Github user dongjoon-hyun commented on the issue:
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Please refer the superset in #17980 .
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Github user dongjoon-hyun commented on the issue:
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BTW, the latest version is maintained in #17980.
Recently, Spark Vector format is changed.
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Github user dongjoon-hyun commented on the issue:
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Hi, I didn't try that, but that's not a concept of Spark data source table.
Please don't expect that. :)
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Github user cenyuhai commented on the issue:
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@dongjoon-hyun I have a question: does this orc data sources reader support
a table contains multiple file format
for example:
table/
day=2017-09-01 RCFile
day=2017-0
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Github user dongjoon-hyun commented on the issue:
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Retest this please.
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Github user dongjoon-hyun commented on the issue:
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Retest this please.
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Github user dongjoon-hyun commented on the issue:
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Hi, All.
For further discussion and easy comparision, I made another PR (#17943)
except `ColumnarBatch`.
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Github user dongjoon-hyun commented on the issue:
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Hi, @rxin , @marmbrus , @gatorsmile , @sameeragarwal .
Could you give us your opinions on this approach in Spark SQL part, too?
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Github user dongjoon-hyun commented on the issue:
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Yep. Since this is an approach adding new dependency on Apache ORC, the
non-vectorized PR also will need more supports(or approval) from the
committers. I'll wait for more opinions at the curr
Github user viirya commented on the issue:
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@dongjoon-hyun It is good for me. We can reduce the size of this PR too and
mitigate review job.
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Github user dongjoon-hyun commented on the issue:
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@cloud-fan and @viirya .
Shall we remove the vectorized part from this PR?
- The non-vectorized ORCFileFormat is mandatory and also the performance is
better than the current one.
Github user viirya commented on the issue:
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From the current benchmark, seems the performance has not obvious
improvement, compared with the vectorized Hive ORC reader #13775.
Maybe with more efficient batch approach as @cloud-fan sugg
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Retest this please.
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