[jira] [Resolved] (SPARK-4787) Clean up SparkContext after DAGScheduler initialization errors

["Josh Rosen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-4787?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Josh Rosen resolved SPARK-4787.
-------------------------------
       Resolution: Fixed
    Fix Version/s: 1.2.1
                   1.0.3
                   1.1.2

Issue resolved by pull request 3809
[https://github.com/apache/spark/pull/3809]

> Clean up SparkContext after DAGScheduler initialization errors
> --------------------------------------------------------------
>
>                 Key: SPARK-4787
>                 URL: https://issues.apache.org/jira/browse/SPARK-4787
>             Project: Spark
>          Issue Type: Sub-task
>          Components: Spark Core
>    Affects Versions: 1.1.0
>            Reporter: Jacky Li
>             Fix For: 1.1.2, 1.0.3, 1.3.0, 1.2.1
>
>
> If an exception occurs when creating the DAGScheduler, then other resources in SparkContext may be leaked / not cleaned up.
> *Edit (joshrosen):* this issue originally was a duplicate of SPARK-4194, but I've converted it into a subtask and revised it to relfect the scope of the PR opened against it.  The original PR description is reproduced below:
> When client creates a SparkContext, currently there are many val to initialize during object initialization. But when there is failure initializing these val, like throwing an exception, the resources in this SparkContext is not released properly. 
> For example, SparkUI object is created and bind to the HTTP server during initialization using
> {{ui.foreach(_.bind())}}
> but if anything goes wrong after this code (say throwing an exception when creating DAGScheduler), the SparkUI server is not stopped, thus the port bind will fail again in the client when creating another SparkContext. So basically this leads to a situation that the client can not create another SparkContext in the same process, which I think it is not reasonable.
> So, I suggest to refactor the SparkContext code to release resource when there is failure during in initialization.
>  



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