[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user jiangxb1987 commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r73821493
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -64,10 +64,17 @@ object PhysicalOperation extends PredicateHelper {
val substitutedFields =
fields.map(substitute(aliases)).asInstanceOf[Seq[NamedExpression]]
(Some(substitutedFields), filters, other,
collectAliases(substitutedFields))
- case Filter(condition, child) if condition.deterministic =>
+ case Filter(condition, child) =>
val (fields, filters, other, aliases) =
collectProjectsAndFilters(child)
-val substitutedCondition = substitute(aliases)(condition)
-(fields, filters ++
splitConjunctivePredicates(substitutedCondition), other, aliases)
+
+// Deterministic parts of filter condition placed before
non-deterministic predicates could
+// be pushed down safely.
+val (pushDown, rest) =
--- End diff --
@cloud-fan Now that I could insert a `Scanner` operator over
`CatalogRelation` in `Optimizer`, but I noticed a relation may also be
something like `l: LogicalRelation(relation: CatalogRelation, _, _)`, in this
case, we couldn't analyze the class `LogicalRelation` because it's in package
`spark-sql` while `Optimizer` is in `spark-catalyst`, thus we are not able to
determine whether a `Scanner` should be added. I think we don't want to add
`Scanner` over every `BaseRelation`.
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r73325120
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -64,10 +64,17 @@ object PhysicalOperation extends PredicateHelper {
val substitutedFields =
fields.map(substitute(aliases)).asInstanceOf[Seq[NamedExpression]]
(Some(substitutedFields), filters, other,
collectAliases(substitutedFields))
- case Filter(condition, child) if condition.deterministic =>
+ case Filter(condition, child) =>
val (fields, filters, other, aliases) =
collectProjectsAndFilters(child)
-val substitutedCondition = substitute(aliases)(condition)
-(fields, filters ++
splitConjunctivePredicates(substitutedCondition), other, aliases)
+
+// Deterministic parts of filter condition placed before
non-deterministic predicates could
+// be pushed down safely.
+val (pushDown, rest) =
--- End diff --
yup, thanks!
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user jiangxb1987 commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r73318885
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -64,10 +64,17 @@ object PhysicalOperation extends PredicateHelper {
val substitutedFields =
fields.map(substitute(aliases)).asInstanceOf[Seq[NamedExpression]]
(Some(substitutedFields), filters, other,
collectAliases(substitutedFields))
- case Filter(condition, child) if condition.deterministic =>
+ case Filter(condition, child) =>
val (fields, filters, other, aliases) =
collectProjectsAndFilters(child)
-val substitutedCondition = substitute(aliases)(condition)
-(fields, filters ++
splitConjunctivePredicates(substitutedCondition), other, aliases)
+
+// Deterministic parts of filter condition placed before
non-deterministic predicates could
+// be pushed down safely.
+val (pushDown, rest) =
--- End diff --
@cloud-fan Do you mean something like adding in basicLogicalOperators the
following:
case class Scanner( projectionList: Seq[NamedExpression], filters:
Seq[Expression], child: LogicalPlan) extends UnaryNode
And pass that to the planner instead of applying PhysicalOperation?
I'm willing to take this work. Thanks!
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r73305098
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -64,10 +64,17 @@ object PhysicalOperation extends PredicateHelper {
val substitutedFields =
fields.map(substitute(aliases)).asInstanceOf[Seq[NamedExpression]]
(Some(substitutedFields), filters, other,
collectAliases(substitutedFields))
- case Filter(condition, child) if condition.deterministic =>
+ case Filter(condition, child) =>
val (fields, filters, other, aliases) =
collectProjectsAndFilters(child)
-val substitutedCondition = substitute(aliases)(condition)
-(fields, filters ++
splitConjunctivePredicates(substitutedCondition), other, aliases)
+
+// Deterministic parts of filter condition placed before
non-deterministic predicates could
+// be pushed down safely.
+val (pushDown, rest) =
--- End diff --
after an offline discussion with @liancheng , we think it would be better
to have a wrapper node for scan(table scan or file scan), and this wrapper node
can also hold project list and filter conditions. Then in optimizer we can
improve the ColumnPrunning and FilterPushdown rules to push down into this
wrapper node. After this we don't need `PhysicalOperator` anymore and the
planner can match on the wrapper node directly.
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user jiangxb1987 commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r73176693
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -64,10 +64,17 @@ object PhysicalOperation extends PredicateHelper {
val substitutedFields =
fields.map(substitute(aliases)).asInstanceOf[Seq[NamedExpression]]
(Some(substitutedFields), filters, other,
collectAliases(substitutedFields))
- case Filter(condition, child) if condition.deterministic =>
+ case Filter(condition, child) =>
val (fields, filters, other, aliases) =
collectProjectsAndFilters(child)
-val substitutedCondition = substitute(aliases)(condition)
-(fields, filters ++
splitConjunctivePredicates(substitutedCondition), other, aliases)
+
+// Deterministic parts of filter condition placed before
non-deterministic predicates could
+// be pushed down safely.
+val (pushDown, rest) =
--- End diff --
Thank you @cloud-fan for pointing that out, I realized my previous thoughts
were wrong. I fully agree with @liancheng 's improvement idea. Will update
related code as well as new testcases tomorrow.
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r73156772
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -64,10 +64,17 @@ object PhysicalOperation extends PredicateHelper {
val substitutedFields =
fields.map(substitute(aliases)).asInstanceOf[Seq[NamedExpression]]
(Some(substitutedFields), filters, other,
collectAliases(substitutedFields))
- case Filter(condition, child) if condition.deterministic =>
+ case Filter(condition, child) =>
val (fields, filters, other, aliases) =
collectProjectsAndFilters(child)
-val substitutedCondition = substitute(aliases)(condition)
-(fields, filters ++
splitConjunctivePredicates(substitutedCondition), other, aliases)
+
+// Deterministic parts of filter condition placed before
non-deterministic predicates could
+// be pushed down safely.
+val (pushDown, rest) =
--- End diff --
I also think that silently dropping nondeterministic filters can be
dangerous. Maybe we should just return all operators beneath the top-most
nondeterministic filter as the bottom operator?
For example, say we have a plan tree like this:
```
Project a, b
Filter a > 1
Filter b < 3
Filter RAND(42) > 0.5
Filter c < 2
TableScan t
```
We should return the following result:
```
(
// Project list
Seq(a, b),
// Deterministic filters
Seq(b < 3, a > 1),
// The top-most nondeterministic operator filter with all operators
beneath
Filter(
RAND(42) > 0.5,
Filter(c < 2,
TableScan(t)))
)
```
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r73136264
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -64,10 +64,17 @@ object PhysicalOperation extends PredicateHelper {
val substitutedFields =
fields.map(substitute(aliases)).asInstanceOf[Seq[NamedExpression]]
(Some(substitutedFields), filters, other,
collectAliases(substitutedFields))
- case Filter(condition, child) if condition.deterministic =>
+ case Filter(condition, child) =>
val (fields, filters, other, aliases) =
collectProjectsAndFilters(child)
-val substitutedCondition = substitute(aliases)(condition)
-(fields, filters ++
splitConjunctivePredicates(substitutedCondition), other, aliases)
+
+// Deterministic parts of filter condition placed before
non-deterministic predicates could
+// be pushed down safely.
+val (pushDown, rest) =
--- End diff --
Can you write a test about this? The logic in `DataSourceStrategy` shows
that, when we get a scan node with the projects and filters upon it, we will
rebuild the project and filter(with project lists and filter conditions merged)
and wrap the scan node with it. So the filter condition that isn't returned by
`collectProjectsAndFilters` won't get executed.
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user jiangxb1987 commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r73126756
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -64,10 +64,17 @@ object PhysicalOperation extends PredicateHelper {
val substitutedFields =
fields.map(substitute(aliases)).asInstanceOf[Seq[NamedExpression]]
(Some(substitutedFields), filters, other,
collectAliases(substitutedFields))
- case Filter(condition, child) if condition.deterministic =>
+ case Filter(condition, child) =>
val (fields, filters, other, aliases) =
collectProjectsAndFilters(child)
-val substitutedCondition = substitute(aliases)(condition)
-(fields, filters ++
splitConjunctivePredicates(substitutedCondition), other, aliases)
+
+// Deterministic parts of filter condition placed before
non-deterministic predicates could
+// be pushed down safely.
+val (pushDown, rest) =
--- End diff --
@cloud-fan Thanks for your comment! But I did searched the codebase and
found the returned `filters` only used for predicates pushdown or partition
pruning, in both case it should be safe for us to drop the `rest` condition.
Thank you!
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r73117855
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -64,10 +64,17 @@ object PhysicalOperation extends PredicateHelper {
val substitutedFields =
fields.map(substitute(aliases)).asInstanceOf[Seq[NamedExpression]]
(Some(substitutedFields), filters, other,
collectAliases(substitutedFields))
- case Filter(condition, child) if condition.deterministic =>
+ case Filter(condition, child) =>
val (fields, filters, other, aliases) =
collectProjectsAndFilters(child)
-val substitutedCondition = substitute(aliases)(condition)
-(fields, filters ++
splitConjunctivePredicates(substitutedCondition), other, aliases)
+
+// Deterministic parts of filter condition placed before
non-deterministic predicates could
+// be pushed down safely.
+val (pushDown, rest) =
--- End diff --
after think about it more, I think it's not safe to do so.
`collectProjectsAndFilters` should return all deterministic projects and
filters upon a scan node. And the returned filter conditions are not only used
for filter pushdown, but also treated as the whole filters upon this scan node.
So the `rest` conditions here won't get executed.
cc @liancheng to confirm this.
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user jiangxb1987 commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r68557086
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
---
@@ -141,6 +141,14 @@ class PruningSuite extends HiveComparisonTest with
BeforeAndAfter {
Seq("2008-04-08", "11"),
Seq("2008-04-09", "11")))
+ createPruningTest("Partition pruning - with nondeterministic fields",
+"SELECT value, hr FROM srcpart1 WHERE ds = '2008-04-08' AND rand(7) <
1",
--- End diff --
Yes you are right. I thought the deterministic part can always be PPDed
safely but it was not, in fact, the order of each part should also be
considered. For example:
rand() < 0.01 AND partition_col = 'some_value'
should not be PPDed, but
partition_col = 'some_value' AND rand() < 0.01
still could be.
Thank you for your kindly reply!
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r68556327
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
---
@@ -141,6 +141,14 @@ class PruningSuite extends HiveComparisonTest with
BeforeAndAfter {
Seq("2008-04-08", "11"),
Seq("2008-04-09", "11")))
+ createPruningTest("Partition pruning - with nondeterministic fields",
+"SELECT value, hr FROM srcpart1 WHERE ds = '2008-04-08' AND rand(7) <
1",
--- End diff --
Good question. I think technically we can't push down any predicates that
are placed after a non-deterministic predicate. Otherwise number of input rows
may change and lead to wrong query results.
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r68538346
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
---
@@ -141,6 +141,14 @@ class PruningSuite extends HiveComparisonTest with
BeforeAndAfter {
Seq("2008-04-08", "11"),
Seq("2008-04-09", "11")))
+ createPruningTest("Partition pruning - with nondeterministic fields",
+"SELECT value, hr FROM srcpart1 WHERE ds = '2008-04-08' AND rand(7) <
1",
--- End diff --
cc @liancheng @yhuai
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r68538310
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
---
@@ -141,6 +141,14 @@ class PruningSuite extends HiveComparisonTest with
BeforeAndAfter {
Seq("2008-04-08", "11"),
Seq("2008-04-09", "11")))
+ createPruningTest("Partition pruning - with nondeterministic fields",
+"SELECT value, hr FROM srcpart1 WHERE ds = '2008-04-08' AND rand(7) <
1",
--- End diff --
I'm not sure if it's safe to push it down. For non-deterministic
expressions, the order(or number) of input rows matters. If we push down the
deterministic part of filter condition, then the input rows to the remaining
filter condition will change and may result to wrong answer.
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user jiangxb1987 commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r68483394
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -64,10 +64,12 @@ object PhysicalOperation extends PredicateHelper {
val substitutedFields =
fields.map(substitute(aliases)).asInstanceOf[Seq[NamedExpression]]
(Some(substitutedFields), filters, other,
collectAliases(substitutedFields))
- case Filter(condition, child) if condition.deterministic =>
+ case Filter(condition, child) =>
+val (deterministicFilters, nondeterministicFilters) =
splitConjunctivePredicates(condition)
+ .partition(_.deterministic)
val (fields, filters, other, aliases) =
collectProjectsAndFilters(child)
-val substitutedCondition = substitute(aliases)(condition)
-(fields, filters ++
splitConjunctivePredicates(substitutedCondition), other, aliases)
+val substitutedFilters =
deterministicFilters.map(substitute(aliases)(_))
+(fields, filters ++ deterministicFilters, other, aliases)
--- End diff --
@AlekseiS U r right! I have fixed this, thank you!
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
Github user AlekseiS commented on a diff in the pull request:
https://github.com/apache/spark/pull/13893#discussion_r68438486
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -64,10 +64,12 @@ object PhysicalOperation extends PredicateHelper {
val substitutedFields =
fields.map(substitute(aliases)).asInstanceOf[Seq[NamedExpression]]
(Some(substitutedFields), filters, other,
collectAliases(substitutedFields))
- case Filter(condition, child) if condition.deterministic =>
+ case Filter(condition, child) =>
+val (deterministicFilters, nondeterministicFilters) =
splitConjunctivePredicates(condition)
+ .partition(_.deterministic)
val (fields, filters, other, aliases) =
collectProjectsAndFilters(child)
-val substitutedCondition = substitute(aliases)(condition)
-(fields, filters ++
splitConjunctivePredicates(substitutedCondition), other, aliases)
+val substitutedFilters =
deterministicFilters.map(substitute(aliases)(_))
+(fields, filters ++ deterministicFilters, other, aliases)
--- End diff --
Likely there's a bug. I think you want to use "substitutedFilters" instead
of "deterministicFilters" here. I think you can also add a test with
substitution for it.
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[GitHub] spark pull request #13893: [SPARK-14172][SQL] Hive table partition predicate...
GitHub user jiangxb1987 opened a pull request: https://github.com/apache/spark/pull/13893 [SPARK-14172][SQL] Hive table partition predicate not passed down correctly ## What changes were proposed in this pull request? Currently partition predicate is not passed down correctly when condition contains nondeterministic parts. This PR changed the logic in collectProjectsAndFilters() to add the deterministic parts into filters, so that partition predicate can be passed down correctly. ## How was this patch tested? new test in PruningSuite. You can merge this pull request into a Git repository by running: $ git pull https://github.com/jiangxb1987/spark bugfix Alternatively you can review and apply these changes as the patch at: https://github.com/apache/spark/pull/13893.patch To close this pull request, make a commit to your master/trunk branch with (at least) the following in the commit message: This closes #13893 commit ce6f0b8186dd18301e5087d16a5139057c42ab77 Author: èæå Date: 2016-06-24T11:53:08Z [SPARK-14172][SQL] Hive table partition predicate not passed down correctly --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
