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From "Andrew Or (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (SPARK-4759) Deadlock in complex spark job in local mode
Date Mon, 08 Dec 2014 07:29:13 GMT

    [ https://issues.apache.org/jira/browse/SPARK-4759?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14237321#comment-14237321
] 

Andrew Or edited comment on SPARK-4759 at 12/8/14 7:29 AM:
-----------------------------------------------------------

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 = {
      val rdd = sc.parallelize(1 to 100).repartition(5).cache()
      rdd.count()
      val rdd2 = sc.parallelize(1 to 100).repartition(12)
      rdd.union(rdd2).count()
    }
{code}
3. runMyJob()

It should be stuck at task 5/17. Note that with local-cluster and (local) standalone mode,
it pauses a little at 5/17 too, but finishes shortly afterwards.

=== EDIT ===
This seems to reproduce it only on the master branch.


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(): Unit = {
      val rdd = sc.parallelize(1 to 100).repartition(5).cache()
      rdd.count()
      val rdd2 = sc.parallelize(1 to 100).repartition(12)
      rdd.union(rdd2).count()
    }
{code}
3. runMyJob()

It should be stuck at task 5/17. Note that with local-cluster and (local) standalone mode,
it pauses a little at 5/17 too, but finishes shortly afterwards.

- EDIT -
This seems to reproduce it only on the master branch.

> 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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