YuweiXiao commented on code in PR #7423:
URL: https://github.com/apache/hudi/pull/7423#discussion_r1049431796
##########
hudi-common/src/main/java/org/apache/hudi/BaseHoodieTableFileIndex.java:
##########
@@ -406,7 +406,7 @@ protected long getTotalCachedFilesSize() {
.sum();
}
- protected boolean areAllFileSlicesCached() {
+ public boolean areAllFileSlicesCached() {
Review Comment:
Looks like this visibility change is not necessary?
##########
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/HoodieSparkSessionExtension.scala:
##########
@@ -44,6 +40,17 @@ class HoodieSparkSessionExtension extends
(SparkSessionExtensions => Unit)
extensions.injectPostHocResolutionRule(ruleBuilder(_))
}
+ HoodieAnalysis.customOptimizerRules.foreach { ruleBuilder =>
+ extensions.injectOptimizerRule(ruleBuilder(_))
+ }
+
+ /*
+ // CBO is only supported in Spark >= 3.1.x
+ HoodieAnalysis.customPreCBORules.foreach { ruleBuilder =>
+ extensions.injectPreCBORule(ruleBuilder(_))
+ }
+ */
Review Comment:
Is this comment left intended?
##########
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/analysis/HoodiePruneFileSourcePartitions.scala:
##########
@@ -0,0 +1,126 @@
+/*
+ * 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.hudi.analysis
+
+import org.apache.hudi.SparkAdapterSupport.sparkAdapter
+import org.apache.hudi.{HoodieBaseRelation, HoodieFileIndex}
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.catalog.CatalogStatistics
+import org.apache.spark.sql.catalyst.expressions.{And, AttributeReference,
AttributeSet, Expression, ExpressionSet, NamedExpression, PredicateHelper,
SubqueryExpression}
+import org.apache.spark.sql.catalyst.planning.PhysicalOperation
+import
org.apache.spark.sql.catalyst.plans.logical.statsEstimation.FilterEstimation
+import org.apache.spark.sql.catalyst.plans.logical.{Filter, LeafNode,
LogicalPlan, Project}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.execution.datasources.{HadoopFsRelation,
LogicalRelation}
+import
org.apache.spark.sql.hudi.analysis.HoodiePruneFileSourcePartitions.{HoodieRelationMatcher,
exprUtils, getPartitionFiltersAndDataFilters, rebuildPhysicalOperation}
+import org.apache.spark.sql.sources.BaseRelation
+import org.apache.spark.sql.types.StructType
+
+/**
+ * Prune the partitions of Hudi table based relations by the means of pushing
down the
+ * partition filters
+ *
+ * NOTE: [[HoodiePruneFileSourcePartitions]] is a replica in kind to Spark's
[[PruneFileSourcePartitions]]
+ */
+case class HoodiePruneFileSourcePartitions(spark: SparkSession) extends
Rule[LogicalPlan] {
+
+ override def apply(plan: LogicalPlan): LogicalPlan = plan transformDown {
+ case op @ PhysicalOperation(projects, filters, lr @
LogicalRelation(HoodieRelationMatcher(fileIndex), _, _, _))
+ if sparkAdapter.isHoodieTable(lr, spark) && filters.nonEmpty &&
fileIndex.partitionSchema.nonEmpty =>
+
+ val deterministicFilters = filters.filter(f => f.deterministic &&
!SubqueryExpression.hasSubquery(f))
+ val normalizedFilters = exprUtils.normalizeExprs(deterministicFilters,
lr.output)
+
+ val (partitionPruningFilters, _) =
+ getPartitionFiltersAndDataFilters(fileIndex.partitionSchema,
normalizedFilters)
+
+ // NOTE: We should only push-down the predicates [[HoodieFileIndex]],
which we didn't
+ // prune on before
+ if (partitionPruningFilters.nonEmpty &&
!fileIndex.prunedFor(partitionPruningFilters)) {
+ // [[HudiFileIndex]] is a caching one, therefore we don't need to
reconstruct new relation,
+ // instead we simply just refresh the index and update the stats
+ fileIndex.listFiles(partitionPruningFilters, Seq())
+
+ // Change table stats based on the sizeInBytes of pruned files
+ val filteredStats =
FilterEstimation(Filter(partitionPruningFilters.reduce(And), lr)).estimate
+ val colStats = filteredStats.map {
+ _.attributeStats.map { case (attr, colStat) =>
+ (attr.name, colStat.toCatalogColumnStat(attr.name, attr.dataType))
+ }
+ }
+
+ val tableWithStats = lr.catalogTable.map(_.copy(
+ stats = Some(
+ CatalogStatistics(
+ sizeInBytes = BigInt(fileIndex.sizeInBytes),
+ rowCount = filteredStats.flatMap(_.rowCount),
+ colStats = colStats.getOrElse(Map.empty)))
+ ))
+
+ val prunedLogicalRelation = lr.copy(catalogTable = tableWithStats)
+ // Keep partition-pruning predicates so that they are visible in
physical planning
+ rebuildPhysicalOperation(projects, filters, prunedLogicalRelation)
+ } else {
+ op
+ }
+ }
+
+}
+
+private object HoodiePruneFileSourcePartitions extends PredicateHelper {
+
+ private val exprUtils = sparkAdapter.getCatalystExpressionUtils
+
+ private object HoodieRelationMatcher {
+ def unapply(relation: BaseRelation): Option[HoodieFileIndex] = relation
match {
+ case HadoopFsRelation(fileIndex: HoodieFileIndex, _, _, _, _, _) =>
Some(fileIndex)
+ case r: HoodieBaseRelation => Some(r.fileIndex)
+ case _ => None
+ }
+ }
+
+ private def rebuildPhysicalOperation(projects: Seq[NamedExpression],
+ filters: Seq[Expression],
+ relation: LeafNode): Project = {
+ val withFilter = if (filters.nonEmpty) {
+ val filterExpression = filters.reduceLeft(And)
+ Filter(filterExpression, relation)
+ } else {
+ relation
+ }
+ Project(projects, withFilter)
+ }
+
+ def getPartitionFiltersAndDataFilters(partitionSchema: StructType,
+ normalizedFilters: Seq[Expression]):
(Seq[Expression], Seq[Expression]) = {
+ val partitionColumns = normalizedFilters.flatMap { expr =>
+ expr.collect {
+ case attr: AttributeReference if
partitionSchema.names.contains(attr.name) =>
+ attr
+ }
+ }
+ val partitionSet = AttributeSet(partitionColumns)
+ val (partitionFilters, dataFilters) = normalizedFilters.partition(f =>
+ f.references.subsetOf(partitionSet)
+ )
+ val extraPartitionFilter =
+ dataFilters.flatMap(exprUtils.extractPredicatesWithinOutputSet(_,
partitionSet))
Review Comment:
why do we have another extract here after the `filters.parttion` above?
##########
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/analysis/HoodiePruneFileSourcePartitions.scala:
##########
@@ -0,0 +1,126 @@
+/*
+ * 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.hudi.analysis
+
+import org.apache.hudi.SparkAdapterSupport.sparkAdapter
+import org.apache.hudi.{HoodieBaseRelation, HoodieFileIndex}
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.catalog.CatalogStatistics
+import org.apache.spark.sql.catalyst.expressions.{And, AttributeReference,
AttributeSet, Expression, ExpressionSet, NamedExpression, PredicateHelper,
SubqueryExpression}
+import org.apache.spark.sql.catalyst.planning.PhysicalOperation
+import
org.apache.spark.sql.catalyst.plans.logical.statsEstimation.FilterEstimation
+import org.apache.spark.sql.catalyst.plans.logical.{Filter, LeafNode,
LogicalPlan, Project}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.execution.datasources.{HadoopFsRelation,
LogicalRelation}
+import
org.apache.spark.sql.hudi.analysis.HoodiePruneFileSourcePartitions.{HoodieRelationMatcher,
exprUtils, getPartitionFiltersAndDataFilters, rebuildPhysicalOperation}
+import org.apache.spark.sql.sources.BaseRelation
+import org.apache.spark.sql.types.StructType
+
+/**
+ * Prune the partitions of Hudi table based relations by the means of pushing
down the
+ * partition filters
+ *
+ * NOTE: [[HoodiePruneFileSourcePartitions]] is a replica in kind to Spark's
[[PruneFileSourcePartitions]]
+ */
+case class HoodiePruneFileSourcePartitions(spark: SparkSession) extends
Rule[LogicalPlan] {
+
+ override def apply(plan: LogicalPlan): LogicalPlan = plan transformDown {
+ case op @ PhysicalOperation(projects, filters, lr @
LogicalRelation(HoodieRelationMatcher(fileIndex), _, _, _))
+ if sparkAdapter.isHoodieTable(lr, spark) && filters.nonEmpty &&
fileIndex.partitionSchema.nonEmpty =>
+
+ val deterministicFilters = filters.filter(f => f.deterministic &&
!SubqueryExpression.hasSubquery(f))
+ val normalizedFilters = exprUtils.normalizeExprs(deterministicFilters,
lr.output)
+
+ val (partitionPruningFilters, _) =
+ getPartitionFiltersAndDataFilters(fileIndex.partitionSchema,
normalizedFilters)
+
+ // NOTE: We should only push-down the predicates [[HoodieFileIndex]],
which we didn't
+ // prune on before
+ if (partitionPruningFilters.nonEmpty &&
!fileIndex.prunedFor(partitionPruningFilters)) {
Review Comment:
Do we also need to add the `prunedFor` check in the standard read path
(i.e., catalog-based table read)?
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