[SPARK-19426][SQL] Custom coalescer for Dataset

core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala

@@ -1158,8 +1158,17 @@ class RDDSuite extends SparkFunSuite with SharedSparkContext {
  * Took this class out of the test suite to prevent "Task not serializable" exceptions.
  */
 class SizeBasedCoalescer(val maxSize: Int) extends PartitionCoalescer with Serializable {
+
+  def getPartitions(parent: RDD[_]): Array[Partition] = {
+    parent.asInstanceOf[HadoopRDD[Any, Any]].getPartitions
+  }
+
+  def getPartitionSize(partition: Partition): Long = {
+    partition.asInstanceOf[HadoopPartition].inputSplit.value.getLength
+  }
+
   override def coalesce(maxPartitions: Int, parent: RDD[_]): Array[PartitionGroup] = {
-    val partitions: Array[Partition] = parent.asInstanceOf[HadoopRDD[Any, Any]].getPartitions
+    val partitions = getPartitions(parent)
     val groups = ArrayBuffer[PartitionGroup]()
     var currentGroup = new PartitionGroup()
     var currentSum = 0L
@@ -1168,8 +1177,8 @@ class SizeBasedCoalescer(val maxSize: Int) extends PartitionCoalescer with Seria
 
     // sort partitions based on the size of the corresponding input splits
     partitions.sortWith((partition1, partition2) => {
-      val partition1Size = partition1.asInstanceOf[HadoopPartition].inputSplit.value.getLength
-      val partition2Size = partition2.asInstanceOf[HadoopPartition].inputSplit.value.getLength
+      val partition1Size = getPartitionSize(partition1)
+      val partition2Size = getPartitionSize(partition2)
       partition1Size < partition2Size
     })
 
@@ -1185,23 +1194,21 @@ class SizeBasedCoalescer(val maxSize: Int) extends PartitionCoalescer with Seria
       totalSum += splitSize
     }
 
-    while (index < partitions.size) {
+    while (index < partitions.length) {
       val partition = partitions(index)
-      val fileSplit =
-        partition.asInstanceOf[HadoopPartition].inputSplit.value.asInstanceOf[FileSplit]
-      val splitSize = fileSplit.getLength
+      val splitSize = getPartitionSize(partition)
       if (currentSum + splitSize < maxSize) {
         addPartition(partition, splitSize)
         index += 1
-        if (index == partitions.size) {
-          updateGroups
+        if (index == partitions.length) {
+          updateGroups()
         }
       } else {
-        if (currentGroup.partitions.size == 0) {
+        if (currentGroup.partitions.isEmpty) {
           addPartition(partition, splitSize)
           index += 1
         } else {
-          updateGroups
+          updateGroups()
         }
       }
     }

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

@@ -17,12 +17,12 @@
 
 package org.apache.spark.sql.catalyst.plans.logical
 
+import org.apache.spark.rdd.PartitionCoalescer
 import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
 import org.apache.spark.sql.catalyst.catalog.CatalogTable
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression
 import org.apache.spark.sql.catalyst.plans._
-import org.apache.spark.sql.catalyst.plans.logical.statsEstimation._
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 import org.apache.spark.util.random.RandomSampler
@@ -752,6 +752,16 @@ case class Repartition(numPartitions: Int, shuffle: Boolean, child: LogicalPlan)
   require(numPartitions > 0, s"Number of partitions ($numPartitions) must be positive.")
 }
 
+/**
+ * Returns a new RDD that has at most `numPartitions` partitions. This behavior can be modified by
+ * supplying a `PartitionCoalescer` to control the behavior of the partitioning.
+ */
+case class PartitionCoalesce(numPartitions: Int, coalescer: PartitionCoalescer, child: LogicalPlan)
+  extends UnaryNode {
+  require(numPartitions > 0, s"Number of partitions ($numPartitions) must be positive.")
+  override def output: Seq[Attribute] = child.output
+}
+
 /**
  * This method repartitions data using [[Expression]]s into `numPartitions`, and receives
  * information about the number of partitions during execution. Used when a specific ordering or

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

@@ -33,7 +33,7 @@ import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.java.function._
 import org.apache.spark.api.python.{PythonRDD, SerDeUtil}
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.{PartitionCoalescer, RDD}
 import org.apache.spark.sql.catalyst._
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.catalog.CatalogRelation
@@ -2661,6 +2661,35 @@ class Dataset[T] private[sql](
       partitionExprs.map(_.expr), logicalPlan, sparkSession.sessionState.conf.numShufflePartitions)
   }
 
+  /**
+   * Returns a new Dataset that an user-defined `PartitionCoalescer` reduces into fewer partitions.
+   * `userDefinedCoalescer` is the same with a coalescer used in the `RDD` coalesce function.
+   *
+   * If a larger number of partitions is requested, it will stay at the current
+   * number of partitions. Similar to coalesce defined on an `RDD`, this operation results in
+   * a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not
+   * be a shuffle, instead each of the 100 new partitions will claim 10 of the current partitions.
+   *
+   * However, if you're doing a drastic coalesce, e.g. to numPartitions = 1,
+   * this may result in your computation taking place on fewer nodes than
+   * you like (e.g. one node in the case of numPartitions = 1). To avoid this,
+   * you can call repartition. This will add a shuffle step, but means the
+   * current upstream partitions will be executed in parallel (per whatever
+   * the current partitioning is).
+   *
+   * @group typedrel
+   * @since 2.3.0
+   */
+  def coalesce(numPartitions: Int, userDefinedCoalescer: Option[PartitionCoalescer]): Dataset[T] = {
+    userDefinedCoalescer.map { coalescer =>
+      withTypedPlan {
+        PartitionCoalesce(numPartitions, coalescer, logicalPlan)
+      }
+    }.getOrElse {
+      coalesce(numPartitions)
+    }
+  }
+
   /**
    * Returns a new Dataset that has exactly `numPartitions` partitions, when the fewer partitions
    * are requested. If a larger number of partitions is requested, it will stay at the current

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

@@ -394,8 +394,10 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         if (shuffle) {
           ShuffleExchange(RoundRobinPartitioning(numPartitions), planLater(child)) :: Nil
         } else {
-          execution.CoalesceExec(numPartitions, planLater(child)) :: Nil
+          execution.CoalesceExec(numPartitions, planLater(child), None) :: Nil
         }
+      case logical.PartitionCoalesce(numPartitions, coalescer, child) =>
+        execution.CoalesceExec(numPartitions, planLater(child), Some(coalescer)) :: Nil
       case logical.Sort(sortExprs, global, child) =>
         execution.SortExec(sortExprs, global, planLater(child)) :: Nil
       case logical.Project(projectList, child) =>

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

@@ -20,14 +20,13 @@ package org.apache.spark.sql.execution
 import scala.concurrent.{ExecutionContext, Future}
 import scala.concurrent.duration.Duration
 
-import org.apache.spark.{InterruptibleIterator, SparkException, TaskContext}
-import org.apache.spark.rdd.{EmptyRDD, PartitionwiseSampledRDD, RDD}
+import org.apache.spark.{InterruptibleIterator, TaskContext}
+import org.apache.spark.rdd.{EmptyRDD, PartitionCoalescer, PartitionwiseSampledRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode, ExpressionCanonicalizer}
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.metric.SQLMetrics
-import org.apache.spark.sql.execution.ui.SparkListenerDriverAccumUpdates
 import org.apache.spark.sql.types.LongType
 import org.apache.spark.util.ThreadUtils
 import org.apache.spark.util.random.{BernoulliCellSampler, PoissonSampler}
@@ -571,7 +570,8 @@ case class UnionExec(children: Seq[SparkPlan]) extends SparkPlan {
  * current upstream partitions will be executed in parallel (per whatever
  * the current partitioning is).
  */
-case class CoalesceExec(numPartitions: Int, child: SparkPlan) extends UnaryExecNode {
+case class CoalesceExec(numPartitions: Int, child: SparkPlan, coalescer: Option[PartitionCoalescer])
+  extends UnaryExecNode {
   override def output: Seq[Attribute] = child.output
 
   override def outputPartitioning: Partitioning = {
@@ -580,7 +580,7 @@ case class CoalesceExec(numPartitions: Int, child: SparkPlan) extends UnaryExecN
   }
 
   protected override def doExecute(): RDD[InternalRow] = {
-    child.execute().coalesce(numPartitions, shuffle = false)
+    child.execute().coalesce(numPartitions, shuffle = false, coalescer)
   }
 }
 

sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala

@@ -20,10 +20,13 @@ package org.apache.spark.sql
 import java.io.{Externalizable, ObjectInput, ObjectOutput}
 import java.sql.{Date, Timestamp}
 
+import org.apache.spark.Partition
+import org.apache.spark.rdd._
 import org.apache.spark.sql.catalyst.encoders.{OuterScopes, RowEncoder}
 import org.apache.spark.sql.catalyst.plans.{LeftAnti, LeftSemi}
 import org.apache.spark.sql.catalyst.util.sideBySide
 import org.apache.spark.sql.execution.{LogicalRDD, RDDScanExec}
+import org.apache.spark.sql.execution.datasources.{FilePartition, FileScanRDD}
 import org.apache.spark.sql.execution.exchange.{BroadcastExchangeExec, ShuffleExchange}
 import org.apache.spark.sql.execution.streaming.MemoryStream
 import org.apache.spark.sql.functions._
@@ -119,6 +122,39 @@ class DatasetSuite extends QueryTest with SharedSQLContext {
       data: _*)
   }
 
+  test("coalesce, custom") {
+    withTempPath { path =>
+      val maxSplitSize = 512
+      val testData = (1 to 1000).map(i => ClassData(i.toString, i))
+      testData.toDS().repartition(50).write.format("csv").save(path.toString)
+
+      withSQLConf(
+        SQLConf.FILES_MAX_PARTITION_BYTES.key -> (maxSplitSize / 3).toString,
+        SQLConf.FILES_OPEN_COST_IN_BYTES.key -> "0"
+      ) {
+        val ds = spark.read.format("csv")
+          .schema("a STRING, b INT")
+          .load(path.toString)
+          .as[ClassData]
+
+        val coalescedDataSet =
+          ds.coalesce(4, Some(new DatasetSizeBasedPartitionCoalescer(maxSplitSize)))
+
+        assert(coalescedDataSet.rdd.partitions.length <= 50)
+
+        val expectedPartitionCount = ds.rdd.partitions.size
+        val totalPartitionCount = coalescedDataSet.rdd.partitions.map { p1 =>
+          val splitSizes = p1.asInstanceOf[CoalescedRDDPartition].parents.map { p2 =>
+            p2.asInstanceOf[FilePartition].files.map(_.length).sum
+          }
+          assert(splitSizes.sum <= maxSplitSize)
+          splitSizes.size
+        }.sum
+        assert(totalPartitionCount === expectedPartitionCount)
+      }
+    }
+  }
+
   test("as tuple") {
     val data = Seq(("a", 1), ("b", 2)).toDF("a", "b")
     checkDataset(
@@ -1402,3 +1438,14 @@ case class CircularReferenceClassB(cls: CircularReferenceClassA)
 case class CircularReferenceClassC(ar: Array[CircularReferenceClassC])
 case class CircularReferenceClassD(map: Map[String, CircularReferenceClassE])
 case class CircularReferenceClassE(id: String, list: List[CircularReferenceClassD])
+
+class DatasetSizeBasedPartitionCoalescer(maxSize: Int) extends SizeBasedCoalescer(maxSize) {
+
+  override def getPartitions(parent: RDD[_]): Array[Partition] = {
+    parent.firstParent.partitions
+  }
+
+  override def getPartitionSize(partition: Partition): Long = {
+    partition.asInstanceOf[FilePartition].files.map(x => x.length - x.start).sum
+  }
+}