I would be cautious regarding use of spark.cleaner.ttl, as it can lead to
confusing error messages if time-based cleaning deletes resources that are
still needed.  See my comment at
https://issues.apache.org/jira/browse/SPARK-5594?focusedCommentId=14486034&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14486034

On Mon, May 11, 2015 at 10:17 AM, Ganelin, Ilya <[email protected]
> wrote:

>  Also check out the spark.cleaner.ttl property. Otherwise, you will
> accumulate shuffle metadata in the memory of the driver.
>
>
>
> Sent with Good (www.good.com)
>
>
>
> -----Original Message-----
> *From: *Silvio Fiorito [[email protected]]
> *Sent: *Monday, May 11, 2015 01:03 PM Eastern Standard Time
> *To: *stanley; [email protected]
> *Subject: *Re: Does long-lived SparkContext hold on to executor resources?
>
> You want to look at dynamic resource allocation, here:
> http://spark.apache.org/docs/latest/job-scheduling.html#dynamic-resource-allocation
>
>
>
>
>
> On 5/11/15, 11:23 AM, "stanley" <[email protected]> wrote:
>
> >I am building an analytics app with Spark. I plan to use long-lived
> >SparkContexts to minimize the overhead for creating Spark contexts, which
> in
> >turn reduces the analytics query response time.
> >
> >The number of queries that are run in the system is relatively small each
> >day. Would long lived contexts hold on to the executor resources when
> there
> >is no queries running? Is there a way to free executor resources in this
> >type of use cases?
> >
> >
> >
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> >View this message in context:
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