Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/10480#discussion_r50348037
--- Diff: core/src/main/scala/org/apache/spark/api/r/SerDe.scala ---
@@ -355,6 +355,13 @@ private[spark] object SerDe {
writeInt(dos, v.length)
v.foreach(elem => writeObject(dos, elem))
+ // Handle Properties
--- End diff --
@shivaram as you see we are calling 3 different overloads of
`read().jdbc()` in Scala, 4 if counting `write().jdbc()`. I think there would
be 4 approaches to handle `read().jdbc()`:
1. Have 3 JVM helper functions
2. Have 1 helper function and on JVM side figure out which overload to
route to
3. Have 1 helper function and include parameter processing (eg. check
numPartitions/defaultParallelism etc), and overload checks all within JVM - and
leave R to be a thin shim
4. serialize Properties as jobj and work on it on R side
I feel #4 gives us the least overhead (less code) and more flexibility
(since logic like default values for numPartition exists only on R/Python and
not on Scala side).
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