[SPARK-53272][SQL] Refactor SPJ pushdown logic out of BatchScanExec #51979
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.sql.execution | ||
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| import org.apache.spark.sql.catalyst.InternalRow | ||
| import org.apache.spark.sql.catalyst.expressions.RowOrdering | ||
| import org.apache.spark.sql.catalyst.plans.physical.{KeyGroupedPartitioning, KeyGroupedShuffleSpec} | ||
| import org.apache.spark.sql.catalyst.util.InternalRowComparableWrapper | ||
| import org.apache.spark.sql.execution.joins.StoragePartitionJoinParams | ||
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| /** Base trait for a data source scan capable of producing a key-grouped output. */ | ||
| trait KeyGroupedPartitionedScan[T] { | ||
| /** | ||
| * The output partitioning of this scan after applying any pushed-down SPJ parameters. | ||
| * | ||
| * @param basePartitioning The original key-grouped partitioning of the scan. | ||
| * @param spjParams SPJ parameters for the scan. | ||
| */ | ||
| def getOutputKeyGroupedPartitioning( | ||
| basePartitioning: KeyGroupedPartitioning, | ||
| spjParams: StoragePartitionJoinParams): KeyGroupedPartitioning = { | ||
| val expressions = spjParams.joinKeyPositions match { | ||
| case Some(projectionPositions) => | ||
| projectionPositions.map(i => basePartitioning.expressions(i)) | ||
| case _ => basePartitioning.expressions | ||
| } | ||
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| val newPartValues = spjParams.commonPartitionValues match { | ||
| case Some(commonPartValues) => | ||
| // We allow duplicated partition values if | ||
| // `spark.sql.sources.v2.bucketing.partiallyClusteredDistribution.enabled` is true | ||
| commonPartValues.flatMap { | ||
| case (partValue, numSplits) => Seq.fill(numSplits)(partValue) | ||
| } | ||
| case None => | ||
| spjParams.joinKeyPositions match { | ||
| case Some(projectionPositions) => basePartitioning.partitionValues.map { r => | ||
| val projectedRow = KeyGroupedPartitioning.project(expressions, | ||
| projectionPositions, r) | ||
| InternalRowComparableWrapper(projectedRow, expressions) | ||
| }.distinct.map(_.row) | ||
| case _ => basePartitioning.partitionValues | ||
| } | ||
| } | ||
| basePartitioning.copy(expressions = expressions, numPartitions = newPartValues.length, | ||
| partitionValues = newPartValues) | ||
| } | ||
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| /** | ||
| * Re-groups the input partitions for this scan based on the provided SPJ params, returning a list | ||
| * of partitions to be scanned by each scan task. | ||
| * | ||
| * @param p The output KeyGroupedPartitioning of this scan. | ||
| * @param spjParams SPJ parameters for the scan. | ||
| * @param filteredPartitions The input partitions (after applying filtering) to be | ||
| * re-grouped for this scan, initially grouped by partition value. | ||
| * @param partitionValueAccessor Accessor for the partition values (as an [[InternalRow]]) | ||
| */ | ||
| def getInputPartitionGrouping( | ||
| p: KeyGroupedPartitioning, | ||
| spjParams: StoragePartitionJoinParams, | ||
|
||
| filteredPartitions: Seq[Seq[T]], | ||
| partitionValueAccessor: T => InternalRow): Seq[Seq[T]] = { | ||
| assert(spjParams.keyGroupedPartitioning.isDefined) | ||
| val expressions = spjParams.keyGroupedPartitioning.get | ||
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| // Re-group the input partitions if we are projecting on a subset of join keys | ||
| val (groupedPartitions, partExpressions) = spjParams.joinKeyPositions match { | ||
| case Some(projectPositions) => | ||
| val projectedExpressions = projectPositions.map(i => expressions(i)) | ||
| val parts = filteredPartitions.flatten.groupBy(part => { | ||
| val row = partitionValueAccessor(part) | ||
| val projectedRow = KeyGroupedPartitioning.project( | ||
| expressions, projectPositions, row) | ||
| InternalRowComparableWrapper(projectedRow, projectedExpressions) | ||
| }).map { case (wrapper, splits) => (wrapper.row, splits) }.toSeq | ||
| (parts, projectedExpressions) | ||
| case _ => | ||
| val groupedParts = filteredPartitions.map(splits => { | ||
| assert(splits.nonEmpty) | ||
| (partitionValueAccessor(splits.head), splits) | ||
| }) | ||
| (groupedParts, expressions) | ||
| } | ||
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| // Also re-group the partitions if we are reducing compatible partition expressions | ||
| val finalGroupedPartitions = spjParams.reducers match { | ||
| case Some(reducers) => | ||
| val result = groupedPartitions.groupBy { case (row, _) => | ||
| KeyGroupedShuffleSpec.reducePartitionValue(row, partExpressions, reducers) | ||
| }.map { case (wrapper, splits) => (wrapper.row, splits.flatMap(_._2)) }.toSeq | ||
| val rowOrdering = RowOrdering.createNaturalAscendingOrdering( | ||
| partExpressions.map(_.dataType)) | ||
| result.sorted(rowOrdering.on((t: (InternalRow, _)) => t._1)) | ||
| case _ => groupedPartitions | ||
| } | ||
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| // When partially clustered, the input partitions are not grouped by partition | ||
| // values. Here we'll need to check `commonPartitionValues` and decide how to group | ||
| // and replicate splits within a partition. | ||
| if (spjParams.commonPartitionValues.isDefined && spjParams.applyPartialClustering) { | ||
| // A mapping from the common partition values to how many splits the partition | ||
| // should contain. | ||
| val commonPartValuesMap = spjParams.commonPartitionValues | ||
| .get | ||
| .map(t => (InternalRowComparableWrapper(t._1, partExpressions), t._2)) | ||
| .toMap | ||
| val filteredGroupedPartitions = finalGroupedPartitions.filter { | ||
| case (partValues, _) => | ||
| commonPartValuesMap.keySet.contains( | ||
| InternalRowComparableWrapper(partValues, partExpressions)) | ||
| } | ||
| val nestGroupedPartitions = filteredGroupedPartitions.map { case (partValue, splits) => | ||
| // `commonPartValuesMap` should contain the part value since it's the super set. | ||
| val numSplits = commonPartValuesMap | ||
| .get(InternalRowComparableWrapper(partValue, partExpressions)) | ||
| assert(numSplits.isDefined, s"Partition value $partValue does not exist in " + | ||
| "common partition values from Spark plan") | ||
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| val newSplits = if (spjParams.replicatePartitions) { | ||
| // We need to also replicate partitions according to the other side of join | ||
| Seq.fill(numSplits.get)(splits) | ||
| } else { | ||
| // Not grouping by partition values: this could be the side with partially | ||
| // clustered distribution. Because of dynamic filtering, we'll need to check if | ||
| // the final number of splits of a partition is smaller than the original | ||
| // number, and fill with empty splits if so. This is necessary so that both | ||
| // sides of a join will have the same number of partitions & splits. | ||
| splits.map(Seq(_)).padTo(numSplits.get, Seq.empty) | ||
| } | ||
| (InternalRowComparableWrapper(partValue, partExpressions), newSplits) | ||
| } | ||
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| // Now fill missing partition keys with empty partitions | ||
| val partitionMapping = nestGroupedPartitions.toMap | ||
| spjParams.commonPartitionValues.get.flatMap { | ||
| case (partValue, numSplits) => | ||
| // Use empty partition for those partition values that are not present. | ||
| partitionMapping.getOrElse( | ||
| InternalRowComparableWrapper(partValue, partExpressions), | ||
| Seq.fill(numSplits)(Seq.empty)) | ||
| } | ||
| } else { | ||
| // either `commonPartitionValues` is not defined, or it is defined but | ||
| // `applyPartialClustering` is false. | ||
| val partitionMapping = finalGroupedPartitions.map { case (partValue, splits) => | ||
| InternalRowComparableWrapper(partValue, partExpressions) -> splits | ||
| }.toMap | ||
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| // In case `commonPartitionValues` is not defined (e.g., SPJ is not used), there | ||
| // could exist duplicated partition values, as partition grouping is not done | ||
| // at the beginning and postponed to this method. It is important to use unique | ||
| // partition values here so that grouped partitions won't get duplicated. | ||
| p.uniquePartitionValues.map { partValue => | ||
| // Use empty partition for those partition values that are not present | ||
| partitionMapping.getOrElse( | ||
| InternalRowComparableWrapper(partValue, partExpressions), Seq.empty) | ||
| } | ||
| } | ||
| } | ||
| } | ||
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can we move
StoragePartitionJoinParamsto an individual file instead ofBatchScanExec.scala?There was a problem hiding this comment.
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Sure, done.