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https://issues.apache.org/jira/browse/SPARK-12845?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Maciej Bryński updated SPARK-12845:
-----------------------------------
Description:
I have following issue.
I'm connecting two tables with where condition
{code}
select * from t1 join t2 in t1.id1 = t2.id2 where t1.id = 1234
{code}
In this code predicate is only push down to t1.
To have predicates on both table I should run following query:
{code}
select * from t1 join t2 in t1.id1 = t2.id2 where t1.id = 1234 and t2.id2 = 1234
{code}
Spark should present same behaviour for both queries.
During inner join there is no problem with NULL values.
On LEFT / RIGHT join predicates could be pushed down only if there are on
correct side.
On FULL OUTER join we should deal with null values.
was:
I have following issue.
I'm connecting two tables with where condition
{code}
select * from t1 join t2 in t1.id1 = t2.id2 where t1.id = 1234
{code}
In this code predicate is only push down to t1.
To have predicates on both table I should run following query:
{code}
select * from t1 join t2 in t1.id1 = t2.id2 where t1.id = 1234 and t2.id2 = 1234
{code}
Spark should present same behaviour for both queries.
During inner join there is no problem with NULL values.
On LEFT / RIGHT join predicates could be pushed down only if there are on right
side.
On OUTER join we should deal with null values.
> During join Spark should pushdown predicates on joining column to both tables
> -----------------------------------------------------------------------------
>
> Key: SPARK-12845
> URL: https://issues.apache.org/jira/browse/SPARK-12845
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.0
> Reporter: Maciej Bryński
>
> I have following issue.
> I'm connecting two tables with where condition
> {code}
> select * from t1 join t2 in t1.id1 = t2.id2 where t1.id = 1234
> {code}
> In this code predicate is only push down to t1.
> To have predicates on both table I should run following query:
> {code}
> select * from t1 join t2 in t1.id1 = t2.id2 where t1.id = 1234 and t2.id2 =
> 1234
> {code}
> Spark should present same behaviour for both queries.
> During inner join there is no problem with NULL values.
> On LEFT / RIGHT join predicates could be pushed down only if there are on
> correct side.
> On FULL OUTER join we should deal with null values.
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