BryanCutler commented on a change in pull request #23760: [SPARK-26762][SQL][R]
Arrow optimization for conversion from Spark DataFrame to R DataFrame
URL: https://github.com/apache/spark/pull/23760#discussion_r257789034
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File path: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
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@@ -3198,9 +3199,66 @@ class Dataset[T] private[sql](
}
/**
- * Collect a Dataset as Arrow batches and serve stream to PySpark.
+ * Collect a Dataset as Arrow batches and serve stream to SparkR. It sends
+ * arrow batches in an ordered manner with buffering. This is inevitable
+ * due to missing R API that reads batches from socket directly. See
ARROW-4512.
+ * Eventually, this code should be deduplicated by `collectAsArrowToPython`.
*/
- private[sql] def collectAsArrowToPython(): Array[Any] = {
+ private[sql] def collectAsArrowToR(): Array[Any] = {
Review comment:
> Can we unit test this in Scala tests?
It would be difficult to test in Scala because this method sets up a server
in the JVM for the R process to read data from.
@HyukjinKwon maybe you can skip the part of the test in
https://github.com/apache/spark/commit/bf2feec2ef023177d72ac1137dbd1b3a02eb9a89
that uses the delay with the RDD and just add the case that uses a large number
of partitions. That will make a pretty good chance to test partitions coming in
out of order.
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