Support left/right outer join in handling data skew feature (apache#60)

* Support left/right outer join in data skew feature

* Style

* Refactor & style

* Modify logic for non-split conditions(join type and skewed side)

* Refactor

sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/HandleSkewedJoin.scala

@@ -17,9 +17,10 @@
 
 package org.apache.spark.sql.execution.adaptive
 
+import scala.collection.immutable.Nil
 import scala.collection.mutable
 
-import org.apache.spark.sql.catalyst.plans.{Cross, Inner, JoinType, LeftSemi}
+import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.execution.{SortExec, SparkPlan, UnionExec}
 import org.apache.spark.sql.execution.joins.SortMergeJoinExec
@@ -28,6 +29,8 @@ import org.apache.spark.sql.internal.SQLConf
 
 case class HandleSkewedJoin(conf: SQLConf) extends Rule[SparkPlan] {
 
+  private val supportedJoinTypes = Inner :: Cross :: LeftSemi :: LeftOuter:: RightOuter :: Nil
+
   private def isSizeSkewed(size: Long, medianSize: Long): Boolean = {
     size > medianSize * conf.adaptiveSkewedFactor &&
       size > conf.adaptiveSkewedSizeThreshold
@@ -96,15 +99,26 @@ case class HandleSkewedJoin(conf: SQLConf) extends Rule[SparkPlan] {
     equallyDivide(queryStageInput.numMapper, numSplits).toArray
   }
 
+  /**
+   * Base optimization support check: the join type is supported and plan statistics is available.
+   * Note that for some join types(like left outer), whether a certain partition can be optimized
+   * also depends on the filed isSkewAndSupportsSplit.
+   */
   private def supportOptimization(
       joinType: JoinType,
       left: QueryStageInput,
       right: QueryStageInput): Boolean = {
-    (joinType == Inner || joinType == Cross || joinType == LeftSemi) &&
+      supportedJoinTypes.contains(joinType) &&
       left.childStage.stats.getPartitionStatistics.isDefined &&
       right.childStage.stats.getPartitionStatistics.isDefined
   }
 
+  private def supportSplitOnLeftPartition(joinType: JoinType) = joinType != RightOuter
+
+  private def supportSplitOnRightPartition(joinType: JoinType) = {
+    joinType != LeftOuter && joinType != LeftSemi
+  }
+
   private def handleSkewedJoin(
       operator: SparkPlan,
       queryStage: QueryStage): SparkPlan = operator.transformUp {
@@ -131,18 +145,21 @@ case class HandleSkewedJoin(conf: SQLConf) extends Rule[SparkPlan] {
       for (partitionId <- 0 until numPartitions) {
         val isLeftSkew = isSkewed(leftStats, partitionId, leftMedSize, leftMedRowCount)
         val isRightSkew = isSkewed(rightStats, partitionId, rightMedSize, rightMedRowCount)
-        if (isLeftSkew || isRightSkew) {
+        val isSkewAndSupportsSplit =
+          (isLeftSkew && supportSplitOnLeftPartition(joinType)) ||
+            (isRightSkew && supportSplitOnRightPartition(joinType))
+
+        if (isSkewAndSupportsSplit) {
           skewedPartitions += partitionId
-          val leftMapIdStartIndices = if (isLeftSkew) {
+          val leftMapIdStartIndices = if (isLeftSkew && supportSplitOnLeftPartition(joinType)) {
             estimateMapIdStartIndices(left, partitionId, leftMedSize, leftMedRowCount)
           } else {
             Array(0)
           }
-          val rightMapIdStartIndices = if (!isRightSkew || joinType == LeftSemi) {
-            // For left semi join, we don't split the right partition
-            Array(0)
-          } else {
+          val rightMapIdStartIndices = if (isRightSkew && supportSplitOnRightPartition(joinType)) {
             estimateMapIdStartIndices(right, partitionId, rightMedSize, rightMedRowCount)
+          } else {
+            Array(0)
           }
 
           for (i <- 0 until leftMapIdStartIndices.length;

sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/QueryStage.scala

@@ -19,7 +19,8 @@ package org.apache.spark.sql.execution.adaptive
 
 import scala.concurrent.{ExecutionContext, Future}
 import scala.concurrent.duration.Duration
-import org.apache.spark.{MapOutputStatistics, SparkContext, broadcast}
+
+import org.apache.spark.{broadcast, MapOutputStatistics, SparkContext}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._

sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/QueryStageSuite.scala

@@ -343,6 +343,241 @@ class QueryStageSuite extends SparkFunSuite with BeforeAndAfterAll {
     }
   }
 
+  test("adaptive skewed join: left/right outer join and skewed on right side") {
+    val spark = defaultSparkSession
+    spark.conf.set(SQLConf.ADAPTIVE_EXECUTION_JOIN_ENABLED.key, "false")
+    spark.conf.set(SQLConf.ADAPTIVE_EXECUTION_SKEWED_JOIN_ENABLED.key, "true")
+    spark.conf.set(SQLConf.ADAPTIVE_EXECUTION_SKEWED_PARTITION_ROW_COUNT_THRESHOLD.key, 10)
+    withSparkSession(spark) { spark: SparkSession =>
+      val df1 =
+        spark
+          .range(0, 10, 1, 2)
+          .selectExpr("id % 5 as key1", "id as value1")
+      val df2 =
+        spark
+          .range(0, 1000, 1, numInputPartitions)
+          .selectExpr("id % 1 as key2", "id as value2")
+
+      val leftOuterJoin =
+        df1.join(df2, col("key1") === col("key2"), "left").select(col("key1"), col("value2"))
+      val rightOuterJoin =
+        df1.join(df2, col("key1") === col("key2"), "right").select(col("key1"), col("value2"))
+
+      // Before Execution, there is one SortMergeJoin
+      val smjBeforeExecutionForLeftOuter = leftOuterJoin.queryExecution.executedPlan.collect {
+        case smj: SortMergeJoinExec => smj
+      }
+      assert(smjBeforeExecutionForLeftOuter.length === 1)
+
+      val smjBeforeExecutionForRightOuter = leftOuterJoin.queryExecution.executedPlan.collect {
+        case smj: SortMergeJoinExec => smj
+      }
+      assert(smjBeforeExecutionForRightOuter.length === 1)
+
+      // Check the answer.
+      val expectedAnswerForLeftOuter =
+        spark
+          .range(0, 1000)
+          .selectExpr("0 as key", "id as value")
+          .union(spark.range(0, 1000).selectExpr("0 as key", "id as value"))
+          .union(spark.range(0, 10, 1).filter(_ % 5 != 0).selectExpr("id % 5 as key1", "null"))
+      checkAnswer(
+        leftOuterJoin,
+        expectedAnswerForLeftOuter.collect())
+
+      val expectedAnswerForRightOuter =
+        spark
+          .range(0, 1000)
+          .selectExpr("0 as key", "id as value")
+          .union(spark.range(0, 1000).selectExpr("0 as key", "id as value"))
+      checkAnswer(
+        rightOuterJoin,
+        expectedAnswerForRightOuter.collect())
+
+      // For the left outer join case: during execution, the SMJ can not be translated to any sub
+      // joins due to the skewed side is on the right but the join type is left outer
+      // (not correspond with each other)
+      val smjAfterExecutionForLeftOuter = leftOuterJoin.queryExecution.executedPlan.collect {
+        case smj: SortMergeJoinExec => smj
+      }
+      assert(smjAfterExecutionForLeftOuter.length === 1)
+
+      // For the right outer join case: during execution, the SMJ is changed to Union of SMJ + 5 SMJ
+      // joins due to the skewed side is on the right and the join type is right
+      // outer (correspond with each other)
+      val smjAfterExecutionForRightOuter = rightOuterJoin.queryExecution.executedPlan.collect {
+        case smj: SortMergeJoinExec => smj
+      }
+
+      assert(smjAfterExecutionForRightOuter.length === 6)
+      val queryStageInputs = rightOuterJoin.queryExecution.executedPlan.collect {
+        case q: ShuffleQueryStageInput => q
+      }
+      assert(queryStageInputs.length === 2)
+      assert(queryStageInputs(0).skewedPartitions === queryStageInputs(1).skewedPartitions)
+      assert(queryStageInputs(0).skewedPartitions === Some(Set(0)))
+
+    }
+  }
+
+  test("adaptive skewed join: left/right outer join and skewed on left side") {
+    val spark = defaultSparkSession
+    spark.conf.set(SQLConf.ADAPTIVE_EXECUTION_JOIN_ENABLED.key, "false")
+    spark.conf.set(SQLConf.ADAPTIVE_EXECUTION_SKEWED_JOIN_ENABLED.key, "true")
+    spark.conf.set(SQLConf.ADAPTIVE_EXECUTION_SKEWED_PARTITION_ROW_COUNT_THRESHOLD.key, 10)
+    withSparkSession(spark) { spark: SparkSession =>
+      val df1 =
+        spark
+          .range(0, 1000, 1, numInputPartitions)
+          .selectExpr("id % 1 as key1", "id as value1")
+      val df2 =
+        spark
+          .range(0, 10, 1, 2)
+          .selectExpr("id % 5 as key2", "id as value2")
+
+      val leftOuterJoin =
+        df1.join(df2, col("key1") === col("key2"), "left").select(col("key1"), col("value1"))
+      val rightOuterJoin =
+        df1.join(df2, col("key1") === col("key2"), "right").select(col("key1"), col("value1"))
+
+      // Before Execution, there is one SortMergeJoin
+      val smjBeforeExecutionForLeftOuter = leftOuterJoin.queryExecution.executedPlan.collect {
+        case smj: SortMergeJoinExec => smj
+      }
+      assert(smjBeforeExecutionForLeftOuter.length === 1)
+
+      val smjBeforeExecutionForRightOuter = leftOuterJoin.queryExecution.executedPlan.collect {
+        case smj: SortMergeJoinExec => smj
+      }
+      assert(smjBeforeExecutionForRightOuter.length === 1)
+
+      // Check the answer.
+      val expectedAnswerForLeftOuter =
+        spark
+          .range(0, 1000)
+          .selectExpr("0 as key", "id as value")
+          .union(spark.range(0, 1000).selectExpr("0 as key", "id as value"))
+      checkAnswer(
+        leftOuterJoin,
+        expectedAnswerForLeftOuter.collect())
+
+      val expectedAnswerForRightOuter =
+        spark
+          .range(0, 1000)
+          .selectExpr("0 as key", "id as value")
+          .union(spark.range(0, 1000).selectExpr("0 as key", "id as value"))
+          .union(spark.range(0, 10, 1).filter(_ % 5 != 0).selectExpr("null", "null"))
+
+      checkAnswer(
+        rightOuterJoin,
+        expectedAnswerForRightOuter.collect())
+
+      // For the left outer join case: during execution, the SMJ is changed to Union of SMJ + 5 SMJ
+      // joins due to the skewed side is on the left and the join type is left outer
+      // (correspond with each other)
+      val smjAfterExecutionForLeftOuter = leftOuterJoin.queryExecution.executedPlan.collect {
+        case smj: SortMergeJoinExec => smj
+      }
+      assert(smjAfterExecutionForLeftOuter.length === 6)
+
+      // For the right outer join case: during execution, the SMJ can not be translated to any sub
+      // joins due to the skewed side is on the left but the join type is right outer
+      // (not correspond with each other)
+      val smjAfterExecutionForRightOuter = rightOuterJoin.queryExecution.executedPlan.collect {
+        case smj: SortMergeJoinExec => smj
+      }
+
+      assert(smjAfterExecutionForRightOuter.length === 1)
+      val queryStageInputs = leftOuterJoin.queryExecution.executedPlan.collect {
+        case q: ShuffleQueryStageInput => q
+      }
+      assert(queryStageInputs.length === 2)
+      assert(queryStageInputs(0).skewedPartitions === queryStageInputs(1).skewedPartitions)
+      assert(queryStageInputs(0).skewedPartitions === Some(Set(0)))
+
+    }
+  }
+
+  test("adaptive skewed join: left/right outer join and skewed on both sides") {
+    val spark = defaultSparkSession
+    spark.conf.set(SQLConf.ADAPTIVE_EXECUTION_JOIN_ENABLED.key, "false")
+    spark.conf.set(SQLConf.ADAPTIVE_EXECUTION_SKEWED_JOIN_ENABLED.key, "true")
+    spark.conf.set(SQLConf.ADAPTIVE_EXECUTION_SKEWED_PARTITION_ROW_COUNT_THRESHOLD.key, 10)
+    withSparkSession(spark) { spark: SparkSession =>
+      import spark.implicits._
+      val df1 =
+        spark
+          .range(0, 100, 1, numInputPartitions)
+          .selectExpr("id % 1 as key1", "id as value1")
+      val df2 =
+        spark
+          .range(0, 100, 1, numInputPartitions)
+          .selectExpr("id % 1 as key2", "id as value2")
+
+      val leftOuterJoin =
+        df1.join(df2, col("key1") === col("key2"), "left").select(col("key1"), col("value2"))
+      val rightOuterJoin =
+        df1.join(df2, col("key1") === col("key2"), "right").select(col("key1"), col("value2"))
+
+      // Before Execution, there is one SortMergeJoin
+      val smjBeforeExecutionForLeftOuter = leftOuterJoin.queryExecution.executedPlan.collect {
+        case smj: SortMergeJoinExec => smj
+      }
+      assert(smjBeforeExecutionForLeftOuter.length === 1)
+
+      val smjBeforeExecutionForRightOuter = leftOuterJoin.queryExecution.executedPlan.collect {
+        case smj: SortMergeJoinExec => smj
+      }
+      assert(smjBeforeExecutionForRightOuter.length === 1)
+
+      // Check the answer.
+      val expectedAnswerForLeftOuter =
+        spark
+          .range(0, 100)
+          .flatMap(i => Seq.fill(100)(i))
+          .selectExpr("0 as key", "value")
+
+      checkAnswer(
+        leftOuterJoin,
+        expectedAnswerForLeftOuter.collect())
+
+      val expectedAnswerForRightOuter =
+        spark
+          .range(0, 100)
+           .flatMap(i => Seq.fill(100)(i))
+          .selectExpr("0 as key", "value")
+      checkAnswer(
+        rightOuterJoin,
+        expectedAnswerForRightOuter.collect())
+
+      // For the left outer join case: during execution, although the skewed sides include the
+      // right, the SMJ is still changed to Union of SMJ + 5 SMJ joins due to the skewed sides
+      // also include the left, so we split the left skewed partition
+      // (correspondence exists)
+      val smjAfterExecutionForLeftOuter = leftOuterJoin.queryExecution.executedPlan.collect {
+        case smj: SortMergeJoinExec => smj
+      }
+      assert(smjAfterExecutionForLeftOuter.length === 6)
+
+      // For the right outer join case: during execution, although the skewed sides include the
+      // left, the SMJ is still changed to Union of SMJ + 5 SMJ joins due to the skewed sides
+      // also include the right, so we split the right skewed partition
+      // (correspondence exists)
+      val smjAfterExecutionForRightOuter = rightOuterJoin.queryExecution.executedPlan.collect {
+        case smj: SortMergeJoinExec => smj
+      }
+
+      assert(smjAfterExecutionForRightOuter.length === 6)
+      val queryStageInputs = rightOuterJoin.queryExecution.executedPlan.collect {
+        case q: ShuffleQueryStageInput => q
+      }
+      assert(queryStageInputs.length === 2)
+      assert(queryStageInputs(0).skewedPartitions === queryStageInputs(1).skewedPartitions)
+      assert(queryStageInputs(0).skewedPartitions === Some(Set(0)))
+
+    }
+  }
+
   test("row count statistics, compressed") {
     val spark = defaultSparkSession
     withSparkSession(spark) { spark: SparkSession =>