Github user yhuai commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-99545780
Merged in master and branch 1.4. Thanks!
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Github user asfgit closed the pull request at:
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Github user rxin commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-99537523
Thanks. I'm going to merge this.
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Github user liancheng commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-94008753
@adrian-wang @marmbrus Sorry for the late reply. Yeah, the MySQL way also
seems reasonable to me.
In both Spark SQL and MySQL, `IN` is treated more like an oper
Github user marmbrus commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-93867757
The mysql way seems reasonable to me. @liancheng ?
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Github user adrian-wang commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-93671496
If we don't like what hive does here, we can do it in MySQL way: Convert
all expressions in the list to the type of left handle of IN. In fact the main
differences he
Github user marmbrus commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-92554707
Any comments on @liancheng 's suggestion?
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Github user liancheng commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-89621194
The thing that makes me hesitant here is whether we should stick to Hive,
because Hive's behavior is actually error prone and unintuitive. In Hive, `IN`
is implemented
Github user marmbrus commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-89094024
What is the status here? I haven't looked closely but this seems
reasonable to me. However, I'd like to see more hive comparison tests that
specifically test edge case
Github user adrian-wang commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-77998513
@liancheng That's reasonable, not every string could be converted into
numeric types.
Hive specified about this as
/**
* GenericUDFIn
*
* Exampl
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Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/4945#discussion_r26054740
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
---
@@ -269,6 +285,14 @@ trait HiveTypeCoercion {
Github user liancheng commented on a diff in the pull request:
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--- Diff:
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@@ -269,6 +285,14 @@ trait HiveTypeCoercion {
Github user chenghao-intel commented on a diff in the pull request:
https://github.com/apache/spark/pull/4945#discussion_r26044965
--- Diff:
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@@ -220,6 +220,22 @@ trait HiveTypeCoercion {
Github user chenghao-intel commented on a diff in the pull request:
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--- Diff:
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GitHub user adrian-wang opened a pull request:
https://github.com/apache/spark/pull/4945
[SPARK-6201] [SQL] promote string and do widen types for IN
@huangjs
Acutally spark sql will first go through analysis period, in which we do
widen types and promote strings, and then optimi
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