[SPARK-33832][SQL] Support optimize skewed join even if introduce extra shuffle #32816
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@@ -281,25 +281,25 @@ case class AdaptiveSparkPlanExec( | |
| // plans are updated, we can clear the query stage list because at this point the two plans | ||
| // are semantically and physically in sync again. | ||
| val logicalPlan = replaceWithQueryStagesInLogicalPlan(currentLogicalPlan, stagesToReplace) | ||
| val (reOptimizationPhysicalPlan, newLogicalPlan) = reOptimize(logicalPlan) | ||
| val planWithExtraShuffle = rePlanWithExtraShuffle(reOptimizationPhysicalPlan) | ||
| val origCost = costEvaluator.evaluateCost(currentPhysicalPlan) | ||
| val reOptimizationCost = costEvaluator.evaluateCost(reOptimizationPhysicalPlan) | ||
| val extraShuffleCost = costEvaluator.evaluateCost(planWithExtraShuffle) | ||
| def updateCurrentPlan(newPhysicalPlan: SparkPlan): Unit = { | ||
| logOnLevel(s"Plan changed from\n$currentPhysicalPlan\nto\n$newPhysicalPlan") | ||
| cleanUpTempTags(newPhysicalPlan) | ||
| currentPhysicalPlan = newPhysicalPlan | ||
| currentLogicalPlan = newLogicalPlan | ||
| stagesToReplace = Seq.empty[QueryStageExec] | ||
| } | ||
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| if (extraShuffleCost < reOptimizationCost || (extraShuffleCost == reOptimizationCost && | ||
| reOptimizationPhysicalPlan != planWithExtraShuffle)) { | ||
| updateCurrentPlan(planWithExtraShuffle) | ||
| } else if (reOptimizationCost < origCost || | ||
| (reOptimizationCost == origCost && currentPhysicalPlan != reOptimizationPhysicalPlan)) { | ||
| updateCurrentPlan(reOptimizationPhysicalPlan) | ||
| } | ||
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| // Now that some stages have finished, we can try creating new stages. | ||
| result = createQueryStages(currentPhysicalPlan) | ||
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I think
reOptimizeshould return(Seq[SparkPlan], LogicalPlan), and here we pick the spark plan with the least cost between the current spark plan and newPhysicalPlans.There was a problem hiding this comment.
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If the conf is false, the returned
Seq[SparkPlan]will be Nil, so that we don't introduce extra overhead if force skew join is disabled.