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--- Diff:
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@@ -0,0 +1,145 @@
+/*
+ * Licensed
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--- Diff: python/pyspark/sql/udf.py ---
@@ -157,7 +157,17 @@ def _create_judf(self):
spark = SparkSession.builder.getOrCreate()
Github user LiangchangZ commented on the issue:
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> Have you run the unit tests? I wouldn't expect them to pass with this
change.
@jose-torres UT test of ContinuousAggregationSuite, ContinuousSuite failed.
But I think
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retest this please
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LGTM
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[SPARK-24385][SQL] Resolve self-join condition ambiguity for all
BinaryComparisons
## What changes were proposed in this pull request?
In `Dataset.join` we have a small hack for
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--- Diff: python/pyspark/sql/udf.py ---
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sc
Github user jerryshao commented on the issue:
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Do we still have a 2.2 release? If not, then this fix seems obsolete.
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@viirya Thanks for the reply and additional info. My (mis)understanding was
that when I access the individual struct fields that compose a UDT (say, in an
Expression), that I was actually accessing
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Thanks @vanzin @jerryshao for the help.
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cc @JoshRosen too
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Merging to master and branch 2.3.
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@juliuszsompolski I see your point and I can say it is an acceptable
solution. Though it has some problems I think. If we follow this path, we are
saying that: `(a, b) IN (select c, d from ...)`
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@cloud-fan @hvanhovell Do you have any comment/suggestion on this change?
Thanks.
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I think that the way the columns are defined in the subquery should define
the semantics.
E.g.:
`(a, b) IN (select c, d from ...)` - unpack (a, b) and treat it as a multi
column
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jenkins, test this, please
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[SPARK-24408][SQL][DOC] Move abs, bitwiseNOT, isnan, nanvl functions to
math_funcs group
## What changes were proposed in this pull request?
A few math functions (`abs` ,
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@maropu I didn't do a full performance benchmark. I believe the performance
gain can be from predicate pushdown when only one element in the set. This can
be a lot.
I forgot which one, I
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LGTM (I didn't look that carefully though)
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https://github.com/apache/spark/pull/21416
Merged build finished. Test PASSed.
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