[jira] [Commented] (SPARK-17995) Use new attributes for columns from outer joins
[
https://issues.apache.org/jira/browse/SPARK-17995?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15589692#comment-15589692
]
Ryan Blue commented on SPARK-17995:
---
I'm not sure how that would work.
Here's an example. Say I have two tables:
{code}
scala> l.show
+---++
| id|left|
+---++
| 1| a|
| 2| b|
+---++
scala> r.show
+---+-+
| id|right|
+---+-+
| 1|x|
+---+-+
{code}
When I join those two on {{id}} and add an extra derived column, I end up with
this *parsed plan*:
{code}
== Parsed Logical Plan ==
Project [id#5 AS id#165, left#6, right#16, isnull(right#16) AS (right IS
NULL)#166]
+- Join LeftOuter, (id#5 = id#15)
:- Project [_1#2 AS id#5, _2#3 AS left#6]
: +- LocalRelation [_1#2, _2#3]
+- Project [_1#12 AS id#15, _2#13 AS right#16]
+- LocalRelation [_1#12, _2#13]
{code}
That has a reference to {{right#16}} in the outer-most project, which is coming
from {{select(r("right"), ...)}}. That reference -- and any reference like it
in the possible tree above the outer join -- needs to be replaced with a new
attribute that correctly shows the {{right}} column as nullable, which wasn't
inferred when I created the dataframe. It's fairly easy to replace the join
itself, but replacing all references to an attribute above the join is where I
don't see an easy solution.
> Use new attributes for columns from outer joins
> ---
>
> Key: SPARK-17995
> URL: https://issues.apache.org/jira/browse/SPARK-17995
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 1.6.2, 2.0.0, 2.1.0
>Reporter: Ryan Blue
>
> Plans involving outer joins use the same attribute reference (by exprId) to
> reference columns above the join and below the join. This is a false
> equivalence that leads to bugs like SPARK-16181, in which an attributes were
> incorrectly replaced by the optimizer. The column has a different schema
> above the outer join because its values may be null. The fix for that issue,
> [PR #13884](https://github.com/apache/spark/pull/13884) has a TODO comment
> from [~cloud_fan] to fix this by using different attributes instead of
> needing to special-case outer joins in rules and this issue is to track that
> improvement.
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[jira] [Commented] (SPARK-17995) Use new attributes for columns from outer joins
[
https://issues.apache.org/jira/browse/SPARK-17995?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15587348#comment-15587348
]
Wenchen Fan commented on SPARK-17995:
-
Can we just add a `newOuterJoinAttrs: Seq[Attribute]` parameter in the `Join`
class? e.g.
{code}
case class Join(..., newOuterJoinAttrs: Seq[Attribute])
object Join {
def apply(...) = {
val newOuterJoinAttrs = joinType match {
case LeftOuterJoin => right.output.map(_.newInstance)
}
}
}
{code}
> Use new attributes for columns from outer joins
> ---
>
> Key: SPARK-17995
> URL: https://issues.apache.org/jira/browse/SPARK-17995
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 1.6.2, 2.0.0, 2.1.0
>Reporter: Ryan Blue
>
> Plans involving outer joins use the same attribute reference (by exprId) to
> reference columns above the join and below the join. This is a false
> equivalence that leads to bugs like SPARK-16181, in which an attributes were
> incorrectly replaced by the optimizer. The column has a different schema
> above the outer join because its values may be null. The fix for that issue,
> [PR #13884](https://github.com/apache/spark/pull/13884) has a TODO comment
> from [~cloud_fan] to fix this by using different attributes instead of
> needing to special-case outer joins in rules and this issue is to track that
> improvement.
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[jira] [Commented] (SPARK-17995) Use new attributes for columns from outer joins
[ https://issues.apache.org/jira/browse/SPARK-17995?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15586972#comment-15586972 ] Ryan Blue commented on SPARK-17995: --- [~cloud_fan] and [~yhuai], I'd like to help fix this, but I'm not sure the best way. I started to write an analyzer rule that uses transformUp on the initial logical plan, before unresovled aliases are resolved. That rule would find outer joins and generate a map of attributes to replace to the new attribute above the join, with the schema updated to be nullable and with a new exprId. The attributes to replace come from the output of the outer join. Where I ran into trouble was in replacing the attributes in the logical plan above the a join. I don't think it is a good idea to have cases in the rule for every possible plan, so I think we need a method to substitute attributes that is implemented by nodes in the plan. That sounds like a larger patch than I originally thought, so I wanted to make sure I'm going down the right path before I put up a PR for it. What do you think? > Use new attributes for columns from outer joins > --- > > Key: SPARK-17995 > URL: https://issues.apache.org/jira/browse/SPARK-17995 > Project: Spark > Issue Type: Improvement > Components: SQL >Affects Versions: 1.6.2, 2.0.0, 2.1.0 >Reporter: Ryan Blue > > Plans involving outer joins use the same attribute reference (by exprId) to > reference columns above the join and below the join. This is a false > equivalence that leads to bugs like SPARK-16181, in which an attributes were > incorrectly replaced by the optimizer. The column has a different schema > above the outer join because its values may be null. The fix for that issue, > [PR #13884](https://github.com/apache/spark/pull/13884) has a TODO comment > from [~cloud_fan] to fix this by using different attributes instead of > needing to special-case outer joins in rules and this issue is to track that > improvement. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
