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Hi @dbtsai , nice catch!
I think we can also check the nullability here:
https://github.com/apache/spark/pull/21952/files#diff-01fea32e6ec6bcf6f34d06282e08705aR160
If the
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I think range join is a very important feature, but the current
implementation is not robust enough(no spilling to disk). Have you explored
other simpler solutions? I vaguely remember that some
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What does the benchmark result suggest? The result should be `1048576` by
default?
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[SPARK-24598][DOCS] State in the documentation the behavior when arithmetic
operations cause overflow
## What changes were proposed in this pull request?
According to the discussion in
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@felixcheung
Thanks for review.
**1. How small is 'small':**
This optimazition works when the sampled data of the RangePartitioner
covers all the data to sort.
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@cloud-fan I think this is just a wrong behavior (being against SQL
standard is not the only issue), so I am not sure that makes sense to have it
controlled by a config. Anyway, I agree that as it
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I don't think we can change behavior like this without introducing a
config. In Spark 3.0 we will add a "strict mode" and closely follow SQL
standard. But for Spark 2.4, how about we keep the
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Github user HeartSaVioR commented on the issue:
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@tdas
I found the spare time to run performance tests though I've run only one
app for now... I couldn't run the tests concurrently. Please let me know if you
are not confident with
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Github user mahmoudmahdi24 commented on the issue:
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Hello @Satyajitv, please rename the title of this PR properly.
The PR title should be of the form [SPARK-][COMPONENT] Title, where
SPARK- is the relevant JIRA number, COMPONENT
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simplify codegen of array_except
## What changes were proposed in this pull request?
simplify the codegen:
1. only do real codegen if the type can be specialized by the hash set
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class
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