled =>
      createBucketedReadRDD(bucketing, readFile, selectedPartitions, relation)
    case _ =>
      createNonBucketedReadRDD(readFile, selectedPartitions, relation)
  }
}

FileScanRDD

class FileScanRDD(
    @transient private val sparkSession: SparkSession,
    readFunction: (PartitionedFile) => Iterator[InternalRow],
    @transient val filePartitions: Seq[FilePartition])
  extends RDD[InternalRow](sparkSession.sparkContext, Nil) {

  override def compute(split: RDDPartition, context: TaskContext): Iterator[InternalRow] = {

    private[this] val files = split.asInstanceOf[FilePartition].files.toIterator
    private[this] var currentFile: PartitionedFile = null // 根据 currentFile = files.next() 来的, 具体实现我就不贴了 有兴趣的可以自己看下.
    ...
    readFunction(currentFile)
    ...
  }
}