Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/18697
Let't close this then.
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Github user maropu commented on the issue:
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@aray Can you close this for now because it's not active for a long time?
(I'm not sure the current master still has this issue..., so you should check
it first)
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Github user viirya commented on the issue:
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If we have correct `outputPartitioning` on each operators, I think this
issue can be fixed.
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Github user cloud-fan commented on the issue:
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shouldn't we fix `ProjectExec.outputPartitioning`?
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Github user aray commented on the issue:
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@viirya We could certainly make that improvement. I believe it would be a
fairly trivial change to this PR if we were just considering expressions that
have the same canonical representation. However f
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Github user viirya commented on the issue:
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A different view to this problem is, in the following part of query plan:
:- *HashAggregate(keys=[parent#228], functions=[],
output=[level1#270]) hashpartitioning(parent#228, 5)
Github user viirya commented on the issue:
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I'd like to reword the problem description as the current one looks obscure
to me.
Currently we don't care if the output partitioning of an operator contains
the attributes not in the output
Github user gatorsmile commented on the issue:
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I will review this next week.
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Github user rxin commented on the issue:
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cc @cloud-fan @hvanhovell
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Github user aray commented on the issue:
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ping @rxin can someone look at this correctness fix?
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Github user aray commented on the issue:
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retest this please
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Github user aray commented on the issue:
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Plan for the example query before the patch (with partitioning as suffix):
```
*HashAggregate(keys=[parent#228], functions=[], output=[level2#274])
hashpartitioning(parent#228, 5)
+- *HashAg
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