Github user dongjoon-hyun commented on the issue:
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@gatorsmile and @maropu . I really appreciate this effort. Thanks.
Since this is a cloud benchmark, I have one thing to recommend. Can we use
`r3.xlarge` for all benchmarks **consisten
Github user gatorsmile commented on the issue:
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Could you address the comments in the original PR?
- https://github.com/apache/spark/pull/21370#discussion_r194276735
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Github user gatorsmile commented on a diff in the pull request:
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--- Diff: docs/configuration.md ---
@@ -456,6 +456,33 @@ Apart from these, the following properties are also
available, and may be useful
from J
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Thanks! @HyukjinKwon
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oh, I forgot to update the title..yea, I'll do soon.
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@maropu The fix looks good to me. Thanks for changing the solution. Could
you update the PR title and description?
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Merged to master and branch-2.3.
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CC @vanzin @tgravescs would you please help to review, thanks!
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@mgummelt too
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ok, could you rebase this PR
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Sure.
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@HyukjinKwon can we merge this?
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LGTM except for the comment from @HyukjinKwon
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@maropu Could you fix the style?
BTW, based on the latest result, Parquet is generally faster than ORC. cc
@dongjoon-hyun @rdblue
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[SPARK-24386][SS] coalesce(1) aggregates in continuous processing
## What changes were proposed in this pull request?
Provide a continuous processing implementation of coalesce(1), as w
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Sorry in advance if this is the wrong place to be asking this!
Does this PR mean that we'll be able to create SparkContexts using
PySpark's
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Github user maropu commented on the issue:
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@mengxr ok, could you check?
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