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[GitHub] [spark] cloud-fan opened a new pull request, #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
cloud-fan opened a new pull request, #38557:
URL: https://github.com/apache/spark/pull/38557
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This is a followup to https://github.com/apache/spark/pull/36304 to simplify `RowLevelOperationRuntimeGroupFiltering`. It does 3 things:
1. run `OptimizeSubqueries` in the batch `PartitionPruning`, so that `RowLevelOperationRuntimeGroupFiltering` does not need to invoke it manually.
2. skip dpp subquery in `OptimizeSubqueries`, to avoid the issue fixed by https://github.com/apache/spark/pull/33664
3. `RowLevelOperationRuntimeGroupFiltering` creates `InSubquery` instead of `DynamicPruningSubquery`, so that it can be optimized by `OptimizeSubqueries` later. This also avoids unnecessary planning overhead of `DynamicPruningSubquery`, as there is no join and we can only run it as a subquery.
### Why are the changes needed?
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code simplification
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no
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existing tests
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[GitHub] [spark] cloud-fan commented on a diff in pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on code in PR #38557:
URL: https://github.com/apache/spark/pull/38557#discussion_r1017300121
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/dynamicpruning/RowLevelOperationRuntimeGroupFiltering.scala:
##########
@@ -89,10 +88,8 @@ case class RowLevelOperationRuntimeGroupFiltering(optimizeSubqueries: Rule[Logic
buildKeys: Seq[Attribute],
pruningKeys: Seq[Attribute]): Expression = {
- val buildQuery = Project(buildKeys, matchingRowsPlan)
- val dynamicPruningSubqueries = pruningKeys.zipWithIndex.map { case (key, index) =>
- DynamicPruningSubquery(key, buildQuery, buildKeys, index, onlyInBroadcast = false)
- }
- dynamicPruningSubqueries.reduce(And)
+ val buildQuery = Aggregate(buildKeys, buildKeys, matchingRowsPlan)
Review Comment:
I don't see any downside. We can only reuse broadcast if the DPP filter is derived from a join, which doesn't apply here.
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #38557:
URL: https://github.com/apache/spark/pull/38557#discussion_r1017016645
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/dynamicpruning/RowLevelOperationRuntimeGroupFiltering.scala:
##########
@@ -89,10 +88,8 @@ case class RowLevelOperationRuntimeGroupFiltering(optimizeSubqueries: Rule[Logic
buildKeys: Seq[Attribute],
pruningKeys: Seq[Attribute]): Expression = {
- val buildQuery = Project(buildKeys, matchingRowsPlan)
- val dynamicPruningSubqueries = pruningKeys.zipWithIndex.map { case (key, index) =>
- DynamicPruningSubquery(key, buildQuery, buildKeys, index, onlyInBroadcast = false)
- }
- dynamicPruningSubqueries.reduce(And)
+ val buildQuery = Aggregate(buildKeys, buildKeys, matchingRowsPlan)
Review Comment:
Are there any downsides of rewriting `DynamicPruningSubquery` into `DynamicPruningExpression` directly instead of relying on other rules like `PlanDynamicPruningFilters` and `PlanAdaptiveDynamicPruningFilters`? I see some special branches for exchange reuse in those rules that would not apply now.
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[GitHub] [spark] cloud-fan commented on a diff in pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on code in PR #38557:
URL: https://github.com/apache/spark/pull/38557#discussion_r1017318193
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala:
##########
@@ -320,6 +320,9 @@ abstract class Optimizer(catalogManager: CatalogManager)
}
def apply(plan: LogicalPlan): LogicalPlan = plan.transformAllExpressionsWithPruning(
_.containsPattern(PLAN_EXPRESSION), ruleId) {
+ // Do not optimize DPP subquery, as it was created from optimized plan and we should not
+ // optimize it again, to save optimization time and avoid breaking broadcast/subquery reuse.
+ case d: DynamicPruningSubquery => d
Review Comment:
Yes, because this PR adds `OptimizeSubqueries` to the batch `PartitionPruning` and we should not break https://github.com/apache/spark/pull/33664
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[GitHub] [spark] cloud-fan commented on a diff in pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on code in PR #38557:
URL: https://github.com/apache/spark/pull/38557#discussion_r1017300121
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/dynamicpruning/RowLevelOperationRuntimeGroupFiltering.scala:
##########
@@ -89,10 +88,8 @@ case class RowLevelOperationRuntimeGroupFiltering(optimizeSubqueries: Rule[Logic
buildKeys: Seq[Attribute],
pruningKeys: Seq[Attribute]): Expression = {
- val buildQuery = Project(buildKeys, matchingRowsPlan)
- val dynamicPruningSubqueries = pruningKeys.zipWithIndex.map { case (key, index) =>
- DynamicPruningSubquery(key, buildQuery, buildKeys, index, onlyInBroadcast = false)
- }
- dynamicPruningSubqueries.reduce(And)
+ val buildQuery = Aggregate(buildKeys, buildKeys, matchingRowsPlan)
Review Comment:
I don't see any downside. We can only reuse broadcast if the DPP filter is derived from a join, which doesn't apply here. What's better, now this is a normal subquery and we can trigger subquery reuse which is not possible for DPP subqueries.
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[GitHub] [spark] cloud-fan closed pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
Posted by GitBox <gi...@apache.org>.
cloud-fan closed pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
URL: https://github.com/apache/spark/pull/38557
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #38557:
URL: https://github.com/apache/spark/pull/38557#discussion_r1018367180
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/dynamicpruning/RowLevelOperationRuntimeGroupFiltering.scala:
##########
@@ -89,10 +88,8 @@ case class RowLevelOperationRuntimeGroupFiltering(optimizeSubqueries: Rule[Logic
buildKeys: Seq[Attribute],
pruningKeys: Seq[Attribute]): Expression = {
- val buildQuery = Project(buildKeys, matchingRowsPlan)
- val dynamicPruningSubqueries = pruningKeys.zipWithIndex.map { case (key, index) =>
- DynamicPruningSubquery(key, buildQuery, buildKeys, index, onlyInBroadcast = false)
- }
- dynamicPruningSubqueries.reduce(And)
+ val buildQuery = Aggregate(buildKeys, buildKeys, matchingRowsPlan)
Review Comment:
Yeah, DS v2 runtime filtering framework is fairly limited at this point.
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[GitHub] [spark] viirya commented on a diff in pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
Posted by GitBox <gi...@apache.org>.
viirya commented on code in PR #38557:
URL: https://github.com/apache/spark/pull/38557#discussion_r1017316276
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala:
##########
@@ -320,6 +320,9 @@ abstract class Optimizer(catalogManager: CatalogManager)
}
def apply(plan: LogicalPlan): LogicalPlan = plan.transformAllExpressionsWithPruning(
_.containsPattern(PLAN_EXPRESSION), ruleId) {
+ // Do not optimize DPP subquery, as it was created from optimized plan and we should not
+ // optimize it again, to save optimization time and avoid breaking broadcast/subquery reuse.
+ case d: DynamicPruningSubquery => d
Review Comment:
This makes sense. Just wondering that is this particularly related to SPARK-38959?
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[GitHub] [spark] cloud-fan commented on pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on PR #38557:
URL: https://github.com/apache/spark/pull/38557#issuecomment-1309806471
thanks for review, merging to master!
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #38557:
URL: https://github.com/apache/spark/pull/38557#discussion_r1017352098
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/dynamicpruning/RowLevelOperationRuntimeGroupFiltering.scala:
##########
@@ -89,10 +88,8 @@ case class RowLevelOperationRuntimeGroupFiltering(optimizeSubqueries: Rule[Logic
buildKeys: Seq[Attribute],
pruningKeys: Seq[Attribute]): Expression = {
- val buildQuery = Project(buildKeys, matchingRowsPlan)
- val dynamicPruningSubqueries = pruningKeys.zipWithIndex.map { case (key, index) =>
- DynamicPruningSubquery(key, buildQuery, buildKeys, index, onlyInBroadcast = false)
- }
- dynamicPruningSubqueries.reduce(And)
+ val buildQuery = Aggregate(buildKeys, buildKeys, matchingRowsPlan)
Review Comment:
Got it. I was originally worried we could miss some future optimizations given that dynamic pruning for row-level operations would go through a different route compared to the normal DPP.
One alternative could be to extend `DynamicPruningSubquery` with a flag whether it should be optimized or not. Up to you, though.
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[GitHub] [spark] cloud-fan commented on a diff in pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on code in PR #38557:
URL: https://github.com/apache/spark/pull/38557#discussion_r1017361159
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/dynamicpruning/RowLevelOperationRuntimeGroupFiltering.scala:
##########
@@ -89,10 +88,8 @@ case class RowLevelOperationRuntimeGroupFiltering(optimizeSubqueries: Rule[Logic
buildKeys: Seq[Attribute],
pruningKeys: Seq[Attribute]): Expression = {
- val buildQuery = Project(buildKeys, matchingRowsPlan)
- val dynamicPruningSubqueries = pruningKeys.zipWithIndex.map { case (key, index) =>
- DynamicPruningSubquery(key, buildQuery, buildKeys, index, onlyInBroadcast = false)
- }
- dynamicPruningSubqueries.reduce(And)
+ val buildQuery = Aggregate(buildKeys, buildKeys, matchingRowsPlan)
Review Comment:
My rationale is, what we really need is a subquery here. This is completely different from dynamic partition pruning. One limitation is DS v2 runtime filter pushdown only applies to `DynamicPruningExpression`. We can probably fix that and accept normal non-correlated subqueries as well.
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[GitHub] [spark] cloud-fan commented on pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on PR #38557:
URL: https://github.com/apache/spark/pull/38557#issuecomment-1306818720
cc @aokolnychyi @viirya @wangyum
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #38557:
URL: https://github.com/apache/spark/pull/38557#discussion_r1017016645
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/dynamicpruning/RowLevelOperationRuntimeGroupFiltering.scala:
##########
@@ -89,10 +88,8 @@ case class RowLevelOperationRuntimeGroupFiltering(optimizeSubqueries: Rule[Logic
buildKeys: Seq[Attribute],
pruningKeys: Seq[Attribute]): Expression = {
- val buildQuery = Project(buildKeys, matchingRowsPlan)
- val dynamicPruningSubqueries = pruningKeys.zipWithIndex.map { case (key, index) =>
- DynamicPruningSubquery(key, buildQuery, buildKeys, index, onlyInBroadcast = false)
- }
- dynamicPruningSubqueries.reduce(And)
+ val buildQuery = Aggregate(buildKeys, buildKeys, matchingRowsPlan)
Review Comment:
Are there any downsides of rewriting `DynamicPruningSubquery` into `DynamicPruningExpression` directly instead of relying on `PlanDynamicPruningFilters` and `PlanAdaptiveDynamicPruningFilters`?
I see some special branches for exchange reuse in those rules that would not apply now.
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[GitHub] [spark] viirya commented on a diff in pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries
Posted by GitBox <gi...@apache.org>.
viirya commented on code in PR #38557:
URL: https://github.com/apache/spark/pull/38557#discussion_r1017317077
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/dynamicpruning/RowLevelOperationRuntimeGroupFiltering.scala:
##########
@@ -66,7 +65,7 @@ case class RowLevelOperationRuntimeGroupFiltering(optimizeSubqueries: Rule[Logic
}
// optimize subqueries to rewrite them as joins and trigger job planning
Review Comment:
This comment can be removed.
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