Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/13494#discussion_r68882374
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/MetadataOnlyOptimizer.scala
---
@@ -0,0 +1,171 @@
+/*
+ * 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
+
+import org.apache.spark.sql.{AnalysisException, SparkSession}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.catalog.{CatalogRelation,
SessionCatalog}
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.execution.datasources.{HadoopFsRelation,
LogicalRelation}
+
+/**
+ * When scanning only partition columns, get results based on metadata
without scanning files.
+ * It is used for distinct, distinct aggregations or distinct-like
aggregations(example: Max/Min).
+ * First of all, scanning only partition columns are required, then the
rule does the following
+ * things here:
+ * 1. aggregate expression is partition columns,
+ * e.g. SELECT col FROM tbl GROUP BY col or SELECT col FROM tbl GROUP BY
cube(col).
+ * 2. aggregate function on partition columns with DISTINCT,
+ * e.g. SELECT count(DISTINCT col) FROM tbl GROUP BY col.
+ * 3. aggregate function on partition columns which have same result with
DISTINCT keyword.
+ * e.g. SELECT Max(col2) FROM tbl GROUP BY col1.
+ */
+case class MetadataOnlyOptimizer(
+ sparkSession: SparkSession,
+ catalog: SessionCatalog) extends Rule[LogicalPlan] {
+
+ private def canSupportMetadataOnly(a: Aggregate): Boolean = {
+ val aggregateExpressions = a.aggregateExpressions.flatMap { expr =>
+ expr.collect {
+ case agg: AggregateExpression => agg
+ }
+ }.distinct
+ if (aggregateExpressions.isEmpty) {
+ // Support for aggregate that has no aggregateFunction when
expressions are partition columns
+ // example: select partitionCol from table group by partitionCol.
+ // Moreover, multiple-distinct has been rewritted into it by
RewriteDistinctAggregates.
+ true
+ } else {
+ aggregateExpressions.forall { agg =>
+ if (agg.isDistinct) {
+ true
+ } else {
+ // If function can be evaluated on just the distinct values of a
column, it can be used
+ // by metadata-only optimizer.
+ agg.aggregateFunction match {
+ case max: Max => true
+ case min: Min => true
+ case hyperLog: HyperLogLogPlusPlus => true
+ case _ => false
+ }
+ }
+ }
+ }
+ }
+
+ private def convertLogicalToMetadataOnly(
+ project: LogicalPlan,
+ filter: Option[Expression],
+ logical: LogicalRelation,
+ files: HadoopFsRelation): LogicalPlan = {
+ val attributeMap = logical.output.map(attr => (attr.name, attr)).toMap
+ val partitionColumns = files.partitionSchema.map { field =>
+ attributeMap.getOrElse(field.name, throw new AnalysisException(
+ s"Unable to resolve ${field.name} given
[${logical.output.map(_.name).mkString(", ")}]"))
+ }
+ val projectSet = filter.map(project.references ++
_.references).getOrElse(project.references)
+ if (projectSet.subsetOf(AttributeSet(partitionColumns))) {
+ val selectedPartitions =
files.location.listFiles(filter.map(Seq(_)).getOrElse(Seq.empty))
+ val valuesRdd =
sparkSession.sparkContext.parallelize(selectedPartitions.map(_.values), 1)
+ val valuesPlan = LogicalRDD(partitionColumns,
valuesRdd)(sparkSession)
+ valuesPlan
+ } else {
+ logical
+ }
+ }
+
+ private def convertCatalogToMetadataOnly(
+ project: LogicalPlan,
+ filter: Option[Expression],
+ relation: CatalogRelation): LogicalPlan = {
+ val attributeMap = relation.output.map(attr => (attr.name, attr)).toMap
+ val partitionColumns = relation.catalogTable.partitionColumnNames.map
{ column =>
+ attributeMap.getOrElse(column, throw new AnalysisException(
+ s"Unable to resolve ${column} given
[${relation.output.map(_.name).mkString(", ")}]"))
+ }
+ val projectSet = filter.map(project.references ++
_.references).getOrElse(project.references)
+ if (projectSet.subsetOf(AttributeSet(partitionColumns))) {
+ val partitionColumnDataTypes = partitionColumns.map(_.dataType)
+ val partitionValues =
catalog.listPartitions(relation.catalogTable.identifier)
+ .map { p =>
+ InternalRow.fromSeq(
+ partitionColumns.map(a =>
p.spec(a.name)).zip(partitionColumnDataTypes).map {
+ case (rawValue, dataType) => Cast(Literal(rawValue),
dataType).eval(null)
+ })
+ }
+ val valuesRdd =
sparkSession.sparkContext.parallelize(partitionValues, 1)
+ val valuesPlan = LogicalRDD(partitionColumns,
valuesRdd)(sparkSession)
+ valuesPlan
+ } else {
+ relation
+ }
+ }
+
+ private def convertToMetadataOnly(plan: LogicalPlan): LogicalPlan = plan
match {
--- End diff --
We need to think about it more carefully, i.e. how can the partition
information propagate up from table relation?
It's obvious that `Filter` can retain all partition information, but for
others, it's not trivial to explain.
Since this PR definitely need more people to review, how about we only
handle `Filter` for now and improve it later? Then it's easier for other people
to review and get this PR in. Thanks!
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