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Github user srowen commented on the issue:
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That isn't the same case. In one instance you are showing an example of SQL
syntax, and in the other, usage of a programmatic Scala API. The SQL query is
not semantically wrong, but syntactically
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Github user windpiger commented on the issue:
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ok,thanks!
another one is spark.sql("select * from test where key in()") throw a
exception, it is not consistent with the isin of dataset, it is ok?
if is ok, I will try to optimize
Github user srowen commented on the issue:
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Yes all good points, this is more complex than I had imagined, especially
with the good point about needing to support older serialized forms. This may
not be worth it, yes.
It would be
Github user srowen commented on the issue:
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To be clear, the history server already shows UTC always right? this change
depends on that being true.
Yes, I think it's not true of the other UIs. We already have this
inconsistency.
Github user srowen commented on the issue:
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Conceptually, it does seem nicer to resolve this with a connection pool.
There is however some complexity in implementing our own connection pool.
I think you've pointed out a key issue:
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Github user HyukjinKwon commented on a diff in the pull request:
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala ---
@@ -142,8 +142,8 @@ class PCAModel private[ml] (
/**
* Transform
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Github user srowen commented on the issue:
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See my comments from your previous PR; they still apply.
https://github.com/apache/spark/pull/15905
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Can one of the admins verify this patch?
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[SPARK-18471][MLLIB] In LBFGS, avoid sending huge vectors of 0
## What changes were proposed in this pull request?
The zero for the aggregation used to be shipped into a closure
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Jenkins, retest this please.
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Github user lichenglin commented on the issue:
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In fact,I have idea .
We can change the param to "connections pool size on each executor",every
partition task on the same executor use the same connection pool to save data.
we can
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@@ -30,7 +32,7 @@ import
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@@ -40,28 +42,28 @@
* of always copying the data
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@cloud-fan the same tests which failed earlier are failing again. Could you
issue retest request one more time?
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Github user srowen commented on the issue:
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I see, because a user can specify this option through SQL only? Ok, then
that logic makes sense to me.
It's still kinda a coarse way to achieve a limit on concurrent connections.
Some
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GitHub user yanboliang opened a pull request:
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[][][] Fix spark.glm throws errors when fitting on collinear data
## What changes were proposed in this pull request?
* Fix spark.glm throws errors when fitting on collinear data, since
Github user srowen commented on the issue:
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Yes you're exactly right, what I meant was that if "IN (empty set)" is
never generated in SQL, then there's no problem with how it's generated. I
suppose it would still also be correct to make sure
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* of always copying the
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--- Diff:
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---
@@ -42,15 +42,55 @@ trait ExpressionEvalHelper
Github user rxin commented on the issue:
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BTW optimizer technically shouldn't have anything to do with correctness,
so it shouldn't be the optimizer's job to make sure jdbc can connect. I'd just
generate the SQL to "false" (depending on how
Github user rxin commented on the issue:
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+1
I don't think we really gain anything by restricting this use case that we
allowed before.
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@yhuai @liancheng @davies @gatorsmile
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[SPARK-18053][SQL] compare unsafe and safe complex-type values correctly
## What changes were proposed in this pull request?
In Spark SQL, some expression may output safe format values,
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--- Diff: core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
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@@ -234,6 +234,9 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
* In
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--- Diff: core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
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@@ -1014,7 +1015,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
*
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Github user srowen commented on the issue:
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The original issue was that the generated SQL code was, apparently, invalid
for at least one JDBC driver. Semantically there's nothing wrong with "in
(empty set)" right? it's false, always.
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okay, thanks
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We will probably visit this after 2.1 is cut.
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Github user WangTaoTheTonic commented on the issue:
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It's always better to show a timezone in table header, i think, no matter
what the timezone it really uses.
But changing to show GMT/UTC always? I have to say it's a bold move even
GitHub user maropu opened a pull request:
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[SPARK-18478][SQL] Support codegen'd Hive UDFs
## What changes were proposed in this pull request?
This pr is to support codegen'd Hive UDFs.
## How was this patch tested?
Add tests
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Jenkins, retest this please.
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Github user kayousterhout commented on a diff in the pull request:
https://github.com/apache/spark/pull/15644#discussion_r88613426
--- Diff:
core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala ---
@@ -992,6 +994,46 @@ class TaskSetManagerSuite extends
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