szehon-ho commented on code in PR #54330: URL: https://github.com/apache/spark/pull/54330#discussion_r2874720178
########## sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/GroupPartitionsExec.scala: ########## @@ -0,0 +1,209 @@ +/* + * 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, SparkException} +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 Review Comment: i think its not true, its used for all cases of push partition values, not just partially clustered case. maybe we can even take the change to rename it 'expectedPartitionKeys' ########## sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/GroupPartitionsExec.scala: ########## @@ -0,0 +1,209 @@ +/* + * 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, SparkException} +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 => + // There can be multiple `KeyedPartitioning` in an output partitioning of a join, but + // they can only differ in `expressions`. `partitionKeys` must match so we can calculate + // it only once via `groupedPartitions`. + val projectedExpressions = joinKeyPositions.fold(k.expressions)(_.map(k.expressions)) + k.copy(expressions = projectedExpressions, partitionKeys = groupedPartitions.map(_._1)) + }.asInstanceOf[Partitioning] + case o => o + } + } + + /** + * Distributes partitions based on `commonPartitionKeys` and clustering mode. + */ + private def distributeByCommonKeys( Review Comment: what do you think changing to something like: 'alignToExpectedKeys' ? to indicate its aligning the keys to the external expectation? ########## sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/EnsureRequirements.scala: ########## @@ -670,75 +657,57 @@ case class EnsureRequirements( joinType == LeftAnti || joinType == LeftOuter } - // Populate the common partition information down to the scan nodes - private def populateCommonPartitionInfo( + /** + * Unwraps a GroupPartitionsExec to get the underlying child plan. + */ + private def unwrapGroupPartitions(plan: SparkPlan): SparkPlan = plan match { + case g: GroupPartitionsExec => g.child + case other => other + } + + /** + * Applies or updates GroupPartitionsExec with the given parameters. + */ + private def applyGroupPartitions( plan: SparkPlan, - values: Seq[(InternalRow, Int)], joinKeyPositions: Option[Seq[Int]], + mergedPartitionKeys: Seq[(InternalRow, Int)], reducers: Option[Seq[Option[Reducer[_, _]]]], applyPartialClustering: Boolean, - replicatePartitions: Boolean): SparkPlan = plan match { - case scan: BatchScanExec => - val newScan = scan.copy( - spjParams = scan.spjParams.copy( - commonPartitionValues = Some(values), + replicatePartitions: Boolean): SparkPlan = { + plan match { + case g: GroupPartitionsExec => + g.copy( Review Comment: do we need copyTagsFrom to preserve tags? -- This is an automated message from the Apache Git Service. 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