Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/15821#discussion_r108746678
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -2747,6 +2747,17 @@ class Dataset[T] private[sql](
}
}
+ /**
+ * Collect a Dataset as ArrowPayload byte arrays and serve to PySpark.
+ */
+ private[sql] def collectAsArrowToPython(): Int = {
+ val payloadRdd = toArrowPayloadBytes()
+ val payloadByteArrays = payloadRdd.collect()
--- End diff --
I did some experimenting with `toLocalIteratorAndServe` and did not see any
performance gain - it was actually a little worse in local mode at least. It
might be worth checking out again though. There are other ways to go about
serving to python, but the current implementation seemed to be the least
intrusive to Spark.
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