[GitHub] [spark] cloud-fan commented on a change in pull request #31653: [SPARK-33832][SQL] v2. move OptimzieSkewedJoin to query stage preparation

[GitBox <gi...@apache.org>]

cloud-fan commented on a change in pull request #31653:
URL: https://github.com/apache/spark/pull/31653#discussion_r605420767



##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeSkewedJoin.scala
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@@ -251,48 +253,129 @@ object OptimizeSkewedJoin extends CustomShuffleReaderRule {
       }
   }
 
+  /**
+   * A potential stage is from Exchange down.  Actual [[QueryStageExec]] nodes are created
+   * by [[AdaptiveSparkPlanExec.newQueryStage]] bounded by previously created [[QueryStageExec]]
+   * nodes below.
+   * Todo: need better way to identify which join the log msgs below refer to.  Tags?
+   */
+  private def handlePotentialQueryStage(plan: SparkPlan): SparkPlan = {
+    val shuffleStages = collectShuffleStages(plan)
+    val s = ExplainUtils.getAQELogPrefix(shuffleStages)
+
+    if (shuffleStages.length != 2 && !conf.adaptiveForceIfShuffle) {
+      /* Consider Case II.  Shuffle above SMJ1.  We should see 3 SQSE nodes but
+       with adaptiveForceIfShuffle() we should be able to add a new shuffle
+       above SMJ2 to enable skew mitigation of SMJ2.  W/o ability to add a new
+       shuffle skew mitigation is still possible in some cases - to be handled later.
+
+       Add a test for this.
+       See test("skew in deeply nested join - test ShuffleAddedException") and
+       add a similar test with just 2 joins */
+      logInfo(s"OptimizeSkewedJoin: rule is not applied since" +
+        s" shuffleStages.length=${shuffleStages.length} != 2 and " +
+        s"${SQLConf.ADAPTIVE_FORCE_IF_SHUFFLE.key}=false; $s")
+      return plan
+    }
+    val numShufflesBefore = plan.collect {
+      case e: ShuffleExchangeExec => e
+    }.length
+    val mitigatedPlan = optimizeSkewJoin(plan)
+    if (mitigatedPlan eq plan) {
+      return plan
+    }
+    val executedPlan = ensureRequirements.apply(mitigatedPlan)
+    val numNewShuffles = executedPlan.collect {
+      case e: ShuffleExchangeExec => e
+    }.length - numShufflesBefore
+    if(numNewShuffles > 0) {
+      if (conf.adaptiveForceIfShuffle) {
+        logInfo(s"OptimizeSkewedJoin: rule is applied. " +
+          s"$numNewShuffles additional shuffles will be introduced; $s")
+        executedPlan // make sure to return plan with new shuffles
+      } else {
+        logInfo(s"OptimizeSkewedJoin: rule is not applied due" +
+          s" to $numNewShuffles additional shuffles will be introduced; $s")
+        plan
+      }
+    } else {
+      executedPlan
+    }
+  }
+
+  def collectShuffleStages(plan: SparkPlan): Seq[ShuffleQueryStageExec] = plan match {
+    case stage: ShuffleQueryStageExec => Seq(stage)
+    case _ => plan.children.flatMap(collectShuffleStages)
+  }
+  /**
+   * Now this runs as part of queryStagePreparationRules() which means it runs over the whole plan
+   * which may have any number of ExchangeExec nodes, i.e. multiple "query stages"

Review comment:
       I was talking about the default case. If we enable the config and allow extra shuffles, then it's pretty straightforward and we just optimize all the leaf SMJs.




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