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Github user jliwork commented on the pull request:
https://github.com/apache/spark/pull/9920#issuecomment-160546154
@jkbradley @holdenk Could you please take a look my latest fix and let me
know of any further comments? thanks!
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Github user liancheng commented on the pull request:
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@yhuai Here is the background: Parquet filter push-down is disabled when
schema merging is turned on because of [PARQUET-389][1].
I'm somewhat hesitant to have
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@kayousterhout For example, task can failed because some host can't access
source/network, local storage of some host is full, broken class linkage and
other run-time excep
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Can one of the admins verify this patch?
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Github user cloud-fan commented on a diff in the pull request:
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--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
---
@@ -191,6 +193,19 @@ abstract class TreeNode[BaseType <:
Tree
GitHub user gepuro opened a pull request:
https://github.com/apache/spark/pull/10036
[SPARK-12043] wrong comment for "gt" in Column.scala
comment of "gt" is wrong.
```scala
def gt(other: Any): Column = this > other
/**
* Less than.
* {{{
Github user cloud-fan commented on a diff in the pull request:
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--- Diff:
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---
@@ -87,31 +96,293 @@ object JavaTypeInference {
Github user sun-rui commented on the pull request:
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@shivaram, I tried , that does not work. It seems right to put test cases
in R/pkg/inst/tests/testthat, and after installation, the cases cases are
available at 'R/lib/
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Github user shivaram commented on the pull request:
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Hmm the recommendation in testthat [1] seems to be to put the tests in
`R/pkg/tests/testthat` instead of `R/pkg/inst/tests` -- Could we try to do that
? Or are there a
Github user felixcheung commented on the pull request:
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It is. I could add that.
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Github user viirya commented on the pull request:
https://github.com/apache/spark/pull/9940#issuecomment-160516788
@yhuai I think so. We completely skip pushdown filters now if schema
merging is enabled. This patch is to improve that.
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rebased to master
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@tdas removed SparkR DESCRIPTION file changes
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Github user shivaram commented on the pull request:
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Hmm I think its better to just support both forms ? Isn't `c("jar1",
"jar2")` more natural ?
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Github user yhuai commented on the pull request:
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oh, I thought it's a bug fix (so, I was wondering if it's a regression from
1.5 or not). But, it is actually an improvement?
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Github user JoshRosen commented on the pull request:
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By the way, it looks like the test failure which motivated this patch
occurs more frequently than I originally thought. It seems to happen about once
a day, give or ta
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Github user dilipbiswal closed the pull request at:
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Github user dilipbiswal commented on the pull request:
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@cloud-fan Sure.. I will close this PR for now. If you think of a better
approach and need my help in anyway pl let me know. Thanks for your feedback.
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Github user sun-rui commented on a diff in the pull request:
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--- Diff: R/pkg/inst/tests/testthat/test_mllib.R ---
@@ -26,7 +26,7 @@ sc <- sparkR.init()
sqlContext <- sparkRSQL.init(sc)
test_that("glm and
Github user viirya commented on the pull request:
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@tdas Thanks for comment. I would like to close this first.
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Github user suyanNone commented on the pull request:
https://github.com/apache/spark/pull/10031#issuecomment-160507811
no... actually, it is not good ideaExecutor may be killed by Yarn due
to some reason...or just akka disconnected...
and I hope you can close this, I will worki
Github user viirya commented on the pull request:
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Hi @yhuai Can you explain more? What you meant about regression?
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Github user viirya commented on the pull request:
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ping @rxin @cloud-fan @nongli
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Github user cloud-fan commented on the pull request:
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Thanks for working on it!
Actually I'm not sure if this is the right appraoch... We still hande
`explode` and `json_tuple` specially, what's worse, we hardcode
Github user kayousterhout commented on the pull request:
https://github.com/apache/spark/pull/8760#issuecomment-160506398
This seems potentially useful, but adds a lot of complexity. Can you
elaborate on the use cases where this problem has been arising? My vague memory
of previous d
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--- Diff:
core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala ---
@@ -52,16 +52,21 @@ private[spark] object SamplingUtils {
(tri
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Github user yhuai commented on the pull request:
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I am merging it to master and branch 1.6.
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GitHub user yhuai opened a pull request:
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[SPARK-12039] [SQL] Ignore HiveSparkSubmitSuite's "SPARK-9757 Persist
Parquet relation with decimal column".
https://issues.apache.org/jira/browse/SPARK-12039
Since it is pretty flaky in had
Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/10030#discussion_r46105330
--- Diff: R/pkg/inst/tests/testthat/test_mllib.R ---
@@ -26,7 +26,7 @@ sc <- sparkR.init()
sqlContext <- sparkRSQL.init(sc)
test_that("glm
Github user lianhuiwang commented on the pull request:
https://github.com/apache/spark/pull/10032#issuecomment-160500147
LGTM @andrewor14
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--- Diff: R/pkg/inst/tests/testthat/test_sparkSQL.R ---
@@ -832,10 +846,10 @@ test_that("test HiveContext", {
expect_equal(count(df2), 3)
Github user carsonwang commented on the pull request:
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@zsxwing , do you have any further comments regarding how the `SQLListener`
is cleaned up?
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--- Diff: R/pkg/inst/tests/testthat/test_mllib.R ---
@@ -26,7 +26,7 @@ sc <- sparkR.init()
sqlContext <- sparkRSQL.init(sc)
test_that("glm and
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@vanzin , I wrapped the calls to the hooks with
`Utils.tryLogNonFatalError`. I didn't clean up the `SQLListener` after a
application end event because another `SQLCon
Github user zjffdu commented on the pull request:
https://github.com/apache/spark/pull/9490#issuecomment-160493625
@yhuai Only changing JSONRelation/TextRelation can be a short-term
solution. But since this is a general issue for HadoopFsRelation. so it would
be better to do it in Ha
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rebased to master
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ping @cloud-fan @yhuai
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GitHub user felixcheung opened a pull request:
https://github.com/apache/spark/pull/10034
[SPARK-12019][SPARKR] Check param and fix doc for sparkR.init()
and add tests.
Spark submit expects comma-separated list
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Github user xguo27 commented on the pull request:
https://github.com/apache/spark/pull/9543#issuecomment-160482218
Thanks @yhuai for reviewing my code! I have updated per your suggestion.
To answer your question, I personally do not have a use case for this. My
take on the JIR
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/10033#issuecomment-160480747
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Github user AmplabJenkins commented on the pull request:
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Merged build finished. Test FAILed.
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Github user yhuai commented on the pull request:
https://github.com/apache/spark/pull/10033#issuecomment-160478928
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is in worker 6.
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Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/10033#issuecomment-160478873
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Github user hvanhovell commented on the pull request:
https://github.com/apache/spark/pull/9819#issuecomment-160478413
Yes. You can use any Spark aggregate function as a window function. Most
Hive UDAFs should also work except for the pivoted ones...
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Github user yhuai commented on the pull request:
https://github.com/apache/spark/pull/10033#issuecomment-160478206
test this please
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Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/10033#issuecomment-160478184
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Github user yhuai commented on the pull request:
https://github.com/apache/spark/pull/10033#issuecomment-160478218
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is execurted by worker 3. Let me find another worker.
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