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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala ---
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data: Iterator[_],
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retest this please
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--- Diff: python/pyspark/worker.py ---
@@ -84,13 +84,36 @@ def wrap_scalar_pandas_udf(f, return_type):
arrow_return_type =
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> he only other idea I have is to provide an option to raise an error if
the type needs to be cast.
Actually sounds good to me.
I think the problem is we are not quite clear
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ok to test
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We can simplery this:
https://github.com/apache/spark/blob/7ad18ee9f26e75dbe038c6034700f9cd4c0e2baa/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala#L273-L295
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Congratulation, @jiangxb1987
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Could you review and merge https://github.com/wangyum/spark/pull/14 ?
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[SQL][MINOR] Make use of TypeCoercion.findTightestCommonType while
inferring CSV schema.
## What changes were proposed in this pull request?
Current the CSV's infer schema code inlines
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@gatorsmile Thanks.. I will check.
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Hi, @jiangxb1987 .
Could you review (and merge) this PR?
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Hi, @kiszk . Is this still valid?
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Thanks @BryanCutler! Yes, this should not be a bug but is used as a warning
to users that there might be some type conversion they are not noticed at first
glance on the Pandas UDFs. For now the
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Let me post something I wrote recently. Could you add test cases to ensure
that we do not break the "Ignore NULLs" policy
> All the set/aggregate functions ignore NULLs. The typical
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ok to test
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@wangyum . Could you review and merge
https://github.com/wangyum/spark/pull/13 ?
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