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Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/10003#issuecomment-160189345
I'm not sure what the issue is with SBT (do you get download errors or
something?) but you can also see the results of the style checker in the build
output:
GitHub user gatorsmile opened a pull request:
https://github.com/apache/spark/pull/10018
[SPARK-12028] [SQL] get_json_object returns an incorrect result when the
value is null literals
When calling `get_json_object` for the following two cases, both results
are `"null"`:
Github user CK50 commented on the pull request:
https://github.com/apache/spark/pull/10003#issuecomment-160193463
Do I take this as "SBT works fine for me on master"?
Yes, build/sbt fails for me when doing downloads:
Getting org.scala-sbt sbt 0.13.7 ...
::
Github user shivaram commented on the pull request:
https://github.com/apache/spark/pull/10016#issuecomment-160195289
LGTM. Thanks @yanboliang -- BTW were these APIs were present in SparkR 1.5
? If so we'll need to update the release docs about this change.
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Github user zsxwing commented on the pull request:
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I saw this issue in several PR builds. I'm going to merge this one because
it only touches the test codes and it bores other people.
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Jenkins, retest this please
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Github user zsxwing commented on the pull request:
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@tedyu Happy Thanksgiving, too :)
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> Test build #46807 has finished for PR 9991 at commit b694e27.
>
> This patch fails from timeout after a configured wait of `250m`.
> This patch merges
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retest this please
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Github user huaxingao commented on the pull request:
https://github.com/apache/spark/pull/9872#issuecomment-160182654
Fixed title and scala style problem. Thanks!
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Github user liancheng commented on the pull request:
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@rxin Updated. Now it shows `ParquetRelation` and `OrcRelation` etc.
instead of `PhysicalRDD`. Hive metastore table name is also attached. For the
long input paths
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@rxin
I wish I could run dev/lint-scala, but even after hours of struggling I
cannot get build/sbt running. I have downloaded sbt-launcher.jar
manually, but
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Github user srowen commented on a diff in the pull request:
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--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala ---
@@ -280,7 +275,8 @@ final class DataFrameWriter private[sql](df: DataFrame)
Github user srowen commented on the pull request:
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OK
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Github user srowen commented on the pull request:
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Seems OK, but the warning indicates it was interpreted as an absolute path
before, but it wasn't. Hm.
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Github user mineo commented on the pull request:
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I've used the same wording that the docs of `takeOrdered` use.
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This is an orthogonal issue, but it occurs to me that we have a bunch of
dialects, and a bunch of behavior variations, but typically a variation isn't
specific to a
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GitHub user hvanhovell opened a pull request:
https://github.com/apache/spark/pull/10015
[SPARK-12024][SQL] More efficient multi-column counting.
In https://github.com/apache/spark/pull/9409 we enabled multi-column
counting. The approach taken in that PR introduces a bit of
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Github user CK50 commented on a diff in the pull request:
https://github.com/apache/spark/pull/10003#discussion_r46038687
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/jdbc/ProgressCassandraDialect.scala
---
@@ -0,0 +1,51 @@
+/*
+ * Licensed to the Apache
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Github user srowen commented on the pull request:
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I think that's fine, though might say it a little differently. It returns
the top k elements according to some ordering, and the k elements are in order
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Github user srowen commented on the pull request:
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I'd defer to @mengxr on this one for final approval but yes that's
definitely what I had in mind. It's nice that you refactored add and subtract
together
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Github user sun-rui commented on the pull request:
https://github.com/apache/spark/pull/10016#issuecomment-160151261
@yanboliang, thanks for your info. I didn't look at the latest code:)
LGTM
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Github user karlhigley commented on a diff in the pull request:
https://github.com/apache/spark/pull/9843#discussion_r46051533
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala ---
@@ -211,14 +213,17 @@ private object IDFModel {
val n = v.size
Github user karlhigley commented on a diff in the pull request:
https://github.com/apache/spark/pull/9843#discussion_r46051762
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala ---
@@ -211,14 +213,17 @@ private object IDFModel {
val n = v.size
GitHub user yanboliang opened a pull request:
https://github.com/apache/spark/pull/10016
[SPARK-12025] [SparkR] Rename some window rank function names for SparkR
Rename some window rank function names for SparkR.
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Github user selvinsource commented on the pull request:
https://github.com/apache/spark/pull/9397#issuecomment-160121192
@dbtsai thanks for the suggestion, rebasing from master seems to have fixed
it.
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Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/8785#issuecomment-160127622
I guess I'm missing why a deep copy solves a problem of an unserializable
property value. It still exists in the copy right?
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Github user sun-rui commented on the pull request:
https://github.com/apache/spark/pull/10016#issuecomment-160129665
code change looks good. New names have no conflict with R base package.
But I don't see they are deprecated now? Could you point to me the
discussion or JIRA
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I see your point, let's go with the same wording then, yes.
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Github user yanboliang commented on the pull request:
https://github.com/apache/spark/pull/10016#issuecomment-160139585
@sun-rui Thanks for your comments. You can get the issue from the [source
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GitHub user jerryshao opened a pull request:
https://github.com/apache/spark/pull/10017
[SPARK-12002][Streaming][PySpark] Fix python direct stream checkpoint
recovery issue
Currently when serializing `TransformFunction`, `rdd_wrap_func` object is
not serialized into bytearray. So
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GitHub user felixcheung opened a pull request:
https://github.com/apache/spark/pull/10019
[SPARK-12029][SPARKR] Improve column functions signature, param check,
tests, fix doc and add examples
@shivaram @sun-rui
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Can one of the admins verify this patch?
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Github user CodingCat commented on the pull request:
https://github.com/apache/spark/pull/10011#issuecomment-160217707
Thanks, @zsxwing, at least it works for my PR
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Github user yhuai commented on the pull request:
https://github.com/apache/spark/pull/10010#issuecomment-160226172
oh, actually I probably to merge this to branch 1.6 (otherwise, pr for 1.6
branch will not be able to test with hadoop 2.0 profile?).
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Github user felixcheung commented on the pull request:
https://github.com/apache/spark/pull/9654#issuecomment-160202381
Could we please review this? I think it would be great to get matching
coltype<- in Spark 1.6
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