Github user yanboliang commented on the issue:
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Merged into master, thanks for all.
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Github user thunterdb commented on the issue:
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Thank you @yanboliang.
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Github user WeichenXu123 commented on the issue:
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@yanboliang I will update ASAP, thanks!
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Github user yanboliang commented on the issue:
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@WeichenXu123 I left some minor comments, otherwise, LGTM. Thanks.
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Github user viirya commented on the issue:
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@WeichenXu123 Thanks! Looks good.
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Github user WeichenXu123 commented on the issue:
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@viirya Sure! comment updated.
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Github user viirya commented on the issue:
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Sorry can we make the performance data clear? Currently it doesn't say what
the unit of the numbers is.
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Github user yanboliang commented on the issue:
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@thunterdb I'm on travel these days, will do a final pass and merge it on
next Monday/Tuesday. Thanks.
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@yanboliang do you feel comfortable to merge this PR? I think that all the
questions have been addressed.
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Jenkins, test this please.
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Github user yanboliang commented on the issue:
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@WeichenXu123 @thunterdb
Thanks for this great work, we are always happy to see improvement which
can help us to migrate MLlib workload to Dataset based API.
Here are my two cents:
Github user WeichenXu123 commented on the issue:
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@thunterdb
1) The dataframe deserialize from binary data will add overhead, (maybe
there is compaction or not, it depends on the datatype, cc @liancheng ) about
1x performance in my test.
Github user thunterdb commented on the issue:
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cc @hvanhovell as well.
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Github user thunterdb commented on the issue:
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Thank you for the performance numbers @WeichenXu123 , I have a couple of
comments:
- you say that SQL uses adaptive compaction. How bad is that? I assume it
adds some overhead.
- did
Github user WeichenXu123 commented on the issue:
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performance data attached. cc @thunterdb @jkbradley
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@WeichenXu123 thanks! Can you post some performance numbers as well?
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