Github user jeanlyn closed the pull request at:
https://github.com/apache/spark/pull/6682
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Github user jeanlyn commented on the pull request:
https://github.com/apache/spark/pull/6682#issuecomment-109777121
@yhuai ,Thanks for comment.In the current implementation of
`join(BinaryNode)` in master just simply use the one side partitioning as its
partitioning to judge whether
Github user yhuai commented on the pull request:
https://github.com/apache/spark/pull/6682#issuecomment-109790105
Thanks for the example.
For your query, if you write it as
```
select a.value,b.value,c.value,d.value,e.value from
a join b
on a.key = b.key
Github user jeanlyn commented on the pull request:
https://github.com/apache/spark/pull/6682#issuecomment-109871137
@yhuai .Yes,the full outer join cases shuffled the null key to the same
reducer in spark-sql ,and the hive plan generated like:
```sql
explain select
GitHub user jeanlyn opened a pull request:
https://github.com/apache/spark/pull/6682
[SPARK-2205][SPARK-7871][SQL]Advoid redundancy exchange
When only use the output partitioning of `BinaryNode` will probably add
unnecessary `Exchange` like multiway join.
This PR add
Github user jeanlyn commented on the pull request:
https://github.com/apache/spark/pull/6682#issuecomment-109564743
cc @yhuai @chenghao-intel
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Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/6682#issuecomment-109564823
Can one of the admins verify this patch?
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Github user yhuai commented on the pull request:
https://github.com/apache/spark/pull/6682#issuecomment-109641063
@jeanlyn Thanks for working on it. However, I am not sure this is the
change we want for long term. To fundamentally address this issue, we need to
consider what
