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https://issues.apache.org/jira/browse/SPARK-4759?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14237321#comment-14237321
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Andrew Or edited comment on SPARK-4759 at 12/8/14 12:20 AM:
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Hey I came up with a much smaller reproduction for this from your program.
1. Start spark-shell with --master local[N] where N can be anything (or simply
local with 1 core)
2. Copy and paste the following into your REPL
{code}
def runMyJob(): Unit = {
sc.setCheckpointDir("/tmp/spark-test")
val rdd = sc.parallelize(1 to 100).repartition(4).cache()
rdd.checkpoint()
rdd.count()
val rdd2 = sc.parallelize(1 to 100).repartition(4)
rdd.union(rdd2).count()
}
{code}
3. runMyJob(sc)
It should be stuck at task 4/8. Note that with local-cluster and (local)
standalone mode, it pauses a little at 4/8 too, but finishes shortly afterwards.
was (Author: andrewor14):
Hey I came up with a much smaller reproduction for this from your program.
1. Start spark-shell with --master local[N] where N can be anything (or simply
local with 1 core)
2. Copy and paste the following into your REPL
{code}
def runMyJob(sc: SparkContext): Unit = {
sc.setCheckpointDir("/tmp/spark-test")
val rdd = sc.parallelize(1 to 100).repartition(4).cache()
rdd.checkpoint()
rdd.count()
val rdd2 = sc.parallelize(1 to 100).repartition(4)
rdd.union(rdd2).count()
}
{code}
3. runMyJob(sc)
It should be stuck at task 4/8. Note that with local-cluster and (local)
standalone mode, it pauses a little at 4/8 too, but finishes shortly afterwards.
> 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
>
>
> 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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