Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18655#discussion_r128312860
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowConverters.scala
---
@@ -55,145 +51,55 @@ private[sql] class ArrowPayload private[arrow]
(payload: Array[Byte]) extends Se
def asPythonSerializable: Array[Byte] = payload
}
-private[sql] object ArrowPayload {
-
- /**
- * Create an ArrowPayload from an ArrowRecordBatch and Spark schema.
- */
- def apply(
- batch: ArrowRecordBatch,
- schema: StructType,
- allocator: BufferAllocator): ArrowPayload = {
- new ArrowPayload(ArrowConverters.batchToByteArray(batch, schema,
allocator))
- }
-}
-
private[sql] object ArrowConverters {
/**
- * Map a Spark DataType to ArrowType.
- */
- private[arrow] def sparkTypeToArrowType(dataType: DataType): ArrowType =
{
- dataType match {
- case BooleanType => ArrowType.Bool.INSTANCE
- case ShortType => new ArrowType.Int(8 * ShortType.defaultSize, true)
- case IntegerType => new ArrowType.Int(8 * IntegerType.defaultSize,
true)
- case LongType => new ArrowType.Int(8 * LongType.defaultSize, true)
- case FloatType => new
ArrowType.FloatingPoint(FloatingPointPrecision.SINGLE)
- case DoubleType => new
ArrowType.FloatingPoint(FloatingPointPrecision.DOUBLE)
- case ByteType => new ArrowType.Int(8, true)
- case StringType => ArrowType.Utf8.INSTANCE
- case BinaryType => ArrowType.Binary.INSTANCE
- case _ => throw new UnsupportedOperationException(s"Unsupported data
type: $dataType")
- }
- }
-
- /**
- * Convert a Spark Dataset schema to Arrow schema.
- */
- private[arrow] def schemaToArrowSchema(schema: StructType): Schema = {
- val arrowFields = schema.fields.map { f =>
- new Field(f.name, f.nullable, sparkTypeToArrowType(f.dataType),
List.empty[Field].asJava)
- }
- new Schema(arrowFields.toList.asJava)
- }
-
- /**
* Maps Iterator from InternalRow to ArrowPayload. Limit
ArrowRecordBatch size in ArrowPayload
* by setting maxRecordsPerBatch or use 0 to fully consume rowIter.
*/
private[sql] def toPayloadIterator(
rowIter: Iterator[InternalRow],
schema: StructType,
- maxRecordsPerBatch: Int): Iterator[ArrowPayload] = {
- new Iterator[ArrowPayload] {
- private val _allocator = new RootAllocator(Long.MaxValue)
- private var _nextPayload = if (rowIter.nonEmpty) convert() else null
+ maxRecordsPerBatch: Int,
+ context: TaskContext): Iterator[ArrowPayload] = {
- override def hasNext: Boolean = _nextPayload != null
+ val arrowSchema = ArrowUtils.toArrowSchema(schema)
+ val allocator =
+ ArrowUtils.rootAllocator.newChildAllocator("toPayloadIterator", 0,
Long.MaxValue)
- override def next(): ArrowPayload = {
- val obj = _nextPayload
- if (hasNext) {
- if (rowIter.hasNext) {
- _nextPayload = convert()
- } else {
- _allocator.close()
- _nextPayload = null
- }
- }
- obj
- }
-
- private def convert(): ArrowPayload = {
- val batch = internalRowIterToArrowBatch(rowIter, schema,
_allocator, maxRecordsPerBatch)
- ArrowPayload(batch, schema, _allocator)
- }
- }
- }
-
- /**
- * Iterate over InternalRows and write to an ArrowRecordBatch, stopping
when rowIter is consumed
- * or the number of records in the batch equals maxRecordsInBatch. If
maxRecordsPerBatch is 0,
- * then rowIter will be fully consumed.
- */
- private def internalRowIterToArrowBatch(
- rowIter: Iterator[InternalRow],
- schema: StructType,
- allocator: BufferAllocator,
- maxRecordsPerBatch: Int = 0): ArrowRecordBatch = {
-
- val columnWriters = schema.fields.zipWithIndex.map { case (field,
ordinal) =>
- ColumnWriter(field.dataType, ordinal, allocator).init()
- }
+ val root = VectorSchemaRoot.create(arrowSchema, allocator)
+ val arrowWriter = ArrowWriter.create(root)
- val writerLength = columnWriters.length
- var recordsInBatch = 0
- while (rowIter.hasNext && (maxRecordsPerBatch <= 0 || recordsInBatch <
maxRecordsPerBatch)) {
- val row = rowIter.next()
- var i = 0
- while (i < writerLength) {
- columnWriters(i).write(row)
- i += 1
- }
- recordsInBatch += 1
+ context.addTaskCompletionListener { _ =>
+ root.close()
+ allocator.close()
--- End diff --
Yeah, good point. What about closing resources in both ways? So have the
listener close for the case that something fails, otherwise once the the row
iterator is fully consumed then close immediately. I'm not really sure at what
exact point the task completion listener callback is done, is it dependent on
any IO?
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