[SPARK-32056][SQL] Coalesce partitions for repartition by expressions when AQE is enabled

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala

@@ -28,6 +28,7 @@ import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.physical.{HashPartitioning, Partitioning, RangePartitioning, RoundRobinPartitioning}
 import org.apache.spark.sql.catalyst.util.truncatedString
 import org.apache.spark.sql.connector.catalog.Identifier
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.types._
 import org.apache.spark.util.random.RandomSampler
 
@@ -953,16 +954,18 @@ case class Repartition(numPartitions: Int, shuffle: Boolean, child: LogicalPlan)
 }
 
 /**
- * This method repartitions data using [[Expression]]s into `numPartitions`, and receives
+ * This method repartitions data using [[Expression]]s into `optNumPartitions`, and receives
  * information about the number of partitions during execution. Used when a specific ordering or
  * distribution is expected by the consumer of the query result. Use [[Repartition]] for RDD-like
- * `coalesce` and `repartition`.
+ * `coalesce` and `repartition`. If no `optNumPartitions` is given, by default it partitions data
+ * into `numShufflePartitions` defined in `SQLConf`, and could be coalesced by AQE.
  */
 case class RepartitionByExpression(
     partitionExpressions: Seq[Expression],
     child: LogicalPlan,
-    numPartitions: Int) extends RepartitionOperation {
+    optNumPartitions: Option[Int]) extends RepartitionOperation {
 
+  val numPartitions = optNumPartitions.getOrElse(SQLConf.get.numShufflePartitions)
   require(numPartitions > 0, s"Number of partitions ($numPartitions) must be positive.")
 
   val partitioning: Partitioning = {
@@ -990,6 +993,15 @@ case class RepartitionByExpression(
   override def shuffle: Boolean = true
 }
 
+object RepartitionByExpression {
+  def apply(
+      partitionExpressions: Seq[Expression],
+      child: LogicalPlan,
+      numPartitions: Int): RepartitionByExpression = {
+    RepartitionByExpression(partitionExpressions, child, Some(numPartitions))
+  }
+}
+
 /**
  * A relation with one row. This is used in "SELECT ..." without a from clause.
  */

sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala

@@ -2991,17 +2991,9 @@ class Dataset[T] private[sql](
     Repartition(numPartitions, shuffle = true, logicalPlan)
   }
 
-  /**
-   * Returns a new Dataset partitioned by the given partitioning expressions into
-   * `numPartitions`. The resulting Dataset is hash partitioned.
-   *
-   * This is the same operation as "DISTRIBUTE BY" in SQL (Hive QL).
-   *
-   * @group typedrel
-   * @since 2.0.0
-   */
-  @scala.annotation.varargs
-  def repartition(numPartitions: Int, partitionExprs: Column*): Dataset[T] = {
+  private def repartitionByExpression(
+      numPartitions: Option[Int],
+      partitionExprs: Seq[Column]): Dataset[T] = {
     // The underlying `LogicalPlan` operator special-cases all-`SortOrder` arguments.
     // However, we don't want to complicate the semantics of this API method.
     // Instead, let's give users a friendly error message, pointing them to the new method.
@@ -3015,6 +3007,20 @@ class Dataset[T] private[sql](
     }
   }
 
+  /**
+   * Returns a new Dataset partitioned by the given partitioning expressions into
+   * `numPartitions`. The resulting Dataset is hash partitioned.
+   *
+   * This is the same operation as "DISTRIBUTE BY" in SQL (Hive QL).
+   *
+   * @group typedrel
+   * @since 2.0.0
+   */
+  @scala.annotation.varargs
+  def repartition(numPartitions: Int, partitionExprs: Column*): Dataset[T] = {
+    repartitionByExpression(Some(numPartitions), partitionExprs)
+  }
+
   /**
    * Returns a new Dataset partitioned by the given partitioning expressions, using
    * `spark.sql.shuffle.partitions` as number of partitions.
@@ -3027,7 +3033,20 @@ class Dataset[T] private[sql](
    */
   @scala.annotation.varargs
   def repartition(partitionExprs: Column*): Dataset[T] = {
-    repartition(sparkSession.sessionState.conf.numShufflePartitions, partitionExprs: _*)
+    repartitionByExpression(None, partitionExprs)
+  }
+
+  private def repartitionByRange(
+      numPartitions: Option[Int],
+      partitionExprs: Seq[Column]): Dataset[T] = {
+    require(partitionExprs.nonEmpty, "At least one partition-by expression must be specified.")
+    val sortOrder: Seq[SortOrder] = partitionExprs.map(_.expr match {
+      case expr: SortOrder => expr
+      case expr: Expression => SortOrder(expr, Ascending)
+    })
+    withTypedPlan {
+      RepartitionByExpression(sortOrder, logicalPlan, numPartitions)
+    }
   }
 
   /**
@@ -3049,14 +3068,7 @@ class Dataset[T] private[sql](
    */
   @scala.annotation.varargs
   def repartitionByRange(numPartitions: Int, partitionExprs: Column*): Dataset[T] = {
-    require(partitionExprs.nonEmpty, "At least one partition-by expression must be specified.")
-    val sortOrder: Seq[SortOrder] = partitionExprs.map(_.expr match {
-      case expr: SortOrder => expr
-      case expr: Expression => SortOrder(expr, Ascending)
-    })
-    withTypedPlan {
-      RepartitionByExpression(sortOrder, logicalPlan, numPartitions)
-    }
+    repartitionByRange(Some(numPartitions), partitionExprs)
   }
 
   /**
@@ -3078,7 +3090,7 @@ class Dataset[T] private[sql](
    */
   @scala.annotation.varargs
   def repartitionByRange(partitionExprs: Column*): Dataset[T] = {
-    repartitionByRange(sparkSession.sessionState.conf.numShufflePartitions, partitionExprs: _*)
+    repartitionByRange(None, partitionExprs)
   }
 
   /**

sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala

@@ -685,8 +685,9 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case r: logical.Range =>
         execution.RangeExec(r) :: Nil
       case r: logical.RepartitionByExpression =>
+        val canChangeNumParts = r.optNumPartitions.isEmpty
         exchange.ShuffleExchangeExec(
-          r.partitioning, planLater(r.child), canChangeNumPartitions = false) :: Nil
+          r.partitioning, planLater(r.child), canChangeNumParts) :: Nil
       case ExternalRDD(outputObjAttr, rdd) => ExternalRDDScanExec(outputObjAttr, rdd) :: Nil
       case r: LogicalRDD =>
         RDDScanExec(r.output, r.rdd, "ExistingRDD", r.outputPartitioning, r.outputOrdering) :: Nil

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

@@ -28,7 +28,7 @@ import org.apache.spark.sql.catalyst.optimizer.{BuildLeft, BuildRight}
 import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, LogicalPlan}
 import org.apache.spark.sql.execution.{PartialReducerPartitionSpec, ReusedSubqueryExec, ShuffledRowRDD, SparkPlan}
 import org.apache.spark.sql.execution.command.DataWritingCommandExec
-import org.apache.spark.sql.execution.exchange.{BroadcastExchangeExec, Exchange, ReusedExchangeExec}
+import org.apache.spark.sql.execution.exchange.{BroadcastExchangeExec, Exchange, ReusedExchangeExec, ShuffleExchangeExec}
 import org.apache.spark.sql.execution.joins.{BroadcastHashJoinExec, SortMergeJoinExec}
 import org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate
 import org.apache.spark.sql.functions._
@@ -1022,18 +1022,83 @@ class AdaptiveQueryExecSuite
     }
   }
 
-  test("SPARK-31220 repartition obeys initialPartitionNum when adaptiveExecutionEnabled") {
+  test("SPARK-31220 and SPARK-32056 coalesce partitions for repartition by expressions " +
+      "when AQE is enabled") {
     Seq(true, false).foreach { enableAQE =>
       withSQLConf(
         SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> enableAQE.toString,
-        SQLConf.SHUFFLE_PARTITIONS.key -> "6",
-        SQLConf.COALESCE_PARTITIONS_INITIAL_PARTITION_NUM.key -> "7") {
-        val partitionsNum = spark.range(10).repartition($"id").rdd.collectPartitions().length
+        SQLConf.COALESCE_PARTITIONS_ENABLED.key -> "true",
+        SQLConf.COALESCE_PARTITIONS_INITIAL_PARTITION_NUM.key -> "10",
+        SQLConf.SHUFFLE_PARTITIONS.key -> "10") {
+
+        val df1 = spark.range(10).repartition($"id")
+        val df2 = spark.range(10).repartition($"id" + 1)
+
+        val partitionsNum1 = df1.rdd.collectPartitions().length
+        val partitionsNum2 = df2.rdd.collectPartitions().length
+
         if (enableAQE) {
-          assert(partitionsNum === 7)
+          assert(partitionsNum1 < 10)
+          assert(partitionsNum2 < 10)
+
+          // repartition obeys initialPartitionNum when adaptiveExecutionEnabled
+          val plan = df1.queryExecution.executedPlan
+          assert(plan.isInstanceOf[AdaptiveSparkPlanExec])
+          val shuffle = plan.asInstanceOf[AdaptiveSparkPlanExec].executedPlan.collect {
+            case s: ShuffleExchangeExec => s
+          }
+          assert(shuffle.size == 1)
+          assert(shuffle(0).outputPartitioning.numPartitions == 10)
         } else {
-          assert(partitionsNum === 6)
+          assert(partitionsNum1 === 10)
+          assert(partitionsNum2 === 10)
         }
+
+
+        // Don't coalesce partitions if the number of partitions is specified.
+        val df3 = spark.range(10).repartition(10, $"id")
+        val df4 = spark.range(10).repartition(10)
+        assert(df3.rdd.collectPartitions().length == 10)
+        assert(df4.rdd.collectPartitions().length == 10)
+      }
+    }
+  }
+
+  test("SPARK-31220 and SPARK-32056 coalesce partitions for repartition by range " +
+      "when AQE is enabled") {
+    Seq(true, false).foreach { enableAQE =>
+      withSQLConf(
+        SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> enableAQE.toString,
+        SQLConf.COALESCE_PARTITIONS_ENABLED.key -> "true",
+        SQLConf.COALESCE_PARTITIONS_INITIAL_PARTITION_NUM.key -> "10",
+        SQLConf.SHUFFLE_PARTITIONS.key -> "10") {
+
+        val df1 = spark.range(10).toDF.repartitionByRange($"id".asc)
+        val df2 = spark.range(10).toDF.repartitionByRange(($"id" + 1).asc)
+
+        val partitionsNum1 = df1.rdd.collectPartitions().length
+        val partitionsNum2 = df2.rdd.collectPartitions().length
+
+        if (enableAQE) {
+          assert(partitionsNum1 < 10)
+          assert(partitionsNum2 < 10)
+
+          // repartition obeys initialPartitionNum when adaptiveExecutionEnabled
+          val plan = df1.queryExecution.executedPlan
+          assert(plan.isInstanceOf[AdaptiveSparkPlanExec])
+          val shuffle = plan.asInstanceOf[AdaptiveSparkPlanExec].executedPlan.collect {
+            case s: ShuffleExchangeExec => s
+          }
+          assert(shuffle.size == 1)
+          assert(shuffle(0).outputPartitioning.numPartitions == 10)
+        } else {
+          assert(partitionsNum1 === 10)
+          assert(partitionsNum2 === 10)
+        }
+
+        // Don't coalesce partitions if the number of partitions is specified.
+        val df3 = spark.range(10).repartitionByRange(10, $"id".asc)
+        assert(df3.rdd.collectPartitions().length == 10)
       }
     }
   }