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Github user kanzhang closed the pull request at:
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Github user techaddict commented on the pull request:
https://github.com/apache/spark/pull/597#issuecomment-42003703
Mapping rating in case of ImplicitPref to `{r=0 -- 0`, `r0 -- 1}`,
`Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble)` to
`Rating(fields(0).toInt,
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/615#discussion_r1831
--- Diff: core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
---
@@ -83,7 +83,8 @@ private[spark] class Worker(
@volatile var registered =
Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/615#discussion_r12223079
--- Diff: core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
---
@@ -83,7 +83,8 @@ private[spark] class Worker(
@volatile var registered
Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/598#issuecomment-42016578
Can you explain why this resolves any of the issues you identified? See my
comment son SPARK-1698. shade is generally better than assembly for making uber
jars, not least
Github user darose commented on the pull request:
https://github.com/apache/spark/pull/468#issuecomment-42027309
Man oh man, I cannot get this to work no way no how. I tried rebuilding
spark using the jets3t 0.9 jar, then tried rebuilding shark doing the same. I
keep getting a
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GitHub user bouk opened a pull request:
https://github.com/apache/spark/pull/620
Reset the thread classLoader in the Mesos Executor
This is because Mesos calls it with a different environment or something,
the result is that the Spark jar is missing and it can't load classes.
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GitHub user tgravescs opened a pull request:
https://github.com/apache/spark/pull/621
[WIP] SPARK-1676: Cache Hadoop UGIs by default to prevent FileSystem leak
Move the doAs in Executor higher up so that we only have 1 ugi and aren't
leaking filesystems.
Fix spark on yarn to
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GitHub user markhamstra opened a pull request:
https://github.com/apache/spark/pull/622
[SPARK-1620] Handle uncaught exceptions in function run by Akka scheduler
If the intended behavior was that uncaught exceptions thrown in functions
being run by the Akka scheduler would end up
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Github user airhorns commented on the pull request:
https://github.com/apache/spark/pull/620#issuecomment-42080286
@bouk the spark contribution guidelines ask you make a JIRA
https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark
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Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/620#issuecomment-42080589
Would you mind printing the exception that happens when you don't have this
fix? I'm wondering which instantiation fails. In theory I don't think we should
have to do
Github user aarondav commented on the pull request:
https://github.com/apache/spark/pull/607#issuecomment-42081936
Ah, your point about making sure we clean up if we don't cache the UGIs is
definitely correct, no reason not to do that. For your last question, you
might note
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Github user andrewor14 commented on the pull request:
https://github.com/apache/spark/pull/30#issuecomment-42084965
Sure. In the event that we don't figure out what the issue is by the time
we release a voting candidate for 1.0, we should at the very least document the
peculiarity
Github user aarondav commented on the pull request:
https://github.com/apache/spark/pull/621#issuecomment-42086088
This seems pretty reasonable to me, but it assumes that there is no value
in recreating the user and re-transferring the current user's credentials. Is
this the case?
Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/621#discussion_r12251357
--- Diff:
core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala ---
@@ -95,9 +95,13 @@ private[spark] class MesosExecutorBackend
Github user aarondav commented on a diff in the pull request:
https://github.com/apache/spark/pull/621#discussion_r12251478
--- Diff:
core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
---
@@ -94,25 +95,32 @@ private[spark] class
Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/607#issuecomment-42087138
What is your concern with moving it up into the ExecutorBackend?
I'd be ok with using the cache and closeAll (if config off) if the
consensus is that moving it
Github user bouk commented on the pull request:
https://github.com/apache/spark/pull/620#issuecomment-42088169
Mesos `stderr` without this patch on master
```
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0502 17:31:42.672224 14688 exec.cpp:131]
Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/621#discussion_r12252251
--- Diff:
core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
---
@@ -94,25 +95,32 @@ private[spark] class
Github user aarondav commented on a diff in the pull request:
https://github.com/apache/spark/pull/621#discussion_r12252315
--- Diff: core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
---
@@ -24,25 +24,30 @@ import org.apache.hadoop.mapred.JobConf
import
Github user aarondav commented on a diff in the pull request:
https://github.com/apache/spark/pull/621#discussion_r12252364
--- Diff:
core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
---
@@ -94,25 +95,32 @@ private[spark] class
Github user bouk commented on the pull request:
https://github.com/apache/spark/pull/620#issuecomment-42088660
Note that exchanging the line
https://github.com/apache/spark/blob/e53eb4f0159ebd4d72c4bbc3586fdfc66ccacab7/core/src/main/scala/org/apache/spark/SparkEnv.scala#L165
with
Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/590#issuecomment-42089041
I'm getting errors trying to build this against yarn-alpha hadoop-0.23.
Its a huge error message, but here is part of it:
[ERROR]
while
Github user aarondav commented on the pull request:
https://github.com/apache/spark/pull/621#issuecomment-42091219
I have tested this on standalone mode and confirmed that the file handles
do not leak.
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GitHub user aarondav opened a pull request:
https://github.com/apache/spark/pull/623
SPARK-1700: Close socket file descriptors on task completion
This will ensure that sockets do not build up over the course of a job, and
that cancellation successfully cleans up sockets.
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/621#issuecomment-42092592
There is no reason to recreate the user and repopulate the
credentials/token unless the credentials/tokens are being updated in the
ExecutorBackend process. On yarn
Github user sryza commented on the pull request:
https://github.com/apache/spark/pull/621#issuecomment-42092767
To add to what Tom said, there's a distinction between renewing tokens
and repopulating them. Renewing means extending the lifespan of existing
tokens. Repopulating is
Github user dkanoafry commented on the pull request:
https://github.com/apache/spark/pull/620#issuecomment-42093656
fwiw, I experienced the same error with branch-1.0 and mesos 0.17, and
applying the patch in this PR fixes the problem.
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Github user witgo commented on the pull request:
https://github.com/apache/spark/pull/590#issuecomment-42094359
@tgravescs
I tested many times, These are all can pass.
`mvn clean package -DskipTests -Pyarn-alpha -Dhadoop.version=0.23.7
-Phadoop-0.23`
`mvn clean package
Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/590#issuecomment-42094808
Sorry, looks like I missed that the build command changed to need
-Phadoop-0.23.
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Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/620#issuecomment-42095686
Would you mind trying an alternative fix?
```
val cls = Class.forName(name, true,
Utils.getContextOrSparkClassLoader)
```
We recently did
Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/621#issuecomment-42095752
Jenkins, retest this please.
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Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/468#issuecomment-42096004
@srowen I'd prefer not to remove it from the dependency graph if possible
because it will break local builds. The best solution I see is to add a profile
for Hadoop 2.3
Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/613#issuecomment-42096048
Thanks I merged this.
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Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/601#issuecomment-42096177
Thanks Sandy, I've merged this.
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Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/468#issuecomment-42096201
@srowen if you'd like to take a crack at this by the way, please do. I'll
probably look at it on Sunday if no one else has.
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Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/610#discussion_r12254725
--- Diff: bin/compute-classpath.sh ---
@@ -63,14 +64,21 @@ fi
# built with Hive, so first check if the datanucleus jars exist, and then
ensure the
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https://github.com/apache/spark/pull/623#discussion_r12254807
--- Diff: core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
---
@@ -54,7 +54,16 @@ private[spark] class PythonRDD[T: ClassTag](
override
Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/623#issuecomment-42096479
LGTM - were you able to test this in some way?
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Github user asfgit closed the pull request at:
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