Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/15651#discussion_r85411204
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -482,6 +483,33 @@ class Dataset[T] private[sql](
@InterfaceStability.Evolving
def isStreaming: Boolean = logicalPlan.isStreaming
+ @Experimental
+ @InterfaceStability.Evolving
+ def checkpoint(): Dataset[T] = {
+ val internalRdd = queryExecution.toRdd.map(_.copy())
+ internalRdd.checkpoint()
+
+ val physicalPlan = queryExecution.executedPlan
+
+ def firstLeafPartitioning(partitioning: Partitioning): Partitioning = {
+ partitioning match {
+ case p: PartitioningCollection =>
firstLeafPartitioning(p.partitionings.head)
+ case p => p
+ }
+ }
--- End diff --
There can be cases where the optimizer fails to eliminate an unnecessary
shuffle if we strip all the other partitionings. But that's still better than
an exponentially growing `PartitioningCollection`, which basically runs into
the same slow query planning issue this PR tries to solve.
I talked to @yhuai offline about exactly the same issue you brought up
before sending out this PR, and we decided to have a working version first and
optimize it later since we still need feedback from ML people to see whether
the basic mechanism works for their workloads.
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