This is an automated email from the ASF dual-hosted git repository.
wenchen pushed a commit to branch branch-3.5
in repository https://gitbox.apache.org/repos/asf/spark.git
The following commit(s) were added to refs/heads/branch-3.5 by this push:
new aeb0cabbea7 [SPARK-44653][SQL] Non-trivial DataFrame unions should not
break caching
aeb0cabbea7 is described below
commit aeb0cabbea7377b77e7befd937babcfd390816e5
Author: Wenchen Fan <wenc...@databricks.com>
AuthorDate: Fri Aug 4 11:26:36 2023 +0800
[SPARK-44653][SQL] Non-trivial DataFrame unions should not break caching
### What changes were proposed in this pull request?
We have a long-standing tricky optimization in `Dataset.union`, which
invokes the optimizer rule `CombineUnions` to pre-optimize the analyzed plan.
This is to avoid too large analyzed plan for a specific dataframe query pattern
`df1.union(df2).union(df3).union...`.
This tricky optimization is designed to break dataframe caching, but we
thought it was fine as people usually won't cache the intermediate dataframe in
a union chain. However, `CombineUnions` gets improved from time to time (e.g.
https://github.com/apache/spark/pull/35214) and now it can optimize a wide
range of Union patterns. Now it's possible that people union two dataframe, do
something with `select`, and cache it. Then the dataframe is unioned again with
other dataframes and peop [...]
This PR updates `Dataset.union` to only combine adjacent Unions to match
the original purpose.
### Why are the changes needed?
Fix perf regression due to breaking df caching
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new test
Closes #42315 from cloud-fan/union.
Lead-authored-by: Wenchen Fan <wenc...@databricks.com>
Co-authored-by: Wenchen Fan <cloud0...@gmail.com>
Signed-off-by: Wenchen Fan <wenc...@databricks.com>
(cherry picked from commit ce1fe57cdd7004a891ef8b97c77ac96b3719efcd)
Signed-off-by: Wenchen Fan <wenc...@databricks.com>
---
.../spark/sql/catalyst/optimizer/Optimizer.scala | 2 +-
.../main/scala/org/apache/spark/sql/Dataset.scala | 56 ++++++++++++++++++----
.../org/apache/spark/sql/DatasetCacheSuite.scala | 21 ++++++++
3 files changed, 70 insertions(+), 9 deletions(-)
diff --git
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 95cf3aee16f..bb2a86556c0 100644
---
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -157,7 +157,7 @@ abstract class Optimizer(catalogManager: CatalogManager)
// since the other rules might make two separate Unions operators
adjacent.
Batch("Inline CTE", Once,
InlineCTE()) ::
- Batch("Union", Once,
+ Batch("Union", fixedPoint,
RemoveNoopOperators,
CombineUnions,
RemoveNoopUnion) ::
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
index 7b2259a6d99..61c83829d20 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
@@ -42,11 +42,10 @@ import
org.apache.spark.sql.catalyst.catalog.HiveTableRelation
import org.apache.spark.sql.catalyst.encoders._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.json.{JacksonGenerator, JSONOptions}
-import org.apache.spark.sql.catalyst.optimizer.CombineUnions
import org.apache.spark.sql.catalyst.parser.{ParseException, ParserUtils}
import org.apache.spark.sql.catalyst.plans._
import org.apache.spark.sql.catalyst.plans.logical._
-import org.apache.spark.sql.catalyst.trees.TreeNodeTag
+import org.apache.spark.sql.catalyst.trees.{TreeNodeTag, TreePattern}
import org.apache.spark.sql.catalyst.types.DataTypeUtils.toAttributes
import org.apache.spark.sql.catalyst.util.{CharVarcharUtils, IntervalUtils}
import org.apache.spark.sql.catalyst.util.TypeUtils.toSQLId
@@ -2241,6 +2240,51 @@ class Dataset[T] private[sql](
Offset(Literal(n), logicalPlan)
}
+ // This breaks caching, but it's usually ok because it addresses a very
specific use case:
+ // using union to union many files or partitions.
+ private def combineUnions(plan: LogicalPlan): LogicalPlan = {
+ plan.transformDownWithPruning(_.containsPattern(TreePattern.UNION)) {
+ case Distinct(u: Union) =>
+ Distinct(flattenUnion(u, isUnionDistinct = true))
+ // Only handle distinct-like 'Deduplicate', where the keys == output
+ case Deduplicate(keys: Seq[Attribute], u: Union) if AttributeSet(keys)
== u.outputSet =>
+ Deduplicate(keys, flattenUnion(u, true))
+ case u: Union =>
+ flattenUnion(u, isUnionDistinct = false)
+ }
+ }
+
+ private def flattenUnion(u: Union, isUnionDistinct: Boolean): Union = {
+ var changed = false
+ // We only need to look at the direct children of Union, as the nested
adjacent Unions should
+ // have been combined already by previous `Dataset#union` transformations.
+ val newChildren = u.children.flatMap {
+ case Distinct(Union(children, byName, allowMissingCol))
+ if isUnionDistinct && byName == u.byName && allowMissingCol ==
u.allowMissingCol =>
+ changed = true
+ children
+ // Only handle distinct-like 'Deduplicate', where the keys == output
+ case Deduplicate(keys: Seq[Attribute], child @ Union(children, byName,
allowMissingCol))
+ if AttributeSet(keys) == child.outputSet && isUnionDistinct &&
byName == u.byName &&
+ allowMissingCol == u.allowMissingCol =>
+ changed = true
+ children
+ case Union(children, byName, allowMissingCol)
+ if !isUnionDistinct && byName == u.byName && allowMissingCol ==
u.allowMissingCol =>
+ changed = true
+ children
+ case other =>
+ Seq(other)
+ }
+ if (changed) {
+ val newUnion = Union(newChildren)
+ newUnion.copyTagsFrom(u)
+ newUnion
+ } else {
+ u
+ }
+ }
+
/**
* Returns a new Dataset containing union of rows in this Dataset and
another Dataset.
*
@@ -2272,9 +2316,7 @@ class Dataset[T] private[sql](
* @since 2.0.0
*/
def union(other: Dataset[T]): Dataset[T] = withSetOperator {
- // This breaks caching, but it's usually ok because it addresses a very
specific use case:
- // using union to union many files or partitions.
- CombineUnions(Union(logicalPlan, other.logicalPlan))
+ combineUnions(Union(logicalPlan, other.logicalPlan))
}
/**
@@ -2366,9 +2408,7 @@ class Dataset[T] private[sql](
* @since 3.1.0
*/
def unionByName(other: Dataset[T], allowMissingColumns: Boolean): Dataset[T]
= withSetOperator {
- // This breaks caching, but it's usually ok because it addresses a very
specific use case:
- // using union to union many files or partitions.
- CombineUnions(Union(logicalPlan :: other.logicalPlan :: Nil, true,
allowMissingColumns))
+ combineUnions(Union(logicalPlan :: other.logicalPlan :: Nil, true,
allowMissingColumns))
}
/**
diff --git
a/sql/core/src/test/scala/org/apache/spark/sql/DatasetCacheSuite.scala
b/sql/core/src/test/scala/org/apache/spark/sql/DatasetCacheSuite.scala
index 6033b9fee84..a657c6212aa 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DatasetCacheSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DatasetCacheSuite.scala
@@ -273,4 +273,25 @@ class DatasetCacheSuite extends QueryTest
}
}
}
+
+ test("SPARK-44653: non-trivial DataFrame unions should not break caching") {
+ val df1 = Seq(1 -> 1).toDF("i", "j")
+ val df2 = Seq(2 -> 2).toDF("i", "j")
+ val df3 = Seq(3 -> 3).toDF("i", "j")
+
+ withClue("positive") {
+ val unionDf = df1.union(df2).select($"i")
+ unionDf.cache()
+ val finalDf = unionDf.union(df3.select($"i"))
+
assert(finalDf.queryExecution.executedPlan.exists(_.isInstanceOf[InMemoryTableScanExec]))
+ }
+
+ withClue("negative") {
+ val unionDf = df1.union(df2)
+ unionDf.cache()
+ val finalDf = unionDf.union(df3)
+ // It's by design to break caching here.
+
assert(!finalDf.queryExecution.executedPlan.exists(_.isInstanceOf[InMemoryTableScanExec]))
+ }
+ }
}
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org
For additional commands, e-mail: commits-h...@spark.apache.org
