Github user zsxwing commented on the pull request:
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No surprises. Exposed a lot of test failures. Fixing them
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[SPARK-11710] Document new memory management model
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test this please
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Github user marmbrus commented on the pull request:
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This seems fine for now, but its not clear to me why the right answer isn't
to read it in as a higher precision signed type.
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Merging to master and 1.6
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+1
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Github user marmbrus commented on the pull request:
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ok to test
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Github user marmbrus commented on the pull request:
https://github.com/apache/spark/pull/9385#issuecomment-156238962
I'm sorry, I don't see where this `expand()` function you are talking about
is, or why it should not use `semanticEquals`. The whole point of
`semanticEquals` is "are
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Github user squito commented on the pull request:
https://github.com/apache/spark/pull/9610#issuecomment-156244415
you can take this test case if you like:
https://github.com/squito/spark/blob/SPARK-8029_first_wins/core/src/test/scala/org/apache/spark/ShuffleSuite.scala#L351
Github user tnachen commented on the pull request:
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LGTM
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Github user tnachen commented on the pull request:
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I think this is fine for now, I was thinking leaving some comments about
how it should be launched with Marathon but I think later we can add a example
json in the
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Github user JihongMA commented on the pull request:
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@mengxr sure, will take care mean via seperate PR.
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/cc @cloud-fan
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Github user tylerprete closed the pull request at:
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Github user tylerprete commented on the pull request:
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Yeah, sorry, life got busy and haven't made progress on this.
Realistically, I'm not sure I'll get to it right away. If I ever get around
to making it work,
Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/9297#discussion_r44711788
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala ---
@@ -45,25 +46,14 @@ private[sql] object SQLExecution {
GitHub user jkbradley opened a pull request:
https://github.com/apache/spark/pull/9674
[SPARK-11612] [ML] Pipeline and PipelineModel persistence
Pipeline and PipelineModel extend Readable and Writable. Persistence
succeeds only when all stages are Writable.
Note: This PR
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GitHub user mengxr opened a pull request:
https://github.com/apache/spark/pull/9677
[WIP] [SPARK-11672] flaky spark.ml read/write tests
Add callSite info for debugging. I will run Jenkins several times.
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GitHub user jkbradley opened a pull request:
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[SPARK-11712] [ML] Make spark.ml LDAModel be abstract
Per discussion in the initial Pipelines LDA PR
[https://github.com/apache/spark/pull/9513], we should make LDAModel abstract
and create a
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Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/9678#discussion_r44720208
--- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala ---
@@ -468,7 +452,37 @@ class LDAModel private[ml] (
/**
* ::
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GitHub user marmbrus opened a pull request:
https://github.com/apache/spark/pull/9673
[SPARK-11654][SQL] add reduce to GroupedDataset
This PR adds a new method, `reduce`, to `GroupedDataset`, which allows
similar operations to `reduceByKey` on a traditional `PairRDD`.
GitHub user tedyu opened a pull request:
https://github.com/apache/spark/pull/9672
[SPARK-11699] TrackStateRDDSuite fails on Jenkins builds
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Github user marmbrus commented on the pull request:
https://github.com/apache/spark/pull/9664#issuecomment-156218775
This LGTM, but what about functions that are added to the metastore?
Should we also check the metadata hive next?
Eitherway, I think we can merge this now to
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Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/9637#discussion_r44710758
--- Diff: docs/job-scheduling.md ---
@@ -56,36 +56,31 @@ provide another approach to share RDDs.
## Dynamic Resource Allocation
-Spark
Github user NarineK commented on the pull request:
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It seems that there is a problem in java for many output parameters.
Introducing a Result output object would be probably better.
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Github user yhuai commented on the pull request:
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@marmbrus Hive UDFs stored in metastore is a harder problem. For those
functions, we need to talk to metadataHive. But, when we do function lookup, we
do not know which
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Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/9664#discussion_r44714829
--- Diff:
sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala ---
@@ -454,7 +454,7 @@ class HiveContext private[hive](
// Note that
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Github user marmbrus commented on the pull request:
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I'm not sure we should support the exact semantics of hives temporary
tables. I'd be more inclined to intercept such DDL and convert it to a Spark
SQL temporary table
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Github user feynmanliang commented on the pull request:
https://github.com/apache/spark/pull/9678#issuecomment-156249049
LGTM, all comments minor / optional
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Github user marmbrus commented on the pull request:
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I'm inclined to agree with Sean, this seems like code churn for the sake
of code churn.
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Github user squito commented on a diff in the pull request:
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--- Diff:
core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala ---
@@ -93,6 +93,29 @@ private[spark] class
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Github user davis commented on the pull request:
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@davies
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Github user tedyu closed the pull request at:
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Github user mengxr commented on the pull request:
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LGTM. Merged into master and branch-1.6. Thanks! Btw, there is a minor
style issue I marked inline. @JihongMA Could you submit another PR to change
the output of
Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/9678#discussion_r44720409
--- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala ---
@@ -314,31 +314,31 @@ private[clustering] trait LDAParams extends Params
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Github user zsxwing commented on the pull request:
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retest this please
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Github user rxin commented on the pull request:
https://github.com/apache/spark/pull/9666#issuecomment-156221680
Thanks - I've merged this.
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Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/9513#issuecomment-156221541
Hm, good point. OK I'll try that & ping you on the PR.
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Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/9668#issuecomment-156223722
Test FAILed.
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