Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18655#discussion_r128061312
--- 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 --
It seems a little odd to me to tie an iterator to a TaskContext, why not
just close resources as soon as the row iterator is consumed?
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