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[GitHub] [spark] aokolnychyi opened a new pull request, #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
aokolnychyi opened a new pull request, #37749:
URL: https://github.com/apache/spark/pull/37749
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### What changes were proposed in this pull request?
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This PR adapts `V2ExpressionUtils` to support arbitrary transforms with multiple args that are either references or literals.
### Why are the changes needed?
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After PR #36995, data sources can request distribution and ordering that reference v2 functions. If a data source needs a transform with multiple input args or a transform where not all args are references, Spark will throw an exception.
### Does this PR introduce _any_ user-facing change?
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No.
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This PR adapts the test added recently in PR #36995.
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[GitHub] [spark] aokolnychyi commented on pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on PR #37749:
URL: https://github.com/apache/spark/pull/37749#issuecomment-1233450883
@cloud-fan @sunchao @pan3793, could you take a look?
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Re: [PR] [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering [spark]
Posted by "faucct (via GitHub)" <gi...@apache.org>.
faucct commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r1430062134
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -361,6 +361,18 @@ object KeyGroupedPartitioning {
partitionValues: Seq[InternalRow]): KeyGroupedPartitioning = {
KeyGroupedPartitioning(expressions, partitionValues.size, Some(partitionValues))
}
+
+ def supportsExpressions(expressions: Seq[Expression]): Boolean = {
Review Comment:
I have managed to execute a `SortMergeJoin` of data sources partitioned by `(SingleColumnTransform, SortedBucketTransform)` benefitting from the partitioning, but I have had to transform the DataSourceV2Relation to DataSourceV2ScanRelation myself and I think it is the only way because of this check. Could this be dropped?
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Re: [PR] [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering [spark]
Posted by "faucct (via GitHub)" <gi...@apache.org>.
faucct commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r1430050128
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -361,6 +361,18 @@ object KeyGroupedPartitioning {
partitionValues: Seq[InternalRow]): KeyGroupedPartitioning = {
KeyGroupedPartitioning(expressions, partitionValues.size, Some(partitionValues))
}
+
+ def supportsExpressions(expressions: Seq[Expression]): Boolean = {
Review Comment:
Is this expected to fail for both `BucketTransform` and `SortedBucketTransform`, which always have more than 1 child expression?
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r961211655
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
Review Comment:
I added tests but they seem to work only by accident as `satisfies0` in `KeyGroupedPartitioning` returns false and triggers a shuffle. We probably need to adapt the existing logic to be more reliable and explicit.
I see two solutions:
- Explicitly check we have flat transforms with one child ref in `satisfies0` in `KeyGroupedPartitioning`.
- Don't construct `KeyGroupedPartitioning` unless we have flat transforms with one child ref.
I am inclined to go with the first option but let me know if you have a better idea.
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[GitHub] [spark] cloud-fan commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r964730555
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -361,6 +362,25 @@ object KeyGroupedPartitioning {
partitionValues: Seq[InternalRow]): KeyGroupedPartitioning = {
KeyGroupedPartitioning(expressions, partitionValues.size, Some(partitionValues))
}
+
+ def supportsExpressions(expressions: Seq[Expression]): Boolean = {
+ def isSupportedTransform(transform: TransformExpression): Boolean = {
+ transform.children.size == 1 && transform.children.head.isInstanceOf[AttributeReference]
Review Comment:
```suggestion
transform.children.size == 1 && isReference(transform.children.head)
```
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[GitHub] [spark] sunchao commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
sunchao commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r961777107
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
Review Comment:
I'm also +1 on the first option. I also tried on my side, and it's possible to trigger the assertion error with the following example in `EnsureRequirementsSuite`:
```scala
test("multiple named references in transform") {
val plan1 = DummySparkPlan(
outputPartitioning = KeyGroupedPartitioning(
truncate(exprD, exprA) :: bucket(16, exprB) :: Nil, 4)
)
val plan2 = DummySparkPlan(
outputPartitioning = KeyGroupedPartitioning(
truncate(exprD, exprA) :: bucket(16, exprC) :: Nil, 4)
)
val smjExec = SortMergeJoinExec(
exprD :: exprA :: exprB :: Nil, exprD :: exprA :: exprC :: Nil, Inner, None, plan1, plan2)
EnsureRequirements.apply(smjExec)
}
```
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
Review Comment:
I'm also +1 on the first option. I also tried on my side, and it's possible to trigger the assertion error with the following example in `EnsureRequirementsSuite`:
```scala
test("multiple named references in transform") {
val plan1 = DummySparkPlan(
outputPartitioning = KeyGroupedPartitioning(
truncate(exprD, exprA) :: bucket(16, exprB) :: Nil, 4)
)
val plan2 = DummySparkPlan(
outputPartitioning = KeyGroupedPartitioning(
truncate(exprD, exprA) :: bucket(16, exprC) :: Nil, 4)
)
val smjExec = SortMergeJoinExec(
exprD :: exprA :: exprB :: Nil, exprD :: exprA :: exprC :: Nil, Inner, None, plan1, plan2)
EnsureRequirements.apply(smjExec)
}
```
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[GitHub] [spark] sunchao commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
sunchao commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r961777107
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
Review Comment:
I'm also +1 on the first option. I also tried on my side, and it's possible to trigger the assertion error with the following example in `EnsureRequirementsSuite`:
```scala
test("multiple named references in transform") {
val plan1 = DummySparkPlan(
outputPartitioning = KeyGroupedPartitioning(
truncate(exprD, exprA) :: bucket(16, exprB) :: Nil, 4)
)
val plan2 = DummySparkPlan(
outputPartitioning = KeyGroupedPartitioning(
truncate(exprD, exprA) :: bucket(16, exprC) :: Nil, 4)
)
val smjExec = SortMergeJoinExec(
exprD :: exprA :: exprB :: Nil, exprD :: exprA :: exprC :: Nil, Inner, None, plan1, plan2)
EnsureRequirements.apply(smjExec) match {
case SortMergeJoinExec(_, _, _, _,
SortExec(_, _, DummySparkPlan(_, _, left: KeyGroupedPartitioning, _, _), _),
SortExec(_, _, DummySparkPlan(_, _, right: KeyGroupedPartitioning, _, _), _), _) =>
assert(left.expressions === Seq(truncate(exprD, exprA), bucket(16, exprB)))
assert(right.expressions === Seq(truncate(exprD, exprA), bucket(16, exprC)))
case other => fail(other.toString)
}
}
```
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[GitHub] [spark] sunchao commented on pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
sunchao commented on PR #37749:
URL: https://github.com/apache/spark/pull/37749#issuecomment-1239606670
Merged to master. Thanks @aokolnychyi , @cloud-fan , @pan3793 !
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[GitHub] [spark] aokolnychyi commented on pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on PR #37749:
URL: https://github.com/apache/spark/pull/37749#issuecomment-1240466722
Thanks for reviewing, @sunchao @cloud-fan @pan3793!
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[GitHub] [spark] sunchao commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
sunchao commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r960277773
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
Review Comment:
Hmm I remember added this check since storage partitioned join can only handle one named reference in partition transform at the moment. There's an [assertion](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala#L656) in `KeyGroupedShuffleSpec` for this.
Could you add a test in `EnsureRequirementsSuite` for a `NamedTransform` with >1 named references? just to make sure it's not breaking anything.
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[GitHub] [spark] cloud-fan commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r961394236
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
Review Comment:
+1 to this option. We should improve `KeyGroupedPartitioning.satisfies0` late.
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r960216556
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
- val resolvedRefs = refs.map(_.asInstanceOf[NamedReference]).map { r =>
- resolveRef[NamedExpression](r, query)
- }
+ case NamedTransform(name, args) =>
+ val catalystArgs = convertTransformArgs(args, query)
Review Comment:
Changed and adapted the test to cover nested transforms.
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[GitHub] [spark] cloud-fan commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r961394236
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
Review Comment:
+1 to this option. We should improve `KeyGroupedPartitioning.satisfies0` and `KeyGroupedShuffleSpec.isCompatibleWith` later.
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[GitHub] [spark] aokolnychyi closed pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi closed pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
URL: https://github.com/apache/spark/pull/37749
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r960072719
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
- val resolvedRefs = refs.map(_.asInstanceOf[NamedReference]).map { r =>
- resolveRef[NamedExpression](r, query)
- }
+ case NamedTransform(name, args) =>
+ val catalystArgs = convertTransformArgs(args, query)
funCatalogOpt.flatMap { catalog =>
- loadV2FunctionOpt(catalog, name, resolvedRefs).map { bound =>
- TransformExpression(bound, resolvedRefs)
+ loadV2FunctionOpt(catalog, name, catalystArgs).map { bound =>
+ TransformExpression(bound, catalystArgs)
}
}
- case _ =>
- throw new AnalysisException(s"Transform $trans is not currently supported")
+ }
+
+ private def convertTransformArgs(
+ args: Seq[V2Expression],
+ query: LogicalPlan): Seq[Expression] = {
+ args.map {
+ case r: NamedReference =>
+ resolveRef[NamedExpression](r, query)
+ case l: V2Literal[_] =>
+ Literal.create(l.value, l.dataType)
+ case arg =>
+ throw new AnalysisException(
+ s"Only references and literals are supported as transform arguments: $arg")
Review Comment:
Technically, transform args can be arbitrary V2 expressions but I am not sure we want to invest into building a framework for supporting those right now.
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[GitHub] [spark] cloud-fan commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r963255131
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -361,6 +361,18 @@ object KeyGroupedPartitioning {
partitionValues: Seq[InternalRow]): KeyGroupedPartitioning = {
KeyGroupedPartitioning(expressions, partitionValues.size, Some(partitionValues))
}
+
+ def supportsExpressions(expressions: Seq[Expression]): Boolean = {
+ def isSupportedTransform(transform: TransformExpression): Boolean = {
+ transform.children.size == 1 && transform.children.head.isInstanceOf[AttributeReference]
+ }
+
+ expressions.forall {
+ case _: AttributeReference => true
Review Comment:
nested column in catalyst is not `AttributeReference`, but `GetStructField(AttributeReference, ...)`. I think we need to support nested column because we support v2 `NamedReference` before.
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Re: [PR] [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering [spark]
Posted by "faucct (via GitHub)" <gi...@apache.org>.
faucct commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r1430062134
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -361,6 +361,18 @@ object KeyGroupedPartitioning {
partitionValues: Seq[InternalRow]): KeyGroupedPartitioning = {
KeyGroupedPartitioning(expressions, partitionValues.size, Some(partitionValues))
}
+
+ def supportsExpressions(expressions: Seq[Expression]): Boolean = {
Review Comment:
I have managed to execute a `SortMergeJoin` of data sources partitioned by `(SingleColumnTransform, SortedBucketTransform)` benefitting from the partitioning, but I have had to transform the DataSourceV2Relation to DataSourceV2ScanRelation myself and I think it is the only way because of this check. Could it be dropped?
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[GitHub] [spark] cloud-fan commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r960163485
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
- val resolvedRefs = refs.map(_.asInstanceOf[NamedReference]).map { r =>
- resolveRef[NamedExpression](r, query)
- }
+ case NamedTransform(name, args) =>
+ val catalystArgs = convertTransformArgs(args, query)
Review Comment:
what if we simply call `args.map(toCatalyst)` here? This means that we can support nested v2 transforms.
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r961236436
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
Review Comment:
On the other hand, skipping `KeyGroupedPartitioning` means we will never create `KeyGroupedShuffleSpec`, which has that validation logic. Maybe, that's a safer option.
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r962077351
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -361,6 +361,18 @@ object KeyGroupedPartitioning {
partitionValues: Seq[InternalRow]): KeyGroupedPartitioning = {
KeyGroupedPartitioning(expressions, partitionValues.size, Some(partitionValues))
}
+
+ def supportsExpressions(expressions: Seq[Expression]): Boolean = {
+ def isSupportedTransform(transform: TransformExpression): Boolean = {
+ transform.children.size == 1 && transform.children.head.isInstanceOf[AttributeReference]
+ }
+
+ expressions.forall {
+ case _: AttributeReference => true
Review Comment:
It can't be any other `NamedExpression`, can it?
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[GitHub] [spark] cloud-fan commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r963901980
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -361,6 +361,18 @@ object KeyGroupedPartitioning {
partitionValues: Seq[InternalRow]): KeyGroupedPartitioning = {
KeyGroupedPartitioning(expressions, partitionValues.size, Some(partitionValues))
}
+
+ def supportsExpressions(expressions: Seq[Expression]): Boolean = {
+ def isSupportedTransform(transform: TransformExpression): Boolean = {
+ transform.children.size == 1 && transform.children.head.isInstanceOf[AttributeReference]
+ }
+
+ expressions.forall {
+ case _: NamedExpression => true
Review Comment:
`NamedExpression` is not `NamedReference`. e.g. `a + b AS c` is also a `NamedExpression`. We can add a small util function to check nested columns
```
def isReference(e: Expression): Boolean = e match {
case a: Attribute => true
case g: GetStructField => isReference(g.child)
case _ => false
}
```
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r960207282
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
- val resolvedRefs = refs.map(_.asInstanceOf[NamedReference]).map { r =>
- resolveRef[NamedExpression](r, query)
- }
+ case NamedTransform(name, args) =>
+ val catalystArgs = convertTransformArgs(args, query)
Review Comment:
Yeah, why not. Let me give it a try.
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r961043795
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
Review Comment:
Let me check.
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r964020570
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -361,6 +361,18 @@ object KeyGroupedPartitioning {
partitionValues: Seq[InternalRow]): KeyGroupedPartitioning = {
KeyGroupedPartitioning(expressions, partitionValues.size, Some(partitionValues))
}
+
+ def supportsExpressions(expressions: Seq[Expression]): Boolean = {
+ def isSupportedTransform(transform: TransformExpression): Boolean = {
+ transform.children.size == 1 && transform.children.head.isInstanceOf[AttributeReference]
+ }
+
+ expressions.forall {
+ case _: NamedExpression => true
Review Comment:
I was mainly judging from the perspective of `V2ExpressionUtils` but I agree this is safer. Changed.
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[GitHub] [spark] sunchao closed pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
sunchao closed pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
URL: https://github.com/apache/spark/pull/37749
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Re: [PR] [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering [spark]
Posted by "faucct (via GitHub)" <gi...@apache.org>.
faucct commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r1430108415
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -361,6 +361,18 @@ object KeyGroupedPartitioning {
partitionValues: Seq[InternalRow]): KeyGroupedPartitioning = {
KeyGroupedPartitioning(expressions, partitionValues.size, Some(partitionValues))
}
+
+ def supportsExpressions(expressions: Seq[Expression]): Boolean = {
Review Comment:
I guess, this code is supposed to make sure there is only one child, but it was skipped in my execution as `sorted` was empty, though I don't understand what is this requirement for:
https://github.com/apache/spark/pull/35657/files#diff-715d0c2d59a4ddb8a4b5952c1b05be9f035b6d9b0d9670c70b58989dc722b252R86
Making `sorted` empty let me pass this check, though I need it for the join optimization. But I have managed to bring this property back on the physical level using `org.apache.spark.sql.connector.read.SupportsReportOrdering` and execute the `SortMergeJoin` without the exchanges and sorts.
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r960057109
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
Review Comment:
This `if` condition was prohibiting transforms with literals and multiple args.
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[GitHub] [spark] sunchao commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
sunchao commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r961777107
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/V2ExpressionUtils.scala:
##########
@@ -105,18 +105,27 @@ object V2ExpressionUtils extends SQLConfHelper with Logging {
TransformExpression(bound, resolvedRefs, Some(numBuckets))
}
}
- case NamedTransform(name, refs)
- if refs.length == 1 && refs.forall(_.isInstanceOf[NamedReference]) =>
Review Comment:
I'm also +1 on the first option. I also tried on my side, and it's possible to trigger the assertion error with the following example:
```scala
test("multiple named references in transform") {
val plan1 = DummySparkPlan(
outputPartitioning = KeyGroupedPartitioning(
truncate(exprD, exprA) :: bucket(16, exprB) :: Nil, 4)
)
val plan2 = DummySparkPlan(
outputPartitioning = KeyGroupedPartitioning(
truncate(exprD, exprA) :: bucket(16, exprC) :: Nil, 4)
)
val smjExec = SortMergeJoinExec(
exprD :: exprA :: exprB :: Nil, exprD :: exprA :: exprC :: Nil, Inner, None, plan1, plan2)
EnsureRequirements.apply(smjExec) match {
case SortMergeJoinExec(_, _, _, _,
SortExec(_, _, DummySparkPlan(_, _, left: KeyGroupedPartitioning, _, _), _),
SortExec(_, _, DummySparkPlan(_, _, right: KeyGroupedPartitioning, _, _), _), _) =>
assert(left.expressions === Seq(truncate(exprD, exprA), bucket(16, exprB)))
assert(right.expressions === Seq(truncate(exprD, exprA), bucket(16, exprC)))
case other => fail(other.toString)
}
}
```
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r963886001
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -361,6 +361,18 @@ object KeyGroupedPartitioning {
partitionValues: Seq[InternalRow]): KeyGroupedPartitioning = {
KeyGroupedPartitioning(expressions, partitionValues.size, Some(partitionValues))
}
+
+ def supportsExpressions(expressions: Seq[Expression]): Boolean = {
+ def isSupportedTransform(transform: TransformExpression): Boolean = {
+ transform.children.size == 1 && transform.children.head.isInstanceOf[AttributeReference]
+ }
+
+ expressions.forall {
+ case _: AttributeReference => true
Review Comment:
You are right, missed that.
I switched back to `NamedExpression` like in `V2ExpressionUtils`.
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[GitHub] [spark] aokolnychyi commented on a diff in pull request #37749: [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering
Posted by GitBox <gi...@apache.org>.
aokolnychyi commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r962076870
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -361,6 +361,18 @@ object KeyGroupedPartitioning {
partitionValues: Seq[InternalRow]): KeyGroupedPartitioning = {
KeyGroupedPartitioning(expressions, partitionValues.size, Some(partitionValues))
}
+
+ def supportsExpressions(expressions: Seq[Expression]): Boolean = {
Review Comment:
@sunchao @cloud-fan, I went back and forth on where to add this validation. I decided to add it here as it is a current limitation of internal Catalyst `KeyGroupedPartitioning`. It is fine if a data source reports a partitioning with multi-arg transforms, we just can't benefit from it right now.
Let me know what you think. I also added a test to `KeyGroupedPartitioningSuite`.
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Re: [PR] [SPARK-40295][SQL] Allow v2 functions with literal args in write distribution/ordering [spark]
Posted by "faucct (via GitHub)" <gi...@apache.org>.
faucct commented on code in PR #37749:
URL: https://github.com/apache/spark/pull/37749#discussion_r1430062134
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
##########
@@ -361,6 +361,18 @@ object KeyGroupedPartitioning {
partitionValues: Seq[InternalRow]): KeyGroupedPartitioning = {
KeyGroupedPartitioning(expressions, partitionValues.size, Some(partitionValues))
}
+
+ def supportsExpressions(expressions: Seq[Expression]): Boolean = {
Review Comment:
I have managed to execute a `SortMergeJoin` of data sources partitioned by `(SingleColumnTransform, SortedBucketTransform)` benefitting from the partitioning, but I have had to transform the DataSourceV2Relation to DataSourceV2ScanRelation myself and I think it is the only way because of this check.
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