szehon-ho commented on code in PR #54330:
URL: https://github.com/apache/spark/pull/54330#discussion_r2825236785
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -346,43 +348,85 @@ case class CoalescedHashPartitioning(from:
HashPartitioning, partitions: Seq[Coa
}
/**
- * Represents a partitioning where rows are split across partitions based on
transforms defined
- * by `expressions`. `partitionValues`, if defined, should contain value of
partition key(s) in
- * ascending order, after evaluated by the transforms in `expressions`, for
each input partition.
- * In addition, its length must be the same as the number of Spark partitions
(and thus is a 1-1
- * mapping), and each row in `partitionValues` must be unique.
+ * Represents a partitioning where rows are split across partitions based on
transforms defined by
+ * `expressions`. `partitionKeys`, should contain value of partition key(s) in
ascending order,
+ * after evaluated by the transforms in `expressions`, for each input
partition.
+ * `partitionKeys` might not be unique when this partitioning is returned from
a data source, but
+ * the `GroupPartitionsExec` operator can group partitions with the same key
and so make
+ * `partitionKeys` unique.
*
- * The `originalPartitionValues`, on the other hand, are partition values from
the original input
+ * The `originalPartitionKeys`, on the other hand, are partition values from
the original input
Review Comment:
nit: looks like 'originalPartitionKeys' are a copy of 'partitionKeys' before
any grouping is applied, . Can we clarify the comment as its not clear from it
currently
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -346,43 +348,85 @@ case class CoalescedHashPartitioning(from:
HashPartitioning, partitions: Seq[Coa
}
/**
- * Represents a partitioning where rows are split across partitions based on
transforms defined
- * by `expressions`. `partitionValues`, if defined, should contain value of
partition key(s) in
- * ascending order, after evaluated by the transforms in `expressions`, for
each input partition.
- * In addition, its length must be the same as the number of Spark partitions
(and thus is a 1-1
- * mapping), and each row in `partitionValues` must be unique.
+ * Represents a partitioning where rows are split across partitions based on
transforms defined by
+ * `expressions`. `partitionKeys`, should contain value of partition key(s) in
ascending order,
Review Comment:
nit: seems we lost 'if defined', so the comma doesn't make sense anymore
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceRDD.scala:
##########
@@ -56,94 +91,60 @@ class DataSourceRDD(
}
override def compute(split: Partition, context: TaskContext):
Iterator[InternalRow] = {
-
- val iterator = new Iterator[Object] {
- private val inputPartitions = castPartition(split).inputPartitions
- private var currentIter: Option[Iterator[Object]] = None
- private var currentIndex: Int = 0
-
- private val partitionMetricCallback = new
PartitionMetricCallback(customMetrics)
-
- // In case of early stopping before consuming the entire iterator,
- // we need to do one more metric update at the end of the task.
- context.addTaskCompletionListener[Unit] { _ =>
- partitionMetricCallback.execute()
- }
-
- override def hasNext: Boolean = currentIter.exists(_.hasNext) ||
advanceToNextIter()
-
- override def next(): Object = {
- if (!hasNext) throw new NoSuchElementException("No more elements")
- currentIter.get.next()
+ castPartition(split).inputPartition.iterator.flatMap { inputPartition =>
+ val (iter, reader) = if (columnarReads) {
+ val batchReader =
partitionReaderFactory.createColumnarReader(inputPartition)
+ val iter = new MetricsBatchIterator(
+ new PartitionIterator[ColumnarBatch](batchReader, customMetrics),
readerStateThreadLocal)
+ (iter, batchReader)
+ } else {
+ val rowReader = partitionReaderFactory.createReader(inputPartition)
+ val iter = new MetricsRowIterator(
+ new PartitionIterator[InternalRow](rowReader, customMetrics),
readerStateThreadLocal)
+ (iter, rowReader)
}
- private def advanceToNextIter(): Boolean = {
- if (currentIndex >= inputPartitions.length) {
- false
- } else {
- val inputPartition = inputPartitions(currentIndex)
- currentIndex += 1
-
- // TODO: SPARK-25083 remove the type erasure hack in data source scan
- val (iter, reader) = if (columnarReads) {
- val batchReader =
partitionReaderFactory.createColumnarReader(inputPartition)
- val iter = new MetricsBatchIterator(
- new PartitionIterator[ColumnarBatch](batchReader, customMetrics))
- (iter, batchReader)
- } else {
- val rowReader = partitionReaderFactory.createReader(inputPartition)
- val iter = new MetricsRowIterator(
- new PartitionIterator[InternalRow](rowReader, customMetrics))
- (iter, rowReader)
+ // Add completion listener only once per thread (null means no listener
added yet)
+ val readerState = readerStateThreadLocal.get()
+ if (readerState == null) {
+ context.addTaskCompletionListener[Unit] { _ =>
+ // Use the reader and iterator from ThreadLocal (the last ones
created in this thread)
+ val readerState = readerStateThreadLocal.get()
+ if (readerState != null) {
+ // In case of early stopping before consuming the entire iterator,
+ // we need to do one more metric update at the end of the task.
+ CustomMetrics.updateMetrics(
+ readerState.reader.currentMetricsValues.toImmutableArraySeq,
customMetrics)
+ readerState.iterator.forceUpdateMetrics()
+ readerState.reader.close()
}
-
- // Once we advance to the next partition, update the metric callback
for early finish
- val previousMetrics = partitionMetricCallback.advancePartition(iter,
reader)
- previousMetrics.foreach(reader.initMetricsValues)
-
- currentIter = Some(iter)
- hasNext
+ readerStateThreadLocal.remove()
}
+ } else {
+ readerState.metrics.foreach(reader.initMetricsValues)
Review Comment:
are we missing a close, maybe here to old readerState.reader?
cc @viirya i believe fixed a memory leak here to also take a look at the new
approach
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/GroupPartitionsExec.scala:
##########
@@ -0,0 +1,220 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package org.apache.spark.sql.execution.datasources.v2
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.Partition
+import org.apache.spark.rdd.{CoalescedRDD, PartitionCoalescer, PartitionGroup,
RDD}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical.{KeyedPartitioning,
Partitioning}
+import org.apache.spark.sql.catalyst.util.InternalRowComparableWrapper
+import org.apache.spark.sql.connector.catalog.functions.Reducer
+import org.apache.spark.sql.execution.{SparkPlan, UnaryExecNode}
+import org.apache.spark.sql.types.DataType
+
+/**
+ * Physical operator that groups input partitions by their partition keys.
+ *
+ * This operator is used to coalesce partitions from bucketed/partitioned data
sources
+ * where multiple input partitions share the same partition key. It's commonly
used in
+ * storage-partitioned joins to align partitions from different sides of the
join.
+ *
+ * @param child The child plan providing bucketed/partitioned input
+ * @param joinKeyPositions Optional projection to select a subset of the
partitioning key
+ * for join compatibility (e.g., when join keys are a
subset of
+ * partition keys)
+ * @param commonPartitionKeys Optional sequence of expected partition key
values and their
+ * split counts, used for partially clustered data
+ * @param reducers Optional reducers to apply to partition keys for grouping
compatibility
+ * @param applyPartialClustering Whether to apply partial clustering for
skewed data
+ * @param replicatePartitions Whether to replicate partitions across multiple
keys
+ */
+case class GroupPartitionsExec(
+ child: SparkPlan,
+ joinKeyPositions: Option[Seq[Int]] = None,
+ commonPartitionKeys: Option[Seq[(InternalRow, Int)]] = None,
+ reducers: Option[Seq[Option[Reducer[_, _]]]] = None,
+ applyPartialClustering: Boolean = false,
+ replicatePartitions: Boolean = false
+ ) extends UnaryExecNode {
+
+ override def outputPartitioning: Partitioning = {
+ child.outputPartitioning match {
+ case p: Partitioning with Expression =>
+ p.transform {
+ case k: KeyedPartitioning =>
+ val projectedExpressions = projectExpressions(k.expressions)
+ val projectedDataTypes = projectedExpressions.map(_.dataType)
+ k.copy(expressions = projectedExpressions,
+ partitionKeys = groupedPartitions.map(_._1),
+ originalPartitionKeys = projectKeys(k.originalPartitionKeys,
projectedDataTypes))
+ }.asInstanceOf[Partitioning]
+ case o => o
+ }
+ }
+
+ private def projectExpressions(expressions: Seq[Expression]) = {
+ joinKeyPositions match {
+ case Some(projectionPositions) =>
+ projectionPositions.map(expressions)
+ case _ => expressions
+ }
+ }
+
+ private def projectKeys(keys: Seq[InternalRow], dataTypes: Seq[DataType]) = {
+ joinKeyPositions match {
+ case Some(projectionPositions) =>
+ keys.map(KeyedPartitioning.projectKey(_, projectionPositions,
dataTypes))
+ case _ => keys
+ }
+ }
+
+ /**
+ * Extracts the first KeyedPartitioning from the child's output partitioning.
+ * The child must have a KeyedPartitioning in its partitioning scheme.
+ */
+ lazy val firstKeyedPartitioning = {
+ child.outputPartitioning.asInstanceOf[Partitioning with
Expression].collectFirst {
+ case k: KeyedPartitioning => k
+ }.get
Review Comment:
nit: how about add .getOrElse(throw new SparkException("requires child with
KeyedPartitioning")) to be more clear when error happens
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