hvanhovell commented on code in PR #38468:
URL: https://github.com/apache/spark/pull/38468#discussion_r1013189541
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowConverters.scala:
##########
@@ -128,6 +128,65 @@ private[sql] object ArrowConverters extends Logging {
}
}
+ private[sql] def toArrowBatchIterator(
+ rowIter: Iterator[InternalRow],
+ schema: StructType,
+ maxRecordsPerBatch: Int,
+ timeZoneId: String,
+ context: TaskContext): Iterator[(Array[Byte], Long, Long)] = {
+ val arrowSchema = ArrowUtils.toArrowSchema(schema, timeZoneId)
+ val allocator = ArrowUtils.rootAllocator.newChildAllocator(
+ "toArrowBatchIterator", 0, Long.MaxValue)
+
+ val root = VectorSchemaRoot.create(arrowSchema, allocator)
+ val unloader = new VectorUnloader(root)
+ val arrowWriter = ArrowWriter.create(root)
+
+ if (context != null) { // for test at driver
+ context.addTaskCompletionListener[Unit] { _ =>
+ root.close()
+ allocator.close()
+ }
+ }
+
+ new Iterator[(Array[Byte], Long, Long)] {
+
+ override def hasNext: Boolean = rowIter.hasNext || {
+ root.close()
+ allocator.close()
+ false
+ }
+
+ override def next(): (Array[Byte], Long, Long) = {
+ val out = new ByteArrayOutputStream()
+ val writeChannel = new WriteChannel(Channels.newChannel(out))
+
+ var rowCount = 0L
+ var estimatedSize = 0L
+ Utils.tryWithSafeFinally {
+ while (rowIter.hasNext && (maxRecordsPerBatch <= 0 || rowCount <
maxRecordsPerBatch)) {
+ val row = rowIter.next()
+ arrowWriter.write(row)
+ rowCount += 1
+ estimatedSize += SizeEstimator.estimate(row)
+ }
+ arrowWriter.finish()
+ val batch = unloader.getRecordBatch()
+
+ MessageSerializer.serialize(writeChannel, arrowSchema)
Review Comment:
Instead of turning each written result into a separate IPC stream, why not
make the driver send the schema, and then stream back the record batches? I am
not 100% how hard it would be to reassemble this on the python side, on the
scala side it would be fairly straightforward.
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