spark V2ScanRelationPushDown 源码
spark V2ScanRelationPushDown 代码
文件路径：/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/V2ScanRelationPushDown.scala
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package org.apache.spark.sql.execution.datasources.v2
import scala.collection.mutable
import org.apache.spark.sql.catalyst.expressions.{aggregate, Alias, And, Attribute, AttributeMap, AttributeReference, AttributeSet, Cast, Expression, IntegerLiteral, Literal, NamedExpression, PredicateHelper, ProjectionOverSchema, SortOrder, SubqueryExpression}
import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression
import org.apache.spark.sql.catalyst.optimizer.CollapseProject
import org.apache.spark.sql.catalyst.planning.PhysicalOperation
import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, Filter, LeafNode, Limit, LimitAndOffset, LocalLimit, LogicalPlan, Offset, OffsetAndLimit, Project, Sample, Sort}
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.connector.expressions.{SortOrder => V2SortOrder}
import org.apache.spark.sql.connector.expressions.aggregate.{Aggregation, Avg, Count, CountStar, Max, Min, Sum}
import org.apache.spark.sql.connector.expressions.filter.Predicate
import org.apache.spark.sql.connector.read.{Scan, ScanBuilder, SupportsPushDownAggregates, SupportsPushDownFilters, V1Scan}
import org.apache.spark.sql.execution.datasources.DataSourceStrategy
import org.apache.spark.sql.sources
import org.apache.spark.sql.types.{DataType, DecimalType, IntegerType, StructType}
import org.apache.spark.sql.util.SchemaUtils._
object V2ScanRelationPushDown extends Rule[LogicalPlan] with PredicateHelper {
import DataSourceV2Implicits._
def apply(plan: LogicalPlan): LogicalPlan = {
val pushdownRules = Seq[LogicalPlan => LogicalPlan] (
createScanBuilder,
pushDownSample,
pushDownFilters,
pushDownAggregates,
pushDownLimitAndOffset,
buildScanWithPushedAggregate,
pruneColumns)
pushdownRules.foldLeft(plan) { (newPlan, pushDownRule) =>
pushDownRule(newPlan)
}
}
private def createScanBuilder(plan: LogicalPlan) = plan.transform {
case r: DataSourceV2Relation =>
ScanBuilderHolder(r.output, r, r.table.asReadable.newScanBuilder(r.options))
}
private def pushDownFilters(plan: LogicalPlan) = plan.transform {
// update the scan builder with filter push down and return a new plan with filter pushed
case Filter(condition, sHolder: ScanBuilderHolder) =>
val filters = splitConjunctivePredicates(condition)
val normalizedFilters =
DataSourceStrategy.normalizeExprs(filters, sHolder.relation.output)
val (normalizedFiltersWithSubquery, normalizedFiltersWithoutSubquery) =
normalizedFilters.partition(SubqueryExpression.hasSubquery)
// `pushedFilters` will be pushed down and evaluated in the underlying data sources.
// `postScanFilters` need to be evaluated after the scan.
// `postScanFilters` and `pushedFilters` can overlap, e.g. the parquet row group filter.
val (pushedFilters, postScanFiltersWithoutSubquery) = PushDownUtils.pushFilters(
sHolder.builder, normalizedFiltersWithoutSubquery)
val pushedFiltersStr = if (pushedFilters.isLeft) {
pushedFilters.left.get.mkString(", ")
} else {
sHolder.pushedPredicates = pushedFilters.right.get
pushedFilters.right.get.mkString(", ")
}
val postScanFilters = postScanFiltersWithoutSubquery ++ normalizedFiltersWithSubquery
logInfo(
s"""
|Pushing operators to ${sHolder.relation.name}
|Pushed Filters: $pushedFiltersStr
|Post-Scan Filters: ${postScanFilters.mkString(",")}
""".stripMargin)
val filterCondition = postScanFilters.reduceLeftOption(And)
filterCondition.map(Filter(_, sHolder)).getOrElse(sHolder)
}
def pushDownAggregates(plan: LogicalPlan): LogicalPlan = plan.transform {
// update the scan builder with agg pushdown and return a new plan with agg pushed
case agg: Aggregate => rewriteAggregate(agg)
}
private def rewriteAggregate(agg: Aggregate): LogicalPlan = agg.child match {
case PhysicalOperation(project, Nil, holder @ ScanBuilderHolder(_, _,
r: SupportsPushDownAggregates)) if CollapseProject.canCollapseExpressions(
agg.aggregateExpressions, project, alwaysInline = true) =>
val aliasMap = getAliasMap(project)
val actualResultExprs = agg.aggregateExpressions.map(replaceAliasButKeepName(_, aliasMap))
val actualGroupExprs = agg.groupingExpressions.map(replaceAlias(_, aliasMap))
val aggExprToOutputOrdinal = mutable.HashMap.empty[Expression, Int]
val aggregates = collectAggregates(actualResultExprs, aggExprToOutputOrdinal)
val normalizedAggExprs = DataSourceStrategy.normalizeExprs(
aggregates, holder.relation.output).asInstanceOf[Seq[AggregateExpression]]
val normalizedGroupingExpr = DataSourceStrategy.normalizeExprs(
actualGroupExprs, holder.relation.output)
val translatedAggOpt = DataSourceStrategy.translateAggregation(
normalizedAggExprs, normalizedGroupingExpr)
if (translatedAggOpt.isEmpty) {
// Cannot translate the catalyst aggregate, return the query plan unchanged.
return agg
}
val (finalResultExprs, finalAggExprs, translatedAgg, canCompletePushDown) = {
if (r.supportCompletePushDown(translatedAggOpt.get)) {
(actualResultExprs, normalizedAggExprs, translatedAggOpt.get, true)
} else if (!translatedAggOpt.get.aggregateExpressions().exists(_.isInstanceOf[Avg])) {
(actualResultExprs, normalizedAggExprs, translatedAggOpt.get, false)
} else {
// scalastyle:off
// The data source doesn't support the complete push-down of this aggregation.
// Here we translate `AVG` to `SUM / COUNT`, so that it's more likely to be
// pushed, completely or partially.
// e.g. TABLE t (c1 INT, c2 INT, c3 INT)
// SELECT avg(c1) FROM t GROUP BY c2;
// The original logical plan is
// Aggregate [c2#10],[avg(c1#9) AS avg(c1)#19]
// +- ScanOperation[...]
//
// After convert avg(c1#9) to sum(c1#9)/count(c1#9)
// we have the following
// Aggregate [c2#10],[sum(c1#9)/count(c1#9) AS avg(c1)#19]
// +- ScanOperation[...]
// scalastyle:on
val newResultExpressions = actualResultExprs.map { expr =>
expr.transform {
case AggregateExpression(avg: aggregate.Average, _, isDistinct, _, _) =>
val sum = aggregate.Sum(avg.child).toAggregateExpression(isDistinct)
val count = aggregate.Count(avg.child).toAggregateExpression(isDistinct)
avg.evaluateExpression transform {
case a: Attribute if a.semanticEquals(avg.sum) =>
addCastIfNeeded(sum, avg.sum.dataType)
case a: Attribute if a.semanticEquals(avg.count) =>
addCastIfNeeded(count, avg.count.dataType)
}
}
}.asInstanceOf[Seq[NamedExpression]]
// Because aggregate expressions changed, translate them again.
aggExprToOutputOrdinal.clear()
val newAggregates =
collectAggregates(newResultExpressions, aggExprToOutputOrdinal)
val newNormalizedAggExprs = DataSourceStrategy.normalizeExprs(
newAggregates, holder.relation.output).asInstanceOf[Seq[AggregateExpression]]
val newTranslatedAggOpt = DataSourceStrategy.translateAggregation(
newNormalizedAggExprs, normalizedGroupingExpr)
if (newTranslatedAggOpt.isEmpty) {
// Ideally we should never reach here. But if we end up with not able to translate
// new aggregate with AVG replaced by SUM/COUNT, revert to the original one.
(actualResultExprs, normalizedAggExprs, translatedAggOpt.get, false)
} else {
(newResultExpressions, newNormalizedAggExprs, newTranslatedAggOpt.get,
r.supportCompletePushDown(newTranslatedAggOpt.get))
}
}
}
if (!canCompletePushDown && !supportPartialAggPushDown(translatedAgg)) {
return agg
}
if (!r.pushAggregation(translatedAgg)) {
return agg
}
// scalastyle:off
// We name the output columns of group expressions and aggregate functions by
// ordinal: `group_col_0`, `group_col_1`, ..., `agg_func_0`, `agg_func_1`, ...
// e.g. TABLE t (c1 INT, c2 INT, c3 INT)
// SELECT min(c1), max(c1) FROM t GROUP BY c2;
// Use group_col_0, agg_func_0, agg_func_1 as output for ScanBuilderHolder.
// We want to have the following logical plan:
// == Optimized Logical Plan ==
// Aggregate [group_col_0#10], [min(agg_func_0#21) AS min(c1)#17, max(agg_func_1#22) AS max(c1)#18]
// +- ScanBuilderHolder[group_col_0#10, agg_func_0#21, agg_func_1#22]
// Later, we build the `Scan` instance and convert ScanBuilderHolder to DataSourceV2ScanRelation.
// scalastyle:on
val groupOutputMap = normalizedGroupingExpr.zipWithIndex.map { case (e, i) =>
AttributeReference(s"group_col_$i", e.dataType)() -> e
}
val groupOutput = groupOutputMap.unzip._1
val aggOutputMap = finalAggExprs.zipWithIndex.map { case (e, i) =>
AttributeReference(s"agg_func_$i", e.dataType)() -> e
}
val aggOutput = aggOutputMap.unzip._1
val newOutput = groupOutput ++ aggOutput
val groupByExprToOutputOrdinal = mutable.HashMap.empty[Expression, Int]
normalizedGroupingExpr.zipWithIndex.foreach { case (expr, ordinal) =>
if (!groupByExprToOutputOrdinal.contains(expr.canonicalized)) {
groupByExprToOutputOrdinal(expr.canonicalized) = ordinal
}
}
holder.pushedAggregate = Some(translatedAgg)
holder.pushedAggOutputMap = AttributeMap(groupOutputMap ++ aggOutputMap)
holder.output = newOutput
logInfo(
s"""
|Pushing operators to ${holder.relation.name}
|Pushed Aggregate Functions:
| ${translatedAgg.aggregateExpressions().mkString(", ")}
|Pushed Group by:
| ${translatedAgg.groupByExpressions.mkString(", ")}
""".stripMargin)
if (canCompletePushDown) {
val projectExpressions = finalResultExprs.map { expr =>
expr.transformDown {
case agg: AggregateExpression =>
val ordinal = aggExprToOutputOrdinal(agg.canonicalized)
Alias(aggOutput(ordinal), agg.resultAttribute.name)(agg.resultAttribute.exprId)
case expr if groupByExprToOutputOrdinal.contains(expr.canonicalized) =>
val ordinal = groupByExprToOutputOrdinal(expr.canonicalized)
expr match {
case ne: NamedExpression => Alias(groupOutput(ordinal), ne.name)(ne.exprId)
case _ => groupOutput(ordinal)
}
}
}.asInstanceOf[Seq[NamedExpression]]
Project(projectExpressions, holder)
} else {
// scalastyle:off
// Change the optimized logical plan to reflect the pushed down aggregate
// e.g. TABLE t (c1 INT, c2 INT, c3 INT)
// SELECT min(c1), max(c1) FROM t GROUP BY c2;
// The original logical plan is
// Aggregate [c2#10],[min(c1#9) AS min(c1)#17, max(c1#9) AS max(c1)#18]
// +- RelationV2[c1#9, c2#10] ...
//
// After change the V2ScanRelation output to [c2#10, min(c1)#21, max(c1)#22]
// we have the following
// !Aggregate [c2#10], [min(c1#9) AS min(c1)#17, max(c1#9) AS max(c1)#18]
// +- RelationV2[c2#10, min(c1)#21, max(c1)#22] ...
//
// We want to change it to
// == Optimized Logical Plan ==
// Aggregate [c2#10], [min(min(c1)#21) AS min(c1)#17, max(max(c1)#22) AS max(c1)#18]
// +- RelationV2[c2#10, min(c1)#21, max(c1)#22] ...
// scalastyle:on
val aggExprs = finalResultExprs.map(_.transform {
case agg: AggregateExpression =>
val ordinal = aggExprToOutputOrdinal(agg.canonicalized)
val aggAttribute = aggOutput(ordinal)
val aggFunction: aggregate.AggregateFunction =
agg.aggregateFunction match {
case max: aggregate.Max =>
max.copy(child = aggAttribute)
case min: aggregate.Min =>
min.copy(child = aggAttribute)
case sum: aggregate.Sum =>
// To keep the dataType of `Sum` unchanged, we need to cast the
// data-source-aggregated result to `Sum.child.dataType` if it's decimal.
// See `SumBase.resultType`
val newChild = if (sum.dataType.isInstanceOf[DecimalType]) {
addCastIfNeeded(aggAttribute, sum.child.dataType)
} else {
aggAttribute
}
sum.copy(child = newChild)
case _: aggregate.Count =>
aggregate.Sum(aggAttribute)
case other => other
}
agg.copy(aggregateFunction = aggFunction)
case expr if groupByExprToOutputOrdinal.contains(expr.canonicalized) =>
val ordinal = groupByExprToOutputOrdinal(expr.canonicalized)
expr match {
case ne: NamedExpression => Alias(groupOutput(ordinal), ne.name)(ne.exprId)
case _ => groupOutput(ordinal)
}
}).asInstanceOf[Seq[NamedExpression]]
Aggregate(groupOutput, aggExprs, holder)
}
case _ => agg
}
private def collectAggregates(
resultExpressions: Seq[NamedExpression],
aggExprToOutputOrdinal: mutable.HashMap[Expression, Int]): Seq[AggregateExpression] = {
var ordinal = 0
resultExpressions.flatMap { expr =>
expr.collect {
// Do not push down duplicated aggregate expressions. For example,
// `SELECT max(a) + 1, max(a) + 2 FROM ...`, we should only push down one
// `max(a)` to the data source.
case agg: AggregateExpression
if !aggExprToOutputOrdinal.contains(agg.canonicalized) =>
aggExprToOutputOrdinal(agg.canonicalized) = ordinal
ordinal += 1
agg
}
}
}
private def supportPartialAggPushDown(agg: Aggregation): Boolean = {
// We can only partially push down min/max/sum/count without DISTINCT.
agg.aggregateExpressions().isEmpty || agg.aggregateExpressions().forall {
case sum: Sum => !sum.isDistinct
case count: Count => !count.isDistinct
case _: Min | _: Max | _: CountStar => true
case _ => false
}
}
private def addCastIfNeeded(expression: Expression, expectedDataType: DataType) =
if (expression.dataType == expectedDataType) {
expression
} else {
Cast(expression, expectedDataType)
}
def buildScanWithPushedAggregate(plan: LogicalPlan): LogicalPlan = plan.transform {
case holder: ScanBuilderHolder if holder.pushedAggregate.isDefined =>
// No need to do column pruning because only the aggregate columns are used as
// DataSourceV2ScanRelation output columns. All the other columns are not
// included in the output.
val scan = holder.builder.build()
val realOutput = scan.readSchema().toAttributes
assert(realOutput.length == holder.output.length,
"The data source returns unexpected number of columns")
val wrappedScan = getWrappedScan(scan, holder)
val scanRelation = DataSourceV2ScanRelation(holder.relation, wrappedScan, realOutput)
val projectList = realOutput.zip(holder.output).map { case (a1, a2) =>
// The data source may return columns with arbitrary data types and it's safer to cast them
// to the expected data type.
assert(Cast.canCast(a1.dataType, a2.dataType))
Alias(addCastIfNeeded(a1, a2.dataType), a2.name)(a2.exprId)
}
Project(projectList, scanRelation)
}
def pruneColumns(plan: LogicalPlan): LogicalPlan = plan.transform {
case PhysicalOperation(project, filters, sHolder: ScanBuilderHolder) =>
// column pruning
val normalizedProjects = DataSourceStrategy
.normalizeExprs(project, sHolder.output)
.asInstanceOf[Seq[NamedExpression]]
val (scan, output) = PushDownUtils.pruneColumns(
sHolder.builder, sHolder.relation, normalizedProjects, filters)
logInfo(
s"""
|Output: ${output.mkString(", ")}
""".stripMargin)
val wrappedScan = getWrappedScan(scan, sHolder)
val scanRelation = DataSourceV2ScanRelation(sHolder.relation, wrappedScan, output)
val projectionOverSchema =
ProjectionOverSchema(output.toStructType, AttributeSet(output))
val projectionFunc = (expr: Expression) => expr transformDown {
case projectionOverSchema(newExpr) => newExpr
}
val filterCondition = filters.reduceLeftOption(And)
val newFilterCondition = filterCondition.map(projectionFunc)
val withFilter = newFilterCondition.map(Filter(_, scanRelation)).getOrElse(scanRelation)
val withProjection = if (withFilter.output != project) {
val newProjects = normalizedProjects
.map(projectionFunc)
.asInstanceOf[Seq[NamedExpression]]
Project(restoreOriginalOutputNames(newProjects, project.map(_.name)), withFilter)
} else {
withFilter
}
withProjection
}
def pushDownSample(plan: LogicalPlan): LogicalPlan = plan.transform {
case sample: Sample => sample.child match {
case PhysicalOperation(_, filter, sHolder: ScanBuilderHolder) if filter.isEmpty =>
val tableSample = TableSampleInfo(
sample.lowerBound,
sample.upperBound,
sample.withReplacement,
sample.seed)
val pushed = PushDownUtils.pushTableSample(sHolder.builder, tableSample)
if (pushed) {
sHolder.pushedSample = Some(tableSample)
sample.child
} else {
sample
}
case _ => sample
}
}
private def pushDownLimit(plan: LogicalPlan, limit: Int): (LogicalPlan, Boolean) = plan match {
case operation @ PhysicalOperation(_, filter, sHolder: ScanBuilderHolder) if filter.isEmpty =>
val (isPushed, isPartiallyPushed) = PushDownUtils.pushLimit(sHolder.builder, limit)
if (isPushed) {
sHolder.pushedLimit = Some(limit)
}
(operation, isPushed && !isPartiallyPushed)
case s @ Sort(order, _, operation @ PhysicalOperation(project, Nil, sHolder: ScanBuilderHolder))
if CollapseProject.canCollapseExpressions(order, project, alwaysInline = true) =>
val aliasMap = getAliasMap(project)
val aliasReplacedOrder = order.map(replaceAlias(_, aliasMap))
val newOrder = if (sHolder.pushedAggregate.isDefined) {
// `ScanBuilderHolder` has different output columns after aggregate push-down. Here we
// replace the attributes in ordering expressions with the original table output columns.
aliasReplacedOrder.map {
_.transform {
case a: Attribute => sHolder.pushedAggOutputMap.getOrElse(a, a)
}.asInstanceOf[SortOrder]
}
} else {
aliasReplacedOrder.asInstanceOf[Seq[SortOrder]]
}
val normalizedOrders = DataSourceStrategy.normalizeExprs(
newOrder, sHolder.relation.output).asInstanceOf[Seq[SortOrder]]
val orders = DataSourceStrategy.translateSortOrders(normalizedOrders)
if (orders.length == order.length) {
val (isPushed, isPartiallyPushed) =
PushDownUtils.pushTopN(sHolder.builder, orders.toArray, limit)
if (isPushed) {
sHolder.pushedLimit = Some(limit)
sHolder.sortOrders = orders
if (isPartiallyPushed) {
(s, false)
} else {
(operation, true)
}
} else {
(s, false)
}
} else {
(s, false)
}
case p: Project =>
val (newChild, isPartiallyPushed) = pushDownLimit(p.child, limit)
(p.withNewChildren(Seq(newChild)), isPartiallyPushed)
case other => (other, false)
}
private def pushDownOffset(
plan: LogicalPlan,
offset: Int): Boolean = plan match {
case sHolder: ScanBuilderHolder =>
val isPushed = PushDownUtils.pushOffset(sHolder.builder, offset)
if (isPushed) {
sHolder.pushedOffset = Some(offset)
}
isPushed
case Project(projectList, child) if projectList.forall(_.deterministic) =>
pushDownOffset(child, offset)
case _ => false
}
def pushDownLimitAndOffset(plan: LogicalPlan): LogicalPlan = plan.transform {
case offset @ LimitAndOffset(limit, offsetValue, child) =>
val (newChild, canRemoveLimit) = pushDownLimit(child, limit)
if (canRemoveLimit) {
// Try to push down OFFSET only if the LIMIT operator has been pushed and can be removed.
val isPushed = pushDownOffset(newChild, offsetValue)
if (isPushed) {
newChild
} else {
// Keep the OFFSET operator if we failed to push down OFFSET to the data source.
offset.withNewChildren(Seq(newChild))
}
} else {
// Keep the OFFSET operator if we can't remove LIMIT operator.
offset
}
case globalLimit @ OffsetAndLimit(offset, limit, child) =>
// For `df.offset(n).limit(m)`, we can push down `limit(m + n)` first.
val (newChild, canRemoveLimit) = pushDownLimit(child, limit + offset)
if (canRemoveLimit) {
// Try to push down OFFSET only if the LIMIT operator has been pushed and can be removed.
val isPushed = pushDownOffset(newChild, offset)
if (isPushed) {
newChild
} else {
// Still keep the OFFSET operator if we can't push it down.
Offset(Literal(offset), newChild)
}
} else {
// For `df.offset(n).limit(m)`, since we can't push down `limit(m + n)`,
// try to push down `offset(n)` here.
val isPushed = pushDownOffset(child, offset)
if (isPushed) {
// Keep the LIMIT operator if we can't push it down.
Limit(Literal(limit, IntegerType), child)
} else {
// Keep the origin plan if we can't push OFFSET operator and LIMIT operator.
globalLimit
}
}
case globalLimit @ Limit(IntegerLiteral(limitValue), child) =>
val (newChild, canRemoveLimit) = pushDownLimit(child, limitValue)
if (canRemoveLimit) {
newChild
} else {
val newLocalLimit =
globalLimit.child.asInstanceOf[LocalLimit].withNewChildren(Seq(newChild))
globalLimit.withNewChildren(Seq(newLocalLimit))
}
case offset @ Offset(IntegerLiteral(n), child) =>
val isPushed = pushDownOffset(child, n)
if (isPushed) {
child
} else {
offset
}
}
private def getWrappedScan(scan: Scan, sHolder: ScanBuilderHolder): Scan = {
scan match {
case v1: V1Scan =>
val pushedFilters = sHolder.builder match {
case f: SupportsPushDownFilters =>
f.pushedFilters()
case _ => Array.empty[sources.Filter]
}
val pushedDownOperators = PushedDownOperators(sHolder.pushedAggregate, sHolder.pushedSample,
sHolder.pushedLimit, sHolder.pushedOffset, sHolder.sortOrders, sHolder.pushedPredicates)
V1ScanWrapper(v1, pushedFilters, pushedDownOperators)
case _ => scan
}
}
}
case class ScanBuilderHolder(
var output: Seq[AttributeReference],
relation: DataSourceV2Relation,
builder: ScanBuilder) extends LeafNode {
var pushedLimit: Option[Int] = None
var pushedOffset: Option[Int] = None
var sortOrders: Seq[V2SortOrder] = Seq.empty[V2SortOrder]
var pushedSample: Option[TableSampleInfo] = None
var pushedPredicates: Seq[Predicate] = Seq.empty[Predicate]
var pushedAggregate: Option[Aggregation] = None
var pushedAggOutputMap: AttributeMap[Expression] = AttributeMap.empty[Expression]
}
// A wrapper for v1 scan to carry the translated filters and the handled ones, along with
// other pushed down operators. This is required by the physical v1 scan node.
case class V1ScanWrapper(
v1Scan: V1Scan,
handledFilters: Seq[sources.Filter],
pushedDownOperators: PushedDownOperators) extends Scan {
override def readSchema(): StructType = v1Scan.readSchema()
}
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