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https://issues.apache.org/jira/browse/SPARK-4759?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14240079#comment-14240079
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Josh Rosen commented on SPARK-4759:
-----------------------------------
[~andrewor14],
If you wanted to run the reproduction in the shell without the "multiple
SparkContexts" warning, couldn't you just call sc.stop() to stop the shell's
default SparkContext prior to running your reproduction code?
> Deadlock in complex spark job in local mode
> -------------------------------------------
>
> Key: SPARK-4759
> URL: https://issues.apache.org/jira/browse/SPARK-4759
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.1.1, 1.2.0, 1.3.0
> Environment: Java version "1.7.0_51"
> Java(TM) SE Runtime Environment (build 1.7.0_51-b13)
> Java HotSpot(TM) 64-Bit Server VM (build 24.51-b03, mixed mode)
> Mac OSX 10.10.1
> Using local spark context
> Reporter: Davis Shepherd
> Assignee: Andrew Or
> Priority: Critical
> Attachments: SparkBugReplicator.scala, SparkBugReplicatorSmaller.scala
>
>
> The attached test class runs two identical jobs that perform some iterative
> computation on an RDD[(Int, Int)]. This computation involves
> # taking new data merging it with the previous result
> # caching and checkpointing the new result
> # rinse and repeat
> The first time the job is run, it runs successfully, and the spark context is
> shut down. The second time the job is run with a new spark context in the
> same process, the job hangs indefinitely, only having scheduled a subset of
> the necessary tasks for the final stage.
> Ive been able to produce a test case that reproduces the issue, and I've
> added some comments where some knockout experimentation has left some
> breadcrumbs as to where the issue might be.
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