Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/15821#discussion_r109547685
--- 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 --
The conversion going on in `table.to_pandas()` is working on an already
loaded table, but the Arrow Readers can read multiple batches of data and
output a single table. The issue is that pyspark serializers expects the data
to be "framed" with the length so I can not send that directly to the Arrow
Reader. Even with `toLocalIteratorAndServer` I would have to read each batch
of data on the driver, then combine. It would be possible to write the
"framed" stream another stream without the lengths, where it can then be then
be read into a single table - but I'm not sure if that added complexity is
worth it.
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