Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/1229#issuecomment-147326922
@pauloangelo sounds like your RDD is not immutable then, in which case many
bets are off. RDDs are generally always the same whenever you compute them.
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Github user pauloangelo commented on the pull request:
https://github.com/apache/spark/pull/1229#issuecomment-147232717
Hi all,
I'm getting similar problem using kmeans clustering with Spark-1.5.1. The
stacktrace is below.
To overcome the problem, I'm setting runs=1,
Github user pauloangelo commented on the pull request:
https://github.com/apache/spark/pull/1229#issuecomment-147266494
My RDD comes from a HBase table, which is growing. When I suspend the row
insertion, the problem doesn't happen.
The RDD is cached, should the problem
GitHub user mengxr opened a pull request:
https://github.com/apache/spark/pull/1229
[SPARK-2251] fix concurrency issues in random sampler
The following code is very likely to throw an exception:
~~~
val rdd = sc.parallelize(0 until 111, 10).sample(false, 0.1)
Github user AmplabJenkins commented on the pull request:
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Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/1229#discussion_r14252256
--- Diff:
core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala ---
@@ -54,17 +54,17 @@ trait RandomSampler[T, U] extends Pseudorandom with
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Github user mengxr closed the pull request at:
https://github.com/apache/spark/pull/1234
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Github user asfgit closed the pull request at:
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