peter-toth commented on code in PR #3349:
URL: https://github.com/apache/datafusion-comet/pull/3349#discussion_r2754880669
##########
spark/src/main/scala/org/apache/spark/sql/comet/CometExecRDD.scala:
##########
@@ -19,39 +19,207 @@
package org.apache.spark.sql.comet
-import org.apache.spark.{Partition, SparkContext, TaskContext}
-import org.apache.spark.rdd.{RDD, RDDOperationScope}
+import org.apache.spark._
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.execution.ScalarSubquery
import org.apache.spark.sql.vectorized.ColumnarBatch
+import org.apache.spark.util.SerializableConfiguration
+
+import org.apache.comet.CometExecIterator
+import org.apache.comet.serde.OperatorOuterClass
+
+/**
+ * Partition that carries per-partition planning data, avoiding closure
capture of all partitions.
+ */
+private[spark] class CometExecPartition(
+ override val index: Int,
+ val inputPartitions: Array[Partition],
+ val planDataByKey: Map[String, Array[Byte]])
+ extends Partition
/**
- * A RDD that executes Spark SQL query in Comet native execution to generate
ColumnarBatch.
+ * Unified RDD for Comet native execution.
+ *
+ * Solves the closure capture problem: instead of capturing all partitions'
data in the closure
+ * (which gets serialized to every task), each Partition object carries only
its own data.
+ *
+ * Handles three cases:
+ * - With inputs + per-partition data: injects planning data into operator
tree
+ * - With inputs + no per-partition data: just zips inputs (no injection
overhead)
+ * - No inputs: uses numPartitions to create partitions
+ *
+ * NOTE: This RDD does not handle DPP (InSubqueryExec), which is resolved in
+ * CometIcebergNativeScanExec.serializedPartitionData before this RDD is
created. It also handles
+ * ScalarSubquery expressions by registering them with CometScalarSubquery
before execution.
*/
private[spark] class CometExecRDD(
sc: SparkContext,
- partitionNum: Int,
- var f: (Seq[Iterator[ColumnarBatch]], Int, Int) => Iterator[ColumnarBatch])
+ inputRDDs: Seq[RDD[ColumnarBatch]],
+ commonByKey: Map[String, Array[Byte]],
+ @transient perPartitionByKey: Map[String, Array[Array[Byte]]],
+ serializedPlan: Array[Byte],
+ defaultNumPartitions: Int,
+ numOutputCols: Int,
+ nativeMetrics: CometMetricNode,
+ subqueries: Seq[ScalarSubquery],
+ broadcastedHadoopConfForEncryption:
Option[Broadcast[SerializableConfiguration]] = None,
+ encryptedFilePaths: Seq[String] = Seq.empty)
extends RDD[ColumnarBatch](sc, Nil) {
- override def compute(s: Partition, context: TaskContext):
Iterator[ColumnarBatch] = {
- f(Seq.empty, partitionNum, s.index)
+ // Determine partition count: from inputs if available, otherwise from
parameter
+ private val numPartitions: Int = if (inputRDDs.nonEmpty) {
+ inputRDDs.head.partitions.length
+ } else if (perPartitionByKey.nonEmpty) {
+ perPartitionByKey.values.head.length
+ } else {
+ defaultNumPartitions
}
+ // Validate all per-partition arrays have the same length to prevent
+ // ArrayIndexOutOfBoundsException in getPartitions (e.g., from broadcast
scans with
+ // different partition counts after DPP filtering)
+ require(
+ perPartitionByKey.values.forall(_.length == numPartitions),
+ s"All per-partition arrays must have length $numPartitions, but found: " +
+ perPartitionByKey.map { case (key, arr) => s"$key -> ${arr.length}"
}.mkString(", "))
+
override protected def getPartitions: Array[Partition] = {
- Array.tabulate(partitionNum)(i =>
- new Partition {
- override def index: Int = i
- })
+ (0 until numPartitions).map { idx =>
+ val inputParts = inputRDDs.map(_.partitions(idx)).toArray
+ val planData = perPartitionByKey.map { case (key, arr) => key ->
arr(idx) }
+ new CometExecPartition(idx, inputParts, planData)
+ }.toArray
+ }
+
+ override def compute(split: Partition, context: TaskContext):
Iterator[ColumnarBatch] = {
+ val partition = split.asInstanceOf[CometExecPartition]
+
+ val inputs = inputRDDs.zip(partition.inputPartitions).map { case (rdd,
part) =>
+ rdd.iterator(part, context)
+ }
+
+ // Only inject if we have per-partition planning data
+ val actualPlan = if (commonByKey.nonEmpty) {
+ val basePlan = OperatorOuterClass.Operator.parseFrom(serializedPlan)
+ val injected =
+ PlanDataInjector.injectPlanData(basePlan, commonByKey,
partition.planDataByKey)
+ PlanDataInjector.serializeOperator(injected)
+ } else {
+ serializedPlan
+ }
+
+ val it = new CometExecIterator(
+ CometExec.newIterId,
+ inputs,
+ numOutputCols,
+ actualPlan,
+ nativeMetrics,
+ numPartitions,
+ partition.index,
+ broadcastedHadoopConfForEncryption,
+ encryptedFilePaths)
+
+ // Register ScalarSubqueries so native code can look them up
+ subqueries.foreach(sub => CometScalarSubquery.setSubquery(it.id, sub))
+
+ Option(context).foreach { ctx =>
+ ctx.addTaskCompletionListener[Unit] { _ =>
+ it.close()
+ subqueries.foreach(sub => CometScalarSubquery.removeSubquery(it.id,
sub))
+ }
+ }
+
+ it
+ }
+
+ override def getDependencies: Seq[Dependency[_]] =
Review Comment:
You can pass this in (instead of `Nil`) where you extend
`RDD[ColumnarBatch](sc, Nil)`.
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