chaoqin-li1123 commented on code in PR #45977:
URL: https://github.com/apache/spark/pull/45977#discussion_r1583666355
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sql/core/src/main/scala/org/apache/spark/sql/execution/python/PythonStreamingSourceRunner.scala:
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@@ -199,4 +223,30 @@ class PythonStreamingSourceRunner(
logError("Exception when trying to kill worker", e)
}
}
+
+ private val allocator = ArrowUtils.rootAllocator.newChildAllocator(
+ s"stream reader for $pythonExec", 0, Long.MaxValue)
+
+ def readArrowRecordBatches(): Iterator[InternalRow] = {
+ assert(dataIn.readInt() == SpecialLengths.START_ARROW_STREAM)
+ val reader = new ArrowStreamReader(dataIn, allocator)
+ val root = reader.getVectorSchemaRoot()
+ // When input is empty schema can't be read.
+ val schema = ArrowUtils.fromArrowSchema(root.getSchema())
+ assert(schema == outputSchema)
+
+ val vectors = root.getFieldVectors().asScala.map { vector =>
+ new ArrowColumnVector(vector)
+ }.toArray[ColumnVector]
+ val rows = ArrayBuffer[InternalRow]()
Review Comment:
If we call putIterator here we should be able to avoid materializing all
rows at once in scala side, but it doesn't matter that much as we already
materialize all rows in python side.
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