cloud-fan commented on code in PR #38558:
URL: https://github.com/apache/spark/pull/38558#discussion_r1018732458
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sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryTableScanExec.scala:
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@@ -111,10 +112,15 @@ case class InMemoryTableScanExec(
override def output: Seq[Attribute] = attributes
+ private def cachedPlan = relation.cachedPlan match {
+ case adaptive: AdaptiveSparkPlanExec if adaptive.isFinalized =>
adaptive.executedPlan
+ case other => other
Review Comment:
Another idea is to materialize the AQE plan eagerly so that even the first
cache access can be optimized. However, this requires triggering query
execution during query planning, which is a bit risky.
A good practice is to ask users to do query caching eagerly, e.g. do a
`df.count` right after the `df` is cached. Then they won't observe
inconsistencies. Anyway, I think this PR is a net win as it optimizes all the
following cache accesses after the first access. This is important for query
caching as cache is meant to be accessed repeatedly.
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