abstract class MultiFileCoalescingPartitionReaderBase extends FilePartitionReaderBase with MultiFileReaderFunctions
The abstracted multi-file coalescing reading class, which tries to coalesce small ColumnarBatch into a bigger ColumnarBatch according to maxReadBatchSizeRows, maxReadBatchSizeBytes and the checkIfNeedToSplitDataBlock.
Please be note, this class is applied to below similar file format
| HEADER | -> optional
| block | -> repeated
| FOOTER | -> optional
The data driven:
next() -> populateCurrentBlockChunk (try the best to coalesce ColumnarBatch) -> allocate a bigger HostMemoryBuffer for HEADER + the populated block chunks + FOOTER -> write header to HostMemoryBuffer -> launch tasks to copy the blocks to the HostMemoryBuffer -> wait all tasks finished -> write footer to HostMemoryBuffer -> decode the HostMemoryBuffer in the GPU
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- MultiFileCoalescingPartitionReaderBase
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- FilePartitionReaderBase
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Instance Constructors
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new
MultiFileCoalescingPartitionReaderBase(conf: Configuration, clippedBlocks: Seq[SingleDataBlockInfo], partitionSchema: StructType, maxReadBatchSizeRows: Integer, maxReadBatchSizeBytes: Long, maxGpuColumnSizeBytes: Long, numThreads: Int, execMetrics: Map[String, GpuMetric])
- conf
Configuration
- clippedBlocks
the block metadata from the original file that has been clipped to only contain the column chunks to be read
- partitionSchema
schema of partitions
- maxReadBatchSizeRows
soft limit on the maximum number of rows the reader reads per batch
- maxReadBatchSizeBytes
soft limit on the maximum number of bytes the reader reads per batch
- maxGpuColumnSizeBytes
maximum number of bytes for a GPU column
- numThreads
the size of the threadpool
- execMetrics
metrics
Abstract Value Members
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abstract
def
calculateEstimatedBlocksOutputSize(batchContext: BatchContext): Long
Calculate the output size according to the block chunks and the schema, and the estimated output size will be used as the initialized size of allocating HostMemoryBuffer
Calculate the output size according to the block chunks and the schema, and the estimated output size will be used as the initialized size of allocating HostMemoryBuffer
Please be note, the estimated size should be at least equal to size of HEAD + Blocks + FOOTER
- batchContext
the batch building context
- returns
Long, the estimated output size
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abstract
def
calculateFinalBlocksOutputSize(footerOffset: Long, blocks: Seq[DataBlockBase], batchContext: BatchContext): Long
Calculate the final block output size which will be used to decide if re-allocate HostMemoryBuffer
Calculate the final block output size which will be used to decide if re-allocate HostMemoryBuffer
There is no need to re-calculate the block size, just calculate the footer size and plus footerOffset.
If the size calculated by this function is bigger than the one calculated by calculateEstimatedBlocksOutputSize, then it will cause HostMemoryBuffer re-allocating, and cause the performance issue.
- footerOffset
footer offset
- blocks
blocks to be evaluated
- batchContext
the batch building context
- returns
the output size
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abstract
def
checkIfNeedToSplitDataBlock(currentBlockInfo: SingleDataBlockInfo, nextBlockInfo: SingleDataBlockInfo): Boolean
To check if the next block will be split into another ColumnarBatch
To check if the next block will be split into another ColumnarBatch
- currentBlockInfo
current SingleDataBlockInfo
- nextBlockInfo
next SingleDataBlockInfo
- returns
true: split the next block into another ColumnarBatch and vice versa
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abstract
def
getBatchRunner(tc: TaskContext, file: Path, outhmb: HostMemoryBuffer, blocks: ArrayBuffer[DataBlockBase], offset: Long, batchContext: BatchContext): Callable[(Seq[DataBlockBase], Long)]
The sub-class must implement the real file reading logic in a Callable which will be running in a thread pool
The sub-class must implement the real file reading logic in a Callable which will be running in a thread pool
- tc
task context to use
- file
file to be read
- outhmb
the sliced HostMemoryBuffer to hold the blocks, and the implementation is in charge of closing it in sub-class
- blocks
blocks meta info to specify which blocks to be read
- offset
used as the offset adjustment
- batchContext
the batch building context
- returns
Callable[(Seq[DataBlockBase], Long)], which will be submitted to a ThreadPoolExecutor, and the Callable will return a tuple result and result._1 is block meta info with the offset adjusted result._2 is the bytes read
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abstract
def
getFileFormatShortName: String
File format short name used for logging and other things to uniquely identity which file format is being used.
File format short name used for logging and other things to uniquely identity which file format is being used.
- returns
the file format short name
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abstract
def
readBufferToTablesAndClose(dataBuffer: HostMemoryBuffer, dataSize: Long, clippedSchema: SchemaBase, readSchema: StructType, extraInfo: ExtraInfo): GpuDataProducer[Table]
Sent host memory to GPU to decode
Sent host memory to GPU to decode
- dataBuffer
the data which can be decoded in GPU
- dataSize
data size
- clippedSchema
the clipped schema
- readSchema
the expected schema
- extraInfo
the extra information for specific file format
- returns
Table
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abstract
def
writeFileFooter(buffer: HostMemoryBuffer, bufferSize: Long, footerOffset: Long, blocks: Seq[DataBlockBase], batchContext: BatchContext): (HostMemoryBuffer, Long)
Writer a footer for a specific file format.
Writer a footer for a specific file format. If there is no footer for the file format, just return (hmb, offset)
Please be note, some file formats may re-allocate the HostMemoryBuffer because of the estimated initialized buffer size may be a little smaller than the actual size. So in this case, the hmb should be closed in the implementation.
- buffer
The buffer holding (header + data blocks)
- bufferSize
The total buffer size which equals to size of (header + blocks + footer)
- footerOffset
Where begin to write the footer
- blocks
The data block meta info
- batchContext
The batch building context
- returns
the buffer and the buffer size
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abstract
def
writeFileHeader(buffer: HostMemoryBuffer, batchContext: BatchContext): Long
Write a header for a specific file format.
Write a header for a specific file format. If there is no header for the file format, just ignore it and return 0
- buffer
where the header will be written
- batchContext
the batch building context
- returns
how many bytes written
Concrete Value Members
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def
!=(arg0: Any): Boolean
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==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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var
batchIter: Iterator[ColumnarBatch]
- Attributes
- protected
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- FilePartitionReaderBase
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def
clone(): AnyRef
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def
close(): Unit
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def
createBatchContext(chunkedBlocks: LinkedHashMap[Path, ArrayBuffer[DataBlockBase]], clippedSchema: SchemaBase): BatchContext
Return a batch context which will be shared during the process of building a memory file, aka with the following APIs.
Return a batch context which will be shared during the process of building a memory file, aka with the following APIs.
- calculateEstimatedBlocksOutputSize
- writeFileHeader
- getBatchRunner
- calculateFinalBlocksOutputSize
- writeFileFooter It is useful when something is needed by some or all of the above APIs. Children can override this to return a customized batch context.
- chunkedBlocks
mapping of file path to data blocks
- clippedSchema
schema info
- Attributes
- protected
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def
currentMetricsValues(): Array[CustomTaskMetric]
- Definition Classes
- PartitionReader
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
fileSystemBytesRead(): Long
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def
finalize(): Unit
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def
finalizeOutputBatch(batch: ColumnarBatch, extraInfo: ExtraInfo): ColumnarBatch
A callback to finalize the output batch.
A callback to finalize the output batch. The batch returned will be the final output batch of the reader's "get" method.
- batch
the batch after decoding, adding partitioned columns.
- extraInfo
the corresponding extra information of the input batch.
- returns
the finalized columnar batch.
- Attributes
- protected
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def
get(): ColumnarBatch
- Definition Classes
- FilePartitionReaderBase → PartitionReader
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final
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getClass(): Class[_]
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hashCode(): Int
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initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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initializeLogIfNecessary(isInterpreter: Boolean): Unit
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isDone: Boolean
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isTraceEnabled(): Boolean
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val
metrics: Map[String, GpuMetric]
- Definition Classes
- ScanWithMetrics
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final
def
ne(arg0: AnyRef): Boolean
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def
next(): Boolean
- Definition Classes
- MultiFileCoalescingPartitionReaderBase → PartitionReader
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final
def
notify(): Unit
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notifyAll(): Unit
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def
startNewBufferRetry: Unit
You can reset the target batch size if needed for splits...
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